(upbeat music) >> Welcome back to theCUBE's coverage of Women in Data Science 2023 live from Stanford University. This is Lisa Martin. My co-host is Tracy Yuan. We're excited to be having great conversations all day but you know, 'cause you've been watching. We've been interviewing some very inspiring women and some men as well, talking about all of the amazing applications of data science. You're not going to want to miss this next conversation. Our guest is Gabriela de Queiroz, Principal Cloud Advocate Manager of Microsoft. Welcome, Gabriela. We're excited to have you. >> Thank you very much. I'm so excited to be talking to you. >> Yeah, you're on theCUBE. >> Yeah, finally. (Lisa laughing) Like a dream come true. (laughs) >> I know and we love that. We're so thrilled to have you. So you have a ton of experience in the data space. I was doing some research on you. You've worked in software, financial advertisement, health. Talk to us a little bit about you. What's your background in? >> So I was trained in statistics. So I'm a statistician and then I worked in epidemiology. I worked with air pollution and public health. So I was a researcher before moving into the industry. So as I was talking today, the weekly paths, it's exactly who I am. I went back and forth and back and forth and stopped and tried something else until I figured out that I want to do data science and that I want to do different things because with data science we can... The beauty of data science is that you can move across domains. So I worked in healthcare, financial, and then different technology companies. >> Well the nice thing, one of the exciting things that data science, that I geek out about and Tracy knows 'cause we've been talking about this all day, it's just all the different, to your point, diverse, pun intended, applications of data science. You know, this morning we were talking about, we had the VP of data science from Meta as a keynote. She came to theCUBE talking and really kind of explaining from a content perspective, from a monetization perspective, and of course so many people in the world are users of Facebook. It makes it tangible. But we also heard today conversations about the applications of data science in police violence, in climate change. We're in California, we're expecting a massive rainstorm and we don't know what to do when it rains or snows. But climate change is real. Everyone's talking about it, and there's data science at its foundation. That's one of the things that I love. But you also have a lot of experience building diverse teams. Talk a little bit about that. You've created some very sophisticated data science solutions. Talk about your recommendation to others to build diverse teams. What's in it for them? And maybe share some data science project or two that you really found inspirational. >> Yeah, absolutely. So I do love building teams. Every time I'm given the task of building teams, I feel the luckiest person in the world because you have the option to pick like different backgrounds and all the diverse set of like people that you can find. I don't think it's easy, like people say, yeah, it's very hard. You have to be intentional. You have to go from the very first part when you are writing the job description through the interview process. So you have to be very intentional in every step. And you have to think through when you are doing that. And I love, like my last team, we had like 10 people and we were so diverse. Like just talking about languages. We had like 15 languages inside a team. So how beautiful it is. Like all different backgrounds, like myself as a statistician, but we had people from engineering background, biology, languages, and so on. So it's, yeah, like every time thinking about building a team, if you wanted your team to be diverse, you need to be intentional. >> I'm so glad you brought up that intention point because that is the fundamental requirement really is to build it with intention. >> Exactly, and I love to hear like how there's different languages. So like I'm assuming, or like different backgrounds, I'm assuming everybody just zig zags their way into the team and now you're all women in data science and I think that's so precious. >> Exactly. And not only woman, right. >> Tracy: Not only woman, you're right. >> The team was diverse not only in terms of like gender, but like background, ethnicity, and spoken languages, and language that they use to program and backgrounds. Like as I mentioned, not everybody did the statistics in school or computer science. And it was like one of my best teams was when we had this combination also like things that I'm good at the other person is not as good and we have this knowledge sharing all the time. Every day I would feel like I'm learning something. In a small talk or if I was reviewing something, there was always something new because of like the richness of the diverse set of people that were in your team. >> Well what you've done is so impressive, because not only have you been intentional with it, but you sound like the hallmark of a great leader of someone who hires and builds teams to fill gaps. They don't have to know less than I do for me to be the leader. They have to have different skills, different areas of expertise. That is really, honestly Gabriela, that's the hallmark of a great leader. And that's not easy to come by. So tell me, who were some of your mentors and sponsors along the way that maybe influenced you in that direction? Or is that just who you are? >> That's a great question. And I joke that I want to be the role model that I never had, right. So growing up, I didn't have anyone that I could see other than my mom probably or my sister. But there was no one that I could see, I want to become that person one day. And once I was tracing my path, I started to see people looking at me and like, you inspire me so much, and I'm like, oh wow, this is amazing and I want to do do this over and over and over again. So I want to be that person to inspire others. And no matter, like I'll be like a VP, CEO, whoever, you know, I want to be, I want to keep inspiring people because that's so valuable. >> Lisa: Oh, that's huge. >> And I feel like when we grow professionally and then go to the next level, we sometimes we lose that, you know, thing that's essential. And I think also like, it's part of who I am as I was building and all my experiences as I was going through, I became what I mentioned is unique person that I think we all are unique somehow. >> You're a rockstar. Isn't she a rockstar? >> You dropping quotes out. >> I'm loving this. I'm like, I've inspired Gabriela. (Gabriela laughing) >> Oh my God. But yeah, 'cause we were asking our other guests about the same question, like, who are your role models? And then we're talking about how like it's very important for women to see that there is a representation, that there is someone they look up to and they want to be. And so that like, it motivates them to stay in this field and to start in this field to begin with. So yeah, I think like you are definitely filling a void and for all these women who dream to be in data science. And I think that's just amazing. >> And you're a founder too. In 2012, you founded R Ladies. Talk a little bit about that. This is present in more than 200 cities in 55 plus countries. Talk about R Ladies and maybe the catalyst to launch it. >> Yes, so you always start, so I'm from Brazil, I always talk about this because it's such, again, I grew up over there. So I was there my whole life and then I moved to here, Silicon Valley. And when I moved to San Francisco, like the doors opened. So many things happening in the city. That was back in 2012. Data science was exploding. And I found out something about Meetup.com, it's a website that you can join and go in all these events. And I was going to this event and I joke that it was kind of like going to the Disneyland, where you don't know if I should go that direction or the other direction. >> Yeah, yeah. >> And I was like, should I go and learn about data visualization? Should I go and learn about SQL or should I go and learn about Hadoop, right? So I would go every day to those meetups. And I was a student back then, so you know, the budget was very restricted as a student. So we don't have much to spend. And then they would serve dinner and you would learn for free. And then I got to a point where I was like, hey, they are doing all of this as a volunteer. Like they are running this meetup and events for free. And I felt like it's a cycle. I need to do something, right. I'm taking all this in. I'm having this huge opportunity to be here. I want to give back. So that's what how everything started. I was like, no, I have to think about something. I need to think about something that I can give back. And I was using R back then and I'm like how about I do something with R. I love R, I'm so passionate about R, what about if I create a community around R but not a regular community, because by going to this events, I felt that as a Latina and as a woman, I was always in the corner and I was not being able to participate and to, you know, be myself and to network and ask questions. I would be in the corner. So I said to myself, what about if I do something where everybody feel included, where everybody can participate, can share, can ask questions without judgment? So that's how R ladies all came together. >> That's awesome. >> Talk about intentions, like you have to, you had that go in mind, but yeah, I wanted to dive a little bit into R. So could you please talk more about where did the passion for R come from, and like how did the special connection between you and R the language, like born, how did that come from? >> It was not a love at first sight. >> No. >> Not at all. Not at all. Because that was back in Brazil. So all the documentation were in English, all the tutorials, only two. We had like very few tutorials. It was not like nowadays that we have so many tutorials and courses. There were like two tutorials, other documentation in English. So it's was hard for me like as someone that didn't know much English to go through the language and then to learn to program was not easy task. But then as I was going through the language and learning and reading books and finding the people behind the language, I don't know how I felt in love. And then when I came to to San Francisco, I saw some of like the main contributors who are speaking in person and I'm like, wow, they are like humans. I don't know, it was like, I have no idea why I had this love. But I think the the people and then the community was the thing that kept me with the R language. >> Yeah, the community factors is so important. And it's so, at WIDS it's so palpable. I mean I literally walk in the door, every WIDS I've done, I think I've been doing them for theCUBE since 2017. theCUBE has been here since the beginning in 2015 with our co-founders. But you walk in, you get this sense of belonging. And this sense of I can do anything, why not? Why not me? Look at her up there, and now look at you speaking in the technical talk today on theCUBE. So inspiring. One of the things that I always think is you can't be what you can't see. We need to be able to see more people that look like you and sound like you and like me and like you as well. And WIDS gives us that opportunity, which is fantastic, but it's also helping to move the needle, really. And I was looking at some of the Anitab.org stats just yesterday about 2022. And they're showing, you know, the percentage of females in technical roles has been hovering around 25% for a while. It's a little higher now. I think it's 27.6 according to any to Anitab. We're seeing more women hired in roles. But what are the challenges, and I would love to get your advice on this, for those that might be in this situation is attrition, women who are leaving roles. What would your advice be to a woman who might be trying to navigate family and work and career ladder to stay in that role and keep pushing forward? >> I'll go back to the community. If you don't have a community around you, it's so hard to navigate. >> That's a great point. >> You are lonely. There is no one that you can bounce ideas off, that you can share what you are feeling or like that you can learn as well. So sometimes you feel like you are the only person that is going through that problem or like, you maybe have a family or you are planning to have a family and you have to make a decision. But you've never seen anyone going through this. So when you have a community, you see people like you, right. So that's where we were saying about having different people and people like you so they can share as well. And you feel like, oh yeah, so they went through this, they succeed. I can also go through this and succeed. So I think the attrition problem is still big problem. And I'm sure will be worse now with everything that is happening in Tech with layoffs. >> Yes and the great resignation. >> Yeah. >> We are going back, you know, a few steps, like a lot of like advancements that we did. I feel like we are going back unfortunately, but I always tell this, make sure that you have a community. Make sure that you have a mentor. Make sure that you have someone or some people, not only one mentor, different mentors, that can support you through this trajectory. Because it's not easy. But there are a lot of us out there. >> There really are. And that's a great point. I love everything about the community. It's all about that network effect and feeling like you belong- >> That's all WIDS is about. >> Yeah. >> Yes. Absolutely. >> Like coming over here, it's like seeing the old friends again. It's like I'm so glad that I'm coming because I'm all my old friends that I only see like maybe once a year. >> Tracy: Reunion. >> Yeah, exactly. And I feel like that our tank get, you know- >> Lisa: Replenished. >> Exactly. For the rest of the year. >> Yes. >> Oh, that's precious. >> I love that. >> I agree with that. I think one of the things that when I say, you know, you can't see, I think, well, how many females in technology would I be able to recognize? And of course you can be female technology working in the healthcare sector or working in finance or manufacturing, but, you know, we need to be able to have more that we can see and identify. And one of the things that I recently found out, I was telling Tracy this earlier that I geeked out about was finding out that the CTO of Open AI, ChatGPT, is a female. I'm like, (gasps) why aren't we talking about this more? She was profiled on Fast Company. I've seen a few pieces on her, Mira Murati. But we're hearing so much about ChatJTP being... ChatGPT, I always get that wrong, about being like, likening it to the launch of the iPhone, which revolutionized mobile and connectivity. And here we have a female in the technical role. Let's put her on a pedestal because that is hugely inspiring. >> Exactly, like let's bring everybody to the front. >> Yes. >> Right. >> And let's have them talk to us because like, you didn't know. I didn't know probably about this, right. You didn't know. Like, we don't know about this. It's kind of like we are hidden. We need to give them the spotlight. Every woman to give the spotlight, so they can keep aspiring the new generation. >> Or Susan Wojcicki who ran, how long does she run YouTube? All the YouTube influencers that probably have no idea who are influential for whatever they're doing on YouTube in different social platforms that don't realize, do you realize there was a female behind the helm that for a long time that turned it into what it is today? That's outstanding. Why aren't we talking about this more? >> How about Megan Smith, was the first CTO on the Obama administration. >> That's right. I knew it had to do with Obama. Couldn't remember. Yes. Let's let's find more pedestals. But organizations like WIDS, your involvement as a speaker, showing more people you can be this because you can see it, >> Yeah, exactly. is the right direction that will help hopefully bring us back to some of the pre-pandemic levels, and keep moving forward because there's so much potential with data science that can impact everyone's lives. I always think, you know, we have this expectation that we have our mobile phone and we can get whatever we want wherever we are in the world and whatever time of day it is. And that's all data driven. The regular average person that's not in tech thinks about data as a, well I'm paying for it. What's all these data charges? But it's powering the world. It's powering those experiences that we all want as consumers or in our business lives or we expect to be able to do a transaction, whether it's something in a CRM system or an Uber transaction like that, and have the app respond, maybe even know me a little bit better than I know myself. And that's all data. So I think we're just at the precipice of the massive impact that data science will make in our lives. And luckily we have leaders like you who can help navigate us along this path. >> Thank you. >> What advice for, last question for you is advice for those in the audience who might be nervous or maybe lack a little bit of confidence to go I really like data science, or I really like engineering, but I don't see a lot of me out there. What would you say to them? >> Especially for people who are from like a non-linear track where like going onto that track. >> Yeah, I would say keep going. Keep going. I don't think it's easy. It's not easy. But keep going because the more you go the more, again, you advance and there are opportunities out there. Sometimes it takes a little bit, but just keep going. Keep going and following your dreams, that you get there, right. So again, data science, such a broad field that doesn't require you to come from a specific background. And I think the beauty of data science exactly is this is like the combination, the most successful data science teams are the teams that have all these different backgrounds. So if you think that we as data scientists, we started programming when we were nine, that's not true, right. You can be 30, 40, shifting careers, starting to program right now. It doesn't matter. Like you get there no matter how old you are. And no matter what's your background. >> There's no limit. >> There was no limits. >> I love that, Gabriela, >> Thank so much. for inspiring. I know you inspired me. I'm pretty sure you probably inspired Tracy with your story. And sometimes like what you just said, you have to be your own mentor and that's okay. Because eventually you're going to turn into a mentor for many, many others and sounds like you're already paving that path and we so appreciate it. You are now officially a CUBE alumni. >> Yes. Thank you. >> Yay. We've loved having you. Thank you so much for your time. >> Thank you. Thank you. >> For our guest and for Tracy's Yuan, this is Lisa Martin. We are live at WIDS 23, the eighth annual Women in Data Science Conference at Stanford. Stick around. Our next guest joins us in just a few minutes. (upbeat music)

(upbeat techno music) >> Collibra is a company that was founded in 2008 right before the so-called modern big data era kicked into high gear. The company was one of the first to focus its business on data governance. Now, historically, data governance and data quality initiatives, they were back office functions, and they were largely confined to regulated industries that had to comply with public policy mandates. But as the cloud went mainstream the tech giants showed us how valuable data could become, and the value proposition for data quality and trust, it evolved from primarily a compliance driven issue, to becoming a linchpin of competitive advantage. But, data in the decade of the 2010s was largely about getting the technology to work. You had these highly centralized technical teams that were formed and they had hyper-specialized skills, to develop data architectures and processes, to serve the myriad data needs of organizations. And it resulted in a lot of frustration, with data initiatives for most organizations, that didn't have the resources of the cloud guys and the social media giants, to really attack their data problems and turn data into gold. This is why today, for example, there's quite a bit of momentum to re-thinking monolithic data architectures. You see, you hear about initiatives like Data Mesh and the idea of data as a product. They're gaining traction as a way to better serve the the data needs of decentralized business users. You hear a lot about data democratization. So these decentralization efforts around data, they're great, but they create a new set of problems. Specifically, how do you deliver, like a self-service infrastructure to business users and domain experts? Now the cloud is definitely helping with that but also, how do you automate governance? This becomes especially tricky as protecting data privacy has become more and more important. In other words, while it's enticing to experiment, and run fast and loose with data initiatives, kind of like the Wild West, to find new veins of gold, it has to be done responsibly. As such, the idea of data governance has had to evolve to become more automated and intelligent. Governance and data lineage is still fundamental to ensuring trust as data. It moves like water through an organization. No one is going to use data that is entrusted. Metadata has become increasingly important for data discovery and data classification. As data flows through an organization, the continuously ability to check for data flaws and automating that data quality, they become a functional requirement of any modern data management platform. And finally, data privacy has become a critical adjacency to cyber security. So you can see how data governance has evolved into a much richer set of capabilities than it was 10 or 15 years ago. Hello and welcome to theCUBE's coverage of Data Citizens made possible by Collibra, a leader in so-called Data intelligence and the host of Data Citizens 2022, which is taking place in San Diego. My name is Dave Vellante and I'm one of the hosts of our program which is running in parallel to Data Citizens. Now at theCUBE we like to say we extract the signal from the noise, and over the next couple of days we're going to feature some of the themes from the keynote speakers at Data Citizens, and we'll hear from several of the executives. Felix Van de Maele, who is the co-founder and CEO of Collibra, will join us. Along with one of the other founders of Collibra, Stan Christiaens, who's going to join my colleague Lisa Martin. I'm going to also sit down with Laura Sellers, she's the Chief Product Officer at Collibra. We'll talk about some of the the announcements and innovations they're making at the event, and then we'll dig in further to data quality with Kirk Haslbeck. He's the Vice President of Data Quality at Collibra. He's an amazingly smart dude who founded Owl DQ, a company that he sold to Collibra last year. Now, many companies they didn't make it through the Hadoop era, you know they missed the industry waves and they became driftwood. Collibra, on the other hand, has evolved its business, they've leveraged the cloud, expanded its product portfolio and leaned in heavily to some major partnerships with cloud providers as well as receiving a strategic investment from Snowflake, earlier this year. So, it's a really interesting story that we're thrilled to be sharing with you. Thanks for watching and I hope you enjoy the program. (upbeat rock music) Last year theCUBE covered Data Citizens, Collibra's customer event, and the premise that we put forth prior to that event was that despite all the innovation that's gone on over the last decade or more with data, you know starting with the Hadoop movement, we had Data lakes, we had Spark, the ascendancy of programming languages like Python, the introduction of frameworks like Tensorflow, the rise of AI, Low Code, No Code, et cetera. Businesses still find it's too difficult to get more value from their data initiatives, and we said at the time, you know maybe it's time to rethink data innovation. While a lot of the effort has been focused on, you more efficiently storing and processing data, perhaps more energy needs to go into thinking about the people and the process side of the equation. Meaning, making it easier for domain experts to both gain insights from data, trust the data, and begin to use that data in new ways, fueling data products, monetization, and insights. Data Citizens 2022 is back and we're pleased to have Felix Van de Maele who is the founder and CEO of Collibra. He's on theCUBE. We're excited to have you Felix. Good to see you again. >> Likewise Dave. Thanks for having me again. >> You bet. All right, we're going to get the update from Felix on the current data landscape, how he sees it why data intelligence is more important now than ever, and get current on what Collibra has been up to over the past year, and what's changed since Data citizens 2021, and we may even touch on some of the product news. So Felix, we're living in a very different world today with businesses and consumers. They're struggling with things like supply chains, uncertain economic trends and we're not just snapping back to the 2010s, that's clear, and that's really true as well in the world of data. So what's different in your mind, in the data landscape of the 2020s, from the previous decade, and what challenges does that bring for your customers? >> Yeah, absolutely, and and I think you said it well, Dave and the intro that, that rising complexity and fragmentation, in the broader data landscape, that hasn't gotten any better over the last couple of years. When when we talk to our customers, that level of fragmentation, the complexity, how do we find data that we can trust, that we know we can use, has only gotten more more difficult. So that trend that's continuing, I think what is changing is that trend has become much more acute. Well, the other thing we've seen over the last couple of years is that the level of scrutiny that organizations are under, respect to data, as data becomes more mission critical, as data becomes more impactful than important, the level of scrutiny with respect to privacy, security, regulatory compliance, as only increasing as well. Which again, is really difficult in this environment of continuous innovation, continuous change, continuous growing complexity, and fragmentation. So, it's become much more acute. And to your earlier point, we do live in a different world and and the past couple of years we could probably just kind of brute force it, right? We could focus on, on the top line, there was enough kind of investments to be, to be had. I think nowadays organizations are focused or are, are, are are, are, are in a very different environment where there's much more focus on cost control, productivity, efficiency, how do we truly get the value from that data? So again, I think it just another incentive for organization to now truly look at data and to scale with data, not just from a a technology and infrastructure perspective, but how do we actually scale data from an organizational perspective, right? You said at the, the people and process, how do we do that at scale? And that's only, only, only becoming much more important, and we do believe that the, the economic environment that we find ourselves in today is going to be catalyst for organizations to really take that more seriously if, if, if you will, than they maybe have in the have in the past. >> You know, I don't know when you guys founded Collibra, if you had a sense as to how complicated it was going to get, but you've been on a mission to really address these problems from the beginning. How would you describe your, your, your mission and what are you doing to address these challenges? >> Yeah, absolutely. We, we started Collibra in 2008. So, in some sense and the, the last kind of financial crisis and that was really the, the start of Collibra, where we found product market fit, working with large financial institutions to help them cope with the increasing compliance requirements that they were faced with because of the, of the financial crisis. And kind of here we are again, in a very different environment of course 15 years, almost 15 years later, but data only becoming more important. But our mission to deliver trusted data for every user, every use case and across every source, frankly, has only become more important. So, what has been an incredible journey over the last 14, 15 years, I think we're still relatively early in our mission to again, be able to provide everyone, and that's why we call it Data Citizens, we truly believe that everyone in the organization should be able to use trusted data in an easy, easy matter. That mission is is only becoming more important, more relevant. We definitely have a lot more work ahead of us because we still relatively early in that, in that journey. >> Well that's interesting, because you know, in my observation it takes 7 to 10 years to actually build a company, and then the fact that you're still in the early days is kind of interesting. I mean, you, Collibra's had a good 12 months or so since we last spoke at Data Citizens. Give us the latest update on your business. What do people need to know about your current momentum? >> Yeah, absolutely. Again, there's a lot of tailwind organizations that are only maturing their data practices and we've seen that kind of transform or influence a lot of our business growth that we've seen, broader adoption of the platform. We work at some of the largest organizations in the world with its Adobe, Heineken, Bank of America and many more. We have now over 600 enterprise customers, all industry leaders and every single vertical. So it's, it's really exciting to see that and continue to partner with those organizations. On the partnership side, again, a lot of momentum in the org in the, in the market with some of the cloud partners like Google, Amazon, Snowflake, Data Breaks, and and others, right? As those kind of new modern data infrastructures, modern data architectures, are definitely all moving to the cloud. A great opportunity for us, our partners, and of course our customers, to help them kind of transition to the cloud even faster. And so we see a lot of excitement and momentum there. We did an acquisition about 18 months ago around data quality, data observability, which we believe is an enormous opportunity. Of course data quality isn't new but I think there's a lot of reasons why we're so excited about quality and observability now. One, is around leveraging AI machine learning again to drive more automation. And a second is that those data pipelines, that are now being created in the cloud, in these modern data architecture, architectures, they've become mission critical. They've become real time. And so monitoring, observing those data pipelines continuously, has become absolutely critical so that they're really excited about, about that as well. And on the organizational side, I'm sure you've heard the term around kind of data mesh, something that's gaining a lot of momentum, rightfully so. It's really the type of governance that we always believed in. Federated, focused on domains, giving a lot of ownership to different teams. I think that's the way to scale data organizations, and so that aligns really well with our vision and from a product perspective, we've seen a lot of momentum with our customers there as well. >> Yeah, you know, a couple things there. I mean, the acquisition of OwlDQ, you know Kirk Haslbeck and, and their team. It's interesting, you know the whole data quality used to be this back office function and and really confined to highly regulated industries. It's come to the front office, it's top of mind for Chief Data Officers. Data mesh, you mentioned you guys are a connective tissue for all these different nodes on the data mesh. That's key. And of course we see you at all the shows. You're, you're a critical part of many ecosystems and you're developing your own ecosystem. So, let's chat a little bit about the, the products. We're going to go deeper into products later on, at Data Citizens 22, but we know you're debuting some, some new innovations, you know, whether it's, you know, the the under the covers in security, sort of making data more accessible for people, just dealing with workflows and processes, as you talked about earlier. Tell us a little bit about what you're introducing. >> Yeah, absolutely. We we're super excited, a ton of innovation. And if we think about the big theme and like, like I said, we're still relatively early in this, in this journey towards kind of that mission of data intelligence that really bolts and compelling mission. Either customers are still start, are just starting on that, on that journey. We want to make it as easy as possible for the, for organization to actually get started, because we know that's important that they do. And for our organization and customers, that have been with us for some time, there's still a tremendous amount of opportunity to kind of expand the platform further. And again to make it easier for, really to, to accomplish that mission and vision around that Data Citizen, that everyone has access to trustworthy data in a very easy, easy way. So that's really the theme of a lot of the innovation that we're driving, a lot of kind of ease of adoption, ease of use, but also then, how do we make sure that, as clear becomes this kind of mission critical enterprise platform, from a security performance, architecture scale supportability, that we're truly able to deliver that kind of an enterprise mission critical platform. And so that's the big theme. From an innovation perspective, from a product perspective, a lot of new innovation that we're really excited about. A couple of highlights. One, is around data marketplace. Again, a lot of our customers have plans in that direction, How to make it easy? How do we make How do we make available to true kind of shopping experience? So that anybody in the organization can, in a very easy search first way, find the right data product, find the right dataset, that they can then consume. Usage analytics, how do you, how do we help organizations drive adoption? Tell them where they're working really well and where they have opportunities. Homepages again to, to make things easy for, for people, for anyone in your organization, to kind of get started with Collibra. You mentioned Workflow Designer, again, we have a very powerful enterprise platform, one of our key differentiators is the ability to really drive a lot of automation through workflows. And now we provided a, a new Low-Code, No-Code kind of workflow designer experience. So, so really customers can take it to the next level. There's a lot more new product around Collibra protect, which in partnership with Snowflake, which has been a strategic investor in Collibra, focused on how do we make access governance easier? How do we, how do we, how are we able to make sure that as you move to the cloud, things like access management, masking around sensitive data, PIA data, is managed as a much more effective, effective rate. Really excited about that product. There's more around data quality. Again, how do we, how do we get that deployed as easily, and quickly, and widely as we can? Moving that to the cloud has been a big part of our strategy. So, we launch our data quality cloud product, as well as making use of those, those native compute capabilities and platforms, like Snowflake, Databricks, Google, Amazon, and others. And so we are bettering a capability, a capability that we call push down, so we're actually pushing down the computer and data quality, to monitoring into the underlying platform, which again from a scale performance and ease of use perspective, is going to make a massive difference. And then more broadly, we talked a little bit about the ecosystem. Again, integrations, we talk about being able to connect to every source. Integrations are absolutely critical, and we're really excited to deliver new integrations with Snowflake, Azure and Google Cloud storage as well. So that's a lot coming out, the team has been work, at work really hard, and we are really really excited about what we are coming, what we're bringing to market. >> Yeah, a lot going on there. I wonder if you could give us your, your closing thoughts. I mean, you you talked about, you know, the marketplace, you know you think about Data Mesh, you think of data as product, one of the key principles, you think about monetization. This is really different than what we've been used to in data, which is just getting the technology to work has been, been so hard. So, how do you see sort of the future and, you know give us the, your closing thoughts please? >> Yeah, absolutely. And, and I think we we're really at a pivotal moment and I think you said it well. We, we all know the constraint and the challenges with data, how to actually do data at scale. And while we've seen a ton of innovation on the infrastructure side, we fundamentally believe that just getting a faster database is important, but it's not going to fully solve the challenges and truly kind of deliver on the opportunity. And that's why now is really the time to, deliver this data intelligence vision, this data intelligence platform. We are still early, making it as easy as we can, as kind of our, as our mission. And so I'm really, really excited to see what we, what we are going to, how the marks are going to evolve over the next, next few quarters and years. I think the trend is clearly there. We talked about Data Mesh, this kind of federated approach focus on data products, is just another signal that we believe, that a lot of our organization are now at the time, they're understanding need to go beyond just the technology. I really, really think about how to actually scale data as a business function, just like we've done with IT, with HR, with sales and marketing, with finance. That's how we need to think about data. I think now is the time, given the economic environment that we are in, much more focus on control, much more focus on productivity, efficiency, and now is the time we need to look beyond just the technology and infrastructure to think of how to scale data, how to manage data at scale. >> Yeah, it's a new era. The next 10 years of data won't be like the last, as I always say. Felix, thanks so much. Good luck in, in San Diego. I know you're going to crush it out there. >> Thank you Dave. >> Yeah, it's a great spot for an in-person event and and of course the content post-event is going to be available at collibra.com and you can of course catch theCUBE coverage at theCUBE.net and all the news at siliconangle.com. This is Dave Vellante for theCUBE, your leader in enterprise and emerging tech coverage. (upbeat techno music)

(upbeat music) >> Last year, the Cube covered Data Citizens, Collibra's customer event. And the premise that we put forth prior to that event was that despite all the innovation that's gone on over the last decade or more with data, you know, starting with the Hadoop movement. We had data lakes, we had Spark, the ascendancy of programming languages like Python, the introduction of frameworks like TensorFlow, the rise of AI, low code, no code, et cetera. Businesses still find it's too difficult to get more value from their data initiatives. And we said at the time, you know, maybe it's time to rethink data innovation. While a lot of the effort has been focused on more efficiently storing and processing data, perhaps more energy needs to go into thinking about the people and the process side of the equation, meaning making it easier for domain experts to both gain insights from data, trust the data, and begin to use that data in new ways, fueling data products, monetization, and insights. Data Citizens 2022 is back, and we're pleased to have Felix Van de Maele, who is the founder and CEO of Collibra. He's on the Cube. We're excited to have you, Felix. Good to see you again. >> Likewise Dave. Thanks for having me again. >> You bet. All right, we're going to get the update from Felix on the current data landscape, how he sees it, why data intelligence is more important now than ever, and get current on what Collibra has been up to over the past year, and what's changed since Data Citizens 2021. And we may even touch on some of the product news. So Felix, we're living in a very different world today with businesses and consumers. They're struggling with things like supply chains, uncertain economic trends, and we're not just snapping back to the 2010s. That's clear. And that's really true, as well, in the world of data. So what's different in your mind in the data landscape of the 2020s from the previous decade, and what challenges does that bring for your customers? >> Yeah, absolutely. And I think you said it well, Dave, in the intro that rising complexity and fragmentation in the broader data landscape that hasn't gotten any better over the last couple of years. When we talk to our customers, that level of fragmentation, the complexity, how do we find data that we can trust, that we know we can use, has only gotten kind of more difficult. So that trend is continuing. I think what is changing is that trend has become much more acute. Well, the other thing we've seen over the last couple of years is that the level of scrutiny that organizations are under with respect to data, as data becomes more mission critical, as data becomes more impactful and important, the level of scrutiny with respect to privacy, security, regulatory compliance, is only increasing as well. Which again, is really difficult in this environment of continuous innovation, continuous change, continuous growing complexity and fragmentation. So it's become much more acute. And to your earlier point, we do live in a different world, and the past couple of years, we could probably just kind of brute force it, right? We could focus on the top line. There was enough kind of investments to be had. I think nowadays organizations are focused, or are in a very different environment where there's much more focus on cost control, productivity, efficiency. How do we truly get value from that data? So again, I think it's just another incentive for organizations to now truly look at that data and to scale that data, not just from a technology and infrastructure perspective, but how do we actually scale data from an organizational perspective, right? Like you said, the people and process, how do we do that at scale? And that's only becoming much more important. And we do believe that the economic environment that we find ourselves in today is going to be a catalyst for organizations to really take that more seriously if you will than they maybe have in the past. >> You know, I don't know when you guys founded Collibra, if you had a sense as to how complicated it was going to get, but you've been on a mission to really address these problems from the beginning. How would you describe your mission, and what are you doing to address these challenges? >> Yeah, absolutely. We started Collibra in 2008. So in some sense in the last kind of financial crisis. And that was really the start of Collibra, where we found product market fit working with large financial institutions to help them cope with the increasing compliance requirements that they were faced with because of the financial crisis, and kind of here we are again in a very different environment of course, 15 years, almost 15 years later. But data only becoming more important. But our mission to deliver trusted data for every user, every use case, and across every source, frankly has only become more important. So while it's been an incredible journey over the last 14, 15 years, I think we're still relatively early in our mission to, again, be able to provide everyone, and that's why we call it Data Citizens. We truly believe that everyone in the organization should be able to use trusted data in an easy, easy manner. That mission is only becoming more important, more relevant. We definitely have a lot more work ahead of us because we're still relatively early in that journey. >> Well, that's interesting because, you know, in my observation, it takes seven to 10 years to actually build a company, and then the fact that you're still in the early days is kind of interesting. I mean, Collibra's had a good 12 months or so since we last spoke at Data Citizens. Give us the latest update on your business. What do people need to know about your your current momentum? >> Yeah, absolutely. Again, there's a lot of tailwinds, organizations are only maturing their data practices, and we've seen it kind of transform, or influence a lot of our business growth that we've seen, broader adoption of the platform. We work at some of the largest organizations in the world, whether it's Adobe, Heineken, Bank of America, and many more. We have now over 600 enterprise customers, all industry leaders and every single vertical. So it's really exciting to see that and continue to partner with those organizations. On the partnership side, again, a lot of momentum in the market with some of the cloud partners like Google, Amazon, Snowflake, Databricks, and others, right? As those kind of new modern data infrastructures, modern data architectures, are definitely all moving to the cloud. A great opportunity for us, our partners, and of course our customers, to help them kind of transition to the cloud even faster. And so we see a lot of excitement and momentum there. We did an acquisition about 18 months ago around data quality, data observability, which we believe is an enormous opportunity. Of course data quality isn't new, but I think there's a lot of reasons why we're so excited about quality and observability now. One is around leveraging AI, machine learning, again to drive more automation. And the second is that those data pipelines that are now being created in the cloud, in these modern data architectures, they've become mission critical. They've become real time. And so monitoring, observing those data pipelines continuously has become absolutely critical. So we're really excited about that as well. And on the organizational side, I'm sure you've heard a term around kind of data mesh, something that's gaining a lot of momentum, rightfully so. It's really the type of governance that we always believed in. Federated, focused on domains, giving a lot of ownership to different teams. I think that's the way to scale the data organizations, and so that aligns really well with our vision, and from a product perspective, we've seen a lot of momentum with our customers there as well. >> Yeah, you know, a couple things there. I mean, the acquisition of OwlDQ, you know, Kirk Haslbeck and their team, it's interesting, you know, the whole data quality used to be this back office function and really confined to highly regulated industries. It's come to the front office, it's top of mind for chief data officers, data mesh, you mentioned. You guys are a connective tissue for all these different nodes on the data mesh. That's key. And of course we see you at all the shows. You're a critical part of many ecosystems, and you're developing your own ecosystem. So let's chat a little bit about the products. We're going to go deeper into products later on at Data Citizens '22, but we know you're debuting some new innovations, you know, whether it's, you know, the under the covers in security, sort of making data more accessible for people, just dealing with workflows and processes as you talked about earlier. Tell us a little bit about what you're introducing. >> Yeah, absolutely. We're super excited, a ton of innovation. And if we think about the big theme, and like I said, we're still relatively early in this journey towards kind of that mission of data intelligence, that really bold and compelling mission. Either customers are just starting on that journey, and we want to make it as easy as possible for the organization to actually get started, because we know that's important that they do. And for our organization and customers that have been with us for some time, there's still a tremendous amount of opportunity to kind of expand the platform further. And again, to make it easier for, really to accomplish that mission and vision around that data citizen that everyone has access to trustworthy data in a very easy, easy way. So that's really the theme of a lot of the innovation that we're driving, a lot of kind of ease of adoption, ease of use, but also then, how do we make sure that as Collibra becomes this kind of mission critical enterprise platform from a security performance architecture scale, supportability that we're truly able to deliver that kind of an enterprise mission critical platform. And so that's the big theme. From an innovation perspective, from a product perspective, a lot of new innovation that we're really excited about. A couple of highlights. One is around data marketplace. Again, a lot of our customers have plans in that direction. How do we make it easy? How do we make available a true kind of shopping experience so that anybody in your organization can, in a very easy search first way, find the right data product, find the right data set that data can then consume, use its analytics. How do we help organizations drive adoption, tell them where they're working really well, and where they have opportunities. Home pages, again, to make things easy for people, for anyone in your organization, to kind of get started with Collibra. You mentioned workflow designer, again, we have a very powerful enterprise platform. One of our key differentiators is the ability to really drive a lot of automation through workflows. And now we provided a new low code, no code, kind of workflow designer experience. So really customers can take it to the next level. There's a lot more new product around Collibra Protect, which in partnership with Snowflake, which has been a strategic investor in Collibra, focused on how do we make access governance easier? How do we, how are we able to make sure that as you move to the cloud, things like access management, masking around sensitive data, PII data, is managed in a much more effective way. Really excited about that product. There's more around data quality. Again, how do we get that deployed as easily and quickly and widely as we can? Moving that to the cloud has been a big part of our strategy. So we launched our data quality cloud product as well as making use of those native compute capabilities in platforms like Snowflake, Databricks, Google, Amazon, and others. And so we are bettering a capability that we call push down. So we're actually pushing down the computer and data quality, the monitoring, into the underlying platform, which again, from a scale performance and ease of use perspective is going to make a massive difference. And then more broadly, we talked a little bit about the ecosystem. Again, integrations that we talk about, being able to connect to every source. Integrations are absolutely critical, and we're really excited to deliver new integrations with Snowflake, Azure, and Google Cloud Storage as well. So there's a lot coming out. The team has been at work really hard, and we are really, really excited about what we are coming, what we're bringing to markets. >> Yeah, a lot going on there. I wonder if you could give us your closing thoughts. I mean, you talked about the marketplace, you know, you think about data mesh, you think of data as product, one of the key principles. You think about monetization. This is really different than what we've been used to in data, which is just getting the technology to work has been been so hard, so how do you see sort of the future? And, you know, give us your closing thoughts please. >> Yeah, absolutely. And I think we're really at this pivotal moment, and I think you said it well. We all know the constraint and the challenges with data, how to actually do data at scale. And while we've seen a ton of innovation on the infrastructure side, we fundamentally believe that just getting a faster database is important, but it's not going to fully solve the challenges and truly kind of deliver on the opportunity. And that's why now is really the time to deliver this data intelligence vision, the data intelligence platform. We are still early, making it as easy as we can. It's kind of our, as our mission. And so I'm really, really excited to see what we are going to, how the markets are going to evolve over the next few quarters and years. I think the trend is clearly there, when we talk about data mesh, this kind of federated approach, focus on data products is just another signal that we believe that a lot of our organizations are now at the time, they understand the need to go beyond just the technology, how to really, really think about how to actually scale data as a business function, just like we've done with IT, with HR, with sales and marketing, with finance. That's how we need to think about data. I think now's the time given the economic environment that we are in, much more focus on control, much more focus on productivity, efficiency, and now's the time we need to look beyond just the technology and infrastructure to think of how to scale data, how to manage data at scale. >> Yeah, it's a new era. The next 10 years of data won't be like the last, as I always say. Felix, thanks so much, and good luck in San Diego. I know you're going to crush it out there. >> Thank you Dave. >> Yeah, it's a great spot for an in person event, and of course, the content post event is going to be available at collibra.com, and you can of course catch the Cube coverage at thecube.net, and all the news at siliconangle.com. This is Dave Vellante for the Cube, your leader in enterprise and emerging tech coverage. (light music)

(bright upbeat music) >> Welcome back, everyone, to the "Supercloud 22." I'm John Furrier, host of "The Cube." This is the Cloud-erati panel, the distinguished experts who have been there from day one, watching the cloud grow, from building clouds, and all open source stuff as well. Just great stuff. Good friends of "The Cube," and great to introduce back on "The Cube," Adrian Cockcroft, formerly with Netflix, formerly AWS, retired, now commentating here in "The Cube," as well as other events. Great to see you back out there, Adrian. Lori MacVittie, Cloud Evangelist with F5, also wrote a great blog post on supercloud, as well as Dave Vellante as well, setting up the supercloud conversation, which we're going to get into, and Chris Hoff, who's the CTO and CSO of LastPass who's been building clouds, and we know him from "The Cube" before with security and cloud commentary. Welcome, all, back to "The Cube" and supercloud. >> Thanks, John. >> Hi. >> All right, Lori, we'll start with you to get things going. I want to try to sit back, as you guys are awesome experts, and involved from building, and in the trenches, on the front lines, and Adrian's coming out of retirement, but Lori, you wrote the post setting the table on supercloud. Let's start with you. What is supercloud? What is it evolving into? What is the north star, from your perspective? >> Well, I don't think there's a north star yet. I think that's one of the reasons I wrote it, because I had a clear picture of this in my mind, but over the past, I don't know, three, four years, I keep seeing, in research, my own and others', complexity, multi-cloud. "We can't manage it. They're all different. "We have trouble. What's going on? "We can't do anything right." And so digging into it, you start looking into, "Well, what do you mean by complexity?" Well, security. Migration, visibility, performance. The same old problems we've always had. And so, supercloud is a concept that is supposed to overlay all of the clouds and normalize it. That's really what we're talking about, is yet another abstraction layer that would provide some consistency that would allow you to do the same security and monitor things correctly. Cornell University actually put out a definition way back in 2016. And they said, "It's an architecture that enables migration "across different zones or providers," and I think that's important, "and provides interfaces to everything, "makes it consistent, and normalizes the network," basically brings it all together, but it also extends to private clouds. Sometimes we forget about that piece of it, and I think that's important in this, so that all your clouds look the same. So supercloud, big layer on top, makes everything wonderful. It's unicorns again. >> It's interesting. We had multiple perspectives. (mumbles) was like Snowflake, who built on top of AWS. Jerry Chan, who we heard from earlier today, Greylock Penn's "Castles in the Cloud" saying, "Hey, you can have a moat, "you can build an advantage and have differentiation," so startups are starting to build on clouds, that's the native cloud view, and then, of course, they get success and they go to all the other clouds 'cause they got customers in the ecosystem, but it seems that all the cloud players, Chris, you commented before we came on today, is that they're all fighting for the customer's workloads on their infrastructure. "Come bring your stuff over to here, "and we'll make it run better." And all your developers are going to be good. Is there a problem? I mean, or is this something else happening here? Is there a real problem? >> Well, I think the north star's over there, by the way, Lori. (laughing) >> Oh, there it is. >> Right there. The supercloud north star. So indeed I think there are opportunities. Whether you call them problems or not, John, I think is to be determined. Most companies have, especially if they're a large enterprise, whether or not they've got an investment in private cloud or not, have spent time really trying to optimize their engineering and workload placement on a single cloud. And that, regardless of your choice, as we take the big three, whether it's Amazon, Google, or Microsoft, each of them have their pros and cons for various types of workloads. And so you'll see a lot of folks optimizing for a particular cloud, and it takes a huge effort up and down the stack to just get a single cloud right. That doesn't take into consideration integrations with software as a service, instantiated, oftentimes, on top of infrastructure of the service that you need to supplement where the obstruction layer ends in infrastructure of the service. You've seen most IS players starting to now move up-chain, as we predicted years ago, to platform as a service, but platforms of various types. So I definitely see it as an opportunity. Previous employers have had multiple clouds, but they were very specifically optimized for the types of workloads, for example, in, let's say, AWS versus GCP, based on the need for different types and optimized compute platforms that each of those providers ran. We never, in that particular case, thought about necessarily running the same workloads across both clouds, because they had different pricing models, different security models, et cetera. And so the challenge is really coming down to the fact that, what is the cost benefit analysis of thinking about multi-cloud when you can potentially engineer the resiliency or redundancy, all the in-season "ilities" that you might need to factor into your deployments on a single cloud, if they are investing at the pace in which they are? So I think it's an opportunity, and it's one that continues to evolve, but this just reminds me, your comments remind me, of when we were talking about OpenStack versus AWS. "Oh, if there were only APIs that existed "that everybody could use," and you saw how that went. So I think that the challenge there is, what is the impetus for a singular cloud provider, any of the big three, deciding that they're going to abstract to a single abstraction layer and not be able to differentiate from the competitors? >> Yeah, and that differentiation's going to be big. I mean, assume that the clouds aren't going to stay still like AWS and just not stop innovating. We see the devs are doing great, Adrian, open source is bigger and better than ever, but now that's been commercialized into enterprise. It's an ops problem. So to Chris's point, the cost benefit analysis is interesting, because do companies have to spin up multiple operations teams, each with specialized training and tooling for the clouds that they're using, and does that open up a can of worms, or is that a good thing? I mean, can you design for this? I mean, is there an architecture or taxonomy that makes it work, or is it just the cart before the horse, the solution before the problem? >> Yeah, well, I think that if you look at any large vendor... Sorry, large customer, they've got a bit of everything already. If you're big enough, you've bought something from everybody at some point. So then you're trying to rationalize that, and trying to make it make sense. And I think there's two ways of looking at multi-cloud or supercloud, and one is that the... And practically, people go best of breed. They say, "Okay, I'm going to get my email "from Google or Microsoft. "I'm going to run my applications on AWS. "Maybe I'm going to do some AI machine learning on Google, "'cause those are the strengths of the platforms." So people tend to go where the strength is. So that's multi-cloud, 'cause you're using multiple clouds, and you still have to move data and make sure they're all working together. But then what Lori's talking about is trying to make them all look the same and trying to get all the security architectures to be the same and put this magical layer, this unicorn magical layer that, "Let's make them all look the same." And this is something that the CIOs have wanted for years, and they keep trying to buy it, and you can sell it, but the trouble is it's really hard to deliver. And I think, when I go back to some old friends of ours at Enstratius who had... And back in the early days of cloud, said, "Well, we'll just do an API that abstracts "all the cloud APIs into one layer." Enstratius ended up being sold to Dell a few years ago, and the problem they had was that... They didn't have any problem selling it. The problem they had was, a year later, when it came up for renewal, the developers all done end runs around it were ignoring it, and the CIOs weren't seeing usage. So you can sell it, but can you actually implement it and make it work well enough that it actually becomes part of your core architecture without, from an operations point of view, without having the developers going directly to their favorite APIs around them? And I'm not sure that you can really lock an organization down enough to get them onto a layer like that. So that's the way I see it. >> You just defined- >> You just defined shadow shadow IT. (laughing) That's pretty- (crosstalk) >> Shadow shadow IT, yeah. >> Yeah, shadow shadow it. >> Yeah. >> Yeah. >> I mean, this brings up the question, I mean, is there really a problem? I mean, I guess we'll just jump to it. What is supercloud? If you can have the magic outcome, what is it? Enstratius rendered in with automation? The security issues? Kubernetes is hot. What is the supercloud dream? I guess that's the question. >> I think it's got easier than it was five, 10 years ago. Kubernetes gives you a bunch of APIs that are common across lots of different areas, things like Snowflake or MongoDB Atlas. There are SaaS-based services, which are across multiple clouds from vendors that you've picked. So it's easier to build things which are more portable, but I still don't think it's easy to build this magic API that makes them all look the same. And I think that you're going to have leaky abstractions and security being... Getting the security right's going to be really much more complex than people think. >> What about specialty superclouds, Chris? What's your view on that? >> Yeah, I think what Adrian is alluding to, those leaky abstractions, are interesting, especially from the security perspective, 'cause I think what you see is if you were to happen to be able to thin slice across a set of specific types of workloads, there is a high probability given today that, at least on two of the three major clouds, you could get SaaS providers that sit on those same infrastructure of the service clouds for you, string them together, and have a service that technically is abstracted enough from the things you care about to work on one, two, or three, maybe not all of them, but most SaaS providers in the security space, or identity space, data space, for example, coexist on at least Microsoft and AWS, if not all three, with Google. And so you could technically abstract a service to the point that you let that level of abstract... Like Lori said, no computer science problem could not be... So, no computer science problem can't be solved with more layers of abstraction or misdirection... Or redirection. And in that particular case, if you happen to pick the right vendors that run on all three clouds, you could possibly get close. But then what that really talks about is then, if you built your seven-layer dip model, then you really have specialty superclouds spanning across infrastructure of the service clouds. One for your identity apps, one for data and data layers, to normalize that, one for security, but at what cost? Because you're going to be charged not for that service as a whole, but based on compute resources, based on how these vendors charge across each cloud. So again, that cost-benefit ratio might start being something that is rather imposing from a budgetary perspective. >> Lori, weigh in on this, because the enterprise people love to solve complexity with more complexity. Here, we need to go the other way. It's a commodity. So there has to be a better way. >> I think I'm hearing two fundamental assumptions. One, that a supercloud would force the existing big three to implement some sort of equal API. Don't agree with that. There's no business case for that. There's no reason that could compel them to do that. Otherwise, we would've convinced them to do that, what? 10, 15 years ago when we said we need to be interoperable. So it's not going to happen there. They don't have a good reason to do that. There's no business justification for that. The other presumption, I think, is that we would... That it's more about the services, the differentiated services, that are offered by all of these particular providers, as opposed to treating the core IaaS as the commodity it is. It's compute, it's some storage, it's some networking. Look at that piece. Now, pull those together by... And it's not OpenStack. That's not the answer, it wasn't the answer, it's not the answer now, but something that can actually pull those together and abstract it at a different layer. So cloud providers don't have to change, 'cause they're not going to change, but if someone else were to build that architecture to say, "all right, I'm going to treat all of this compute "so you can run your workloads," as Chris pointed out, "in the best place possible. "And we'll help you do that "by being able to provide those cost benefit analysis, "'What's the best performance, what are you doing,' "And then provide that as a layer." So I think that's really where supercloud is going, 'cause I think that's what a lot of the market actually wants in terms of where they want to run their workloads, because we're seeing that they want to run workloads at the edge, "a lot closer to me," which is yet another factor that we have to consider, and how are you going to be moving individual workloads around? That's the holy grail. Let's move individual workloads to where they're the best performance, the security, cost optimized, and then one layer up. >> Yeah, I think so- >> John Considine, who ultimately ran CloudSwitch, that sold to Verizon, as well as Tom Gillis, who built Bracket, are both rolling in their graves, 'cause what you just described was exactly that. (Lori laughing) Well, they're not even dead yet, so I can't say they're rolling in their graves. Sorry, Tom. Sorry, John. >> Well, how do hyperscalers keep their advantage with all this? I mean, to that point. >> Native services and managed services on top of it. Look how many flavors of managed Kubernetes you have. So you have a choice. Roll your own, or go with a managed service, and then differentiate based on the ability to take away and simplify some of that complexity. Doesn't mean it's more secure necessarily, but I do think we're seeing opportunities where those guys are fighting tooth and nail to keep you on a singular cloud, even though, to Lori's point, I agree, I don't think it's about standardized APIs, 'cause I think that's never going to happen. I do think, though, that SaaS-y supercloud model that we were talking about, layering SaaS that happens to span all the three infrastructure of the service are probably more in line with what Lori was talking about. But I do think that portability of workload is given to you today within lots of ways. But again, how much do you manage, and how much performance do you give up by running additional abstraction layers? And how much security do you give up by having to roll your own and manage that? Because the whole point was, in many cases... Cloud is using other people's computers, so in many cases, I want to manage as little of it as I possibly can. >> I like this whole SaaS angle, because if you had the old days, you're on Amazon Web Services, hey, if you build a SaaS application that runs on Amazon, you're all great, you're born in the cloud, just like that generations of startups. Great. Now when you have this super pass layer, as Dave Vellante was riffing on his analysis, and Lori, you were getting into this pass layer that's kind of like SaaS-y, what's the SaaS equation look like? Because that, to me, sounds like a supercloud version of saying, "I have a workload that runs on all the clouds equally." I just don't think that's ever going to happen. I agree with you, Chris, on that one. But I do see that you can have an abstraction that says, "Hey, I don't really want to get in the weeds. "I don't want to spend a lot of ops time on this. "I just want it to run effectively, and magic happens," or, as you said, some layer there. How does that work? How do you see this super pass layer, if anything, enabling a different SaaS game? >> I think you hit on it there. The last like 10 or so years, we've been all focused on developers and developer productivity, and it's all about the developer experience, and it's got to be good for them, 'cause they're the kings. And I think the next 10 years are going to be very focused on operations, because once you start scaling out, it's not about developers. They can deliver fast or slow, it doesn't matter, but if you can't scale it out, then you've got a real problem. So I think that's an important part of it, is really, what is the ops experience, and what is the best way to get those costs down? And this would serve that purpose if it was done right, which, we can argue about whether that's possible or not, but I don't have to implement it, so I can say it's possible. >> Well, are we going to be getting into infrastructure as code moves into "everything is code," security, data, (laughs) applications is code? I mean, "blank" is code, fill in the blank. (Lori laughing) >> Yeah, we're seeing more of that with things like CDK and Pulumi, where you are actually coding up using a real language rather than the death by YAML or whatever. How much YAML can you take? But actually having a real language so you're not trying to do things in parsing languages. So I think that's an interesting trend. You're getting some interesting templates, and I like what... I mean, the counterexample is that if you just go deep on one vendor, then maybe you can go faster and it is simpler. And one of my favorite vendor... Favorite customers right now that I've been talking to is Liberty Mutual. Went very deep and serverless first on AWS. They're just doing everything there, and they're using CDK Patterns to do it, and they're going extremely fast. There's a book coming out called "The Value Flywheel" by Dave Anderson, it's coming out in a few months, to just detail what they're doing, but that's the counterargument. If you could pick one vendor, you can go faster, you can get that vendor to do more for you, and maybe get a bigger discount so you're not splitting your discounts across vendors. So that's one aspect of it. But I think, fundamentally, you're going to find the CIOs and the ops people generally don't like sitting on one vendor. And if that single vendor is a horizontal platform that's trying to make all the clouds look the same, now you're locked into whatever that platform was. You've still got a platform there. There's still something. So I think that's always going to be something that the CIOs want, but the developers are always going to just pick whatever the best tool for building the thing is. And a analogy here is that the developers are dating and getting married, and then the operations people are running the family and getting divorced. And all the bad parts of that cycle are in the divorce end of it. You're trying to get out of a vendor, there's lawyers, it's just a big mess. >> Who's the lawyer in this example? (crosstalk) >> Well... (laughing) >> Great example. (crosstalk) >> That's why ops people don't like lock-in, because they're the ones trying to unlock. They aren't the ones doing the lock-in. They're the ones unlocking, when developers, if you separate the two, are the ones who are going, picking, having the fun part of it, going, trying a new thing. So they're chasing a shiny object, and then the ops people are trying to untangle themselves from the remains of that shiny object a few years later. So- >> Aren't we- >> One way of fixing that is to push it all together and make it more DevOps-y. >> Yeah, that's right. >> But that's trying to put all the responsibilities in one place, like more continuous improvement, but... >> Chris, what's your reaction to that? Because you're- >> No, that's exactly what I was going to bring up, yeah, John. And 'cause we keep saying "devs," "dev," and "ops" and I've heard somewhere you can glue those two things together. Heck, you could even include "sec" in the middle of it, and "DevSecOps." So what's interesting about what Adrian's saying though, too, is I think this has a lot to do with how you structure your engineering teams and how you think about development versus operations and security. So I'm building out a team now that very much makes use of, thanks to my brilliant VP of Engineering, a "Team Topologies" approach, which is a very streamlined and product oriented way of thinking about, for example, in engineering, if you think about team structures, you might have people that build the front end, build the middle tier, and the back end, and then you have a product that needs to make use of all three components in some form. So just from getting stuff done, their ability then has to tie to three different groups, versus building a team that's streamlined that ends up having front end, middleware, and backend folks that understand and share standards but are able to uncork the velocity that's required to do that. So if you think about that, and not just from an engineering development perspective, but then you couple in operations as a foundational layer that services them with embedded capabilities, we're putting engineers and operations teams embedded in those streamlined teams so that they can run at the velocity that they need to, they can do continuous integration, they can do continuous deployment. And then we added CS, which is continuously secure, continuous security. So instead of having giant, centralized teams, we're thinking there's a core team, for example, a foundational team, that services platform, makes sure all the trains are running on time, that we're doing what we need to do foundationally to make the environments fully dev and operator and security people functional. But then ultimately, we don't have these big, monolithic teams that get into turf wars. So, to Adrian's point about, the operators don't like to be paned in, well, they actually have a say, ultimately, in how they architect, deploy, manage, plan, build, and operate those systems. But at the same point in time, we're all looking at that problem across those teams and go... Like if one streamline team says, "I really want to go run on Azure, "because I like their services better," the reality is the foundational team has a larger vote versus opinion on whether or not, functionally, we can satisfy all of the requirements of the other team. Now, they may make a fantastic business case and we play rock, paper, scissors, and we do that. Right now, that hasn't really happened. We look at the balance of AWS, we are picking SaaS-y, supercloud vendors that will, by the way, happen to run on three platforms, if we so choose to expand there. So we have a similar interface, similar capability, similar processes, but we've made the choice at LastPass to go all in on AWS currently, with respect to how we deliver our products, for all the reasons we just talked about. But I do think that operations model and how you build your teams is extremely important. >> Yeah, and to that point- >> And has the- (crosstalk) >> The vendors themselves need optionality to the customer, what you're saying. So, "I'm going to go fast, "but I need to have that optionality." I guess the question I have for you guys is, what is today's trade-off? So if the decision point today is... First of all, I love the go-fast model on one cloud. I think that's my favorite when I look at all this, and then with the option, knowing that I'm going to have the option to go to multiple clouds. But everybody wants lock-in on the vendor side. Is that scale, is that data advantage? I mean, so the lock-in's a good question, and then also the trade-offs. What do people have to do today to go on a supercloud journey to have an ideal architecture and taxonomy, and what's the right trade-offs today? >> I think that the- Sorry, just put a comment and then let Lori get a word in, but there's a lot of... A lot of the market here is you're building a product, and that product is a SaaS product, and it needs to run somewhere. And the customers that you're going to... To get the full market, you need to go across multiple suppliers, most people doing AWS and Azure, and then with Google occasionally for some people. But that, I think, has become the pattern that most of the large SaaS platforms that you'd want to build out of, 'cause that's the fast way of getting something that's going to be stable at scale, it's got functionality, you'd have to go invest in building it and running it. Those platforms are just multi-cloud platforms, they're running across them. So Snowflake, for example, has to figure out how to make their stuff work on more than one cloud. I mean, they started on one, but they're going across clouds. And I think that that is just the way it's going to be, because you're not going to get a broad enough view into the market, because there isn't a single... AWS doesn't have 100% of the market. It's maybe a bit more than them, but Azure has got a pretty solid set of markets where it is strong, and it's market by market. So in some areas, different people in some places in the world, and different vertical markets, you'll find different preferences. And if you want to be across all of them with your data product, or whatever your SaaS product is, you're just going to have to figure this out. So in some sense, the supercloud story plays best with those SaaS providers like the Snowflakes of this world, I think. >> Lori? >> Yeah, I think the SaaS product... Identity, whatever, you're going to have specialized. SaaS, superclouds. We already see that emerging. Identity is becoming like this big SaaS play that crosses all clouds. It's not just for one. So you get an evolution going on where, yes, I mean, every vendor who provides some kind of specific functionality is going to have to build out and be multi-cloud, as it were. It's got to work equally across them. And the challenge, then, for them is to make it simple for both operators and, if required, dev. And maybe that's the other lesson moving forward. You can build something that is heaven for ops, but if the developers won't use it, well, then you're not going to get it adopted. But if you make it heaven for the developers, the ops team may not be able to keep it secure, keep everything. So maybe we have to start focusing on both, make it friendly for both, at least. Maybe it won't be the perfect experience, but gee, at least make it usable for both sides of the equation so that everyone can actually work in concert, like Chris was saying. A more comprehensive, cohesive approach to delivery and deployment. >> All right, well, wrapping up here, I want to just get one final comment from you guys, if you don't mind. What does supercloud look like in five years? What's the Nirvana, what's the steady state of supercloud in five to 10 years? Or say 10 years, make it easier. (crosstalk) Five to 10 years. Chris, we'll start with you. >> Wow. >> Supercloud, what's it look like? >> Geez. A magic pane, a single pane of glass. (laughs) >> Yeah, I think- >> Single glass of pain. >> Yeah, a single glass of pain. Thank you. You stole my line. Well, not mine, but that's the one I was going to use. Yeah, I think what is really fascinating is ultimately, to answer that question, I would reflect on market consolidation and market dynamics that happens even in the SaaS space. So we will see SaaS companies combining in focal areas to be able to leverage the positions, let's say, in the identity space that somebody has built to provide a set of compelling services that help abstract that identity problem or that security problem or that instrumentation and observability problem. So take your favorite vendors today. I think what we'll end up seeing is more consolidation in SaaS offerings that run on top of infrastructure of the service offerings to where a supercloud might look like something I described before. You have the combination of your favorite interoperable identity, observability, security, orchestration platforms run across them. They're sold as a stack, whether it be co-branded by an enterprise vendor that sells all of that and manages it for you or not. But I do think that... You talked about, I think you said, "Is this an innovator's dilemma?" No, I think it's an integrator's dilemma, as it has always ultimately been. As soon as you get from Genesis to Bespoke Build to product to then commoditization, the cycle starts anew. And I think we've gotten past commoditization, and we're looking at niche areas. So I see just the evolution, not necessarily a revolution, of what we're dealing with today as we see more consolidation in the marketplace. >> Lori, what's your take? Five years, 10 years, what does supercloud look like? >> Part of me wants to take the pie in the sky unicorn approach. "No, it will be beautiful. "One button, and things will happen," but I've seen this cycle many times before, and that's not going to happen. And I think Chris has got it pretty close to what I see already evolving. Those different kinds of super services, basically. And that's really what we're talking about. We call them SaaS, but they're... X is a service. Everything is a service, and it's really a supercloud that can run anywhere, but it presents a different interface, because, well, it's easier. And I think that's where we're going to go, and that's just going to get more refined. And yes, a lot of consolidation, especially on the observability side, but that's also starting to consume the security side, which is really interesting to watch. So that could be a little different supercloud coming on there that's really focused on specific types of security, at least, that we'll layer across, and then we'll just hook them all together. It's an API first world, and it seems like that's going to be our standard for the next while of how we integrate everything. So superclouds or APIs. >> Awesome. Adrian... Adrian, take us home. >> Yeah, sure. >> What's your- I think, and just picking up on Lori's point that these are web services, meaning that you can just call them from anywhere, they don't have to run everything in one place, they can stitch it together, and that's really meant... It's somewhat composable. So in practice, people are going to be composable. Can they compose their applications on multiple platforms? But I think the interesting thing here is what the vendors do, and what I'm seeing is vendors running software on other vendors. So you have Google building platforms that, then, they will support on AWS and Azure and vice versa. You've got AWS's distro of Kubernetes, which they now give you as a distro so you can run it on another platform. So I think that trend's going to continue, and it's going to be, possibly, you pick, say, an AWS or a Google software stack, but you don't run it all on AWS, you run it in multiple places. Yeah, and then the other thing is the third tier, second, third tier vendors, like, I mean, what's IBM doing? I think in five years time, IBM is going to be a SaaS vendor running on the other clouds. I mean, they're already halfway there. To be a bit more controversial, I guess it's always fun to... Like I don't work for a corporate entity now. No one tells me what I can say. >> Bring it on. >> How long can Google keep losing a billion dollars a quarter? They've either got to figure out how to make money out of this thing, or they'll end up basically being a software stack on another cloud platform as their, likely, actual way they can make money on it. Because you've got to... And maybe Oracle, is that a viable cloud platform that... You've got to get to some level of viability. And I think the second, third tier of vendors in five, 10 years are going to be running on the primary platform. And I think, just the other final thing that's really driving this right now. If you try and place an order right now for a piece of equipment for your data center, key pieces of equipment are a year out. It's like trying to buy a new fridge from like Sub-Zero or something like that. And it's like, it's a year. You got to wait for these things. Any high quality piece of equipment. So you go to deploy in your data center, and it's like, "I can't get stuff in my data center. "Like, the key pieces I need, I can't deploy a whole system. "We didn't get bits and pieces of it." So people are going to be cobbling together, or they're going, "No, this is going to cloud, because the cloud vendors "have a much stronger supply chain to just be able "to give you the system you need. "They've got the capacity." So I think we're going to see some pandemic and supply chain induced forced cloud migrations, just because you can't build stuff anymore outside the- >> We got to accelerate supercloud, 'cause they have the supply. They are the chain. >> That's super smart. That's the benefit of going last. So I'm going to scoop in real quick. I can't believe we can call this "Web3 Supercloud," because none of us said "Web3." Don't forget DAO. (crosstalk) (indistinct) You have blockchain, blockchain superclouds. I mean, there's some very interesting distributed computing stuff there, but we'll have to do- >> (crosstalk) We're going to call that the "Cubeverse." The "Cubeverse" is coming. >> Oh, the "Cubeverse." All right. >> We will be... >> That's very meta. >> In the metaverse, Cubeverse soon. >> "Stupor cloud," perhaps. But anyway, great points, Adrian and Lori. Loved it. >> Chris, great to see you. Adrian, Lori, thanks for coming on. We've known each other for a long time. You guys are part of the cloud-erati, the group that has been in there from day one, and watched it evolve, and you get the scar tissue to prove it, and the experience. So thank you so much for sharing your commentary. We'll roll this up and make it open to everybody as additional content. We'll call this the "outtakes," the longer version. But really appreciate your time, thank you. >> Thank you. >> Thanks so much. >> Okay, we'll be back with more "Supercloud 22" right after this. (bright upbeat music)

>>From the cube studios in Palo Alto, in Boston, bringing you data driven insights from the cube and ETR. This is breaking analysis with Dave ante. >>When Frank sluman took service, now public many people undervalued the company, positioning it as just a better help desk tool. You know, it turns out that the firm actually had a massive Tam expansion opportunity in it. SM customer service, HR, logistics, security marketing, and service management. Generally now stock price followed over the years, the stellar execution under Slootman and CFO, Mike scar Kelly's leadership. Now, when they took the reins at snowflake expectations were already set that they'd repeat the feet, but this time, if anything, the company was overvalued out of the gate, the thing is people didn't really better understand the market opportunity this time around, other than that, it was a bet on Salman's track record of execution and on data, pretty good bets, but folks really didn't appreciate that snowflake. Wasn't just a better data warehouse that it was building what they call a data cloud, and we've turned a data super cloud. >>Hello and welcome to this. Week's Wikibon cube insights powered by ETR in this breaking analysis, we'll do four things. First. We're gonna review the recent narrative and concerns about snowflake and its value. Second, we're gonna share survey data from ETR that will confirm precisely what the company's CFO has been telling anyone who will listen. And third, we're gonna share our view of what snowflake is building IE, trying to become the defacto standard data platform, and four convey our expectations for the upcoming snowflake summit. Next week at Caesar's palace in Las Vegas, Snowflake's most recent quarterly results they've been well covered and well documented. It basically hit its targets, which for snowflake investors was bad news wall street piled on expressing concerns about Snowflake's consumption, pricing model, slowing growth rates, lack of profitability and valuation. Given the, given the current macro market conditions, the stock dropped below its IPO offering price, which you couldn't touch on day one, by the way, as the stock opened well above that and, and certainly closed well above that price of one 20 and folks express concerns about some pretty massive insider selling throughout 2021 and early 2022, all this caused the stock price to drop quite substantially. >>And today it's down around 63% or more year to date, but the only real substantive change in the company's business is that some of its largest consumer facing companies, while still growing dialed back, their consumption this past quarter, the tone of the call was I wouldn't say contentious the earnings call, but Scarelli, I think was getting somewhat annoyed with the implication from some analyst questions that something is fundamentally wrong with Snowflake's business. So let's unpack this a bit first. I wanna talk about the consumption pricing on the earnings call. One of the analysts asked if snowflake would consider more of a subscription based model so that they could better weather such fluctuations and demand before the analyst could even finish the question, CFO Scarelli emphatically interrupted and said, no, <laugh> the analyst might as well have asked, Hey Mike, have you ever considered changing your pricing model and screwing your customers the same way most legacy SaaS companies lock their customers in? >>So you could squeeze more revenue out of them and make my forecasting life a little bit easier. <laugh> consumption pricing is one of the things that makes a company like snowflake so attractive because customers is especially large customers facing fluctuating demand can dial and their end demand can dial down usage for certain workloads that are maybe not yet revenue producing or critical. Now let's jump to insider trading. There were a lot of insider selling going on last year and into 2022 now, I mean a lot sloop and Scarelli Christine Kleinman. Mike SP several board members. They sold stock worth, you know, many, many hundreds of millions of dollars or, or more at prices in the two hundreds and three hundreds and even four hundreds. You remember the company at one point was valued at a hundred billion dollars, surpassing the value of service now, which is this stupid at this point in the company's tenure and the insider's cost basis was very often in the single digit. >>So on the one hand, I can't blame them. You know what a gift the market gave them last year. Now also famed investor, Peter Linsey famously said, insiders sell for many reasons, but they only buy for one. But I have to say there wasn't a lot of insider buying of the stock when it was in the three hundreds and above. And so yeah, this pattern is something to watch our insiders buying. Now, I'm not sure we'll keep watching snowflake. It's pretty generous with stock based compensation and insiders still own plenty of stock. So, you know, maybe not, but we'll see in future disclosures, but the bottom line is Snowflake's business. Hasn't dramatically changed with the exception of these large consumer facing companies. Now, another analyst pointed out that companies like snap, he pointed to company snap, Peloton, Netflix, and face Facebook have been cutting back. >>And Scarelli said, and what was a bit of a surprise to me? Well, I'm not gonna name the customers, but it's not the ones you mentioned. So I, I thought I would've, you know, if I were the analyst I would've follow up with, how about Walmart target visa, Amex, Expedia price line, or Uber? Any of those Mike? I, I doubt he would've answered me anything. Anyway, the one thing that Scarelli did do is update Snowflake's fiscal year 2029 outlook to emphasize the long term opportunity that the company sees. This chart shows a financial snapshot of Snowflake's current business using a combination of quarterly and full year numbers in a model of what the business will look like. According to Scarelli in Dave ante with a little bit of judgment in 2029. So this is essentially based on the company's framework. Snowflake this year will surpass 2 billion in revenues and targeting 10 billion by 2029. >>Its current growth rate is 84% and its target is 30% in the out years, which is pretty impressive. Gross margins are gonna tick up a bit, but remember Snowflake's cost a good sold they're dominated by its cloud cost. So it's got a governor. There has to pay AWS Azure and Google for its infrastructure. But high seventies is a, is a good target. It's not like the historical Microsoft, you know, 80, 90% gross margin. Not that Microsoft is there anymore, but, but snowflake, you know, was gonna be limited by how far it can, how much it can push gross margin because of that factor. It's got a tiny operating margin today and it's targeting 20% in 2029. So that would be 2 billion. And you would certainly expect it's operating leverage in the out years to enable much, much, much lower SGNA than the current 54%. I'm guessing R and D's gonna stay healthy, you know, coming in at 15% or so. >>But the real interesting number to watch is free cash flow, 16% this year for the full fiscal year growing to 25% by 2029. So 2.5 billion in free cash flow in the out years, which I believe is up from previous Scarelli forecast in that 10, you know, out year view 2029 view and expect the net revenue retention, the NRR, it's gonna moderate. It's gonna come down, but it's still gonna be well over a hundred percent. We pegged it at 130% based on some of Mike's guidance. Now today, snowflake and every other stock is well off this morning. The company had a 40 billion value would drop well below that midday, but let's stick with the 40 billion on this, this sad Friday on the stock market, we'll go to 40 billion and who knows what the stock is gonna be valued in 2029? No idea, but let's say between 40 and 200 billion and look, it could get even ugly in the market as interest rates rise. >>And if inflation stays high, you know, until we get a Paul Voker like action, which is gonna be painful from the fed share, you know, let's hope we don't have a repeat of the long drawn out 1970s stagflation, but that is a concern among investors. We're gonna try to keep it positive here and we'll do a little sensitivity analysis of snowflake based on Scarelli and Ante's 2029 projections. What we've done here is we've calculated in this chart. Today's current valuation at about 40 billion and run a CAGR through 2029 with our estimates of valuation at that time. So if it stays at 40 billion valuation, can you imagine snowflake grow into a 10 billion company with no increase in valuation by the end, by by 2029 fiscal 2029, that would be a major bummer and investors would get a, a 0% return at 50 billion, 4% Kager 60 billion, 7%. >>Kegar now 7% market return is historically not bad relative to say the S and P 500, but with that kind of revenue and profitability growth projected by snowflake combined with inflation, that would again be a, a kind of a buzzkill for investors. The picture at 75 billion valuation, isn't much brighter, but it picks up at, at a hundred billion, even with inflation that should outperform the market. And as you get to 200 billion, which would track by the way, revenue growth, you get a 30% plus return, which would be pretty good. Could snowflake beat these projections. Absolutely. Could the market perform at the optimistic end of the spectrum? Sure. It could. It could outperform these levels. Could it not perform at these levels? You bet, but hopefully this gives a little context and framework to what Scarelli was talking about and his framework, not with notwithstanding the market's unpredictability you're you're on your own. >>There. I can't help snowflake looks like it's going to continue either way in amazing run compared to other software companies historically, and whether that's reflected in the stock price. Again, I, I, I can't predict, okay. Let's look at some ETR survey data, which aligns really well with what snowflake is telling the street. This chart shows the breakdown of Snowflake's net score and net score. Remember is ETS proprietary methodology that measures the percent of customers in their survey that are adding the platform new. That's the lime green at 19% existing snowflake customers that are ex spending 6% or more on the platform relative to last year. That's the forest green that's 55%. That's a big number flat spend. That's the gray at 21% decreasing spending. That's the pinkish at 5% and churning that's the red only 1% or, or moving off the platform, tiny, tiny churn, subtract the red from the greens and you get a net score that, that, that nets out to 68%. >>That's an, a very impressive net score by ETR standards. But it's down from the highs of the seventies and mid eighties, where high seventies and mid eighties, where snowflake has been since January of 2019 note that this survey of 1500 or so organizations includes 155 snowflake customers. What was really interesting is when we cut the data by industry sector, two of Snowflake's most important verticals, our finance and healthcare, both of those sectors are holding a net score in the ETR survey at its historic range. 83%. Hasn't really moved off that, you know, 80% plus number really encouraging, but retail consumer showed a dramatic decline. This past survey from 73% in the previous quarter down to 54%, 54% in just three months time. So this data aligns almost perfectly with what CFO Scarelli has been telling the street. So I give a lot of credibility to that narrative. >>Now here's a time series chart for the net score and the provision in the data set, meaning how penetrated snowflake is in the survey. Again, net score measures, spending velocity and a specific platform and provision measures the presence in the data set. You can see the steep downward trend in net score this past quarter. Now for context note, the red dotted line on the vertical axis at 40%, that's a bit of a magic number. Anything above that is best in class in our view, snowflake still a well, well above that line, but the April survey as we reported on May 7th in quite a bit of detail shows a meaningful break in the snowflake trend as shown by ETRS call out on the bottom line. You can see a steady rise in the survey, which is a proxy for Snowflake's overall market penetration. So steadily moving up and up. >>Here's a bit of a different view on that data bringing in some of Snowflake's peers and other data platforms. This XY graph shows net score on the vertical axis and provision on the horizontal with the red dotted line. At 40%, you can see from the ETR callouts again, that snowflake while declining in net score still holds the highest net score in the survey. So of course the highest data platforms while the spending velocity on AWS and Microsoft, uh, data platforms, outperforms that have, uh, sorry, while they're spending velocity on snowflake outperforms, that of AWS and, and Microsoft data platforms, those two are still well above the 40% line with a stronger market presence in the category. That's impressive because of their size. And you can see Google cloud and Mongo DB right around the 40% line. Now we reported on Mongo last week and discussed the commentary on consumption models. >>And we referenced Ray Lenchos what we thought was, was quite thoughtful research, uh, that rewarded Mongo DB for its forecasting transparency and, and accuracy and, and less likelihood of facing consumption headwinds. And, and I'll reiterate what I said last week, that snowflake, while seeing demand fluctuations this past quarter from those large customers is, is not like a data lake where you're just gonna shove data in and figure it out later, no schema on, right. Just throw it into the pond. That's gonna be more discretionary and you can turn that stuff off. More likely. Now you, you bring data into the snowflake data cloud with the intent of driving insights, which leads to actions, which leads to value creation. And as snowflake adds capabilities and expands its platform features and innovations and its ecosystem more and more data products are gonna be developed in the snowflake data cloud and by data products. >>We mean products and services that are conceived by business users. And that can be directly monetized, not just via analytics, but through governed data sharing and direct monetization. Here's a picture of that opportunity as we see it, this is our spin on our snowflake total available market chart that we've published many, many times. The key point here goes back to our opening statements. The snowflake data cloud is evolving well beyond just being a simpler and easier to use and more elastic cloud database snowflake is building what we often refer to as a super cloud. That is an abstraction layer that companies that, that comprises rich features and leverages the underlying primitives and APIs of the cloud providers, but hides all that complexity and adds new value beyond that infrastructure that value is seen in the left example in terms of compressed cycle time, snowflake often uses the example of pharmaceutical companies compressing time to discover a drug by years. >>Great example, there are many others this, and, and then through organic development and ecosystem expansion, snowflake will accelerate feature delivery. Snowflake's data cloud vision is not about vertically integrating all the functionality into its platform. Rather it's about creating a platform and delivering secure governed and facile and powerful analytics and data sharing capabilities to its customers, partners in a broad ecosystem so they can create additional value. On top of that ecosystem is how snowflake fills the gaps in its platform by building the best cloud data platform in the world, in terms of collaboration, security, governance, developer, friendliness, machine intelligence, etcetera, snowflake believes and plans to create a defacto standard. In our view in data platforms, get your data into the data cloud and all these native capabilities will be available to you. Now, is that a walled garden? Some might say it is. It's an interesting question and <laugh>, it's a moving target. >>It's definitely proprietary in the sense that snowflake is building something that is highly differentiatable and is building a moat around it. But the more open snowflake can make its platform. The more open source it uses, the more developer friendly and the great greater likelihood people will gravitate toward snowflake. Now, my new friend Tani, she's the creator of the data mesh concept. She might bristle at this narrative in favor, a more open source version of what snowflake is trying to build, but practically speaking, I think she'd recognize that we're a long ways off from that. And I also think that the benefits of a platform that despite requiring data to be inside of the data cloud can distribute data globally, enable facile governed, and computational data sharing, and to a large degree be a self-service platform for data, product builders. So this is how we see snow, the snowflake data cloud vision evolving question is edge part of that vision on the right hand side. >>Well, again, we think that is going to be a future challenge where the ecosystem is gonna have to come to play to fill those gaps. If snowflake can tap the edge, it'll bring even more clarity as to how it can expand into what we believe is a massive 200 billion Tam. Okay, let's close on next. Week's snowflake summit in Las Vegas. The cube is very excited to be there. I'll be hosting with Lisa Martin and we'll have Frank son as well as Christian Kleinman and several other snowflake experts. Analysts are gonna be there, uh, customers. And we're gonna have a number of ecosystem partners on as well. Here's what we'll be looking for. At least some of the things, evidence that our view of Snowflake's data cloud is actually taking shape and evolving in the way that we showed on the previous chart, where we also wanna figure out where snowflake is with it. >>Streamlet acquisition. Remember streamlet is a data science play and an expansion into data, bricks, territory, data, bricks, and snowflake have been going at it for a while. Streamlet brings an open source Python library and machine learning and kind of developer friendly data science environment. We also expect to hear some discussion, hopefully a lot of discussion about developers. Snowflake has a dedicated developer conference in November. So we expect to hear more about that and how it's gonna be leveraging further leveraging snow park, which it has previously announced, including a public preview of programming for unstructured data and data monetization along the lines of what we suggested earlier that is building data products that have the bells and whistles of native snowflake and can be directly monetized by Snowflake's customers. Snowflake's already announced a new workload this past week in security, and we'll be watching for others. >>And finally, what's happening in the all important ecosystem. One of the things we noted when we covered service now, cause we use service now as, as an example because Frank Lupin and Mike Scarelli and others, you know, DNA were there and they're improving on that service. Now in his post IPO, early adult years had a very slow pace. In our view was often one of our criticism of ecosystem development, you know, ServiceNow. They had some niche SI uh, like cloud Sherpa, and eventually the big guys came in and, and, and began to really lean in. And you had some other innovators kind of circling the mothership, some smaller companies, but generally we see sluman emphasizing the ecosystem growth much, much more than with this previous company. And that is a fundamental requirement in our view of any cloud or modern cloud company now to paraphrase the crazy man, Steve bomber developers, developers, developers, cause he screamed it and ranted and ran around the stage and was sweating <laugh> ecosystem ecosystem ecosystem equals optionality for developers and that's what they want. >>And that's how we see the current and future state of snowflake. Thanks today. If you're in Vegas next week, please stop by and say hello with the cube. Thanks to my colleagues, Stephanie Chan, who sometimes helps research breaking analysis topics. Alex, my is, and OS Myerson is on production. And today Andrew Frick, Sarah hiney, Steven Conti Anderson hill Chuck all and the entire team in Palo Alto, including Christian. Sorry, didn't mean to forget you Christian writer, of course, Kristin Martin and Cheryl Knight, they helped get the word out. And Rob ho is our E IIC over at Silicon angle. Remember, all these episodes are available as podcast, wherever you listen to search breaking analysis podcast, I publish each week on wikibon.com and Silicon angle.com. You can email me directly anytime David dot Valante Silicon angle.com. If you got something interesting, I'll respond. If not, I won't or DM me@deteorcommentonmylinkedinpostsandpleasedocheckoutetr.ai for the best survey data in the enterprise tech business. This is Dave Valante for the insights powered by ETR. Thanks for watching. And we'll see you next week. I hope if not, we'll see you next time on breaking analysis.

(upbeat music) >> Hello, and welcome to theCUBE's presentation of the a AWS startup showcase, data as code is the theme. This is season two episode two of the ongoing series covering the exciting startups from the AWS ecosystem and we talk about data analytics. I'm your old John Furrier with the cube, and we have Javier De La Torre. who's the founder and chief strategy officer of Carto, which is doing some amazing innovation around geographic information systems or GIS. Javier welcome to the cube for this showcase. >> Thank you. Thank you for having me. >> So, you know, one of the things that you guys are bringing to the table is spatial analytic data that now moves into spatial relations, which is, you know, we know about geofencing. You're seeing more data coming from satellites, ground stations, you name it. Things are coming into the market from a data perspective, that's across the board and geo's one of them GIS systems. This is what you guys are doing in the rise of SQL in particular with spatial. This is a huge new benefit to the world. Can you take a minute to explain what Carto's doing and what spatial SQL is? >> Sure. Yeah. So like you said, like data, obviously we know is growing very fast and as you know now, being leveraged by many organizations in many different ways. There's one part of data, one dimension that is location. We like to say that everything happens somewhere. So therefore everything can be analyzed and understood based on the location. So we like to put an example, if all your neighbors get an alarm in their homes, the likelihood that you will get an alarm increases, right? So that's obvious we are all affected by our surroundings. What is spatial analytics, this type of analytics does is try to uncover those spacial relations so that you can model, you can predict where something is going to happen, or, you know, like, or optimize it, you know, like where else you want it to happen, right? So that's at the core of it. Now, this is something that as an industry has been done for many years, like the GIS or geographic information systems have existed for a long time. But now, and this is what Carto really brings to the table. We're looking at really the marketizing it, so that it's in the hands of any analyst, our vision is that you need to go five years, to a geography school to be able to do this type of spatial analysis. And the way that we want to make that happen is what we call with the rise of a spatial SQL. We add these capabilities around spatial analytics based on the language that is very, very popular for a analysts, which is SQL. So what we do is enables you to do this spatial analysis on top of the well known and well used SQL methods. >> It's interesting the cloud native and the cloud scale wave and now data as code has shown that the old school, the old guard, the old way of doing things, you mentioned data warehousing, okay, as one. BI tools in particular have always been limited. And the scope of the limitation was the environment was different. You have to have domain expertise, rich knowledge of the syntax. Usually it's for an application developer, not for like real time and building it into the CICD pipeline, or just from a workflow standpoint, making it available. The so-called democratization, this is where this connects. And so I got to ask you, what are you most excited about in the innovations at Carto? Can you share some of the things that people might know about or might not know about that's happening at Carto, that takes advantage of this cloud native wave because companies are now on this bandwagon. >> Yeah, no, it is. And cloud native analytics is probably the most disruptive kind of like trend that we've seen over the few years, in our particular space on the spatial it has tremendous effects on the way that we provide our service. So I'd like to kind of highlight four main reasons why cloud analytics, cloud native is super important to us. So the first one is obviously is a scalability, the working with the sizes of data that we work now in terms of location was just not possible or before. So for someone that is performing now analysis on autonomous car, or you're like that has any sensorized GPS on a device and is collecting hundreds of billions of points. If you want to do analysis on that type of data, cloud native allows you to do that in a scalable way, but it also is very cost effective. That is something that you'll see very quickly when your data grows a lot, which is that this computing storage separation, the idea that is store your data at cloud prices, but then use them with these data warehouses that we work in this private, makes for a very, very cost effective solution. But then, you know, there is other two, obviously one of them being SQL and spatial SQL that like means we like to say that SQL is becoming the lingua franca for analytics. So it's used by many products that you can connect through the usage of SQL, but I think like you coming towards why I think it's even more interesting it's like, in the cloud the concept like we all are serving, we are all living in the same infrastructure enables us that we can distribute a spatial data sets to a customer that they can join it on their database on SQL without having to move the data from one another, like in the case of Redshift or Amazon Redshift car connects and you using something called a spectrum, we can connect live to data that is stored on S3. And I think that is going to disrupt a lot the way that we think about data distributions and how cost effective it is. I think, it has a lot of your like potential on it. And in that sense what Carto is providing on top of it in the format of formats like parquet, which is a very popular with big data format. We adding geo parquet, we are specializing this big data technology for doing the spatial analysis. And that to me it is very exciting because it's putting some of the best tools at the hands of doing the space analytics for something that we're not able to do before. So to me, this is one area that I'm very, very excited. >> Well, I want to back up for a second. So you mentioned parquet and the standards around that format. And also you mentioned Redshift, so let me get this right. So you just saying that you can connect into Redshift. So I'm a customer and I have Redshift I'm using, I got my S3, I'm using Redshift for analysis. You're saying you can plug right into Redshift. >> Yes. And this is a very, very, very important part because what Carto does is leverage Redshift computing infrastructure to essentially kind of like do all the analysis. So what we do is we bring a spatial analysis where the data is, where Redshift is versus in the past, what we will do is take the data where the analysis was and that sense, it's at the core of cloud native. >> Okay. This is really where I see the exciting shift where data as code now becomes a reality is that you bring the... It redefines architecture, the script is flipped. The architecture has been redefined. You're making the data move to the environments that needs to move when it has to, if it doesn't have to move you bring compute to it. So you're seeing new kinds of use cases. So I have to ask you on the use cases and examples for Carto AWS customers with spatial analytics, what are some of the examples on how your clients are using cloud native spatial analytics or Carto? >> Yeah. So one, for example, that we've seen a lot, on the AWS ecosystem, obviously because of its suites and its position. We work together with another service in the AWS ecosystem called Amazon Location. So that actually provides you access to maps and SDKs for navigation. So it means that you are like a company that is delivering food or any other goods in the city. We have like hundreds or thousands of drivers around the city moving, doing all these deliveries. And each of these drivers they have an app and they're collecting actively their location, their position, right? So you get all the data and then it gets stored on something like a Redshift data cluster on S3 as well. There's different architectures in there, but now you essentially have like a full log of the activity that is happening on the ground from your business. So what Carto does on top of that data is you connect your data into Carto. And now you can do analysis, for example, for finding out where you user may be placed, another distribution center, you know, for optimizing your delivering routes, or like if you're in the restaurant business where you might want to have a new dark kitchen, right? So all this type of analysis based on, since I know where you're doing your operations, I can post analyze the data and then provide you a different way that you can think about solving your operation. So that's an example of a great use case that we're seeing right now. >> Talk to me about about the traditional BI tools out there, because you mentioned earlier, they lack the specific capabilities. You guys bring that to the table. What about the scalability limitations? Can you talk about where that is? Is there limitations there, obviously, if they don't have the capabilities, you can't scale that's one, but you know, as you start plugging into Redshift, scale and performance matters, what's the issue there? Can you unpack that a little bit real quick? >> Yeah. It goes back to the particulars of the spacial data, location data, like in the use case, like I was describing you very quickly are going to end up with really a lot of your like terabytes, if not petabytes of data very quickly, if you're start aggregating all this data, because it gets created by sensors. So volumes in our world kind of tends to grow a lot now. So when you work with BI tools, there's two things that you have to take in consideration. BI tools are great for seeing things like for example, if all you want to see is where your customers are, a BI tool is great. Seeing, creating a map and seeing your customers. That's totally in the world of BI. But if you want to understand why your customers are there, or where else could they be, you're going to need to perform what we call a spatial analysis. You're going to have to create a spatial model. You're going to have to, and for that BI tools will not give you that that's one side, the other it talks about the volumes that I was describing. Most of these BI tools can handle certain aggregations. Like, for example, if you are reading, if you're connecting your, let's say 10 billion data set to a BI tool, the BI tool will do some aggregations because you cannot display 10,000 rows on a BI tool and that's okay, you get aggregations and that works. But when it comes to a map, you cannot aggregate the data on the map. You actually want to see all the data on the map, and that's what Carto provides you. It allows you to make maps that sees all the data, not just aggregated by county or aggregated by other kind of like area, you see all your data on the map. >> You know, what's interesting is that location based service has been around for a long time. You know, when mobile started even hitting the scene, you saw it get better mashups, Google Maps, all this Google API mashups, things like that. You know, developers are used to it, but they could never get to the promised land on the big data side, because they just didn't have the compute. But now you add in geofencing, geo information, you now have access to this new edge like data, right? So I have to ask you on the mobile side, are you guys working with any 5G or edge providers? Because I can almost imagine that the spatial equation gets more complicated and more data full when you start blowing out edge data, like with 5G, you got more, more things happening at the edge. It's only going to fill in more data points. Can you share that's how that use case is going with mobile, mobile carriers or 5G? >> Yeah, that's totally, yeah. It's totally the case. Well, first, even before, you know, like we are there, we actually helping a lot of telcos on actually planning the 5G deployment. Where do you place your antennas is a very, very important topic when you're like talking about 5G. Because you know, like 5G networks require a lot of density. So it's a lot about like, okay, where do I start deploying my infrastructure to ensure the customers like meet, like have the best service and the places where I want to kind of like go first So like... >> You mean like the RF maps, like understanding how RF propagates. >> Well, that's one signal, but the other is like, imagine that your telco is more interested on, you know, let's say on a certain kind of like consumer profile, like young people that are using the one type of service. Well, we know where these demographics kind of lives. So you might want to start kind of like deploying your 5G in those areas, right. Versus if you go to more commercial and more kind of like residential areas, there might be other demographics. So that's one part around market analysis. Then the second part is once these 5G networks are in place, you're right. I mean, one of the premises that kind of like these news technologies give us is because the network is much smarter. You can have all these edge cases, there's much more location data that can be collected. So what we see now is a rise on the amount of what we call telemetry. That for example, the IOT space can make around location. And that's now enabled because of 5G. So I think 5G is going to be one of those trends that are going to make like more and more data coming into, I mean, more location, data available for analysis. >> So how does that, I mean, this is a great conversation because everyone can realize they're at a stadium and they see multiple bars but they can't get bandwidth. So they got a back haul problem or not enough signal. Everyone knows when they're driving their car, they know they can relate to the consumer side of it. So I get how the spatial data grows. What's the impact to Carto and specifically the cloud, because if you have more data coming in, you need the actionable insight. So I can see the use case, oh, put the antenna here. That's an actionable business decision, more content, more revenue, more happy customers, but where else is the impact to you guys and the spatial piece of it? >> Yeah. Well, I mean like there's many, many factors, right? So one of them, for example, on the telco, one of the things where we realize impact is that it gives the visibility to the operator, for example, around the quality of service. Like, okay, are my customers getting the quality of services where I want? Or like you said, like if there sitting outside a concert the quality of service in one particular area is dropping very fast. So the idea of like being able to now in real time, kind of like detect location issues, like I'm having an issue in this place. That means that then now I can act, I can drive up bandwidth, put more capacity et cetera right. So I think the biggest impact that we are seeing we are going to see on the upcoming years is that like more and more use cases going towards real time. So where, like before it was like, well, now that it has happened, I'm going to analyze it. I'm going to look at, you know, like how I could do better next time towards a more of like an industry where Carto ourselves, we are embedded in more real time type of, you know, like analytics where it's okay, if this happens, then do that, right. So it's going to be more personalized at the level that like in the code environment, it has to be art of a full kind of like pipeline kind of like type of analysis. That's already programmatically prepared to act on real time. >> That's great and it's a good segue. My next question, as more and more companies adopt cloud native analytics, what trends are you seeing out of the key to watch? Obviously you're seeing more developers coming on site, on the scene, open sources growing, what's the big cloud native analytics trends for Carto and geographic information. >> Yeah. So I think you know like the, we were talking before the cloud native now is unstoppable, but one of the things that we are seeing that is still needs to be developed and we are seeing progress is around a standardization, for example, around like data sets that are provided by different providers. What I mean with that is like, you as an organization, you're going to be responsible for your data like that you create on your cloud, right. On S3, or, you know and then you going to have a competing engine, like Redshift and you're going to have all that set up, but then you also going to have to think about like, okay, how do I ingest data from third party providers that are important for my analysis? So for example, Carto provides a lot of demographics, human mobility. we aggregate and clean up and prepare lot of spacial data so that we can then enrich your business. So for us, how we deliver that into your cloud native solution is a very important factor. And we haven't seen yet enough standardization around that. And that's one of the things, what we are pushing, you know, with the concept of geo Parquet of standardizing that body. That's one, then there is another, this is more what I like to say that you know, we are helping companies figure out their own geographies. What we mean by that is like most companies, when they start thinking about like how they interact, on the space, on the location, some of them will work like by zip codes and other by cities, they organize their operations based on a geography in a way, or technically what we call a geographic support system. Well, nowadays, like the most advance companies are defining their geographies in a continuous spectrum in what we call global grid system or spatial indexes that allows them to understand the business, not just as a set of regions, but as a continuous space. And that is now possible because of the technologies that we are introducing around spatial indexes at the cloud native infrastructure. And it provides a great a way to match data with resources and operate at scale. To me those two trends are going to be like very, very important because of the capabilities that cloud native brings to our spatial industry. >> So it changes the operation. So it's data as ops, data as code, is data ops, like infrastructures code means cloud DevOps. So I got to ask you because that's cool. Spatial index is a whole another way to think of it, rather than you go hyper local, super local, you get local zones for AWS and regions. Things are getting down to the granular levels I see that. So I have to ask you, what does data as code mean to you and what does it mean to Carto? Because you're kind of teasing at this new way because it's redefining the operation, the data operations, data engineering. So data as code is real. What does that mean to you? >> No, I think we already seeing it happening to me and to Carto what I will describe data as code is when an organization has moved from doing an analysis after the fact, like where they're like post kind of like analysis in a way to where they're actually kind of like putting analytics on their operational cycle. So then they need to really code it. They need to make these analysis, put them and insert them into the architecture bus, if you want to say of the organization. So if I get a customer, happens to be in this location, I'm going to trigger that and then this is going to do that. Or if this happens, I'm need to open up. And this is where if an organization is going to react in more real time, and we know that organizations need to drive in that direction, the only way that they can make that happen is if they operationalize analytics on their daily operations. And that can only happen with data as code. >> Yeah. And that's interesting. Look at ML ops, AI ops, people talk about that. This is data, so developers meets operations, that's the cloud, data meets code that's operations, that's data business. >> You got it. And add to that, the spacial with Carto and we go it. >> Yeah, because every piece of data now is important. And the spatial's key real quick before we close out, what is the index thing? Explain the benefit real quick of a spatial index. >> Yes. So the spatial index is well everybody can understand how we organize societies politically, right? Our countries, you have like states and then you have like counties and you have all these different kind, what we call administrative boundaries, right? That's a way that we organize information too, right? A spatial index is when you divide the world, not in administrative boundaries, but you actually make a grid. Imagine that you just essentially make a grid of the world. right? And you make that grid so that in every cell you can then split it into, let's say for example, four more cells. So you now have like an organization. You split the world in a grid that you can have multiple resolutions think like Google maps when you see the entire world, but you can zoom in and you end up seeing, you know, like one particular place, so that's one thing. So what a spatial indexes allows you is to technically put, you know like your location, not based coordinate, but actually on one grid place on an index. And we use that then later to correlate, let's say your data with someone else data, as we can use what we call this spatial indexes to do joints very, very fast and we can do a lot of operations with it. So it is a new way to do spatial computing based on this type of indexes, but for more than anything for an organization, what spatial index allows is that you don't need to work on zip codes or in boundaries on artificial boundaries. I mean, your customer doesn't change because he goes from this place to the road, to the other side of the road, this is the same place. It's an arbitrary in location. It's a spatial index break out all of that. You're like you break with your zip codes, you break. And you essentially have a continuous geography, that actually is a much closer look up to the reality. >> It's like the forest and the trees and the bark of the tree. (Javier laughing) You can see everything. >> That's it, you can get a look at everything. >> Javi, great to have you on. In real quick closing give a quick plug for the company, summarize what you do, what you're looking into, how many people you got, when you're hiring, what's the key goals for the company? >> Yeah, sure. So Carto is a company, now we are around 200 people. Our vision is that spatial analytics is something that every organization should do. So we really try to enable organizations with the best data and analysis around spatial. And we do all that cloud native on top of your data warehouse. So what we are really in enabling these organizations is to take that cloud native approach that they're already embracing it also to spatial analysis. >> Javi, founder, chief strategy officer for Carto. Great to have you on data as code, all data's real, all data has impact, operational impact with data is the new big trend. Thanks for coming on and sharing the company story and all your key innovations. Thank you. >> Thanks to you. >> Okay. This is the startup showcase. Data as code, season two episode two of the ongoing series. Every episode will explore new topics and new exciting companies pioneering this next cloud native wave of innovation. I'm John Furrier, your host of theCUBE. Thanks for watching. (upbeat music)

>>Hey everyone. Welcome to this cube conversation featuring Carto I'm Lisa Martin. And today we're excited to be joined by Javier Delatorre, the founder and chief strategy officer at Carto. We're going to be talking about how Carto is bringing cloud native spatial analysis to the cloud with AWS. How do you are great to have you on the program? Talk to us about cartel. What do you guys do? >>Great. So, uh, part two is a location intelligence platform, but we really use some neighboring organizations to work with location data on the AWS cloud. So essentially enabling organizations to analyze what do they, what should they open new stores? Whereas today probably the new internet, in essence, understanding the locations. I mentioned just helping them to figure out where to do things >>From Carter's perspective. Talk to me about why spatial analysis, location data is important. What power does it give to businesses in any industry? >>Right. I mean, we like to say that everything happens somewhere, right? So we understand that, you know, like the physical world is a very important dimension. So understanding where things happens and the relation within space is a pretty fundamental dimension when it comes to another. I like to put examples of, um, before your neighbors, uh, install alarms in their phones, the likelihood that you will get an alarm is also versus quite a lot. So that's eight years old, says that we are influenced by things that happens around us. And if you can model and understand those spacial relations, you can then look to optimize or predict what is going to happen based on where things are happening. And this is something that we've seen a lot, for example, with the pandemic, but now we're seeing, you know, like many organizations utilizing it for yeah. For finding out where they can find new customers, stores, like say, where did they deploy the new infrastructure? Everything that the ANZ has a spatial component. And that's what is spatial analytics and location intelligence allows you to do? >>Give me some examples of spatial data. And the first thing that pops into my mind is GPS. But I know that there's a lot more than that. >>Uh, GPS has been one of the most important types of data for, so since you know, the inability of GPS and, and with mobile and different sensors are staring at it, we've seen an incredible amount of location data coming into place, but you're right. There's many other types of location data that people tend not to be so aware. I'd say any company that is handling customers, you know, they're likely going to have their addresses. So we have the address of the customer. You have a location already, we'll have, we'll call that the process of geocoding. We transform an address that coordinates, right? But you also have the same, you know, with bees, you have the same, uh, with many different sip codes, it's many different ways that you can represent location. And once you identify those, uh, location bits in your data, then you can start thinking about what type of analysis you can do with them. So it is, like I said, like in many, many places, but definitely the, the rise of, uh, GPS and sensors have been very dramatic. Now we see in also like acute stream of location data coming, for example, from satellites, you know, with all these constellations of satellites, capturing daily images on from earlier, that is also giving us a lot of contextual information. But so it is, you know, mobile phones, when you connect to cell towers, there's many different businesses that are now kind of giving us location data. >>So you alluded to that earlier, a lot more businesses are using location data in their strategies. Talk to me about the acceleration that you seen of that in the last couple of years alone. >>Yeah. So I think one thing that we see in, you know, like massively on the industry obviously is these companies are going through the digital transformation. They are applying analytics to bigger and bigger areas of their, of their, of their business, right. And in a way to showcase, to kind of came as while the last time I mentioned that a lot of organizations started to look at, and over the last few years, we've seen that change in a lot. We've seen it within the many more organized spaces. Now making the questions around where things happens, how does actually matter to my business. So this is celebration, you know, has the sensitive men that many more people are now starting to look at, not only seeing things on a map, like, you know, where my customers are, where my warehouses are, my logistics supply chain, where is it located? >>Now, we're starting to see many more organizations looking at questions about how can I predict where something is going to happen, or how can I optimize my business process so that, um, you know, I, I try to reduce the number of kilometers that I have to drive miles. So, um, I guess it's a mix of the need for sustainability optimizing the business process. And the fact that more and more organizations are starting to do much more deep transformation that now location data has become a much more interesting aspect for many more organizations. So I think all these things together has to make in a way that perfect storm. And now we've seen a lot of the men too, um, for companies that want to go will be John seeing things in a map to understanding why things happen in those spaces. And that's, I think that like, again, a multitude of drivers, you know, that is supposed to in this industry. >>Can you talk about some of the key use cases and maybe some of the vertical industries where you've really seen this takeoff in the last couple of years? >>Yes. And I think he's just in a way, one of the most interesting factors of our industry traditional industries have been on the area around security in the public sector was very much on the military and the, in the, in the, uh, intelligence ecosystem. But now we've seen tremendous adoption on industries like retail, right, where they are lying now consolidating what is their, what is their physical presence? Where do they open stores? You know, like, uh, food chains, what do they open restaurants? And it's a much more analytical process now towards making businesses because, and that involves the usage of location intelligence and space analytics. We do touch one, but we still also like tremendous increase in usage on things like telcos telecommunication. Now with all the deployment of 5g networks, fiber optics, most of those operators require a very good understanding of where you should apply your networks, which, which areas you want to go start first tablet, smart CapEx car, like a strategy. >>So that's telco, I would say it's also has been a tremendous increase. Um, the public secretary is obviously very important, you know, especially, you know, with a lot of the, in a way we all got to master or do you know why geography matters? You know, how to understand your location. Um, and the last one that I would say that it's also connected very much with climate change, transportation and logistics are very, very important factor now. So understanding what is the best strategies for last mile delivery, how to organize your warehouses to better meet your needs. Those are the places that now we're seeing really growing really fast. >>So tremendous amount of use cases, a lot of opportunity there for optimization. How have companies traditionally analyze spatial data and why does that need to change? >>Yeah, so, um, I mean, to a certain extent, I would like to say that there's not been, um, that much use of location data. And that I think is one of the most exciting parts that for many organizations, this is the first time that they're looking at location as a, as a need. I mentioned that they need to understand. So there were, there were several organizations doing already a spatial analytics, but right now it's really, we really see in the expansion of our industry and you're not catching up in, in major, uh, major companies. So those are not like more advanced, you know, we'll have used so-called the traditional GIS systems. GIS is a, is a type of software. That's been existing for many years, but it's only the second used by a very small needs of analysts. You have to go almost four years to school, you know, to become a GIS expert and then do GIS analyst. >>This is right now trending dramatically. And I think, you know, Carter's part of that, uh, transition to necessity, making best patient analysis and GIS part of just the generic general analytics. And I think this is one of the most exciting times that we have, because we've seen the demo by station of his face. And it takes now to imagine why there are, so now we've seen, you know, like analysts that, you know, used to be just to know how to make a map. So things are not with a map, you know, where, where something was happening. Now we starting to see them making much more interesting plastics. So I'm like, okay, if it happens here, where else could they be happened? Right. So that's what I, right now, they, the, the, the huge statements, I'd say, I'd say like many organizations is the first time they go into jail. People like me for being very passionate about the possibilities of really improving processes. I mean, this is super, super exciting time. >>I can definitely feel your passion here through zoom, or talk to me a little bit about how cartel and AWS are helping organizations to embrace the democratization of spatial data and really unlock its super powers. >>Yeah. Well, I mean, obviously, you know, that AWS as the leader on the cloud, in a way that has fundamentally changed the way that we think about like analytics, right? So, um, not only the clouds provide us with the scalability, scalability, affordable the scale of anything. So that's one of the things that, you know, has been incredibly, um, transformative in our industry, uh, with AWS. Now we can do analysis at the scale that wasn't possible before. So that's, that's, that's one thing. So for us, you know, what we've embarked with AWS is rethinking how we can do a spatial analytics in the cloud. We're calling it car to cloud native is providing a full cloud native approach towards performing the spatial analytics, traditional GIS. And for us to utilize this game, even as huge amount of scalability, we use services like Retsef the now with their server last capabilities, we like a, an organization have their data already on that data warehouse on breaths test and using Kartra space. >>now they can do a special ethics directly on the warehouse. This is one of the biggest characteristics of cartel made by being the first cloud data platform. Every computing that we do actually gets pushed down to the warehouse. So the customer is already using the computing engine that they're already, they've been using it for many other things they're paying for already. And they give us scalability. Uh, also very cost-effectiveness this storage competed in separation that the rest of service provides. It makes it very competitive from a call like a cost perspective, and then also is very convenient. So it means that you can use just traditional sequel that are many analysts, know how to use it within the tools that they've been using for many. So I think the participation is essential to read safe, and then also with incorporating the Amazon location services. So we can talk to, and it certainly provides a cloud native it's scalable, affordable, efficient, and much more easy to use solution to performance, space analytics that anything that has been done before. >>It's a tremendous amount of opportunity. It sounds like we're just scratching the surface, but really interesting things that cartoon was doing and how you're enabling organizations in every industry to accelerate the use of spatial data. Javier, thank you so much for joining me on the program today. Fascinating information and best of luck to you. >>Thank you very much >>For Javier Delatorre I'm Lisa Martin. You're watching the cubes stay right here for more coverage of the hybrid tech event world.

(upbeat music) >> Hello and welcome to today's session of the 2021 AWS Global Public Sector Partner Awards Program. This session's award is going to be profiling the Most Customer Obsessed Mission-based Win in the education domain. I'm your host, Donald Klein, with theCUBE. And today we are joined by Fernando Castillo. He's the Business Development Manager at CloudHesive, and then also Luis Muñoz, who's the Information Director at the Unibersidad de Los Lagos. >> Okay, everyone. Welcome to today's session. All right. Fernando, thanks for taking some time out and joining us today. Wanted to start with you and wanted to hear a little bit of background about CloudHesive. Obviously, you're a company that had won an award last year, but you're back on this year, again. Want you give us some a little bit of the story of CloudHesive, and what kind of services you provide? (speaking in foreign language) >> Translator: Thank you very much, Donald. Yes, CloudHesive is a managed consulting service provider in the cloud. We are AWS Partner and since 2014 we have been providing solutions focusing on security, trustability, and scalability in the cloud. Accompany companies to their main objective, which is reducing operational costs and increasing their productivity as they move forward in the adaption of cloud services. >> Very good. Okay. And then Luis, I'm going to turn to you now, want you talk to us a little bit about your role there at the Unibersidad de Los Lagos, and how you started this project? (speaking in foreign language) >> Translator: Good afternoon. I belong to the academic department of the engineering department at the University of Los Lagos and the director of the IT of this school. For several years, for about five years, we've been analyzing the deployment of these automation at universities of Chile. Since it's not a common item in the country, we've done several benchmarking worldwide, especially in Spain, Mexico, Columbia, and places where it's more developed. And eventually, we have to take some demos that allowed us to make some decisions. This topic was not going to be considered in 2020, but it happened because of a political situation, social political in Chile in 2019. So we have to move forward the process, but we had already made a global analysis and this was one of the reasons why we have to get closer to AWS Partners and this allowed us to move this process forward within the university. >> Okay. Very good. All right. Well then, what I'm going to do now is I'm going to come back to you, Fernando, and I want you to talk a little bit about the overall goal of what you were trying to help the university with. (speaking in foreign language) >> Translator: Well, within the main objectives we had in the project was to have a platform that would support a concurrent load of thousands of students, especially in University of Los Lagos. They had requested to have around 15,000 students and the main complication or the main challenge was to keep a virtual attendance, which is now known as learning management system, but also having the possibility of having video classes in two days, something similar to what we are doing today, but with 50 or up to 100 students. This was one of the main objectives of the project. >> Okay, understood. So the goal is here to deploy this platform and open source platform and make it available for about 15,000 students. Okay. Now coming back to you, Luis, there was a time constraint here, correct? You needed to get the system going very quickly. Maybe you could explain why you needed to accelerate this program so quickly. (speaking in foreign language) >> Translator: Well, literally, the pandemic conditions in the country started to be more evident and more severe since the first week of March in 2020. And so we have to make the decision, the double-sided decision of choosing an infrastructure that we could not buy at that time, given the emergency, logistic emergency of the pandemic at the server's room and to keep a stable platform for that number of users, student and professors of university. So we started conversations to make this scale up and move everything to the cloud. This was the first decision. So we decided to use Amazon and with CloudHesive, we were able to organize the academics charter in the same platform. So as to move no longer than three weeks so that we could give classes, online classes with the students while we were learning this new normal, which was virtual distance education. This was very difficult of every morning, afternoon, and evening of work, but this allowed us not to fall behind in the first semester of the educational needs of the students. With this modality, we have around 5% more students that we used to last year in 2020, in March 2020. And this allowed us to have a more visible structure for those who were questioning this new modality and we were applied to take this new modality in the end. >> Okay. So because of the pandemic, you had to accelerate the deployment of this learning management system very quickly. And you had to learn how to manage the system at the same time that you were deploying it. Okay. Understood. So a lot of challenges there. All right. So then maybe coming back to you, Fernando. Wanted you talk about your role and how CloudHesive helped with this sort of this very rapid deployment of this LMS system. (speaking in foreign language) >> Translator: Well, talking about the challenges and how we were able to get to the objective, within the plan, deployment and development have to accompany the University of Los Lagos not only with the use of the platform, but also how to change management. One of the biggest challenges was to do a security audit, the deployment of scalable infrastructures. And one of the main topics was, one of the main challenges for CloudHesive that we can now talk about and obtained objective was to do the tests from the point of view of scalability and security getting into 15,000 students, concurrent students, stimulating the workload of the university, keeping 99.5 availability of the platform. Going back to the challenges, it's not only the scalability and stability. Nowadays, the University of Los Lagos platform can continue to grow, as Luis mentioned, without the need to look for new resources. But with our implementation, deployment and development, we already have a scalable resource as they increase the number of professors and students to their university. >> Okay. Understood, understood. Now, maybe talk a little bit just to continue with that point. Maybe talk for a minute about how you leverage the AWS platform in order to be able to accelerate this project. What aspects of your partnership with AWS enabled you to deploy the system so quickly? (speaking in foreign language) >> Translator: Well, talking about that, we based on a referential architecture of AWS, which is an open source middle platform, and within these competencies and within things, they belong to the education. We also have the problems, the presence of (indistinct), which allows us to deploy new solution and new integrations. So this allowed us as the team to, within weeks, to develop new features that would allow us to deal with each of the requirements of the universities, specifically. So within the first week, the University of Los Lagos had the connectivity with the academic sector. On the second week, they had the infrastructure to support out two-way videos. And on the third week, they already had the platform completely deployed with all the security safeguards that we already have in all of our products and services. So having worked hand-in-hand with AWS allowed us to have success in time with this platform. >> Wow. So that's fantastic. You were able to deploy this entire system from the connection with the academics to the video infrastructure to actually getting all the security implementations in place. You were able to do that in a three week cycle, is that correct? >> Yeah, that's correct. >> Fantastic. Okay. So Luis, coming back to you then, so working with CloudHesive as a partner to help deploy the platform on AWS gave you fantastic speed and agility to get the system working. Maybe talk a little bit now about the challenges of getting students and educators to adapt the system, and what kind of successes you had? (speaking in foreign language) >> Translator: First of all, they have to, we need to need to know the geography, the landscape of the university. The geography is very varied. We have mountains and lakes and so forth, and connectivity concepts are very difficult in this area. In addition, University of Los Lagos has the characteristic of receiving students from very poor sectors within the region. So this means that more than 80% have a free education, as there are few universities that exist in the country. So one of the technological challenges was for these students to receive the mechanisms and technology to have the connectivity they needed. After that, we had a very big training plan with the deployment company, CloudHesive, with the permissions, and eventually together, we were able to go beyond students and professors. And I remember we had 50% students and professors logged in to the platform, and nowadays, we have 100% students and professors logged in having classes in the platform. But most importantly, nowadays, we have an analytical control because of an integration with CloudHesive, with certain tools that allow us to gather data in real time. And we can do a follow-up of the student that is closer actually from the previous situation when we didn't have this technology. If the student is not logged in, we can reach them directly or indirectly to know, what is happening with his meeting, which is the kind of support, academic, social or economic support that they need. Before, it was harder to get this. So we have a communion between technology and social services that we can provide as a university. And of course, the adaptability of CloudHesive in as much as most of the requirements that we needed. So as to have a good response, they've been very providing, they provided a very robust service in this terms. >> Fantastic. So you were able to reach 100% percent of your target audience very quickly. Is that correct? Great. >> Yes. >> And maybe just to kind of follow up one more. Just talk a little bit about the future of your program. Now that you've worked so hard to establish the system and to connect your students and your teachers and to optimize the system, what is your plan to use it going forward? Are you looking to expand it? What would you say are your goals? (speaking in foreign language) >> Translator: First of all, for better or for worse, this modality came here to stay. The pandemic may end, but it generated opportunities that nationwide, it moved forward at least seven or eight times faster, these kinds of possibilities. So it's hard to use or waste this opportunity with the face-to-face classes. The university nowadays, thanks to the platform and the work done by CloudHesive and AWS, the university won ministry projects from the Ministry of Education in the country, have a strengthening plans for other kinds of services that were not incorporated before, like the idea of virtual library, research work, academic development work, of training and cultural transformation as well. But eventually, they are happening in this virtually environments. And the university won this possibility through the ministry, bridging the gap between the academic sector and the students. And in order to elaborate a little bit more from the previous question, we did a survey last year and ended not long ago. And most professors said that 80%, more than 80% said that the virtual environment was considered as good or very good. So we have a very good assessment in order to participate in this project that were won by the university and they are nowadays being applied. So this generates development in the academic sector, in research, in library, in content creation, global communication, working together with other universities with work postgraduate courses and other universities without the need of getting out of home. So this is a very competitive advantage that we didn't have before. And since 2020, we were able to develop. >> Fantastic. Well, congratulations on a really well put together program. And I'm excited to hear that you've won an award in your country and that you're planning to expand the system more broadly. I think that's a fantastic success story. So maybe just to wrap this up here with you Fernando, why don't you talk a little bit about, so obviously, you guys were very critical in helping this system be deployed very quickly, but very securely at the same time. How do you see your role going forward in enabling these types of situations, this distance learning type formats? (speaking in foreign language) >> Translator: Well, just as Luis said, taking this project with the University of Los Lagos, this showed the importance of looking at technological advances and to improve the universities and research centers and how to focus on innovation and bringing the future education down. For us, the data generated in this virtual interactions are very valuable and having a clear perspective, so as to organize this data for, to make more effective decisions that allow us to act in real time. This is what we are focusing on right now. So as to keep, I mean, prove, and being able to provide new tools, the research centers and universities to operate quickly, safely, and cost effectively. >> Okay, fantastic. So really, the real lesson learned here is by working with a partner like yourself, you were able take an open source learning management system and then deploy it very quickly, manage it, and then secure it in a way that allowed the university then to do their work. So I think that's a really great end-to-end delivery story. So I think, maybe if you want to make one last comment, Fernando, about your role in any kind of future expansion for this type of work. (speaking in foreign language) >> Translator: Yes, of course. I would like to thank Amazon and University of Los Lagos, of course for giving us the chance to work together and develop this project successfully. And answering your question, I would like to say that this is a good incentive to build more robust solutions, as long as we have our focus on our clients, when working and as a final comment, I would just would like to thank you and hope to see you again with a new project. >> Okay, well, congratulations to you both on winning this award. And for CloudHesive, this is your second year in a row of winning a Public Sector Award. So with that, I'm going to sign off today and I'm going to thank you both for attending. Today, we've had Fernando Castillo, the Business Development Manager from CloudHesive and then Luis Muñoz, the Information Director at the Uniberisdad de Los Lagos, and thank you both for attending. This is Donald Klein for theCUBE, until next time. (bright music)

>>At the beginning of the last decade, the technology industry was a buzzing because we were on the cusp of a new era of data. The promise of so-called big data was that it would enable data-driven organizations to tap a new form of competitive advantage. Namely insights from data at a much lower cost. The problem was data became plentiful, but insights. They remained scarce, a rash of technical complexity combined with a lack of trust due to conflicting data sources and inconsistent definitions led to the same story that we've heard for decades. We spent a ton of time and money to create a single version of the truth. And we're further away than we've ever been before. Maybe as an industry, we should be approaching this problem differently. Perhaps it should start with the idea that we have to change the way we serve business users. I E those who understand data context, and with me to discuss the evolving data space, his company, and the upcoming data citizens conference is Felix van de Mala, the CEO and founder of Collibra. Felix. Welcome. Great to see you. >>Great to see you. Great to be here. >>So tell us a little bit about Collibra and the problem that you're solving. Maybe you could double click on my upfront narrative. >>Yeah, I think you said it really well. Uh, we've seen so much innovation over the last couple of years in data, the exploding volume complexity of data. We've seen a lot of innovation of how to store and process that data, that, that volume of data more effectively or more cost-effectively, but fundamentally the source of the problem as being able to really derive insights from that data effectively when it's for an AI model or for reporting, it's still as difficult as it was, let's say 10 years ago. And if only in a way it's only become more, uh, more difficult. And so what we fundamentally believe is that next to that innovation on the infrastructure side of data, you really need to look at the people on process side of data. There's so many more people that today consume and produce data to do their job. >>That's why we talk about data citizens. They have to make it easier for them to find the right data in a way that they can trust that there's confidence in that data to be able to make decisions and to be able to trust the output of that, uh, of that model. And that's really what is focused on initially around governance. Uh, how do you make sure people actually are companies know what data they have and make sure they can trust it and they can use it in a compliant way. And now we've extended that into the only data intelligence platform today in the industry where we just make it easier for organizations to truly unite around the data across the whole organization, wherever that data is stored on premise and the cloud, whoever is actually using or consuming data. Uh, that's why we talk about data citizens. I >>Think you're right. I think it is more complex. There's just more of it. And there's more pressure on individuals to get advantage from it. But I, to ask you what sets Culebra apart, because I'd like you to explain why you're not just another data company chasing a problem with w it's going to be an incremental solution. It's really not going to change anything. What, what sets Collibra apart? >>Yeah, that's a really good question. And I think what's fundamentally sets us apart. What makes us unique is that we look at data or the problem around data as truly a business problem and a business function. So we fundamentally believe that if you believe that data is an asset, you really have to run it as a, as a, as a strategic business functions, just like your, um, uh, your HR function, your people function, your it functioning says a marketing function. You have a system to run that function. Now you have Salesforce to run sales and marketing. You have service now to run your, it function. You have Workday to run your people function, but you need the same system to really run your data from. And that's really how we think about GDPR. So we not another kind of faster, better database we know than other data management tool that makes the life of a single individual easier, which really a business application that focuses on how do we bring people together and effective rate so that they can collaborate around the data. It creates efficiency. So you don't have to do things ad hoc. You can easily find the right information. You can collaborate effectively. And it creates the confidence to actually be able to do something with the outcomes of it, the results of all of that work. And so fundamentally I'm looking at the problem as a, as a business function that needs a business system. We call it the system of record or system of engagement for the, for the data function, I think is absolutely critical and, and really unique in the, in our approach. So >>Data citizens are big user conference, data citizens, 21, it's coming up June 16th and 17th, the cubes stoked because we love talking about data. This is the first time we're bringing the cube to that event. So we're really gearing up for it. And I wonder if it could tell us a little bit about the history and the evolution of the data citizens conference? >>Absolutely. I think the first one is set at six years ago where we had a small event at a hotel downtown New York. Uh, most of the customers as their user conference, a lot of the banks, which are at the time of the main customers at 60 people. So very small events, and it exploded ever since, uh, this year we expect over 5,000 people. So it's really expanded beyond just the user conference to really become more of almost the community conference and the industry, um, the conference. So we're really excited, a big part of what we do, why we care so much about the conference. That's an opportunity to build that data citizens community. That's what we hear from our customers, from all attendees that come to the conference, uh, bring those people to get us all care about the same topic and are passionate about doing more at data, uh, being able to connect, uh, connect people together as a big part of that. So we've always, uh, we're always looking forwards, uh, through the event, uh, from that perspective >>Competition, of course, for virtual events these days with them, what's in it for me, what, who should attend and what can attendees expect from data citizens? 21. >>Yeah, absolutely. The good thing about the virtual event, uh, event is that everybody can attend. It's free, it's open from across the road, of course, but what we want for people to take away as attendees is that you learn something at pragmatics or the next day on the job, you can do something. You've learned something very specific. We've also been, um, um, excited and looked at what is possible from an innovation perspective. And so that's how we look at the events. We bring a lot of, um, uh, customers on my realization that they're going to share their best practices, very specifically, how they are, how they are handling data governance, how they're doing data, data, cataloging, how they're doing data privacy. So very specific best practices and tips on how to be successful, but then also industry experts that can paint the picture of where we going as an industry, what are the best practices? >>What do we need to think about today to be ready for what's going to come tomorrow? So that's a big focus. We, of course, we're going to talk about and our product. What are we, what do we have in store from a product roadmap and innovation perspective? How are we helping these organizations get their foster and not aspect as we were being in a lot of partners as well? Um, and so that's a big part of that broader ecosystem, uh, which is, which is really interesting. And I finally, like I said, it's really around the community, right? And that's what we hear continuously from the attendees. Just being able to make these connections, learn new people, learn what they're doing, how they've, uh, kind of, um, solved certain challenges. We hear that's a really big part of, uh, of the value proposition. So as an attendee, uh, the good thing is you can, you can join from anywhere. Uh, all of the content is going to be available on demand. So later it's going to be available for you to have to look at as well. Plus you're going to be farther out. You're going to become part of that data, citizens community, which has a really thriving and growing community where you're going to find a lot of like-minded people with the same passion, the same interest that McConnell learned the most from, well, I'd rather >>Like the term data citizen. I consider myself a data citizen, and it has implications just in terms of putting data in the hands of, of business users. So it's sort of central to this event, obviously. W what is a data citizen to Collibra? >>Yeah, it's, it's a really core part of our mission and our vision that we believe that today everyone needs data to do their job. Everyone in that sense has become a data citizen in the sense that they need to be able to easily access trustworthy data. We have to make it easy for people to easily find the right data that they can trust that they can understand. And I can do something like with and make their job easier. On the other hand, like a citizen, you have rights and you have responsibilities as a data citizen. You also have the responsibility to treat that data in the right way to make sure from a privacy and security perspective, that data is a as again, like I said, treated in the right way. And so that combination of making it easy, making it accessible, democratizing it, uh, but also making sure we treat data in the right way is really important. And that's a core part of what we believe that everyone is going to become a data citizen. And so, um, that's a big part of our mission. I like that >>We're to enter into a contract, I'll do my part and you'll give me access to that data. I think that's a great philosophy. So the call to action here, June 16th and 17th, go register@citizensdotcollibra.com go register because it's not just the normal mumbo jumbo. You're going to get some really interesting data. Felix, I'll give you the last word. >>No, like I said, it's like you said, go register. It's a great event. It's a great community to be part of June 16 at 17, you can block it in your calendar. So go to citizens up pretty bad outcome. It's going to be a, it's going to be a great event. Thanks for helping >>Us preview. Uh, this event is going to be a great event that really excited about Felix. Great to see you. And we'll see you on June 16th and 17th. Absolutely. All right. Thanks for watching everybody. This is Dave Volante for the cube. We'll see you next time.

(upbeat music) >> At the beginning of last decade technology industry was a buzzing because we were on the cusp of a new era of data. The promise of so-called big data was that it would enable data-driven organizations to tap a new form of competitive advantage. Namely insights from data at a much lower cost. The problem was data became plentiful, but insights, they remain scarce. A rash of technical complexity combined with a lack of trust due to conflicting data sources and inconsistent definitions led to the same story that we've heard for decades. We spent a ton of time and money to create a single version of the truth. And we're further away than we've ever been before. Maybe as an industry, we should be approaching this problem differently. Perhaps it should start with the idea that we have to change the way we serve business users i.e. those who understand data context. And with me, to discuss the evolving data space, his company and the upcoming Data Citizens Conference is Felix Van De Maele, the CEO and Founder, of Collibra. Felix, welcome. Great to see you. >> Great to see you. Great to be here. >> So tell us a little bit about Collibra and the problem that you're solving. Maybe you could double click on my upfront narrative. >> Yeah, I think you said it really well. We've seen so much innovation over the last couple of years in data, the exploding volume complexity of data. We've seen a lot of innovation of how to store and process that data, that volume of data more effectively, more cost-effectively. But fundamentally the source of the problem as being able to really derive insights from that data effectively when it's for an AI model or for reporting is still as difficult as it was let's say 10 years ago. And it only... In a way it's only become more difficult. And so what we fundamentally believe is that next to that innovation on the infrastructure side of data you really need to look at the people on process side of data. There are so many more people that today consume and produce data to do their job. That's why we talk about data citizens. They have to make it easier for them to find the right data in a way that they can trust that there's confidence in that data to be able to make decisions and to be able to trust the algorithm of that model. And that's really what Collibra is focused on. Initially, around governance. How do you make sure people actually or companies know what data they have and make sure they can trust it and they can use it in a compliant way. And now we've extended that into the only data intelligence platform today in the industry where we just make it easier for organizations to truly unite around the data across the whole organization. wherever that data stored on premise and the cloud whoever is actually using or consuming that data. That's why we talk about data citizens. >> I think you're right. I think yours is more complex. There's more of it. And there's more pressure on individuals to get advantage from it. But I want to ask you, what sets Collibra apart because I'd like you to explain why you're not just another data company chasing a problem with it's going to be an incremental solution, it's really not going to change anything. What sets Collibra apart? >> Yeah, that's a really good question. And what fundamentally sets us apart, or makes us unique is that we look at data or the problem around data as truly a business owner and a business function. So we fundamentally believe that if you believe that data is an asset, you really have to run it as a strategic business function. Just like you run your HR function, your people function, your IT function your sales and marketing function. You have a system to run that function. And you have Salesforce to run sales and marketing. You have service now to run your IT function. You have word day to run your people function. Like you need the same system to really run your data function. And that's really how we think about Collibra. So we're not another kind of faster better database. We're not another data management tool that makes the life of a single individual easier. We're truly a business application that focuses on how do we bring people together and effective rates so that they can collaborate around the data. It creates efficiency. So you don't have to do things ad hoc. You can easily find the right information. You can collaborate effectively. And it creates the confidence to actually be able to do something with the outcomes or with the results of all of that work. And so fundamentally, looking at the problem as a business function that needs a business system. We call it the system of record or system of engagement. For the data function, I think it's absolutely a critical and really unique in our approach. >> So Data Citizens your big user conference. Data Citizens '21 it's coming up June 16th and 17th cubes stoked because we love talking about data. This is the first time we're bringing theCUBE to that event. And so we're really gearing up for it. And I wonder if you can tell us a little bit about the history and the evolution of the Data Citizens conference? >> Absolutely. I think the first one it started six years ago where we had a small event at a hotel downtown New York mostly customers as their user conference, a lot of the banks, which are at the time are the main customers at 60 people. So very small events. And it's exploded ever since this year, we expect over 5,000 people. So it's really expanded beyond just a user conference to really become more of almost a community conference and an industry conference. So we're really excited. A big part of what we do, why we care so much about the conference. That's an opportunity to build that data citizens community. That's where we hear from our customers, from all attendees that come to the conference, bring those people together that all care about the same topic and are passionate about doing more with data, being able to connect people together as a big part of that. So we've always... We're always looking forward to event from that perspective. >> Well, a lot of competition of course, for virtual events these days with them. What's in it for me? Who should attend? And what can attendees expect from Data Citizens '21? >> Yeah, absolutely. The good thing about the virtual event is that everybody can attend. It's free, it's open from across the world, of course. But what we want for people to take away as attendees is that you learn something pragmatic. So the next day on the job, you can do something. You've learned something very specific. We've also been excited and looked at what is possible from an innovation perspective? And so that's how we look at the event. We bring a lot of customers and organization that are going to share their best practices. Very specifically, how they're handling data governance. How they're doing data cataloging. How they're doing data privacy. So very specific best practices and tips on how to be successful, but then also industry experts that can paint a picture of where we're going as an industry, what are the best practices? What do we need to think about today to be ready for what's going to come tomorrow? So that's a big focus. We, of course, we're going to talk about Collibra and our product. What do we have in store from a product roadmap. And innovation perspective, how we're helping these organizations get there faster and all that aspect as we bring in a lot of partners as well. And so that's a big part of that broader ecosystem which is really interesting. And I finally, like I said it's really around the community. That's what we hear continuously from the attendees. Just being able to make these connections, learn new people, learn what they're doing how they've kind of solved certain challenges. We hear that's a really big part of the value proposition. So as an attendee, the good thing is you can join from anywhere. All of the content is going to be available on demand. So later it's going to be available for you to have to look at as well. Plus you're going to be part, or you're going to become part of that data citizens community. Which is a really thriving and growing community where you're going to find a lot of like-minded people with the same passion, the same interest, that we can all learn a lot from. >> I rather like the term data citizen. I consider myself a data citizen and it has implications just in terms of putting data in the hands of business users. So it's just sort of central to this event, obviously. What is a data citizen to Collibra? >> Yeah. It's a really core part of our mission and our vision that we believe that today everyone needs data to do their job. Everyone in that sense has become a data citizen in the sense that they need to be able to easily access trustworthy data. We have to make it easy for people to easily find the right data that they can trust, that they can understand and they can do something with and make their job easier. On the other hand, like a citizen, you have rights and you have responsibilities. As a data citizen, you also have the responsibility to treat that data in the right way. To make sure from a privacy and security perspective, that data is as again like I said, treated in the right way. And so that combination of making it easy, making it accessible, democratizing it but also making sure we treat data in the right way is really important. And it's a core part of what we believe that everyone is going to become a data citizen. And so that's a big part of our mission. >> I like that. We're going to enter into a contract. I'll do my part and you'll give me access to that data. I think that's a great philosophy. So the call to action here, June 16th and 17th go register at citizens.collibra.com go register because it's not just the normal mumbo jumbo. You're going to get some really interesting data. Felix, I'll give you the last word. >> No, like I said, like you said, go register. It's a great event. It's a great community to be part of at June 16th and 17th you can block it in your calendar. So go to citizens.collibra.com. It's going to be a great event. >> Well, thanks for helping us preview this event. It's going to be a great event that we're really excited about. Felix, great to see you. And we'll see you on June 16th and 17th. >> Absolutely. >> All right. Thanks for watching everyone. This is Dave Vellante for theCUBE. We'll see you next time. (upbeat music)

>> Announcer: From around the globe, it's theCUBE. With digital coverage of Dockercon live 2020. Brought to you by, Docker, and its ecosystem partners. >> Hey, welcome back everyone to the DockerCon 2020, #DockerCon20. This is The Cube virtual coverage with Docker on their event here. And we're in the studio in Palo Alto, I'm John Furrier your host of theCUBE, we're here with a great guest to talk about Docker Desktop, the Microsoft relationship, and the key news that's coming out. Ben De St Paer-Gotch is the product manager for Docker Desktop. Ben, great for coming on, thanks for spending the time with me. >> Thanks for having me, I really appreciate it. >> So obviously, this is a virtual conference, we wish we could be in person, but given the state of affairs we're going to do remotely, but the momentum Docker has is phenomenal, it's always been great with containers. It's the number one downloaded app around for developers. Microsoft just had their Build conference, which was again virtual as well, or digital, as they say, it's interchangeable. But clear momentum now with Docker as containers actually is the standard, you guys are doing great. What's the key news out of the Microsoft world for people who missed it last week with MS Build? >> Yeah, so last year at Build, Microsoft announced WSO2 to the Windows subsystem with Linux two. (mumbles) The mapping between the windows (mumbles) Which, went really well but it just didn't provide the same centered needed Linux experience. Last year, they announced Windows subsystem Linux two, (Provides an actual Linux one on windows machine, and we've been working hard with Microsoft over the last year to integrate proper desktop as a main desktop application for working with containers with WSO2. A build this year, Microsoft has gone on and announced that WSO2 is going to have a few new features, and it's going to have new features. (mumbles) Mention Linux graphical, Linux applications, you can access the file system, the installation is going to become a slicker which I guess I'm the most excited about that pitch. But the most exciting announcement is, they will be bringing GPU support to WSO2 which means that we will be able to provide and give you support through Docker desktop or container workloads that peoples are working on. And now we're launching Gray and Agua through containers and docks and desktops and Windows which is really cool because we haven't been able to do that before. >> So is this the first GPU support on Microsoft Windows for Docker, with Docker? >> It's, yeah, it's the first GPU Support for Docker Desktop or Mac or Windows. So, previously the hypervisor hasn't passed through the GPU, pretty much, which meant that we couldn't access it from Docker desktop. So Docker desktop isn't about a lightweight VM we sorts of plumb all that in for you. But we're limited about what we could get access to from the hypervisor, Microsoft putting this through and giving us access for the first time, we can actually, we can go. >> Not to go on a side tangent here, but you know, all these virtual events, and I was watching some of the build stuff as well, as well as us immediate streamers and doing stuff, you can see people's home rigs. And you talk to any Developer, video streamer, or anyone who is working remotely, if you don't have the best GPU's in there, I mean, this has just become, I mean, quite frankly, you need the GPU's. So this is important, it's not only from a vanity standpoint performance. Having that support, I'm going to want the best GPU's, I'm always going to be upgrading my machine for that extra power. What's the impact? What does it mean for me as a Developer? Does it increase stuff? What's the bottom line? >> As a Developer, it means you actually have access to it. So, especially when you're doing workloads on the CPU, you've got minimum amounts of power utilization you can do. When you're running workloads for an L Development, you have a lot of power up process you've got to log, to do your mobile training. So, in an element cycle, you're likely to have your application which you're going to use to produce a modeling, you're going to have training data. Taking that training data and producing a model requires lots of panel processing which is an enormous calculations in producing with finer waitings. Doing that on a CPU has to be done on a serial fashion rather than parallel, which is huge and intensive and takes a really long time. Whereas on a GPU, you can do all of that in parallel which massively reduces the amount of time it will take to run those training functions. Either just straight up in Linux or running them in a container, which as the more of people are looking at running container with workloads, it's how I first, the first team that I was on actually used Docker. I was working in Amazon Alexa, and my team picked up the opportunity to run our workload in container. And that was my first experience, so even though my team backed down, so I could see the system. >> Yeah, ML workloads automations could be critical of that performance. Okay, let's get into some of the momentum with Microsoft, you guys have obviously, builds over, we're here now at DockerCon, there's news. Could you share some of the tidbits for what's being talked about now with Docker and DockerCon. >> Yeah, absolutely, so, along with everything else we've been doing, we've been partnering with Microsoft trying to make the best experience generally with Docker desktop, and with WSO2 and with the VSCO. I've been working closely with Microsoft guys to actually try and improve our experience in Windows as it is today, and to improve some of those integrations with VSCO, and also working with the VSCO team on the Docker plugin for VSCO to give our feedback, and to hear feedback from those guys on the errors and issues they're seeing with Docker desktop and to really try to produce the best experience we can on Windows. End to end, from very front end running all the way through that first push, that first run on the cloud using Docker. >> So what is some of the new product management processes and customer support things that you guys are doing? This comes up a lot, obviously, we had a great conversation around shift left with security. That's great news there. You start to see a lot of this added value for Developers, wanted their support right? So how do I get things I need, and from a customer standpoint? It's kind of a moving train this world and it's only getting better and better from a Developer standpoint. But there's more complexity, it's got to be abstract the way you've got, you know, this new abstraction layers developing. You've got a lot of automation. How does the customer get the support they need in the same agile way that Developers are cranking out code? >> It's a really good question, it's something I think we're still working on as well. So, we're trying to working out and one of the big things I'm trying to work out is, how to make it easier for people to get started with Docker, and how do we also make sure with the things we build, we don't leave a cliff edge instead of a lining path. You don't get to a certain point in an easy process, and then the next step, takes you straight off a cliff, so that's not useful for anyone. So, producing those parts and those ways for people to learn and actually progress is something we're really trying to work out. How to make it natural from the first experience all the way through. From an actual support perspective, the other thing we're looking at, is we're trying to do more things in the open. We're really trying at Docker to bring as many of the new features and pieces we're developing which we have to do that in the open with community visibility, so that if people really want it fixed, they can open the PR and they can help us out. And then the last thing that my team really stood out was our Docker of having actions. As creators, someone already finished, could you do this? Someone else had a PR and emerged it. So, to a certain extent, you've got your one side which had you on board and this ever growth spiral and you keep learning. The other side is how'd you fix the board when you find an issue? In that one, we're really trying to work with the community, a lot more than we have in the last couple of years. >> Awesome, some folks watching, hit him up on Twitter, he's the Product Manager for Docker Desktop among other things. You guys are very transparent, you've got your Twitter handle on the lower third. People can chime in or just jump on the chat, we'll follow up and get you the info. Final question for you Ben, as you look at this reality we're in, there's kind of a holistic kind of moment now where people kind of realizing the new realities here. You're looking at the.. you get the keys to the kingdom with Docker Desktop, okay. You got some momentum with Microsoft, the developer role is moving fast and fast as the head room increases for capabilities with automation. And I know you mentioned a few of those things. GPU is now available. What's the future look like for these Developers? The next short, medium and long term? What's your view as you look out over the landscape because you've got to look at the product roadmap, your engagement with the community. Can you share some insight into how you're thinking about Docker Desktop going forward? >> Yeah, absolutely. So, I think what really interesting point as you say, which is that, if you look at sort of a lot of the Developer side of things that have sort of come out in the last like six months, six to eighteen months. The things I see, I see daily like you mention, things like orchestrating for containers gaining momentum. If you think about crossing the Kaizen model, we're just passed the early Dockers now. We're kind of into the early majority, but we're going to start to move over the next few years into the late majority. What that really means is that people here have been using one of two of these technologies. Maybe you've been using cloud, maybe you've been using Edge, maybe you've been using containers, maybe you've been using CICD, maybe you are using Expiration, maybe you're not. Maybe you've got a Microservice application, maybe it's a little bit of a mole rat. What we're really going to see is, you're going to start to see, all of these changes intersecting and overlapping. And people who have started to pick up model two of these will start to pick up all of them. And that's probably going to happen as we move into the majority of users. So from a what's coming instead of a lot of those thing that you see in best practice in the ideal Developer setup, so a beautiful CICD, a more of an orchestrated environment, Microservice architecture, we're going to see a lot more of that becoming the norm. But I think along with that, we'll also see a level of recognition coming along that a single Microservice alone doesn't provide value. And that's it's going to be some of those groups of services that will provide the user outcome. And that's where my focus is at the end which is you know, an authentication service is great but it doesn't provide value unless you give access to something as authentic. >> It's been issued that the new Docker is all about Developer experience. This is really the core mission. I mean, since the sale of the piece of morantis, Docker has retrenched and reinvented, but stayed core to its principles. Just share with the Developers who've been watching that are coming back into the ecosystem, what is this new Docker vibe? Share your thoughts. >> The new Docker vibe is about working in the open, and it's about solving problems for Developments. The original goal of Docker was to make it easy to pack and ship. It was to reduce Developer friction. As we move more into, sort of, the enterprise space, we worry more about Ops and DevOps. We're not trying to re-focus on Developer and if you sort of think there's two parts to the Developer life cycle, where you've got your work, where you're doing your creative work, where you're writing code. And then you've sort of got your part of the inner loop. And then you've got your part where you're trying to get that code out to production, you're trying to get your value to someone else. Instead of your outer loop, we're really trying to focus on the inner loop And sort of our mantra is that any bit for a Developer should spend as much as their time as possible creating new and exciting things and we're onto those holes that reduce those boring, Monday, repetitive tasks, that we're really trying to work out how we take those boring repetitive pieces and how do we make them just vanish like magic from new users or how do we reduce the friction for the experience from users? From both desktop and hub, we're really trying to bring those two together to achieve that. >> You know what's great about folks who have been in the class since day one. All of us have scar tissue experiences, you know the one thing that's constant is constant change. And one of the things that you guys have done at Docker, and hats off to the whole, you know, original team, is that brand of Docker has symbolized quality openness, and set the standard, I mean, if you look back and containers were really coming around, it's not a new concept. But Docker really set the industry on this path and it's been great to follow every DockerCon at TheCube coverage, but more importantly, as the demand for Developers to build these next wave of Cambrian explosion of applications. It's going to be more important than ever to have more of these abstractions, more of these tools in this real time, more Developers experience because there's more building going on. And it's not just one cloud, it's all clouds, it's all things. >> Yeah, I think it was like when IDC analyzed the future report a couple years ago, I think it was maybe the 2018 one. They said that maybe 2017. They said to date, we've built 500 millions applications worldwide and by 2023, we'll build another 500 million. The rate of creation is just insane, it's exponential growth of us producing more and more applications and connecting more and more devices to do them. The sheer volume of creation and the rate of new technology supporting, even with the rate of companies adopting, I guess more of a warm cloud. I think it's like 60 percent of companies are now more than one cloud provider. Maybe even more, maybe it's like 80 percent. It's ridiculous. >> I was just having this debate on Twitter about this multi-cloud. Someone tried to call us out saying, "Oh you guys were pooing on multi-cloud in 2016 and 18." I go "Look at, no one was Pooping on multi-cloud, it didn't exist." I had multiple clouds but there was no real use case. Now you're starting to see the use cases, where yeah, I had multiple clouds and I got Azure here, I got this over here. But no one wakes up and spreads their workloads wrong. This is going back a few years. Certainly the hybrid was developing, but I think now you're starting to see with networking and some of these inter-operable dynamics, you start to see innovation pockets in wide spaces in large market opportunities for start-ups and companies to thread the clouds together at the right place. So I think multi-cloud is becoming apparent from a use case stand point. Still a ton of work to do, I mean direct connects, got SLA's, I mean all kinds of stuff at the networking level but it is real. It's going to be one of those realities that everyone has, at least one or two, if not three. It could be optimization, this is what Developers do right? Solve problems. >> Yeah, absolutely, I mean if nothing else, I've encounter a couple of companies even just where redundancy is handled by multi-cloud strategy. If you want to achieve more nines and you're just balancing workloads between two clouds. >> I mean, the Zoom news was really a testament to that because everyone got into a twist over that. Oh Zoom moves off Amazon, no they didn't move off Amazon, they went to Oracle, they got Adge, they're everywhere. Why wouldn't they be? They need to pass it, they fail over, they need fall tolerance, I mean, these are basic distributing computing concepts that is one on one. You've got to have these co-locations. And optimization for those clouds and the apps on Microsoft as well, so why wouldn't you do it? >> Exactly. And that's that hybrid, that multi-cloud, compounding that some of which you said earlier, that over changes when you're looking at how you go to CICD, how you're bundling these applications, creating more applications than ever. Coming back, sort of, with more AI workloads, much like GPU and you combine that with, sort of, last in the growth of age devices as well. It sort of makes for a really interesting future. And Docker is sort of, that summation SOV, what we're using to frame how we're thinking about our product and what we should be building. >> Great, for the audience out there, hit him up on Twitter, Ben's available, they're out in the open, if you're interested in how Docker makes life easier on the Windows platform, with the GPU support, they've got security now built in, shifting left. Give these guys a call and of course, we love the mission, out in the open. It's theCUBE's mission as well and great to chat with you. Ben, thanks for spending the time with me today. >> Been an absolute pleasure, thank you for having me. >> Okay, just TheCube's coverage, the virtual Cube with DockerCon co-creating together out in the open. DockerCon20, #Docker20, I'm John Fer with TheCube, stay tuned for our next segment, and thanks for watching. (ambient music)

>> Announcer: Live from Las Vegas, it's theCUBE, covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel, along with its ecosystem partners. >> Well, welcome back here on theCUBE. We're at AWS re:Invent 2019. And every once in a while, we have one of these fascinating interviews that really reaches beyond the technological prowess that's available today into almost the human fascination of work, and that's what we have here. >> Big story. >> Dave Vellante, John Walls. We're joined by Sebastien De Halleux, who is the CEO, oh, COO, rather, of a company called Saildrone, and what they feature is wind-powered flying robots, and they've undertaken a project called Seabed 2030 that will encompass mapping the world's oceans. 85% of the oceans, we know nothing about. >> That's right. >> And, yeah, they're going to combine this tremendous technology with 100 of these flying drones. So, Sebastien, we're really excited to have you here. Thanks for joining us, and wow, what a project! So, just paint the high-level view, I mean, not to have a pun here, but just to share with folks at home a little bit about the motivation of this and what gap you're going to fill. Then we'll get into the technology. >> So I think, you know, the first question is to realize the role of oceans and how they affect you on land and all of us. Half the air you breathe, half the oxygen you breathe, comes from the ocean. They cover 70% of the planet and drive global weather, they drive all the precipitation. They also drive sea-level rise, which affects coastal communities. They provide 20% of the protein, all the fish that we all eat. So, you know, it's a very, very important survival system for all of us on land. The problem is, it's also a very hostile environment, very dangerous, and so, we know very little about it. Because we study it with a few ships and buoys, but that's really a few hundred data points to cover 70% of the planet, whereas on land, we have billions of data points that are connected. So, that's why we're trying to fundamentally address, is deploying sensors in the ocean using autonomous surface vehicles, what we call Saildrones, which are essentially, think of them as autonomous sailboats, seven meters, 23 feet, long, bright orange thing with a five-meter-tall sail, which is harnessing wind power for propulsion and solar power for the onboard electronics. >> And then you've got sonar attached to that, that is what's going to do the-- >> The mapping itself. >> The underwater mapping, right, so you can look for marine life, you can look for geographical or topographical anomalies and whatever, and so, it's a multidimensional look using this sonar that, I think, is powered down to seven kilometers, right? >> That's right. >> So that's how far down, 20,000, 30,000 feet. >> That's right. >> So you're going to be able to derive information from it. >> You essentially describe it as, you're painting the ocean with sound. >> That's absolutely right, whereas if you wanted to take a picture of land, you could fly an airplane or satellite and take a photograph, light does not travel through water that well. And so, we use sound instead of light, but the same principle, which is that we send those pulses of sound down, and the echo we listen to from the seabed, or from fish or critters in the water column. And so, yes, we paint the ocean with sound, and then we use machine learning to transform this data into biomass, statistical biomass distribution, for example, or a 3-D surface of the seabed, after processing the sound data. >> And you have to discern between different objects, right? I mean, you (laughs) showed one picture of a seal sunbathing on one of these drones, right? Or is there a boat on the horizon? How do you do that? >> It's an extremely hard problem, because if a human is at sea looking through binoculars at things on the horizon, you're going to become seasick, right? So imagine the state of the algorithm trying to process this in a frame where every pixel is moving all the time, unlike on land, where you have at least a static frame of reference. So it's a very hard problem, and one of the first problems is training data. Where do you get all this training data? So our drones, hundreds of drones, take millions of pictures of the ocean, and then we train the algorithm using either labeled datasets or other source of data, and we teach them what is a boat on the horizon, what does that look like, and what's a bird, what's a seal. And then, in some hard cases, when you have a whale under the Saildrone or a seal lying on it, we have a lot of fun pushing it on our blog and asking the experts to really classify it. (Dave and John laugh) You know, what are we looking at? Well, you see a fin, is it a shark? Is it a dolphin? Is it a whale? It can get quite heated. >> I hope it's a dolphin, I hope it's a dolphin. (Sebastien laughs) All right, so, I want to get into the technology, but I'm just thinking about the practical operation of this. They're wind-powered. >> Sebastien: Yes. >> But they just can't go on forever, right? I mean, they have to touch down at some point somehow, right? They're going to hit water. How do you keep this operational when you've got weather situations, you've got some days maybe where wind doesn't exist or there's not enough there to keep it upright, keep it operational, I mean. >> It's a very good question. I mean, the ocean is often described as one of the toughest environments in the universe, because you have corrosive force, you have pounding waves, you have things you can hit, marine mammals, whales who can breach on you, so it's a very hard problem. They leave the dock on their own, and they sail around the world for up to a year, and then they come back to the same dock on their own. And they harvest all of their energy from the environment. So, wind for propulsion, and there's always wind on the ocean. As soon as you have a bit of pressure differential, you have wind. And then, sunlight and hydrogeneration for electrical power, which powers the onboard computers, the sensors, and the satellite link that tells it to get back to shore. >> It's all solar-powered. >> Exactly, so, no fuel, no engine, no carbon emission, so, a very environmentally friendly solution. >> So, what is actually on them, well, first of all, you couldn't really do this without the cloud, right? >> That's right. >> And maybe you could describe why that is. And I'm also interested in, I mean, it's the classic edge use case. >> Sure, the ultimate edge. >> I mean, if you haven't seen Sebastien's keynote, you got to. There's just so many keynotes here, but it should be on your top 10 list, so Google Saildrone keynote AWS re:Invent 2019 and watch it. It was really outstanding. >> Sebastien: Thank you. >> But help us understand, what's going on in the cloud and what's going on on the drone? >> So it is really an AWS-powered solution, because the drones themselves have a low level of autonomy. All they know how to do is to go from Point A to Point B and take wave, current, and wind into consideration. All the intelligence happens shoreside. So, shoreside, we crunch huge amounts of datasets, numerical models that describe pressure field and wind and wave and current and sea ice and all kinds of different parameters, we crunch this, we optimize the route, and we send those instructions via satellite to the vehicle, who then follow the mission plan. And then, the vehicle collects data, one data point every second, from about 25 different sensors, and sends this data back via satellite to the cloud, where it's crunched into products that include weather forecasts. So you and I can download the Saildrone Forecast app and look at a very beautiful picture of the entire Earth, and look at, where is it going to rain? Where is it going to wind? Should I have my barbecue outside? Or, is a hurricane coming down towards my region? So, this entire chain, from the drone to the transmission to the compute to the packaging to the delivery in near real time into your hand, is all done using AWS cloud. >> Yeah, so, I mean, a lot of people use autonomous vehicles as the example and say, "Oh, yeah, that could never be done in the cloud," but I think we forget sometimes, there are thousands of use cases where you don't need, necessarily, that real-time adjustment like you do in an autonomous vehicle. So, your developers are essentially interacting with the cloud and enabling this, right? >> Absolutely, so we are, as I said, really, the foundation for our data infrastructure is AWS, and not just for the data storage, we're talking about petabytes and petabytes of data if you think about mapping 70% of the world, right, but also on the compute side. So, running weather models, for example, requires supercomputers, and this is how it's traditionally done, so our team has taken those supercomputing jobs and brought them into AWS using all the new instances like C3 and C5 and P3, and all this high-performance computing, you can now move from old legacy supercomputers into the cloud, and so, that really is an amazing new capability that did not exist even five years ago. >> Sebastien, did you ever foresee the day where you might actually have some compute locally, or even some persistent-- >> So on the small Saildrones, which is the majority of our fleet, which is going to number a thousand Saildrones at scale, there is very little compute, because the amount of electrical power available is quite low. >> Is not available, yeah. >> However, on the larger Saildrone, which we announced here, which is called the Surveyor-- >> How big, 72 feet, yeah. >> Which is a 72-foot machine, so this has a significant amount of compute, and it has onboard machine learning and onboard AI that processes all the sonar data to send the finished product back to shore. Because, you know, no matter how fast satellite connectivity's evolving, it's always a small pipe, so you cannot send all the raw data for processing on shore. >> I just want to make a comment. So people often ask Andy Jassy, "You say you're misunderstood. "What are you most misunderstood about?" I think this is one of the most misunderstood things about AWS. The edge is going to be won by developers, and Amazon is basically taking its platform and allowing it to go to the edge, and it's going to be a programmable edge, and that's why I really love the strategy. But please, yeah. >> Yeah, no, we talked about this project, you know, Seabed 2030, but you talked about weather forecasts, and whatever. Your client base already, NASA, NOAA, research universities, you've got an international portfolio. So, you've got a whole (laughs) business operation going. I don't want to give people at home the idea that this is the only thing you have going on. You have ongoing data collection and distribution going on, so you're meeting needs currently, right? >> That's right, we supply governments around the world, from the U.S. government, of course, to Canada, Mexico, Japan, Australia, the European Union, well, you name it. If you've got a coastline, you've got a data problem. And no government has ever come and told us, "We have enough ships or enough data on the oceans." And so, we are really servicing a global user base by using this infrastructure that can provide you a thousand times more data and a whole lot of new insights that can be derived from that data. >> And what's your governance structure? Are you a commercial enterprise, or are you going-- >> We are a commercial enterprise, yes, we're based in San Francisco. We're backed by long-term impact venture capital. We've been revenue-generating since day one, and we just offer a tremendous amount of value for a much cheaper cost. >> You used the word impact. There's a lot of impact funds that are sort of emerging now. At the macro, talk about the global impact that you guys hope to have, and the outcome that you'd like to see. >> Yeah, you know, our planetary data is all about understanding things that impact humanity, right? Right now, here at home, you might have a decent weather forecast, but if you go to another continent, would that still be the case? Is there an excuse for us to not address this disparity of information and data? And so, by running global weather model and getting global datasets, you can really deliver an impact at very low marginal cost for the entire global population with the same level of quality that we enjoy here at home. That's really an amazing kind of impact, because, you know, rich and developed nations can afford very sophisticated infrastructure to count your fish and establish fishing quarters, but other countries cannot. Now, they can, and this is part of delivering the impact, it's leveraging this amazing infrastructure and putting it in the hands, with a simple product, of someone whether they live on the islands of Tuvalu or in Chicago. >> You know, it's part of our mission to share stories like this, that's how we have impact, so thank you so much for-- >> I mean, we-- >> The work that you're doing and coming on theCUBE. >> This is cool. We talk about data lakes, this is data oceans. (Dave laughs) This is big-time stuff, like, serious storage. All right, Sebastien, thank you. Again, great story, and we wish you all the best and look forward to following this for the next 10 years or so. Seabed 2030, check it out. Back with more here from AWS re:Invent 2019. You're watching us live, right here on theCUBE. (upbeat pop music)

>>law from Las Vegas. It's the Q covering a ws re invent 2019. Brought to you by Amazon Web service is and in along with its ecosystem partners. >>Hey, welcome back. Everyone's two cubes. Live coverage I'm John for with the Cube were here reinvent date, too, as it winds down Walter Wall interviews two sets here. We want to think Intel, big sponsor of this, said we without Intel, we wouldn't have this great content. They support our mission at the Q. We really appreciate it. We're here and strengthen the signal the noise on our seventh reinvent of the eight years that they've been here. We've been documenting history, and we got a great panel lined up here. They got Sebastian to holler Who's the CEO? Sale Drone. Henry Stalls, Stool The VP of Science and Technology and Bowery Farming. Great use case around the food supply and Janet his era space weather scientists at NASA. The Kilo Physics division. We got a great lineup here. Great panel. Welcome to the Cube. Thanks for coming. Thank you. Okay. We'll start with you, Jen. And you're doing some super cool space exploration. You're looking at super storms in space. What's your story? >>Yeah, I work at NASA and NASA has in its mandate to understand how to protect life on Earth and in space from events like space, weather and other things. And I'm working with Amazon right now to understand how storms in space get amplified into super storms in space, which now people understand, can have major impacts on infrastructures head earth like power grits. >>So there's impact. >>There's a >>guy's measuring that, not like a supernova critical thing like >>that >>of, like, practical space. >>Actually, the idea that the perception of the world of the other risks of space weather changed dramatically in 1989 when Superstorm actually caused the collapse of a power grid in Canada and the currents flowing in the ground from the storm entered the power grid and it collapsed in 90 seconds. It couldn't even intervene. >>Wow, some serious issues. We want to get into the machine learning and how you guys are applying. But let's get through here, and we're doing some pretty cool stuff that's really important. Mission. Food supply and global food supply something that you're doing. What I think it might explain. >>Yeah, Bowery were growing food for a better future by revolutionizing agriculture. And to do that, we're building these ah network of large warehouse scale indoor farms where we go all sorts of produce indoors 365 days a year, using zero pesticides using hydroponic systems and led technology. So it's really exciting. And at the core of it is some technology we call the Bowery operating system, which is how we leverage software hardware in a I tow, operate and learn from our farm. >>I'm looking forward to digging into that Sebastian sale drone. You're doing some stuff you're sailing around the world. You got nice chance that you now tell your story. >>Sadly, no way. Use wind powered robots to study the 20% of the planet that's currently really data scarce. And that's the oceans on. So we measure things like biomass, which is how many fish down in the ocean. We measure the input of energy, which impacts weather and climate. We mapped the seabed on. We do all kinds of different tasks which are very, very expensive to do with few ships >>and to report now that climate change is on everyone's agenda, understanding potentially blind spots. Super important, right? >>That's what I'm trying to, You know, this whole question of if it's a question of what? When and what and how much. And so, you know, the ice is melting, the Gulf Stream is changing, and Nina is wrecking havoc. But we just do not understand this because we just don't have the data. In city, we use satellites where they have very low resolution. They cannot see through the water where you ships. No, has 16 ships he in the U. S. So we have to do better. We have to translate this into a big data problem. So that's what we're doing. We have 1000 sale drones on our plan with 100 water right now. And so we're trying to instrument old oceans all the time, >>you know, and data scales your friend because you don't want more data. Yes. Talk about what you're working on. What kind of a I in machine learning are you doing? You just gathering day. Then you're pumping it up to the cloud via satellites or what's going on there? >>One of the one of the use cases trying to understand you know who's out there. What are they doing? Another doing anything illegal. So to do this, you need to use cameras and look at the horizon and detect. You know whether you have vessels. And if those vessels are not transmitting the position, it means that they're trying to stay hidden on the ocean. And so we use machine learning and I that we train on on AWS to try to understand what where those things are. It's hard enough on land at sea. It's very hard because every pixel is moving. You have waves. The horizon is moving, the skies moving, the ship is moving. And so trying to solve this problem is a completely new thing that's called maritime domain awareness on, and it's something that has never been done before. >>And what's the current status of the project? >>So wave been live for about four years now we have 100 sail drones were building one a day towards the goal of having 1000 which we covered all the planet in a six by six degrees squares on. We are operationally active in the Arctic in the tropical Pacific. In the Atlantic. We just circumnavigated Antarctica, So it's the thing. That's really it's out there. But it's very far from from from land, >>So the spirit of cloud and agility static buoy goes away. You want to put the sale drones out there to gather and move around and capture. >>That's what the buoy is. You know, a massive steel thing, which has a full mile long cable, and it's it's headed to the silo in a fix stations one point and the ocean goes by. You having and robots means that you can go where you know something interesting is happening where you have a hurricane where you might have an atmospheric river where you might have a natural catastrophe or man made catastrophe. So this intelligence of the platform is really important in the navigation. That platform requires intelligence. And on the other side, getting 1000 times more data allows you to understand things better, just like Michael is doing. >>It isn't a non profit of four profit venture. >>It's a for profit company. So we said raw data a fraction of the cost of existing solution to try to create this kind of transformative impact on understanding what's happening >>that's super exciting for all the maritime folks out there because I love the ocean myself. Henry, you you're tackling real big mission. How using technology. I can almost imagine the instrumentation must be off the charts. What's your opportunity? Looked like? A tech perspective >>s o The level of control we have in our farms is really unparalleled. Weaken tune Just about every parameter that goes into growing our plans from temperature humidity Co Two light intensity day night cycles list keeps going on. And so to do Maur with fewer resource is to grow Maurin our farms. We're doing something called science a scale where we can pull different levers and make changes to recipes in real time. And we're using a I tow, understand the impact that those changes have and to guide us going from millions of different permutations. Trillions of permutations, really too. The perfect outdone >>converging. You jittery? Look at the product outcome. You circle that dated back is all on Amazon >>way. Do operate on Amazon. Yeah, and we're using deep learning technology to analyze pictures that come from cameras all over our farms. So we actually have eyes on every single crop that grows in our facilities and So we process those, learn from the data and and funnel that back into the >>like, Maybe put more light on this or do that kind of make a just a conditions. Is that that thing? That's >>exactly it. And we grow lots of different types of plants. We grow butter, head lettuce, romaine, kale, spinach, arugula, basil, cilantro. So there's a lot of different things we grow, and each of them require different, different little tweaks here and there. Toe produced over the best tasting and most nutritious product. >>That's cool, Janet Space. Lastly, on one inspection, we're gonna live on Mars someday. So you might be a weather forecaster for what route to take to Mars. But right now, the practical matter is Israel correlation between these storms. What kind of data problem are you looking at? What is the machine learning? What are some of the cool things you're working on? >>It? We have a big date, a problem because storms of that magnitude are very rare. So it's hard for us to find enough data to train a I we can't actually train a we have to use, you know, learning that doesn't require us to train it, but we've decided to take the approach that these super storms are like anomalies on the normal weather patterns. So we're trying to use the kind of a I that you used to detect anomalies like people who are trying to break into to do bank fraud or, you know, do a Web server tax. We use that same kind of software to tryto identify anomalies that are the space weather and look at the patterns between sort of a normal, more of a normal storm and a space with a huge space weather event to see how they patterns. Comparing how you're amplifying the regular storm into this big Superstorm activity. >>So it sounds like you have to be prepared for identifying the anomaly. See you looking at anomalies to figure out where the anomaly might be ready to be ready to get the anomaly. >>Yeah, you look at the background, and then what sticks out of the background that doesn't look like the background is is identified as the anomaly. And that's the storms that air happening, which are quite rare, >>all three of you guys to do some real cutting edge cool projects. I guess my question would be for the folks that are putting their toe in the water for machine learning. They tend to be new use cases like what you guys are doing, whether it's just a company tryingto read, factor themselves or we become reborn in the cloud ran legacy stuff. When you hear it, Amazon reinvent. This is the big question for these folks that are here. You guys are on the front end of a really cool projects. What's your advice that the people are trying to get in that mindset? >>So I think I think you know the way the way to think about this is if you're good at something and if you think you have the solution for something, how can you make that a 1,000,000 times more efficient? And so the problem is, there's just not enough capacity in the world, usually to treat data sets that a 1,000,000 times larger. And this is where machine learning should be thought about it as an extension of what humans really good at using a pair of eyes, ears or whatever or the sense. And so in our case. For example, counting fish acoustician, train acoustician, look at sonar data and understand schools of fish and can recognize them. And by using this knowledge base, we can train machines to do this on a much grander scale. And when you're doing a much grander scale, you derive. Ah, holding tight to >>your point is that humans are critical. I'm the process. So scaling the human capabilities and maybe filling in another scale issues or >>that's what a machine learning is. It's the greatest enabler of our time. It enables us to do things which are impossible to do before because we just didn't have enough people to do them at scale. >>AKI is being able to ask questions, right? And so if you have the questions to ask, you can apply this technology in a way that's never really been before possible. >>You're Jake. >>Yeah, I am actually someone who didn't know anything about a Ira ml when I started. I'm on. I'm a research scientist. That space weather. So coming into this, I'm working with E m L Solutions Lab here and putting a I experts with with experts and space brother we're getting we're doing things that are gonna give us new advances. I mean, We're already seeing things we didn't know before. So I think that if you partner with people who really have strong a I knowledge, you can use your knowledge of science to really get to the really important issues. >>Okay, I have to ask the final lightning round question. What is the coolest thing that you've done with your project that you've either observed implemented? That is super cool. Super cool. What's the coolest thing >>well in in terms of us were using anomaly detection to identify storms and in the first round through it actually identified every single Superstorm, which was not the major super storms, but it did. But it also started identifying other anomalous events, and when you went looked at him, they were anomalous events. So we're seeing things. It's picking out the weird things that are happening in space weather. It's kind of exciting and interesting. >>I worked for a day with you. I would love to just leave these anomalies every what's the coolest thing that you've seen or done with your project? >>I think the fact that we've built our own custom hardware own camera systems, uh, and that we feed those through algorithms that tell us something about what's happening minute by minute with plans as they grow to see pictures of plants minute by minute, they dance and it's truly it's It's remarkable. >>Wow! Fascinating Machin >>We've counted every single fish on the West Coast, the United States, every single air from Canada to Mexico. I thought I >>was pretty >>good. I didn't think it was possible. >>Very cool. But what's the number? >>Yeah, If I could tell you, I would. But I'm not allowed to tell you the jam. >>And you know where the salmon are, where they're running all that good stuff. Awesome. Well, congratulations, You guys doing some amazing work is pioneering a great example of just what's coming. And I love this angle of making larger human impact using technology. Where you guys a shaping technology for good things. Really, really exciting. Thanks for coming on, John Kerry. We're here live in Vegas for re invent 2019. Stay with more coverage. Day three coming tomorrow back with more After this break, when a fake intel for making it all happened presented by Intel Without their sponsorship, we wouldn't be able to bring this great content. Thanks for watching

>>Bly from Boston, Massachusetts. It's the cube covering ifs world conference 2019 brought to you by ifs. >>Okay. We're back in Boston, Massachusetts ifs world day one. You walked into cube Dave Vellante with Paul Gillen boss Devoss is here. He's the director of ISF I F S labs and Bob Dico who's the vice president of AI and RPA at ifs jets. Welcome. Good to see you again. Good morning bossy. We're on last year. I'm talking about innovation ifs labs. First of all, tell us about ifs labs and what you've been up to in the last 12 months. Well, I have has Lapsis a functioning as the new technology incubator. Fire Fest writes over continuously looking at opportunities to bring innovation into, into product and help our customers take advantage of all the new things out there to yeah. To, to create better businesses. And one of the things I talked about last year is how we want to be close to our customers. And I think, uh, that's what we have been doing over the pasta pasta year. Really be close to our customers. So Bob, you got, you got the cool title, AI, RPA, all the hot cool topics. So help us understand what role you guys play as ifs. As a software developer, are you building AI? Are you building RPA? Are you integrating it? Yes, yes. Get your paint. >>I mean, our value to our customers comes from wrapping up the technology, the AI, the RPA, the IOT into product in a way that it's going to help their business. So it's going to be easy to use. They're not going to need to be a technical specialist to take advantage of it. It's going to be embedded in the product in a way they can take advantage of very easily that that's the key for us as a software developer. We don't want to offer them a platform that they can just go and do their own thing. We want to sort of control it, make it easier for them. >>So I presume it's not a coincidence that you guys are on together. So this stuff starts in the labs and then your job is to commercialize it. Right? So, so take machine intelligence for example. I mean it can be so many things to so many different people. Take us back to sort of, you know, the starting point, you know, within reason of your work on machine intelligence, what you were thinking at the time, maybe some of the experiments that you did and how it ends up in the product. Oh, very good question. Right? So I think we start at a, Oh, well first of all, I think ifs has been using a machine learning at, at various points in our products for many, many years of Trumbull in our dynamic scheduling engine. We have been using neural networks to optimize fuel serve scheduling for quite some many years. >>But I think, um, if we go back like two years, what we sold is that, uh, there, there's a real potential, um, in our products that if you will take machine learning algorithms inside of the product to actually, um, help ultimately certain decisions in there, um, that could potentially help our business quite a bit. And the role of ifs lapse back in the day as that we just started experimenting, right? So we went out to different customers. Uh, we started engaging with them to see, okay, what kind of data do we have, what kind of use cases are there? And basically based on that, we sort of developed a vision around AI and a division back in the day was based on on three important aspects, human machine interaction optimization and automation. And that kind of really lended well with our customer use case. We talked quite a bit about that or the previous world conference. >>So at that point we basically decided, okay, you know what, we need to make serious work of this, uh, experimenting as boots. But at a certain point you have to conclude that the experiments were successful, which we did. And at that point we decided to look at, okay, how can we make this into a product and how to normally go system. We started engaging with them more intensively and starting to hand over in this guys, we decided the most also a good moment to bring somebody on board that actually has even more experience and knowledge in AI and what we already had as hive as labs. But that could basically take over the Baton. And say, okay, now I am going to run with it and actually start commercializing and productizing that still in collaboration with IVIS laps. But yeah, taking that next step in the road and then then Bob came onboard. >>Christian Pedersen made the point during the keynote this morning that you have to avoid the, the appeal of technology for technology's sake. You have to have it. I start with the business use case. You are both very technology, very deep into the technology. How do you keep disciplined to avoid letting the technology lead your, your activities? >>Well, both. Yeah. So, so I think a good example is what we see this world's going fronts as well. It is staying closer to customer and, and, and accepting and realizing that there is no, um, there's no use in just creating technology for sake of technology as you say yourself. So what we did here for example, is that we showcase collaboration projects with, with customers. So, for example, we show showcase a woman chair pack, which um, as a, as a manufacturing of spouting pouches down here in Massachusetts actually, uh, and they wanted to invest in robotics to get our widows. So what we basically did is actually wind into their factory literally on the factory floor and start innovating there. So instead of just thinking about, okay, how do robotics and AI for subrogations or one of our older products work together, we set, let's experiment on the shop floor off a customer instead of inside of the ivory towers. Sometimes our competitors to them, they'll start to answer your question. >>Sure. I can pick up a little, a little feasible. Yeah. Well, so in, I think the really important thing, and again, Christian touched on it this morning is not the individual technologies themselves. It's how they work together. Um, we see a lot of the underlying technologies becoming more commoditized. That's not where companies are really starting to differentiate algorithms after a while become algorithms. There's a good way of doing things. They might evolve slightly over time, but effectively you can open source a lot of these things. You can take advantage, the value comes from that next layer up. How you take those technologies together, how you can create end to end processes. So if we take something like predictive, we would have an asset. We would have sensors on that asset that would be providing real time data, uh, to an IOT system. We can combine that with historical maintenance data stored within a classic ERP system. >>We can pull that together, use machine learning on it to make a prediction for when that machine is gonna break down. And based on that prediction, we can raise a work order and if we do that over enough assets, we can then optimize our technicians. So instead of having to wait for it to break down, we can know in advance, we can plan for people to be in the right the right place. It's that end to end process where the value is. We have to bring that together in a way that we can offer it to our customers. There's certainly, you know, a lot of talk in the press about machines replacing humans. Machine of all machines have always replaced humans. But for the first time in history, it's with cognitive functions. Now it's, people get freaked out. A little bit about that. I'm hearing a theme of, of augmentation, you know, at this event. >>But I wonder if you could share your thoughts with regard to things like AI automation, robotic process automation. How are customers, you know, adopting them? Is there sort of concern up front? I mean we've talked to a number of RPA customers that, you know, initially maybe are hesitant but then say, wow, I'm automating all those tasks that I hate and sort of lean in. But at the same time, you know, it's clear that this could have an effect on people's jobs and lives. What are your thoughts? Sure. Do you want to kick off on them? Yeah, I'll know. Yeah, absolutely. That's fine. So I think in terms of the, the automation, the low level tasks, as you say, that can free up people to focus on higher value activities. Something like RPA, those bots, they can work 24, seven, they can do it error free. >>Um, it's often doing work that people don't enjoy anyway. So that tends to actually raise morale, raise productivity, and allow you to do tasks faster. And the augmentation, I think is where it gets very interesting because you need to, you often don't want to automate all your decisions. You want people to have the final say, but you want to provide them more information, better, more pertinent ways of making that decision. And so it's very important. If you can do that, then you've got to build the trust with them. If you're going to give them an AI decision that's just out of a black box and just say, there's a 70% chance of this happening. And what I founded in my career is that people don't tend to believe that or they start questioning it and that's where you have difficulty. So this is where explainable AI comes in. >>I do to be able to state clearly why that prediction is being made, what are the key drivers going into it? Or if that's not possible, at least giving them the confidence to see, well, you're not sure about this prediction. You can play around with it. You can see I'm right, but I'm going to make you more comfortable and then hopefully you're going to understand and, and sort of move with it. And then it starts sort of finding its way more naturally into the workplace. So that's, I think the key to building up successful open sexually. What it is is it's sort of giving a human the, the, the parameters the and saying, okay, now you can make the call as to whether or not you want to place that bet or make a different decision or hold off and get more data. Is that right? >>Uh, yeah. I think a lot of it is about setting the threshold and the parameters with within which you want to operate. Often if a model is very confident, either you know, a yes or a no, you probably be quite happy to let it automate. Take that three, it's the borderline decision where it gets interesting. You probably would still want someone to look over it, but you want them to do it consistently. You want them to do it using all the information to hand and say that's what you do. You're presented to them. And to add to that, um, I think we also should not forget they said a lot of our customers, a lot of companies are, are actually struggling finding quality stuff, right? I mean aging of the workforce riots, we're, we're old. I'm retiring eventually. Right? So aging of the workforce is a potential issue. >>Funding, lack of quality. Stop. So if I go back to the chair pack example I was just talking about, um, and, and, and some of the benefits they get out of that robotics projects, um, um, is of course they're saving money right there. They're saving about one point $5 million a year on money on that project, but their most important benefits for them, it's actually the fact that I have been able to move the people from the work floor doing that into higher scope positions, effectively countering the labor shortage today. They were limited in their operations, but in fact, I had two few quality stuff. And by putting the robots in, they were able to reposition those people and that's for them the most important benefits. So I think there's always a little bit of a balance. Um, but I also think we eventually need robots. >>We need ultimation to also keep up with the work that needs to be done. Maybe you can speak to Bobby, you can speak to software robots. We've, Pete with people think of robots, they tend to think of machines, but in fact software robots are, where are the a, the real growth is right now, the greatest growth is right now. How pervasive will software robots be in the workplace do you think in the three to five years? >> I think the software robots as they are now within the RPA space, um, they fulfill a sort of part of the Avril automation picture, but they're never going to be the whole thing. I see them very much as bringing different systems together, moving data between systems, allowing them to interact more effectively. But, um, within systems themselves, uh, you know, the bots can only really scratched the surface. >>They're interacting with software in the same way a human would on the whole by clicking buttons going through, et cetera, beneath the surface. Uh, you know, for example, within the ifs products we have got data understanding how people interact with our products. We can use machine learning on that data to learn, to make recommendations to do things that our software but wouldn't be able to see. So I think it's a combination. There's software bots, they're kind of on the outside looking in, but they're very good at bringing things together. And then insight you've got that sort of deeper automation to take real advantage of the individual pieces of software. >> This may be a little out there, but you guys >>are, you guys are deep into, into the next generation lot to talk right now about quantum and how we could see workable quantum computers within the next two to two to three years. How, what do you think the, the outlook is there? How is that going to shake things up? So >>let me answer this. We were actually a having an active project and I for slabs currently could looking at quantum computing, right? Um, there's a lot of promise in it. Uh, there's also a lot of unfilled, unfulfilled problems in that, right? But if you look at the, the potential, I think where it really starts playing, um, into, uh, into benefits is if the larger the, the, the optimization problems, the larger the algorithms are that we have to run, the more benefits it actually starts bringing us. So if you're asking me for an for an outlook, I say there is potential definitely, especially in optimization problems. Right. Um, but I also think that the realistic outlook is quite far out. Uh, yes, we're all experimenting it and I think it's our responsibility as ifs or ciphers laps to also look on what it could potentially mean for applications as we FSI Fs. >>But my personal opinion is the odd Lucas. Yeah. So what comes five to 10 years out? What comes first? Quantum computing or fully autonomous driverless vehicles? Oh, that's a tricky question. I mean, I would say in terms of the practical commercial application, it's going to be the latter in that much so that's quite a ways off. Yeah, I think so. Of course. Question back on on RPA, what are you guys exactly doing on RPA? Are you developing your own robotic process automation software or are you integrating, doing both say within the products? We, you know, if we think of RPA as, as this means of interacting with the graphical user interface in a way that a human would within the product. Um, we, we're thinking more in terms of automating processes using the machine learning as I mentioned, to learn from experience, et cetera. Uh, in a way that will take advantage of things like our API eighth, an API APIs that are discussed on main stage today. >>RPA is very much our way of interacting with other systems, allowing other systems when trapped with ifs, allowing us to, to send messages out. So we need to make it as easy as possible for those bots to call us. Uh, you know, that can be by making our screens nice and accessible and easy to use. But I think the way that RPA is going, a lot of the major vendors are becoming orchestrators really. They're creating these, these studios where you can drag and drop different components into to do ACR, provide cognitive services and you know, elements that you could drag and drop in would be to say, ah, take data from a file and load it into ifs and put it in a purchase order. And you can just drag that in and then it doesn't really matter how it connects to YFS. It can do that via the API. And I think it probably will say it's creating the ability to talk to ifs. That's the most important thing for us. So you're making your products a RPA ready, friendly >>you, it sounds like you're using it for your own purposes, but you're not an RPA vendor per se. You know what I'm saying? Okay. Here's how you do an automation. You're gonna integrate that with other RPA leadership product. I think we would really take a more firm partner approach to it. Right? So if a customer, I mean, there's different ways of integrating systems to get our RPA as a Google on there. There's other ways as well, right? That if a customer actually, um, wants to integrate the systems together using RPA, very good choice, we make sure that our products are as ready as much for that as possible. Of course we will look at the partner ecosystem to make sure that we have sufficient and the right partners in there that a customer has as a choice in what we recommends. But basically we say where we want to be agnostic to what kind of RPA feminists sits in there that was standing there was obviously a lot of geopolitical stuff going on with tariffs and the like. >>So not withstanding that, do you feel as though things like automation, RPA, AI will swing the pendulum back to onshore manufacturing, whether it's Europe or, or U S or is the costs still so dramatically advantageous to, you know, manufacture in China? Well, that pendulum swing in your opinion as a result of automation? Um, I have a good, good question. Um, I'm not sure it's will completely swing, but it will definitely be influenced. Right. One of the examples I've seen in the RPA space ride wire a company before we would actually have an outsourcing project in India where people would just type over D uh, DDD, the purchase orders right now. Now in RPA bolts scans. I didn't, so they don't need the Indian North shore anymore. But it's always a balance between, you know, what's the benefit of what's the cost of developing technology and that's, and it's, and, and it's almost like a macro economical sort of discussion. >>One of the discussions I had with my colleagues in Sri Lanka, um, and, and maybe completely off topic example, we were talking about carwash, right? So us in the, in the Western world we have car wash where you drive your car through, right? They don't have them in Sri Lankan. All the car washes are by hands. But the difference is because labor is cheaper there that it's actually cheaper to have people washing your car while we'd also in the us for example, that's more expensive than actually having a machine doing it. Right. So it is a, it's a macro economical sort of question that is quite interesting to see how that develops over the next couple of years. All right, Jess. Well thanks very much for coming on the cube. Great discussion. Really appreciate it. Thank you very much. You're welcome. All right. I'll keep it right there, but he gave a latte. Paul Gillen moved back. Ifs world from Boston. You watch in the queue.

>>live from Atlanta, Georgia. It's the Q covering answerable best 2019. Brought to you by Red hat. >>Hey, welcome back to the Cuban Live coverage in simple fest. Two days of coverage. Day one, wrapping up. I'm John forwards. Accused Too many men. My guest co host today, our next two guests at his van. Van Velzen. Okay, welcome to the Cube. You're an engineer at I n G Bank and Robert de Bock, product owner, engineer I n g. Bank. Hey, guys, Thanks for coming on. Thank you. Have the practitioner on. Well, first of all, we have a lot of great feedback from the practitioners here. And also people in deploying answerable and other other cool Dev ops Tools on automation is at the top of the list. Yes, More efficient. Getting things done. Focus. You got satisfaction in job because things go awaiting time savings. I'm saving security drives a conversation and re skilling opportunities. Love. These are cutting edge. Things you got to do is take a minute to explain what you guys do. What a night. What a night. Angie bank. >>Yeah. I work in a team that provides redhead images for other teams. in 90 to consume to use two insane she ate way. Also live from playbooks, amendable code and rolls to manage those things. And he's very scattered, which sort of decentralized, which is a good thing. In my opinion, it's ready for scaling. In that case, I used to work with Dennis are lots in the tower team, so take it away. >>Okay, so I still work at the answer, built our squad What we do, it's ah, We make sure that the instable tower service keeps running 24 7 and we also ensure that we, uh, provide updates next to all this. We also have unanswerable community where we basically support our end users, which are their love. So, uh, from some numbers, I heard we have 1200 applications teams that are using our service. Um, and they all have, like, answerable playbook, sensible rolls, questions, difficulties with, uh, with anything. And we're basically there to support them as well. >>So 1200 teams are using answerable, Yes, inside the bank. Yes. Yeah, like >>it's set up very decentralized. And I think what I hear from instable fest that is not very common. I still think it's very good thing to do. We try to basically give these teams all the tools they need to do their stuff on. What I hear hear mostly is that there's essential team off administrators pushing the buttons for them. Towers. Great answer was great in that case, I think, for our case is really it's a perfect fit. >>E guess help Explain. Is this do you provide? You know, he said it's not centralized, but is this you know, here's best practices here. Some play boat out. How do you end? You support them? Because they're a little bit those relationships. >>Okay. Okay. Um so what we do is we basically all the rules and get ah ah, good lap. So it's an own premise. Get environment. You can search in this. Get for rules. Uh, not like all rules are easily to be found when searching for them. So that's why there are these communities to share what you have made. Um, >>plus these teams, they can themselves pick and choose. Some will try to rewrite everything That's fine. Others can can benefit from existing coat, so it's just a good trick. Thio enable these team to participate on it really different. Some people make it all themselves another >>next to this. So we basically have these 12 on the teams do their own thing. But next to this, we also have a self service portal where they can choose, like from, uh, generic finks like us. But your machine at new disc. So New capacity Cp use memory. That's all being done through a portal s so they don't need to do anything on their own for this they can, but most of them choose the easy way off using this portal. This portal basically doesn't a vehicle to instable tower, which executes a sensible playbook and some other stuffs. Maybe some AP eyes. And this is one of the things you guys create A manage these books. So, um >>and if you go back in time so the alternative way, which we happily got rid off, is to do it ourselves. I think it was before we we work together. Way had batch weekends, for example, and it >>was no very different. No life. Oh, that's working on weekends, >>weekends and, for example, he used to patch machine some 10,000 or so, and we were not aware what was important. What? Not so you you'd stop the whole pitch. Oh, this machine has a problem. Let's stop everything in focus and that's >>not important. Was like a complete order. >>And the other way around Also this machine. I guess it's not that important. Let's just >>continue this >>Sunday morning. Oh, my God. Everything's broken. >>Can you give us a little flavor of kind of the spectrum of solutions that you leverage answerable on >>tap? Yeah. We, uh I think what we see Moses for Lennox machines, eso fetching is a big one. We got a second operation, so there's a few of them. The deployment also depends on and small. So if you order a new machine, answer was involved somewhere to do to make it happen on network on board and the Windows teams are very interested. I'm not sure if we notice on board yet. To >>be honest, I know we did some book in the boss so a couple of months ago, using wind around when you needed set on policies there, But you can see that the networking teams were getting more momentum. Uh, five. There's some suffer suffer to find switches Bob. I don't know. The, uh Never mind the name, but ah, you can see some momentum in the in the networking. Uh, it's not Morgan departments >>configuration network networking with the activists. So that's where the action is in the >>network. Um, there were some cool talks also here on five workshops. So you can see there is, um, that there is some attention on these modules and integrations as well. >>What's your guy's goal here for the show? What brought you here? I'll see Big user. >>Yeah. So what do you think was like sharing our own thing? We did. They talk this morning. Ah, regarding and programming A really cool we wanted to share. It is this behavioral thing, and and >>we'll talk about take a minute and programming. >>So, um, basically, it's, ah programming with the whole team and making sure that you get something done with all the knowledge in the team. So you don't have to align off the words or if some other if you're Kulik says from basically session, you can do better using this staying. It's all, um it's It's all done during the decision >>as basically a good way to get a team up to speed. So in a team that's probably a few few people that are very quick and understand the concept and few starters or so So >>you guys decentralized, which makes sense for scale. I get that. So this sounds like you can operate decentralized, but where danceable. You can still have that common a book Switch >>teams, for example. So it used to be very specific. H team would have their own type of coat. Now that more answers used people can switch a little easier to to another product of surface because the languages have lied, shared, steal it, steal. It's quite >>well happy with this, right? I am. I really, really have to work on the weekend. That's good. I think >>the good thing is that you have one generic way of working. So his playbook is readable by all engineers. And if you want to learn this thing, you just do the inevitable course. So you know what this thing is? A mosque and roll, and it's all like >>way. We do see horrible >>koto. Come on, don't throw your college under the bus. But here's the international tough question can see is what we have been here. I want you guys to test this. We hear that there's a lot of time savings involved. Yes, with answer. True or false. That's true order of magnitude. What? What kind of saving way talking about? I >>think it depends on the thing because we saw a huge I don't know, except numbers. But this this os patching that Really? Really Uh, >>yes. Now, especially waas. Two people working a full time basically collecting, who needs to do what? The win. And then for a weekend, 10 15 people or so. So, uh, that's reduced now to sort of nothing. Yes, some maintenance to that playbook and roll. But I mean, yeah, it's difficult to express what message? So >>no one's getting phone call? Hey, come in on the weekend. So 15 people on the weekend jam and then to Fulton will just managing it all Go away. >>Yeah, not needed, but not needed. But they basically they can do something else, so those people are still there. But now they're not doing Os patching and doing all the excel sheets and keeping order off. The systems are important, and this shall be the first, and then they because way are basically doing the thing they know better. This application team knows their dependency, so they know they. But first I need to patch the database machine and then there during the front end or Andi. It's difficult to do this so they do it themselves. >>That's Dev Ops. That's that's the way it's supposed to be, right? >>So you've matured this thes deployments over time. As you look back, What key learnings do you have that maybe you'd recommend to your peers toe? You know how things could run a little bit smoother >>next time, a good amount of time. So they're stools. That's not the problem, So answer is great, but there's others to their great Give it time to sink in with the people. So you start something and you have to have a pretty strong team to do the long the long stretch with it and give it some time, maybe a year or so before everyone's on board it. In our case, in the beginning, we spend lots of time on this community model where we basically organized small meet ups or get together, too, show things or to hear problems and try to express them. That really helped a lot. And by now it's starting to get normal, more normal. So all the teams do sensible, basically. And problem starts slowly disappearing. Also. So So >>one of the things, um, that will be better. Probably in our scenario. Housekeeping metrics. So what are the improvements over time? I don't know how to measure this. No, no, no aspect. But it will be better if you had, like, better numbers like we did hair Very good. Or this is something like, what did the community thing bring way indirectly what the results are Because the engineers are doing things really, really things. They're really patching the replication. And they're really, um, restarting their own machines, for example, when there is something wrong. Whatever. Um, but our days related to our community thing or all that's really related to Sensible Tower >>last. I think we we are very technical focus. So So we like it as a nerd, so to say, to do things but what the business value is, for example, I'm not so interested or less interested so way typically, like the technology, so it could be good to have some someone onboard and your team that says, Yeah, but this is the problem. It's crossed. This amount of money and that solved now are improved. >>Well, they assume the applications are doing a good job. So you guys helped those guys out. They get to do their own thing. They do the heavy lifting. They're doing the coding anyway for those guys that were coming in managing full time on the 15 or so on the weekend. What are they doing now? >>Most are spread across. All the application teams go back. But the other side there is now it's our team that was not there s. So that's the price you have to pay. And that's a serious team. I mean, it's far six people now 86 people and 100 machines or so. So it is a serious amount of time, but it makes it at least much more constant. So people are not surprised by machines being patched, and Monday they come back into the half broken or so. So it's a lot more control now, so I don't know if you can express it in price, but at least it's more stable >>more consistent. >>Well, one of the things that we hear here and I want to get your thoughts as we wrap up is as you go forward, you got answerable 1200 teams using it. You got a lot of collaboration. The work cultures change. Sounds like a shower. Team steps service everything else. So some scale building out what's next? Because as it becomes a platform. Okay, you have to enable something. There has value there. Okay, technical nerd value and then business value >>scaling, uh, because we continuously see this thing growing like more application teams are adapting answerable, invincible tower. So, um, right now we have, like, a cluster. We have different clusters running. Go into much detail, but we can see that the load is getting higher and higher, so we need to skill. Um, and this is sort of difficult, but red. That is really supporting in this because they're going to change some things at the application level two to allow scaling even better. Um, >>plus, also, for most teams, they're starting their configuration. Everything is coat process. They're not there yet. As soon as they discover the power of it, I'm sure that's being used a lot. A lot more. And plus, there's other countries that are going to be connected. So you have a lot of work >>because your engineering doing some getting down and dirty with the code, automating everything. >>Yeah. Yeah. So, um, what else do we >>Oh, what's the coolest thing you've done that you've automated? >>Uh >>uh, Pick your favorite. >>So but the child during Encircle Tower and with answerable, um, let me think about this. >>I I really like the patching that saved us so much work. And, uh, I think also one of the next goes to make much more simpler. So we as a company, we're complex and the people also like complexity. That's wrong. We should change >>that. Patching up our >>offense, Melissa Simplicity. So we should really use that. >>You don't want any open holes in the network housely and assistance >>about your previous question. Like I have sort of a finger and all these small things. So it's sort of what I did. It's more like an A team thing. We created the OS patch playbooks, the configure stuff, the second day offs. So we did this as a team >>like sports but the playbooks together run the play. Some defense on security >>and programming. So you're doing >>this as a team, which is very cool. Has a scoreboard look good? Winning? >>Yeah, Yeah, yeah, yeah. We're looking at the graphite. Uh, it's girl. >>Final question. How you enjoying the show here? Having a good time? What's the vibe here? What's it like here? Share for the people who aren't here. What's going on? What's the vibe with >>a conversation? It's great. We went to some sessions yesterday really technical stuff with developers. And this was really amazing because you heard details that that are not in the India in the talks today and tomorrow. Um, yeah, it's great. It's great community. It's just I really I really enjoy it because you can. It's You can have, like one on one conversations go into depth. I was showing something I created, and this guy's we'll hold. This is really great in the It's cool. It's just if you it's really great. It's really >>cool. Really? Yeah, for me also, it feels like coming home, So I know these people and I think the first day, the collaboration day, what's it called and I'm not sure you community, that's it's great because it's been a bit rough and unpolished in today's more polished and more presented and prepared to, uh, both are great. >>Good. Give the hard feedback. >>Yeah, you meet all the people. So, for example, I used instable a lot, and then I'm getting up. I see all these names. Like, who would that be there walking here and shake hands like, Oh, that's >>why guys like your code looking good. Yeah. Looks good. A contributor. Summit contributed. Okay. Sorry. After it for >>anyone that goes to visit that day, too. That's just great. >>It's great to see people face to face that, you know, online for their digital identity or the code >>you can You can't complain about stuff out on. Do you know that you don't hurt them or something with just commenting on get like after this issue and this issue and this issue. Then you can see them in person. And then you >>him a high five assault, you know? Hey, >>it's really very cool. >>Guys. Great conversations were coming on cue. Thanks, Dennis. Appreciate Robert. Thanks for coming on. Skew coverage here Day one of two days of live coverage here inside the Cube here in Atlanta, Georgia for Ansel Fest is the cute I'm John 1st 2 minute. Thanks for watching.