>> The Cube presents Dell Technologies World brought to you by Dell. (crowd murmuring) >> Welcome back to Las Vegas. It's The Cube live at Dell Technologies World 2022. This is day two of our coverage Lisa Martin, with Dave Vellante. We've had a lot of great conversations all day today half a day yesterday. We've got another great conversation coming up about ESG environmental, social and governance. Please welcome JJ Davis, the Chief Corporate Affairs Officer at Dell Technologies. Welcome to the program. >> Hi, thanks for having me. >> Hey, hey. >> It's great to be here. >> ESG is a very popular topic. >> Yes. >> It's one thing to talk about another thing to actually have a plan, have a strategy, have those 20, 30 moonshot goals and implement. Talk to us about what ESG means for Dell Technologies and some of these great things, that you have going on. >> Absolutely. So you said it, I mean it can be acronym soup. When you think about, is it social impact? Is it corporate social responsibility? Is it ESG and the beauty of having an environmental social governance strategy is we now are bringing ESG much closer to the corporate strategy and how we meet the needs of all of our stakeholders. So I'd love to just back it up for a minute and think about the purpose of Dell Technologies is to create technologies that advance human potential. Our vision is to be the most essential technology company for the data era. The way we do that is we're growing and modernizing our core businesses like PC servers and storage while we're building the technology ecosystem of the future. Well guess what? ESG is embedded in all of that because the future is more sustainable, built by people that represent our customer base with a workforce that is more diverse and a workplace that is more inclusive. We put human rights and the needs of people at the center of what we do as well as the needs of the planet. And when I get to put together purpose planet and profit and bring that strategy together in partnership with so many leaders of across the company and meeting the demands of our customers. ESG is just a part of the way we do business now >> It's part of the DNA. >> Yeah. >> Talk to us about some of the key priorities from a climate perspective, for example. >> Sure. >> What are some of Dell's key focus areas where that's concerned? >> So when we think about our ESG priorities as a whole there are four climate, circular, economy, diverse workplace and digital inclusion. And so within our sustainability pillar of our strategy or the E, we are committed to being net zero across scopes 1, 2 and 3 emissions by 2050. We are revamping our product energy goal right now to relaunch that. When we think about our customers 95% of our big customer RFPs ask about sustainability and our commitment and what we'll be doing to help them because they're going to be reliant on technology to meet their own sustainability and climate goals, whether it's green IT or IT for green and they're going to really be looking to us to help them. >> You know, I love this purpose planet profit. >> Yeah. >> You and I have talked about this a little bit. It's actually good business. Explain why ESG is good business? >> Well, I mean, used to social impact kind of sat off to the side. We might have been called do gooders or people that are passionate about things that maybe don't align to the corporate strategy. And now when you think about business round table and Michael Dell as a member and they came out with their purpose of a company statement it'll be three years in August to really redefine the purpose of a company to meet the needs of all stakeholders from employees, to customers, to shareholders as well. And so we know that new hires and new buyers demand more of their employer and of the companies they buy from. They want their own personal values to align with that of the company they work for or buy from. And so now we need to the needs of our business commitments, but also if companies don't take a leadership role, we're screwed, we're not going to be able to reverse the negative impacts. So climate change and technology plays a big role. >> Yeah. "The earth gets the last at bat," as they say. >> Yeah. >> From an accountability perspective that you mentioned 95% of RFPs are coming in and customers are looking for- >> Yes. >> Dell Technologies's commitment to ESG. Talk about the accountability to your customers to all customers where ESG is concerned and how is it measured? >> Sure. So we've been spending a lot of time over the last year, year and a half on the G of ESG the governance. And so we have been doing this for a couple decades really moving the needle on social impact. Michael talked about it in his key note, that this is in our DNA like you said. But now we have to be able to really measure. You can't manage what you can't measure. We have put a lot of governance around, what do we disclose and why Michael Dell is an active participant in the world economic forum, common metrics project because, you know, there's too many metrics and frameworks to know what companies need to be measuring and how we hold ourselves accountable and what we ultimately report to our shareholders. And so there's a lot of work to get more clarity there. You're seeing the SEC put out new rules around climate and human rights. And so when you start to get regulated that changes the game in terms of how transparent you need to be. And then what are the third party assurances that you need to have to validate the data that you're reporting on? We do have an annual ESG report that comes out every June where we report across several moonshot goals across sustainability, inclusive culture, transforming lives and ethics and privacy. Then we have sub goals. There's probably about 25 in total. And we're going to tell you our stakeholders every year how we're doing against our 20, 30 commitment. And I think it's that level of transparency and measurement that we have to hold ourselves accountable to and our customers do as well. >> Can you share a little bit about where you are on the 2030 moonshot that was announced about a couple years ago at the beginning of 20, yeah, towards the beginning of 2020. Where is Dell on the that, what's your moonscape look like? >> Yeah, sure. So we are announcing our update from calendar year 21 in June. So I'm not going to get the numbers exactly right. But if you take sustainability so one of our moonshot goals is around 100% of our packaging by 2030 will be made of recycled or renewable content. We're over 90% now. So we're going to probably restate that goal and evolve it or meet it early and set a new one. In terms of product contents. We have a goal that is 50% of our product contents will be from recycled over renewable materials. That's a little harder, plastic is easy, steel is hard. And so we're still working through how across the main components that go into our machines. How does that become more renewed and sustainable? If you think about 50% women in our workforce 25% African American or Hispanic in our US workforce we're making really good progress. And we have scaled programs that are helping us deliver on those commitments. >> Yeah. I think I'm quoting JJ Davis, correct me if I'm wrong but, "ESG marries who we are with what we do." What do you mean by that? >> So when you think about what we do, we build technology that delivers or advances human progress. We help our customers solve their biggest problems but really who we are. We are a founder-led company and Michael Dell was a purpose led driven CEO before that was even a term. And so he always wanted to have an ethical company that just did business above and beyond what the law required. And we'd been recycling PC for more than 20 years. And so we are an inclusive culture where we can bring our full selves to work and we are entrepreneurial. And, you know, if we have an idea and you raise that idea or a problem, you see then oftentimes the management will say, "Okay you go fix that." And so I think just what we do, we build technology. Who we are, is we're problem solvers for our customers. And that is good for business and good for the environment and what it is society really expects of us. And we're empowered to make a difference. Feels good. >> One of, I'm curious to get your perspective on , you know, the events of the last two years. One of the things that's happened is the great resignation. I think we all all know multiple people who have decided they're moving forward, lots of opportunity but where is Dell's ESG strategy as a differentiator for people going, I get it, I support that, that's the kind of company I want to work for? >> Our Chief Human Resources Officer Jen Saavedra calls it, "The great reshuffle." I think that's maybe a more positive way to look at it. And, you know, I've had people actually join my team because they are really positive on our mission and not just our proactive strategy around ESG but how we have handled our response to social issues. >> Yeah. >> I mean, who knew that company CEOs would be expected to speak out on voter access or LGBTQ rights and, you know. So a lot of people are coming to work for us because we are very measured in where we weigh in and what we stand for, how we speak out. But they're also really buying into our ESG strategy. I would also say our flexible work commitment. It's a big part of our DNI strategy as well and helps us attract and retain diverse talent. You can live and work wherever you want to proximity the headquarters is no longer criteria for advancement. And that's going to be a really big differentiator companies that get this right will win the talent war. And that means they'll better serve their customers. >> When you took over this role, I'm guessing you kind of did a scan to see who else was out there, what others were doing, not just in Tech. >> Sure. >> Not just in North America, but globally. What did you find? Where do you get your inspiration? Are there any organizations out there that are really models that you get inspiration from? Or is it so new? You are the model. Can you just talk about that? >> Well I mean, I think we're doing a really good job and we're pretty advanced, but nobody has this figured out and frankly, we need to do it together. This is a space where you don't actually want to compete. >> Right. >> You want to partner. And so we have our own sustainability advisory aboard and companies like Boeing or on that. I serve on a sustain the advisory board from McLaren and Unilever's chief sustainability officers there. That is a company that is really inspirational to us. And so partners like Intel, they're very involved in 50. So the next 50% that needs to get connected to the internet and participate in the digital economy. We're big partner, as you know we're their largest customer. And so there's a lot going on across our competition our customers and our partners. And we're all inspiring each other and figuring it out together. Cause it's evolving so fast. Nobody has all the answers. >> But that's a great point. The evolution is happening so quickly and every day you turn on the news and there's something else that needs to be responded to. >> Yeah. >> I mean, think that from a strategic perspective from that overall vision perspective, it sounds like what and there's been some announcements this week. >> Yeah. >> That respect to issue. What's been some of the feedback from the part of ecosystem, from customers, from investors on this laser focused vision that Dell has with respect to sustainability and ESG? >> So Cassandra Garber, our head of ESG just finished out of cycle road show with investors and had really good conversations. They're asking a lot of questions about our strategy. They're asking questions about executive compensation tied to ESG as an example. Our customers are very positive and responding. They're looking for technology solutions. As I mentioned to meet their own climate commitments. And from our channel partners they really want to partner on our initiatives and really go do good and make an impact together. And we're getting really good feedback. >> So carrot or stick, it's probably not 100% that the channel partners or even suppliers, you know, some just don't have the resource possibly or maybe they don't share your values. >> Right. >> So how do you approach that? Is it through inspiration? Is it through a little tap in the head or a little headlock? How do you deal with that? >> It's both. I mean, our suppliers have to adhere to the contract and the RSA code of conduct that they have to sign on to uphold. And so we very much hold them accountable just like we do our ourselves. And so that is more compliance driven but we do have partners like Western's Green in our supply chain who we're really involved with us in some early work around recycled gold and partners that are involved with us in setting up the ocean plastic supply chain. And so we have great partnership but there are things they have to do from a human rights perspective or commitment to the environment that are required. From a channel partner perspective, you know, we want to incent them. We want to make money together. We are for profit businesses after all. And ESG can be a part of that. And if you don't have the resources to drive your own take back initiative, then we can do that in partnership through our asset recovery services which partners can sell and then use our infrastructure to take back and recycle old equipment. >> I mean, I feel like a lot of my questions are two-way but you feel as though you're in influencing public policy or a public policy is influencing you? >> Both. I mean, early on when the SEC was looking at the climate rules that they just put out, there was, I think we submitted a six page response to their, you know, ask for inquiry and response. And so that's good. We're able to talk to each other and have conversations and shape things, but ultimately we'll be regulated in these areas and that's fine. We just got to make sure that we're ready. >> Great. >> It's always good to have that push and pull it's like with the pandemic all the silver linings that have come out of the acceleration, we talk about that all the time on this show. The acceleration of digital transformation, we were talking about the acceleration of retail in the intelligence store. >> Right. >> And as consumers, we expect that, but that push and pull sometimes those forcing functions are necessary to be able to drive forward. >> For sure. >> Yeah. >> Yeah. >> My last question for you is Dell just came off it's most successful year. >> Yes. >> First time hitting north of 100 billion. >> Yes. >> In the company's history. What are some of the things that we think is the moonshot goals, we're only in 2020. >> I know. >> But as time is going by so quickly, what are some of the things that you are personally looking forward to from a corporate affairs ESG perspective say the next like three to five years? >> Well, I'm really excited about some of the groundwork we've laid in digital inclusion. We just made some new hires there. We're connecting the dots, you know, and we have a lot of initiatives that can really if we can scale them, make a big impact. So we have student tech crew, it's where high school students serve as the technical support in their local high school and get certified. So they are job ready the minute they graduate. If they don't want to go to community college or university they can go right into the workforce. How do we marry that up with other skill building initiatives that we have? And if you add 1 plus 1 it equals 3. And I think this year will be a really big accelerator for us in the area of digital inclusion and how we bring connectivity, community services and support and digital skills together. Because that's what, you know, those that aren't participating in the digital economy we need to partner and really deliver on the promise of what it means to be in technology and at least have the skills to compete >> Right. Start eliminating that digital divide. JJ, thank you for joining David and me today talking about ESG- >> Thank you. >> corporate affairs, such an interesting focused efforts that Dell is really wrapped around. And it sounds like there's that push pull from the customers, from policy, but ultimately going in a great direction that can be measured. Thank you for your insights and your time. >> Thank you. >> For JJ and Dave Vellante I'm Lisa Martin. You've been watching The Cube live from Las Vegas. This is the end of day 2 of our coverage of Dell Technologies World. We thank you for watching. You can find all of our content on replay on theCUBE.net. And of course, we will be here tomorrow with John Farrier and Dave Nicholson as well. Have a great night. We'll see you tomorrow. (upbeat music)

>> theCUBE presents Dell technologies World, brought to you by Dell. >> Hey everyone. Welcome back to theCUBE's live coverage of Dell Technologies World 2022, from the show floor, the Venetian in lively Las Vegas. Lisa Martin here with Dave Vellante. We're having a little reunion with our guests that we haven't seen in a couple years. Please welcome back to theCUBE, Doug Schmitt, President of Dell Technologies and Services. Alex Barretto joins us as well, the Senior Vice President at Emerging Services and Technology. Guys, it's great to see you in 3D. >> I know great to be back. >> Yes. >> Its Awesome to be back. >> Isn't it great? >> And fantastic. >> It is. >> We were talking about how we have to get our sea legs back about, even just interacting with in life. >> That's exactly right. Being able to see everybody be back in person at these events. And it's great to see everybody it's like coming back to family. >> It is, it's been a reunion since Sunday. One of the, obviously the last two years have been quite challenging for everybody, for customers. Dell is coming off it's best year ever in FY22, over 100 billion in revenues, 17% growth year over year, astounding growth. The voice of the customer is always strong here at Dell technologies. But Doug, talk to us about some of the things that have been going on services perspective and how you really stepped in to help customers across industries succeed in the dynamic times we've been living in. >> Well. Yeah, thank you, and you're right. Coming off a very great, great year. And I think as you can see behind us and in the room here just great solutions for our customers. And that's what it's about, delivering the outcomes. And service is a huge piece of that, of making sure we bring all that together, deliver the outcomes our customers are looking for. If you look at the overall services organization just to take a step back just a little bit, we are a team around over 60,000 folks in 170 countries. And look, it's about this life cycle of services we provide. Everything from consulting to deployment to our support, manage services, security, education services, residency services, all the way to asset sustainability and recovery. So we can get all of the material back in and recycle it. So we have a great suite of services, and it's bringing all that together for the customer again to deliver with the products and the solutions and the software, the outcomes they're looking for. You asked a little bit about just to kind of double click that, about what our customers really saying, kind of what they're hearing, what we're hearing. I think there's three things. When I think about what they're looking for, one is the trusted advisor. You heard this during Michael's keynotes speech, that is key. They're navigating through the digital transformation, hybrid cloud, all of these things. Determining what they need to do to deliver their outcomes. And Dell can bring that trusted advisor status to them. So we can consult with them professional services, help bring that. The second thing is really around that life cycle services I talked about, all those different services that we bring. We allow our customers clearly the choice to say what pieces of the services do they need. Now we think we can bring everything together into a managed solution for them, but if there's certain pieces that they need to just, double click on, we can help with that. And then look, the third item that I'm hearing and that we can bring and that we have for them is flexible consumption. They can choose the way they want to consume the technology. You consume it by usage. You can consume by month, by quarter, or if you want the stability of long term contracts one, two, three years we'll do that. So really it's about trusted advisor and choice to help them deliver their outcomes. >> So a lot changed during the isolation economy. You guys obviously had to support new initiatives. First of all, budgets got squeezed in 2020. Then boom back, so they sort of slingshot it, real focus on obviously client solutions, remote work, endpoint security, identity access, VDI. Now in the post isolation economy, it's like, okay, some of the stuff at HQ you maybe needs to be updated, maybe we're rethinking the network. So, what are you hearing from customers? Where are they in their digital transformations, Alex? You know, what's hot. >> Yeah, so we actually recently created an emerging services group. And the reason for that is exactly what you're alluding today. So we actually talked in that group everything in this emerging. So APEX, telco, edge, data management, all the things our customers are asking for and we are convening new solutions, new services to meet their needs, and all that is housing in one unit, and we're thinking about the product management, the technology that goes with it, and we're working partnership with our customers to actually build and develop solutions that they're looking for. >> Yeah, there was no as a service really. I mean, you could do it with financial machinations before, now it's becoming much more mainstream. I mean, I know it's not a hundred percent of your business and maybe never will be. >> Yeah. >> But that's a whole new mindset. What else is changing in the business that you guys see? >> Well, yeah, I think there's, I think that's what comes back to what we saw, first of all we listen to the customers, follow what their needs are, and you're right. As far as the, as a service, I think it's back to that choice. If they want to purchase or consume as flexible or as needed, we'll do that. They want the contracts, the standard CapEx model, we'll do that as well. Look, there's three things. Professional services is really changing as well. We're seeing the needs again for going in and being able to deliver the services to customers, but also manage that in a lot of cases, they're asking us to take the workloads from them so that they can go and change their transformation, and their digitalization is one of the things that we're clearly hearing. And I know you're hearing the second one, security. I mean that is top of mind for everyone. And I, we have launched a lot of services around this. Some of those like MDR or Managed Detection Response our cyber vault, as well as our APEX cyber recovery services as well that we've announced here. So security's number two. And then the third one is this sustainability, again very important for us and our customers, is we have a 2030 goal around this as I'm sure or you've heard, but more importantly, that's something I know my team and I and everyone at Dell, that's a great personal feeling too. When you're getting up and you're doing something that you know, is right, really just doing it to help the customers as well is just an extra added benefit. So those would be the three things professional services changing, doing more and more of the manage take workloads off, two is the security, and the third is the sustainability clearly. >> We talked with JJ Davis yesterday, and we're talking a lot about ESG and how a tremendous percentage of RFPs come in wanting to know what is Dell technologies doing from an environmental, social, governance perspective. That it's really your customers wanting to work with companies like Dell who have a focused clear agenda on ESG. One thing that I'm curious when you talk about the increase in advantage services, the great resignation. We've all, that's been happening now for a couple years. It's probably going to persist for a while. Customers suddenly, labor shortages and the supply chain issues. How have you helped organizations deal with some of the challenges that they're going through from a labor perspective is that why one of the reasons the managed services is we're seeing an increase there. >> Yeah. I'm sure that can be and I wouldn't doubt that, you mean in terms of our customer is wanting more and more the managed and the professional. Yeah, I think that is a piece of it, but I also think part of that is that speed matters and customers are looking for the additional assistance to take things off, that they may have traditionally done so that they can, they can really get this transformation, this hybrid cloud, getting things moving very, very quickly. There's just so much to be done in terms of data management and bringing information to their end user customers. And they want to spend more time doing that. And so I'm hearing that more, but you are right. There's absolutely, there's absolutely the times where we have a residency service, we, and that has been growing very, very fast. And that tends to be why they ask for it, is because people have either left or are leaving >> Alex, Doug really kind of alluded to an area that I want to probe a little bit. And it's that's, I was talking to Jen Felch recently she's going to be on soon. And the, you mentioned security, Doug, as the top initiative clearly. And the distance between number two is widening, but number two is cloud migration. Now I asked Jen about that, because internally Dell has its own cloud. And I said, how do you interpret that? Or how do you, what's your second priority? She goes, well, I would translate that into modernization. So we're essentially building our own cloud is how I interpreted it. So my question to you is, are you seeing that with customers, how closely do you work with your own IT to take those learnings to your customers? And what does modernization actually mean to your customers? >> Yeah, that's a great question. It's actually the essence of why we're here. Talking to our customers and showcasing what we do within services, what we do within IT. Jen and I talk very often about her roadmap, our roadmap, and we want to showcase that to our customers because it's a proof point, it's a proof point of how they can do the transformation on their own. Do we have a whole slue of products from a services standpoint that are tied with what Jen is doing as well? And that's what we bring to market. So whether that's on APEX, that we announced right here two days ago, the cyber recovery services available now, that's working very closely with our IT counterparts. And we have a whole slue of roadmap with high performance computing, to be announced soon and machine learning operations, all that is to meet the customer needs, and what they're asking for. And if you look at the emergence of needs from a customer standpoint, it goes in a multitude of uses. We have telco customers, they have very specific needs and we're looking to meet those needs. We have the traditional customers, which may be going at a slower speed in their adoption of the cloud, we're there to help them. And we're all about to hybrid cloud. Hybrid cloud is a hundred percent of our strategy. So whether you want to go cloud based, whether you want to be OnPrem or you want to be hybrid, we're there to solve your needs. >> What's the partner story in terms of delivering services, we know that the Dell technologies' partner ecosystem is massive. We know how important partners are to the growth. I think I saw 59 billion in revenue came through the channel last year alone. How do you enable partners to deliver some of those key services that you talked about? >> To leverage the partners for the, on the broader ecosystem for that? >> Yes. >> Yes, well, you're right. We do have a very large partner network and we're very flexible on that. Again, it sounds like we are flexible in everything and we are by the way, for our customers and our partners, 'cause look it is about delivering first of all, how our customers want their service. I do like this idea and we talk about modernization, transformation, digitalization all these things are kind of the same thing about going in and looking about how we're improving the overall infrastructure and these outcomes. And to that end, we work with the customer on what they're looking for. And then we'll either do a couple things with working with the partners. Either we take prime and we'll take that and take the pieces that they can deliver and we can deliver together. But again, it's with the customer in mind of how they want to do that, working with the customer. We do have code delivery services as well. And look, we're very open with our partners about if they want to be prime and then leverage those same lifecycle services we have. What this is about is about getting this transformation and this technology and these so into the hands of the customers in the best way possible. >> So, I could white label as a partner. Could I white label your services? >> We don't have the white label. >> Okay. >> We do have co-delivery. >> Okay. So that's what I could do. I can say, okay, I'm bringing this value. Dell's bringing that value. You're visible to the customer. >> That's correct. >> Which is I presume a benefit to the customer. >> Correct, correct. >> The trust that you've built up. >> Now that gets, just the white label you would say like our ProSeries, ProSupport, ProDeploy, ProManage, all of those things. Isn't a white label, but at the same time our customers especially in the professional service side of it could be the prime, which would be the same thing as a label. >> How are client? This is kind of interesting thought I had the other day. How are client services changing? Do you see the point where, I mean, maybe you're doing it already. It's just a full manage all my client devices and just take that away from me, and Dell you take care of that and I'll pay you a monthly fee. >> Well, yeah, we are seeing that. And one of the things that they like the best about is doing that management, is bringing kind of the AI and the BI to it that we can with our support assist and all of the data that we give back, we're actually able to help manage those environments much better. And in terms of an end to end, keep things updated, upgraded, manage it. But more importantly, what we see when we do have those client managed services end to end, the customers are actually coming back and asking us to help improve their operational performance. And, and what I mean by that is, all of a sudden you'll see things where the trouble tickets are coming in 'cause we're seeing that. And we're actually going back in with that information to help alleviate or improve their operational processes, so that they're able to function and spend more time on their business outcomes >> And reduce that complexity, sorry, Dave. >> No worries. How about the tip of the spear, the consulting piece? What are you seeing there? Are we going through and as we modernize, are we going through another wave of application rationalization, people trying to figure out their digital transformation, what to double down on? What to retire? What to sun set? What's that like? >> Yeah, I think it's similar to the managed service conversation we just had. It's really pivoting to technology. Even in the services space, it was all about our physical footprint. Five, six years ago, our physical capabilities, the number of people, depots et cetera that we had, right now, our customers and even internally what we're pivoting towards is technology. They want to know how are you going to do is solve our problems, whether it's consulting or managed services using technology. Precisely to the point that Doug was making, because they want insights, value add from the services we provide, not just consult for me, not just manage my service, but provide me value added service on top of that so that I can actually differentiate my services, my solutions and that's where we're building, that's what delivering really leveraging technology. You look at the number of software engineers we have, data scientists, the algorithms we're building now inside services. It's really become a technology hub, whereas it used to be a physical hub. >> I'm just going to, oh, I'm sorry please. >> No, go ahead. >> Follow up. >> Where it's really headed is, if you look at this it's going to become this outcome based services. When I talk about outcome based services, it's not managing just the IT infrastructure, that you have to do, you have to modernize and transform. However you want to say that to customers. But in addition to that, they're looking for us to take that information and help change their business models as well, with the data and the and the insights we're getting back. >> Their operating model. >> Absolutely. >> But changing that in the last couple years and pivoting over and over again, to survive and to thrive, talk to us, Alex about the emerging services and how you've maybe a particular customer example of how you've helped an organization radically transform in the last two years to be competitive and to be thriving in this new economy in which we're living. >> Yeah. I think a great example is Dish. If you look at Dish, they're actually launching one of the first Open RAN networks. Leveraging the power of 5G. And we're working very closely with them on the services and solutions to enable them to deliver that service to their customers. And that's a new area for us, a new area for them. So we're actually working together in innovating and coming up with solutions and bringing those to the market. It's a great example. >> Lot of collaboration guys, thank you so much for joining us. Great to see you back in person again after couple years, probably three. We appreciate your time and your insights. >> Thanks guys. >> Thanks for having us. >> Our pleasure. Dave Vellante, Lisa Martin here, you're watching theCUBE's live from Dell Technologies World 2022. Stick around. Be right back with our next guest. (gentle music)

>>Well, good day, everybody. And it's great to have you with us here on the cube. As we continue our key conversations as a part of the AWS startup showcase, please welcome Jennifer Johnson. Insidery, Jennifer's the chief marketing and strategy officer at amplitude, which is a global leader in product intelligence, and she tells her friends collar JJ. And so today it's still all JJ, how are you doing? I'm doing great, John, how are you doing very well. Thanks for being with us. We appreciate the time. Um, first off, tell us a little bit about amplitude about your work and job for those who might not be familiar. And also, I like to hear a little more about product intelligence about that concept. It's certainly taken on probably a pretty different meaning in this digital world that we're in today. That's right. That's right. Well, so I've been at amplitude. >>I joined in October of 2020. So, uh, not that long. Uh, and let me tell you, I, anyone who knows me knows that I am a CMO, but I am also a category designer. So I look at, uh, I look at companies, I look at opportunities as market creation opportunities, and we're going to talk about that because that's a big reason why I joined amplitude and why I'm so excited for the future of amplitude. Um, and so when we think about our website today says product intelligence. If you read between the lines and I tell you I'm a category designer, you might understand that maybe that will evolve over time, but what product intelligence actually means is it, is it really connects digital products to revenue. And what do I mean by that? And we all know that everything is digital. I don't need to tell you that everything is digital. >>We have the whole world just moved to digital. Um, and it's interesting because we think about digital and we think about the door dashes and the Peloton of the world, but really it's every company and every industry, um, you know, are some of our largest customers are hundred-year-old companies, right? And they have had to not just because of the last year in the pandemic, but they've been really thinking about how do we disrupt ourselves. Really? It's not even about disrupting the industry. It's actually about disrupting their own business around digital. So digital really, isn't a nice to have anymore. It's existential. And we all, I think we all know that at this point. Um, but you know, if the whole world has moved to digital and I think I read something that IDC wrote, we're going to spend $6.8 trillion by 2023 on digital transformation. We're spending an enormous, I mean, I think enormous has even an understatement amount of money on digital. >>So what is the next thing that you have to do once you've spent all this time and money and effort and probably millions of dollars, billions per company actually transforming is you have to actually optimize it and you have to figure out what your, what digital products and digital investments you're making. You have to make sure that actually connect to business outcomes. Things like, uh, revenue, things like lifetime value of things like loyalty, things that drive your business forward. And that's really where product intelligence and the future where amplitude is going is so critical. Because if you think about actually one of our customers said it best the customers of yesterday or the companies of yesterday. They put a website in front of their old way of doing things, their old products, their old way of doing things and call it a digital, like we just put a website in front of it. >>So it's digital. That is no longer the case. Now it's about redesigning your business and transforming value through new digital products and services. So digital products are actually the future of how businesses will operate in the new era. And so what happens is companies say, okay, we need to go build all these new products and services. And we have these goals of growth and revenue, and we hope the revenue comes out the other end, but there's really no way for, or no really effective way for companies to actually figure out how to manage and measure that in between you build a product, you put it out to market. Revenue comes out the other end, but how do you actually know if you're building the right things in the first place? How do you know what, uh, what features, what behaviors, what actions, what combinations of those actually lead to things like engagement and revenue and loyalty, and then how do you actually go and double down on those? >>And what I mean by that is adapting the experience. If you know, something works and you know that every customer that looks like that person will do this and you can predict an outcome. Why wouldn't you serve that up to every single person that looks like that. And really that whole notion of prediction and understanding, and prediction and adapting, that's really where amplitude plays a role. And that's what got me really excited about joining amplitude and really excited about the future is every company is a digital company and really companies have to completely rethink how they manage digital because it isn't just putting a website in front of it anymore. >>Yeah. I mean, you you've hit on something to them. In fact, we've got a lot to unpack here, which is great. Um, but, but you, you talk about that. Digital's lost, right? You got to have it's existential now you're dealing with business, which I think is absolutely correct, but because it's everybody and it is everywhere and you've got a lot of categories, right. Um, as a chief strategy officer, uh, you can't be all things to all people. You can't go off in every which way, but, so how are you focusing then your efforts in terms of identifying the key categories of prime categories, as opposed to looking at this huge landscape, and that could be overwhelming, you know, in some respects, how are you focusing? >>Yeah. I mean, there's, there's two ways to look at it and it is, you know, every company is a digital company, but really any company that has any kind of a digital product or an app digital app, anything that's digital is a as a relevant target for, for amplitude. Um, traditionally we have focused with probably no surprise. We focused on the, probably the, what I'd say the digital native companies, the companies that are more mature, but really they grew up through digitally through digital native. Those are the door dashes, the Postmates, the Uber's, the Lyft's right. Um, and those companies were just built by design to think this way, right? We're building products. Our app is our business. Our product is our business. So we need to make sure that we deeply understand how the interactions with our customers through that experience actually translates. And how do we continue to tweak and test and optimize and digitally native companies tend to understand that inherently. >>So that's been a lot of the early adopters of amplitude have been those digitally native companies. Now what we're seeing and no surprise is there's a really long tail of companies and more traditional industries. I mean, everything from, uh, you know, hospitality and restaurants, obviously media is going through a huge digital disruption right now. Um, automotive, I mean, any, any company that's looking at, how do we build new ways to engage and provide experiences to our customers through any kind of a digital means digital, digital product and app. Those are relevant targets for amplitude. So I think, you know, people think, Oh, it's every, every, uh, industry looks very different, but the commonality is everyone needs to move to digital. And the great thing for amplitude and for the market at large is a lot of our customers are these digitally native, what I would call the thought leaders around digital. And so if we can help bring that, bring those best practices and bring that approach to some of the more traditional companies in traditional industries and help them become more like the Peloton and the door dashes of the world. Then that's great for everybody, >>You know, JJ, when you talk about this transformation that's going on and the spaces in which is going on, which is everywhere right now, I imagine there are still some folks who might be a little reluctant, right? And you talked about slapping the new website and the old material, and they think they're done and they wash your hands and they go away and it's not that simple. Right. Um, so what's that conversation like to people who maybe aren't willing to jump in to take that risk as they see it, whereas, you know, it's an essential to their business. >>Yeah. So, you know, I do think that every disruption, technologically speaking or other is really change management and digital is no different, right? It's not just about moving to digital, it's changing the way that you're organized, it's changing your business structure, your, your strategy priorities. So I think that that organizations know they have to go there now. And even the ones that are reluctant, I'd say, if they're reluctant, they're probably going to get disrupted. So I think everyone understands they need to go there. Our role is really to help organizations get there without, I mean, digital, the, the word that usually follows digital is transformation. And I think a lot of people think that digital transformation needs to be this, you know, three to five year strategic journey and costs millions of dollars with armies of consultants. And really what we're helping to do is help organizations just answer the question, how is our product tied to our revenue? >>And we do that by bringing the data to the teams that actually need it. And it was really, it was really surprising to me to understand the process and some of these really large enterprises around how product and marketing teams, uh, get data. And, and a lot of times, if you have a question about something, if you're a product, if you're a product manager, obviously you want to understand how is our product doing what features are resonating? What features are leading to things like engagement or revenue or subscriptions or loyalty or whatever it is, right. As a marketer, you also want to know that as a marketer, you also want to know what campaigns are we driving that are actually creating value. Are there things that we should be doing? Are there areas we should double down on? And so the process is if you have a question about something or a hypothesis that you want to answer, a lot of times you have to send this request to some centralized data team or a data science team. >>Uh, you know, organizations have, you know, large B2C organizations. Most of them have armies of data scientists and business intelligence platforms. And you send a request and you might get an answer back in a few weeks, maybe a month. And, uh, maybe it's the right answer or usually what happens. And I think we can all relate to this. As you ask a question and you get data back and then it sparks five more questions. And so that whole process is the cyclical thing that I always say, if by the time you actually figure out the answer to your question, it's enough time to get Amazon in the new digital era. And so what we're actually doing is helping to bring that data, which we all know is the crown jewel of any organization. We're bringing that data and we're democratizing it and bringing it to all the teams that actually need it, lock, unlock it from data scientists and BI and bring it to the teams that need it, whether it's product, whether it's marketing, whether it's sales, whether it's customer success. >>And the greatest thing is it's not as a tool for everyone. And then all of a sudden you have these silo tools marketing as their tool product has their tools. CS has their tool is you actually have one platform, one system, and one source of data that all those teams use. So marketing doesn't say, well, yeah, my mind says this and it looks at it from this lens. And product says, well, my data says this, but it looks at it from this lens. All of a sudden you've removed that entire conversation or that entire debate. And that changes everything. It changes the way that companies get insights into customer behavior. It changes the way that they build products. It changes the way that the teams work together, product and marketing can now work off of a common set of data. And so really amplitude is helping to drive that change. >>And you don't have to do it through a three year implementation with an army of consultants that come in. It's something that can be done very easily. And so, and it, you know, I know everyone wants an easy button. Um, it is quite easy though. It's not, it's not the, the three-year or even the one year transformation. It's actually a way to, to bring that data to the teams that need it quickly. Um, the other thing I'd say to it is it's bringing the right data to them. Um, I was reading something from Gardner that said 85% of marketing analytics tools. Now these are tools that usually track things like ad attribution, website visits and how that, you know, how that relates to revenue well in a customer acquisition scenario, while you just want to know what ads actually lead to a cart, uh, put someone going to a cart, someone purchasing that was probably sufficient, but in the, in the new world, that's just not answering the same question. >>Like if you need to add, answer a question of what features, what behaviors, what actions within the product actually drive business outcomes, knowing what ads people clicked on and what web visits that you know, that, that, you know people had. And that's not going to answer it. That's not, it's just answering a totally different question. And 85% of companies are using marketing analytics tools to actually answer questions like what features we need to build. So that's another key point here is companies need to answer this question. They know they do. They just don't have the tools to do it and the data to do it. So they're using tools that were designed for a completely different purpose. And so really that's another great thing about amplitude is we're actually giving them the actual, the right data to answer the questions. >>So if you're, if you're somebody who's headlights, you know, for down the road, then in terms of, you know, you're looking for behavior, straights and patterns, you're looking for increased customer engagements, right. They have all these wonderful tools now, you know, not that you're missing anything, but where do you think that you could even sharpen the pencil a little bit more so that down the road here, what, what do you think technologically, you are capable or you would like to be able to, there's a making that an even richer in case even a bigger, a deeper dig? >>Well, I mean, so we, we have this, this, uh, immense deep, fast, smart database of customer behavior. So if you think of it, it's almost like the possibilities are endless. Anything that you need to be able to know or any question you could ask of your data to know what combinations of features, what combinations of behaviors actually lead to things like retention or churn or revenue. And then you can actually start to model those into cohorts. If I know that a customer does these five things in this order, and they're five times more likely to churn, well, then any customer that actually doesn't just look like that based on your demographics, who you are, where you live, et cetera, but based on actually what you do in the product, we can start to cohort them and say, this person actually looks like this other person based on their behavior. >>And therefore we might actually personalize an experience for them. We might send them an offer if we think they're going to turn, because we know they're likely to turn base cause other people that look like them do, um, or we're not going to send them anything because we already know they're loyal. So they're already likely to buy. So it's answering more questions, but then it's also, how do you actually use that to really personalize experiences? And I, that word is so overused, but in this way, I mean, it's not about I'm going to serve you a piece of content because I know what industry you work in, or I know where you live. I'm actually going to personalize your experience because I know that you, John, as an individual, do these things. And therefore I know that you are either a loyal customer or you've got a high likelihood to churn, et cetera. >>And then I'm going to personalize an experience. That's a good experience for you, but also it could experience for the business. So I think there's more, um, types of analytics. There's more ways to personalize and build experiences. I think in the, in the modern way, not the old demographic way. Um, but also even every organization around the company, like everyone touches the customer. So, you know, customer experience, as we know, is, is, you know, I hate to call it, call it the buzzword. Of course, everybody wants a great customer experience, but everybody talks about customer experience. Anyone who touches the customer as part of customer experience, which is basically the whole company. And so if you think about today, there's obviously product teams, marketing teams are heavy users of, of amplitude, but going forward, I mean, imagine a world where, you know, anytime, you know, anytime you have a touch point with a customer, you can use this, this insight into what they're actually doing in the product to, to get some level of, of intelligence that you didn't have before and use it to proactively give them a better experience, right? >>Whether it's, you know, uh, you know, at renewal time or you know, that they're likely to do something. So you offer something that gives them a better experience or you're in customer service. And wouldn't it be great to actually know if someone's logging a support ticket, what they're actually doing in the product it's going to help you give them a better support experience, et cetera, et cetera. I mean, the options here I think are because of the data that we have and the way that we can, like you said, build these patterns and pattern match, what features and actions lead to outcomes. Uh, I think the options are limitless. And I think this is the new way, like customers, that companies that understand this is the Holy grail of the new way of, of digital and understanding your customers and having this intelligence into the product is the new way to engage the customers that get that are going to be the customers that win. >>What is the new game you're right. I think limitless is a really good word too, because the capabilities that you're developing and the product and services you're providing. Um, so thanks for sharing the time and the insight and pleasure to have you on the queue. Thanks for being here. It's been great. Thank you, John. You've got jumbles here on the cube to conversation on AWS startup showcase. In fact, we have Jennifer Johnson.

(upbeat music) >> Well, good day, everybody. And it's great to have you with us here on the theCUBE. As we continue our CUBE Conversations as a part of the AWS startup showcase. Pleased to welcome Jennifer Johnson in today. Jennifer is the Chief Marketing and Strategy Officer at Amplitude, which is a global leader in product intelligence. And she tells me her friends call her JJ. And so, today it's... Hello JJ, how are you doing? >> I'm doing great, John. How are you? >> I'm doing very well. Thanks for being with us, we appreciate the time. First off, tell us a little bit about Amplitude, about your work in general for those who might not be familiar, and also, I'd like to hear a little more about product intelligence and about that concept, if you will, and how that has certainly taken on probably a pretty different meaning in this digital world that we're in today. >> That's right. Well, so I've been at Amplitude, I joined in October of 2020. So not that long. And let me tell you, anyone who knows me knows that I am a CMO, but I am also a Category Designer. So, I look at companies, I look at opportunities as market creation opportunities. And we're going to talk about that 'cause that's a big reason why I joined Amplitude and why I'm so excited for the future of Amplitude. And so when we think about... Our website today says product intelligence. If you read between the lines and I tell you I'm a category designer, you might understand that maybe that will evolve over time. But what product intelligence actually means is, is that it really connects digital products to revenue. And what do I mean by that? And we all know that everything is digital. I don't need to tell you that everything is digital. The whole world just moved to digital. And it's interesting because, we think about digital and we think about the DoorDashs and the Pelotons of the world, but really it's every company in every industry. Some of our largest customers are 100-year old companies. And they have had to, not just because of the last year in the pandemic, but they've been really thinking about how do we disrupt ourselves, really. It's not even about disrupting the industry. It's actually about disrupting their own business around digital. So digital really, isn't a nice to have anymore. It's existential. And we all, I think we all know that at this point. But, if the whole world has moved to digital and I think I read something that IDC wrote, we're going to spend $6.8 trillion by 2023 on digital transformation. We're spending an enormous, I mean, I think enormous is even an understatement amount of money on digital. So what is the next thing that you have to do, once you've spent all this time and money and effort in probably millions of dollars, billions per company actually transforming, is you have to actually optimize it. And you have to figure out what digital products and digital investments you're making. You have to make sure that they actually connect to business outcomes. Things like, revenue, things like lifetime value, things like loyalty, things that drive your business forward. And that's really where product intelligence and the future where Amplitude is going is so critical. Because if you think about... Actually, one of our customers said it best. The customers of yesterday or the companies of yesterday, they put a website in front of their old way of doing things, their old products, their old way of doing things and called it digital. Like we just put a website in front of it so it's digital. That is no longer the case. Now it's about redesigning your business and transforming value through new digital products and services. So digital products are actually, the future of how businesses will operate in the new era. And so what happens is, companies say, "Okay, we need to go build all these new products "and services. "And we have these goals of growth and revenue "and we hope the revenue comes out the other end." But there's really no way for... Or no really effective way for companies to actually figure out how to manage and measure that in-between. You build a product, you put it out to market, revenue comes out the other end, but how do you actually know if you're building the right things in the first place? How do you know what features, what behaviors, what actions, what combinations of those, actually lead to things like engagement and revenue and loyalty. And then how do you actually go and double down on those? And what I mean by that is adapting the experience. If you know something works, and you know that every customer that looks like that person will do this, and you can predict an outcome, why wouldn't you serve that up to every single person that looks like that? And really that whole notion of prediction and understanding and prediction and adapting, that's really where Amplitude plays a role. And that's what got me really excited about joining Amplitude and really excited about the future is, every company is a digital company and really companies have to completely rethink how they manage digital because it isn't just putting website in front of it anymore. >> Yeah I mean, you've hit on something there. In fact, we've got a lot to unpack here, which is great. But you talk about that digital (mumbles) you got to have. It's existential now to doing your business which I think is absolutely correct. But because it's everybody, and it is everywhere and you've got a lot of categories, as a Chief Strategy Officer, I mean, you can't be all things to all people. You can't go off in every which way, so how are you focusing then in your efforts in terms of identifying maybe key categories or prime categories, as opposed to, looking at this huge landscape, and that can be overwhelming in some respects how are you focusing then? >> Yeah. I mean, there's two ways to look at it. And it is... Every company is a digital company, but really any company that has any kind of a digital product or a digital app, anything that's digital is a relevant target for Amplitude. Traditionally, we have focused with probably no surprise, we focused on the, probably what I'd say the digital native companies, the companies that are more mature, but really they grew up through digital native. Those are the DoorDashs, the Postmates, the Ubers, the Lyfts. And those companies were just built by design to think this way. "We're building products. "Our app is our business. Our product is our business." So we need to make sure that we deeply understand how the interactions with our customers through that experience actually translates, and how do we continue to tweak and test and optimize. And digitally native companies, tend to understand that inherently. So that's been a lot of the early adopters of Amplitude have been those digitally native companies. Now what we're seeing, and no surprise is, there's a really long tail of companies in more traditional industries. I mean, everything from, hospitality and restaurants. Obviously media is going through a huge digital disruption right now. Automotive. I mean, any company that's looking at how do we build new ways to engage and provide experiences to our customers through any kind of a digital means, a digital product, an app, those are relevant targets for Amplitude. So I think people think, "Oh, it's..." Every industry looks very different but the commonality is everyone needs to move to digital. And the great thing for Amplitude and for the market at large is a lot of our customers are these digitally native, what I would call the thought leaders around digital. And so if we can help bring that, bring those best practices and bring that approach to some of the more traditional companies, in traditional industries and help them become more like the Pelotons and the DoorDashs of the world, then that's great for everybody. >> You know, JJ, when you talk about, this transformation that's going on and the spaces in which is going on which is everywhere right now, I imagine there are still some folks who might be a little reluctant. And you talked about slapping a new website on the old material and they think they're done and they wash their hands and they go away. And it's not that simple. So what's that conversation like to people who maybe aren't willing to jump in, to take that "risk" as they see it, whereas you know, it's an essential to their business. >> Yeah. So, I do think that every disruption technologically speaking or other, is really change management. And digital's no different. It's not just about moving to digital, it's changing the way that you're organized. It's changing your business structure, your strategy, your priorities. So, I think that organizations know they have to go there now. And even the ones that are reluctant, I'd say if they're reluctant they're probably going to get disrupted. So I think everyone understands they need to go there. Our role is really to help organizations get there, without... I mean, digital, the word that usually follows digital is transformation. And I think a lot of people think that digital transformation needs to be this, three to five year strategic journey, and cost millions of dollars with armies of consultants. And really what we're helping to do is, help organizations just answer the question, "how is our product tied to our revenue?" And we do that by bringing the data to the teams that actually need it. And it was really surprising to me to understand the process in some of these really large enterprises, around how product and marketing teams get data. And a lot of times if you have a question about something, if you're a product manager obviously you want to understand how is our product doing? What features are resonating? What features are leading to things like engagement or revenue or subscriptions or loyalty or whatever it is. As a marketer you also want to know that. As a marketer you also want to know, what campaigns are we driving that are actually creating value. Are there things that we should be doing? Are there areas we should double down on? And so the process is if you have a question about something or a hypothesis that you want to answer, a lot of times you have to send this request to some centralized data team or a data science team. Organizations have, large B2C organizations. Most of them have armies of data scientists and business intelligence platforms. And you send a request and you might get an answer back in a few weeks, maybe a month and maybe it's the right answer or usually what happens, and I think we can all relate to this. Is you ask a question and you get data back and then it sparks five more questions. And so that whole process is the cyclical thing that I always say, by the time you actually figure out the answer to your question, it's enough time to get Amazoned in the new digital era. And so what we're actually doing is helping to bring that data which we all know is the crown jewel of any organization. We're bringing that data and we're democratizing it and bringing it to all the teams that actually need it. Unlock it from data scientists and BI, and bring it to the teams that need it, whether it's product, whether it's marketing, whether it's sales, whether it's customer success. And the greatest thing is it's not a tool for everyone. And then all of a sudden you have these siloed tools, marketing has their tool, product has their tool, CS has their tool. Is you actually have one platform, one system, and one source of data that all those teams use. So marketing doesn't say, "Well yeah, my mind says this "and it looks at it from this lens." And product says, "Well, my data says this, "but it looks at it from this lens." All of a sudden you've removed that entire conversation or that entire debate. And that changes everything. It changes the way that companies get insights into customer behavior. It changes the way that they build products. It changes the way that the teams work together. Product and marketing can now work off of a common set of data. And so really Amplitude is helping to drive that change. And you don't have to do it through a three-year implementation with an army of consultants that come in. It's something that can be done very easily. And so, I know everyone wants an easy button. It is quite easy though. It's not the three-year or even the one-year transformation. It's actually a way to bring that data to the teams that need it quickly. The other thing I'd say to it is, it's bringing the right data to them. I was reading something from Gartner that said, 85% of marketing analytics tools, now these are tools that usually track things like ad attribution, website visits, and how that relates to revenue. Well in a customer acquisition scenario, well, you just want to know what ads actually lead to a cart. Put someone going to a cart, someone purchasing that was probably sufficient, but in the new world, that's just not answering the same question. Like if you need to answer a question of what features, what behaviors, what actions within the product actually drive business outcomes, knowing what ads people clicked on and what web visits that people had, that's not going to answer... It's just answering a totally different question. And 85% of companies are using marketing analytics tools to actually answer questions like what features, do we need to build? So that's another key point here is, companies need to answer this question. They know they do. They just don't have the tools to do it and the data to do it. So they're using tools that were designed for a completely different purpose. And so really that's another great thing about Amplitude, is we're actually giving them the actual, the right data to answer the questions. >> So, if you're somebody's headlights, for down the road, then in terms of, you're looking for behavior, straights and patterns. You're looking for increased customer engagements, and you have all these wonderful tools now, not that you're missing anything, but where do you think that you could even sharpen the pencil a little bit more so that down the road here, what do you think technologically you are capable or that you would like to be able to deliver, because of making that an even richer engagement, even a bigger, a deeper dig. >> Yeah. Well, I mean, so, we have this immense deep, fast, smart database of customer behavior. So if you think of it, it's almost like the possibilities are endless. Anything that you need to be able to know or any question you could ask of your data to know what combinations of features, what combinations of behaviors actually lead to things like retention or churn or revenue. And then you can actually start to model those into cohorts. If I know that a customer does these five things in this order, and they're five times more likely to churn, well then, any customer that actually, doesn't just look like that based on your demographics, who you are, where you live, et cetera, but based on actually what you do in the product. We can start to cohort them and say, "this person actually looks like this other person "based on their behavior." And therefore we might actually personalize an experience for them. We might send them an offer if we think they're going to churn because we know they're likely to churn base 'cause other people that look like them do. Or we're not going to send them anything because we already know they're loyal. So they're already likely to buy. So it's answering more questions, but then it's also, how do you actually use that to, really personalize experiences? And that word is so overused, but in this way, I mean, it's not about I'm going to serve you a piece of content because I know what industry you work in, or I know where you live. I'm actually going to personalize your experience because I know that you, John, as an individual, do these things and therefore I know that you are either, a loyal customer, or you've got a high likelihood to churn, et cetera. And then I'm going to personalize an experience, that's a good experience for you but also a good experience for the business. So, I think there's more types of analytics. There's more ways to personalize and build experiences. I think in the modern way, not the old demographic way. But also, even every organization around the company, like everyone touches the customer. So, customer experience as we know is, I hate to call it the buzzword. Of course, everybody wants a great customer experience but everybody talks about customer experience. Anyone who touches the customer is part of customer experience, which is basically the whole company. And so if you think about, today, there's obviously product teams, marketing teams, are heavy users of Amplitude. But going forward, I mean, imagine a world where, anytime you have a touch point with a customer, you can use this insight into what they're actually doing in the product to get some level of intelligence that you didn't have before, and use it to proactively give them a better experience. Whether it's, at renewal time, or you know that they're likely to do something so you offer something that gives them a better experience or you're in customer service. And wouldn't it be great to actually know if someone's logging a support ticket. What they're actually doing in the product is going to help you give them a better support experience, et cetera, et cetera. I mean, the options here I think are, because of the data that we have and the way that we can, like you said, build these patterns and pattern match what features and actions lead to outcomes, I think the options are limitless. And I think this is the new way. Like companies that understand this is the Holy grail of the new way of digital and understanding your customers and having this intelligence into the product is the new way to engage, the customers that get that are going to be the customers that win. >> Well, it is a new game, you're right. I think limitless is a really good word too because the capabilities that you're developing and the product and services you're providing, really are limitless. So thanks for sharing the time and the insight, a pleasure to have you on theCUBE. Thanks for being here. >> Thank you. It's been great. Thank you, John. >> You've got John Walls here on theCUBE, CUBE Conversation on the AWS startup showcase. I'm talking with Jennifer Johnson from Amplitude. (soft music)

>>don't talk mhm, >>Okay, thing is my presentation on coherent nonlinear dynamics and combinatorial optimization. This is going to be a talk to introduce an approach we're taking to the analysis of the performance of coherent using machines. So let me start with a brief introduction to easing optimization. The easing model represents a set of interacting magnetic moments or spins the total energy given by the expression shown at the bottom left of this slide. Here, the signal variables are meditate binary values. The Matrix element J. I. J. Represents the interaction, strength and signed between any pair of spins. I. J and A Chive represents a possible local magnetic field acting on each thing. The easing ground state problem is to find an assignment of binary spin values that achieves the lowest possible value of total energy. And an instance of the easing problem is specified by giving numerical values for the Matrix J in Vector H. Although the easy model originates in physics, we understand the ground state problem to correspond to what would be called quadratic binary optimization in the field of operations research and in fact, in terms of computational complexity theory, it could be established that the easing ground state problem is np complete. Qualitatively speaking, this makes the easing problem a representative sort of hard optimization problem, for which it is expected that the runtime required by any computational algorithm to find exact solutions should, as anatomically scale exponentially with the number of spends and for worst case instances at each end. Of course, there's no reason to believe that the problem instances that actually arrives in practical optimization scenarios are going to be worst case instances. And it's also not generally the case in practical optimization scenarios that we demand absolute optimum solutions. Usually we're more interested in just getting the best solution we can within an affordable cost, where costs may be measured in terms of time, service fees and or energy required for a computation. This focuses great interest on so called heuristic algorithms for the easing problem in other NP complete problems which generally get very good but not guaranteed optimum solutions and run much faster than algorithms that are designed to find absolute Optima. To get some feeling for present day numbers, we can consider the famous traveling salesman problem for which extensive compilations of benchmarking data may be found online. A recent study found that the best known TSP solver required median run times across the Library of Problem instances That scaled is a very steep route exponential for end up to approximately 4500. This gives some indication of the change in runtime scaling for generic as opposed the worst case problem instances. Some of the instances considered in this study were taken from a public library of T SPS derived from real world Veil aside design data. This feels I TSP Library includes instances within ranging from 131 to 744,710 instances from this library with end between 6880 13,584 were first solved just a few years ago in 2017 requiring days of run time and a 48 core to King hurts cluster, while instances with and greater than or equal to 14,233 remain unsolved exactly by any means. Approximate solutions, however, have been found by heuristic methods for all instances in the VLS i TSP library with, for example, a solution within 0.14% of a no lower bound, having been discovered, for instance, with an equal 19,289 requiring approximately two days of run time on a single core of 2.4 gigahertz. Now, if we simple mindedly extrapolate the root exponential scaling from the study up to an equal 4500, we might expect that an exact solver would require something more like a year of run time on the 48 core cluster used for the N equals 13,580 for instance, which shows how much a very small concession on the quality of the solution makes it possible to tackle much larger instances with much lower cost. At the extreme end, the largest TSP ever solved exactly has an equal 85,900. This is an instance derived from 19 eighties VLSI design, and it's required 136 CPU. Years of computation normalized to a single cord, 2.4 gigahertz. But the 24 larger so called world TSP benchmark instance within equals 1,904,711 has been solved approximately within ophthalmology. Gap bounded below 0.474%. Coming back to the general. Practical concerns have applied optimization. We may note that a recent meta study analyzed the performance of no fewer than 37 heuristic algorithms for Max cut and quadratic pioneer optimization problems and found the performance sort and found that different heuristics work best for different problem instances selected from a large scale heterogeneous test bed with some evidence but cryptic structure in terms of what types of problem instances were best solved by any given heuristic. Indeed, their their reasons to believe that these results from Mexico and quadratic binary optimization reflected general principle of performance complementarity among heuristic optimization algorithms in the practice of solving heart optimization problems there. The cerise is a critical pre processing issue of trying to guess which of a number of available good heuristic algorithms should be chosen to tackle a given problem. Instance, assuming that any one of them would incur high costs to run on a large problem, instances incidence, making an astute choice of heuristic is a crucial part of maximizing overall performance. Unfortunately, we still have very little conceptual insight about what makes a specific problem instance, good or bad for any given heuristic optimization algorithm. This has certainly been pinpointed by researchers in the field is a circumstance that must be addressed. So adding this all up, we see that a critical frontier for cutting edge academic research involves both the development of novel heuristic algorithms that deliver better performance, with lower cost on classes of problem instances that are underserved by existing approaches, as well as fundamental research to provide deep conceptual insight into what makes a given problem in, since easy or hard for such algorithms. In fact, these days, as we talk about the end of Moore's law and speculate about a so called second quantum revolution, it's natural to talk not only about novel algorithms for conventional CPUs but also about highly customized special purpose hardware architectures on which we may run entirely unconventional algorithms for combinatorial optimization such as easing problem. So against that backdrop, I'd like to use my remaining time to introduce our work on analysis of coherent using machine architectures and associate ID optimization algorithms. These machines, in general, are a novel class of information processing architectures for solving combinatorial optimization problems by embedding them in the dynamics of analog, physical or cyber physical systems, in contrast to both MAWR traditional engineering approaches that build using machines using conventional electron ICS and more radical proposals that would require large scale quantum entanglement. The emerging paradigm of coherent easing machines leverages coherent nonlinear dynamics in photonic or Opto electronic platforms to enable near term construction of large scale prototypes that leverage post Simoes information dynamics, the general structure of of current CM systems has shown in the figure on the right. The role of the easing spins is played by a train of optical pulses circulating around a fiber optical storage ring. A beam splitter inserted in the ring is used to periodically sample the amplitude of every optical pulse, and the measurement results are continually read into a refugee A, which uses them to compute perturbations to be applied to each pulse by a synchronized optical injections. These perturbations, air engineered to implement the spin, spin coupling and local magnetic field terms of the easing Hamiltonian, corresponding to a linear part of the CME Dynamics, a synchronously pumped parametric amplifier denoted here as PPL and Wave Guide adds a crucial nonlinear component to the CIA and Dynamics as well. In the basic CM algorithm, the pump power starts very low and has gradually increased at low pump powers. The amplitude of the easing spin pulses behaviors continuous, complex variables. Who Israel parts which can be positive or negative, play the role of play the role of soft or perhaps mean field spins once the pump, our crosses the threshold for parametric self oscillation. In the optical fiber ring, however, the attitudes of the easing spin pulses become effectively Qantas ized into binary values while the pump power is being ramped up. The F P J subsystem continuously applies its measurement based feedback. Implementation of the using Hamiltonian terms, the interplay of the linear rised using dynamics implemented by the F P G A and the threshold conversation dynamics provided by the sink pumped Parametric amplifier result in the final state of the optical optical pulse amplitude at the end of the pump ramp that could be read as a binary strain, giving a proposed solution of the easing ground state problem. This method of solving easing problem seems quite different from a conventional algorithm that runs entirely on a digital computer as a crucial aspect of the computation is performed physically by the analog, continuous, coherent, nonlinear dynamics of the optical degrees of freedom. In our efforts to analyze CIA and performance, we have therefore turned to the tools of dynamical systems theory, namely, a study of modifications, the evolution of critical points and apologies of hetero clinic orbits and basins of attraction. We conjecture that such analysis can provide fundamental insight into what makes certain optimization instances hard or easy for coherent using machines and hope that our approach can lead to both improvements of the course, the AM algorithm and a pre processing rubric for rapidly assessing the CME suitability of new instances. Okay, to provide a bit of intuition about how this all works, it may help to consider the threshold dynamics of just one or two optical parametric oscillators in the CME architecture just described. We can think of each of the pulse time slots circulating around the fiber ring, as are presenting an independent Opio. We can think of a single Opio degree of freedom as a single, resonant optical node that experiences linear dissipation, do toe out coupling loss and gain in a pump. Nonlinear crystal has shown in the diagram on the upper left of this slide as the pump power is increased from zero. As in the CME algorithm, the non linear game is initially to low toe overcome linear dissipation, and the Opio field remains in a near vacuum state at a critical threshold. Value gain. Equal participation in the Popeo undergoes a sort of lazing transition, and the study states of the OPIO above this threshold are essentially coherent states. There are actually two possible values of the Opio career in amplitude and any given above threshold pump power which are equal in magnitude but opposite in phase when the OPI across the special diet basically chooses one of the two possible phases randomly, resulting in the generation of a single bit of information. If we consider to uncoupled, Opio has shown in the upper right diagram pumped it exactly the same power at all times. Then, as the pump power has increased through threshold, each Opio will independently choose the phase and thus to random bits are generated for any number of uncoupled. Oppose the threshold power per opio is unchanged from the single Opio case. Now, however, consider a scenario in which the two appeals air, coupled to each other by a mutual injection of their out coupled fields has shown in the diagram on the lower right. One can imagine that depending on the sign of the coupling parameter Alfa, when one Opio is lazing, it will inject a perturbation into the other that may interfere either constructively or destructively, with the feel that it is trying to generate by its own lazing process. As a result, when came easily showed that for Alfa positive, there's an effective ferro magnetic coupling between the two Opio fields and their collective oscillation threshold is lowered from that of the independent Opio case. But on Lee for the two collective oscillation modes in which the two Opio phases are the same for Alfa Negative, the collective oscillation threshold is lowered on Lee for the configurations in which the Opio phases air opposite. So then, looking at how Alfa is related to the J. I. J matrix of the easing spin coupling Hamiltonian, it follows that we could use this simplistic to a p o. C. I am to solve the ground state problem of a fair magnetic or anti ferro magnetic ankles to easing model simply by increasing the pump power from zero and observing what phase relation occurs as the two appeals first start delays. Clearly, we can imagine generalizing this story toe larger, and however the story doesn't stay is clean and simple for all larger problem instances. And to find a more complicated example, we only need to go to n equals four for some choices of J J for n equals, for the story remains simple. Like the n equals two case. The figure on the upper left of this slide shows the energy of various critical points for a non frustrated and equals, for instance, in which the first bifurcated critical point that is the one that I forget to the lowest pump value a. Uh, this first bifurcated critical point flows as symptomatically into the lowest energy easing solution and the figure on the upper right. However, the first bifurcated critical point flows to a very good but sub optimal minimum at large pump power. The global minimum is actually given by a distinct critical critical point that first appears at a higher pump power and is not automatically connected to the origin. The basic C am algorithm is thus not able to find this global minimum. Such non ideal behaviors needs to become more confident. Larger end for the n equals 20 instance, showing the lower plots where the lower right plot is just a zoom into a region of the lower left lot. It can be seen that the global minimum corresponds to a critical point that first appears out of pump parameter, a around 0.16 at some distance from the idiomatic trajectory of the origin. That's curious to note that in both of these small and examples, however, the critical point corresponding to the global minimum appears relatively close to the idiomatic projector of the origin as compared to the most of the other local minima that appear. We're currently working to characterize the face portrait topology between the global minimum in the antibiotic trajectory of the origin, taking clues as to how the basic C am algorithm could be generalized to search for non idiomatic trajectories that jump to the global minimum during the pump ramp. Of course, n equals 20 is still too small to be of interest for practical optimization applications. But the advantage of beginning with the study of small instances is that we're able reliably to determine their global minima and to see how they relate to the 80 about trajectory of the origin in the basic C am algorithm. In the smaller and limit, we can also analyze fully quantum mechanical models of Syrian dynamics. But that's a topic for future talks. Um, existing large scale prototypes are pushing into the range of in equals 10 to the 4 10 to 5 to six. So our ultimate objective in theoretical analysis really has to be to try to say something about CIA and dynamics and regime of much larger in our initial approach to characterizing CIA and behavior in the large in regime relies on the use of random matrix theory, and this connects to prior research on spin classes, SK models and the tap equations etcetera. At present, we're focusing on statistical characterization of the CIA ingredient descent landscape, including the evolution of critical points in their Eigen value spectra. As the pump power is gradually increased. We're investigating, for example, whether there could be some way to exploit differences in the relative stability of the global minimum versus other local minima. We're also working to understand the deleterious or potentially beneficial effects of non ideologies, such as a symmetry in the implemented these and couplings. Looking one step ahead, we plan to move next in the direction of considering more realistic classes of problem instances such as quadratic, binary optimization with constraints. Eso In closing, I should acknowledge people who did the hard work on these things that I've shown eso. My group, including graduate students Ed winning, Daniel Wennberg, Tatsuya Nagamoto and Atsushi Yamamura, have been working in close collaboration with Syria Ganguly, Marty Fair and Amir Safarini Nini, all of us within the Department of Applied Physics at Stanford University. On also in collaboration with the Oshima Moto over at NTT 55 research labs, Onda should acknowledge funding support from the NSF by the Coherent Easing Machines Expedition in computing, also from NTT five research labs, Army Research Office and Exxon Mobil. Uh, that's it. Thanks very much. >>Mhm e >>t research and the Oshie for putting together this program and also the opportunity to speak here. My name is Al Gore ism or Andy and I'm from Caltech, and today I'm going to tell you about the work that we have been doing on networks off optical parametric oscillators and how we have been using them for icing machines and how we're pushing them toward Cornum photonics to acknowledge my team at Caltech, which is now eight graduate students and five researcher and postdocs as well as collaborators from all over the world, including entity research and also the funding from different places, including entity. So this talk is primarily about networks of resonate er's, and these networks are everywhere from nature. For instance, the brain, which is a network of oscillators all the way to optics and photonics and some of the biggest examples or metal materials, which is an array of small resonate er's. And we're recently the field of technological photonics, which is trying thio implement a lot of the technological behaviors of models in the condensed matter, physics in photonics and if you want to extend it even further, some of the implementations off quantum computing are technically networks of quantum oscillators. So we started thinking about these things in the context of icing machines, which is based on the icing problem, which is based on the icing model, which is the simple summation over the spins and spins can be their upward down and the couplings is given by the JJ. And the icing problem is, if you know J I J. What is the spin configuration that gives you the ground state? And this problem is shown to be an MP high problem. So it's computational e important because it's a representative of the MP problems on NPR. Problems are important because first, their heart and standard computers if you use a brute force algorithm and they're everywhere on the application side. That's why there is this demand for making a machine that can target these problems, and hopefully it can provide some meaningful computational benefit compared to the standard digital computers. So I've been building these icing machines based on this building block, which is a degenerate optical parametric. Oscillator on what it is is resonator with non linearity in it, and we pump these resonate er's and we generate the signal at half the frequency of the pump. One vote on a pump splits into two identical photons of signal, and they have some very interesting phase of frequency locking behaviors. And if you look at the phase locking behavior, you realize that you can actually have two possible phase states as the escalation result of these Opio which are off by pie, and that's one of the important characteristics of them. So I want to emphasize a little more on that and I have this mechanical analogy which are basically two simple pendulum. But there are parametric oscillators because I'm going to modulate the parameter of them in this video, which is the length of the string on by that modulation, which is that will make a pump. I'm gonna make a muscular. That'll make a signal which is half the frequency of the pump. And I have two of them to show you that they can acquire these face states so they're still facing frequency lock to the pump. But it can also lead in either the zero pie face states on. The idea is to use this binary phase to represent the binary icing spin. So each opio is going to represent spin, which can be either is your pie or up or down. And to implement the network of these resonate er's, we use the time off blood scheme, and the idea is that we put impulses in the cavity. These pulses air separated by the repetition period that you put in or t r. And you can think about these pulses in one resonator, xaz and temporarily separated synthetic resonate Er's if you want a couple of these resonator is to each other, and now you can introduce these delays, each of which is a multiple of TR. If you look at the shortest delay it couples resonator wanted to 2 to 3 and so on. If you look at the second delay, which is two times a rotation period, the couple's 123 and so on. And if you have and minus one delay lines, then you can have any potential couplings among these synthetic resonate er's. And if I can introduce these modulators in those delay lines so that I can strength, I can control the strength and the phase of these couplings at the right time. Then I can have a program will all toe all connected network in this time off like scheme, and the whole physical size of the system scales linearly with the number of pulses. So the idea of opium based icing machine is didn't having these o pos, each of them can be either zero pie and I can arbitrarily connect them to each other. And then I start with programming this machine to a given icing problem by just setting the couplings and setting the controllers in each of those delight lines. So now I have a network which represents an icing problem. Then the icing problem maps to finding the face state that satisfy maximum number of coupling constraints. And the way it happens is that the icing Hamiltonian maps to the linear loss of the network. And if I start adding gain by just putting pump into the network, then the OPI ohs are expected to oscillate in the lowest, lowest lost state. And, uh and we have been doing these in the past, uh, six or seven years and I'm just going to quickly show you the transition, especially what happened in the first implementation, which was using a free space optical system and then the guided wave implementation in 2016 and the measurement feedback idea which led to increasing the size and doing actual computation with these machines. So I just want to make this distinction here that, um, the first implementation was an all optical interaction. We also had an unequal 16 implementation. And then we transition to this measurement feedback idea, which I'll tell you quickly what it iss on. There's still a lot of ongoing work, especially on the entity side, to make larger machines using the measurement feedback. But I'm gonna mostly focused on the all optical networks and how we're using all optical networks to go beyond simulation of icing Hamiltonian both in the linear and non linear side and also how we're working on miniaturization of these Opio networks. So the first experiment, which was the four opium machine, it was a free space implementation and this is the actual picture off the machine and we implemented a small and it calls for Mexico problem on the machine. So one problem for one experiment and we ran the machine 1000 times, we looked at the state and we always saw it oscillate in one of these, um, ground states of the icing laboratoria. So then the measurement feedback idea was to replace those couplings and the controller with the simulator. So we basically simulated all those coherent interactions on on FB g. A. And we replicated the coherent pulse with respect to all those measurements. And then we injected it back into the cavity and on the near to you still remain. So it still is a non. They're dynamical system, but the linear side is all simulated. So there are lots of questions about if this system is preserving important information or not, or if it's gonna behave better. Computational wars. And that's still ah, lot of ongoing studies. But nevertheless, the reason that this implementation was very interesting is that you don't need the end minus one delight lines so you can just use one. Then you can implement a large machine, and then you can run several thousands of problems in the machine, and then you can compare the performance from the computational perspective Looks so I'm gonna split this idea of opium based icing machine into two parts. One is the linear part, which is if you take out the non linearity out of the resonator and just think about the connections. You can think about this as a simple matrix multiplication scheme. And that's basically what gives you the icing Hambletonian modeling. So the optical laws of this network corresponds to the icing Hamiltonian. And if I just want to show you the example of the n equals for experiment on all those face states and the history Graham that we saw, you can actually calculate the laws of each of those states because all those interferences in the beam splitters and the delay lines are going to give you a different losses. And then you will see that the ground states corresponds to the lowest laws of the actual optical network. If you add the non linearity, the simple way of thinking about what the non linearity does is that it provides to gain, and then you start bringing up the gain so that it hits the loss. Then you go through the game saturation or the threshold which is going to give you this phase bifurcation. So you go either to zero the pie face state. And the expectation is that Theis, the network oscillates in the lowest possible state, the lowest possible loss state. There are some challenges associated with this intensity Durban face transition, which I'm going to briefly talk about. I'm also going to tell you about other types of non aerodynamics that we're looking at on the non air side of these networks. So if you just think about the linear network, we're actually interested in looking at some technological behaviors in these networks. And the difference between looking at the technological behaviors and the icing uh, machine is that now, First of all, we're looking at the type of Hamilton Ian's that are a little different than the icing Hamilton. And one of the biggest difference is is that most of these technological Hamilton Ian's that require breaking the time reversal symmetry, meaning that you go from one spin to in the one side to another side and you get one phase. And if you go back where you get a different phase, and the other thing is that we're not just interested in finding the ground state, we're actually now interesting and looking at all sorts of states and looking at the dynamics and the behaviors of all these states in the network. So we started with the simplest implementation, of course, which is a one d chain of thes resonate, er's, which corresponds to a so called ssh model. In the technological work, we get the similar energy to los mapping and now we can actually look at the band structure on. This is an actual measurement that we get with this associate model and you see how it reasonably how How? Well, it actually follows the prediction and the theory. One of the interesting things about the time multiplexing implementation is that now you have the flexibility of changing the network as you are running the machine. And that's something unique about this time multiplex implementation so that we can actually look at the dynamics. And one example that we have looked at is we can actually go through the transition off going from top A logical to the to the standard nontrivial. I'm sorry to the trivial behavior of the network. You can then look at the edge states and you can also see the trivial and states and the technological at states actually showing up in this network. We have just recently implement on a two D, uh, network with Harper Hofstadter model and when you don't have the results here. But we're one of the other important characteristic of time multiplexing is that you can go to higher and higher dimensions and keeping that flexibility and dynamics, and we can also think about adding non linearity both in a classical and quantum regimes, which is going to give us a lot of exotic, no classical and quantum, non innate behaviors in these networks. Yeah, So I told you about the linear side. Mostly let me just switch gears and talk about the nonlinear side of the network. And the biggest thing that I talked about so far in the icing machine is this face transition that threshold. So the low threshold we have squeezed state in these. Oh, pios, if you increase the pump, we go through this intensity driven phase transition and then we got the face stays above threshold. And this is basically the mechanism off the computation in these O pos, which is through this phase transition below to above threshold. So one of the characteristics of this phase transition is that below threshold, you expect to see quantum states above threshold. You expect to see more classical states or coherent states, and that's basically corresponding to the intensity off the driving pump. So it's really hard to imagine that it can go above threshold. Or you can have this friends transition happen in the all in the quantum regime. And there are also some challenges associated with the intensity homogeneity off the network, which, for example, is if one opioid starts oscillating and then its intensity goes really high. Then it's going to ruin this collective decision making off the network because of the intensity driven face transition nature. So So the question is, can we look at other phase transitions? Can we utilize them for both computing? And also can we bring them to the quantum regime on? I'm going to specifically talk about the face transition in the spectral domain, which is the transition from the so called degenerate regime, which is what I mostly talked about to the non degenerate regime, which happens by just tuning the phase of the cavity. And what is interesting is that this phase transition corresponds to a distinct phase noise behavior. So in the degenerate regime, which we call it the order state, you're gonna have the phase being locked to the phase of the pump. As I talked about non degenerate regime. However, the phase is the phase is mostly dominated by the quantum diffusion. Off the off the phase, which is limited by the so called shallow towns limit, and you can see that transition from the general to non degenerate, which also has distinct symmetry differences. And this transition corresponds to a symmetry breaking in the non degenerate case. The signal can acquire any of those phases on the circle, so it has a you one symmetry. Okay, and if you go to the degenerate case, then that symmetry is broken and you only have zero pie face days I will look at. So now the question is can utilize this phase transition, which is a face driven phase transition, and can we use it for similar computational scheme? So that's one of the questions that were also thinking about. And it's not just this face transition is not just important for computing. It's also interesting from the sensing potentials and this face transition, you can easily bring it below threshold and just operated in the quantum regime. Either Gaussian or non Gaussian. If you make a network of Opio is now, we can see all sorts off more complicated and more interesting phase transitions in the spectral domain. One of them is the first order phase transition, which you get by just coupling to Opio, and that's a very abrupt face transition and compared to the to the single Opio phase transition. And if you do the couplings right, you can actually get a lot of non her mission dynamics and exceptional points, which are actually very interesting to explore both in the classical and quantum regime. And I should also mention that you can think about the cup links to be also nonlinear couplings. And that's another behavior that you can see, especially in the nonlinear in the non degenerate regime. So with that, I basically told you about these Opio networks, how we can think about the linear scheme and the linear behaviors and how we can think about the rich, nonlinear dynamics and non linear behaviors both in the classical and quantum regime. I want to switch gear and tell you a little bit about the miniaturization of these Opio networks. And of course, the motivation is if you look at the electron ICS and what we had 60 or 70 years ago with vacuum tube and how we transition from relatively small scale computers in the order of thousands of nonlinear elements to billions of non elements where we are now with the optics is probably very similar to 70 years ago, which is a table talk implementation. And the question is, how can we utilize nano photonics? I'm gonna just briefly show you the two directions on that which we're working on. One is based on lithium Diabate, and the other is based on even a smaller resonate er's could you? So the work on Nana Photonic lithium naive. It was started in collaboration with Harvard Marko Loncar, and also might affair at Stanford. And, uh, we could show that you can do the periodic polling in the phenomenon of it and get all sorts of very highly nonlinear processes happening in this net. Photonic periodically polls if, um Diabate. And now we're working on building. Opio was based on that kind of photonic the film Diabate. And these air some some examples of the devices that we have been building in the past few months, which I'm not gonna tell you more about. But the O. P. O. S. And the Opio Networks are in the works. And that's not the only way of making large networks. Um, but also I want to point out that The reason that these Nana photonic goblins are actually exciting is not just because you can make a large networks and it can make him compact in a in a small footprint. They also provide some opportunities in terms of the operation regime. On one of them is about making cat states and Opio, which is, can we have the quantum superposition of the zero pie states that I talked about and the Net a photonic within? I've It provides some opportunities to actually get closer to that regime because of the spatial temporal confinement that you can get in these wave guides. So we're doing some theory on that. We're confident that the type of non linearity two losses that it can get with these platforms are actually much higher than what you can get with other platform their existing platforms and to go even smaller. We have been asking the question off. What is the smallest possible Opio that you can make? Then you can think about really wavelength scale type, resonate er's and adding the chi to non linearity and see how and when you can get the Opio to operate. And recently, in collaboration with us see, we have been actually USC and Creole. We have demonstrated that you can use nano lasers and get some spin Hamilton and implementations on those networks. So if you can build the a P. O s, we know that there is a path for implementing Opio Networks on on such a nano scale. So we have looked at these calculations and we try to estimate the threshold of a pos. Let's say for me resonator and it turns out that it can actually be even lower than the type of bulk Pip Llano Pos that we have been building in the past 50 years or so. So we're working on the experiments and we're hoping that we can actually make even larger and larger scale Opio networks. So let me summarize the talk I told you about the opium networks and our work that has been going on on icing machines and the measurement feedback. And I told you about the ongoing work on the all optical implementations both on the linear side and also on the nonlinear behaviors. And I also told you a little bit about the efforts on miniaturization and going to the to the Nano scale. So with that, I would like Thio >>three from the University of Tokyo. Before I thought that would like to thank you showing all the stuff of entity for the invitation and the organization of this online meeting and also would like to say that it has been very exciting to see the growth of this new film lab. And I'm happy to share with you today of some of the recent works that have been done either by me or by character of Hong Kong. Honest Group indicates the title of my talk is a neuro more fic in silica simulator for the communities in machine. And here is the outline I would like to make the case that the simulation in digital Tektronix of the CME can be useful for the better understanding or improving its function principles by new job introducing some ideas from neural networks. This is what I will discuss in the first part and then it will show some proof of concept of the game and performance that can be obtained using dissimulation in the second part and the protection of the performance that can be achieved using a very large chaos simulator in the third part and finally talk about future plans. So first, let me start by comparing recently proposed izing machines using this table there is elected from recent natural tronics paper from the village Park hard people, and this comparison shows that there's always a trade off between energy efficiency, speed and scalability that depends on the physical implementation. So in red, here are the limitation of each of the servers hardware on, interestingly, the F p G, a based systems such as a producer, digital, another uh Toshiba beautification machine or a recently proposed restricted Bozeman machine, FPD A by a group in Berkeley. They offer a good compromise between speed and scalability. And this is why, despite the unique advantage that some of these older hardware have trust as the currency proposition in Fox, CBS or the energy efficiency off memory Sisters uh P. J. O are still an attractive platform for building large organizing machines in the near future. The reason for the good performance of Refugee A is not so much that they operate at the high frequency. No, there are particular in use, efficient, but rather that the physical wiring off its elements can be reconfigured in a way that limits the funding human bottleneck, larger, funny and phenols and the long propagation video information within the system. In this respect, the LPGA is They are interesting from the perspective off the physics off complex systems, but then the physics of the actions on the photos. So to put the performance of these various hardware and perspective, we can look at the competition of bringing the brain the brain complete, using billions of neurons using only 20 watts of power and operates. It's a very theoretically slow, if we can see and so this impressive characteristic, they motivate us to try to investigate. What kind of new inspired principles be useful for designing better izing machines? The idea of this research project in the future collaboration it's to temporary alleviates the limitations that are intrinsic to the realization of an optical cortex in machine shown in the top panel here. By designing a large care simulator in silicone in the bottom here that can be used for digesting the better organization principles of the CIA and this talk, I will talk about three neuro inspired principles that are the symmetry of connections, neural dynamics orphan chaotic because of symmetry, is interconnectivity the infrastructure? No. Next talks are not composed of the reputation of always the same types of non environments of the neurons, but there is a local structure that is repeated. So here's the schematic of the micro column in the cortex. And lastly, the Iraqi co organization of connectivity connectivity is organizing a tree structure in the brain. So here you see a representation of the Iraqi and organization of the monkey cerebral cortex. So how can these principles we used to improve the performance of the icing machines? And it's in sequence stimulation. So, first about the two of principles of the estimate Trian Rico structure. We know that the classical approximation of the car testing machine, which is the ground toe, the rate based on your networks. So in the case of the icing machines, uh, the okay, Scott approximation can be obtained using the trump active in your position, for example, so the times of both of the system they are, they can be described by the following ordinary differential equations on in which, in case of see, I am the X, I represent the in phase component of one GOP Oh, Theo f represents the monitor optical parts, the district optical Parametric amplification and some of the good I JoJo extra represent the coupling, which is done in the case of the measure of feedback coupling cm using oh, more than detection and refugee A and then injection off the cooking time and eso this dynamics in both cases of CNN in your networks, they can be written as the grand set of a potential function V, and this written here, and this potential functionally includes the rising Maccagnan. So this is why it's natural to use this type of, uh, dynamics to solve the icing problem in which the Omega I J or the eyes in coping and the H is the extension of the icing and attorney in India and expect so. Not that this potential function can only be defined if the Omega I j. R. A. Symmetric. So the well known problem of this approach is that this potential function V that we obtain is very non convicts at low temperature, and also one strategy is to gradually deformed this landscape, using so many in process. But there is no theorem. Unfortunately, that granted conventions to the global minimum of There's even Tony and using this approach. And so this is why we propose, uh, to introduce a macro structures of the system where one analog spin or one D O. P. O is replaced by a pair off one another spin and one error, according viable. And the addition of this chemical structure introduces a symmetry in the system, which in terms induces chaotic dynamics, a chaotic search rather than a learning process for searching for the ground state of the icing. Every 20 within this massacre structure the role of the er variable eyes to control the amplitude off the analog spins toe force. The amplitude of the expense toe become equal to certain target amplitude a uh and, uh, and this is done by modulating the strength off the icing complaints or see the the error variable E I multiply the icing complaint here in the dynamics off air d o p. O. On then the dynamics. The whole dynamics described by this coupled equations because the e I do not necessarily take away the same value for the different. I thesis introduces a symmetry in the system, which in turn creates security dynamics, which I'm sure here for solving certain current size off, um, escape problem, Uh, in which the X I are shown here and the i r from here and the value of the icing energy showing the bottom plots. You see this Celtics search that visit various local minima of the as Newtonian and eventually finds the global minimum? Um, it can be shown that this modulation off the target opportunity can be used to destabilize all the local minima off the icing evertonians so that we're gonna do not get stuck in any of them. On more over the other types of attractors I can eventually appear, such as limits I contractors, Okot contractors. They can also be destabilized using the motivation of the target and Batuta. And so we have proposed in the past two different moderation of the target amateur. The first one is a modulation that ensure the uh 100 reproduction rate of the system to become positive on this forbids the creation off any nontrivial tractors. And but in this work, I will talk about another moderation or arrested moderation which is given here. That works, uh, as well as this first uh, moderation, but is easy to be implemented on refugee. So this couple of the question that represent becoming the stimulation of the cortex in machine with some error correction they can be implemented especially efficiently on an F B. G. And here I show the time that it takes to simulate three system and also in red. You see, at the time that it takes to simulate the X I term the EI term, the dot product and the rising Hamiltonian for a system with 500 spins and Iraq Spain's equivalent to 500 g. O. P. S. So >>in >>f b d a. The nonlinear dynamics which, according to the digital optical Parametric amplification that the Opa off the CME can be computed in only 13 clock cycles at 300 yards. So which corresponds to about 0.1 microseconds. And this is Toby, uh, compared to what can be achieved in the measurements back O C. M. In which, if we want to get 500 timer chip Xia Pios with the one she got repetition rate through the obstacle nine narrative. Uh, then way would require 0.5 microseconds toe do this so the submission in F B J can be at least as fast as ah one g repression. Uh, replicate pulsed laser CIA Um, then the DOT product that appears in this differential equation can be completed in 43 clock cycles. That's to say, one microseconds at 15 years. So I pieced for pouring sizes that are larger than 500 speeds. The dot product becomes clearly the bottleneck, and this can be seen by looking at the the skating off the time the numbers of clock cycles a text to compute either the non in your optical parts or the dog products, respect to the problem size. And And if we had infinite amount of resources and PGA to simulate the dynamics, then the non illogical post can could be done in the old one. On the mattress Vector product could be done in the low carrot off, located off scales as a look at it off and and while the guide off end. Because computing the dot product involves assuming all the terms in the product, which is done by a nephew, GE by another tree, which heights scarce logarithmic any with the size of the system. But This is in the case if we had an infinite amount of resources on the LPGA food, but for dealing for larger problems off more than 100 spins. Usually we need to decompose the metrics into ah, smaller blocks with the block side that are not you here. And then the scaling becomes funny, non inner parts linear in the end, over you and for the products in the end of EU square eso typically for low NF pdf cheap PGA you the block size off this matrix is typically about 100. So clearly way want to make you as large as possible in order to maintain this scanning in a log event for the numbers of clock cycles needed to compute the product rather than this and square that occurs if we decompose the metrics into smaller blocks. But the difficulty in, uh, having this larger blocks eyes that having another tree very large Haider tree introduces a large finding and finance and long distance start a path within the refugee. So the solution to get higher performance for a simulator of the contest in machine eyes to get rid of this bottleneck for the dot product by increasing the size of this at the tree. And this can be done by organizing your critique the electrical components within the LPGA in order which is shown here in this, uh, right panel here in order to minimize the finding finance of the system and to minimize the long distance that a path in the in the fpt So I'm not going to the details of how this is implemented LPGA. But just to give you a idea off why the Iraqi Yahiko organization off the system becomes the extremely important toe get good performance for similar organizing machine. So instead of instead of getting into the details of the mpg implementation, I would like to give some few benchmark results off this simulator, uh, off the that that was used as a proof of concept for this idea which is can be found in this archive paper here and here. I should results for solving escape problems. Free connected person, randomly person minus one spring last problems and we sure, as we use as a metric the numbers of the mattress Victor products since it's the bottleneck of the computation, uh, to get the optimal solution of this escape problem with the Nina successful BT against the problem size here and and in red here, this propose FDJ implementation and in ah blue is the numbers of retrospective product that are necessary for the C. I am without error correction to solve this escape programs and in green here for noisy means in an evening which is, uh, behavior with similar to the Cartesian mission. Uh, and so clearly you see that the scaring off the numbers of matrix vector product necessary to solve this problem scales with a better exponents than this other approaches. So So So that's interesting feature of the system and next we can see what is the real time to solution to solve this SK instances eso in the last six years, the time institution in seconds to find a grand state of risk. Instances remain answers probability for different state of the art hardware. So in red is the F B g. A presentation proposing this paper and then the other curve represent Ah, brick a local search in in orange and silver lining in purple, for example. And so you see that the scaring off this purpose simulator is is rather good, and that for larger plant sizes we can get orders of magnitude faster than the state of the art approaches. Moreover, the relatively good scanning off the time to search in respect to problem size uh, they indicate that the FPD implementation would be faster than risk. Other recently proposed izing machine, such as the hope you know, natural complimented on memories distance that is very fast for small problem size in blue here, which is very fast for small problem size. But which scanning is not good on the same thing for the restricted Bosman machine. Implementing a PGA proposed by some group in Broken Recently Again, which is very fast for small parliament sizes but which canning is bad so that a dis worse than the proposed approach so that we can expect that for programs size is larger than 1000 spins. The proposed, of course, would be the faster one. Let me jump toe this other slide and another confirmation that the scheme scales well that you can find the maximum cut values off benchmark sets. The G sets better candidates that have been previously found by any other algorithms, so they are the best known could values to best of our knowledge. And, um or so which is shown in this paper table here in particular, the instances, uh, 14 and 15 of this G set can be We can find better converse than previously known, and we can find this can vary is 100 times faster than the state of the art algorithm and CP to do this which is a very common Kasich. It s not that getting this a good result on the G sets, they do not require ah, particular hard tuning of the parameters. So the tuning issuing here is very simple. It it just depends on the degree off connectivity within each graph. And so this good results on the set indicate that the proposed approach would be a good not only at solving escape problems in this problems, but all the types off graph sizing problems on Mexican province in communities. So given that the performance off the design depends on the height of this other tree, we can try to maximize the height of this other tree on a large F p g a onda and carefully routing the components within the P G A and and we can draw some projections of what type of performance we can achieve in the near future based on the, uh, implementation that we are currently working. So here you see projection for the time to solution way, then next property for solving this escape programs respect to the prime assize. And here, compared to different with such publicizing machines, particularly the digital. And, you know, 42 is shown in the green here, the green line without that's and, uh and we should two different, uh, hypothesis for this productions either that the time to solution scales as exponential off n or that the time of social skills as expression of square root off. So it seems, according to the data, that time solution scares more as an expression of square root of and also we can be sure on this and this production show that we probably can solve prime escape problem of science 2000 spins, uh, to find the rial ground state of this problem with 99 success ability in about 10 seconds, which is much faster than all the other proposed approaches. So one of the future plans for this current is in machine simulator. So the first thing is that we would like to make dissimulation closer to the rial, uh, GOP oh, optical system in particular for a first step to get closer to the system of a measurement back. See, I am. And to do this what is, uh, simulate Herbal on the p a is this quantum, uh, condoms Goshen model that is proposed described in this paper and proposed by people in the in the Entity group. And so the idea of this model is that instead of having the very simple or these and have shown previously, it includes paired all these that take into account on me the mean off the awesome leverage off the, uh, European face component, but also their violence s so that we can take into account more quantum effects off the g o p. O, such as the squeezing. And then we plan toe, make the simulator open access for the members to run their instances on the system. There will be a first version in September that will be just based on the simple common line access for the simulator and in which will have just a classic or approximation of the system. We don't know Sturm, binary weights and museum in term, but then will propose a second version that would extend the current arising machine to Iraq off F p g. A, in which we will add the more refined models truncated, ignoring the bottom Goshen model they just talked about on the support in which he valued waits for the rising problems and support the cement. So we will announce later when this is available and and far right is working >>hard comes from Universal down today in physics department, and I'd like to thank the organizers for their kind invitation to participate in this very interesting and promising workshop. Also like to say that I look forward to collaborations with with a file lab and Yoshi and collaborators on the topics of this world. So today I'll briefly talk about our attempt to understand the fundamental limits off another continues time computing, at least from the point off you off bullion satisfy ability, problem solving, using ordinary differential equations. But I think the issues that we raise, um, during this occasion actually apply to other other approaches on a log approaches as well and into other problems as well. I think everyone here knows what Dorien satisfy ability. Problems are, um, you have boolean variables. You have em clauses. Each of disjunction of collaterals literally is a variable, or it's, uh, negation. And the goal is to find an assignment to the variable, such that order clauses are true. This is a decision type problem from the MP class, which means you can checking polynomial time for satisfy ability off any assignment. And the three set is empty, complete with K three a larger, which means an efficient trees. That's over, uh, implies an efficient source for all the problems in the empty class, because all the problems in the empty class can be reduced in Polian on real time to reset. As a matter of fact, you can reduce the NP complete problems into each other. You can go from three set to set backing or two maximum dependent set, which is a set packing in graph theoretic notions or terms toe the icing graphs. A problem decision version. This is useful, and you're comparing different approaches, working on different kinds of problems when not all the closest can be satisfied. You're looking at the accusation version offset, uh called Max Set. And the goal here is to find assignment that satisfies the maximum number of clauses. And this is from the NPR class. In terms of applications. If we had inefficient sets over or np complete problems over, it was literally, positively influenced. Thousands off problems and applications in industry and and science. I'm not going to read this, but this this, of course, gives a strong motivation toe work on this kind of problems. Now our approach to set solving involves embedding the problem in a continuous space, and you use all the east to do that. So instead of working zeros and ones, we work with minus one across once, and we allow the corresponding variables toe change continuously between the two bounds. We formulate the problem with the help of a close metrics. If if a if a close, uh, does not contain a variable or its negation. The corresponding matrix element is zero. If it contains the variable in positive, for which one contains the variable in a gated for Mitt's negative one, and then we use this to formulate this products caused quote, close violation functions one for every clause, Uh, which really, continuously between zero and one. And they're zero if and only if the clause itself is true. Uh, then we form the define in order to define a dynamic such dynamics in this and dimensional hyper cube where the search happens and if they exist, solutions. They're sitting in some of the corners of this hyper cube. So we define this, uh, energy potential or landscape function shown here in a way that this is zero if and only if all the clauses all the kmc zero or the clauses off satisfied keeping these auxiliary variables a EMS always positive. And therefore, what you do here is a dynamics that is a essentially ingredient descend on this potential energy landscape. If you were to keep all the M's constant that it would get stuck in some local minimum. However, what we do here is we couple it with the dynamics we cooperated the clothes violation functions as shown here. And if he didn't have this am here just just the chaos. For example, you have essentially what case you have positive feedback. You have increasing variable. Uh, but in that case, you still get stuck would still behave will still find. So she is better than the constant version but still would get stuck only when you put here this a m which makes the dynamics in in this variable exponential like uh, only then it keeps searching until he finds a solution on deer is a reason for that. I'm not going toe talk about here, but essentially boils down toe performing a Grady and descend on a globally time barren landscape. And this is what works. Now I'm gonna talk about good or bad and maybe the ugly. Uh, this is, uh, this is What's good is that it's a hyperbolic dynamical system, which means that if you take any domain in the search space that doesn't have a solution in it or any socially than the number of trajectories in it decays exponentially quickly. And the decay rate is a characteristic in variant characteristic off the dynamics itself. Dynamical systems called the escape right the inverse off that is the time scale in which you find solutions by this by this dynamical system, and you can see here some song trajectories that are Kelty because it's it's no linear, but it's transient, chaotic. Give their sources, of course, because eventually knowledge to the solution. Now, in terms of performance here, what you show for a bunch off, um, constraint densities defined by M overran the ratio between closes toe variables for random, said Problems is random. Chris had problems, and they as its function off n And we look at money toward the wartime, the wall clock time and it behaves quite value behaves Azat party nominally until you actually he to reach the set on set transition where the hardest problems are found. But what's more interesting is if you monitor the continuous time t the performance in terms off the A narrow, continuous Time t because that seems to be a polynomial. And the way we show that is, we consider, uh, random case that random three set for a fixed constraint density Onda. We hear what you show here. Is that the right of the trash hold that it's really hard and, uh, the money through the fraction of problems that we have not been able to solve it. We select thousands of problems at that constraint ratio and resolve them without algorithm, and we monitor the fractional problems that have not yet been solved by continuous 90. And this, as you see these decays exponentially different. Educate rates for different system sizes, and in this spot shows that is dedicated behaves polynomial, or actually as a power law. So if you combine these two, you find that the time needed to solve all problems except maybe appear traction off them scales foreign or merely with the problem size. So you have paranormal, continuous time complexity. And this is also true for other types of very hard constraints and sexual problems such as exact cover, because you can always transform them into three set as we discussed before, Ramsey coloring and and on these problems, even algorithms like survey propagation will will fail. But this doesn't mean that P equals NP because what you have first of all, if you were toe implement these equations in a device whose behavior is described by these, uh, the keys. Then, of course, T the continue style variable becomes a physical work off. Time on that will be polynomial is scaling, but you have another other variables. Oxidative variables, which structured in an exponential manner. So if they represent currents or voltages in your realization and it would be an exponential cost Al Qaeda. But this is some kind of trade between time and energy, while I know how toe generate energy or I don't know how to generate time. But I know how to generate energy so it could use for it. But there's other issues as well, especially if you're trying toe do this son and digital machine but also happens. Problems happen appear. Other problems appear on in physical devices as well as we discuss later. So if you implement this in GPU, you can. Then you can get in order off to magnitude. Speed up. And you can also modify this to solve Max sad problems. Uh, quite efficiently. You are competitive with the best heuristic solvers. This is a weather problems. In 2016 Max set competition eso so this this is this is definitely this seems like a good approach, but there's off course interesting limitations, I would say interesting, because it kind of makes you think about what it means and how you can exploit this thes observations in understanding better on a low continues time complexity. If you monitored the discrete number the number of discrete steps. Don't buy the room, Dakota integrator. When you solve this on a digital machine, you're using some kind of integrator. Um and you're using the same approach. But now you measure the number off problems you haven't sold by given number of this kid, uh, steps taken by the integrator. You find out you have exponential, discrete time, complexity and, of course, thistles. A problem. And if you look closely, what happens even though the analog mathematical trajectory, that's the record here. If you monitor what happens in discrete time, uh, the integrator frustrates very little. So this is like, you know, third or for the disposition, but fluctuates like crazy. So it really is like the intervention frees us out. And this is because of the phenomenon of stiffness that are I'll talk a little bit a more about little bit layer eso. >>You know, it might look >>like an integration issue on digital machines that you could improve and could definitely improve. But actually issues bigger than that. It's It's deeper than that, because on a digital machine there is no time energy conversion. So the outside variables are efficiently representing a digital machine. So there's no exponential fluctuating current of wattage in your computer when you do this. Eso If it is not equal NP then the exponential time, complexity or exponential costs complexity has to hit you somewhere. And this is how um, but, you know, one would be tempted to think maybe this wouldn't be an issue in a analog device, and to some extent is true on our devices can be ordered to maintain faster, but they also suffer from their own problems because he not gonna be affect. That classes soldiers as well. So, indeed, if you look at other systems like Mirandizing machine measurement feedback, probably talk on the grass or selected networks. They're all hinge on some kind off our ability to control your variables in arbitrary, high precision and a certain networks you want toe read out across frequencies in case off CM's. You required identical and program because which is hard to keep, and they kind of fluctuate away from one another, shift away from one another. And if you control that, of course that you can control the performance. So actually one can ask if whether or not this is a universal bottleneck and it seems so aside, I will argue next. Um, we can recall a fundamental result by by showing harder in reaction Target from 1978. Who says that it's a purely computer science proof that if you are able toe, compute the addition multiplication division off riel variables with infinite precision, then you could solve any complete problems in polynomial time. It doesn't actually proposals all where he just chose mathematically that this would be the case. Now, of course, in Real warned, you have also precision. So the next question is, how does that affect the competition about problems? This is what you're after. Lots of precision means information also, or entropy production. Eso what you're really looking at the relationship between hardness and cost of computing off a problem. Uh, and according to Sean Hagar, there's this left branch which in principle could be polynomial time. But the question whether or not this is achievable that is not achievable, but something more cheerful. That's on the right hand side. There's always going to be some information loss, so mental degeneration that could keep you away from possibly from point normal time. So this is what we like to understand, and this information laws the source off. This is not just always I will argue, uh, in any physical system, but it's also off algorithm nature, so that is a questionable area or approach. But China gets results. Security theoretical. No, actual solar is proposed. So we can ask, you know, just theoretically get out off. Curiosity would in principle be such soldiers because it is not proposing a soldier with such properties. In principle, if if you want to look mathematically precisely what the solar does would have the right properties on, I argue. Yes, I don't have a mathematical proof, but I have some arguments that that would be the case. And this is the case for actually our city there solver that if you could calculate its trajectory in a loss this way, then it would be, uh, would solve epic complete problems in polynomial continuous time. Now, as a matter of fact, this a bit more difficult question, because time in all these can be re scared however you want. So what? Burns says that you actually have to measure the length of the trajectory, which is a new variant off the dynamical system or property dynamical system, not off its parameters ization. And we did that. So Suba Corral, my student did that first, improving on the stiffness off the problem off the integrations, using implicit solvers and some smart tricks such that you actually are closer to the actual trajectory and using the same approach. You know what fraction off problems you can solve? We did not give the length of the trajectory. You find that it is putting on nearly scaling the problem sites we have putting on your skin complexity. That means that our solar is both Polly length and, as it is, defined it also poorly time analog solver. But if you look at as a discreet algorithm, if you measure the discrete steps on a digital machine, it is an exponential solver. And the reason is because off all these stiffness, every integrator has tow truck it digitizing truncate the equations, and what it has to do is to keep the integration between the so called stability region for for that scheme, and you have to keep this product within a grimace of Jacoby in and the step size read in this region. If you use explicit methods. You want to stay within this region? Uh, but what happens that some off the Eigen values grow fast for Steve problems, and then you're you're forced to reduce that t so the product stays in this bonded domain, which means that now you have to you're forced to take smaller and smaller times, So you're you're freezing out the integration and what I will show you. That's the case. Now you can move to increase its soldiers, which is which is a tree. In this case, you have to make domain is actually on the outside. But what happens in this case is some of the Eigen values of the Jacobean, also, for six systems, start to move to zero. As they're moving to zero, they're going to enter this instability region, so your soul is going to try to keep it out, so it's going to increase the data T. But if you increase that to increase the truncation hours, so you get randomized, uh, in the large search space, so it's it's really not, uh, not going to work out. Now, one can sort off introduce a theory or language to discuss computational and are computational complexity, using the language from dynamical systems theory. But basically I I don't have time to go into this, but you have for heart problems. Security object the chaotic satellite Ouch! In the middle of the search space somewhere, and that dictates how the dynamics happens and variant properties off the dynamics. Of course, off that saddle is what the targets performance and many things, so a new, important measure that we find that it's also helpful in describing thesis. Another complexity is the so called called Makarov, or metric entropy and basically what this does in an intuitive A eyes, uh, to describe the rate at which the uncertainty containing the insignificant digits off a trajectory in the back, the flow towards the significant ones as you lose information because off arrows being, uh grown or are developed in tow. Larger errors in an exponential at an exponential rate because you have positively up north spawning. But this is an in variant property. It's the property of the set of all. This is not how you compute them, and it's really the interesting create off accuracy philosopher dynamical system. A zay said that you have in such a high dimensional that I'm consistent were positive and negatively upon of exponents. Aziz Many The total is the dimension of space and user dimension, the number off unstable manifold dimensions and as Saddam was stable, manifold direction. And there's an interesting and I think, important passion, equality, equality called the passion, equality that connect the information theoretic aspect the rate off information loss with the geometric rate of which trajectory separate minus kappa, which is the escape rate that I already talked about. Now one can actually prove a simple theorems like back off the envelope calculation. The idea here is that you know the rate at which the largest rated, which closely started trajectory separate from one another. So now you can say that, uh, that is fine, as long as my trajectory finds the solution before the projective separate too quickly. In that case, I can have the hope that if I start from some region off the face base, several close early started trajectories, they kind of go into the same solution orphaned and and that's that's That's this upper bound of this limit, and it is really showing that it has to be. It's an exponentially small number. What? It depends on the end dependence off the exponents right here, which combines information loss rate and the social time performance. So these, if this exponents here or that has a large independence or river linear independence, then you then you really have to start, uh, trajectories exponentially closer to one another in orderto end up in the same order. So this is sort off like the direction that you're going in tow, and this formulation is applicable toe all dynamical systems, uh, deterministic dynamical systems. And I think we can We can expand this further because, uh, there is, ah, way off getting the expression for the escaped rate in terms off n the number of variables from cycle expansions that I don't have time to talk about. What? It's kind of like a program that you can try toe pursuit, and this is it. So the conclusions I think of self explanatory I think there is a lot of future in in, uh, in an allo. Continue start computing. Um, they can be efficient by orders of magnitude and digital ones in solving empty heart problems because, first of all, many of the systems you like the phone line and bottleneck. There's parallelism involved, and and you can also have a large spectrum or continues time, time dynamical algorithms than discrete ones. And you know. But we also have to be mindful off. What are the possibility of what are the limits? And 11 open question is very important. Open question is, you know, what are these limits? Is there some kind off no go theory? And that tells you that you can never perform better than this limit or that limit? And I think that's that's the exciting part toe to derive thes thes this levian 10.

>>from around the globe. It's the Cube with digital coverage of enterprise data automation an event Siri's brought to you by Iot. Tahoe. >>Okay, we're back. Welcome back to data Automated. A J ahora is CEO of I o Ta ho, JJ. Good to see you. How have things in London? >>Big thing. Well, thinking well, where we're making progress, I could see you hope you're doing well and pleasure being back here on the Cube. >>Yeah, it's always great to talk to. You were talking enterprise data automation. As you know, with within our community, we've been pounding the whole data ops conversation. Little different, though. We're gonna We're gonna dig into that a little bit. But let's start with a J how you've seen the response to Covert and I'm especially interested in the role that data has played in this pandemic. >>Yeah, absolutely. I think everyone's adapting both essentially, um, and and in business, the customers that I speak to on day in, day out that we partner with, um they're busy adapting their businesses to serve their customers. It's very much a game of and showing the week and serve our customers to help their customers um, you know, the adaptation that's happening here is, um, trying to be more agile, kind of the most flexible. Um, a lot of pressure on data. A lot of demand on data and to deliver more value to the business, too. Serve that customer. >>Yeah. I mean, data machine intelligence and cloud, or really three huge factors that have helped organizations in this pandemic. And, you know, the machine intelligence or AI piece? That's what automation is all about. How do you see automation helping organizations evolve maybe faster than they thought they might have to >>Sure. I think the necessity of these times, um, there's there's a says a lot of demand doing something with data data. Uh huh. A lot of a lot of businesses talk about being data driven. Um, so interesting. I sort of look behind that when we work with our customers, and it's all about the customer. You know, the mic is cios invested shareholders. The common theme here is the customer. That customer experience starts and ends with data being able to move from a point that is reacting. So what the customer is expecting and taking it to that step forward where you can be proactive to serve what that customer's expectation to and that's definitely come alive now with they, um, the current time. >>Yes. So, as I said, we've been talking about data ops a lot. The idea being Dev Ops applied to the data pipeline. But talk about enterprise data automation. What is it to you and how is it different from data off? >>Yeah, Great question. Thank you. I am. I think we're all familiar with felt more more awareness around. So as it's applied, Teoh, uh, processes methodologies that have become more mature of the past five years around devil that managing change, managing an application, life cycles, managing software development data about, you know, has been great. But breaking down those silos between different roles functions and bringing people together to collaborate. Andi, you know, we definitely see that those tools, those methodologies, those processes, that kind of thinking, um, landing itself to data with data is exciting. We're excited about that, Andi shifting the focus from being I t versus business users to you know who are the data producers. And here the data consumers in a lot of cases, it concert in many different lines of business. So in data role, those methods those tools and processes well we look to do is build on top of that with data automation. It's the is the nuts and bolts of the the algorithms, the models behind machine learning that the functions. That's where we investors our R and D and bringing that in to build on top of the the methods, the ways of thinking that break down those silos on injecting that automation into the business processes that are going to drive a business to serve its customers. It's, um, a layer beyond Dev ops data ops. They can get to that point where well, I think about it is, Is the automation behind the automation we can take? I'll give you an example. Okay, a bank where we did a lot of work to do make move them into accelerating that digital transformation. And what we're finding is that as we're able to automate the jobs related to data a managing that data and serving that data that's going into them as a business automating their processes for their customer. Um, so it's it's definitely having a compound effect. >>Yeah, I mean I think that you did. Data ops for a lot of people is somewhat new to the whole Dev Ops. The data ops thing is is good and it's a nice framework. Good methodology. There is obviously a level of automation in there and collaboration across different roles. But it sounds like you're talking about so supercharging it, if you will, the automation behind the automation. You know, I think organizations talk about being data driven. You hear that? They have thrown around a lot of times. People sit back and say, We don't make decisions without data. Okay? But really, being data driven is there's a lot of aspects there. There's cultural, but it's also putting data at the core of your organization, understanding how it effects monetization. And, as you know, well, silos have been built up, whether it's through M and a, you know, data sprawl outside data sources. So I'm interested in your thoughts on what data driven means and specifically Hi, how Iot Tahoe plays >>there. Yeah, I'm sure we'll be happy. That look that three David, we've We've come a long way in the last four years. We started out with automating some of those simple, um, to codify. Um, I have a high impact on organization across the data, a data warehouse. There's data related tasks that classify data on and a lot of our original pattern. Senai people value that were built up is is very much around. They're automating, classifying data across different sources and then going out to so that for some purpose originally, you know, some of those simpler I'm challenges that we have. Ah, custom itself, um, around data privacy. You know, I've got a huge data lake here. I'm a telecoms business. I've got millions of six subscribers. Um, quite often the chief data office challenges. How do I cover the operational risk? Where, um, I got so much data I need to simplify my approach to automating, classifying that data. Recent is you can't do that manually. We can for people at it. And the the scale of that is is prohibitive, right? Often, if you had to do it manually by the time you got a good picture of it, it's already out of date. Then, starting with those those simple challenges that we've been able to address, we're then going on and build on that to say, What else do we serve? What else do we serve? The chief data officer, Chief marketing officer on the CFO. Within these times, um, where those decision makers are looking for having a lot of choices in the platform options that they say that the tooling they're very much looking for We're that Swiss army. Not being able to do one thing really well is is great, but more more. Where that cost pressure challenge is coming in is about how do we, um, offer more across the organization, bring in those business lines of business activities that depend on data to not just with a T. Okay, >>so we like the cube. Sometimes we like to talk about Okay, what is it? And then how does it work? And what's the business impact? We kind of covered what it is but love to get into the tech a little bit in terms of how it works. And I think we have a graphic here that gets into that a little bit. So, guys, if you bring that up, I wonder if you could tell us and what is the secret sauce behind Iot Tahoe? And if you could take us through this slot. >>Sure. I mean, right there in the middle that the heart of what we do It is the intellectual property. Yeah, that was built up over time. That takes from Petra genius data sources Your Oracle relational database, your your mainframe. If they lay in increasingly AP eyes and devices that produce data and that creates the ability to automatically discover that data, classify that data after it's classified them have the ability to form relationships across those different, uh, source systems, silos, different lines of business. And once we've automated that that we can start to do some cool things that just puts a contact and meaning around that data. So it's moving it now from bringing data driven on increasingly well. We have really smile, right people in our customer organizations you want do some of those advanced knowledge tasks, data scientists and, uh, quants in some of the banks that we work with. The the onus is on, then, putting everything we've done there with automation, pacifying it, relationship, understanding that equality policies that you apply to that data. I'm putting it in context once you've got the ability to power. A a professional is using data, um, to be able to put that data and contacts and search across the entire enterprise estate. Then then they can start to do some exciting things and piece together the tapestry that fabric across that different systems could be crm air P system such as s AP on some of the newer cloud databases that we work with. Snowflake is a great Well, >>yes. So this is you're describing sort of one of the one of the reasons why there's so many stove pipes and organizations because data is gonna locked in the silos of applications. I also want to point out, you know, previously to do discovery to do that classification that you talked about form those relationship to glean context from data. A lot of that, if not most of that in some cases all that would have been manual. And of course, it's out of date so quickly. Nobody wants to do it because it's so hard. So this again is where automation comes into the the the to the idea of really becoming data driven. >>Sure. I mean the the efforts. If we if I look back, maybe five years ago, we had a prevalence of daily technologies at the cutting edge. Those have said converging me to some of these cloud platforms. So we work with Google and AWS, and I think very much is, as you said it, those manual attempts to try and grasp. But it is such a complex challenge at scale. I quickly runs out of steam because once, um, once you've got your hat, once you've got your fingers on the details Oh, um, what's what's in your data estate? It's changed, you know, you've onboard a new customer. You signed up a new partner, Um, customer has no adopted a new product that you just Lawrence and there that that slew of data it's keeps coming. So it's keeping pace with that. The only answer really is is some form of automation. And what we found is if we can tie automation with what I said before the expertise the, um, the subject matter expertise that sometimes goes back many years within an organization's people that augmentation between machine learning ai on and on that knowledge that sits within inside the organization really tends to involve a lot of value in data? >>Yes, So you know Well, a J you can't be is a smaller company, all things to all people. So your ecosystem is critical. You working with AWS? You're working with Google. You got red hat. IBM is as partners. What is attracting those folks to your ecosystem and give us your thoughts on the importance of ecosystem? >>Yeah, that's that's fundamental. So I mean, when I caimans, we tell her here is the CEO of one of the, um, trends that I wanted us to to be part of was being open, having an open architecture that allowed one thing that was nice to my heart, which is as a CEO, um, a C I O where you've got a budget vision and you've already made investments into your organization, and some of those are pretty long term bets. They should be going out 5 10 years, sometimes with CRM system training up your people, getting everybody working together around a common business platform. What I wanted to ensure is that we could openly like it using ap eyes that were available, the love that some investment on the cost that has already gone into managing in organizations I t. But business users to before So part of the reason why we've been able to be successful with, um, the partners like Google AWS and increasingly, a number of technology players. That red hat mongo DB is another one where we're doing a lot of good work with, um, and snowflake here is, um it's those investments have been made by the organizations that are our customers, and we want to make sure we're adding to that, and they're leveraging the value that they've already committed to. >>Okay, so we've talked about kind of what it is and how it works, and I want to get into the business impact. I would say what I would be looking for from from this would be Can you help me lower my operational risk? I've got I've got tasks that I do many year sequential, some who are in parallel. But can you reduce my time to task? And can you help me reduce the labor intensity and ultimately, my labor costs? And I put those resources elsewhere, and ultimately, I want to reduce the end and cycle time because that is going to drive Telephone number R. A. Y So, um, I missing anything? Can you do those things? And maybe you could give us some examples of the tiara y and the business impact. >>Yeah. I mean, the r a y David is is built upon on three things that I mentioned is a combination off leveraging the existing investment with the existing state, whether that's home, Microsoft, Azure or AWS or Google IBM. And I'm putting that to work because, yeah, the customers that we work with have had made those choices. On top of that, it's, um, is ensuring that we have you got the automation that is working right down to the level off data, a column level or the file level so we don't do with meta data. It is being very specific to be at the most granular level. So as we've grown our processes and on the automation, gasification tagging, applying policies from across different compliance and regulatory needs, that an organization has to the data, everything that then happens downstream from that is ready to serve a business outcome. It could be a customer who wants that experience on a mobile device. A tablet oh, face to face within, within the store. I mean game. Would you provision the right data and enable our customers do that? But their customers, with the right data that they can trust at the right time, just in that real time moment where decision or an action is being expected? That's, um, that's driving the r a y two b in some cases, 20 x but and that's that's really satisfying to see that that kind of impact it is taking years down to months and in many cases, months of work down to days. In some cases, our is the time to value. I'm I'm impressed with how quickly out of the box with very little training a customer and think about, too. And you speak just such a search. They discovery knowledge graph on DM. I don't find duplicates. Onda Redundant data right off the bat within hours. >>Well, it's why investors are interested in this space. I mean, they're looking for a big, total available market. They're looking for a significant return. 10 X is you gotta have 10 x 20 x is better. So so that's exciting and obviously strong management and a strong team. I want to ask you about people and culture. So you got people process technology we've seen with this pandemic that processes you know are really unpredictable. And the technology has to be able to adapt to any process, not the reverse. You can't force your process into some static software, so that's very, very important. But the end of the day you got to get people on board. So I wonder if you could talk about this notion of culture and a data driven culture. >>Yeah, that's that's so important. I mean, current times is forcing the necessity of the moment to adapt. But as we start to work their way through these changes on adapt ah, what with our customers, But that is changing economic times. What? What we're saying here is the ability >>to I >>have, um, the technology Cartman, in a really smart way, what those business uses an I T knowledge workers are looking to achieve together. So I'll give you an example. We have quite often with the data operations teams in the companies that we, um, partnering with, um, I have a lot of inbound enquiries on the day to day level. I really need this set of data they think it can help my data scientists run a particular model? Or that what would happen if we combine these two different silence of data and gets the Richmond going now, those requests you can, sometimes weeks to to realize what we've been able to do with the power is to get those answers being addressed by the business users themselves. And now, without without customers, they're coming to the data. And I t folks saying, Hey, I've now built something in the development environment. Why don't we see how that can scale up with these sets of data? I don't need terabytes of it. I know exactly the columns and the feet in the data that I'm going to use on that gets seller wasted in time, um, angle to innovate. >>Well, that's huge. I mean, the whole notion of self service and the lines of business actually feeling like they have ownership of the data as opposed to, you know, I t or some technology group owning the data because then you've got data quality issues or if it doesn't line up there their agenda, you're gonna get a lot of finger pointing. So so that is a really important. You know a piece of it. I'll give you last word A J. Your final thoughts, if you would. >>Yeah, we're excited to be the only path. And I think we've built great customer examples here where we're having a real impact in in a really fast pace, whether it helping them migrate to the cloud, helping the bean up their legacy, Data lake on and write off there. Now the conversation is around data quality as more of the applications that we enable to a more efficiently could be data are be a very robotic process automation along the AP, eyes that are now available in the cloud platforms. A lot of those they're dependent on data quality on and being able to automate. So business users, um, to take accountability off being able to so look at the trend of their data quality over time and get the signals is is really driving trust. And that trust in data is helping in time. Um, the I T teams, the data operations team, with do more and more quickly that comes back to culture being out, supply this technology in such a way that it's visual insensitive. Andi. How being? Just like Dev Ops tests with with a tty Dave drops putting intelligence in at the data level to drive that collaboration. We're excited, >>you know? You remind me of something. I lied. I don't want to go yet. It's OK, so I know we're tight on time, but you mentioned migration to the cloud. And I'm thinking about conversation with Paula from Webster Webster. Bank migrations. Migrations are, you know, they're they're a nasty word for for organizations. So our and we saw this with Webster. How are you able to help minimize the migration pain and and why is that something that you guys are good at? >>Yeah. I mean, there were many large, successful companies that we've worked with. What's There's a great example where, you know, I'd like to give you the analogy where, um, you've got a lot of people in your teams if you're running a business as a CEO on this bit like a living living grade. But imagine if those different parts of your brain we're not connected, that with, um, so diminish how you're able to perform. So what we're seeing, particularly with migration, is where banks retailers. Manufacturers have grown over the last 10 years through acquisition on through different initiatives, too. Um, drive customer value that sprawl in their data estate hasn't been fully dealt with. It sometimes been a good thing, too. Leave whatever you're fired off the agent incent you a side by side with that legacy mainframe on your oracle, happy and what we're able to do very quickly with that migration challenges shine a light on all the different parts. Oh, data application at the column level or higher level if it's a day late and show an enterprise architect a CDO how everything's connected, where they may not be any documentation. The bright people that created some of those systems long since moved on or retired or been promoted into so in the rose on within days, being out to automatically generate Anke refreshed the states of that data across that man's game on and put it into context, then allows you to look at a migration from a confidence that you did it with the back rather than what we've often seen in the past is teams of consultant and business analysts. Data around this spend months getting an approximation and and a good idea of what it could be in the current state and try their very best to map that to the future Target state. Now, without all hoping out, run those processes within hours of getting started on, um well, that picture visualize that picture and bring it to life. You know, the Yarra. Why, that's off the bat with finding data that should have been deleted data that was copies off on and being able to allow the architect whether it's we're working on gcb or migration to any other clouds such as AWS or a multi cloud landscape right now with yeah, >>that visibility is key. Teoh sort of reducing operational risks, giving people confidence that they can move forward and being able to do that and update that on an ongoing basis, that means you can scale a J. Thanks so much for coming on the Cube and sharing your insights and your experience is great to have >>you. Thank you, David. Look towards smoking in. >>Alright, keep it right there, everybody. We're here with data automated on the Cube. This is Dave Volante and we'll be right back. Short break. >>Yeah, yeah, yeah, yeah

>>from around the globe. It's the Cube with digital coverage of enterprise data automation, an event Siri's brought to you by Iot. Tahoe Welcome everybody to Enterprise Data Automation. Ah co created digital program on the Cube with support from my hotel. So my name is Dave Volante. And today we're using the hashtag data automated. You know, organizations. They really struggle to get more value out of their data, time to data driven insights that drive cost savings or new revenue opportunities. They simply take too long. So today we're gonna talk about how organizations can streamline their data operations through automation, machine intelligence and really simplifying data migrations to the cloud. We'll be talking to technologists, visionaries, hands on practitioners and experts that are not just talking about streamlining their data pipelines. They're actually doing it. So keep it right there. We'll be back shortly with a J ahora who's the CEO of Iot Tahoe to kick off the program. You're watching the Cube, the leader in digital global coverage. We're right back right after this short break. Innovation impact influence. Welcome to the Cube disruptors. Developers and practitioners learn from the voices of leaders who share their personal insights from the hottest digital events around the globe. Enjoy the best this community has to offer on the Cube, your global leader. High tech digital coverage from around the globe. It's the Cube with digital coverage of enterprise, data, automation and event. Siri's brought to you by Iot. Tahoe. Okay, we're back. Welcome back to Data Automated. A J ahora is CEO of I O ta ho, JJ. Good to see how things in London >>Thanks doing well. Things in, well, customers that I speak to on day in, day out that we partner with, um, they're busy adapting their businesses to serve their customers. It's very much a game of ensuring the week and serve our customers to help their customers. Um, you know, the adaptation that's happening here is, um, trying to be more agile. Got to be more flexible. Um, a lot of pressure on data, a lot of demand on data and to deliver more value to the business, too. So that customers, >>as I said, we've been talking about data ops a lot. The idea being Dev Ops applied to the data pipeline, But talk about enterprise data automation. What is it to you. And how is it different from data off >>Dev Ops, you know, has been great for breaking down those silos between different roles functions and bring people together to collaborate. Andi, you know, we definitely see that those tools, those methodologies, those processes, that kind of thinking, um, lending itself to data with data is exciting. We look to do is build on top of that when data automation, it's the it's the nuts and bolts of the the algorithms, the models behind machine learning that the functions. That's where we investors, our r and d on bringing that in to build on top of the the methods, the ways of thinking that break down those silos on injecting that automation into the business processes that are going to drive a business to serve its customers. It's, um, a layer beyond Dev ops data ops. They can get to that point where well, I think about it is is the automation behind new dimension. We've come a long way in the last few years. Boy is, we started out with automating some of those simple, um, to codify, um, I have a high impact on organization across the data a cost effective way house. There's data related tasks that classify data on and a lot of our original pattern certain people value that were built up is is very much around that >>love to get into the tech a little bit in terms of how it works. And I think we have a graphic here that gets into that a little bit. So, guys, if you bring that up, >>sure. I mean right there in the middle that the heart of what we do it is, you know, the intellectual property now that we've built up over time that takes from Hacha genius data sources. Your Oracle Relational database. Short your mainframe. It's a lay and increasingly AP eyes and devices that produce data and that creates the ability to automatically discover that data. Classify that data after it's classified. Them have the ability to form relationships across those different source systems, silos, different lines of business. And once we've automated that that we can start to do some cool things that just puts of contact and meaning around that data. So it's moving it now from bringing data driven on increasingly where we have really smile, right people in our customer organizations you want I do some of those advanced knowledge tasks data scientists and ah, yeah, quants in some of the banks that we work with, the the onus is on, then, putting everything we've done there with automation, pacifying it, relationship, understanding that equality, the policies that you can apply to that data. I'm putting it in context once you've got the ability to power. Okay, a professional is using data, um, to be able to put that data and contacts and search across the entire enterprise estate. Then then they can start to do some exciting things and piece together the the tapestry that fabric across that different system could be crm air P system such as s AP and some of the newer brown databases that we work with. Snowflake is a great well, if I look back maybe five years ago, we had prevalence of daily technologies at the cutting edge. Those are converging to some of the cloud platforms that we work with Google and AWS and I think very much is, as you said it, those manual attempts to try and grasp. But it is such a complex challenges scale quickly runs out of steam because once, once you've got your hat, once you've got your fingers on the details Oh, um, what's what's in your data state? It's changed, You know, you've onboard a new customer. You signed up a new partner. Um, customer has, you know, adopted a new product that you just Lawrence and there that that slew of data keeps coming. So it's keeping pace with that. The only answer really is is some form of automation >>you're working with AWS. You're working with Google, You got red hat. IBM is as partners. What is attracting those folks to your ecosystem and give us your thoughts on the importance of ecosystem? >>That's fundamental. So, I mean, when I caimans where you tell here is the CEO of one of the, um, trends that I wanted us CIO to be part of was being open, having an open architecture allowed one thing that was close to my heart, which is as a CEO, um, a c i o where you go, a budget vision on and you've already made investments into your organization, and some of those are pretty long term bets. They should be going out 5 10 years, sometimes with the CRM system training up your people, getting everybody working together around a common business platform. What I wanted to ensure is that we could openly like it using AP eyes that were available, the love that some investment on the cost that has already gone into managing in organizations I t. But business users to before. So part of the reason why we've been able to be successful with, um, the partners like Google AWS and increasingly, a number of technology players. That red hat mongo DB is another one where we're doing a lot of good work with, um and snowflake here is, um Is those investments have been made by the organizations that are our customers, and we want to make sure we're adding to that. And they're leveraging the value that they've already committed to. >>Yeah, and maybe you could give us some examples of the r A y and the business impact. >>Yeah, I mean, the r a y David is is built upon on three things that I mentioned is a combination off. You're leveraging the existing investment with the existing estate, whether that's on Microsoft Azure or AWS or Google, IBM, and I'm putting that to work because, yeah, the customers that we work with have had made those choices. On top of that, it's, um, is ensuring that we have got the automation that is working right down to the level off data, a column level or the file level we don't do with meta data. It is being very specific to be at the most granular level. So as we've grown our processes and on the automation, gasification tagging, applying policies from across different compliance and regulatory needs that an organization has to the data, everything that then happens downstream from that is ready to serve a business outcome now without hoping out which run those processes within hours of getting started And, um, Bill that picture, visualize that picture and bring it to life. You know, the PR Oh, I that's off the bat with finding data that should have been deleted data that was copies off on and being able to allow the architect whether it's we're working on GCB or a migration to any other clouds such as AWS or a multi cloud landscape right off the map. >>A. J. Thanks so much for coming on the Cube and sharing your insights and your experience is great to have you. >>Thank you, David. Look who is smoking in >>now. We want to bring in the customer perspective. We have a great conversation with Paul Damico, senior vice president data architecture, Webster Bank. So keep it right there. >>Utah Data automated Improve efficiency, Drive down costs and make your enterprise data work for you. Yeah, we're on a mission to enable our customers to automate the management of data to realise maximum strategic and operational benefits. We envisage a world where data users consume accurate, up to date unified data distilled from many silos to deliver transformational outcomes, activate your data and avoid manual processing. Accelerate data projects by enabling non I t resources and data experts to consolidate categorize and master data. Automate your data operations Power digital transformations by automating a significant portion of data management through human guided machine learning. Yeah, get value from the start. Increase the velocity of business outcomes with complete accurate data curated automatically for data, visualization tours and analytic insights. Improve the security and quality of your data. Data automation improves security by reducing the number of individuals who have access to sensitive data, and it can improve quality. Many companies report double digit era reduction in data entry and other repetitive tasks. Trust the way data works for you. Data automation by our Tahoe learns as it works and can ornament business user behavior. It learns from exception handling and scales up or down is needed to prevent system or application overloads or crashes. It also allows for innate knowledge to be socialized rather than individualized. No longer will your companies struggle when the employee who knows how this report is done, retires or takes another job, the work continues on without the need for detailed information transfer. Continue supporting the digital shift. Perhaps most importantly, data automation allows companies to begin making moves towards a broader, more aspirational transformation, but on a small scale but is easy to implement and manage and delivers quick wins. Digital is the buzzword of the day, but many companies recognized that it is a complex strategy requires time and investment. Once you get started with data automation, the digital transformation initiated and leaders and employees alike become more eager to invest time and effort in a broader digital transformational agenda. Yeah, >>everybody, we're back. And this is Dave Volante, and we're covering the whole notion of automating data in the Enterprise. And I'm really excited to have Paul Damico here. She's a senior vice president of enterprise Data Architecture at Webster Bank. Good to see you. Thanks for coming on. >>Nice to see you too. Yes. >>So let's let's start with Let's start with Webster Bank. You guys are kind of a regional. I think New York, New England, uh, leave headquartered out of Connecticut, but tell us a little bit about the >>bank. Yeah, Webster Bank is regional, Boston. And that again in New York, Um, very focused on in Westchester and Fairfield County. Um, they're a really highly rated bank regional bank for this area. They, um, hold, um, quite a few awards for the area for being supportive for the community. And, um, are really moving forward. Technology lives. Currently, today we have, ah, a small group that is just working toward moving into a more futuristic, more data driven data warehouse. That's our first item. And then the other item is to drive new revenue by anticipating what customers do when they go to the bank or when they log into there to be able to give them the best offer. The only way to do that is you have timely, accurate, complete data on the customer and what's really a great value on off something to offer that >>at the top level, what were some of what are some of the key business drivers there catalyzing your desire for change >>the ability to give the customer what they need at the time when they need it? And what I mean by that is that we have, um, customer interactions and multiple weights, right? And I want to be able for the customer, too. Walk into a bank, um, or online and see the same the same format and being able to have the same feel, the same look and also to be able to offer them the next best offer for them. >>Part of it is really the cycle time, the end end cycle, time that you're pressing. And then there's if I understand it, residual benefits that are pretty substantial from a revenue opportunity >>exactly. It's drive new customers, Teoh new opportunities. It's enhanced the risk, and it's to optimize the banking process and then obviously, to create new business. Um, and the only way we're going to be able to do that is that we have the ability to look at the data right when the customer walks in the door or right when they open up their app. >>Do you see the potential to increase the data sources and hence the quality of the data? Or is that sort of premature? >>Oh, no. Um, exactly. Right. So right now we ingest a lot of flat files and from our mainframe type of runnin system that we've had for quite a few years. But now that we're moving to the cloud and off Prem and on France, you know, moving off Prem into, like, an s three bucket Where that data king, we can process that data and get that data faster by using real time tools to move that data into a place where, like, snowflake Good, um, utilize that data or we can give it out to our market. The data scientists are out in the lines of business right now, which is great, cause I think that's where data science belongs. We should give them on, and that's what we're working towards now is giving them more self service, giving them the ability to access the data in a more robust way. And it's a single source of truth. So they're not pulling the data down into their own like tableau dashboards and then pushing the data back out. I have eight engineers, data architects, they database administrators, right, um, and then data traditional data forwarding people, Um, and because some customers that I have that our business customers lines of business, they want to just subscribe to a report. They don't want to go out and do any data science work. Um, and we still have to provide that. So we still want to provide them some kind of read regiment that they wake up in the morning and they open up their email. And there's the report that they just drive, um, which is great. And it works out really well. And one of the things. This is why we purchase I o waas. I would have the ability to give the lines of business the ability to do search within the data, and we read the data flows and data redundancy and things like that and help me cleanup the data and also, um, to give it to the data. Analysts who say All right, they just asked me. They want this certain report and it used to take Okay, well, we're gonna four weeks, we're going to go. We're gonna look at the data, and then we'll come back and tell you what we dio. But now with Iot Tahoe, they're able to look at the data and then, in one or two days of being able to go back and say, Yes, we have data. This is where it is. This is where we found that this is the data flows that we've found also, which is what I call it is the birth of a column. It's where the calm was created and where it went live as a teenager. And then it went to, you know, die very archive. >>In researching Iot Tahoe, it seems like one of the strengths of their platform is the ability to visualize data the data structure, and actually dig into it. But also see it, um, and that speeds things up and gives everybody additional confidence. And then the other pieces essentially infusing ai or machine intelligence into the data pipeline is really how you're attacking automation, right? >>Exactly. So you're able to let's say that I have I have seven cause lines of business that are asking me questions. And one of the questions I'll ask me is, um, we want to know if this customer is okay to contact, right? And you know, there's different avenues so you can go online to go. Do not contact me. You can go to the bank And you could say, I don't want, um, email, but I'll take tests and I want, you know, phone calls. Um, all that information. So seven different lines of business asked me that question in different ways once said Okay to contact the other one says, You know, just for one to pray all these, you know, um, and each project before I got there used to be siloed. So one customer would be 100 hours for them to do that and analytical work, and then another cut. Another of analysts would do another 100 hours on the other project. Well, now I can do that all at once, and I can do those type of searches and say yes we already have that documentation. Here it is. And this is where you can find where the customer has said, You know, you don't want I don't want to get access from you by email, or I've subscribed to get emails from you. I'm using Iot typos eight automation right now to bring in the data and to start analyzing the data close to make sure that I'm not missing anything and that I'm not bringing over redundant data. Um, the data warehouse that I'm working off is not, um a It's an on prem. It's an oracle database. Um, and it's 15 years old, so it has extra data in it. It has, um, things that we don't need anymore. And Iot. Tahoe's helping me shake out that, um, extra data that does not need to be moved into my S three. So it's saving me money when I'm moving from offering on Prem. >>What's your vision or your your data driven organization? >>Um, I want for the bankers to be able to walk around with on iPad in their hands and be able to access data for that customer really fast and be able to give them the best deal that they can get. I want Webster to be right there on top, with being able to add new customers and to be able to serve our existing customers who had bank accounts. Since you were 12 years old there and now our, you know, multi. Whatever. Um, I want them to be able to have the best experience with our our bankers. >>That's really what I want is a banking customer. I want my bank to know who I am, anticipate my needs and create a great experience for me. And then let me go on with my life. And so that's a great story. Love your experience, your background and your knowledge. Can't thank you enough for coming on the Cube. >>No, thank you very much. And you guys have a great day. >>Next, we'll talk with Lester Waters, who's the CTO of Iot Toe cluster takes us through the key considerations of moving to the cloud. >>Yeah, right. The entire platform Automated data Discovery data Discovery is the first step to knowing your data auto discover data across any application on any infrastructure and identify all unknown data relationships across the entire siloed data landscape. smart data catalog. Know how everything is connected? Understand everything in context, regained ownership and trust in your data and maintain a single source of truth across cloud platforms, SAS applications, reference data and legacy systems and power business users to quickly discover and understand the data that matters to them with a smart data catalog continuously updated ensuring business teams always have access to the most trusted data available. Automated data mapping and linking automate the identification of unknown relationships within and across data silos throughout the organization. Build your business glossary automatically using in house common business terms, vocabulary and definitions. Discovered relationships appears connections or dependencies between data entities such as customer account, address invoice and these data entities have many discovery properties. At a granular level, data signals dashboards. Get up to date feeds on the health of your data for faster improved data management. See trends, view for history. Compare versions and get accurate and timely visual insights from across the organization. Automated data flows automatically captured every data flow to locate all the dependencies across systems. Visualize how they work together collectively and know who within your organization has access to data. Understand the source and destination for all your business data with comprehensive data lineage constructed automatically during with data discovery phase and continuously load results into the smart Data catalog. Active, geeky automated data quality assessments Powered by active geek You ensure data is fit for consumption that meets the needs of enterprise data users. Keep information about the current data quality state readily available faster Improved decision making Data policy. Governor Automate data governance End to end over the entire data lifecycle with automation, instant transparency and control Automate data policy assessments with glossaries, metadata and policies for sensitive data discovery that automatically tag link and annotate with metadata to provide enterprise wide search for all lines of business self service knowledge graph Digitize and search your enterprise knowledge. Turn multiple siloed data sources into machine Understandable knowledge from a single data canvas searching Explore data content across systems including GRP CRM billing systems, social media to fuel data pipelines >>Yeah, yeah, focusing on enterprise data automation. We're gonna talk about the journey to the cloud Remember, the hashtag is data automate and we're here with Leicester Waters. Who's the CTO of Iot Tahoe? Give us a little background CTO, You've got a deep, deep expertise in a lot of different areas. But what do we need to know? >>Well, David, I started my career basically at Microsoft, uh, where I started the information Security Cryptography group. They're the very 1st 1 that the company had, and that led to a career in information, security. And and, of course, as easy as you go along with information security data is the key element to be protected. Eso I always had my hands and data not naturally progressed into a roll out Iot talk was their CTO. >>What's the prescription for that automation journey and simplifying that migration to the cloud? >>Well, I think the first thing is understanding what you've got. So discover and cataloging your data and your applications. You know, I don't know what I have. I can't move it. I can't. I can't improve it. I can't build upon it. And I have to understand there's dependence. And so building that data catalog is the very first step What I got. Okay, >>so So we've done the audit. We know we've got what's what's next? Where do we go >>next? So the next thing is remediating that data you know, where do I have duplicate data? I may have often times in an organization. Uh, data will get duplicated. So somebody will take a snapshot of the data, you know, and then end up building a new application, which suddenly becomes dependent on that data. So it's not uncommon for an organization of 20 master instances of a customer, and you can see where that will go. And trying to keep all that stuff in sync becomes a nightmare all by itself. So you want to sort of understand where all your redundant data is? So when you go to the cloud, maybe you have an opportunity here to do you consolidate that that data, >>then what? You figure out what to get rid of our actually get rid of it. What's what's next? >>Yes, yes, that would be the next step. So figure out what you need. What, you don't need you Often times I've found that there's obsolete columns of data in your databases that you just don't need. Or maybe it's been superseded by another. You've got tables have been superseded by other tables in your database, so you got to kind of understand what's being used and what's not. And then from that, you can decide. I'm gonna leave this stuff behind or I'm gonna I'm gonna archive this stuff because I might need it for data retention where I'm just gonna delete it. You don't need it. All were >>plowing through your steps here. What's next on the >>journey? The next one is is in a nutshell. Preserve your data format. Don't. Don't, Don't. Don't boil the ocean here at music Cliche. You know, you you want to do a certain degree of lift and shift because you've got application dependencies on that data and the data format, the tables in which they sent the columns and the way they're named. So some degree, you are gonna be doing a lift and ship, but it's an intelligent lift and ship. The >>data lives in silos. So how do you kind of deal with that? Problem? Is that is that part of the journey? >>That's that's great pointed because you're right that the data silos happen because, you know, this business unit is start chartered with this task. Another business unit has this task and that's how you get those in stance creations of the same data occurring in multiple places. So you really want to is part of your cloud migration. You really want a plan where there's an opportunity to consolidate your data because that means it will be less to manage. Would be less data to secure, and it will be. It will have a smaller footprint, which means reduce costs. >>But maybe you could address data quality. Where does that fit in on the >>journey? That's that's a very important point, you know. First of all, you don't want to bring your legacy issues with U. S. As the point I made earlier. If you've got data quality issues, this is a good time to find those and and identify and remediate them. But that could be a laborious task, and you could probably accomplish. It will take a lot of work. So the opportunity used tools you and automate that process is really will help you find those outliers that >>what's next? I think we're through. I think I've counted six. What's the What's the lucky seven >>Lucky seven involved your business users. Really, When you think about it, you're your data is in silos, part of part of this migration to cloud as an opportunity to break down the silos. These silence that naturally occurs are the business. You, uh, you've got to break these cultural barriers that sometimes exists between business and say so. For example, I always advise there's an opportunity year to consolidate your sensitive data. Your P I. I personally identifiable information and and three different business units have the same source of truth From that, there's an opportunity to consolidate that into one. >>Well, great advice, Lester. Thanks so much. I mean, it's clear that the Cap Ex investments on data centers they're generally not a good investment for most companies. Lester really appreciate Lester Water CTO of Iot Tahoe. Let's watch this short video and we'll come right back. >>Use cases. Data migration. Accelerate digitization of business by providing automated data migration work flows that save time in achieving project milestones. Eradicate operational risk and minimize labor intensive manual processes that demand costly overhead data quality. You know the data swamp and re establish trust in the data to enable data signs and Data analytics data governance. Ensure that business and technology understand critical data elements and have control over the enterprise data landscape Data Analytics ENABLEMENT Data Discovery to enable data scientists and Data Analytics teams to identify the right data set through self service for business demands or analytical reporting that advanced too complex regulatory compliance. Government mandated data privacy requirements. GDP Our CCP, A, e, p, R HIPPA and Data Lake Management. Identify late contents cleanup manage ongoing activity. Data mapping and knowledge graph Creates BKG models on business enterprise data with automated mapping to a specific ontology enabling semantic search across all sources in the data estate data ops scale as a foundation to automate data management presences. >>Are you interested in test driving the i o ta ho platform Kickstart the benefits of data automation for your business through the Iot Labs program? Ah, flexible, scalable sandbox environment on the cloud of your choice with set up service and support provided by Iot. Top Click on the link and connect with the data engineer to learn more and see Iot Tahoe in action. Everybody, we're back. We're talking about enterprise data automation. The hashtag is data automated and we're going to really dig into data migrations, data migrations. They're risky, they're time consuming and they're expensive. Yousef con is here. He's the head of partnerships and alliances at I o ta ho coming again from London. Hey, good to see you, Seth. Thanks very much. >>Thank you. >>So let's set up the problem a little bit. And then I want to get into some of the data said that migration is a risky, time consuming, expensive. They're they're often times a blocker for organizations to really get value out of data. Why is that? >>I think I mean, all migrations have to start with knowing the facts about your data. Uh, and you can try and do this manually. But when you have an organization that may have been going for decades or longer, they will probably have a pretty large legacy data estate so that I have everything from on premise mainframes. They may have stuff which is probably in the cloud, but they probably have hundreds, if not thousands of applications and potentially hundreds of different data stores. >>So I want to dig into this migration and let's let's pull up graphic. It will talk about We'll talk about what a typical migration project looks like. So what you see, here it is. It's very detailed. I know it's a bit of an eye test, but let me call your attention to some of the key aspects of this, uh and then use if I want you to chime in. So at the top here, you see that area graph that's operational risk for a typical migration project, and you can see the timeline and the the milestones That Blue Bar is the time to test so you can see the second step. Data analysis. It's 24 weeks so very time consuming, and then let's not get dig into the stuff in the middle of the fine print. But there's some real good detail there, but go down the bottom. That's labor intensity in the in the bottom, and you can see hi is that sort of brown and and you could see a number of data analysis data staging data prep, the trial, the implementation post implementation fixtures, the transition to be a Blu, which I think is business as usual. >>The key thing is, when you don't understand your data upfront, it's very difficult to scope to set up a project because you go to business stakeholders and decision makers, and you say Okay, we want to migrate these data stores. We want to put them in the cloud most often, but actually, you probably don't know how much data is there. You don't necessarily know how many applications that relates to, you know, the relationships between the data. You don't know the flow of the basis of the direction in which the data is going between different data stores and tables. So you start from a position where you have pretty high risk and probably the area that risk you could be. Stack your project team of lots and lots of people to do the next phase, which is analysis. And so you set up a project which has got a pretty high cost. The big projects, more people, the heavy of governance, obviously on then there, then in the phase where they're trying to do lots and lots of manual analysis, um, manual processes, as we all know, on the layer of trying to relate data that's in different grocery stores relating individual tables and columns, very time consuming, expensive. If you're hiring in resource from consultants or systems integrators externally, you might need to buy or to use party tools. Aziz said earlier the people who understand some of those systems may have left a while ago. CEO even higher risks quite cost situation from the off on the same things that have developed through the project. Um, what are you doing with Ayatollah? Who is that? We're able to automate a lot of this process from the very beginning because we can do the initial data. Discovery run, for example, automatically you very quickly have an automated validator. A data met on the data flow has been generated automatically, much less time and effort and much less cars stopped. >>Yeah. And now let's bring up the the the same chart. But with a set of an automation injection in here and now. So you now see the sort of Cisco said accelerated by Iot, Tom. Okay, great. And we're gonna talk about this, but look, what happens to the operational risk. A dramatic reduction in that, That that graph and then look at the bars, the bars, those blue bars. You know, data analysis went from 24 weeks down to four weeks and then look at the labor intensity. The it was all these were high data analysis, data staging data prep trialling post implementation fixtures in transition to be a you all those went from high labor intensity. So we've now attacked that and gone to low labor intensity. Explain how that magic happened. >>I think that the example off a data catalog. So every large enterprise wants to have some kind of repository where they put all their understanding about their data in its price States catalog. If you like, imagine trying to do that manually, you need to go into every individual data store. You need a DB, a business analyst, reach data store. They need to do an extract of the data. But it on the table was individually they need to cross reference that with other data school, it stores and schemers and tables you probably with the mother of all Lock Excel spreadsheets. It would be a very, very difficult exercise to do. I mean, in fact, one of our reflections as we automate lots of data lots of these things is, um it accelerates the ability to water may, But in some cases, it also makes it possible for enterprise customers with legacy systems take banks, for example. There quite often end up staying on mainframe systems that they've had in place for decades. I'm not migrating away from them because they're not able to actually do the work of understanding the data, duplicating the data, deleting data isn't relevant and then confidently going forward to migrate. So they stay where they are with all the attendant problems assistance systems that are out of support. You know, you know, the biggest frustration for lots of them and the thing that they spend far too much time doing is trying to work out what the right data is on cleaning data, which really you don't want a highly paid thanks to scientists doing with their time. But if you sort out your data in the first place, get rid of duplication that sounds migrate to cloud store where things are really accessible. It's easy to build connections and to use native machine learning tools. You well, on the way up to the maturity card, you can start to use some of the more advanced applications >>massive opportunities not only for technology companies, but for those organizations that can apply technology for business. Advantage yourself, count. Thanks so much for coming on the Cube. Much appreciated. Yeah, yeah, yeah, yeah

>> Announcer: Live from San Diego, California, it's theCUBE! Covering KubeCon and CloudNativeCon. Brought to you by Red Hat, the Cloud Native Computing Foundation, and its ecosystem partners. >> Welcome back, this is theCUBE's fourth year of coverage at KubeCon CloudNativeCon, we're here in San Diego, it's 2019, I'm Stu Miniman, my host for this afternoon is Justin Warren, and happy to welcome two guests from the newly minted platinum member of the CNCF, NetApp, sitting to my right is Matt Baldwin, who is the director of cloud native and Kubernetes engineering, and sitting to his right is Rob Esker, who does product and strategy for Kubernetes, and is also a forward member on the CNCF, thank you both for joining us. >> Thank you. >> Thanks for having us. >> All right, so Matt, maybe start with you, NetApp, companies that know, I've got plenty of history with NetApp there, what I've been hearing from NetApp for the last few years is, the core of NetApp has always been software, and it is a multicloud world. I've been hearing this message since before the cloud native and Kubernetes piece was going. Of course there's been some acquisitions, and NetApp continuing to go through its transformations, if you will. So help us understand NetApp's positioning in this ecosystem. >> In Kubernetes? >> Yes. >> Okay, so, what we're doing is, we're building a product that allows you to manage cloud-native workloads on top of Kubernetes, so we've solved the infrastructure problem, and that's kind of the old problem we're bored to death talking about that problem, but what we try to do is try to provide a single pane of glass to manage on-premise workloads and off-premise workloads, and so that's what we're trying to do, we're trying to say, it's now more about the app taxonomy in Kubernetes, and then what type of tooling do you build to manage that application in Kubernetes, and so that's what we're building right now, that's where we're headed with the hybrid multicloud. >> There's a piece of it, though, that does draw from the historical strengths of NetApp, of course. So we're building, we are essentially already in market a capability that allows you to deploy Kubernetes, in an agnostic way, using pure open unmodified Kubernetes, on all of the major public clouds, but also on-prem. But over time, and some of this is already evident, you'll see it married to the storage and data management capabilities that we draw from the historical NetApp, and that we're starting to deploy into those public clouds. >> With the idea that you should be able to take a project, so a project being in a namespace, namespace having an application in it, so you have multiple deployments, I should be able to protect that namespace, or that project, I should be able to move that, and that data goes with it, so that we're very data-aware, that's what we're trying to do with our software is, make it very data-aware and have that align with apps inside of Kubernetes. >> Yeah, so Rob, maybe step back for a second, one of the things we've heard a few times at this show before, and it was talked about in the keynote this morning, is that it is project over company when it comes to the CNCF. Project over company, so it's about the ecosystem, the CNCF tries not to be opinionated, so it's okay for multiple projects to fit in a space. NetApp moving up to a platinum sponsor level, participated here, NetApp's got lots of histories in participating and driving standards, helping move where the industry's going, where does NetApp see its position in participating in the foundation and participating in this ecosystem? >> Yeah, so great question, and actually, I love it, it's one of my favorite topics, so, I think the way we look at it is, oftentimes projects, to the extent they become ubiquitous, define a standard, a defacto standard, so not necessarily ratified by some standards body, and so we're very interested in making sure that in the scenario where you want to employ this standard, from a technology integration perspective, our capabilities can operate as an implementation behind the standard. So you get the distinguishing qualities of our capabilities, our products and our services, vis-a-vis, or in the context of the standard, but we're not trying to take you down a walled garden path in a proprietary journey, if you will. We would rather compel you to work with us on the basis of the value, not necessarily operating off a proprietary set of interfaces. So Kubernetes, broadly perceive it as a defacto standard at this point, there's still some work to be done on rounding out the edges, a lot of it underway this week, it's definitely the case that there's an appeal to making this more offerable by, pardon the expression, mere mortals, and we think we can offer some help in that respect as well. >> Yeah, where is its usability? I mean, that's the reason I started stacked on cloud, was that there was a usability problem with Kubernetes. I had a usability problem with Kubernetes. That's what we're trying, that's how I'm looking at the landscape, and I look at all the projects inside of the CNCF, and I look at my role is, our role is to, how do we tie these together, how do we make these so they're very very usable to the users, and how we're engaging with the community is to try to align this, basically pure upstream projects, and create a usability layer on top of that. But we're not going to, we don't want to ever say we're going to fork any of these projects, but we're going to contribute back into these projects. >> So that's one concern that I have heard from some customers, which speaking of which, some of them yesterday, one of the concerns they had was that, when you add that manageability onto the base Kubernetes layer, that often, various vendors become rather opinionated about which way we think this is a good way to do that, and when you're trying to maintain that compatibility across the ecosystem, so some customers say, "Well I actually don't want to have to be too closely welded "to any one vendor, 'cause part of the benefit "of Kubernetes is I can move my workloads around." So how do you navigate what is the right level of opinion to have, and which part should actually just be part of a common standard? >> Think it needs to be along the lines of best practices, is how we do it. So, let's take network policy, for example, applying a sane, default network policy to every namespace. Defining a sane, default pod security policy, building a cluster in a best practices fashion, with security turned on, hardening done, where you would've done this already as a user, so we're not locking you in in any way there. So that's, we're not trying, I'm not trying to curate any type of opinion of the product, what we're trying to do is harmonize your experience across all this ecosystem, so that you don't ever have to think about, "I'm building a cluster on top of Amazon, "so I got to worry about how do I manage this on Amazon." I don't want you to have to think about those providers anymore. And then on top of those, on top of that infrastructure, I want to have a way that you're thinking about managing the applications on those environments in the exact same way, so I'm scaling, or I'm protecting an application on-premise, in the identical way I'm doing it in the cloud. >> So if it's the same everywhere, what's the value that you're providing that means that I should choose your option than something else? >> So, we do have, this is where we have controllers that live inside of the clusters, that manage this stuff for the users. So, you could rebuild what we're doing, but you would have to roll it all by hand. But you could, we don't stand in the way of your operations either, so if we go down, you don't go down, type of idea. But we do have controllers, we're using CRDs, and so our app management technology, our controllers are just watching for a workload to come into the environment, and then we show that in the interface, but you can just walk away as well, if you wanted to. >> There's also a constellation of other services that we're building around, this experience, that do draw, again, from some of the storage and data management capabilities, so staple sets, your traditional workloads that want to interact with or transact data against a block or a shared file system. We're providing capabilities for sophisticated qualities of persistence that can exist in all of those same public clouds, but moreover, over time, we're going to be, and on-premise as well, we're going to be able to actually move, migrate, place, cache, per policy, your persistent data, with your workloads, as you move, migrate, scale, burst, whatever the model is, as you move across and between clouds. >> How far down that pathway do you think we are, 'cause one criticism of Kubernetes is that a lot of the tooling that we're used to from more traditional ways of operating this kind of infrastructure, isn't really there yet, hence the question about, we actually need to make this easier to use. How far down that pathway are we? >> I'd argue that the tooling that I've built has already solved some of those problems. So I think we're pretty far down the path. Now, what we haven't done is open sourced all of my tooling, right, to make it easier on everybody else. >> Rob, NetApp's got strong partnerships across the cloud platforms, I had a chance to interview George at the Google Cloud event, I know you partner of the year, I believe, on some of these stuff, help us understand how some of the things Matt and the team are building interact with the public clouds, you look at Anthos, and Azure Arc, and of course Amazon has many different ways you can do your container and management piece there. Talk a little bit about that relationship and how, both with those partners and then across those partners, work. >> Yeah, it's, how much time do we have, so there's certainly a lot of facets to that, but drawing from the Google experience, we just announced the general availability of Cloud Volumes ONTAP, so the ability to stand up and manage your own ONTAP instance in Google's cloud. Likewise, we announced the general availability of the Cloud Volume service, which gives you the managed push button as a service experience of shared file system on demand, at Google, I believe it was either today or yesterday, in London, I guess maybe I'll blame that on the time zone conversion, not knowing what day it was, but the point is, that's now generally available. Some of those capabilities are going to be able to be connected to our ability from MKS, to deploy a on-demand Kubernetes cluster, and deploy applications from a marketplace experience, in a common way, not just with Google but Azure, with Amazon, and so frankly the story does differ a little bit from one cloud to the next, but the endeavor is to provide common capabilities across all of them. It's also the case that we do have people that are very opinionated about, I want to live only in the Google or the Microsoft or the Amazon ecosystem, we're trying to deliver a rich experience for those folks as well, even if you don't value the agnostic multicloud experience. >> Yeah, and Matt, I'm sure you have a viewpoint on this, but it's that skillset that's really challenging. I was at the Microsoft show, and you've got people, it's not just about .NET, they're embracing and open to all of these environments, but people tend to have the environments that they're used to, and for multicloud to be a reality, it needs to be a little bit easier for me to go between them, but it's still, we're making progress but there's work to do. >> Matt: Yeah, what's the question? >> Yeah, so, I know you're building tools and everything, but what more do we need to do, where are some of the areas that you're hopeful for, but where are the areas that we need to go further? >> So for me it's coming down to the data side. I need to be able to say that, when I turn on data services, inside of Kubernetes, I need to be able to have that workload go anywhere, because as a developer, I'm running a production, I'm running an Amazon, but maybe I'm doing tests locally on my bare metal environments, right, I want to be able to maybe sink down some of my data that I'm working with in production down to my test environment. That stuff's missing, there's no one doing that right now, and that's where we're headed, that's the path, that's where we're headed. >> Yeah, I'm glad you brought that up, actually, 'cause one of the things that I feel like I heard a little bit last year but it is highlighted more this year, is we're talking a little bit more to the application developers because, Kubernetes is a piece of the infrastructure, but it's about-- >> It's the kernel. >> Yeah, it's the kernel there, so, how do we make sure we're spanning between what the app developer needs and still making sure that infrastructure is taken care of, because storage and networking are still hard. >> It is, yeah, I mean I'm approaching, I'm thinking more along the lines of, I'm trying to think more about app developers, personally, than infrastructure at this point. For me, so I can give you a cluster in three minutes, right, so I don't really have to worry about that problem. We also put Istio on top of the clusters, so it's like we're trying to create this whole narrative that you can manage that environment on day one, day two type operations. But, and that's for an IT manager, right, so inside of our product, how I'm addressing this is you have personas, and so you have this concept, you have an IT manager, they can do these things, they can set limits, but for the developer, who's building the applications or the services and pushing those up into the environment, they need to have a sense of freedom, and so on that side of the house, I'm trying not to break them out of their tooling, so part of our product ties into Git, so we have cd, so you just do a git push, git commit to a branch, and we can target multiple clusters. But at no point did the developer actually draft DAML, or anything, we basically create the container for you, create the deployment, bring it online, and I feel like there's these lines, and the IT guys need to be able to say, "I need to create the guardrails for the devs, "but I don't want to make it seem like "I'm creating guardrails for the devs, "'cause the devs don't like that." So that's how I'm balancing it. >> Okay, 'cause that has always been the tension, in that there's a lot of talk about DevOps, but you go and talk to application developers, and they don't want to have anything to do with infrastructure, they just want to program to an API and get things done, they would like this infrastructure to be seamless. >> Yeah, and what we do, also what I'm giving them is service dashboards, because as a developer, you know, because now you're in charge of your QA, you're writing your tests, you're pushing it through CI, it's going to CD. You own your service and production, right? And so we're delivering dashboards as well for services that the developers are running, so they can dig in and say, "Oh, here's an issue," or "Here's where the issue's probably going to be at, "I'm going to go fix this." And we're trying to create that type of scenario for a developer, and for an IT manager. >> Slightly different angle on it, if I'm understanding the question correctly, part of the complexity of infrastructure is something we're also trying to provide a deterministic sort of easy button capability for, perhaps you're familiar with NetApp's Nason ATI product, which we kind of expand that as hybrid cloud infrastructure. If the intention is to make it a simple, private cloud capability, and indeed, our NetApp Kubernetes service operates directly off of it, it's a big part of actually how we deliver cloud services from it. So the point is that, if you're that application developer, if you want the effective NKS on-prem, the endeavor with our NetApp ATI product is to give you that sort of easy button experience, because you didn't really want to be a storage admin or a network admin, you didn't want to get into the, be mired in the details of infra, so that's obviously work in progress, but we think we're definitely headed down the right direction. >> It does seem that a lot of enterprises want to have the cloudlike experience, but they want to be able to bring it home, we're seeing that a lot more. >> Yeah, so this turnkey on-premise, turnkey cloud on-premise, and, with NKS we can, the same auto-scaling, so take the dynamic nature of Kubernetes, so I have a base cluster size of say four worker nodes, right, but my workload's going to maybe need to have more nodes, so my auto-scaler's going to increase the size of my cluster and decrease the size, right? Pretty much everybody only can do that in the public cloud. I can do that in public cloud and on-premise, now. And so that's what we're trying to deliver, and that's pretty cool stuff, I think. >> Well there's a lot of advantages to enterprises operating in that way, because people out here, I can go and buy them or hire them, and say "Hey, we need you to operate this gear," and you've already done it elsewhere, you can do it in cloud, you can do it on-site, I can now run my operations the same across, no matter where my applications live, which saves me a lot of money on training costs, on development costs, and generally it makes for a much more smooth and seamless experience. >> So Rob, if you could, just love your takeaway on NetApp's participation here at the event, and what you want people to take away from the show this year. >> So it's certainly the case that we're doing a lot of great work, we like people to become aware of it. NetApp of course is not, I think we talked about this in perhaps other contexts, not strictly a storage and data management company only. We do draw from the strengths of that as we're providing full stack capabilities, in a way that are interconnected with public cloud, things like our NetApp Kubernetes service as really the foundational glue in many ways, to how we deliver the application runtime, but over time we'll build a constellation of data-centric capabilities around that as well. >> Matt, I would just love to get your viewpoint as someone that built a company in this ecosystem, there's so many startups here, give us kind of that founder viewpoint of being in this sort of ecosystem. >> Of the ecosystem... So this is, I came into the ecosystem at the beginning. I would have to say that it does feel different at this point, I'm going to speak as Matt, not as NetApp. And so my thinking has always been it feels a lot like, you're a big fan of that rock band, right, and you go to a local club, and we all get to know each other at that local club, and there's maybe 500 of us or 1000 of us, and then that band gets signed to Warner Brothers, and goes to the top, and now there's 20,000 people or 12,000 people. That's how it feels to me right now. I think, but what I like about it is that, it just shows the power of the community is now at a point where it's drawing in cities now, not just a small collection of a tribe of people. And I think that's a very powerful thing with this community, and like all the, what are they called, the Kubernetes Summits that they're doing, we didn't have any of those back when we first got going, I mean it was tough to fill the room, and now we can fill the room, and it's amazing, and what I like seeing is people moving past the problem of Kubernetes itself, and moving into what other problems can I solve on top of Kubernetes, so you're starting to see all these really exciting startups doing really neat things, and I really like, like this vendor hall I really like, 'cause you get to see all the new guys, but there's a lot of neat stuff going on, and I'm excited to see where the community goes in the next five years, but it's, we've gone from zero to 60 insanely fast, 'cause you guys were at the original KubeCon, I think, as well. >> It's our fourth year doing theCUBE at this show, but absolutely, we've watched it since the early days. I'm not supposed to mention OpenStack at this show, but we remember talking to JJ and some of the early people there, and we interviewed Craig McLuckie back in his Google days, and the like, so we've been fortunate to be on here since really day zero here, and definitely great energy, congrats so much on the progress, I really appreciate the updates on everything going, as you said, we've reached a certain state, and adding more value on top of this whole environment. >> Yeah, we're in junior high now, right, and we were in grade school for a few years. >> All right, well Matt and Rob, thank you so much for the update, hopefully not an awkward dance tonight for the junior people. For Justin Warren, I'm Stu Miniman, back with more coverage here from KubeCon CloudNativeCon 2019 in San Diego. Thank you for watching theCUBE. (techno music)

>> Announcer: From New York, it's theCUBE covering Escape/19. (techno music) >> Hey welcome back to theCUBE's coverage of the first inaugural, Multi-Cloud Conference in New York City. It's called Escape/2019. I'm here with Brendan O'Leary, Senior Solutions Architect with GitLab. Is that right, Senior Solutions Architect? >> Brendan: Close enough, Manager, you know. >> Manager, architect, you work at GitLab, you're technical, so we'll have a good chat here. Welcome to theCUBE, good to see you. >> Thanks for having me. >> First Multi-Cloud Conference, really we love to go to the inaugural anything. >> Sure. >> Just in case it's not around next year, we can say we were here. It looks like it's got some legs, some interesting conversations I see in the hallways. You know, you guys are a big part of this revolution. GitLab, your company, you're providing opensource repositories, free, to get people to get started, as well you got paid stuff, as well. Hot area. GitHub was acquired by Microsoft. Some say Microsoft's not going to meddle with that. We'll see, but still, a super-important part of the community that you guys are involved in. >> It's true. We're seeing this multi-cloud revolution, if you want to call it that, with a lot of our customers, right? It's no longer that you pick one cloud, and that's where everything's going to run. You're going to have acquisitions. You're going to have the desire to negotiate and have a negotiating position with your vendors. You're going to want to use functionality that's maybe only in one of the clouds. And so we're really seeing this multi-cloud become more of a norm. And that's why we think it's critical to have a DevOps platform that's independent from that, so that you can deploy everywhere. >> So what's the lock-in spec? I mean, basically the thesis is that if you want to negotiating leverage, you want to have multi-cloud. I get the whole, "there's multiple clouds," because, upgrade to Office 365, you got Azure, basically. So, multi-vendor, multi-cloud, totally buy it. But what's the lock-in spec that's getting people agitated, or thinking about multi-cloud? >> Yeah, I think it's interesting, because there's both, of course, the technical side. Like I said, you might have functionality that you want to run that's only available on one cloud. But, the finance folks, and everyone else gets concerned about, "Hey, are we going to get locked into some vendor, "where we don't have any ability to negotiate?" And so I think that is part of it, and I read, as part of prepping for my talk here, a 2019 state-of-cloud report that said 84% of enterprises, today, are using more than one cloud. So I think that's indicative of that desire to not-- You may have a primary cloud where you deploy things, but you're going to use more than one. >> I think that's a fair reality. I mean, probably more, I mean, if you count all these, how they're bundling apps in there. What's your talk going to be about? Is it today or tomorrow? >> So, I'm talking tomorrow, and I'm talking about a framework for making decisions about multi-cloud. 'Cause again, I think that a lot of the times we get bogged down in the technology, and picking features over what we're really looking for, which is the business value of being able to have a single view, a single application, a single platform for your developers to be able to deploy, kind of no matter where it's going to end up, in the end, right? We don't want the developer having to think about that, necessarily, when they're building the application. We want to deliver value to our customers, right? And so we want them to be doing that differentiated work. >> Me and Armon were talking earlier, HashiCorp, CTO of HashiCorp, and he was talking about workflows, and I was talking about, okay, workloads. So, if you just take those two concepts, workflows and workloads, and just strip out any other technical conversation, what's the framework? Because, these are real issues. Those are the--that's the continuity issue for the business, not the tech. So, fill in the blanks around that. How does that--how do I get multi-cloud out of making sure my workflows aren't disrupted, and my workloads are kicking ass and doing their job? >> Yeah, I would say that that's a great question, and we love HashiCorp and what they've done for our space, and for multi-cloud, in general. They're a great partner for us. But I think the key is, the workflow you generally want to be the same, no matter where you're deploying, right? You want to have confidence that the code your building is secure, it's going to work, it's been tested, and, no matter where it deploys in the end, you want to have that same kind of workflow for your developers. But you also want to have workload portability, right? So, when you're talking about the ability to have a negotiating position, or the ability to run in multiple clouds, the same application, you know, have disaster recovery, have not just this monolith--mono-cloud environment, you have to have workload portability, as well. >> Well, Brendan, I'm not sure if they're taping your interview. Hope they are. If they are, then we'll get those copies in our video on cloud. But, you've got a framework for multi-cloud, and with the reality that everyone wants, or has either inherited, or has, or will want a multi-vendor environment, what is that framework for negotiating, or setting up the foundation? Because the theme here, my interviews here, and the hallway conversations, two things: One is foundational discussions around multi-cloud, I mean, early, thought leaders laying out, here's some lines to think about. And then, two, data. So, two, interesting, common threads, here: foundational thinking and data. >> I think that foundational thinking's important, because I think that's really what my framework gets to is, hey, we want to look at not just the technology, and not those answers. We want to look at, what are the business metrics that we're driving towards, right? 'Cause, in the end, again, that's what we want to be driving in software is our businesses. And, so, what are the business metrics that we're going to use, and how can we make it efficient? How can we make it governed? And how can we make it visible across those clouds? I think those are the three things to be focused on. >> And is there a certain way? So is it more, situational, based upon the environment, because maybe there's weights of certain variables over others? >> I think so. I think, depending on your environment, right? You maybe in a more highly regulated environment where governance is the number one, it's the king. But I think everyone has those governance concerns, right? None of us want to wake up to a security call that we should have known about, right? >> How's things going on in your world? GitLab, you guys are doing great. Good to see you guys got a big round of funding, recently. >> Going great. >> GitHub just sold for billions of dollars. That's a nice comp. >> Yeah, no, I say it's nice when someone sells a house in your neighborhood for a lot of money, right? But, yeah, no, what we see from that is the industry moving toward this single tool for your DevOps lifecycle, for your DevOps tool chain, and your DevOps lifecycle. We want to be able to have one way that developers deploy code, and we're seeing that kind of consolidation in the market. And we've had great success with that, so far. Our stated pubic desire is to go public next year. And we're on track for that, right now. So, we're looking forward to it. >> You know what's interesting and I love is the subtext to all this plot, which is, there's a human equation in all this, right? The human capital, human resource, the people-side of the equation, the cultural shifts in these companies, your customers, now. Any observational commentary that you can share around how DevOps has kind of gone mainstream? Any cultural shifts around people and their behaviors and their affinity towards certain things? >> Yeah, it's an interesting question. I saw an article yesterday about a CIO who was being promoted to CEO, as the current CEO stepped down, and how that was kind of a novel thing. But the article was actually talking about how we're going to see more of that, right? Businesses, eight years ago, Marc Andreesen said that software is eating the world. Well, I think software has eaten the world, and we're seeing that in our businesses, as every company becomes a software company. >> And open source, JJ would argue at OSS Capital, that there's new business models emerging, as well. And new opportunities, as well, for everyone involved. Open source software, cloud computing, multi-cloud, it's a great wave. >> It is a big wave, and, you know, GitLab's based on an open-source project, right? And so, just, we were founded only back in 2014, as a company, but we've come to find a business model that works, open-core, and we think there's a lot of opportunity in the market for folks to follow, and open source to have an even bigger impact than it's already had on the market. >> Final question for you, Brendan. What do you think about this conference, some of the hallway conversations, what's the vibe? For the folks that aren't here, what's it like? >> Oh, I mean, I think it's great. I think there's been a lot of great discussions, again, about very foundational things, about, hey, how do we look at this as a business leaders? But, then, I've also had great discussions about the technology and about Kubernetes, about those kinds of things that really enable us to have those kinds of conversations. >> Some good relationships being developed here. People know each other, too. >> Exactly, yeah, people I haven't seen in a long time, or people that I work with that I haven't seen 'cause we're all remote. >> It's great to see it in New York, too. >> Yeah, I love it in New York. So, I'm from DC, so it's a quick train ride up, but I love coming up, though. >> Not like us in California, big plane ride. Brendan, thank you so much for coming on theCUBE. Appreciate it. >> Yeah, great, thank you very much for having me. >> I'm John Furrier, here at the first, inaugural conference, Escape/19, back with more of that after this short break. (techno music)

>> Announcer: From New York, it's theCUBE covering Escape/19. (techno music) >> Hey welcome back to theCUBE's coverage of the first inaugural, Multi-Cloud Conference in New York City. It's called Escape/2019. I'm here with Brendan O'Leary, Senior Solutions Architect with GitLab. Is that right, Senior Solutions Architect? >> Brendan: Close enough, Manager, you know. >> Manager, architect, you work at GitLab, you're technical, so we'll have a good chat here. Welcome to theCUBE, good to see you. >> Thanks for having me. >> First Multi-Cloud Conference, really we love to go to the inaugural anything. >> Sure. >> Just in case it's not around next year, we can say we were here. It looks like it's got some legs, some interesting conversations I see in the hallways. You know, you guys are a big part of this revolution. GitLab, your company, you're providing opensource repositories, free, to get people to get started, as well you got paid stuff, as well. Hot area. GitHub was acquired by Microsoft. Some say Microsoft's not going to meddle with that. We'll see, but still, a super-important part of the community that you guys are involved in. >> It's true. We're seeing this multi-cloud revolution, if you want to call it that, with a lot of our customers, right? It's no longer that you pick one cloud, and that's where everything's going to run. You're going to have acquisitions. You're going to have the desire to negotiate and have a negotiating position with your vendors. You're going to want to use functionality that's maybe only in one of the clouds. And so we're really seeing this multi-cloud become more of a norm. And that's why we think it's critical to have a DevOps platform that's independent from that, so that you can deploy everywhere. >> So what's the lock-in spec? I mean, basically the thesis is that if you want to negotiating leverage, you want to have multi-cloud. I get the whole, "there's multiple clouds," because, upgrade to Office 365, you got Azure, basically. So, multi-vendor, multi-cloud, totally buy it. But what's the lock-in spec that's getting people agitated, or thinking about multi-cloud? >> Yeah, I think it's interesting, because there's both, of course, the technical side. Like I said, you might have functionality that you want to run that's only available on one cloud. But, the finance folks, and everyone else gets concerned about, "Hey, are we going to get locked into some vendor, "where we don't have any ability to negotiate?" And so I think that is part of it, and I read, as part of prepping for my talk here, a 2019 state-of-cloud report that said 84% of enterprises, today, are using more than one cloud. So I think that's indicative of that desire to not-- You may have a primary cloud where you deploy things, but you're going to use more than one. >> I think that's a fair reality. I mean, probably more, I mean, if you count all these, how they're bundling apps in there. What's your talk going to be about? Is it today or tomorrow? >> So, I'm talking tomorrow, and I'm talking about a framework for making decisions about multi-cloud. 'Cause again, I think that a lot of the times we get bogged down in the technology, and picking features over what we're really looking for, which is the business value of being able to have a single view, a single application, a single platform for your developers to be able to deploy, kind of no matter where it's going to end up, in the end, right? We don't want the developer having to think about that, necessarily, when they're building the application. We want to deliver value to our customers, right? And so we want them to be doing that differentiated work. >> Me and Armon were talking earlier, HashiCorp, CTO of HashiCorp, and he was talking about workflows, and I was talking about, okay, workloads. So, if you just take those two concepts, workflows and workloads, and just strip out any other technical conversation, what's the framework? Because, these are real issues. Those are the--that's the continuity issue for the business, not the tech. So, fill in the blanks around that. How does that--how do I get multi-cloud out of making sure my workflows aren't disrupted, and my workloads are kicking ass and doing their job? >> Yeah, I would say that that's a great question, and we love HashiCorp and what they've done for our space, and for multi-cloud, in general. They're a great partner for us. But I think the key is, the workflow you generally want to be the same, no matter where you're deploying, right? You want to have confidence that the code your building is secure, it's going to work, it's been tested, and, no matter where it deploys in the end, you want to have that same kind of workflow for your developers. But you also want to have workload portability, right? So, when you're talking about the ability to have a negotiating position, or the ability to run in multiple clouds, the same application, you know, have disaster recovery, have not just this monolith--mono-cloud environment, you have to have workload portability, as well. >> Well, Brendan, I'm not sure if they're taping your interview. Hope they are. If they are, then we'll get those copies in our video on cloud. But, you've got a framework for multi-cloud, and with the reality that everyone wants, or has either inherited, or has, or will want a multi-vendor environment, what is that framework for negotiating, or setting up the foundation? Because the theme here, my interviews here, and the hallway conversations, two things: One is foundational discussions around multi-cloud, I mean, early, thought leaders laying out, here's some lines to think about. And then, two, data. So, two, interesting, common threads, here: foundational thinking and data. >> I think that foundational thinking's important, because I think that's really what my framework gets to is, hey, we want to look at not just the technology, and not those answers. We want to look at, what are the business metrics that we're driving towards, right? 'Cause, in the end, again, that's what we want to be driving in software is our businesses. And, so, what are the business metrics that we're going to use, and how can we make it efficient? How can we make it governed? And how can we make it visible across those clouds? I think those are the three things to be focused on. >> And is there a certain way? So is it more, situational, based upon the environment, because maybe there's weights of certain variables over others? >> I think so. I think, depending on your environment, right? You maybe in a more highly regulated environment where governance is the number one, it's the king. But I think everyone has those governance concerns, right? None of us want to wake up to a security call that we should have known about, right? >> How's things going on in your world? GitLab, you guys are doing great. Good to see you guys got a big round of funding, recently. >> Going great. >> GitHub just sold for billions of dollars. That's a nice comp. >> Yeah, no, I say it's nice when someone sells a house in your neighborhood for a lot of money, right? But, yeah, no, what we see from that is the industry moving toward this single tool for your DevOps lifecycle, for your DevOps tool chain, and your DevOps lifecycle. We want to be able to have one way that developers deploy code, and we're seeing that kind of consolidation in the market. And we've had great success with that, so far. Our stated pubic desire is to go public next year. And we're on track for that, right now. So, we're looking forward to it. >> You know what's interesting and I love is the subtext to all this plot, which is, there's a human equation in all this, right? The human capital, human resource, the people-side of the equation, the cultural shifts in these companies, your customers, now. Any observational commentary that you can share around how DevOps has kind of gone mainstream? Any cultural shifts around people and their behaviors and their affinity towards certain things? >> Yeah, it's an interesting question. I saw an article yesterday about a CIO who was being promoted to CEO, as the current CEO stepped down, and how that was kind of a novel thing. But the article was actually talking about how we're going to see more of that, right? Businesses, eight years ago, Marc Andreesen said that software is eating the world. Well, I think software has eaten the world, and we're seeing that in our businesses, as every company becomes a software company. >> And open source, JJ would argue at OSS Capital, that there's new business models emerging, as well. And new opportunities, as well, for everyone involved. Open source software, cloud computing, multi-cloud, it's a great wave. >> It is a big wave, and, you know, GitLab's based on an open-source project, right? And so, just, we were founded only back in 2014, as a company, but we've come to find a business model that works, open-core, and we think there's a lot of opportunity in the market for folks to follow, and open source to have an even bigger impact than it's already had on the market. >> Final question for you, Brendan. What do you think about this conference, some of the hallway conversations, what's the vibe? For the folks that aren't here, what's it like? >> Oh, I mean, I think it's great. I think there's been a lot of great discussions, again, about very foundational things, about, hey, how do we look at this as a business leaders? But, then, I've also had great discussions about the technology and about Kubernetes, about those kinds of things that really enable us to have those kinds of conversations. >> Some good relationships being developed here. People know each other, too. >> Exactly, yeah, people I haven't seen in a long time, or people that I work with that I haven't seen 'cause we're all remote. >> It's great to see it in New York, too. >> Yeah, I love it in New York. So, I'm from DC, so it's a quick train ride up, but I love coming up, though. >> Not like us in California, big plane ride. Brendan, thank you so much for coming on theCUBE. Appreciate it. >> Yeah, great, thank you very much for having me. >> I'm John Furrier, here at the first, inaugural conference, Escape/19, back with more of that after this short break. (techno music)

>> from Cambridge, Massachusetts. It's three Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Hi already. We're back in Cambridge, Massachusetts at the M I t. Chief data officer Information quality event. Hashtag m i t cdo i Q. And I'm David Dante. He's Paul Gillen. Day one of our two day coverage of this event. This is the Cube, the leader in live tech coverage. Joe Caserta is here is the president of Caserta and Doug Laney, who is principal data strategist at Caserta, both Cube alarm guys. Great to see you again, Joe. What? Did you pick up this guy? How did that all came on here a couple of years ago? We had a great conversation. I read the book, Loved it. So congratulations. A nice pickup. >> We're very fortunate to have. >> Thanks. So I'm fortunate to be here, >> so Okay, well, what attracted you to Cassard? Oh, >> it's Joe's got a tremendous reputation. His his team of consultants has a great reputation. We both felt there was an opportunity to build some data strategy competency on top of that and leverage some of those in Phanom. Its ideas that I've been working on over the years. >> Great. Well, congratulations. And so, Joe, you and I have talked many times. And the reason I like talking because you know what's going on in the market place? You could you could siphon. What's riel? What's hype? So what do you see? It is the big trends in this data space, and then we'll get into it. Yeah, sure. Um, trends >> are chief data officer has been evolving over the last couple of years. You know, when we started doing this several years ago, there was just a handful of people, maybe 30 40 people. Now, there's 450 people here today, and it's been evolving. People are still trying to find their feet. Exactly what the chief date officers should be doing where they are in the hierarchy. Should they report to the c e o the C I O u the other CDO, which is a digital officer. So I think you know, hierarchically. That's still figuring it out politically. They're figuring it out, but technically also, they're still trying to figure it out. You know what's been happening over the past three years is the evolution of data going from traditional data warehousing and business intelligence. To get inside out of data just isn't working anymore. Eso evolving that moving it forward to more modern data engineering we've been doing for the past couple of years with quote unquote big data on That's not working anymore either, right? Because it's been evolving so fast. So now we're on, like, maybe Data three dato. And now we're talking about just pure automate everything. We have to automate everything. And we have to change your mindset from from having output of a data solution to an outcome to date a solution. And that's why I hired Doug, because way have to figure out not only had to get this data and look at it and analyze really had to monetize it, right? It's becoming a revenue stream for your business if you're doing it right and Doug is the leader in the industry, how to figure that >> you keep keep premise of your book was you gotta start valuing data and its fundamental you put forth a number of approaches and techniques and examples of companies doing that. Since you've published in phenomena Microsoft Apple, Amazon, Google and Facebook. Of the top five market value cos they've surpassed all the financial service is guys all ExxonMobil's and any manufacturer? Automobile makers? And what of a data companies, right? Absolutely. But intrinsically we know there's value their way any closer to the prescription that you put forth. >> Yeah, it's really no surprise and extra. We found that data companies have, ah, market to book value. That's nearly 33 times the market average, so Apple and others are much higher than that. But on average, if you look at the data product companies, they're valued much higher than other companies, probably because data can be reused in multiple ways. That's one of the core tenets of intra nomics is that Data's is non depleted ble regenerative, reusable asset and that companies that get that an architect of businesses based on those economics of information, um, can really perform well and not just data companies, but >> any company. That was a key takeaway of the book. The data doesn't conform to the laws of scarcity. Every says data is the new oil. It's like, No, it's not more valuable. So what are some examples in writing your book and customers that you work with. Where do you see Cos outside of these big data driven firms, breaking new ground and uses of data? I >> think the biggest opportunity is really not with the big giant Cos it's really with. Most of our most valuable clients are small companies with large volumes of data. You know if and the reason why they can remain small companies with large volumes of data is the thing that holds back the big giant enterprises is they have so much technical. Dad, it's very hard. They're like trying to, you know, raise the Titanic, right? You can't really. It's not agile enough. You need something that small and agile in order to pivot because it is changing so fast every time there's a solution created, it's obsolete. We have to greet the new solution on dhe when you have a big old processes. Big old technologies, big old mind sets on big old cultures. It's very hard to be agile. >> So is there no hope? I mean, the reason I ask the question was, What hope can you give some of these smokestack companies that they can become data centric? Yeah, What you >> see is that there was a There was a move to build big, monolithic data warehouses years ago and even Data Lakes. And what we find is that through the wealth of examples of companies that have benefited in significant ways from data and analytics, most of those solutions are very vocational. They're very functionally specific. They're not enterprise class, yada, yada, kind of kind of projects. They're focused on a particular business problem or monetizing or leveraging data in a very specific way, and they're generating millions of dollars of value. But again they tend to be very, very functionally specific. >> The other trend that we're seeing is also that the technology and the and the end result of what you're doing with your data is one thing. But really, in order to make that shift, if your big enterprises culture to really change all of the people within the organization to migrate from being a conventional wisdom run company to be a data really analytics driven company, and that takes a lot of change management, a lot of what we call data therapy way actually launched a new practice within the organization that Doug is actually and I are collaborating on to really mature because that is the next wave is really we figured out the data part. We figured out the technology part, but now it's the people part people. Part is really why we're not way ahead of where we even though we're way ahead of where we were a couple of years ago, we should be even further. Culturally, it's very, very challenging, and we need to address that head on. >> And that zeta skills issue that they're sort of locked into their existing skill sets and processes. Or is it? It's fear of the unknown what we're doing, you know? What about foam? Oh, yeah, Well, I mean, there are people >> jumping into bed to do this, right? So there is that part in an exciting part of it. But there's also just fear, you know, and fear of the unknown and, you know, part of what we're trying to do. And why were you trying Thio push Doug's book not for sales, but really just to share the knowledge and remove the mystery and let people see what they can actually do with this data? >> Yeah, it's more >> than just date illiteracy. So there's a lot of talk of the industry about data literacy programs and educating business people on the data and educating data people on the business. And that's obviously important. But what Joe is talking about is something bigger than that. It's really cultural, and it's something that is changed to the company's DNA. >> So where do you attack that problem? It doesn't have to go from the top down. You go into the middle. It has to >> be from the top down. It has to be. It has to be because my boss said to do it all right. >> Well, otherwise they well, they might do it. But the organization's because if you do, it >> is a grassroots movement on Lee. The folks who are excited, right? The foam of people, right? They're the ones who are gonna be excited. But they're going to evolve in adopt anyway, right? But it's the rest of the organization, and that needs to be a top down, Um, approach. >> It was interesting hearing this morning keynote speakers. You scored a throw on top down under the bus, but I had the same reaction is you can't do it without that executive buying. And of course, we defined, I guess in the session what that was. Amazon has an interesting concept for for any initiative, like every initiative that's funded has to have what they call a threaded leader. Another was some kind of And if they don't, if they don't have a threat of leader, there's like an incentive system tau dime on initiative. Kill it. It kind of forces top down. Yeah, you know, So >> when we interview our clients, we have a litmus test and the limits. It's kind of a ready in this test. Do you have the executive leadership to actually make this project successful? And in a lot of cases, they don't And you know, we'll have to say will call us when you're ready, you know, or because one of the challenges another part of the litmus test is this IittIe driven. If it's I t driven is gonna be very tough to get embraced by the rest of the business. So way need to really be able to have that executive leadership from the business to say this is something that we need >> to do to survive. Yeah, and, you know, with without the top down support. You could play small ball. But if you're playing the Yankees, you're gonna win one >> of the reasons why when it's I t driven, it's very challenging is because the people part right is a different budget from the i T budget. And when we start talking about data therapy, right and human resource is and training and education of just culture and data literacy, which is not necessary technical, that that becomes a challenge internally figuring out, like how to pay for Andi how to get it done with a corporate politics. >> So So the CDO crowd definitely parts of your book that they should be adopting because to me, there their main job is okay. How does data support the monetization of my organization? Raising revenue, cutting costs, improving productivity, saving lives. You call it value. And so that seems to be the starting point. At the same time. In this conference, you grew out of the ashes of back room information quality of the big data height, but exploded and have kind of gone full circle. So But I wonder, I mean, is the CDO crowd still focused on that monetization? Certainly I think we all agree they should be, but they're getting sucked back into a governance role. Can they do both, I guess, is >> my question. Well, governance has been, has been a big issue the past few years with all of the new compliance regulation and focus on on on ensuring compliance with them. But there's often a just a pendulum swing back, and I think there's a swing back to adding business value. And so we're seeing a lot of opportunities to help companies monetize their data broadly in a variety of ways. A CZ you mentioned not just in one way and, um, again those you need to be driven from the top. We have a process that we go through to generate ideas, and that's wonderful. Generating ideas. No is fairly straightforward enough. But then running them through kind of a feasibility government, starting with you have the executive support for that is a technology technologically feasible, managerially feasible, ethically feasible and so forth. So we kind of run them through that gauntlet next. >> One of my concerns is that chief data officer, the level of involvement that year he has in these digital initiatives again is digital initiative of Field of Dreams. Maybe it is. But everywhere you go the CEO is trying to get digital right, and it seems like the chief data officer is not necessarily front and center in those. Certainly a I projects, which are skunk works. But it's the chief digital officer that's driving it. So how how do you see in those roles playoff >> In the less panel that I've just spoken, very similar question was asked. And again, we're trying to figure out the hierarchy of where the CDO should live in an organization. Um, I find that the biggest place it fails typically is if it rolls up to a C I. O. Right. If you think the data is a technical issue, you're wrong, Right? Data is a business issue, Andi. I also think for any company to survive today, they have to have a digital presence. And so digital presence is so tightly coupled to data that I find the best success is when the chief date officer reports directly to the chief digital officer. Chief Digital officer has a vision for the user experience for the customer customers Ella to figure out. How do we get that customer engaged and that directly is dependent on insight. Right on analytics. You know, if the four of us were to open up, any application on our phone, even for the same product, would have four different experiences based on who we are, who are peers are what we bought in the past, that's all based on analytics. So the business application of the digital presence is tightly couple tow Analytics, which is driven by the chief state officer. >> That's the first time I've heard that. I think that's the right organizational structure. Did see did. JJ is going to be sort of the driver, right? The strategy. That's where the budget's gonna go and the chief date office is gonna have that supporting role that's vital. The enabler. Yeah, I think the chief data officer is a long term play. Well, we have a lot of cheap date officers. Still, 10 years from now, I think that >> data is not a fad. I think Data's just become more and more important. And will they ultimately leapfrog the chief digital officer and report to the CEO? Maybe someday, but for now, I think that's where they belong. >> You know what's company started managing their labor and workforce is as an actual asset, even though it's not a balance sheet. Asked for obvious reasons in the 19 sixties that gave rise to the chief human resource officer, which we still see today and his company start to recognize information as an asset, you need an executive leader to oversee and be responsible for that asset. >> Conceptually, it's always been data is an asset and a liability. And, you know, we've always thought about balancing terms. Your book sort of put forth a formula for actually formalizing. That's right. Do you think it's gonna happen our lifetime? What exactly clear on it, what you put forth in your book in terms of organizations actually valuing data specifically on the balance sheet. So that's >> an accounting question and one that you know that you leave to the accounting professionals. But there have been discussion papers published by the accounting standards bodies to discuss that issue. We're probably at least 10 years away, but I think respective weather data is that about what she'd asked or not. It's an imperative organizations to behave as if it is one >> that was your point it's probably not gonna happen, but you got a finger in terms that you can understand the value because it comes >> back to you can't manage what you don't measure and measuring the value of potential value or quality of your information. Or what day do you have your in a poor position to manage it like one. And if you're not manage like an asset, then you're really not probably able to leverage it like one. >> Give us a little commercial for I do want to say that I do >> think in our lifetime we will see it become an asset. There are lots of intangible assets that are on the books, intellectual property contracts. I think data that supports both of those things are equally is important. And they will they will see the light. >> Why are those five companies huge market cap winners, where they've surpassed all the evaluation >> of a business that the data that they have is considered right? So it should be part of >> the assets in the books. All right, we gotta wraps, But give us Give us the The Caserta Commercial. Well, concert is >> a consultancy that does essentially three things. We do data advisory work, which, which Doug is heading up. We do data architecture and strategy, and we also do just implementation of solutions. Everything from data engineering gate architecture and data science. >> Well, you made a good bet on data. Thanks for coming on, you guys. Great to see you again. Thank you. That's a wrap on day one, Paul. And I'll be back tomorrow for day two with the M I t cdo m I t cdo like you. Thanks for watching. We'll see them all.

(lively classical music) >> Hello everyone, welcome to this special Cube conversation, we are here at the Power Panel Conversation. I'm John Furrier, in Palo Alto, California, theCUBE studies we have remote on the line here, talk about the cloud technology's impact on entrepreneurship and startups and overall ecosystem is Jim Long, who's the CEO of Didja, which is a startup around disrupting digital TV, also has been an investor and a serial entrepreneur, Sarbjeet Johal, who's the in-cloud influencer of strategy and investor out of Berkeley, California, The Batchery, and also Joseph Jacks, CUBE alumni, actually you guys are all CUBE alumni, so great to have you on. Joseph Jacks is the founder and general partner of OSS Capital, Open Source Software Capital, a new fund that's been raised specifically to commercialize and fund startups around open source software. Guys, we got a great panel here of experts, thanks for joining us, appreciate it. >> Go Bears! >> Nice to be here. >> So we have a distinguished panel, it's the Power Panel, we're on cloud technos, first I'd like to get you guys' reaction you know, you're to seeing a lot of negative news around what Facebook has become, essentially their own hyper-scale cloud with their application. They were called the digital, you know, renegades, or digital gangsters in the UK by the Parliament, which was built on open source software. Amazon's continuing to win, Azure's doing their thing, bundling Office 365, making it look like they've got more revenue with their catching up, Google, and then you got IBM and Oracle, and then you got an ecosystem that's impacted by this large scale, so I want to get your thoughts on first point here. Is there room for more clouds? There's a big buzzword around multiple clouds. Are we going to see specialty clouds? 'Causes Salesforce is a cloud, so is there room for more cloud? Jim, why don't you start? >> Well, I sure hope so. You know, the internet has unfortunately become sort of the internet of monopolies, and that doesn't do anyone any good. In fact, you bring up an interesting point, it'd be kind of interesting to see if Facebook created a social cloud for certain types of applications to use. I've no idea whether that makes any sense, but Amazon's clearly been the big gorilla now, and done an amazing job, we love using them, but we also love seeing, trying out different services that they have and then figuring out whether we want to develop them ourselves or use a specialty service, and I think that's going to be interesting, particularly in the AI area, stuff like that. So I sure hope more clouds are around for all of us to take advantage of. >> Joseph, I want you to weigh in here, 'cause you were close to the Kubernetes trend, in fact we were at a OpenStack event when you started Kismatic, which is the movement that became KubeCon Cloud Native, many many years ago, now you're investing in open source. The world's built on open source, there's got to be room for more clouds. Your thoughts on the opportunities? >> Yeah, thanks for having me on, John. I think we need a new kind of open collaborative cloud, and to date, we haven't really seen any of the existing major sort of large critical mass cloud providers participate in that type of model. Arguably, Google has probably participated and contributed the most in the open source ecosystem, contributing TensorFlow and Kubernetes and Go, lots of different open source projects, but they're ultimately focused on gravitating huge amounts of compute and storage cycles to their cloud platform. So I think one of the big missing links in the industry is, as we continue to see the rise of these large vertically integrated proprietary control planes for computing and storage and applications and services, I think as the open source community and the open source ecosystem continues to grow and explode, we'll need a third sort of provider, one that isn't based on monopoly or based on a traditional proprietary software business like Microsoft kind of transitioning their enterprise customers to services, sort of Amazon in the first camp vertically integrated many a buffet of all these different compute, storage, networking services, application, middleware. Microsoft focused on sort of building managed services of their software portfolio. I think we need a third model where we have sort of an open set of interfaces and an open standards based cloud provider that might be a pure software company, it might be a company that builds on the rails and the infrastructure that Amazon has laid down, spending tens of billions in cap ex, or it could be something based on a project like Kubernetes or built from the community ecosystem. So I think we need something like that just to sort of provide, speed the innovation, and disaggregate the services away from a monolithic kind of closed vendor like Amazon or Azure. >> I want to come back to that whole startup opportunity, but I want to get Sarbjeet in here, because we've been in the B2B area with just last week at IBM Think 2019. Obviously they're trying to get back into the cloud game, but this digital transformation that has been the cliche for almost a couple of years now, if not five or plus. Business has got to move to the cloud, so there's a whole new ball game of complete cultural shift. They need stability. So I want to talk more about this open cloud, which I love that conversation, but give me the blocking and tackling capabilities first, 'cause I got to get out of that old cap ex model, move to an operating model, transform my business, whether it's multi clouds. So Sarbjeet, what's your take on the cloud market for say, the enterprise? >> Yeah, I think for the enterprise... you're just sitting in that data center and moving those to cloud, it's a cumbersome task. For that to work, they actually don't need all the bells and whistles which Amazon has in the periphery, if you will. They need just core things like compute, network, and storage, and some other sort of services, maybe database, maybe data share and stuff like that, but they just want to move those applications as is to start with, with some replatforming and with some changes. Like, they won't make changes to first when they start moving those applications, but our minds are polluted by this thinking. When we see a Facebook being formed by a couple of people, or a company of six people sold for a billion dollars, it just messes up with our mind on the enterprise side, hey we can do that too, we can move that fast and so forth, but it's sort of tragic that we think that way. Well, having said that, and I think we have talked about this in the past. If you are doing anything in the way of systems innovation, if your building those at, even at the enterprise, I think cloud is the way to go. To your original question, if there's room for newer cloud players, I think there is, provided that we can detach the platforms from the environments they are sitting on. So the proprietariness has to kinda, it has to be lowered, the degree of proprietariness has to be lower. It can be through open source I think mainly, it can be from open technologies, they don't have to be open source, but portable. >> JJ was mentioning that, I think that's a big point. Jim Long, you're an entrepreneur, you've been a VC, you know all the VCs, been around for a while, you're also, you're an entrepreneur, you're a serial entrepreneur, starting out at Cal Berkeley back in the day. You know, small ideas can move fast, and you're building on Amazon, and you've got a media kind of thing going on, there's a cloud opportunity for you, 'cause you are cloud native, 'cause you're built in the cloud. How do you see it playing out? 'Cause you're scaling with Amazon. >> Well, so we obviously, as a new startup, don't have the issues the enterprise folks have, and I could really see the enterprise customers, what we used to call the Fortune 500, for example, getting together and insisting on at least a base set of APIs that Amazon and Microsoft et cetera adopt, and for a startup, it's really about moving fast with your own solution that solves a problem. So you don't necessarily care too much that you're tied into Amazon completely because you know that if you need to, you can make a change some day. But they do such a good job for us, and their costs, while they can certainly be lower, and we certainly would like more volume discounts, they're pretty darn amazing across the network, across the internet, we do try to price out other folks just for the heck of it, been doing that recently with CDNs, for example. But for us, we're actually creating a hybrid cloud, if you will, a purpose-built cloud to support local television stations, and we do think that's going to be, along with using Amazon, a unique cloud with our own APIs that we will hopefully have lots of different TV apps use our hybrid cloud for part of their application to service local TV. So it's kind of a interesting play for us, the B2B part of it, we're hoping to be pretty successful as well, and we hope to maybe have multiple cloud vendors in our mix, you know. Not that our users will know who's behind us, maybe Amazon, for something, Limelight for another, or whatever, for example. >> Well you got to be concerned about lock-in as you become in the cloud, that's something that everybody's worried about. JJ, I want to get back to you on the investment thesis, because you have a cutting edge business model around investing in open source software, and there's two schools of thought in the open source community, you know, free contribution's great, and let tha.t be organic, and then there's now commercialization. There's real value being created in open source. You had put together a chart with your team about the billions of dollars in exits from open source companies. So what are you investing in, what do you see as opportunities for entrepreneurs like Jim and others that are out there looking at scaling their business? How do you look at success, what's your advice, what do you see as leading indicators? >> I think I'll broadly answer your question with a model that we've been thinking a lot about. We're going to start writing publicly about it and probably eventually maybe publish a book or two on it, and it's around the sort of fundamental perspective of creating value and capturing value. So if you model a famous investor and entrepreneur in Silicon Valley who has commonly modeled these things using two different letter variables, X and Y, but I'll give you the sort of perspective of modeling value creation and value capture around open source, as compared to closed source or proprietary software. So if you look at value creation modeled as X, and value capture modeled as Y, where X and Y are two independent variables with a fully proprietary software company based approach, whether you're building a cloud service or a proprietary software product or whatever, just a software company, your value creation exponent is typically bounded by two things. Capital and fundraising into the entity creating the software, and the centralization of research and development, meaning engineering output for producing the software. And so those two things are tightly coupled to and bounded to the company. With commercial open source software, the exact opposite is true. So value creation is decoupled and independent from funding, and value creation is also decentralized in terms of the research and development aspect. So you have a sort of decentralized, community-based, crowd-sourced, or sort of internet, global phenomena of contributing to a code base that isn't necessarily owned or fully controlled by a single entity, and those two properties are sort of decoupled from funding and decentralized R and D, are fundamentally changing the value creation kind of exponent. Now let's look at the value capture variable. With proprietary software company, or proprietary technology company, you're primarily looking at two constituents capturing value, people who pay for accessing the service or the software, and people who create the software. And so those two constituents capture all the value, they capture, you know, the vendor selling the software captures maybe 10 or 20% of the value, and the rest of the value, I would would express it say as the customer is capturing the rest of the value. Most economists don't express value capture as capturable by an end user or a customer. I think that's a mistake. >> Jim, you're-- >> So now... >> Okay, Jim, your reaction to that, because there's an article went around this weekend from Motherboard. "The internet was built on free labor "of open source developers. "Is that sustainable?" So Jim, what's your reaction to JJ's comments about the interactions and the dynamic between value creation, value capture, free versus sustainable funding? >> Well if you can sort of mix both together, that's what I would like, I haven't really ever figured out how to make open source work in our business model, but I haven't really tried that hard. It's an intriguing concept for sure, particularly if we come up with APIs that are specific to say, local television or something like that, and maybe some special processes that do things that are of interest to the wider community. So it's something I do plan to look at because I do agree that if you, I mean we use open source, we use this thing called FFmpeg, and several other things, and we're really happy that there's people out there adding value to them, et cetera, and we have our own versions, et cetera, so we'd like to contribute to the community if we could figure out how. >> Sarbjeet, your reactions to JJ's thesis there? >> I think two things. I will comment on two different aspects. One is the lack of standards, and then open source becoming the standard, right. I think open source kind of projects take birth and life in its own, because we have lack of standard, 'cause these different vendors can't agree on standards. So remember we used to have service-oriented architecture, we have Microsoft pushing some standards from one side and IBM pushing from other, SOAP versus xCBL and XML, different sort of paradigms, right, but then REST API became the de facto standard, right, it just took over, I think what REST has done for software in last about 10 years or so, nothing has done that for us. >> well Kubernetes is right now looking pretty good. So if you look at JJ, Kubernetes, the movement you were really were pioneering on, it's having similar dynamic, I mean Kubernetes is becoming a forcing function for solidarity in the community of cloud native, as well as an actual interoperable orchestration layer for multiple clouds and other services. So JJ, your thoughts on how open source continues as some of these new technologies, like Kubernetes, continue to hit the scene. Is there any trajectory change in open source that you see, that you could share, I'd love to get your insights on what's next behind, you know, the rise of Kubernetes is happening, what's next? >> I think more abstractly from Kubernetes, we believe that if you just look at the rate of innovation as a primary factor for progress and forward change in the world, open source software has the highest rate of innovation of any technology creation phenomena, and as a consequence, we're seeing more standards emerge from the open source ecosystem, we're seeing more disruption happen from the open source ecosystem, we're seeing more new technology companies and new paradigms and shifts happen from the open source ecosystem, and kind of all progress across the largest, most difficult sort of compound, sensitive problems, influenced and kind of sourced from the open source ecosystem and the open source world overall. Whether it's chip design, machine learning or computing innovations or new types of architectures, or new types of developer paradigms, you know, biological breakthroughs, there's kind of things up and down the technology spectrum that have a lot to sort of thank open source for. We think that the future of technology and the future of software is really that open source is at the core, as opposed to the periphery or the edges, and so today, every software technology company, and cloud providers included, have closed proprietary cores, meaning that where the core is, the data path, the runtime, the core business logic of the company, today that core is proprietary software or closed source software, and yet what is also true, is at the edges, the wrappers, the sort of crust, the periphery of every technology company, we have lots of open source, we have client libraries and bindings and languages and integrations, configuration, UIs and so on, but the cores are proprietary. We think the following will happen over the next few decades. We think the future will gradually shift from closed proprietary cores to open cores, where instead of a proprietary core, an open core is where you have core open source software project, as the fundamental building block for the company. So for example, Hadoop caused the creation of MapR and Cloudera and Hortonworks, Spark caused the creation of Databricks, Kafka caused the creation of Confluent, Git caused the creation of GitHub and GitLab, and this type of commercial open source software model, where there's a core open source project as the kernel building block for the company, and then an extension of intellectual property or wrappers around that open source project, where you can derive value capture and charge for licensed product with the company, and impress customer, we think that model is where the future is headed, and this includes cloud providers, basically selling proprietary services that could be based on a mixture of open source projects, but perhaps not fundamentally on a core open source project. Now we think generally, like abstractly, with maybe somewhat of a reductionist explanation there, but that open core future is very likely, fundamentally because of the rate of innovation being the highest with the open source model in general. >> All right, that's great stuff. Jim, you're a historian of tech, you've lived it. Your thoughts on some of the emerging trends around cloud, because you're disrupting linear TV with Didja, in a new way using cloud technology. How do you see cloud evolving? >> Well, I think the long lines we discussed, certainly I think that's a really interesting model, and having the open source be the center of the universe, then figure out how to have maybe some proprietary stuff, if I can use that word, around it, that other people can take advantage of, but maybe you get the value capture and build a business on that, that makes a lot of sense, and could certainly fit in the TV industry if you will from where I sit... Bring services to businesses and consumers, so it's not like there's some reason it wouldn't work, you know, it's bound to, it's bound to figure out a way, and if you can get a whole mass of people around the world working on the core technology and if it is sort of unique to what mission of, or at least the marketplace you're going after, that could be pretty interesting, and that would be great to see a lot of different new mini-clouds, if you will, develop around that stuff would be pretty cool. >> Sarbjeet, I want you to talk about scale, because you also have experience working with Rackspace. Rackspace was early on, they were trying to build the cloud, and OpenStack came out of that, and guess what, the world was moving so fast, Amazon was a bullet train just flying down the tracks, and it just felt like Rackspace and their cloud, you know OpenStack, just couldn't keep up. So is scale an issue, and how do people compete against scale in your mind? >> I think scale is an issue, and software chops is an issue, so there's some patterns, right? So one pattern is that we tend to see that open source is now not very good at the application side. You will hardly see any applications being built as open source. And also on the extreme side, open source is pretty sort of lame if you will, at very core of the things, like OpenStack failed for that reason, right? But it's pretty good in the middle as Joseph said, right? So building pipes, building some platforms based on open source, so the hooks, integration, is pretty good there, actually. I think that pattern will continue. Hopefully it will go deeper into the core, which we want to see. The other pattern is I think the software chops, like one vendor has to lead the project for certain amount of time. If that project goes into sort of open, like anybody can grab it, lot of people contribute and sort of jump in very quickly, it tends to fail. That's what happened to, I think, OpenStack, and there were many other reasons behind that, but I think that was the main reason, and because we were smaller, and we didn't have that much software chops, I hate to say that, but then IBM could control like hundred parties a week, at the project >> They did, and look where they are. >> And so does HP, right? >> And look where they are. All right, so I'd love to have a Power Panel on open source, certainly JJ's been in the thick of it as well as other folks in the community. I want to just kind of end on lightweight question for you guys. What have you guys learned? Go down the line, start with Jim, Sarbjeet, and then JJ we'll finish with you. Share something that you've learned over the past three months that moved you or that people should know about in tech or cloud trends that's notable. What's something new that you've learned? >> In my case, it was really just spending some time in the last few months getting to know our end users a little bit better, consumers, and some of the impact that having free internet television has on their lives, and that's really motivating... (distorted speech) Something as simple as you might take for granted, but lower income people don't necessarily have a TV that works or a hotel room that has a TV that works, or heaven forbid they're homeless and all that, so it's really gratifying to me to see people sort of tuning back into their local media through television, just by offering it on their phone and laptops. >> And what are you going to do as a result of that? Take a different action, what's the next step for you, what's the action item? >> Well we're hoping, once our product gets filled out with the major networks, et cetera, that we actually provide a community attachment to it, so that we have over-the-air television channels is the main part of the app, and then a side part of the app could be any IP stream, from city council meetings to high schools, to colleges, to local community groups, local, even religious situations or festivals or whatever, and really try to tie that in. We'd really like to use local television as a way to strengthening all local media and local communities, that's the vision at least. >> It's a great mission you guys have at Didja, thanks for sharing that. Sarbjeet, what have learned over the past quarter, three months that was notable for you and the impact and something that changed you a little bit? >> What actually I have gravitated towards in last three to six months is the blockchain, actually. I was light on that, like what it can do for us, and is there really a thing behind it, and can we leverage it. I've seen more and more actually usage of that, and sort of full SCM, supply chain management and healthcare and some other sort of use cases if you will. I'm intrigued by it, and there's a lot of activity there. I think there's some legs behind it, so I'm excited about that. >> And are doing a blockchain project as a result, or are you still tire-kicking? >> No actually, I will play with it, I'm a practitioner, I play with it, I write code and play with it and see (Jim laughs) what does that level of effort it takes to do that, and as you know, I wrote the Alexa scale couple of weeks back, and play with AI and stuff like that. So I try to do that myself before I-- >> We're hoping blockchain helps even out the TV ad economy and gets rid of middle men and makes more trusting transactions between local businesses and stuff. At least I say that, I don't really know what I'm talking about. >> It sounds good though. You get yourself a new round of funding on that sound byte alone. JJ, what have you learned in the past couple months that's new to you and changed you or made you do something different? >> I've learned over the last few months, OSS Capital is a few months and change old, and so just kind of getting started on that, and it's really, I think potentially more than one decade, probably multi-decade kind of mostly consensus building effort. There's such a huge lack of consensus and agreement in the industry. It's a fascinatingly polarizing area, the sort of general topic of open source technology, economics, value creation, value capture. So my learnings over the past few months have just intensified in terms of the lack of consensus I've seen in the industry. So I'm trying to write a little bit more about observations there and sort of put thoughts out, and that's kind of been the biggest takeaway over the last few months for me. >> I'm sure you learned about all the lawyer conversations, setting up a fund, learnings there probably too right, (Jim laughs) I mean all the detail. All right, JJ, thanks so much, Sarbjeet, Jim, thanks for joining me on this Power Panel, cloud conversation impact, to entrepreneurship, open source. Jim Long, Sarbjeet Johal and Joseph Jacks, JJ, thanks for joining us, theCUBE Conversation here in Palo Alto, I'm John Furrier, thanks for watching. >> Thanks John. (lively classical music)

(bright symphony music) >> Hello, I'm John Furrier, the founder of SiliconANGLE Media and co-host of theCUBE. We're here in Paulo Alto at our studio here. I'm joining with Joseph Jacks, the founder and general partner of OSS Capital. Open Source Software Capital, is what OSS stands for. He's also the founder of KubeCon which now is part of the CNCF. It's a huge conference around Kubernetes. He's a cloud guy. He knows open source. Very well respected in the industry and also a great guest and friend of theCUBE, CUBE alumni. Joseph, great to see you. Also known as JJ. JJ, good to see you. >> Thank you for having me on again, John. >> Hey, great to have you come on. I know we've talked many times on theCUBE, but you've got some exciting news. You got a new firm, OSS Capital. Open Source Software, not operational support like a telco, but this is an investment opportunity where you're making investments. Congratulations. >> Thank you. >> So I know you can't talk about some of the specifics on the funds size, but you are actually going to go out, talk to entrepreneurs, make some equity investments. Around open source software. What's the thesis? How did you get here, why did you do it? What's motivating you, and what's the thesis? >> A lot of questions in there. Yeah, I mean this is a really profoundly huge year for open source software. On a bunch of different levels. I think the biggest kind of thing everyone anchors towards is GitHub being acquired by Microsoft. Just a couple of weeks ago, we had the two huge hadoop vendors join forces. That, I think, surprised a lot of people. MuleSoft, which is a big opensource middleware company, getting acquired by Salesforce just a year after going public. Just a huge outcome. I think one observation, just to sort of like summarize the year 2018, is actually, starting in January, almost on sort of like a monthly basis, we've observed a major sort of opensource software company outcome. And sort of kicking off the year, we had CoreOS getting acquired by Red Hat. Brandon and Alex, the founders over there, built a really interesting company in the Kubernetes ecosystem. And I think in February, Al Fresco, which is an open source content portal taking privatization outcome from a private equity firm, I believe in March we had Magento getting acquired by Adobe, which an open source based CMS. PHP CMS. So just a lot of activity for significant outcomes. Multibillion dollar outcomes of commercial open source companies. And open source software is something like 20 years old. 20 years in the making. And this year in particular, I've just seen just a huge amount of large scale outcomes that have been many years in the making from companies that have taken lots of venture funding. And in a lot of cases, sort of partially focused funding from different investors that have an affinity for open source software and sort of understand the uniqueness of the open source model when it's applied to business, when it's applied to company building. But more sort of opportunistic and sort of affinity oriented, as opposed to a pure focus. So that's kind of been part of the motivation. I'd say the more authentically compelling motivation for doing this is that it just needs to exist. This is sort of a model that is happening by necessity. We're seeing more and more software companies be open source software companies. So open source first. They're built in a distributed way. They're leveraging engineers and talent around the world. They're just part of this open source kind of philosophy. And they are fundamentally kind of commercial open source software companies. We felt that if you had a firm basically designed in a way to exclusively focus on those kind of companies, and where the firmware actually backed and supported by the founders of the largest commercial open source companies in the world before sort of the last decade. That could actually deliver a lot of value. So we've been sort of blogging a little bit about this. >> And you wrote a great post on it. I read about open source monetization. But I think one of the things I'm seeing as well that supports your thesis, and I like to get your reaction to it because I think this is something that's not really talked about, but open source is still young. I mean, you go back. I remember the days when we used to have to hide in the shadows to get licenses and pirate stuff and do all those crazy stuff. But now, it's only a couple decades away. The leaders that were investing were usually entrepreneurs that've been successful. The Rob Bearns, the Amar Wadhwa, the guy that did Spring. All these different open source. Linux, obviously, great success story. But there hasn't any been any institutional. Yeah, you got benchmark, other things, done some investments. A discipline around open source. Where open source is now table stakes in all software development. Cloud is scaling, scaling out globally. There's no real foc- There's never been a firm that's been focused on- Just open source from a commercial, while maintaining the purity and ethos of open source. I mean, is that. >> You agree? >> That's true. >> 100%, yeah. That's been the big part of creating the firm is aligning and solving for a pure focused structure. And I think what I'll say abstractly is this sort of venture capital, venture style approach to funding enterprise technology companies, software companies in general, has been to kind of find great entrepreneurs and in an abstract way that can build great technology companies. Can bring them to market, can sell them, and can scale them, and so on. And either create categories, or dominate existing categories, and disrupt incumbents, and so on. And I think while that has worked for quite a while, in the venture industry overall, in the 50, 60 years of the venture industry, lots of successful firms, I think what we're starting to see is a necessary shift toward accounting for the fundamental differences of opensource software as it relates to new technology getting created and going, and new software companies kind of coming into market. So we actually fundamentally believe that commercial open source software companies are fundamentally different. Functionally in almost every way, as compared to proprietary closed source software companies of the last 30 years. And the way we've sort of designed our firm and we'll about ten people pretty soon. We're just about a month in. We're growing the team quickly, but we're sort of a small, focused team. >> A ten's not focused small, I mean, I know venture firms that have two billion in management that don't have more than 20 people. >> Well, we have portfolio partners that are focused in different functional areas where commercial open source software companies have really fundamental differences. If you were to sort of stack rank, by function, where commercial open source software companies are really fundamentally different, sort of top to bottom. Legal would be, probably, the very top of the list. Right, in terms of license compliance management, structuring all the sort of protections and provisions around how intellectual property is actually shipped to and sold to customers. The legal licensing aspects. The commercial software licensing. This is quite a polarizing hot topic these days. The second big functional area where we have a portfolio partner focused on this is finance. Finance is another area where commercial open source software companies have to sort of behaviorally orient and apply that function very, very differently as compared to proprietary software companies. So we're crazy honored and excited to have world experts and very respected leaders in those different areas sort of helping to provide sort of different pillars of wisdom to our portfolio companies, our portfolio founders, in those different functional areas. And we provide a really focused kind of structure for them. >> Well I want to ask you the kind of question that kind of bridges the old way and new way, 'cause I definitely see you guys definitely being new and different, which is good. Or as Andy Jassy would say, you can be misunderstood for a while, but as you become successful, people will start understanding what you do. And that's a great example of Amazon. The pattern with success is traditionally the same. If we kind of encapsulate the difference between open source old and new, and that is you have something of value, and you're disrupting the market and collecting rents from it. Or revenue, or profit. So that's commercial, that's how businesses run. How are you guys going to disrupt with open source software the next generation value creation? We know how value's created, certainly in software that opensource has shown a path on how to create value in writing software if code is value and functionality's value. But to commercialize and create revenue, which is people paying something for something. That's a little bit different kind of value extraction from the value creation. So open source software can create value in functionality and value product. Now you bring it to the market, you get paid for it, you have to disrupt somebody, you have to create something. How are you looking at that? What's the vision of the creation, the extraction of value, who's disrupted, is it greenfield new opportunities? What's your vision? >> A lot of nuance and complexity in that question. What I would say is- >> Well, open source is creating products. >> Well, open source is the basis for creating products in a different kind of way. I'll go back to your question around let's just sort of maybe simplify it as the value creation and the value capture dynamics, right? We've sort of written a few posts about this, and it's subtle, but it's easy to understand if you look at it from a fundamental kind of perspective. We actually believe, and we'll be publishing research on this, and maybe even sort of more principled scientific, perhaps, even ways of looking at it. And then blog posts and research. We believe that open source software will always generate or create orders of magnitude more value than any constituent can capture. Right, and that's a fundamental way of looking at it. So if you see how cloud providers are capturing value that open source creates, whether it's Elasticsearch, or Postgres, or MySQL or Hadoop. And then commercial open source software companies that capture value that open source software creates, whether it's companies like Confluent around Kafka, or Cloudera around Hadoop, or Databricks around Apache Spark. Or whether it's the creators of those projects. The creators of Spark and Hadoop and Elasticsearch, sometimes many of them are the founders of those companies I mentioned, and sometimes they're not. We just believe regardless of how that sort of value is captured by the cloud providers, the commercial vendors, or the creators, the value created relative to the value captured will always be orders and orders of magnitude greater. And this is expressed in another way, which this may be easier to understand, it's a sort of reinforcing this kind of assertion that there's orders of magnitude value created far greater than what can be captured. If you were to do a survey, which we're currently in the process of doing, and I'm happy to sort of say that publicly for the first time here, of all the commercial open source software companies that have projects with large significant adoption, whether, say for example, it's Docker, with millions of users, or Apache Hadoop. How many Hadoop deployments there are. How many customers' companies are there running Hadoop deployments. Or it may be even MySQL. How many MySQL installations are there. And then you were to sort of survey those companies and see how many end users are there relative to how many customers are paying for the usage of the project. It would probably be something like if there were a million users of a given project, the company behind that project or the cloud provider, or say the end user, the developer behind the project, is unlikely to capture more than, say, 1% or a couple percent of those end users to companies, to paying companies, to paying customers. And many times, that's high. Many times, 1% to 2% is very high. Often, what we've seen actually anecdotally, and we're doing principled research around this, and we'll have data here across a large number of companies, many times it's a fraction of 1%. Which is just sort of maybe sometimes 10% of 1%, or even smaller. >> So the practitioners will be making more money than the actual vendors? >> Absolutely right. End users and practitioners always stand to benefit far greater because of the fundamental nature of open source. It's permissionless, it's disaggregated, the value creation dynamics are untethered, and it is fundamentally freely available to use, freely available to contribute to, with different constraints based on the license. However, all those things are sort of like disaggregating the creating of technology into sort of an unbounded network. And that's really, really incredible. >> Okay, so first of all, I agree with your premise 100%. We've seen it with CUBE, where videos are free. >> And that's a good thing. All those things are good. >> And Dave Vellante says this all the time on theCUBE. And we actually pointed this out and called this in the Hadoop ecosystem in 2012. In fact, we actually said that on theCUBE, and it turned out to be true, 'cause look at Hortonworks and Cloudera had to merge because, again, the market changed very quickly >> Value Creation. >> Because value >> Was created around them in the immediate cloud, etc. So the question is, that changes the valuation mechanisms. So if this true, which we believe it is. Just say it is. Then the traditional net present value cash flow metric of the value of the firm, not your firm, but, like, if I'm an open source firm, I'm only one portion of the extraction. I'm a supplier, and I'm an enabler, the valuation on cash flow might not be as great as the real impact. So the question I have for you, have you thought about the valuation? 'Cause now you're thinking about bigger construct community network effects. These are new dynamics. I don't think anyone's actually crunched a valuation model around this. So if someone knew that, say for example, an open source project created all this value, and they weren't necessarily harvesting it from a cash flow perspective, there might be other ways to monetize it. Have you though about that, and what's your reaction to that concept? 'Cause capitalism would kind of shake down the system. 'Cause why would someone be motivated to participate if they're not capturing any value? So if the value shifts, are they still going to be able to participate? You follow the logic I'm trying to- >> I definitely do. I think what I would say to that is we expect and we encourage and we will absolutely heavily invest in more business model innovation in the area of open source. So what I mean by that is, and it's important to sort of qualify a few things there. There's a huge amount of polarization and lack of consensus, lack of industry consensus on what it actually means to have or implement an open source based business model. In fact there's a lot of people who just sort of point blankedly assert that an opensource business model does not exist. We believe that many business models for monetizing and commercializing open source exist. We've blogged and written about a few of them. Their services and training and support. There's open core, which is very effective in sort of a spectrum of ways to implement open core. Around the core, you can have a thin crust or a thick crust. There's SAS. There are hardware based distribution models, things like Sourcefire, and Cumulus Networks. And there are also network based approaches. For example, project called Storj or Stor-J. Being developed and run now by Ben Golub, who's the former CEO of Docker. >> CUBE alumni. >> Ben's really great open source veteran. This is a network, kind of decentralized network based approach of sort of right sizing the production and consumption of the resource of a storage based open source project in a decentralized network. So those are sort of four or five ways to commercializing value, however, four or five ways of commercializing value, however what we believe is that there will be more business model innovation. There will be more developments around how you can better capture more, or in different ways, the value that open source creates. However, what I will say though, is it is unrealistic to expect two things. It is unrealistic and, in fact, unfair to expect that any of those constituents will contribute back to open source proportional to the value that they received from it, or the benefit, and I'm actually paraphrasing Doug Cutting there, who tweeted this a couple of years ago. Very profoundly deep, wise tweet, which I very strongly agree with. And it is also unrealistic to expect a second thing, which is that any of those constituents can capture a material portion of the value that open source creates, which I would assert is many trillions of dollars, perhaps tens of trillions of dollars. It's really hard to quantify that. And it's not just dollars in economic sense, it's dollars in productivity time saved, new markets, new areas, and so on. >> Yeah, I think this is interesting, and I think that we'll be an open book at that. But I will say that what I've observed in looking through all these CUBE interviews, I think that business model innovation absolutely is something that is an IP. >> We need it. Well, it's now intellectual property, the business model isn't, hey I went to business school, learned this at Babson or Harvard, I learned this business model. We're going to do SAS premium. Okay, I get that. There's going to be very interesting new innovations coming, and I think that's the new IP. 'Cause open source, if it's community based, there's going to be formulas. So that's going to be really inter- Okay, so now let's get back to actual funding itself. You guys are doing early stage. Can you take us through the approach? >> We're very focused on early stage, investing, and backing teams that are, just sort of welcoming the idea of a commercial entity around their open source project. Or building a business fundamentally dependent on an open source project or maybe even more than one. The reason for that is this is really where there's a lot of structural inefficiency in supporting and backing those types of founders. >> I think one of the things with ... is with that acquisition. They were pure on the open source side, doing a great job, didn't want to push the business model too hard because the open source, let's face it, you got people like, eh, I don't want to get caught on the business side, and get revenue, perverse incentives might come up, or fear of incentives that might be different or not aligned. Was a great a value. >> I think so. >> So Red Hat got a steal on that one. But as you go forward, there's going to be certainly a lot more stuff. We're seeing a lot of it now in CNCF, for instance. I want to get your thoughts on this because, being the co founder of KubeCon, and donating it to the CNCF, Kubernetes is the hottest thing on the planet, as we talked about many years ago. What's your take on that, now? I see exciting things happening. What is the impact of Kubernetes, in your opinion, to the world, and where do you see that evolving rapidly, and where is the focus here as the people should be paying attention to? >> I think that Kubernetes replaces EC2. Kubernetes is a disaggregated API for distributed computing anywhere. And it happens to be portable and able to run on any kind of computer infrastructure, which sort of makes it like a liquid disaggregated EC2-like API. Which a lot of people have been sort of chasing and trying to implement for many years with things like OpenStack or Eucalyptus. But interestingly, Kubernetes is sort of the right abstraction for distributed computing, because it meets people where they are architecturally. It's sort of aligned with this current movement around distributed systems first designs. Microservices, packaging things in small compartmentalized units. >> Good for integrating of existing stuff. >> Absolutely, and it's very composable, un-opinionated architecturally. So you can sort of take an application and structure it in any given way, and as long as it has this sort of isolation boundary of a container, you can run it on Kubernetes without needing to sort of retrofit the architecture, which is really awesome. I think Kubernetes is a foundational part of the next kind of computing paradigm in the same way that Linux was foundational to the computing paradigm that gave rise to the internet. We had commodity hardware meeting open source based sort of cost reduction and efficiency, which really Linux enabled, and the movement toward scale out data center infrastructure that supported the Internet's sort of maturity and infrastructure. I think we're starting to see the same type of repeat effect thanks to Kubernetes basically being really well received by engineers, by the cloud providers. It's now the universal sort of standard for running container based applications on the different cloud providers. >> And think having the non-technical opinion posture, as you said, architectural posture, allows it to be compatible with a new kind of heterogeneous. >> Heterogeneity is critical. >> Heterogeneity is key, 'cause it's not just within the environment, it's also within each vendor, or customer has more heterogeneity. So, okay, now that's key. So multi cloud, I want to get your thoughts on multi cloud, because now this goes into some of things that might build on top of if Kubernetes continues to go down the road that you say it does. Then the next question is, stateful applications, service meshes. >> A lot of buzz words. A lot of buzz words in there. Stateful application's real because at a certain point in time, you have a maturity curve with critical infrastructure that starts to become appealing for stateful mission critical storage systems, which is typically where you have all the crown jewels of a given company's infrastructure, whether it's a transactional system, or reading and writing core customer, or financial service information, or whatever it is. So Kubernetes' starting to hit this maturity curve where people are migrating really serious mission critical storage workloads onto that platform. And obviously we're going to start to see even more critical work loads. We're starting to see Edge workloads because Kubernetes is a pretty low footprint system, so you can run it on Edge devices, you can even run it on microcontrollers. We're sort of past the experimental, you know, fun and games was Raspberry Pi, sort of towers, and people actually legitimately doing real world Edge kind of deployments with Kubernetes. We're absolutely starting to see multi-geo, multi-replication, multi-cloud sort of style architectures becoming real, as well. Because Kubernetes is this API that the industry's agreeing upon sufficiently. We actually have agreement around this sort of surface area for distributed system style computing that if cloud providers can actually standardize on in a way that lets application specific vendors or new types of application deployment models innovate further, then we can really unlock this sort of tight coupling of proprietary services inside cloud providers and disaggregate it. Which is really exciting, and I forget the Netscape, Jim Barksdale. Bundling, un-bundling. We're starting to see the un-bundling of proprietary cloud computing service API's. Things like Kinesis, and ALB and ELB and proprietary storage services, and these other sticky services get un-bundled because of two big things. Open source, obviously, we have open source alternative data paths. And then we have Kubernetes which allows us to sort of disaggregate things out pretty easily. >> I want to hear your thoughts, one final concept, before we break, 'cause I was having a private conversation with three people besides myself. A big time CIO of a company that if I said the name everyone would go, oh my god, that guy is huge, he's seen it all going back many, many ways. Currently done a lot of innovation. A hardcore network chip guy who knows networking, old school infrastructure. And then a cloud native application founder who knows a lot about software development and is state-of-the-art cloud native. So cloud native, all experienced, old-school, kind of about my age, a cloud native app developer, a big time CIO, and a chip networking kind of infrastructure guy. And we're talking, and one thing that came out, I want to get you thoughts on this, he says, so what's going on with DevOps, how do you see this service mesh, is a stay for (mumbles) on top of the stack, no stacks, horizontally scalable. And the comment that came out was storage and networking have had this relationship with everything since day one. Network moves a packet from point A to point B, and nothing happens in between, maybe some inspection. And storage goes from here now to the then, because you store it. He goes, that premise moves up the stacks, so then the cloud native guy goes, well that's what's happening up at the top, there's a lot of moving things around, workloads and or services, provisioning services, and then from now to then state. In real time. And what dawned on the next conversation the CIO goes, well this is exactly our challenge. We have under the hood infrastructure being programmable, >> We're having some trouble with the connection. Please try again. >> My phone's calling me. >> Programmable connections. >> So you got the programmable on the top of the stack too, so the CIO said, that's exactly the problem we're trying to solve. We're trying to solve some of these network storage concepts now at an application level. Your thoughts to that. >> Well, I think if I could tease apart everything you just said, which is profound synthesis of a lot of different things, I think we've started to see application logic leak out of application code itself into dedicated layers that are really good at doing one specific thing. So traditionally we had some crud style kind of behavioral semantics implemented around business logic. And then, inside of that, you also had libraries for doing connectivity and lookups and service discovery and locking and key management and encryption and coordination with other types of applications. And all that stuff was sort of shoved into the single big application binary. And now, we're starting to see all those language runtime specific parts of application code sort of crack or leak out into these dedicated, highly scalable, Unix philosophy oriented sort of like layers. So things like Envoy are really just built for the sort of nervous system layer of application communication fabric up and down the layer two through layer seven sort of protocol transport stack, which is really profound. We're seeing things like Vault from Hashicorp handle secure key storage persistence of application dedication, authorization, metadata and information to sort of access different systems and end points. And that's a dedicated sort of stateful layer that you can sort of fragment out and delegate sort of application specific functionality to, which is really great for scalability reasons. And on, and on, and on. So we've started to see that, and I think one way of looking at that is it's a cycle. It's the sort of bundling and un-bundling aspect. >> One of the granny level services are getting a really low level- >> Yeah, it's a sort of like bundling and un-bundling and so we've got all this un-bundling happening out of application code to these dedicated layers. The bundling back may happen. I've actually seen a few Bay Area companies go like, we're going back to the monolith 'cause it actually gives us lots of efficiencies in things that we though were trade offs before. We're actually comfortable with a big monorepo, and one or two core languages, and we're going to build everything into these big binaries, and everyone's going to sort of live in the same source code repository and break things out through folders or whatever. There's a lot of really interesting things. I don't want to say we're sort of clear on where this bundling, un-bundling is happening, but I do think that there's a lot of un-bundling happening right now. And there's a lot of opportunity there. >> And the open source, obviously, driving it. So final question for you, how many deals have you done? Can you talk a little bit about the firm? And exciting things and plans that you have going forward. >> Yeah, we're going to be making a lot of announcements over the next few months, and we're, I guess, extremely thrilled. I don't want to say overwhelmed, 'cause we're able to handle all of the volume and inquiries and inbound interest. We're really honored and thrilled by the reception over the last couple weeks from announcing the firm on the first of October, sort of before the Hortonworks Cloudera merger. The JFrog funding announcement that week. The Elastic IPO. Just a lot of really awesome things happened that week. This is obviously before Microsoft open sourced all their patents. We'll be announcing more investments that we've made. We announced our first one on the first of October as well with the announcement of the firm. We've made a good number of investments. We're not able to talk to much about our first initiative, but you'll hear more about that in the near future. >> Well, we're excited. I think it's the timing's perfect. I know you've been working on this kind of vision for a while, and I think it's really great timing. Congratulations, JJ >> Thank you so much. Thanks for having me on. >> Joesph Jacks, also known as JJ, founder and general partner of OSS Capital, Open Source Software Capital, co founder of KubeCon, which is now part of the CNCF. A real great player in the community and the ecosystem, great to have him on theCUBE, thanks for coming in. I'm John Furrier, thanks for watching. >> Thanks, John. (bright symphony music)

>> Announcer: Live, from Copenhagen, Denmark, it's theCUBE. Covering KubeCon and CloudNativeCon Europe 2018. Brought to you by the Cloud Native Computing Foundation and its Ecosystem Partners. >> Well everyone, welcome back to the live coverage of theCUBE here in Copenhagen, Denmark for KubeCon, Kubernetes Con 2018, part of the CNCF, Cloud Native Compute Foundation, part of the Linux Foundation. I'm John Furrier with Lauren Cooney, the founder of Spark Labs, breaking down day two, wrapping up our coverage of KubeCon and all the success that we've seen with Kubernetes, I thought it would be really appropriate to bring on the cofounder of KubeCon originally, Joseph Jacks, known as JJ in the industry, a good friend of theCUBE and part of the early formation of what is now Cloud Native. We were all riffing on that at the time. welcome back to theCUBE, great to see you. >> Thank you for having me John. >> So, for the story, for the folks out there, you know Cloud Native was really seen by the devops community, and infrastructure code was no secret to the insiders in the timeframes from 2010 through 2015, 16 timeframe, but really it was an open stack summit. A lot of people were kind of like, hey, you know, Google's got Kubernetes, they're going to open it up and this could be a real game changer, container, Docker was flying off the shelves. So we just kind of saw, right, and you were there and we were talking so there was a group of us. You were one of them. And you founded KubeCon, and bolted into the, at that time, the satellite Linux Foundation events, and then you pass it off as a good community citizen to the CNCF, so I wanted to just make sure that people knew that. What a great success. What's your impression? I mean, are you blown away? >> I am definitely blown away. I mean I think the size and scale of the European audience is remarkable. We had something like slightly less than half this in Austin last year. So to see more than that come here in Europe I think shows the global kind of growth curve as well as like, I think, Dan and someone else was asking sort of raise your hand if you've been to Kubecon Austin and very few actually, so there's a lot of new people showing up in Europe. I think it just shows the demand-- >> And Dan's been traveling around. I've seen him in China, some events I've been to. >> Joseph: All over. >> He's really working hard so props to him. We gave him some great props earlier. But he also told us Shanghai is coming online. >> Joseph: Yeah. >> So you got Shanghai, you to Barcelona next year for the European show, and of course Seattle. This is a community celebrating right now because there's a lot of high fives going on right now because there's a lot of cool, we've got some sort of core standard, defacto standard, now let's go to work. What are you working on now? You got a stealth startup? Share a little bit about it. I know you don't want to give the details out, but where is it kind of above the stack? Where you going to be playing? >> Sure, so we're not talking too much in terms of specifics and we're pretty stealthy, but I can tell you what I'm personally very excited about in terms of where Kubernetes is going and kind of where this ecosystem is starting to mature for practitioners, for enterprises. So one of the things that I think Kubernetes is starting to bring to bear is this idea of commoditizing distributed systems for everyday developers, for everyday enterprises. And I think that that is sort of the first time in sort of maybe, maybe the history of software development, software engineering and building applications, we're standardizing on a set of primitives, a set of building blocks for distributed system style programming. You know we had in previous eras things like Erlang and fault tolerant programming and frameworks, but those were sort of like pocketed into different programming communities and different types of stacks. I think Kubernetes is the one sort of horizontal technology that the industry's adopting and it's giving us these amazing properties, so I think some of the things that we're focusing on or excited about involve sort of the programming layer on top of Kubernetes in simplifying the experience of kind of bringing all stateful and enterprise workloads and different types of application paradigms natively into Kubernetes without requiring a developer to really understand and learn the Kubernetes primitives themselves. >> That's next level infrastructure as code. Yeah so as Kubernetes becomes more successful, as Kubernetes succeeds at a larger and larger scale, people simply shouldn't have to know or understand the internals. There's a lot of people, I think Kelsey and a few other people, started to talk about Kubernetes as the Linux kernel of distributed computing or distributed systems, and I think that's a really great way of looking at it. You know, do programmers make file system calls directly when they're building their applications? Do they script directly against the kernel for maybe some very high performance things. But generally speaking when you're writing a service or you're writing a microservice or some business logic, you're writing at a higher level of abstraction and a language that's doing some IO and maybe some reading and writing files, but you're using higher level abstractions. So I think by the same token, the focus today with Kubernetes is people are learning this API. I think over time people are going to be programming against that API at a higher level. And what are you doing here, the show? Obviously you're (mumbles) so you're doing some (mumbles) intelligence. Conversations you've been in, can you share your opinion of what's going on here? Your thoughts on the content program, the architecture, the decisions they've made. >> I think we've just, so lots of questions in there. What am I doing here? I just get so energized and I'm so, I just get reinvigorated kind of being here and talking to people and it's just super cool to see a lot of old faces, people who've been here for a while, and you know, one of the things that excites me, and this is just like proof that the event's gotten so huge. I walk around and I see a lot of familiar faces, but more than 80, 90% of people I've never seen before, and I'm like wow this has like gotten really super huge mainstream. Talking with some customers, getting a good sense of kind of what's going on. I think we've seen two really huge kind of trends come out of the event. One is this idea of multicloud sort of as a focus area, and you've talked with Bassam at Upbound and the sort of multicloud control plane, kind of need and demand out there in the community and the user base. I think what Bassam's doing is extremely exciting. The other, so multicloud is a really big paradigm that most companies are sort of prioritizing. Kubernetes is available now on all the cloud providers, but how do we actually adopt it in a way that is agnostic to any cloud provider service. That's one really big trend. The second big thing that I think we're starting to see, just kind of across a lot of talks is taking the Kubernetes API and extending it and wrapping it around stateful applications and stateful workloads, and being able to sort of program that API. And so we saw the announcement from Red Hat on the operator framework. We've seen projects like Kube Builder and other things that are really about sort of building native custom Kubernetes APIs for your applications. So extensibility, using the Kubernetes API as a building block, and then multicloud. I think those are really two huge trends happening here. >> What is your view on, I'm actually going to put you on test here. So Red Hat made a bet on Kubernetes years ago when it was not obvious to a lot of the other big wales. >> Joseph: From the very beginning really. >> Yeah from the very beginning. And that paid off huge for Red Hat as an example. So the question is, what bets should people be making if you had to lay down some thought leadership on this here, 'cause you obviously are in the middle of it and been part of the beginning. There's some bets to be made. What are the bets that the IBMs and the HPs and the Cisco's and the big players have to make and what are the bets the startups have to make? >> Well yeah, there's two angles to that. I mean, I think the investment startups are making, are different set of investments and motivated differently than the multinational, huge, you know, technology companies that have billions of dollars. I think in the startup category, startups just should really embrace Kubernetes for speeding the way they build reliable and scalable applications. I think really from the very beginning Kubernetes is becoming kind of compelling and reasonable even at a very small scale, like for two or three node environment. It's becoming very easy to run and install and manage. Of course it gives you a lot of really great properties in terms of actually running, building your systems, adopting microservices, and scaling out your application. And that's what's sort of like a direct end user use case, startups, kind of building their business, building their stack on Kubernetes. We see companies building products on top of Kubernetes. You see a lot of them here on the expo floor. That's a different type of vendor startup ecosystem. I think there's lots of opportunities there. For the big multinationals, I think one really interesting thing that hasn't really quite been done yet, is sort of treating Kubernetes as a first-class citizen as opposed to a way to commercialize and enter a new market. I think one of the default ways large technology companies tend to look at something hypergrowth like Kubernetes and TensorFlow and other projects is wrapping around it and commercializing in some way, and I think a deeper more strategic path for large companies could be to really embed Kubernetes in the core kind of crown jewel IP assets that they have. So I'll give you an example, like, for let's just take SAP, I'll just pick on SAP randomly, for no reason. This is one of the largest enterprise software companies in the world. I would encourage the co-CEOs of SAP, for example. >> John: There's only one CEO now. >> Is there one CEO now? Okay. >> John: Snabe left. It's now (drowned out by talking). >> Oh, okay, gotcha. I haven't been keeping up on the SAP... But let's just say, you know, a CEO boardroom level discussion of replatforming the entire enterprise application stack on something like Kubernetes could deliver a ton of really core meaningful benefits to their business. And I don't think like deep super strategic investments like that at that level are being made quite yet. I think at a certain point in time in the future they'll probably start to be made that way. But that's how I would like look at smart investments on the bigger scale. >> We're not seeing scale yet with Kubernetes, just the toe is in the water. >> I think we're starting to see scale, John. I think we are. >> John: What's the scale number in clusters? >> I'll give you the best example, which came up today, and actually really surprised me which I think was a super compelling example. The largest retailer in China, so essentially the Amazon of China, JD.com, is running in production for years now at 20,000 compute nodes with Kubernetes, and their largest cluster is a 5,000 node cluster. And so this is pushing the boundary of the sort of production-- >> And I think that may be the biggest one I've heard. >> Yeah, that's certainly, I mean for a disclosed user that's pretty huge. We're starting to see people actually talk publicly about this which is remarkable. And there are huge deployments out there. >> We saw Tyler Jewell come on from WSO2. He's got a new thing called Ballerina. New programming language, have you seen that? >> Joseph: I have, I have. >> Thoughts on that? What's your thoughts on that? >> You know, I think that, so I won't make any particular specific comments on Ballerina, I'm not extremely informed on it. I did play with a little bit, I don't want to give any of my opinions, but what I'd say, and I think Tyler actually mentioned this, one of the things that I believe is going to be a big deal in the coming years, is so, trying to think of Kubernetes as an implementation detail, as the kernel, do you interact directly with that? Do you learn that interface directly? Are you sort of kind of optimizing your application to be sort of natively aware of those abstractions? I think the answer to all of those questions is no, and Kubernetes is sort of delegated as a compiler target, and so frankly like directionally speaking, I think what Ballerina's sort of design is aspiring towards is the right one. Compile time abstraction for building distributed systems is probably the next logical progression. I like to think of, and I think Brendan Burns has started to talk about this over the last year or two. Everyone's writing assembly code 'cause we're swimming yaml and configuration based designs and systems. You know, sort of pseudodeclarative, but more imperative in static configurations. When in reality we shouldn't be writing these assembly artifacts. We should be delegating all of this complexity to a compiler in the same way that you know, we went from assembly to C to higher level languages. So I think over time that starts to make a lot of sense, and we're going to see a lot of innovation here probably. >> What's your take on the community formation? Obviously, it's growing, so, any observations, any insight for the folks watching what's happening in the community, patterns, trends you'd see, like, don't like. >> I think we could do a better job of reducing politics amongst the really sort of senior community leaders, particularly who have incentives behind their sort of agendas and sort of opinions, since they work for various, you know, large and small companies. >> Yeah, who horse in this race. >> Sure, and there's, whether they're perverse incentives or not, I think net the project has such a high quality genuine, like humble, focused group of people leading it that there isn't much pollution and negativity there. But I think there could be a higher standard in some cases. Since the project is so huge and there are so many very fast moving areas of evolution, there tends to be sort of a fast curve toward many cooks being in the kitchen, you know, when new things materialize and I think that could be better handled. But positive side, I think like the project is becoming incredibly diverse. I just get super excited to see Aparna from Google leading the project at Google, both on the hosted Saas offering and the Kubernetes project. People like Liz and others. And I just think it's an awesome, welcoming, super diverse community. And people should really highlight that more. 'Cause I think it's a unique asset of the project. >> Well you're involved in some deep history. I think we're going to be looking this as moment where there was once a KubeCon that was not part of the CNCF, and you know, you did the right thing, did a good thing. You could have kept it to yourself and made some good cash. >> It's definitely gotten really big, and it's way beyond me now at this point. >> Those guys did a good job with CNCF. >> They're doing phenomenal. I think vast majority of the credit, at this scale, goes to Chris Anasik and Dan Conn, and the events team at the Linux Foundation, CNCF, and obviously Kelsey and Liz and Michelle Noorali and many others. But blood, sweat, and tears. It's no small feat pulling off an event like this. You know, corralling the CFP process, coordinating speakers, setting the themes, it's a really huge job. >> And now they got to deal with all the community, licenses, Lauren your thoughts? >> Well they're consistent across Apache v2 I believe is what Dan said, so all the projects under the CNCF are consistently licensed. So I think that's great. I think they actually have it together there. You know, I do share your concerns about the politics that are going on a little bit back and forth, the high level, I tend to look back at history a little bit, and for those of us that remember JBoss and the JBoss fork, we're a little bit nervous, right? So I think that it's important to take a look at that and make sure that that doesn't happen. Also, you know, open stack and the stuff that we've talked about before with distros coming out or too many distros going to be hitting the street, and how do we keep that more narrow focused, so this can go across-- >> Yeah, I started this, I like to list rank and iterate things, and I started with this sheet of all the vendors, you know, all the Kubernetes vendors, and then Linux Foundation, or CNCF took it over, and they've got a phenomenal sort of conformance testing and sort of compliance versioning sheet, which lists all the vendors and certification status and updates and so on and I think there's 50 or 60 companies. On one hand I think that's great, because it's more innovation, lots of service providers and offerings, but there is a concern that there might be some fragmentation, but again, this is a really big area of focus, and I think it's being addressed. Yeah, I think the right ones will end up winning, right? >> Joseph: Right, for sure. >> and that's what's going to be key. >> Joseph: Healthy competition. >> Yes. >> All right final question. Let's go around the horn. We'll start with you JJ, wrapping up KubeCon 2018, your thoughts, summary, what's happened here? What will we talk about next year about what happened this week in Denmark? >> I think this week in Denmark has been a huge turning point for the growth in Europe and sort of proof that Kubernetes is on like this unstoppable inflection, growth curve. We usually see a smaller audience here in Europe, relative to the domestic event before it. And we're just seeing the numbers get bigger and bigger. I think looking back we're also going to see just the quality of end users and the end user community and more production success stories starting to become front and center, which I think is really awesome. There's lots of vendors here. But I do believe we have a huge representation of end users and companies actually sharing what they're doing pragmatically and really changing their businesses from Financial Times to Cern and physics projects, and you know, JD and other huge companies. I think that's just really awesome. That's a unique thing of the Kubernetes project. There's some hugely transformative companies doing awesome things out there. >> Lauren your thoughts, summary of the week in Denmark? >> I think it's been awesome. There's so much innovation happening here and I don't want to overuse that word 'cause I think it's kind of BS at some point, but really these companies are doing new things, and they're taking this to new levels. I think that hearing about the excitement of the folks that are coming here to actually learn about Kubernetes is phenomenal, and they're going to bring that back into their companies, and you're going to see a lot more actually coming to Europe next year. I also true multicloud would be phenomenal. I would love that if you could actually glue those platforms together, per se. That's really what I'm looking for. But also security. I think security, there needs to be a security seg. We talked to customers earlier. That's something they want to see. I think that that needs to be something that's brought to the table. >> That's awesome. My view is very simple. You know I think they've done a good job in CNCF and Linux Foundation, the team, building the ecosystem, keeping the governance and the technical and the content piece separate. I think they did a good job of showing the future state that we'd like to get to, which is true multicloud, workload portability, those things still out of reach in my opinion, but they did a great job of keeping the tight core. And to me, when I hear words like defacto standard I think of major inflection points where industries have moved big time. You think of internetworking, you think of the web, you think of these moments where that small little tweak created massive new brands and created a disruptor enabler that just created, changed the game. We saw Cisco coming out of that movement of IP with routers you're seeing 3Com come out of that world. I think that this change, this new little nuance called Kubernetes is going to be absolutely a defacto standard. I think it's definitely an inflection point and you're going to see startups come up with new ideas really fast in a new way, in a new modern global architecture, new startups, and I think people are going to be blown away. I think you're going to see fast rising growth companies. I think it's going to be an investment opportunity whether it's token economics or a venture backer private equity play. You're going to see people come out of the wood work, real smart entrepreneur. I think this is what people have been waiting for in the industry so I mean, I'm just super excited. And so thanks for coming on. >> Thank you for everything you do for the community. I think you truly extract the signal from the noise. I'm really excited to see you keep coming to the show, so it's really awesome. >> I appreciate your support, and again we're co-developing content in the open. Lauren great to host with you this week. >> Thank you, it's been awesome. >> And you got a great new venture, high five there. High five to the founder of KubeCon. This is theCUBE, not to be confused with KubeCon. And we're theCUBE, C-U-B-E. I'm John Furrier, thanks for watching. It's a wrap of day two global coverage here exclusively for KubeCon 2018, CNCF and the Linux Foundation. Thanks for watching. (techno music)

from Austin Texas it's the cube covering cube con and cloud native con 2017 brought to you by Red Hat the Lenox foundations and the cubes ecosystem partners well everyone welcome back to our exclusive coverage from the cube here in Austin Texas we're live on the floor at cloud native con and cube con cubic on like kubernetes gone not the cube con us but cute con we're Michele norelli who's the senior software engineer at Microsoft also the co-chair with Kelsey Heights our great event record-setting attendance I'm John ferry your host with stew minimun Michele welcome to the cube thank you so much for having me so people don't know about if they might have watch the street if you had a stream you're on stage keynoting and managing the whole program here congratulations more attendees here at this event than all the other cube cause of cloud native combined shows the growth and interest in a new way to develop new way to engage with other developers and create value yeah kubernetes has been the heart of it explain cloud native con and cube con what's the difference because I love cloud native but what's this Cooper Denny's thing I love that too yeah was it related a intertwine Wayne take him into his plane there's a there's a really big kubernetes audience and community and they need time to engage and just like work with each other and learn from each other and that's where coop Connie came from soku-kun with the original conference and the first one was a November in Seattle in 2016 and I was actually at that wine was a few hundred people and it was just so small people were actually asking like what is a pod what is kubernetes which are fine questions asked today as well but it was everyone was asking this question nobody was past that point and then you know kubernetes was donated to the CNCs and there were also these other cloud native projects that came about in the space and so we wanted a conference that encompasses both all of the cloud native projects as well as serbs the kubernetes community as well so that's where both of them came from some of the other cloud native projects have their own conferences like Prometheus has prom time and that's been growing as well I think the last one was 200 people up from 70 the last so I gotta ask you because we even cover us we were there at the cube con I was actually having drinks with Luke Tucker at JJ we're like hey we should do this Cuban Eddie's thing and bolted onto the Linux Foundation so you're president creates with the whole team it's been fun to watch Wow yeah but it's the tale of two stories in the community in the industry companies that got funded and we're building open-source and our participants who are building projects out and then a new onboarding of new developers coming into the community a lot of first-timers here you're seeing a visibility into the success of cloud yeah and they're Rieger engaged so you got a lot of folks who have invested into the community and new entrants a migration into the community yeah what does that dynamic mean to the CN CF how is that impacting how you structure in the programming and what are some of the insiders talking about what it is what's the reality yeah I think a lot of it has to do with you know this is a really positive community and there are just like so many people working together and collaborating not just because they I mean it looks like nice to be in a positive community right but you kind of have to like these problems are really hard and it's good to learn from different organizations that have like come across these projects or problems starting in the in the space before and they'll come and collaborate I think some of the things that we've been talking about inside the community is how to actually how to onboard people so the kubernetes community is starting up a new mentorship program to help people that are new to the community start learning how to review code and then PR code and and be productive members in the community and whatever they whatever area they want miss Michelle want to hear about kind of some of the breadth and depth of the community here yeah you know we went there's so many announcements there's a bunch of wando's yeah it's a brand new project I think what it was four projects a year ago and it's now 14 you know right how does somebody's supposed to get their arms around it should they be beat me about that you know where should somebody start you know what do you recommend yeah start with the that's a great question by the way I think that people should start with with a solution to a problem they already have so just know that people have run into these problems before and you should just go into the thing that you know about first and then if that leads you to a different problem and there's a solution that the CNCs you know has already come across then you can go into and dive into the other palms for example I am really interested in kubernetes and have been in that space but I think tracing is really interesting too and I want to start learning how to incorporate that into my workflow as well so show you you're also one of the diversity chairs yeah for the event you talk about kind of a diverse global nature of this community yeah we are spread across all time zone so I actually want to share an experience I have as a sake lead in kubernetes so at first I really wanted to serve all of the time zones and so we have these weekly sick meetings at 9:30 a.m. Pacific and I was like no maybe we should have like alternate meetings like alternate weekly meetings for other time zones but after talking to those the people in the other time sounds like they're very far off actually like China Asia Pacific I realize that they're actually more interested in reading notes and watching videos which is something I didn't actually know you know it's it's you think like oh you have to serve every community in the same way but what I've learned and face to face yeah base to base exactly and that's not actually how that's not how actually everybody wants to interact and so that's been an interesting thing I've learned from the diverse nature and this in the space let's see a challenges I mean we've been talking we're just that reinvent last week at Amazon obviously the number of services that they're rolling out is pretty strong there's a leader in the cloud but as multi cloud becomes the choice for most most enterprises and businesses the service requirements the baseline is got to be established seeing your community rolling out a lot of great new services but storage old storage is transferring to machine learning in AI and you got I Oh tea right around the corner new new kinds of applications yeah okay it's changing the game on the old card storage and security obviously two important areas you got to store the data data is that the card of the value proposition and then security security how are you guys dealing with that those challenges those political grounds that people are have a lot of making a lot of money in an old storage you mean ship a storage drive and here's an architecture those are being disrupted yeah I think they I mean they'll continue to be disrupted I think people are just going to bring in new and new more new and new use cases and then people will come and meet them meet those customers where they are and people just have to change I guess get used to it yeah shifter die yeah I think that some that that we are getting to that point but I can't only time will tell we'll see what are something exciting things that you see from the new developers I just recognize some friends here that I've haven't that dark wondering the community are new and they're kind of like licking their chops like wow what an excitement I could feel value and I could have a distribution I got a community and I can make money and then Dan said you know project products profits you put the product profit motive right on the table but he's clear at the same not pay to play it's okay to have profits if you have a good product for me project I buy that but the new developers like that because as an end scoreboard what are you guys doing with that new community what survived there around those kinds of opportunities you guys creating any programs for them or yeah I think just to just they can get involved you know I think knowledge is power perspective is power also so being involved helps give you a perspective to see where those gaps are and then come up with those services that are profitable or those tools that are profitable and I think this space can be very lucrative based on the number of people he sponsors I think he said he said the show was wondering if you can comment when you're building the schedule how do you balance you know all those platinum sponsors versus you know some of the you know practitioner companies that are also getting involved how do you there are there are different levels of sponsorship right like you mentioned the events team has a sponsorship section or sponsorship team and they handle most of placing sponsors and all of that and so they'll get whatever level they want but actually Kelsey and I do a lot of research and see like what's happening in the community what's interesting what's new and and we'll find time to highlight that as well which one is research what's your role in Microsoft share with the audience what are you working on what's your day-to-day job is it just foundation work are you doing coding what do you coding what's your fav is the VI MX what do you prefer yes my work is 30% community and 70% engineering I really love engineering but I also really love the community and just getting these opportunities to give back you know build skills as well learning how to speak in front of people as well these are both valuable skills to learn and it gives me an opportunity to just give back what I've learned so I appreciate those but I mostly work on developer tools that are open source that help people use containers and kubernetes a little more easily so I work on projects like Helms drafts and Brigade and these are just like things that we've seen the pain points that we've experienced and we want to kind of share our solutions with them so draft is the one I've been working on a lot have you heard of drops okay let me do the two second draft is a tool for application developers to build containerized apps without really understanding or having to understand all of what is kubernetes and containers so that's my favorite space to know you know one of the things we look at coming in here is there's that balance between there's complexity but there's flexibility you know I've heard Kelsey talking about our on when I talk to customer they're like oh I love kubernetes because I take vault and I take envoy and I take all these different things that put together and it does what I want but a lot of people are daunted and they say oh I want to I want to just go to Microsoft Azure and they'll take care of that so how do you look at that and what is the balance that we should be looking for as an industry yeah we've been emphasizing in the community a lot on plug ability across contracts it's like a theme that I think almost every project hurts and a word that you'll hear a lot I'm sure you already have heard a lot and I think that's because you can't meet everyone's needs so you build this modular component that does one thing very well and then you learn how to extend it and or you give people the ability to extend it and so that's really great for scaling a project I I do really appreciate the clouds coming out all of them with their own managed services because it's hard to operate and understand all of these things it's it takes a lot of depth in knowledge context and just prior experience and so I think that'll just make it a lot easier for people to onboard onto these technologies I was going to ask you I was going to ask so you brought up fug ability we saw you know Netflix on stage was his phenomenal of the culture yeah dynamic I think that the Schumer important conversation you know something we've been talking about silage is a real part of what we're seeing tech being a part of but the the things that popped out at me in the keynote were service mesh and pluggable architecture so I want to get your thoughts for the folks that aren't there is that in the trenches and inside the ropes what is a pluggable architecture and what is a service mesh these days because you got lyft and uber and all these great companies who have built hyper scale and large-scale systems in open source and now our big tech success stories donating these kinds of approaches pluggable architectures and service man talk a minute to explain so pluggable architectures this is why you have one layer of your stuff there's a piece of software that does something does one thing very well but you know every I like to say that every company is a snowflake and that's okay and so you may have some workflow or need that is specific to your company and so we shouldn't limit you to just what we think is the right solution to a problem we should allow you to extend or extend these pieces of software with modular components or just extensible components that that work for you does that make a little more sense yeah I work on helm and we also have a pluggable architecture because we were just getting so many requests from the community and it didn't make sense to put everything in the core code based if we did if we accepted one thing it would really just interrupt somebody else's workflow so that that's helped us a lot in in my personal experience I really like plug water it's actually that means you can go build a really kick butt app yeah nail it down to your specifications but decoupler from a core or avoiding kind the old spaghetti code mindset but kind of creating a model where it can be leveraged yeah plugin we all know plugins are but right so so that someone else could take advantage of it exactly yeah a service mesh that's evolved yeah heard a lot of that what is that yeah it's um so developers this is actually the lift story is really interesting to me so at lyft developers were really uneasy about moving from the monolith to the micro-services architecture just because they didn't early understand the network component and we're like network reliability would not be so reliable would fail and time service meshes have allowed engineers at lyft to understand where their failures happen and in terms like of a network standpoint and so you're basically abstracting with network layer and allowing more transparency into it this is like very useful for when you have lots of Micra services and you want this kind of reliability and stability awesome so one point 9s coming Spence support Windows that's what key and now a congratulations just go to the next level I mean growth talk about the growth because it's fun for us to watch you know kind of a small group core young community less than three years old really to kubernetes kind of had some traction but it really is going to be commoditized and that's not a bad thing so how do you what's your take on this what's the vibe what's that what's the current feeling inside the community right now excited pinching ourselves no I think everybody's in awe everybody is in awe and we're just like we want to make this the best experience possible in terms of an open source experience you know we want to welcome people to the community we want to serve the people's needs and we just we just want to do a good job because this is really fun and I think the people working on these problems are having a lot of fun with with seeing this kind of growth and support it's been great certainly for US president creation president and creation of this whole movement it's been fun to watch a document final question what should people expect this week what is the show going to hopefully do what's your prediction what's your purpose here what should people expect this week and the folks that didn't make it what do they miss okay there are so many things happening it's insane you're going to get a little bit of everything there's lots of different tracks lots of diverse content I think I'm when I go to conferences in my personal experience I really love technical salons those are really great because you can get your hands dirty and you can get questions answered by the people who created the project that's an experience that is is really powerful for me I went to the first open tracing salon and that's where I kind of got my hands dirty with tracing and been siegelman who's doing the keynote today this afternoon was the person who was teaching me how to like do this stuff so yeah it was awesome like some marketing fluff no it's not and it's just like it's it's real experienced very expert like experts you know in the in the space teaching you these things so that that definitely can't be replicated I think the cig sessions will be really cool there's a big focus on not just learning stuff but also collaborating and and just talking about things before they get documented so that's a really good experience here it's an action-packed schedule I tweeted that it feels like I'm you know when Burning Man had like a hundred people announced this big thing I think this is the beginning of a amazing industry people are cool they're helpful they're getting you're getting involved answering questions open-book here yeah at cloud native Punk you've got thanks Michele Farrelly been coming on co-chair senior engineer at Microsoft great to have her on the cube great keynote great color great fun exciting times here at cloud native con I'm John furry the founders look at angle media with too many men my co-hosts more live coverage after the short break