>>From the Bellagio hotel in Las Vegas, it's the cube covering UI path forward for brought to you by UI path. >>It's the Q we are live in Las Vegas at the Bellagio. Lisa Martin, with Dave Volante, we're covering UI path forward for this is our second day of coverage. We've had a lot of great conversations with customers at UI path partners, their senior leaders. And next up, we're going to be talking to, I'm going to say the queen of citizen developer nests. We're not just going to create that title for you. Sydney, Madison Prescott. She's the global head of intelligent automation at Spotify Sydney. Welcome to the program. I >>Am so excited >>To be here. We're excited to have you. So one of my, as we were talking before we went live, we both are big fans of Spotify. I don't know what we would do without it in our personal lives. But talk to me a little bit about Spotify automation, UI path. And I don't want to get into you your book, what you've done for citizen developers. >>Perfect. So Spotify is on a very interesting journey. Uh, we began the journey during the pandemic and we were speaking about this a little bit earlier. And so our journey began with trying to understand how we would tackle, uh, still wanting to upskill our employees, despite the fact that we were in the middle of this kind of global crisis. And so through that endeavor, we decided to actually split out our different, uh, automation capabilities into citizen developer and unattended automation. And we did all of this through a center of excellence. So a centralized, uh, COE, which would facilitate the growth of the automations, uh, whether on the citizen developer side or the unattended side. And through this programs, we set up, uh, several different trainings where we could facilitate the growth of the citizen developer community through five day, what we call bot boot camps and the bot boot camp is in essence, um, five day training, about four and a half, five hours a day, where we take anyone at Spotify who would like to upskill in this type of automation. And we teach them everything from the basics of robotic process automation, all the way to, you know, what are all of the Spotify specific things that you have to do in order to maintain a robust, uh, citizen developer footprint within your team. And so through that, uh, that entire journey, it's been quite amazing. We started with a very small footprint in our accounting team, and we have scaled now to over 100 citizen developers, uh, in a variety of functions within Spotify. >>And what was the role that you came to Spotify to do? Cause you came there, went there right before the everything happened. >>Yes. So I was actually, uh, brought into Spotify to stand up and scale out our intelligent automation center of excellence. >>So the center of excellence is, is sort of the main spring of knowledge, training innovation. And then the, the citizen developer piece, it sounds like you're pushing out distributing that knowledge. Right. And so I'm interested in that sort of architecture of automation is that you've got a combination of centralized expertise and decentralized innovation. Can you talk about that a little bit? >>Yeah. So it's very interesting actually. So we facilitate the citizen developer program through the center of excellence. So you can think of the center of excellence as the foundation of that knowledge. And our goal is to democratize that knowledge throughout the enterprise. And the way we do that is through the training. Uh, we facilitate the governance of the program. So making sure that all of the infrastructure is properly set up, uh, enabling the citizens, if they need support, just talking about ideation, uh, even so far as up-skilling as well. So upskilling all the way to a power user, uh, whereby those users could become true innovators and facilitate a wide variety of automations within their teams. And was it >>The events of the last 18 months? It really catalyzed this and kind of led you to really become a big advocate for citizen led development. >>It did. So we initially were starting with just the center of excellence and an unintended footprint, and we quickly pivoted and realized that we needed to in order to scale, uh, significantly given the, the situation working virtually, uh, we are a distributed team around the world that it was critical for our success that we could, uh, really distribute to this workout. And we felt that the best way to do that was through standing up a citizen developer program. >>The things that I'm trying to understand is the relationship between automation and data. And I look at Spotify in many respects is a data company, at least a company who really understands data. And I see you building all these awesome data products. I'm a subscriber as well, but you know, you've added podcasts, you've got contributors to those podcasts. You've obviously got artists and you know, these people obviously have to be paid. You have this sort of interesting ecosystem and these are all data products, if you will, that you guys build. And it's very cool sort of business model. What's the relationship between data and automation? >>Well, it is a big relationship. I would actually say it is probably the pivotal relationship because in order to tell that compelling story of digital transformation, we have to understand the data behind all of the automations that we're generating. Um, and this is whether it says in developer or COE built. Um, and so for us, it's, it's a critical component of our success that we can pinpoint those key metrics that we are looking at and tracking, you know, what does success mean for our center of excellence? What does it mean for our citizen led program? And this is everything from, you know, increased data quality to risk mitigation of different internal regulatory risk. Uh, it could be something as simple as our saved on automation. So it's, uh, a wide variety of attributes that we're looking at to really pinpoint where the successes are coming from and where we can improve maybe where we need to improve our automation footprint in a given business. >>Why did you write this book? >>Great question. So I believe in citizen development, I think it is a very unique approach to spreading out the way that you can transform your business. And so I saw a lot of struggles as I've gone through I'm in the industry with understanding citizen development, uh, the premise of it, and also understanding the technology behind it. Um, I am a big fan of studio X. And so the book specifically focuses on studio X. Um, it really introducing users to what is studio X and how really teaching individuals, how to upskill themselves, um, just through the use of the book, very intuitive and hopefully taking away some of the fear that the users may have about walking through a platform like studio S >>So what do I have to know to actually, can I read your book and then start coding? Is it by >>That is the goal. Yes. So the goal for the book is very hands-on. So it is, it is a book for, um, the novice business user, uh, someone who is not familiar with RPA, someone who may not even be familiar with UI path, they would be able to pick up this book, go through the set of exercises. It's very robust out over 400 pages. So it really packed a lot of knowledge in there, but the goal would be by the time you walk through every single exercise and complete the book, you would not only understand RPA. You would also understand UI path as a, as a service provider platform. You'd also understand the nuances of studio X. >>So in theory, someone like myself could get your book, download the community edition, start building automations, right? >>Yes, exactly. Exactly. >>You have to Google a few things, but yeah. >>Yes. And it comes with a very robust code code set up. They're able to actually look at the code and review, uh, examples of the code, uh, in a source code repository. So again, it's very novice users it's meant for, to help facilitate just the learning of someone who is maybe curious about RPA, curious about UI path, or just curious about studio app. >>I already have the use case. >>You do have these guys I'm interested in doing it too. I mean, I can tell that it's a passion project of yours that you fundamentally believe in. You know, we saw this morning data from IDC and we've seen lots of different data sources that talk about, oh, automation taking jobs, people being fearful, organizations, not being ready at the same time. We've had such a tumultuous last 18 months that organizations that weren't digital are probably gone and organizations that aren't, how did there was this massive uptake in automation because suddenly you couldn't get bodies into buildings. So tell me about how this book is a facility, first of all, tell us the name of it. And then as a facilitator of those employees who might be worried about their jobs being taken by bots, >>That is a great question. So the, the name of the book is robotic process automation, a citizen developers guide to hyper automation using UI path studio X. And I would say I've heard a lot of the conversation surrounding the loss of jobs, the potential fear, uh, we all we know as humans, we are generally unfortunately, a little resistant to change and, you know, the, I'll say the digital revolution that we're going through, uh, within the workforce, whether it is hybrid work, whether it is completely virtual work, it is a bit daunting. And I understand that fear, I think in alignment with the conversation that we had heard about earlier at forward there, RPA has the ability to generate a massive amount of not only improvements within different industries, but jobs as well. Right? And for someone who is looking at this kind of ever changing landscape, and they're wondering, where do I fit in? >>Am I going to get pushed out of a, of a general, you know, uh, industry? I would say that that fear turned that into power, turned that into ambition. Um, the level of upskilling that you can do on your own, whether it's using UiPath academy, whether it's reaching out to your center of excellence, it's incredible. Um, there's a wide variety of different ways that you can upskill yourself. And in essence, you become, um, a powerful player in your environment because not only do you have the business acumen, you now have the technical acumen, and that is everything. I mean, when we talk about transformation, we talk about where our industry's going. Um, there's a saying that, you know, every company now must be a technology company, right? And so this is the key, even as workers, even as employees, we all must be technologists. And so the real question is, think of yourself and think of this concept. I like to call human augmentation. How can you augment yourself through UI path, through the use of RPA to become that up-skilled worker, that next level worker who will be integral to the success of any company moving from, >>We talk a lot about upscaling. Now, of course, part of that upskilling I presume is learning how to use robotic process automation and the tooling, but it seems that there's more to it than that. And, and you just strike me as a person that's creative, you have a power persona. So what are these sort of intangible skills that, that I need to succeed in this new world? And can I learn them? >>That's a great question. I think one of the biggest skills, being able to think outside of the box, that is huge. Uh, and again, this goes back to at least question about what does it take and what should you, you should really think outside of the box about your own career, about your team, about your company, um, how you can improve upon what is already there, um, or how can you build something completely new that has never been thought of before. And so I think that's the biggest skill. The ability to, um, innovate, think, think innovatively and think outside of the box. Um, I believe it's, it's something that is maybe a little intuitive to some individuals, but you can also learn, you can learn to, um, get out of your own way, so to speak, uh, so that you can actually start to come up with these really creative ways to address, uh, whether it's complex business problems, uh, whether it's at an industry level or even just within your internal enterprise. >>And creativity is actually one of the attributes. And I guess it might not be in your DNA, but if you, you know, it's like humor, humor, right? If you hang out with funny people, you know, if you hang out with creative people, you can, you can learn about apply. >>That's right. That's right. But in the beginning of the pandemic, you know, one of the things that I think we all want, you seem to have a ton of motivation and ambition as Dave was saying. And, and I'm someone that normally has that too, but in the first year of pandemic, that was hard. It's hard to get motivated. It was hard to know where do I fit in? How do you advise? And now of course, when you publish the book six months ago, we're about a year into the pandemic. Things are looking better because here we are in Las Vegas at an in-person event with humans. But how do you, how do you see, how do you recommend to folks that are, that don't have technology backgrounds like you don't, I don't to motivate themselves to go, you can take the control, take it. And everybody don't, we all want control as people and take control of your career path. There are a lot of opportunities out there. How do you advise people navigating this challenging sort of mental state with there's so many opportunities sitting right here? Yes >>That's so I think it, it goes back to the getting out of your own way. It also goes back to really taking a look at assess assessing your own skillset, um, assessing your own personal drivers. What motivates you, uh, whether that is in your personal life, whether that is in your professional life, and then taking a look also at those motivators, how can I look at those and what use can I get out of those to help me to transform my own personal skill set and really grow out, uh, my, my capabilities, right, as a professional it's, it's all about really thinking through, uh, your, I'll say your professional background or role as ever-changing ever-growing. And as long as you approach it with a mindset of constantly growing constantly upskilling, I mean, honestly, the sky is truly the limit. >>I a weird question. If, if, if, if mastering word is a one and let's say learning, um, learning how to use Excel, macros is let's call it a three. Uh, all in the spectrum goes out to a, be a building, a complex, uh, you know, uh, AI model, data science kind of ML model. If that's a 10, where does learning how to code RPA as a citizen developer fit on that spectrum? Good question. >>Oh, that's a great question. I would say somewhere between, Hmm. I would say somewhere between maybe three and four around there, because you there. So again, we, we have so many tools that we can use to help upscale the set of sense at this point that we can really walk them through the nuances, uh, at a pace that is conducive to really retaining the knowledge. So I don't think it's, it's definitely not the level of, let's say, building out a complex, like machine learning model or something of that nature. It may be a little bit more in alignment with, um, if someone is up-skilled and macros, or you may be up-skilled in some other type of scripting, uh, language similar to that, I might even say sometimes a little bit, maybe a little bit less difficult than that, uh, depending on what you're trying to automate, right. The process you're trying to automate the company, >>But inside of a day, I can do something fairly simple, right? Yeah. >>Yes. So we actually, the, the training that we have at Spotify, we train our users from novice. Absolutely no understanding, no knowledge of RPA to building able, being able to build a bot in five days. And those are five half days sessions that the citizen developers attend. And by the morning of the fifth day, they actually have built a bot. And so it's, and it's very powerful, uh, being able to, to upskill someone and show them, I can take you from, you know, absolutely no understanding of RPA to actually having something, a bot that you can showcase that you can run within as little as five half days. I mean, it's very compelling to any business user, right? >>The business impact. Soon as you guys are too young to remember, but there's this thing called Lotus 1, 2, 3, we used to have to go to Lotus class slash file retrieve for you folks who remember this was all keyboard based, but it was game changing in terms of your personal productivity. And it sounds like there's a similar but much, much larger impact with RPA >>Impact. Talk to me about the impact of the program, especially in the last, this year, here we are in October, you mentioned started small, and now there's about a hundred folks. Talk to me about the appetite of that as we've seen this massive acceleration and the need to automate for everyday things that we expect as consumers, whether we're ordering food or buying something online. >>Hmm. So it really is a different mindset in terms of thinking through the way that we work differently. And so we really approached it with, if you're an accountant, think of what is the future role and responsibility of an accountant in this new digital, uh, I'll say environment. And through that, we have been able to really push this idea or this concept of up-skilling as a key element of personal professional success and also team success. Um, and that has been a game changer. So there's a lot of value that comes out of the cohesiveness between the personal desire to upskill and continue to, uh, be a, you know, a consummate professional in whatever role you're in, but also to help your team right, to be, to be, you know, a standout, uh, team player in terms of the skills that you're bringing to the table as both an accountant and someone that has now the power of automation within your skillset. Okay. >>And ask you one more question. And that is something that Dave brought up yesterday as we were, he was sitting on a panel with, and he was the only male, which is not common in our industry. How have you seen the role of females in technology changing? And I'm imagine you do work in stem. Imagine you're a motivational speaker you should be if you're not. Um, but how have you seen the role of females in technology changing in since there's so much opportunity there? >>Yes. That is a great question. I believe that RPA specifically, uh, is an incredible driver of women and influencing more women to enter into stem fields, primarily because it is such an innovative technology. There are so many roles he said that are open, just opening up. Uh, probably I've heard different numbers in terms of acceleration of growth over the next five to 10 years. So we're looking at a plethora of opportunities and these are brand new roles that women who are curious about stem, curious about being a technologist can dive right into from wherever they are. So whether they are a tax professional today, whether they are working within, you know, uh, counting, whether they're working with an internal audit, they have the opportunity now to test the waters. Um, and quite often it is such a, it's such a fascinating field. And as I said, there's so much potential around it and for growth and just for changing, uh, different industries, that it's a great driver for women to actually enter into, uh, stem technology, uh, and really drive change, facilitate change, and have more women at the table, so to speak. Okay. >>And you didn't, you didn't start in tech, in stem, right? I did not. Do you have a law degree or no, you have a Ms. >>So yes, little studies and then I actually, I'm a philosopher. So I started by my degree is in philosophy. I love >>This logic. Yes. I love how you've applied that to >>Yeah. Yes. I was not initially in stem and it was actually through an internship at a technology firm, uh, while I was in college that I don't first open to technology. And it just immediately captivated me just in terms of working, you know, that the speed, the pace, uh, just being able to solve these complex business problems at scale around the world. It was absolutely fascinating to me, obviously still is, but I think testing the waters in that way, um, as I was just talking before, it helped me to understand, I had never envisioned a career in technology, but having an opportunity to test the waters really enabled me to see that, wow, this is something where I have a skillset and it brings out a passion within me that I didn't know that I had. So it was a, it was a win-win. >>That's awesome. No worries. Last question. Where can folks go to get your book? >>Yes. So anywhere books are sold, uh, definitely on Amazon. Uh, we, if you are here at forward, we also are, have a book signing, so you can come find me. I'll be on the patio signing books and, uh, yeah, it's, it's everywhere. And I would love to hear feedback. And we're thinking about a second one. So please let us know how you like the, uh, the activities that are in there. >>Thank you. Congratulations. And Dave's going to pick one up so he can start. >>Yeah. The use case. I'm dying to dig >>In, do a breathing analysis on it, and he was great to have you on the program. Your energy is fantastic. You really open up opportunities for people. I hope that more people will watch this and understand that while the really the sky is really the limit. And, uh, thank you for your time. Absolutely. >>Thank you. It's a pleasure >>For Dave Volante. I'm Lisa Martin. We are live in Vegas at the Bellagio UI path forward for you right back with our next guest.

from around the globe it's thecube with coverage of kubecon and cloudnativecon north america 2020 virtual brought to you by red hat the cloud native computing foundation and ecosystem partners hey welcome back everybody jeff frick here with thecube coming to you from our palo alto studios with our ongoing coverage of kubecon cloud nativecon north america 2020 virtual it's virtual like everything else that we're doing in 2020 we're really excited by our next guest we're going to dive into a company that you probably know a little bit on the surface but probably don't know a lot of the stuff that's going on behind the surface so we're really excited to have our next guest he is jai chakrabarti he is the director of engineering for core infrastructure at spotify jai great to see you great to be here with you today so as a as a long-standing uh spotify fan and and customer and premium customer and family playing customer just so there's no question i'm a big fan the infrastructure to deliver what i want to hear basically any sound any song from the entire world it seems like i don't know what the actual uh percentage of every published song you guys have you know kind of at my fingertips searchable available now to listen to is an amazing accomplishment i can't imagine how big and significant and complicated the infrastructure you guys must be managing and and not only that but kind of the meteoric growth over the last several years so first off just talk a little bit about spotify scale how you guys think about it is there some things that you can share to help people really understand you know some of the some of the big iron that's behind giving me the songs i want to hear absolutely and thank you for the opportunity to let me talk about this so it's a as you say it's a pretty mammoth project to be able to deliver just about any song that's in the world or now any podcast that you might want to listen to to hundreds of millions of fans and also enable creators to be able to share their content with the consumers who are interested in consuming that content so some of the metrics that go behind us are we have thousands of microservices running in production we were one of the early adopters of microservices at scale and continued to build on that foundation with early entrants to dockerize services and now of course largely on kubernetes we also have thousands of data pipelines hundreds of uh websites as well as micro app features and we're doing about 20 000 deployments a day to give you kind of a scale of how fast things are changing and for us speed is a great virtue as we're testing out features doing ab tests and trying to roll out the next best thing for the audio network it's amazing and i'm and i'm curious in terms of execution on the business side i mean clearly you're in many many countries you know you're global are all the licensing agreements for the music different by country are you just like super micromanaging um you know kind of the the revenue streams and the licensing by geo or is is that just as complex as it feels like it might be or is there some some simplicity or some scale that you can bring to uh to bring a little bit of of clarification there yeah so that is an area of complexity as well um so you know licensing across the broad set of content that we have as well as the number of publishers and creators that we have to make sure that everything is well accounted for is also kind of a source of complexity in our organizational makeup and then and then the the piece that i don't think a lot of people know is you guys are huge consumers and contributors back to open source and clearly we're here at q con cloud native con you've talked already about kubernetes and containers but i wonder before we get into some of the specifics if you can talk about philosophically the role of open source and why you know you guys are such a big open source company versus kind of back in the old days when you would have a lot of proprietary technology that you would try to develop and keep in-house as part of the as part of the secret sauce yeah thank you for that question so philosophically we are big proponents of open source we believe in giving back to the community we believe that when we as a community come together to solve these problems at scale the end result is much better than if we were to try it alone if any one company were to try it alone so some of the projects that we've contributed or invested a lot of time in are envoy for example which we use to power our perimeter at spotify or kubernetes which we use for deployment purposes as many companies do but there are also a number of other open source projects that we're committing to so for example with cloud bigtable we have produced an auto scaler that's now fairly widely used to be able to manage costs better with cloud bigtable we've also invested in a open source time series database called heroic to manage millions of data points for a metrics platform and scales so those are just a few examples but philosophically we believe this isn't something that we want to do alone and we want to leverage and do this together with the community right another one that you didn't mention there but you've talked about i want to dig into is backstage and as you mentioned you have a lot of developer teams working on a lot of projects like i saw a statistic maybe in github of the number of of github projects you guys are working on it's a it's a lot so what is backstage all about give us the story there yeah so at spotify we have almost somewhere around 500 engineering teams and so you can think about backstage as kind of like a central nervous system to be able to help engineers interface across the wide landscape that is spotify's engineering ecosystems so if you're an engineer you can go into backstage and you can manage your services your data pipelines your micro features you can see what other teams are doing what the organizational structure is you can get recommendations and insights on your tech health so you can see where you might need to invest more time and get some recommendations on how to get back to the blessed stock so it's really a one-stop developer portal that engineers spend the bulk of their time in today we open sourced it uh earlier this year and we've been absolutely thrilled with the response we've gotten thus far a number of companies have already started using it and contributing back so we've seen you know a lot of contributions coming back to backstage which is of course one of the ideas to be able to get some of the great ideas uh on backstage so we're really excited about that and specifically within backstage something that my team has just released into the open is a product called cost insights so one of the problems that we were dealing with at spotify is how do we sustainably look at cloud costs but do it in a way that isn't like a compliance exercise isn't a focus on traditional top top down cost controls but really taps into developers innate desire to work on optimization because all of us who come from an engineering background know that optimization is fun at the same time premature optimization is the root of all evil as the saying goes and so what we've done within our cost insights product and backstage is really try to find a good balance between engineering love for optimization and letting people know what are the areas where cloud spend really matters so if making an investment here isn't going to move the needle for us we let people know that this isn't worth your time to worry about so let me unpack you touch on a couple things first off you talked about it gives you an assessment of your engineering health so does that mean that it's kind of uh compliance within a standard is that looking for i guess not quite red flags yet but yellow flags of things that that are known potential issues down the road is it you know tapping into maybe higher cost services or microservices versus less that maybe there's a less expensive way so so how do you define health and how do you you know keep track of people getting away from health and then you know steering them back to being more healthy yeah that's a great question so we have this concept at spotify called golden state which is a reflection of how far away are you from all of the blessed frameworks libraries that we recommend to engineers and the way we think about golden state is there ought to be clear value adds to going to a new service a new library version and so the way we try to express it is unless of course there's a kind of a direct security concern and there aren't really too many ways to get around that but we really tried to preserve engineering autonomy and say if you go to this new framework for example you're going to save this much time on average so the recommendations that you'll find there are going to be highly specific so for example if you adopt uh you know an auto scaler for bigtable you're going to save this much time and spend this much less that's in general how we phrase these things okay and then on the cost insights i mean clearly when a dev is working on a new feature or new uh you know experimenting maybe with a bunch of new features and you're you're setting up multiple a b testing this and that are they are they not really working worrying about cost at the front end of that or is really kind of the cost optimization and you mentioned you know don't optimize too early does that come kind of after the fact and after you've you know moved some new things into production they have potential and now we do maybe a second order kind of analysis of the appropriateness of that feature because i imagine if they're just if you're just trying to come up with new features and exploring and trying new things not really worrying about the you're not worrying about the cloud bill right you're just trying to get some feature functionality and make sure you don't have too many bugs and make sure you're going to get some good client value and some new customer experience yeah yeah no and and we agree with that perspective so we think about the world in terms of startup scale-ups and mature businesses at spotify so there are a lot of teams who are experimenting with new ideas that fall into the startup category and by and large they are not going to be worrying about costs that being said we as infrastructure teams have the notice on us to think about how do we provide shared services and frameworks that abstract away a lot of these questions around how do you properly manage your costs right so that that is on us as infrastructure teams but really our perspective is for startups to move as quickly as they can and really if that's an idea that's viable and you get to what we call the scale-up stage or you get to the mature business stage where it really is a core part of our business then that's where you know you might start to get some nudges or recommendations and cost insights so interesting so i'd love to you know your background you came from financial services and trading where clearly speed matters accuracy matters you know that that's i mean basically financial services is is a software game at this stage of the game and it's a speed game and i saw another interesting uh video getting ready for this i think it was with gustav soderstrom talking about the competitive advantage of the early days really being speed and speed to return a result and speed to start that stream and it just struck me very much like you know the early days of google which was that was their whole speed thing and they even told you how fast you got a return on your search when you're thinking about optimizing now with the huge suite of features and functionalities that you have how do you think about speed is it still speed number one how is kind of the priority changed and what are some of the design priorities that when things go from experiment to start to be into the scale realm and hopefully be successful in production that that need to be thought about and potentially rank ordered um in in the proper way yeah yeah that's it's a great question and so you know i'll just refer to daniel x quote around this which is we aim to fail faster than anyone else and so for us as a company and with our growth trajectory and investing in the areas that we are looking to invest into it's still absolutely critical that we move fast that we get the ideas of the startup phase out to be vetted and validated if we can go to the next phase to the scale-up phase so i see that just as important today if not more than when i first joined spotify uh you know over four years ago at this point and regarding financial services um there are certainly you know touch points in terms of the amount of data that we're processing and the scale of technology that it requires to process that kind of data but one of the things that i really love about spotify of course is that we get to move fast which is sometimes of course going to be a lot more difficult when you're talking about the financial service arena and various uh compliance bodies that are overseeing any changes that you might make yeah you guys are you guys were running a little bit ahead of the regs i think which is pretty typical uh in the music business napster was running a little bit ahead of the regs and you know then we saw the evolution with the itunes and then you know you guys really really nailing the streaming service really for the first time and and opening up this new con consumption bottle and i wonder if you could talk about you know kind of keeping the customer experience first and making sure that that's a positive thing i can't help but think of of the netflix experience where they spend so much time on people's interaction with the application to to get them to try new things a recommendation engine such an important piece of the of the puzzle and i think what you guys have really nailed is the discovery piece because it's one thing to be able to quickly access a favorite song and be able to listen to it but everyone loves discovery right and discovery is kind of an interesting and interesting process and you guys have taken a really scientific approach in terms of cataloging music and and different attributes of music and then using those to help drive the recommendation engine i wonder if you can share you know kind of your thoughts in terms of being you know kind of ultimately driven by the customer experience and their interaction with the application and these things called you know music or podcast which is such a such a a a very personal thing to interact with yeah so from the perspective of core infrastructure you know it's spotify our goal is to really enable the scale in which we are processing the amount of audio content that goes through our system and so podcast of course is a new category that wasn't there when i originally joined spotify but it's really to provide a platform so these experiments can be done seamlessly so we can have different ways of looking at discovery looking at user segmentation and being able to come up with new ways that are going to be compelling to our customers so that's very exciting and fulfilling for us to be able to provide that platform by which our sister teams can iterate very quickly knowing that they have the guard rails uh which you know in our on-premise days at times was a struggle and where we're in a very different place now yeah so last question before i let you go we're at cubecon cloudnativecon um and and it's just an interesting thing that i always think about when you're managing engineering teams that are heavily open source participants and you know it's such a big piece now of of a lot of engineers motivation to be active participants in open source and to and to show their work to others outside the company but at the same time they have to get company work done so i just wonder if you could share your perspective of how do you manage open source contributions how do you keep them you know working on company projects but also make sure you allocate time and priorities to open source contributions because that is a really important piece of the motivation for a lot of engineers it's not just working for the company and getting paid at the india at the end of every two weeks yeah it's a key motivation as you say and it's key to our recruiting strategy and also how we think about retaining engineers and spotify so there are different mechanisms that we use and there's a lot of focus that's modified on coming up with development plans for engineers that actually make sense um so you know i would say that all the way from the oft quoted 20 time is something that you might hear at spotify where you have engineers who are working on open source 20 of the time or you might see a variety of customized customized options depending on who the engineer is where they want to grow and really i think the key here is providing the right support structures so even if you have the time are you getting the mentorship are you getting the right kind of support system so you know how to connect with the community and so you have other like-minded people who are bouncing ideas and you don't feel like you're doing it yourself so that's something that i feel really excited about that we've grown those support structures over the last few years eyes have also been very intentional about giving engineers time to work on open source and you give them as much as 20 i'd never heard that before yeah in some cases some i mean if that is what where an engineer really wants to focus and grow there are a number of folks at spotify who are spending up to 20 of their time on open source wow that's amazing that that is a uh that's a it's just it's such a great commitment for the company to the engineer if that's their priority and then everyone's going to benefit from it both the engineer the company as well as the community so really a forward-looking you know point of view to take that long-term view versus the you know maybe we should only give them 10 we're losing 10 of their time working on a project so that is super super progressive and i'm sure you must be seeing great roi on it or you wouldn't continue to be such huge proponents of open source and such huge contributors back so that's that's a great story yeah terrific i mean you know we we want those contributions to be in line with where we're growing as a company and we see a lot of opportunities uh where that is happening so like envoy or kubernetes um just to name a couple of examples where folks have devoted time in those areas well thanks for uh thanks for sharing some of the the story behind the scenes you know again household name what what a tremendous success story and and and uh you know i'm a movie customer so i'm definitely a customer though no no doubt about it so uh thank you for your contributions congrats to the team and uh and really loved the story of how you guys are contributing back and and doing a lot more than just making great music available to us all and a great channel for uh for creators to get their stuff out there so thanks again thanks so much for your time i really appreciate it all right he's jai i'm jeff you're watching the cube's continuing coverage of kubecon cloud nativecon north america 2020 thanks for watching we'll see you next [Music] time you

>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. >>Yeah, yeah. Hi. And welcome to the Cube. I'm your host, Sonia Atari. And we're live at Stanford University, covering the fifth annual Woods Women in Data Science Conference. Joining us today is Lillian Kearse. Keo, who's the Insights manager at Spotify. Slowly and welcome to the Cube. Thank you so much for having me. So tell us a little bit about your role at a Spotify. >>Yeah, So I'm actually one of the few insights managers in the personalization team. Um, and within my little group, we think about data and algorithms that help power the larger personalization experiences throughout Spotify. So, from your limits to discover weekly to your year and wrap stories to your experience on home and the search results, that's >>awesome. Can you tell us a little bit more about the personalization? Um, team? >>Yes. We actually have a variety of different product areas that come together to form the personalization mission, which is the mission is like the term that we use for a big department at Spotify, and we collaborate across different product areas to understand what are the foundational data sets and the foundational machine learning tools that are needed to be able to create features that a user can actually experience in the app? >>Great. Um, and so you're going to be on the career panel today? How do you feel about that? I'm >>really excited. Yeah, Yeah, the would seem is in a great job of bringing together Diverse is very, uh, it's overused term. Sometimes they're a very diverse group of people with lots of different types of experiences, which I think is core. So how I think about data science, it's a wide definition. And so I think it's great to show younger and mid career women all of the different career paths that we can all take. >>And what advice would you would you give to? Women were coming out of college right now about data science. >>Yeah, so my my big advice is to follow your interests. So there's so many different types of data science problems. You don't have to just go into a title that says data scientists or a team that says Data scientist, You can follow your interest into your data science. Use your data science skills in ways that might require a lot of collaboration or mixed methods, or work within a team where there are different types of different different types of expertise coming together to work on problems. >>And speaking of mixed methods, insights is a team that's a mixed methods research groups. So tell us more about that. Yes, I >>personally manage a data scientist, Um, user researcher and the three of us collaborate highly together across their disciplines. We also collaborate across research science, the research science team right into the product and engineering teams that are actually delivering the different products that users get to see. So it's highly collaborative, and the idea is to understand the problem. Space deeply together, be able to understand. What is it that we're trying to even just form in our head is like the need that a user work and human and user human has, um, in bringing in research from research scientists and the product side to be able to understand those needs and then actually have insights that another human, you know, a product owner you can really think through and understand the current space and like the product opportunities >>and to understand that user insight do use a B testing. >>We use a lot of >>a B testing, so that's core to how we think about our users at Spotify. So we use a lot of a B testing. We do a lot of offline experiments to understand the potential consequences or impact that certain interventions can have. But I think a B testing, you know, there's so much to learn about best practices there and where you're talking about a team that does foundational data and foundational features. You also have to think about unintended or second order effects of algorithmic a B test. So it's been just like a huge area of learning in a huge area of just very interesting outcomes. And like every test that we run, we learn a lot about not just the individual thing. We're testing with just the process overall. >>And, um, what are some features of Spotify that customers really love anything? Anything >>that's like we know use a daily mix people absolutely love every time that I make a new friend and I saw them what they work on there like I was just listening to my daily makes this morning discover weekly for people who really want >>to stay, >>you know, open to new music is also very popular. But I think the one that really takes it is any of the end of year wrapped campaigns that we have just the nostalgia that people have, even just for the last year. But in 2019 we were actually able to do 10 years, and that amount of nostalgia just went through the roof like people were just like, Oh my goodness, you captured the time that I broke up with that, you >>know, the 1st 5 years ago, or just like when I discovered that I love Taylor Swift, even though I didn't think I like their or something like that, you know? >>Are there any surprises or interesting stories that you have about, um, interesting user experiences? Yeah. >>I mean, I could give I >>can give you an example from my experience. So recently, A few a few months ago, I was scrolling through my home feed, and I noticed that one of the highly rated things for me was women in >>country, and I was like, Oh, that's kind of weird. I don't consider >>myself a country fan, right? And I was like having this moment where I went through this path of Wait, That's weird. Why would Why would this recommend? Why would the home screen recommend women in country, country music to me? And then when I click through it, um, it would show you a little bit of information about it because it had, you know, Dolly Parton. It had Margo Price and it had the high women and those were all artistes. And I've been listening to a lot, but I just had not formed an identity as a country music. And then I click through It was like, Oh, this is a great play list and I listen to it and it got me to the point where I was realizing I really actually do like country music when the stories were centered around women, that it was really fun to discover other artists that I wouldn't have otherwise jumped into as well. Based on the fact that I love the story writing and the song, writing these other country acts that >>so quickly discovered that so you have a degree in industrial mathematics, went to a liberal arts college on purpose because you want to try out different classes. So how is that diversity of education really helped >>you in your Yes, in my undergrad is from Smith College, which is a liberal arts school, very strong liberal arts foundation. And when I went to visit, one of the math professors that I met told me that he, you know, he considers studying math, not just to make you better at math, but that it makes you a better thinker. And you can take in much more information and sort of question assumptions and try to build a foundation for what? The problem that you're trying to think through is. And I just found that extremely interesting. And I also, you know, I haven't undeclared major in Latin American studies, and I studied like neuroscience and quantum physics for non experts and film class and all of these other things that I don't know if I would have had the same opportunity at a more technical school, and I just found it really challenging and satisfying to be able to push myself to think in different ways. I even took a poetry writing class I did not write good poetry, but the experience really stuck with me because it was about pushing myself outside of my own boundaries. >>And would you recommend having this kind of like diverse education to young women now who are looking >>and I absolutely love it? I mean, I think, you know, there's some people believe that instead of thinking about steam, we should be talking instead of thinking about stem. Rather, we should be talking about steam, which adds the arts education in there, and liberal arts is one of them. And I think that now, in these conversations that we have about biases in data and ML and AI and understanding, fairness and accountability, accountability bitterly, it's a hardware. Apparently, I think that a strong, uh, cross disciplinary collaborative and even on an individual level, cross disciplinary education is really the only way that we're gonna be able to make those connections to understand what kind of second order effects for having based on the decisions of parameters for a model. In a local sense, we're optimizing and doing a great job. But what are the global consequences of those decisions? And I think that that kind of interdisciplinary approach to education as an individual and collaboration as a team is really the only way. >>And speaking about bias. Earlier, we heard that diversity is great because it brings out new perspectives, and it also helps to reduce that unfair bias. So how it Spotify have you managed? Or has Spotify managed to create a more diverse team? >>Yeah, so I mean, it starts with recruiting. It starts with what kind of messaging we put out there, and there's a great team that thinks about that exclusively. And they're really pushing all of us as managers. As I seizes leaders to really think about the decisions in the way that we talk about things and all of these micro decisions that we make and how that creates an inclusive environments, it's not just about diversity. It's also about making people feel like this is where they should be. On a personal level, you know, I talk a lot with younger folks and people who are trying to just figure out what their place is in technology, whether it be because they come from a different culture, >>there are, >>you know, they might be gender, non binary. They might be women who feel like there is in a place for them. It's really about, You know, the things that I think about is because you're different. Your voice is needed even more. You know, like your voice matters and we need to figure out. And I always ask, How can I highlight your voice more? You know, how can I help? I have a tiny, tiny bit of power and influence. You know, more than some other folks. How can I help other people acquire that as well? >>Lilian, thank you so much for your insight. Thank you for being on the Cube. Thank you. I'm your host, Sonia today. Ari. Thank you for watching and stay tuned for more. Yeah, yeah.

(upbeat music) >> Hello everyone. Welcome to theCube's presentation of the "AWS Startup Showcase." The topic this episode is AI and machine learning, top startups building foundational model infrastructure. This is season three, episode one of the ongoing series covering exciting startups from the AWS ecosystem. And this time we're talking about AI and machine learning. I'm your host, John Furrier. I'm excited I'm joined today by Robert Nishihara, who's the co-founder and CEO of a hot startup called Anyscale. He's here to talk about Ray, the open source project, Anyscale's infrastructure for foundation as well. Robert, thank you for joining us today. >> Yeah, thanks so much as well. >> I've been following your company since the founding pre pandemic and you guys really had a great vision scaled up and in a perfect position for this big wave that we all see with ChatGPT and OpenAI that's gone mainstream. Finally, AI has broken out through the ropes and now gone mainstream, so I think you guys are really well positioned. I'm looking forward to to talking with you today. But before we get into it, introduce the core mission for Anyscale. Why do you guys exist? What is the North Star for Anyscale? >> Yeah, like you mentioned, there's a tremendous amount of excitement about AI right now. You know, I think a lot of us believe that AI can transform just every different industry. So one of the things that was clear to us when we started this company was that the amount of compute needed to do AI was just exploding. Like to actually succeed with AI, companies like OpenAI or Google or you know, these companies getting a lot of value from AI, were not just running these machine learning models on their laptops or on a single machine. They were scaling these applications across hundreds or thousands or more machines and GPUs and other resources in the Cloud. And so to actually succeed with AI, and this has been one of the biggest trends in computing, maybe the biggest trend in computing in, you know, in recent history, the amount of compute has been exploding. And so to actually succeed with that AI, to actually build these scalable applications and scale the AI applications, there's a tremendous software engineering lift to build the infrastructure to actually run these scalable applications. And that's very hard to do. So one of the reasons many AI projects and initiatives fail is that, or don't make it to production, is the need for this scale, the infrastructure lift, to actually make it happen. So our goal here with Anyscale and Ray, is to make that easy, is to make scalable computing easy. So that as a developer or as a business, if you want to do AI, if you want to get value out of AI, all you need to know is how to program on your laptop. Like, all you need to know is how to program in Python. And if you can do that, then you're good to go. Then you can do what companies like OpenAI or Google do and get value out of machine learning. >> That programming example of how easy it is with Python reminds me of the early days of Cloud, when infrastructure as code was talked about was, it was just code the infrastructure programmable. That's super important. That's what AI people wanted, first program AI. That's the new trend. And I want to understand, if you don't mind explaining, the relationship that Anyscale has to these foundational models and particular the large language models, also called LLMs, was seen with like OpenAI and ChatGPT. Before you get into the relationship that you have with them, can you explain why the hype around foundational models? Why are people going crazy over foundational models? What is it and why is it so important? >> Yeah, so foundational models and foundation models are incredibly important because they enable businesses and developers to get value out of machine learning, to use machine learning off the shelf with these large models that have been trained on tons of data and that are useful out of the box. And then, of course, you know, as a business or as a developer, you can take those foundational models and repurpose them or fine tune them or adapt them to your specific use case and what you want to achieve. But it's much easier to do that than to train them from scratch. And I think there are three, for people to actually use foundation models, there are three main types of workloads or problems that need to be solved. One is training these foundation models in the first place, like actually creating them. The second is fine tuning them and adapting them to your use case. And the third is serving them and actually deploying them. Okay, so Ray and Anyscale are used for all of these three different workloads. Companies like OpenAI or Cohere that train large language models. Or open source versions like GPTJ are done on top of Ray. There are many startups and other businesses that fine tune, that, you know, don't want to train the large underlying foundation models, but that do want to fine tune them, do want to adapt them to their purposes, and build products around them and serve them, those are also using Ray and Anyscale for that fine tuning and that serving. And so the reason that Ray and Anyscale are important here is that, you know, building and using foundation models requires a huge scale. It requires a lot of data. It requires a lot of compute, GPUs, TPUs, other resources. And to actually take advantage of that and actually build these scalable applications, there's a lot of infrastructure that needs to happen under the hood. And so you can either use Ray and Anyscale to take care of that and manage the infrastructure and solve those infrastructure problems. Or you can build the infrastructure and manage the infrastructure yourself, which you can do, but it's going to slow your team down. It's going to, you know, many of the businesses we work with simply don't want to be in the business of managing infrastructure and building infrastructure. They want to focus on product development and move faster. >> I know you got a keynote presentation we're going to go to in a second, but I think you hit on something I think is the real tipping point, doing it yourself, hard to do. These are things where opportunities are and the Cloud did that with data centers. Turned a data center and made it an API. The heavy lifting went away and went to the Cloud so people could be more creative and build their product. In this case, build their creativity. Is that kind of what's the big deal? Is that kind of a big deal happening that you guys are taking the learnings and making that available so people don't have to do that? >> That's exactly right. So today, if you want to succeed with AI, if you want to use AI in your business, infrastructure work is on the critical path for doing that. To do AI, you have to build infrastructure. You have to figure out how to scale your applications. That's going to change. We're going to get to the point, and you know, with Ray and Anyscale, we're going to remove the infrastructure from the critical path so that as a developer or as a business, all you need to focus on is your application logic, what you want the the program to do, what you want your application to do, how you want the AI to actually interface with the rest of your product. Now the way that will happen is that Ray and Anyscale will still, the infrastructure work will still happen. It'll just be under the hood and taken care of by Ray in Anyscale. And so I think something like this is really necessary for AI to reach its potential, for AI to have the impact and the reach that we think it will, you have to make it easier to do. >> And just for clarification to point out, if you don't mind explaining the relationship of Ray and Anyscale real quick just before we get into the presentation. >> So Ray is an open source project. We created it. We were at Berkeley doing machine learning. We started Ray so that, in order to provide an easy, a simple open source tool for building and running scalable applications. And Anyscale is the managed version of Ray, basically we will run Ray for you in the Cloud, provide a lot of tools around the developer experience and managing the infrastructure and providing more performance and superior infrastructure. >> Awesome. I know you got a presentation on Ray and Anyscale and you guys are positioning as the infrastructure for foundational models. So I'll let you take it away and then when you're done presenting, we'll come back, I'll probably grill you with a few questions and then we'll close it out so take it away. >> Robert: Sounds great. So I'll say a little bit about how companies are using Ray and Anyscale for foundation models. The first thing I want to mention is just why we're doing this in the first place. And the underlying observation, the underlying trend here, and this is a plot from OpenAI, is that the amount of compute needed to do machine learning has been exploding. It's been growing at something like 35 times every 18 months. This is absolutely enormous. And other people have written papers measuring this trend and you get different numbers. But the point is, no matter how you slice and dice it, it' a astronomical rate. Now if you compare that to something we're all familiar with, like Moore's Law, which says that, you know, the processor performance doubles every roughly 18 months, you can see that there's just a tremendous gap between the needs, the compute needs of machine learning applications, and what you can do with a single chip, right. So even if Moore's Law were continuing strong and you know, doing what it used to be doing, even if that were the case, there would still be a tremendous gap between what you can do with the chip and what you need in order to do machine learning. And so given this graph, what we've seen, and what has been clear to us since we started this company, is that doing AI requires scaling. There's no way around it. It's not a nice to have, it's really a requirement. And so that led us to start Ray, which is the open source project that we started to make it easy to build these scalable Python applications and scalable machine learning applications. And since we started the project, it's been adopted by a tremendous number of companies. Companies like OpenAI, which use Ray to train their large models like ChatGPT, companies like Uber, which run all of their deep learning and classical machine learning on top of Ray, companies like Shopify or Spotify or Instacart or Lyft or Netflix, ByteDance, which use Ray for their machine learning infrastructure. Companies like Ant Group, which makes Alipay, you know, they use Ray across the board for fraud detection, for online learning, for detecting money laundering, you know, for graph processing, stream processing. Companies like Amazon, you know, run Ray at a tremendous scale and just petabytes of data every single day. And so the project has seen just enormous adoption since, over the past few years. And one of the most exciting use cases is really providing the infrastructure for building training, fine tuning, and serving foundation models. So I'll say a little bit about, you know, here are some examples of companies using Ray for foundation models. Cohere trains large language models. OpenAI also trains large language models. You can think about the workloads required there are things like supervised pre-training, also reinforcement learning from human feedback. So this is not only the regular supervised learning, but actually more complex reinforcement learning workloads that take human input about what response to a particular question, you know is better than a certain other response. And incorporating that into the learning. There's open source versions as well, like GPTJ also built on top of Ray as well as projects like Alpa coming out of UC Berkeley. So these are some of the examples of exciting projects in organizations, training and creating these large language models and serving them using Ray. Okay, so what actually is Ray? Well, there are two layers to Ray. At the lowest level, there's the core Ray system. This is essentially low level primitives for building scalable Python applications. Things like taking a Python function or a Python class and executing them in the cluster setting. So Ray core is extremely flexible and you can build arbitrary scalable applications on top of Ray. So on top of Ray, on top of the core system, what really gives Ray a lot of its power is this ecosystem of scalable libraries. So on top of the core system you have libraries, scalable libraries for ingesting and pre-processing data, for training your models, for fine tuning those models, for hyper parameter tuning, for doing batch processing and batch inference, for doing model serving and deployment, right. And a lot of the Ray users, the reason they like Ray is that they want to run multiple workloads. They want to train and serve their models, right. They want to load their data and feed that into training. And Ray provides common infrastructure for all of these different workloads. So this is a little overview of what Ray, the different components of Ray. So why do people choose to go with Ray? I think there are three main reasons. The first is the unified nature. The fact that it is common infrastructure for scaling arbitrary workloads, from data ingest to pre-processing to training to inference and serving, right. This also includes the fact that it's future proof. AI is incredibly fast moving. And so many people, many companies that have built their own machine learning infrastructure and standardized on particular workflows for doing machine learning have found that their workflows are too rigid to enable new capabilities. If they want to do reinforcement learning, if they want to use graph neural networks, they don't have a way of doing that with their standard tooling. And so Ray, being future proof and being flexible and general gives them that ability. Another reason people choose Ray in Anyscale is the scalability. This is really our bread and butter. This is the reason, the whole point of Ray, you know, making it easy to go from your laptop to running on thousands of GPUs, making it easy to scale your development workloads and run them in production, making it easy to scale, you know, training to scale data ingest, pre-processing and so on. So scalability and performance, you know, are critical for doing machine learning and that is something that Ray provides out of the box. And lastly, Ray is an open ecosystem. You can run it anywhere. You can run it on any Cloud provider. Google, you know, Google Cloud, AWS, Asure. You can run it on your Kubernetes cluster. You can run it on your laptop. It's extremely portable. And not only that, it's framework agnostic. You can use Ray to scale arbitrary Python workloads. You can use it to scale and it integrates with libraries like TensorFlow or PyTorch or JAX or XG Boost or Hugging Face or PyTorch Lightning, right, or Scikit-learn or just your own arbitrary Python code. It's open source. And in addition to integrating with the rest of the machine learning ecosystem and these machine learning frameworks, you can use Ray along with all of the other tooling in the machine learning ecosystem. That's things like weights and biases or ML flow, right. Or you know, different data platforms like Databricks, you know, Delta Lake or Snowflake or tools for model monitoring for feature stores, all of these integrate with Ray. And that's, you know, Ray provides that kind of flexibility so that you can integrate it into the rest of your workflow. And then Anyscale is the scalable compute platform that's built on top, you know, that provides Ray. So Anyscale is a managed Ray service that runs in the Cloud. And what Anyscale does is it offers the best way to run Ray. And if you think about what you get with Anyscale, there are fundamentally two things. One is about moving faster, accelerating the time to market. And you get that by having the managed service so that as a developer you don't have to worry about managing infrastructure, you don't have to worry about configuring infrastructure. You also, it provides, you know, optimized developer workflows. Things like easily moving from development to production, things like having the observability tooling, the debug ability to actually easily diagnose what's going wrong in a distributed application. So things like the dashboards and the other other kinds of tooling for collaboration, for monitoring and so on. And then on top of that, so that's the first bucket, developer productivity, moving faster, faster experimentation and iteration. The second reason that people choose Anyscale is superior infrastructure. So this is things like, you know, cost deficiency, being able to easily take advantage of spot instances, being able to get higher GPU utilization, things like faster cluster startup times and auto scaling. Things like just overall better performance and faster scheduling. And so these are the kinds of things that Anyscale provides on top of Ray. It's the managed infrastructure. It's fast, it's like the developer productivity and velocity as well as performance. So this is what I wanted to share about Ray in Anyscale. >> John: Awesome. >> Provide that context. But John, I'm curious what you think. >> I love it. I love the, so first of all, it's a platform because that's the platform architecture right there. So just to clarify, this is an Anyscale platform, not- >> That's right. >> Tools. So you got tools in the platform. Okay, that's key. Love that managed service. Just curious, you mentioned Python multiple times, is that because of PyTorch and TensorFlow or Python's the most friendly with machine learning or it's because it's very common amongst all developers? >> That's a great question. Python is the language that people are using to do machine learning. So it's the natural starting point. Now, of course, Ray is actually designed in a language agnostic way and there are companies out there that use Ray to build scalable Java applications. But for the most part right now we're focused on Python and being the best way to build these scalable Python and machine learning applications. But, of course, down the road there always is that potential. >> So if you're slinging Python code out there and you're watching that, you're watching this video, get on Anyscale bus quickly. Also, I just, while you were giving the presentation, I couldn't help, since you mentioned OpenAI, which by the way, congratulations 'cause they've had great scale, I've noticed in their rapid growth 'cause they were the fastest company to the number of users than anyone in the history of the computer industry, so major successor, OpenAI and ChatGPT, huge fan. I'm not a skeptic at all. I think it's just the beginning, so congratulations. But I actually typed into ChatGPT, what are the top three benefits of Anyscale and came up with scalability, flexibility, and ease of use. Obviously, scalability is what you guys are called. >> That's pretty good. >> So that's what they came up with. So they nailed it. Did you have an inside prompt training, buy it there? Only kidding. (Robert laughs) >> Yeah, we hard coded that one. >> But that's the kind of thing that came up really, really quickly if I asked it to write a sales document, it probably will, but this is the future interface. This is why people are getting excited about the foundational models and the large language models because it's allowing the interface with the user, the consumer, to be more human, more natural. And this is clearly will be in every application in the future. >> Absolutely. This is how people are going to interface with software, how they're going to interface with products in the future. It's not just something, you know, not just a chat bot that you talk to. This is going to be how you get things done, right. How you use your web browser or how you use, you know, how you use Photoshop or how you use other products. Like you're not going to spend hours learning all the APIs and how to use them. You're going to talk to it and tell it what you want it to do. And of course, you know, if it doesn't understand it, it's going to ask clarifying questions. You're going to have a conversation and then it'll figure it out. >> This is going to be one of those things, we're going to look back at this time Robert and saying, "Yeah, from that company, that was the beginning of that wave." And just like AWS and Cloud Computing, the folks who got in early really were in position when say the pandemic came. So getting in early is a good thing and that's what everyone's talking about is getting in early and playing around, maybe replatforming or even picking one or few apps to refactor with some staff and managed services. So people are definitely jumping in. So I have to ask you the ROI cost question. You mentioned some of those, Moore's Law versus what's going on in the industry. When you look at that kind of scale, the first thing that jumps out at people is, "Okay, I love it. Let's go play around." But what's it going to cost me? Am I going to be tied to certain GPUs? What's the landscape look like from an operational standpoint, from the customer? Are they locked in and the benefit was flexibility, are you flexible to handle any Cloud? What is the customers, what are they looking at? Basically, that's my question. What's the customer looking at? >> Cost is super important here and many of the companies, I mean, companies are spending a huge amount on their Cloud computing, on AWS, and on doing AI, right. And I think a lot of the advantage of Anyscale, what we can provide here is not only better performance, but cost efficiency. Because if we can run something faster and more efficiently, it can also use less resources and you can lower your Cloud spending, right. We've seen companies go from, you know, 20% GPU utilization with their current setup and the current tools they're using to running on Anyscale and getting more like 95, you know, 100% GPU utilization. That's something like a five x improvement right there. So depending on the kind of application you're running, you know, it's a significant cost savings. We've seen companies that have, you know, processing petabytes of data every single day with Ray going from, you know, getting order of magnitude cost savings by switching from what they were previously doing to running their application on Ray. And when you have applications that are spending, you know, potentially $100 million a year and getting a 10 X cost savings is just absolutely enormous. So these are some of the kinds of- >> Data infrastructure is super important. Again, if the customer, if you're a prospect to this and thinking about going in here, just like the Cloud, you got infrastructure, you got the platform, you got SaaS, same kind of thing's going to go on in AI. So I want to get into that, you know, ROI discussion and some of the impact with your customers that are leveraging the platform. But first I hear you got a demo. >> Robert: Yeah, so let me show you, let me give you a quick run through here. So what I have open here is the Anyscale UI. I've started a little Anyscale Workspace. So Workspaces are the Anyscale concept for interactive developments, right. So here, imagine I'm just, you want to have a familiar experience like you're developing on your laptop. And here I have a terminal. It's not on my laptop. It's actually in the cloud running on Anyscale. And I'm just going to kick this off. This is going to train a large language model, so OPT. And it's doing this on 32 GPUs. We've got a cluster here with a bunch of CPU cores, bunch of memory. And as that's running, and by the way, if I wanted to run this on instead of 32 GPUs, 64, 128, this is just a one line change when I launch the Workspace. And what I can do is I can pull up VS code, right. Remember this is the interactive development experience. I can look at the actual code. Here it's using Ray train to train the torch model. We've got the training loop and we're saying that each worker gets access to one GPU and four CPU cores. And, of course, as I make the model larger, this is using deep speed, as I make the model larger, I could increase the number of GPUs that each worker gets access to, right. And how that is distributed across the cluster. And if I wanted to run on CPUs instead of GPUs or a different, you know, accelerator type, again, this is just a one line change. And here we're using Ray train to train the models, just taking my vanilla PyTorch model using Hugging Face and then scaling that across a bunch of GPUs. And, of course, if I want to look at the dashboard, I can go to the Ray dashboard. There are a bunch of different visualizations I can look at. I can look at the GPU utilization. I can look at, you know, the CPU utilization here where I think we're currently loading the model and running that actual application to start the training. And some of the things that are really convenient here about Anyscale, both I can get that interactive development experience with VS code. You know, I can look at the dashboards. I can monitor what's going on. It feels, I have a terminal, it feels like my laptop, but it's actually running on a large cluster. And I can, with however many GPUs or other resources that I want. And so it's really trying to combine the best of having the familiar experience of programming on your laptop, but with the benefits, you know, being able to take advantage of all the resources in the Cloud to scale. And it's like when, you know, you're talking about cost efficiency. One of the biggest reasons that people waste money, one of the silly reasons for wasting money is just forgetting to turn off your GPUs. And what you can do here is, of course, things will auto terminate if they're idle. But imagine you go to sleep, I have this big cluster. You can turn it off, shut off the cluster, come back tomorrow, restart the Workspace, and you know, your big cluster is back up and all of your code changes are still there. All of your local file edits. It's like you just closed your laptop and came back and opened it up again. And so this is the kind of experience we want to provide for our users. So that's what I wanted to share with you. >> Well, I think that whole, couple of things, lines of code change, single line of code change, that's game changing. And then the cost thing, I mean human error is a big deal. People pass out at their computer. They've been coding all night or they just forget about it. I mean, and then it's just like leaving the lights on or your water running in your house. It's just, at the scale that it is, the numbers will add up. That's a huge deal. So I think, you know, compute back in the old days, there's no compute. Okay, it's just compute sitting there idle. But you know, data cranking the models is doing, that's a big point. >> Another thing I want to add there about cost efficiency is that we make it really easy to use, if you're running on Anyscale, to use spot instances and these preemptable instances that can just be significantly cheaper than the on-demand instances. And so when we see our customers go from what they're doing before to using Anyscale and they go from not using these spot instances 'cause they don't have the infrastructure around it, the fault tolerance to handle the preemption and things like that, to being able to just check a box and use spot instances and save a bunch of money. >> You know, this was my whole, my feature article at Reinvent last year when I met with Adam Selipsky, this next gen Cloud is here. I mean, it's not auto scale, it's infrastructure scale. It's agility. It's flexibility. I think this is where the world needs to go. Almost what DevOps did for Cloud and what you were showing me that demo had this whole SRE vibe. And remember Google had site reliability engines to manage all those servers. This is kind of like an SRE vibe for data at scale. I mean, a similar kind of order of magnitude. I mean, I might be a little bit off base there, but how would you explain it? >> It's a nice analogy. I mean, what we are trying to do here is get to the point where developers don't think about infrastructure. Where developers only think about their application logic. And where businesses can do AI, can succeed with AI, and build these scalable applications, but they don't have to build, you know, an infrastructure team. They don't have to develop that expertise. They don't have to invest years in building their internal machine learning infrastructure. They can just focus on the Python code, on their application logic, and run the stuff out of the box. >> Awesome. Well, I appreciate the time. Before we wrap up here, give a plug for the company. I know you got a couple websites. Again, go, Ray's got its own website. You got Anyscale. You got an event coming up. Give a plug for the company looking to hire. Put a plug in for the company. >> Yeah, absolutely. Thank you. So first of all, you know, we think AI is really going to transform every industry and the opportunity is there, right. We can be the infrastructure that enables all of that to happen, that makes it easy for companies to succeed with AI, and get value out of AI. Now we have, if you're interested in learning more about Ray, Ray has been emerging as the standard way to build scalable applications. Our adoption has been exploding. I mentioned companies like OpenAI using Ray to train their models. But really across the board companies like Netflix and Cruise and Instacart and Lyft and Uber, you know, just among tech companies. It's across every industry. You know, gaming companies, agriculture, you know, farming, robotics, drug discovery, you know, FinTech, we see it across the board. And all of these companies can get value out of AI, can really use AI to improve their businesses. So if you're interested in learning more about Ray and Anyscale, we have our Ray Summit coming up in September. This is going to highlight a lot of the most impressive use cases and stories across the industry. And if your business, if you want to use LLMs, you want to train these LLMs, these large language models, you want to fine tune them with your data, you want to deploy them, serve them, and build applications and products around them, give us a call, talk to us. You know, we can really take the infrastructure piece, you know, off the critical path and make that easy for you. So that's what I would say. And, you know, like you mentioned, we're hiring across the board, you know, engineering, product, go-to-market, and it's an exciting time. >> Robert Nishihara, co-founder and CEO of Anyscale, congratulations on a great company you've built and continuing to iterate on and you got growth ahead of you, you got a tailwind. I mean, the AI wave is here. I think OpenAI and ChatGPT, a customer of yours, have really opened up the mainstream visibility into this new generation of applications, user interface, roll of data, large scale, how to make that programmable so we're going to need that infrastructure. So thanks for coming on this season three, episode one of the ongoing series of the hot startups. In this case, this episode is the top startups building foundational model infrastructure for AI and ML. I'm John Furrier, your host. Thanks for watching. (upbeat music)

(upbeat instrumental) >> Hello and welcome to this CUBE conversation. I'm John Furrier, host of theCUBE, here in Palo Alto, California. Got a great conversation with Robert Nishihara who's the co-founder and CEO of Anyscale. Robert, great to have you on this CUBE conversation. It's great to see you. We did your first Ray Summit a couple years ago and congratulations on your venture. Great to have you on. >> Thank you. Thanks for inviting me. >> So you're first time CEO out of Berkeley in Data. You got the Databricks is coming out of there. You got a bunch of activity coming from Berkeley. It's like a, it really is kind of like where a lot of innovations going on data. Anyscale has been one of those startups that has risen out of that scene. Right? You look at the success of what the Data lakes are now. Now you've got the generative AI. This has been a really interesting innovation market. This new wave is coming. Tell us what's going on with Anyscale right now, as you guys are gearing up and getting some growth. What's happening with the company? >> Yeah, well one of the most exciting things that's been happening in computing recently, is the rise of AI and the excitement about AI, and the potential for AI to really transform every industry. Now of course, one of the of the biggest challenges to actually making that happen is that doing AI, that AI is incredibly computationally intensive, right? To actually succeed with AI to actually get value out of AI. You're typically not just running it on your laptop, you're often running it and scaling it across thousands of machines, or hundreds of machines or GPUs, and to, so organizations and companies and businesses that do AI often end up building a large infrastructure team to manage the distributed systems, the computing to actually scale these applications. And that's a, that's a, a huge software engineering lift, right? And so, one of the goals for Anyscale is really to make that easy. To get to the point where, developers and teams and companies can succeed with AI. Can build these scalable AI applications, without really you know, without a huge investment in infrastructure with a lot of, without a lot of expertise in infrastructure, where really all they need to know is how to program on their laptop, how to program in Python. And if you have that, then that's really all you need to succeed with AI. So that's what we've been focused on. We're building Ray, which is an open source project that's been starting to get adopted by tons of companies, to actually train these models, to deploy these models, to do inference with these models, you know, to ingest and pre-process their data. And our goals, you know, here with the company are really to make Ray successful. To grow the Ray community, and then to build a great product around it and simplify the development and deployment, and productionization of machine learning for, for all these businesses. >> It's a great trend. Everyone wants developer productivity seeing that, clearly right now. And plus, developers are voting literally on what standards become. As you look at how the market is open source driven, a lot of that I love the model, love the Ray project love the, love the Anyscale value proposition. How big are you guys now, and how is that value proposition of Ray and Anyscale and foundational models coming together? Because it seems like you guys are in a perfect storm situation where you guys could get a real tailwind and draft off the the mega trend that everyone's getting excited. The new toy is ChatGPT. So you got to look at that and say, hey, I mean, come on, you guys did all the heavy lifting. >> Absolutely. >> You know how many people you are, and what's the what's the proposition for you guys these days? >> You know our company's about a hundred people, that a bit larger than that. Ray's been going really quickly. It's been, you know, companies using, like OpenAI uses Ray to train their models, like ChatGPT. Companies like Uber run all their deep learning you know, and classical machine learning on top of Ray. Companies like Shopify, Spotify, Netflix, Cruise, Lyft, Instacart, you know, Bike Dance. A lot of these companies are investing heavily in Ray for their machine learning infrastructure. And I think it's gotten to the point where, if you're one of these, you know type of businesses, and you're looking to revamp your machine learning infrastructure. If you're looking to enable new capabilities, you know make your teams more productive, increase, speed up the experimentation cycle, you know make it more performance, like build, you know, run applications that are more scalable, run them faster, run them in a more cost efficient way. All of these types of companies are at least evaluating Ray and Ray is an increasingly common choice there. I think if they're not using Ray, if many of these companies that end up not using Ray, they often end up building their own infrastructure. So Ray has been, the growth there has been incredibly exciting over the, you know we had our first in-person Ray Summit just back in August, and planning the next one for, for coming September. And so when you asked about the value proposition, I think there's there's really two main things, when people choose to go with Ray and Anyscale. One reason is about moving faster, right? It's about developer productivity, it's about speeding up the experimentation cycle, easily getting their models in production. You know, we hear many companies say that they, you know they, once they prototype a model, once they develop a model, it's another eight weeks, or 12 weeks to actually get that model in production. And that's a reason they talk to us. We hear companies say that, you know they've been training their models and, and doing inference on a single machine, and they've been sort of scaling vertically, like using bigger and bigger machines. But they, you know, you can only do that for so long, and at some point you need to go beyond a single machine and that's when they start talking to us. Right? So one of the main value propositions is around moving faster. I think probably the phrase I hear the most is, companies saying that they don't want their machine learning people to have to spend all their time configuring infrastructure. All this is about productivity. >> Yeah. >> The other. >> It's the big brains in the company. That are being used to do remedial tasks that should be automated right? I mean that's. >> Yeah, and I mean, it's hard stuff, right? It's also not these people's area of expertise, and or where they're adding the most value. So all of this is around developer productivity, moving faster, getting to market faster. The other big value prop and the reason people choose Ray and choose Anyscale, is around just providing superior infrastructure. This is really, can we scale more? You know, can we run it faster, right? Can we run it in a more cost effective way? We hear people saying that they're not getting good GPU utilization with the existing tools they're using, or they can't scale beyond a certain point, or you know they don't have a way to efficiently use spot instances to save costs, right? Or their clusters, you know can't auto scale up and down fast enough, right? These are all the kinds of things that Ray and Anyscale, where Ray and Anyscale add value and solve these kinds of problems. >> You know, you bring up great points. Auto scaling concept, early days, it was easy getting more compute. Now it's complicated. They're built into more integrated apps in the cloud. And you mentioned those companies that you're working with, that's impressive. Those are like the big hardcore, I call them hardcore. They have a good technical teams. And as the wave starts to move from these companies that were hyper scaling up all the time, the mainstream are just developers, right? So you need an interface in, so I see the dots connecting with you guys and I want to get your reaction. Is that how you see it? That you got the alphas out there kind of kicking butt, building their own stuff, alpha developers and infrastructure. But mainstream just wants programmability. They want that heavy lifting taken care of for them. Is that kind of how you guys see it? I mean, take us through that. Because to get crossover to be democratized, the automation's got to be there. And for developer productivity to be in, it's got to be coding and programmability. >> That's right. Ultimately for AI to really be successful, and really you know, transform every industry in the way we think it has the potential to. It has to be easier to use, right? And that is, and being easier to use, there's many dimensions to that. But an important one is that as a developer to do AI, you shouldn't have to be an expert in distributed systems. You shouldn't have to be an expert in infrastructure. If you do have to be, that's going to really limit the number of people who can do this, right? And I think there are so many, all of the companies we talk to, they don't want to be in the business of building and managing infrastructure. It's not that they can't do it. But it's going to slow them down, right? They want to allocate their time and their energy toward building their product, right? To building a better product, getting their product to market faster. And if we can take the infrastructure work off of the critical path for them, that's going to speed them up, it's going to simplify their lives. And I think that is critical for really enabling all of these companies to succeed with AI. >> Talk about the customers you guys are talking to right now, and how that translates over. Because I think you hit a good thread there. Data infrastructure is critical. Managed services are coming online, open sources continuing to grow. You have these people building their own, and then if they abandon it or don't scale it properly, there's kind of consequences. 'Cause it's a system you mentioned, it's a distributed system architecture. It's not as easy as standing up a monolithic app these days. So when you guys go to the marketplace and talk to customers, put the customers in buckets. So you got the ones that are kind of leaning in, that are pretty peaked, probably working with you now, open source. And then what's the customer profile look like as you go mainstream? Are they looking to manage service, looking for more architectural system, architecture approach? What's the, Anyscale progression? How do you engage with your customers? What are they telling you? >> Yeah, so many of these companies, yes, they're looking for managed infrastructure 'cause they want to move faster, right? Now the kind of these profiles of these different customers, they're three main workloads that companies run on Anyscale, run with Ray. It's training related workloads, and it is serving and deployment related workloads, like actually deploying your models, and it's batch processing, batch inference related workloads. Like imagine you want to do computer vision on tons and tons of, of images or videos, or you want to do natural language processing on millions of documents or audio, or speech or things like that, right? So the, I would say the, there's a pretty large variety of use cases, but the most common you know, we see tons of people working with computer vision data, you know, computer vision problems, natural language processing problems. And it's across many different industries. We work with companies doing drug discovery, companies doing you know, gaming or e-commerce, right? Companies doing robotics or agriculture. So there's a huge variety of the types of industries that can benefit from AI, and can really get a lot of value out of AI. And, but the, but the problems are the same problems that they all want to solve. It's like how do you make your team move faster, you know succeed with AI, be more productive, speed up the experimentation, and also how do you do this in a more performant way, in a faster, cheaper, in a more cost efficient, more scalable way. >> It's almost like the cloud game is coming back to AI and these foundational models, because I was just on a podcast, we recorded our weekly podcast, and I was just riffing with Dave Vellante, my co-host on this, were like, hey, in the early days of Amazon, if you want to build an app, you just, you have to build a data center, and then you go to now you go to the cloud, cloud's easier, pay a little money, penny's on the dollar, you get your app up and running. Cloud computing is born. With foundation models in generative AI. The old model was hard, heavy lifting, expensive, build out, before you get to do anything, as you mentioned time. So I got to think that you're pretty much in a good position with this foundational model trend in generative AI because I just looked at the foundation map, foundation models, map of the ecosystem. You're starting to see layers of, you got the tooling, you got platform, you got cloud. It's filling out really quickly. So why is Anyscale important to this new trend? How do you talk to people when they ask you, you know what does ChatGPT mean for Anyscale? And how does the financial foundational model growth, fit into your plan? >> Well, foundational models are hugely important for the industry broadly. Because you're going to have these really powerful models that are trained that you know, have been trained on tremendous amounts of data. tremendous amounts of computes, and that are useful out of the box, right? That people can start to use, and query, and get value out of, without necessarily training these huge models themselves. Now Ray fits in and Anyscale fit in, in a number of places. First of all, they're useful for creating these foundation models. Companies like OpenAI, you know, use Ray for this purpose. Companies like Cohere use Ray for these purposes. You know, IBM. If you look at, there's of course also open source versions like GPTJ, you know, created using Ray. So a lot of these large language models, large foundation models benefit from training on top of Ray. And, but of course for every company training and creating these huge foundation models, you're going to have many more that are fine tuning these models with their own data. That are deploying and serving these models for their own applications, that are building other application and business logic around these models. And that's where Ray also really shines, because Ray you know, is, can provide common infrastructure for all of these workloads. The training, the fine tuning, the serving, the data ingest and pre-processing, right? The hyper parameter tuning, the and and so on. And so where the reason Ray and Anyscale are important here, is that, again, foundation models are large, foundation models are compute intensive, doing you know, using both creating and using these foundation models requires tremendous amounts of compute. And there there's a big infrastructure lift to make that happen. So either you are using Ray and Anyscale to do this, or you are building the infrastructure and managing the infrastructure yourself. Which you can do, but it's, it's hard. >> Good luck with that. I always say good luck with that. I mean, I think if you really need to do, build that hardened foundation, you got to go all the way. And I think this, this idea of composability is interesting. How is Ray working with OpenAI for instance? Take, take us through that. Because I think you're going to see a lot of people talking about, okay I got trained models, but I'm going to have not one, I'm going to have many. There's big debate that OpenAI is going to be the mother of all LLMs, but now, but really people are also saying that to be many more, either purpose-built or specific. The fusion and these things come together there's like a blending of data, and that seems to be a value proposition. How does Ray help these guys get their models up? Can you take, take us through what Ray's doing for say OpenAI and others, and how do you see the models interacting with each other? >> Yeah, great question. So where, where OpenAI uses Ray right now, is for the training workloads. Training both to create ChatGPT and models like that. There's both a supervised learning component, where you're pre-training this model on doing supervised pre-training with example data. There's also a reinforcement learning component, where you are fine-tuning the model and continuing to train the model, but based on human feedback, based on input from humans saying that, you know this response to this question is better than this other response to this question, right? And so Ray provides the infrastructure for scaling the training across many, many GPUs, many many machines, and really running that in an efficient you know, performance fault tolerant way, right? And so, you know, open, this is not the first version of OpenAI's infrastructure, right? They've gone through iterations where they did start with building the infrastructure themselves. They were using tools like MPI. But at some point, you know, given the complexity, given the scale of what they're trying to do, you hit a wall with MPI and that's going to happen with a lot of other companies in this space. And at that point you don't have many other options other than to use Ray or to build your own infrastructure. >> That's awesome. And then your vision on this data interaction, because the old days monolithic models were very rigid. You couldn't really interface with them. But we're kind of seeing this future of data fusion, data interaction, data blending at large scale. What's your vision? How do you, what's your vision of where this goes? Because if this goes the way people think. You can have this data chemistry kind of thing going on where people are integrating all kinds of data with each other at large scale. So you need infrastructure, intelligence, reasoning, a lot of code. Is this something that you see? What's your vision in all this? Take us through. >> AI is going to be used everywhere right? It's, we see this as a technology that's going to be ubiquitous, and is going to transform every business. I mean, imagine you make a product, maybe you were making a tool like Photoshop or, or whatever the, you know, tool is. The way that people are going to use your tool, is not by investing, you know, hundreds of hours into learning all of the different, you know specific buttons they need to press and workflows they need to go through it. They're going to talk to it, right? They're going to say, ask it to do the thing they want it to do right? And it's going to do it. And if it, if it doesn't know what it's want, what it's, what's being asked of it. It's going to ask clarifying questions, right? And then you're going to clarify, and you're going to have a conversation. And this is going to make many many many kinds of tools and technology and products easier to use, and lower the barrier to entry. And so, and this, you know, many companies fit into this category of trying to build products that, and trying to make them easier to use, this is just one kind of way it can, one kind of way that AI will will be used. But I think it's, it's something that's pretty ubiquitous. >> Yeah. It'll be efficient, it'll be efficiency up and down the stack, and will change the productivity equation completely. You just highlighted one, I don't want to fill out forms, just stand up my environment for me. And then start coding away. Okay well this is great stuff. Final word for the folks out there watching, obviously new kind of skill set for hiring. You guys got engineers, give a plug for the company, for Anyscale. What are you looking for? What are you guys working on? Give a, take the last minute to put a plug in for the company. >> Yeah well if you're interested in AI and if you think AI is really going to be transformative, and really be useful for all these different industries. We are trying to provide the infrastructure to enable that to happen, right? So I think there's the potential here, to really solve an important problem, to get to the point where developers don't need to think about infrastructure, don't need to think about distributed systems. All they think about is their application logic, and what they want their application to do. And I think if we can achieve that, you know we can be the foundation or the platform that enables all of these other companies to succeed with AI. So that's where we're going. I think something like this has to happen if AI is going to achieve its potential, we're looking for, we're hiring across the board, you know, great engineers, on the go-to-market side, product managers, you know people who want to really, you know, make this happen. >> Awesome well congratulations. I know you got some good funding behind you. You're in a good spot. I think this is happening. I think generative AI and foundation models is going to be the next big inflection point, as big as the pc inter-networking, internet and smartphones. This is a whole nother application framework, a whole nother set of things. So this is the ground floor. Robert, you're, you and your team are right there. Well done. >> Thank you so much. >> All right. Thanks for coming on this CUBE conversation. I'm John Furrier with theCUBE. Breaking down a conversation around AI and scaling up in this new next major inflection point. This next wave is foundational models, generative AI. And thanks to ChatGPT, the whole world's now knowing about it. So it really is changing the game and Anyscale is right there, one of the hot startups, that is in good position to ride this next wave. Thanks for watching. (upbeat instrumental)

(upbeat music) >> Hello, everybody. This is Dave Vellante. Welcome back to Supercloud2, where we're exploring the intersection of data analytics and the future of cloud. In this segment, we're going to look at how the Supercloud will support a new class of applications, not just work that runs on multiple clouds, but rather a new breed of apps that can orchestrate things in the real world. Think Uber for many types of businesses. These applications, they're not about codifying forms or business processes. They're about orchestrating people, places, and things in a business ecosystem. And I'm pleased to welcome my colleague and friend, George Gilbert, former Gartner Analyst, Wiki Bond market analyst, former equities analyst as my co-host. And we're thrilled to have Tristan Handy, who's the founder and CEO of DBT Labs and Bob Muglia, who's the former President of Microsoft's Enterprise business and former CEO of Snowflake. Welcome all, gentlemen. Thank you for coming on the program. >> Good to be here. >> Thanks for having us. >> Hey, look, I'm going to start actually with the SuperCloud because both Tristan and Bob, you've read the definition. Thank you for doing that. And Bob, you have some really good input, some thoughts on maybe some of the drawbacks and how we can advance this. So what are your thoughts in reading that definition around SuperCloud? >> Well, I thought first of all that you did a very good job of laying out all of the characteristics of it and helping to define it overall. But I do think it can be tightened a bit, and I think it's helpful to do it in as short a way as possible. And so in the last day I've spent a little time thinking about how to take it and write a crisp definition. And here's my go at it. This is one day old, so gimme a break if it's going to change. And of course we have to follow the industry, and so that, and whatever the industry decides, but let's give this a try. So in the way I think you're defining it, what I would say is a SuperCloud is a platform that provides programmatically consistent services hosted on heterogeneous cloud providers. >> Boom. Nice. Okay, great. I'm going to go back and read the script on that one and tighten that up a bit. Thank you for spending the time thinking about that. Tristan, would you add anything to that or what are your thoughts on the whole SuperCloud concept? >> So as I read through this, I fully realize that we need a word for this thing because I have experienced the inability to talk about it as well. But for many of us who have been living in the Confluence, Snowflake, you know, this world of like new infrastructure, this seems fairly uncontroversial. Like I read through this, and I'm just like, yeah, this is like the world I've been living in for years now. And I noticed that you called out Snowflake for being an example of this, but I think that there are like many folks, myself included, for whom this world like fully exists today. >> Yeah, I think that's a fair, I dunno if it's criticism, but people observe, well, what's the big deal here? It's just kind of what we're living in today. It reminds me of, you know, Tim Burns Lee saying, well, this is what the internet was supposed to be. It was supposed to be Web 2.0, so maybe this is what multi-cloud was supposed to be. Let's turn our attention to apps. Bob first and then go to Tristan. Bob, what are data apps to you? When people talk about data products, is that what they mean? Are we talking about something more, different? What are data apps to you? >> Well, to understand data apps, it's useful to contrast them to something, and I just use the simple term people apps. I know that's a little bit awkward, but it's clear. And almost everything we work with, almost every application that we're familiar with, be it email or Salesforce or any consumer app, those are applications that are targeted at responding to people. You know, in contrast, a data application reacts to changes in data and uses some set of analytic services to autonomously take action. So where applications that we're familiar with respond to people, data apps respond to changes in data. And they both do something, but they do it for different reasons. >> Got it. You know, George, you and I were talking about, you know, it comes back to SuperCloud, broad definition, narrow definition. Tristan, how do you see it? Do you see it the same way? Do you have a different take on data apps? >> Oh, geez. This is like a conversation that I don't know has an end. It's like been, I write a substack, and there's like this little community of people who all write substack. We argue with each other about these kinds of things. Like, you know, as many different takes on this question as you can find, but the way that I think about it is that data products are atomic units of functionality that are fundamentally data driven in nature. So a data product can be as simple as an interactive dashboard that is like actually had design thinking put into it and serves a particular user group and has like actually gone through kind of a product development life cycle. And then a data app or data application is a kind of cohesive end-to-end experience that often encompasses like many different data products. So from my perspective there, this is very, very related to the way that these things are produced, the kinds of experiences that they're provided, that like data innovates every product that we've been building in, you know, software engineering for, you know, as long as there have been computers. >> You know, Jamak Dagani oftentimes uses the, you know, she doesn't name Spotify, but I think it's Spotify as that kind of example she uses. But I wonder if we can maybe try to take some examples. If you take, like George, if you take a CRM system today, you're inputting leads, you got opportunities, it's driven by humans, they're really inputting the data, and then you got this system that kind of orchestrates the business process, like runs a forecast. But in this data driven future, are we talking about the app itself pulling data in and automatically looking at data from the transaction systems, the call center, the supply chain and then actually building a plan? George, is that how you see it? >> I go back to the example of Uber, may not be the most sophisticated data app that we build now, but it was like one of the first where you do have users interacting with their devices as riders trying to call a car or driver. But the app then looks at the location of all the drivers in proximity, and it matches a driver to a rider. It calculates an ETA to the rider. It calculates an ETA then to the destination, and it calculates a price. Those are all activities that are done sort of autonomously that don't require a human to type something into a form. The application is using changes in data to calculate an analytic product and then to operationalize that, to assign the driver to, you know, calculate a price. Those are, that's an example of what I would think of as a data app. And my question then I guess for Tristan is if we don't have all the pieces in place for sort of mainstream companies to build those sorts of apps easily yet, like how would we get started? What's the role of a semantic layer in making that easier for mainstream companies to build? And how do we get started, you know, say with metrics? How does that, how does that take us down that path? >> So what we've seen in the past, I dunno, decade or so, is that one of the most successful business models in infrastructure is taking hard things and rolling 'em up behind APIs. You take messaging, you take payments, and you all of a sudden increase the capability of kind of your median application developer. And you say, you know, previously you were spending all your time being focused on how do you accept credit cards, how do you send SMS payments, and now you can focus on your business logic, and just create the thing. One of, interestingly, one of the things that we still don't know how to API-ify is concepts that live inside of your data warehouse, inside of your data lake. These are core concepts that, you know, you would imagine that the business would be able to create applications around very easily, but in fact that's not the case. It's actually quite challenging to, and involves a lot of data engineering pipeline and all this work to make these available. And so if you really want to make it very easy to create some of these data experiences for users, you need to have an ability to describe these metrics and then to turn them into APIs to make them accessible to application developers who have literally no idea how they're calculated behind the scenes, and they don't need to. >> So how rich can that API layer grow if you start with metric definitions that you've defined? And DBT has, you know, the metric, the dimensions, the time grain, things like that, that's a well scoped sort of API that people can work within. How much can you extend that to say non-calculated business rules or governance information like data reliability rules, things like that, or even, you know, features for an AIML feature store. In other words, it starts, you started pragmatically, but how far can you grow? >> Bob is waiting with bated breath to answer this question. I'm, just really quickly, I think that we as a company and DBT as a product tend to be very pragmatic. We try to release the simplest possible version of a thing, get it out there, and see if people use it. But the idea that, the concept of a metric is really just a first landing pad. The really, there is a physical manifestation of the data and then there's a logical manifestation of the data. And what we're trying to do here is make it very easy to access the logical manifestation of the data, and metric is a way to look at that. Maybe an entity, a customer, a user is another way to look at that. And I'm sure that there will be more kind of logical structures as well. >> So, Bob, chime in on this. You know, what's your thoughts on the right architecture behind this, and how do we get there? >> Yeah, well first of all, I think one of the ways we get there is by what companies like DBT Labs and Tristan is doing, which is incrementally taking and building on the modern data stack and extending that to add a semantic layer that describes the data. Now the way I tend to think about this is a fairly major shift in the way we think about writing applications, which is today a code first approach to moving to a world that is model driven. And I think that's what the big change will be is that where today we think about data, we think about writing code, and we use that to produce APIs as Tristan said, which encapsulates those things together in some form of services that are useful for organizations. And that idea of that encapsulation is never going to go away. It's very, that concept of an API is incredibly useful and will exist well into the future. But what I think will happen is that in the next 10 years, we're going to move to a world where organizations are defining models first of their data, but then ultimately of their business process, their entire business process. Now the concept of a model driven world is a very old concept. I mean, I first started thinking about this and playing around with some early model driven tools, probably before Tristan was born in the early 1980s. And those tools didn't work because the semantics associated with executing the model were too complex to be written in anything other than a procedural language. We're now reaching a time where that is changing, and you see it everywhere. You see it first of all in the world of machine learning and machine learning models, which are taking over more and more of what applications are doing. And I think that's an incredibly important step. And learned models are an important part of what people will do. But if you look at the world today, I will claim that we've always been modeling. Modeling has existed in computers since there have been integrated circuits and any form of computers. But what we do is what I would call implicit modeling, which means that it's the model is written on a whiteboard. It's in a bunch of Slack messages. It's on a set of napkins in conversations that happen and during Zoom. That's where the model gets defined today. It's implicit. There is one in the system. It is hard coded inside application logic that exists across many applications with humans being the glue that connects those models together. And really there is no central place you can go to understand the full attributes of the business, all of the business rules, all of the business logic, the business data. That's going to change in the next 10 years. And we'll start to have a world where we can define models about what we're doing. Now in the short run, the most important models to build are data models and to describe all of the attributes of the data and their relationships. And that's work that DBT Labs is doing. A number of other companies are doing that. We're taking steps along that way with catalogs. People are trying to build more complete ontologies associated with that. The underlying infrastructure is still super, super nascent. But what I think we'll see is this infrastructure that exists today that's building learned models in the form of machine learning programs. You know, some of these incredible machine learning programs in foundation models like GPT and DALL-E and all of the things that are happening in these global scale models, but also all of that needs to get applied to the domains that are appropriate for a business. And I think we'll see the infrastructure developing for that, that can take this concept of learned models and put it together with more explicitly defined models. And this is where the concept of knowledge graphs come in and then the technology that underlies that to actually implement and execute that, which I believe are relational knowledge graphs. >> Oh, oh wow. There's a lot to unpack there. So let me ask the Colombo question, Tristan, we've been making fun of your youth. We're just, we're just jealous. Colombo, I'll explain it offline maybe. >> I watch Colombo. >> Okay. All right, good. So but today if you think about the application stack and the data stack, which is largely an analytics pipeline. They're separate. Do they, those worlds, do they have to come together in order to achieve Bob's vision? When I talk to practitioners about that, they're like, well, I don't want to complexify the application stack cause the data stack today is so, you know, hard to manage. But but do those worlds have to come together? And you know, through that model, I guess abstraction or translation that Bob was just describing, how do you guys think about that? Who wants to take that? >> I think it's inevitable that data and AI are going to become closer together? I think that the infrastructure there has been moving in that direction for a long time. Whether you want to use the Lakehouse portmanteau or not. There's also, there's a next generation of data tech that is still in the like early stage of being developed. There's a company that I love that is essentially Cross Cloud Lambda, and it's just a wonderful abstraction for computing. So I think that, you know, people have been predicting that these worlds are going to come together for awhile. A16Z wrote a great post on this back in I think 2020, predicting this, and I've been predicting this since since 2020. But what's not clear is the timeline, but I think that this is still just as inevitable as it's been. >> Who's that that does Cross Cloud? >> Let me follow up on. >> Who's that, Tristan, that does Cross Cloud Lambda? Can you name names? >> Oh, they're called Modal Labs. >> Modal Labs, yeah, of course. All right, go ahead, George. >> Let me ask about this vision of trying to put the semantics or the code that represents the business with the data. It gets us to a world that's sort of more data centric, where data's not locked inside or behind the APIs of different applications so that we don't have silos. But at the same time, Bob, I've heard you talk about building the semantics gradually on top of, into a knowledge graph that maybe grows out of a data catalog. And the vision of getting to that point, essentially the enterprise's metadata and then the semantics you're going to add onto it are really stored in something that's separate from the underlying operational and analytic data. So at the same time then why couldn't we gradually build semantics beyond the metric definitions that DBT has today? In other words, you build more and more of the semantics in some layer that DBT defines and that sits above the data management layer, but any requests for data have to go through the DBT layer. Is that a workable alternative? Or where, what type of limitations would you face? >> Well, I think that it is the way the world will evolve is to start with the modern data stack and, you know, which is operational applications going through a data pipeline into some form of data lake, data warehouse, the Lakehouse, whatever you want to call it. And then, you know, this wide variety of analytics services that are built together. To the point that Tristan made about machine learning and data coming together, you see that in every major data cloud provider. Snowflake certainly now supports Python and Java. Databricks is of course building their data warehouse. Certainly Google, Microsoft and Amazon are doing very, very similar things in terms of building complete solutions that bring together an analytics stack that typically supports languages like Python together with the data stack and the data warehouse. I mean, all of those things are going to evolve, and they're not going to go away because that infrastructure is relatively new. It's just being deployed by companies, and it solves the problem of working with petabytes of data if you need to work with petabytes of data, and nothing will do that for a long time. What's missing is a layer that understands and can model the semantics of all of this. And if you need to, if you want to model all, if you want to talk about all the semantics of even data, you need to think about all of the relationships. You need to think about how these things connect together. And unfortunately, there really is no platform today. None of our existing platforms are ultimately sufficient for this. It was interesting, I was just talking to a customer yesterday, you know, a large financial organization that is building out these semantic layers. They're further along than many companies are. And you know, I asked what they're building it on, and you know, it's not surprising they're using a, they're using combinations of some form of search together with, you know, textual based search together with a document oriented database. In this case it was Cosmos. And that really is kind of the state of the art right now. And yet those products were not built for this. They don't really, they can't manage the complicated relationships that are required. They can't issue the queries that are required. And so a new generation of database needs to be developed. And fortunately, you know, that is happening. The world is developing a new set of relational algorithms that will be able to work with hundreds of different relations. If you look at a SQL database like Snowflake or a big query, you know, you get tens of different joins coming together, and that query is going to take a really long time. Well, fortunately, technology is evolving, and it's possible with new join algorithms, worst case, optimal join algorithms they're called, where you can join hundreds of different relations together and run semantic queries that you simply couldn't run. Now that technology is nascent, but it's really important, and I think that will be a requirement to have this semantically reach its full potential. In the meantime, Tristan can do a lot of great things by building up on what he's got today and solve some problems that are very real. But in the long run I think we'll see a new set of databases to support these models. >> So Tristan, you got to respond to that, right? You got to, so take the example of Snowflake. We know it doesn't deal well with complex joins, but they're, they've got big aspirations. They're building an ecosystem to really solve some of these problems. Tristan, you guys are part of that ecosystem, and others, but please, your thoughts on what Bob just shared. >> Bob, I'm curious if, I would have no idea what you were talking about except that you introduced me to somebody who gave me a demo of a thing and do you not want to go there right now? >> No, I can talk about it. I mean, we can talk about it. Look, the company I've been working with is Relational AI, and they're doing this work to actually first of all work across the industry with academics and research, you know, across many, many different, over 20 different research institutions across the world to develop this new set of algorithms. They're all fully published, just like SQL, the underlying algorithms that are used by SQL databases are. If you look today, every single SQL database uses a similar set of relational algorithms underneath that. And those algorithms actually go back to system R and what IBM developed in the 1970s. We're just, there's an opportunity for us to build something new that allows you to take, for example, instead of taking data and grouping it together in tables, treat all data as individual relations, you know, a key and a set of values and then be able to perform purely relational operations on it. If you go back to what, to Codd, and what he wrote, he defined two things. He defined a relational calculus and relational algebra. And essentially SQL is a query language that is translated by the query processor into relational algebra. But however, the calculus of SQL is not even close to the full semantics of the relational mathematics. And it's possible to have systems that can do everything and that can store all of the attributes of the data model or ultimately the business model in a form that is much more natural to work with. >> So here's like my short answer to this. I think that we're dealing in different time scales. I think that there is actually a tremendous amount of work to do in the semantic layer using the kind of technology that we have on the ground today. And I think that there's, I don't know, let's say five years of like really solid work that there is to do for the entire industry, if not more. But the wonderful thing about DBT is that it's independent of what the compute substrate is beneath it. And so if we develop new platforms, new capabilities to describe semantic models in more fine grain detail, more procedural, then we're going to support that too. And so I'm excited about all of it. >> Yeah, so interpreting that short answer, you're basically saying, cause Bob was just kind of pointing to you as incremental, but you're saying, yeah, okay, we're applying it for incremental use cases today, but we can accommodate a much broader set of examples in the future. Is that correct, Tristan? >> I think you're using the word incremental as if it's not good, but I think that incremental is great. We have always been about applying incremental improvement on top of what exists today, but allowing practitioners to like use different workflows to actually make use of that technology. So yeah, yeah, we are a very incremental company. We're going to continue being that way. >> Well, I think Bob was using incremental as a pejorative. I mean, I, but to your point, a lot. >> No, I don't think so. I want to stop that. No, I don't think it's pejorative at all. I think incremental, incremental is usually the most successful path. >> Yes, of course. >> In my experience. >> We agree, we agree on that. >> Having tried many, many moonshot things in my Microsoft days, I can tell you that being incremental is a good thing. And I'm a very big believer that that's the way the world's going to go. I just think that there is a need for us to build something new and that ultimately that will be the solution. Now you can argue whether it's two years, three years, five years, or 10 years, but I'd be shocked if it didn't happen in 10 years. >> Yeah, so we all agree that incremental is less disruptive. Boom, but Tristan, you're, I think I'm inferring that you believe you have the architecture to accommodate Bob's vision, and then Bob, and I'm inferring from Bob's comments that maybe you don't think that's the case, but please. >> No, no, no. I think that, so Bob, let me put words into your mouth and you tell me if you disagree, DBT is completely useless in a world where a large scale cloud data warehouse doesn't exist. We were not able to bring the power of Python to our users until these platforms started supporting Python. Like DBT is a layer on top of large scale computing platforms. And to the extent that those platforms extend their functionality to bring more capabilities, we will also service those capabilities. >> Let me try and bridge the two. >> Yeah, yeah, so Bob, Bob, Bob, do you concur with what Tristan just said? >> Absolutely, I mean there's nothing to argue with in what Tristan just said. >> I wanted. >> And it's what he's doing. It'll continue to, I believe he'll continue to do it, and I think it's a very good thing for the industry. You know, I'm just simply saying that on top of that, I would like to provide Tristan and all of those who are following similar paths to him with a new type of database that can actually solve these problems in a much more architected way. And when I talk about Cosmos with something like Mongo or Cosmos together with Elastic, you're using Elastic as the join engine, okay. That's the purpose of it. It becomes a poor man's join engine. And I kind of go, I know there's a better answer than that. I know there is, but that's kind of where we are state of the art right now. >> George, we got to wrap it. So give us the last word here. Go ahead, George. >> Okay, I just, I think there's a way to tie together what Tristan and Bob are both talking about, and I want them to validate it, which is for five years we're going to be adding or some number of years more and more semantics to the operational and analytic data that we have, starting with metric definitions. My question is for Bob, as DBT accumulates more and more of those semantics for different enterprises, can that layer not run on top of a relational knowledge graph? And what would we lose by not having, by having the knowledge graph store sort of the joins, all the complex relationships among the data, but having the semantics in the DBT layer? >> Well, I think this, okay, I think first of all that DBT will be an environment where many of these semantics are defined. The question we're asking is how are they stored and how are they processed? And what I predict will happen is that over time, as companies like DBT begin to build more and more richness into their semantic layer, they will begin to experience challenges that customers want to run queries, they want to ask questions, they want to use this for things where the underlying infrastructure becomes an obstacle. I mean, this has happened in always in the history, right? I mean, you see major advances in computer science when the data model changes. And I think we're on the verge of a very significant change in the way data is stored and structured, or at least metadata is stored and structured. Again, I'm not saying that anytime in the next 10 years, SQL is going to go away. In fact, more SQL will be written in the future than has been written in the past. And those platforms will mature to become the engines, the slicer dicers of data. I mean that's what they are today. They're incredibly powerful at working with large amounts of data, and that infrastructure is maturing very rapidly. What is not maturing is the infrastructure to handle all of the metadata and the semantics that that requires. And that's where I say knowledge graphs are what I believe will be the solution to that. >> But Tristan, bring us home here. It sounds like, let me put pause at this, is that whatever happens in the future, we're going to leverage the vast system that has become cloud that we're talking about a supercloud, sort of where data lives irrespective of physical location. We're going to have to tap that data. It's not necessarily going to be in one place, but give us your final thoughts, please. >> 100% agree. I think that the data is going to live everywhere. It is the responsibility for both the metadata systems and the data processing engines themselves to make sure that we can join data across cloud providers, that we can join data across different physical regions and that we as practitioners are going to kind of start forgetting about details like that. And we're going to start thinking more about how we want to arrange our teams, how does the tooling that we use support our team structures? And that's when data mesh I think really starts to get very, very critical as a concept. >> Guys, great conversation. It was really awesome to have you. I can't thank you enough for spending time with us. Really appreciate it. >> Thanks a lot. >> All right. This is Dave Vellante for George Gilbert, John Furrier, and the entire Cube community. Keep it right there for more content. You're watching SuperCloud2. (upbeat music)

(lively music) >> Good afternoon cloud community and welcome back to beautiful Las Vegas, Nevada. We are here at AWS re:Invent, day four of our wall to wall coverage. It is day four in the afternoon and we are holding strong. I'm Savannah Peterson, joined by my fabulous co-host Paul Gillen. Paul, how you doing? >> I'm doing well, fine Savannah. You? >> You look great. >> We're in the home stretch here. >> Yeah, (laughs) we are. >> You still look fresh as a daisy. I don't know how you do it. >> (laughs) You're too kind. You're too kind, but I'm vain enough to take that compliment. I'm very excited about the conversation that we're going to have up next. We get to get a little DevRel and we got a little swagger on the stage. Welcome, Austin. How you doing? >> Hey, great to be here. Thanks for having me. >> Savannah: Yeah, it's our pleasure. How's the show been for you so far? >> Busy, exciting. Feels a lot like, you know it used to be right? >> Yeah, I know. A little reminiscent of the before times. >> Well, before times. >> Before we dig into the technical stuff, you're the most intriguingly dressed person we've had on the show this week. >> Austin: I feel extremely underdressed. >> Well, and we were talking about developer fancy. Talk to me a little bit about your approach to fashion. Wasn't expecting to lead with this, but I like this but I like this actually. >> No, it's actually good with my PR. You're going to love it. My approach, here's the thing, I give free advice all the time about developer relations, about things that work, have worked, and don't work in community and all that stuff. I love talking about that. Someone came up to me and said, "Where do you get your fashion tips from? What's the secret Discord server that I need to go on?" I'm like, "I will never tell." >> Oh, okay. >> This is an actual trait secret. >> Top secret. Wow! Talk about. >> If someone else starts wearing the hat, then everyone's going to be like, "There's so many white guys." Look, I'm a white guy with a beard that works in technology. >> Savannah: I've never met one of those. >> Exactly, there's none of them at all. So, you have to do something to kind stand out from the crowd a little bit. >> I love it, and it's a talk trigger. We're talking about it now. Production team loved it. It's fantastic. >> It's great. >> So your DevRel for Lightstep, in case the audience isn't familiar tell us about Lightstep. >> So Lightstep is a cloud native observability platform built at planet scale, and it powers observability at some places you've heard of like Spotify, GitHub, right? We're designed to really help developers that are working in the cloud with Kubernetes, with these huge distributed systems, understand application performance and being able to find problems, fix problems. We're also part of the ServiceNow family and as we all know ServiceNow is on a mission to help the world of work work better by powering digital transformation around IT and customer experiences for their many, many, many global 2000 customers. We love them very much. >> You know, it's a big love fest here. A lot of people have talked about the collaboration, so many companies working together. You mentioned unified observability. What is unified observability? >> So if you think about a tradition, or if you've heard about this traditional idea of observability where you have three pillars, right? You have metrics, and you have logs, and you have traces. All those three things are different data sources. They're picked up by different tools. They're analyzed by different people for different purposes. What we believe and what we're working to accomplish right now is to take all that and if you think those pillars, flip 'em on their side and think of them as streams of data. If we can take those streams and integrate them together and let you treat traces and metrics and logs not as these kind of inviolate experiences where you're kind of paging between things and going between tab A to tab B to tab C, and give you a standard way to query this, a standard way to display this, and letting you kind of find the most relevant data, then it really unlocks a lot of power for like developers and SREs to spend less time like managing tools. You know, figuring out where to build their query or what dashboard to check, more just being able to like kind of ask a question, get an answer. When you have an incident or an outage that's the most important thing, right? How quickly can you get those answers that you need so that you can restore system health? >> You don't want to be looking in multiple spots to figure out what's going on. >> Absolutely. I mean, some people hear unified observability and they go to like tool consolidation, right? That's something I hear from a lot of our users and a lot of people in re:Invent. I'll talk to SREs, they're like, "Yeah, we've got like six or seven different metrics products alone, just on services that they cover." It is important to kind of consolidate that but we're really taking it a step lower. We're looking at the data layer and trying to say, "Okay, if the data is all consistent and vendor neutral then that gives you flexibility not only from a tool consolidation perspective but also you know, a consistency, reliability. You could have a single way to deploy your observability out regardless of what cloud you're on, regardless if you're using Kubernetes or Fargate or whatever else. or even just Bare Metal or EC2 Bare Metal, right? There's been so much historically in this space. There's been a lot of silos and we think that unify diversability means that we kind of break down those silos, right? The way that we're doing it primarily is through a project called OpenTelemetry which you might have heard of. You want to talk about that in a minute? . >> Savannah: Yeah, let's talk about it right now. Why don't you tell us about it? Keep going, you're great. You're on a roll. >> I am. >> Savannah: We'll just hang out over here. >> It's day four. I'm going to ask the questions and answer the questions. (Savannah laughs) >> Yes, you're right. >> I do yeah. >> Open Tele- >> OpenTelemetry . >> Explain what OpenTelemetry is first. >> OpenTelemetry is a CNCF project, Cloud Native Computing Foundation. The goal is to make telemetry data, high quality telemetry data, a builtin feature of cloud native software right? So right now if you wanted to get logging data out, depending on your application stack, depending on your application run time, depending on language, depending on your deployment environment. You might have a lot... You have to make a lot of choices, right? About like, what am I going to use? >> Savannah: So many different choices, and the players are changing all the time. >> Exactly, and a lot of times what people will do is they'll go and they'll say like, "We have to use this commercial solution because they have a proprietary agent that can do a lot of this for us." You know? And if you look at all those proprietary agents, what you find very quickly is it's very commodified right? There's no real difference in what they're doing at a code level and what's stopped the industry from really adopting a standard way to create this logs and metrics and traces, is simply just the fact that there was no standard. And so, OpenTelemetry is that standard, right? We've got dozens of companies many of them like very, many of them here right? Competitors all the same, working together to build this open standard and implementation of telemetry data for cloud native software and really any software right? Like we support over 12 languages. We support Kubernetes, Amazon. AWS is a huge contributor actually and we're doing some really exciting stuff with them on their Amazon distribution of OpenTelemetry. So it's been extremely interesting to see it over the past like couple years go from like, "Hey, here's this like new thing that we're doing over here," to really it's a generalized acceptance that this is the way of the future. This is what we should have been doing all along. >> Yeah. >> My opinion is there is a perception out there that observability is kind of a commodity now that all the players have the same set of tools, same set of 15 or 17 or whatever tools, and that there's very little distinction in functionality. Would you agree with that? >> I don't know if I would characterize it that way entirely. I do think that there's a lot of duplicated effort that happens and part of the reason is because of this telemetry data problem, right? Because you have to wind up... You know, there's this idea of table stakes monitoring that we talk about right? Table stakes monitoring is the stuff that you're having to do every single day to kind of make sure your system is healthy to be able to... When there's an alert, gets triggered, to see why it got triggered and to go fix it, right? Because everyone has the kind of work on that table stake stuff and then build all these integrations, there's very little time for innovation on top of that right? Because you're spending all your time just like working on keeping up with technology. >> Savannah: Doing the boring stuff to make sure the wheels don't fall off, basically. >> Austin: Right? What I think the real advantage of OpenTelemetry is that it really, from like a vendor perspective, like it unblocks us from having to kind of do all this repetitive commodified work. It lets us help move that out to the community level so that... Instead of having to kind of build, your Kubernetes integration for example, you can just have like, "Hey, OpenTelemetry is integrated into Kubernetes and you just have this data now." If you are a commercial product, or if you're even someone that's interested in fixing a, scratching a particular itch about observability. It's like, "I have this specific way that I'm doing Kubernetes and I need something to help me really analyze that data. Well, I've got the data now I can just go create a project. I can create an analysis tool." I think that's what you'll see over time as OpenTelemetry promulgates out into the ecosystem is more people building interesting analysis features, people using things like machine learning to analyze this large amount, large and consistent amount of OpenTelemetry data. It's going to be a big shakeup I think, but it has the potential to really unlock a lot of value for our customers. >> Well, so you're, you're a developer relations guy. What are developers asking for right now out of their observability platforms? >> Austin: That's a great question. I think there's two things. The first is that they want it to just work. It's actually the biggest thing, right? There's so many kind of... This goes back to the tool proliferation, right? People have too much data in too many different places, and getting that data out can still be really challenging. And so, the biggest thing they want is just like, "I want something that I can... I want a lot of these questions I have to ask, answered already and OpenTelemetry is going towards it." Keep in mind it's the project's only three years old, so we obviously have room to grow but there are people running it in production and it works really well for them but there's more that we can do. The second thing is, and this isn't what really is interesting to me, is it's less what they're asking for and more what they're not asking for. Because a lot of the stuff that you see people, saying around, "Oh, we need this like very specific sort of lower level telemetry data, or we need this kind of universal thing." People really just want to be able to get questions or get questions answered, right? They want tools that kind of have these workflows where you don't have to be an expert because a lot of times this tooling gets locked behind sort of is gate kept almost in a organization where there are teams that's like, "We're responsible for this and we're going to set it up and manage it for you, and we won't let you do things outside of it because that would mess up- >> Savannah: Here's your sandbox and- >> Right, this is your sandbox you can play in and a lot of times that's really useful and very tuned for the problems that you saw yesterday, but people are looking at like what are the problems I'm going to get tomorrow? We're deploying more rapidly. We have more and more intentional change happening in the system. Like it's not enough to have this reactive sort of approach where our SRE teams are kind of like or this observability team is building a platform for us. Developers want to be able to get in and have these kind of guided workflows really that say like, "Hey, here's where you're starting at. Let's get you to an answer. Let's help you find the needle in the haystack as it were, without you having to become a master of six different or seven different tools." >> Savannah: Right, and it shouldn't be that complicated. >> It shouldn't be. I mean we've certainly... We've been working on this problem for many years now, starting with a lot of our team that started at Google and helped build Google's planet scale monitoring systems. So we have a lot of experience in the field. It's actually one... An interesting story that our founder or now general manager tells BHS, Ben Sigelman, and he told me this story once and it's like... He had built this really cool thing called Dapper that was a tracing system at Google, and people weren't using it. Because they were like, "This is really cool, but I don't know how to... but it's not relevant to me." And he's like, the one thing that we did to get to increase usage 20 times over was we just put a link. So we went to the place that people were already looking for that data and we added a link that says, "Hey, go over here and look at this." It's those simple connections being able to kind of draw people from like point A to point B, take them from familiar workflows into unfamiliar ones. You know, that's how we think about these problems right? How is this becoming a daily part of someone's usage? How is this helping them solve problems faster and really improve their their life? >> Savannah: Yeah, exactly. It comes down to quality of life. >> Warner made the case this morning that computer architecture should be inherently event-driven and that we are moving toward a world where the person matters less than what the software does, right? The software is triggering events. Does this complicate observability or simplify it? >> Austin: I think that at the end of the day, it's about getting the... Observability to me in a lot of ways is about modeling your system, right? It's about you as a developer being able to say this is what I expect the system to do and I don't think the actual application architecture really matters that much, right? Because it's about you. You are building a system, right? It can be event driven, can be support request response, can be whatever it is. You have to be able to say, "This is what I expect to... For these given inputs, this is the expected output." Now maybe there's a lot of stuff that happens in the middle that you don't really care about. And then, I talk to people here and everyone's talking about serverless right? Everyone... You can see there's obviously some amazing statistics about how many people are using Lambda, and it's very exciting. There's a lot of stuff that you shouldn't have to care about as a developer, but you should care about those inputs and outputs. You will need to have that kind of intermediate information and understand like, what was the exact path that I took through this invented system? What was the actual resources that were being used? Because even if you trust that all this magic behind the scenes is just going to work forever, sometimes it's still really useful to have that sort of lower level abstraction, to say like, "Well, this is what actually happened so that I can figure out when I deployed a new change, did I make performance better or worse?" Or being able to kind of segregate your data out and say like... Doing AB testing, right? Doing canary releases, doing all of these things that you hear about as best practices or well architected applications. Observability is at the core of all that. You need observability to kind of do any of, ask any of those higher level interesting questions. >> Savannah: We are here at ReInvent. Tell us a little bit more about the partnership with AWS. >> So I would have to actually probably refer you to someone at Service Now on that. I know that we are a partner. We collaborate with them on various things. But really at Lightstep, we're very focused on kind of the open source part of this. So we work with AWS through the OpenTelemetry project, on things like the AWS distribution for OpenTelemetry which is really... It's OpenTelemetry, again is really designed to be like a neutral standard but we know that there are going to be integrators and implementers that need to package up and bundle it in a certain way to make it easy for their end users to consume it. So that's what Amazon has done with ADOT which is the shortening for it. So it's available in several different ways. You can use it as like an SDK and drop it into your application. There's Lambda layers. If you want to get Lambda observability, you just add this extension in and then suddenly you're getting OpenTelemetry data on the other side. So it's really cool. It's been a really exciting to kind of work with people on the AWS side over the past several years. >> Savannah: It's exciting, >> I've personally seen just a lot of change. I was talking to a PM earlier this week... It's like, "Hey, two years ago I came and talked to you about OpenTelemetry and here we are today. You're still talking about OpenTelemetry." And they're like, "What changes?" Our customers have started coming to us asking for OpenTelemetry and we see the same thing now. >> Savannah: Timing is right. >> Timing is right, but we see the same thing... Even talking to ServiceNow customers who are... These very big enterprises, banks, finance, healthcare, whatever, telcos, it used to be... You'd have to go to them and say like, "Let me tell you about distributed tracing. Let me tell you about OpenTelemetry. Let me tell you about observability." Now they're coming in and saying, "Yeah, so we're standard." If you think about Kubernetes and how Kubernetes, a lot of enterprises have spent the past five-six years standardizing, and Kubernetes is a way to deploy applications or manage containerized applications. They're doing the same journey now with OpenTelemetry where they're saying, "This is what we're betting on and we want partners we want people to help us go along that way." >> I love it, and they work hand in hand in all CNCF projects as well that you're talking about. >> Austin: Right, so we're integrated into Kubernetes. You can find OpenTelemetry and things like kept in which is application standards. And over time, it'll just like promulgate out from there. So it's really exciting times. >> A bunch of CNCF projects in this area right? Prometheus. >> Prometheus, yeah. Yeah, so we inter-operate with Prometheus as well. So if you have Prometheus metrics, then OpenTelemetry can read those. It's a... OpenTelemetry metrics are like a super set of Prometheus. We've been working with the Prometheus community for quite a while to make sure that there's really good compatibility because so many people use Prometheus you know? >> Yeah. All right, so last question. New tradition for us here on theCUBE. We're looking for your 32nd hot take, Instagram reel, biggest theme, biggest buzz for those not here on the show floor. >> Oh gosh. >> Savannah: It could be for you too. It could be whatever for... >> I think the two things that are really striking to me is one serverless. Like I see... I thought people were talking about servers a lot and they were talking about it more than ever. Two, I really think it is observability right? Like we've gone from observability being kind of a niche. >> Savannah: Not that you're biased. >> Huh? >> Savannah: Not that you're biased. >> Not that I'm biased. It used to be a niche. I'd have to go niche thing where I would go and explain what this is to people and nowpeople are coming up. It's like, "Yeah, yeah, we're using OpenTelemetry." It's very cool. I've been involved with OpenTelemetry since the jump, since it was started really. It's been very exciting to see and gratifying to see like how much adoption we've gotten even in a short amount of time. >> Yeah, absolutely. It's a pretty... Yeah, it's been a lot. That was great. Perfect soundbite for us. >> Austin: Thanks, I love soundbites. >> Savannah: Yeah. Awesome. We love your hat and your soundbites equally. Thank you so much for being on the show with us today. >> Thank you for having me. >> Savannah: Hey, anytime, anytime. Will we see you in Amsterdam, speaking of KubeCon? Awesome, we'll be there. >> There's some real exciting OpenTelemetry stuff coming up for KubeCon. >> Well, we'll have to get you back on theCUBE. (talking simultaneously) Love that for us. Thank you all for tuning in two hour wall to wall coverage here, day four at AWS re:Invent in fabulous Las Vegas, Nevada, with Paul Gillin. I'm Savannah Peterson and you're watching theCUBE, the leader in high tech coverage. (lively music)

>>Hello, wonderful humans and welcome back to The Cube's Thrilling live coverage of AWS Reinvent here in Las Vegas, Nevada. I'm joined by my fantastic co-host, John Farer. John, things are really ramping up in here. Day one. >>Yep, it's packed already. I heard 70,000 maybe attendees really this year. I just saw that on Twitter. Again, it continues to show that over the past 10 years we've been here, you're seeing some of the players that were here from the beginning growing up and getting bigger and stronger, becoming more platforms, not just point solutions. You're seeing new entrants coming in, new startups, and the innovation you start to see happening, it's really compelling to fun to watch. And our next segment, we have multi 10 time Cube alumni coming on and a first timer, so it should be great. We'll get into some of the innovation, >>Not only as this guest went on the cube 10 times, he also spoke at the first AWS reinvent, just like you were covering it here with Cube. But without further ado, please welcome Ronan and Kevin from NetApp. Thank you gentlemen, both for being here and for matching in your dark blue. How's the show going for you? Ronan, I'm gonna ask you first, you've been here since the beginning. How does it feel in 2022? >>First, it's amazing to see so many people, right? So many humans in one place, flesh and blood. And it's also amazing to see, it's such a celebration for people in the cloud, right? Like this is our, this is our event, the people in the cloud. I'm really, really happy to be here and be in the cube as well. >>Fantastic. It, it is a party, it's a cloud party. Yes. How are you feeling being here, Kevin? I'm >>Feeling great. I mean, going all the way back to the early days of Spot T, which was the start that eventually got acquired as Spot by NetApp. I mean this was, this was our big event. This is what we lived for. We've gone, I've gone from everything, one of the smaller booths out here on the floor all the way up to the, the huge booth that we have today. So we've kind of grown along with the AWS ecosystem and it's just a lot of fun to get here, see all the customers and talk to everybody. >>That's a lot of fun. Fun. That's the theme that we've been talking about. And we wrote a story about on, on Silicon Angle, more that growth from that getting in and getting bigger, not just an ISV or part of the startup showcase or ecosystem. The progression of the investment on how cloud has changed deliverables. You've been part of that wave. What's the biggest walk away, what's, and what's the most important thing going on now cuz it's not stopping. You got new interests coming in and the folks are rising with the tide and getting platforms built around their products. >>Yeah, I would say, you know, years ago is, is cloud in my decision path and now it's cloud is in my decision path. How much is it and how am I going to use it? And I think especially coming up over the next year, macroeconomic events and everything going on is how do I make my next dollar in the cloud go further than my last dollar? Because I know I'm gonna be there, I know I'm gonna be growing in the cloud, so how do I effectively use it to run my business going forward? >>All right, take a minute to explain Spot now part of NetApp. What's the story? What take us through for the folks that aren't familiar with the journey, where it's come from, where it's today? >>Sure. So SPOT is all about cloud optimization. We help all of our customers deploy scale and optimize their applications in the cloud. And what we do is everything from VMs to containers to any type of custom application you want to deploy, we analyze those applications, we find the best price point to run them, we right size them, we do the automation so your DevOps team doesn't have to do it. And we basically make the whole cloud serverless for you at the end of the day. So whatever you're doing in the cloud, we'll manage that for you from the lowest level of the stack all the way up to the highest level financials. >>Is this what you call the evolved cloud state? >>It is in the evolve clouds a little bit more, and Ronan can touch on that a little bit too. The Evolve clouds not only the public cloud but also the cloud that you're building OnPrem, right? A lot of big companies, it's not necessarily a hundred percent one way or the other. The Evolve cloud is which cloud am I on? Am I on an OnPrem cloud and a public cloud or am I on multiple public clouds in an OnPrem cloud? And I think Ronan, you probably have an opinion on that too. >>Yeah, and and I think what we are hearing from our customers is that many of them are in a situation where a lot of their data has been built for years on premises. They're accelerating their move to the cloud, some of them are accelerating, they're moving into multiple cloud and that situation of an on-prem that is becoming cloudy and cloudy all the time. And then accelerated cloud adoption. This is what the customers are calling the Evolve cloud and that's what we're trying to support them in that journey. >>How many customers are you supporting in this Evolve cloud? You made it seem like you can just turnkey this for everyone, which I am here >>For it. Yeah, just to be clear, I mean we have thousands of customers, right? Everything from your small startups, people just getting going with a few VMs all the way to people scaling to tens and thousands of VMs in the cloud or even beyond VM services and you know, tens of millions of spend a month. You know, people are putting a lot of investment into the cloud and we have all walks of life under our, you know, customer portfolio. >>You know, multi-cloud has been a big topic in the industry. We call it super cloud. Cause we think super cloud kind of more represents the destination to multi-cloud. I mean everyone has multiple clouds, but they're best of breed defaults. They're not by design in most cases, but we're starting to see traction towards that potential common level services fix to late. See, I still think we're on the performance game now, so I have to ask, ask you guys. Performance has becoming back in VO speeds and feeds back during the data center days. Well, I wouldn't wanna talk speeds and feeds of solutions and then cloud comes in. Now we're at the era of cloud where people are moving their workloads there. There's a lot more automation going on, A lot more, as you said, part of the decision. It is the path. Yeah. So they say, now I wanna run my workloads on the better, faster infrastructure. No developer wants to run their apps on the slower hardware. >>I think that's a tall up for you. Ronan go. >>I mean, I put out my story, no developer ever said, give me the slower software performance and and pay more fast, >>Fastest find too fastest. >>Speed feeds your back, >>Right? And and performance comes in different, in different parameters, right? They think it is come throughput, it comes through latency. And I think even a stronger word today is price performance, right? How much am I paying for the performance that that I need? NetApp is actually offering a very, very big advantage for customers on both the high end performance as well as in the dollar per performance. That is, that is needed. This is actually one of the key differentiator that Fsx for NetApp on top is an AWS storage based on the NetApp on top storage operating system. This is one of the biggest advantages it is offering. It is SAP certified, for example, where latency is the key, is the key item. It is offering new and fastest throughput available, but also leveraging some advanced features like tiering and so on, is offering unique competitive advantage in the dollar for performance specifically. >>And why, why is performance important now, in your opinion? Obviously besides the obvious of no one wants to run their stuff on the slower infrastructure, but why are some people so into it now? >>I think performance as a single parameter is, is definitely a key influencer of the user experience. None, none of us will, will compromise our our experience. The second part is performance is critical when scale is happening, right? And especially with the scale of data performance to handle massive amounts of data is is becoming more and more critical. The last thing that I'll emphasize is again is the dollar for performance. The more data you have, the more you need to handle, the more critical for you is to handle it in a cost effective way. This is kind of, that's kind of in the, in the, in the secret sauce of the success of every workload. >>There isn't a company or person here who's not thinking about doing more faster for cheaper. So you're certainly got your finger on the pulse With that, I wanna talk about a, a customer case study. A little birdie told me that a major US airline recently just had a mass of when we're where according to my notes response time and customer experience was improved by 17 x. Now that's the type of thing that cuts cost big time. Can one of you tell me a little bit more about that? >>Yeah, so I think we all flew here somehow, right? >>Exactly. It's airlines matter. Probably most folks listening, they're >>Doing very well right now. Yes, the >>Airlines and I think we all also needed to deal with changes in the flights with, with really enormous amount of complexity in managing a business like that. We actually rank and choose what, what airline to use among other things based on the level of service that they give us. And especially at the time of crunch, a lot of users are looking through a lot of data to try to optimize, >>Plus all of them who just work this holiday weekend sidebar >>E Exactly right. Can't even, and Thanksgiving is one of these crunch times that are in the middle of this. So 70 x improvement in performance means a loss seven >>Zero or >>17 1 7 1 7 x Right? >>Well, and especially when we're talking about it looks like 50,000, 50,000 messages per minute that this customer was processing. Yes. That that's a lot. That's almost a thousand messages a second. Wow. I think my math tees up there. Yeah. >>It does allow them to operate in the next level of scale and really increase their support for the customer. It also allows them to be more efficient when it comes to cost. Now they need less infrastructure to give better service across the board. The nice thing is that it didn't require them for a lot of work. Sometimes when the customers are doing their journey to the cloud, one of the things that kind of hold them back is like, is either the fear or, or maybe is the, the concern of how much effort will it take me to achieve the same performance or even a better performance in the cloud? They are a live example that not only can you achieve, you can actually exceed the performance that I have on premises and really give customer a better service >>Customer a better service. And reliability is extremely important there. 99.9%. 99% >>99. Yes. >>Yes. That second nine obviously being very important, especially when we're talking about the order of magnitude of, of data and, and actions being taken place. How much of a priority is, is reliability and security for y'all as a team? >>So reliability is a key item for, for everybody, especially in crunch times. But reliability goes beyond the nines. Specifically reliability goes into how simple it is for you to enable backup n dr, how protected are you against ransomware? This is where netup and, and including the fsx for NETUP on top richness of data management makes a huge difference. If you are able to make your copy undeletable, that is actually a game changer when it comes to, to data protection. And this is, this is something that in the past requires a lot of work, opening vaults and other things. Yeah. Now it becomes a very simple configuration that is attached to every net up on top storage, no matter where it is. >>We heard some news at VMware explorer this past fall. Early fall. You guys were there. We saw the Broadcom acquisition. Looks like it's gonna get finalized maybe sooner than later. Lot of, so a lot of speculation around VMware. Someone called the VMware like where is VMware as in where they now, nice pun it was, it was actually Nutanix people, they go at each other all the time. But Broadcom's gonna keep vse and that's where the bread and butter, that's the, that's the goose that lays the Golden eggs. Customers are there. How do you guys see your piece there with VMware cloud on AWS that integrates solution? You guys have a big part of that ecosystem. We've covered it for years. I mean we've been to every VM world now called explorer. You guys have a huge customer base with VMware customers. What's the, what's the outlook? >>Yeah, and, and I think the important part is that a big part of the enterprise workloads are running on VMware and they will continue to run on VMware in, in, in the future. And most of them will try to run in a hybrid mode if not moving completely to the cloud. The cloud give them unparallel scale, it give them DR and backup opportunities. It does a lot of goodness to that. The partnership that NetApp brings with both VMware as well ass as well as other cloud vendors is actually a game changer. Because the minute that you go to the cloud, things like DR and backup have a different economics connected to them. Suddenly you can do compute less dr definitely on backup you can actually achieve massive savings. NetApp is the only data store that is certified to run with VMware cloud. And that actually opens to the customer's huge opportunity for unparalleled data protection as well as real, real savings, hard savings. And customers that look today and they say, I'm gonna shrink my data center, I'm gonna focus on, on moving certain things to the cloud, DR and backup and especially DR and backup VMware might be one of the easiest, fastest things to take into the cloud. And the partnership betweens VMware and NetApp might actually give you >>And the ONAP is great solution. Fsx there? Yes. I think you guys got a real advantage here and I want to get into something that's kind of a gloom and doom. I don't have to go negative on this one, Savannah, but they me nervous John. But you know, if you look at the economic realities you got a lot of companies like that are in the back of a Druva, Netta, Druva, cohesive rub. Others, you know, they, you know, there's a, their generational cloud who breaks through. What's the unique thing? Because you know there's gonna be challenges in the economy and customers are gonna vote with their wallets and they start to see as they make these architectural decisions, you guys are in the middle of it. There's not, there may not be enough to go around and the musical chairs might stop or, or not, I'm not sure. But I feel like if there's gonna be a consolidation, what does that look like? What are customers thinking? Backup recovery, cloud. That's a unique thing. You mentioned economics, it's not, you can't take the old strategy and put it there from five, 10 years ago. What's different now? >>Yeah, I think when it comes to data protection, there is a real change in, in the technology landscape that opened the door for a lot of new vendors to come and offer. Should we expect consolidation? I think microeconomic outside and other things will probably drive some of that to happen. I think there is one more parameter, John, that I wanna mention in this context, which is simplicity. Many of the storage vendors, including us, including aws, you wanna make as much of the backup NDR at basically a simple checkbox that you choose together with your main workload. This is another key capabilities that is, that is being, bringing and changing the market, >>But it also needs to move up. So it's not only simplicity, it's also about moving to the applications that you use, use, and just having it baked in. It's not about you going out and finding a replication. It's like what Ronan said, we gotta make it simple and then we gotta bake it into what they use. So one of our most recent acquisitions of Insta Cluster allows us to provide our customers with open source databases and data streaming services. When those sit on top of on tap and they sit on top of spots, infrastructure optimization, you get all that for free through the database that you use. So you don't worry about it. Your database is replicated, it's highly available, and it's running at the best cost. That's where it's going. >>Awesome. >>You also recently purchased Cloud Checker as well. Yes. Do you just purchase wonderful things all the time? We >>Do. We do. We, >>I'm not >>The, if he walk and act around and then we find the best thing and then we, we break out the checkbook, no, but more seriously, it, it rounds out what customers need for the cloud. So a lot of our customers come from storage, but they need to operate the entire cloud around the storage that they have. Cloud Checker gives us that financial visibility across every single dollar that you spend in the cloud and also gives us a better go to market motion with our MSPs and our distributors than we had in the past. So we're really excited about what cloud checker can unlock for us in >>The future. Makes a lot of sense and congratulations on all the extremely exciting things going on. Our final and closing question for our guests on this year's show is we would love your, your Instagram hot take your 32nd hot take on the most important stories, messages, themes of AWS reinvent 2022. Ronan, I'm gonna start with you cause you have a smirk >>And you do it one day ahead of the keynotes, one day ahead with you. >>You can give us a little tease a little from you. >>I think that pandemic or no pandemic face to face or no face to face, the innovation in the cloud is, is actually breaking all records. And I think this year specifically, you will see a lot of focus on data and scale. I think that's, these are two amazing things that you'll see, I think doubling down. But I'm also anxious to see tomorrow, so I'll learn more about it. >>All right. We might have to chat with you a little bit after tomorrow. Is keynotes and whatnot coming up? What >>About you? I think you're gonna hear a lot about cost. How much are you spending? How far are your dollars going? How are you using the cloud to the best of your abilities? How, how efficient are you being with your dollars in the cloud? I think that's gonna be a huge topic. It's on everybody's mind. It's the macro economics situation right now. I think it's gonna be in every session of the keynote tomorrow. All >>Right, so every >>Session. Every session, >>A bulk thing. John, we're gonna have >>That. >>I'm with him. You know, all S in general, you >>Guys have, and go look up what I said. >>Yeah, >>We'll go back and look at, >>I'm gonna check on you >>On that. The record now states. There you go, Kevin. Thank both. Put it down so much. We hope that it's a stellar show for Spotify, my NetApp. Thank you. And that we have you 10 more times and more than just this once and yeah, I, I can't wait to see, well, I can't wait to hear when your predictions are accurate tomorrow and we get to learn a lot more. >>No, you gotta go to all the sessions down just to check his >>Math on that. Yeah, no, exactly. Now we have to do our homework just to call him out. Not that we're competitive or those types of people at all. John. No. On that note, thank you both for being here with us. John, thank you so much. Thank you all for tuning in from home. We are live from Las Vegas, Nevada here at AWS Reinvent with John Furrier. My name is Savannah Peterson. You're watching the Cube, the leader in high tech coverage.

>>Good morning and welcome back to Detroit, Michigan. My name is Savannah Peterson and I'm here on set of the cube, my co-host John Farer. How you doing this morning, John? >>Doing great. Feeling fresh. Day two of three days of coverage, feeling >>Fresh. That is that for being in the heat of the conference. I love that attitude. It's gonna >>Be a great day today. We'll see you at the end of the day. Yeah, >>Well, we'll hold him to it. All right, everyone hold 'em accountable. Very excited to start the day off with an internet, a legend as well as a cube og. We are joined this morning by Matt Klein. Matt, welcome to the show. >>Thanks for having me. Good to see you. Yep. >>It's so, what's the vibe? Day two, Everyone's buzzing. What's got you excited at the show? You've been here before, but it's been three years you >>Mentioned. I, I was saying it's been three years since I've been to a conference, so it's been interesting for me to see what is, what is the same and what is different pre and post covid. But just really great to see everyone here again and nice to not be sitting in my home by myself. >>You know, Savannah said you're an OG and we were referring before we came on camera that you were your first came on the Cub in 2017, second Cuban event. But you were, I think, on the first wave of what I call the contributor momentum, where CNCF really got the traction. Yeah. You were at Lift, Envoy was contributed and that was really hyped up and I remember that vividly. It was day zero they called it back then. Yeah. And you got so much traction. People are totally into it. Yeah. Now we've got a lot of that going on now. Right. A lot of, lot of day Zero events. They call 'em co, co-located events. You got web assembly, a lot of other hype out there. What do you see out there that you like? How would you look at some of these other Sure. Communities that are developing, What's the landscape look like as you look out? Because Envoy set the table, what is now a standard >>Practice. Yeah. What's been so interesting for me just to come here to the conference is, you know, we open source Envoy in 2016. We donated in 2017. And as you mentioned at that time, Envoy was, you know, everyone wanted to talk about Envoy. And you know, much to my amazement, Envoy is now pervasive. I mean, it's used everywhere around the world. It's like, never in my wildest dreams would I have imagined that it would be so widely used. And it's almost gotten to the point where it's become boring. You know, It's just assumed that Envoy is, is everywhere. And now we're hearing a lot about Eeb p f and Web assembly and GI ops and you know, AI and a bunch of other things. So it's, it's actually great. It's made me very happy that it's become so pervasive, but it's also fun. Yeah. We mention to, to look around all other stuff >>Like congratulate. It's just a huge accomplishment really. I think it's gonna be historic, historical moment for the industry too. But I like how it progressed. I mean, I don't mind hype cycles as long as it's some vetting. Sure. Of course. You know, use cases that are clearly defined, but you gotta get that momentum in the community, but then you start gotta get down to, to business. Yep. So, so to speak and get it deployed, get traction. Yep. What should projects look like? And, and give us the update on Envoy. Cause you guys have a, a great use case of how you got traction. Right. Take us through some of the early days of what made Envoy successful in your opinion. Great question. >>Yeah. You know, I, I think Envoy is fairly unique around this conference in the sense that Envoy was developed by Lyft, which is an end user company. And many of the projects in this ecosystem, you know, no judgment, for better or worse, they are vendor backed. And I think that's a different delivery mechanism when it's coming from an end user where you're solving a, a particular business case. So Envoy was really developed for Lyft in a, you know, very early scaling days and just, you know, trying to help Lyft solve its business problems. So I think when Envoy was developed, we were, you know, scaling, we were falling over and actually many other companies were having similar problems. So I think Envoy became very widely deployed because many companies were having similar issues. So Envoy just became pervasive among lift peer companies. And then we saw a lot of vendor uptake in the service mesh space in the API gateway space among large internet providers. So, I I I, I think it's just, it's an interesting case because I think when you're solving real problems on the ground, in some ways it's easier to actually get adoption than if you're trying to develop it from a commercial backing. >>And that's the class, I mean, almost, It's almost like open source product market fit. It is in its own way. Cause you have a problem. Absolutely. Other people have the same problem finding >>Too. I mean, it's, it's designed thinking from >>A different, When, when I talk to people about open source, I like to tell people that I do not think it's any different than starting a company. I actually think it's all the same problems finding pro product, market fit, hiring, like finding contributors and maintainers, like doing PR and marketing. Yeah. Getting team together, traction, getting, getting funding. I mean, you have to have money to do all these things. Yeah. So I think a lot of people think of open source as I, I don't know, you know, this fantastic collaborative effort and, and it is that, but there's a lot more to it. Yeah. And it is much more akin to starting a >>Company. Let's, let's just look at that for a second. Cause I think that's a good point. And I was having a conversation in the hallway two nights ago on this exact point. If the power dynamics of a startup in the open source, as you point out, is just different, it's community based. So there are things you just gotta be mindful of. It's not top down. >>Exactly. It's not like, >>Right. You know, go take that hill. It's really consensus based, but it is a startup. All those elements are in place. Absolutely. You need leadership, you gotta have debates, alignment, commit, You gotta commit to a vision. Yep. You gotta make adjustments. Build the trajectory. So based on that, I mean, do you see more end user traction? Cause I was, we were talking also about Intuit, they donated some of their tow code R goes out there. Yep. R go see the CDR goes a service. Where's the end user contributions to these days? Do you feel like it's good, still healthy? >>I, I mean, I, I'm, I'm biased. I would like to see more. I think backstage outta Spotify is absolutely fantastic. That's an area just in terms of developer portals and developer efficiency that I think has been very underserved. So seeing Backstage come outta Spotify where they've used it for years, and I think we've already seen they had a huge date, you know, day one event. And I, I think we're gonna see a lot more out of that >>Coming from, I'm an end user, pretend I'm an end user, so pretend I have some code. I want to, Oh man, I'm scared. I don't am I'm gonna lose my competitive edge. What's the, how do you talk to the enterprise out there that might be thinking about putting their project out there for whether it's the benefit of the community, developing talent, developing the product? >>Sure. Yeah. I would say that I, I would ask everyone to think through all of the pros and cons of doing that because it's not for free. I mean, doing open source is costly. It takes developer time, you know, it takes management time, it takes budgeting dollars. But the benefits if successful can be huge, right? I mean, it can be just in terms of, you know, getting people into your company, getting users, getting more features, all of that. So I would always encourage everyone to take a very pragmatic and realistic view of, of what is required to make that happen. >>What was that decision like at Lyft >>When you I I'm gonna be honest, it was very naive. I I think we've, of that we think we need to know. No, just didn't know. Yeah. I think a lot of us, myself included, had very minimal open source experience. And had we known, or had I known what would've happened, I, I still would've done it. But I, I'm gonna be honest, the last seven years have aged me what I feel like is like 70 or a hundred. It's been a >>But you say you look out in the landscape, you gotta take pride, look at what's happened. Oh, it's, I mean, it's like you said, it >>Matured fantastic. I would not trade it for anything, but it has, it has been a journey. What >>Was the biggest surprise? What was the most eye opening thing about the journey for you? >>I, I think actually just the recognition of all of the non-technical things that go into making these things a success. I think at a conference like this, people think a lot about technology. It is a technology conference, but open source is business. It really is. I mean, it, it takes money to keep it going. It takes people to keep >>It going. You gotta sell people on the concepts. >>It takes leadership to keep it going. It takes internal, it takes marketing. Yeah. So for me, what was most eyeopening is over the last five to seven years, I feel like I actually have not developed very many, if any technical skills. But my general leadership skills, you know, that would be applicable again, to running a business have applied so well to, to >>Growing off, Hey, you put it out there, you hear driving the ship. It's good to do that. They need that. It really needs it. And the results speak for itself and congratulations. Yeah. Thank you. What's the update on the project? Give us an update because you're seeing, seeing a lot of infrastructure people having the same problem. Sure. But it's also, the environments are a little bit different. Some people have different architectures. Absolutely different, more cloud, less cloud edges exploding. Yeah. Where does Envoy fit into the landscape they've seen and what's the updates? You've got some new things going on. Give the updates on what's going on with the project Sure. And then how it sits in the ecosystem vis-a-vis what people may use it for. >>Yeah. So I'm, from a core project perspective, honestly, things have matured. Things have stabilized a bit. So a lot of what we focus on now are less Big bang features, but more table stakes. We spend a lot of time on security. We spend a lot of time on software supply chain. A topic that you're probably hearing a lot about at this conference. We have a lot of software supply chain issues. We have shipped Quicken HTB three over the last year. That's generally available. That's a new internet protocol still work happening on web assembly where ha doing a lot of work on our build and release pipeline. Again, you would think that's boring. Yeah. But a lot of people want, you know, packages for their fedora or their ADU or their Docker images. And that takes a lot of effort. So a lot of what we're doing now is more table stakes, just realizing that the project is used around the world very widely. >>Yeah. The thing that I'm most interested in is, we announced in the last six months a project called Envoy Gateway, which is layered on top of Envoy. And the goal of Envoy Gateway is to make it easier for people to run Envoy within Kubernetes. So essentially as an, as an ingress controller. And Envoy is a project historically, it is a very sophisticated piece of software, very complicated piece of software. It's not for everyone. And we want to provide Envoy Gateway as a way of onboarding more users into the Envoy ecosystem and making Envoy the, the default API gateway or edge proxy within Kubernetes. But in terms of use cases, we see Envoy pervasively with service mesh, API gateway, other types of low balancing cases. I mean, honestly, it's, it's all over the place at >>This point. I'm curious because you mentioned it's expanded beyond your wildest dreams. Yeah. And how could you have even imagined what Envoy was gonna do? Is there a use case or an application that really surprised you? >>You know, I've been asked that before and I, it's hard for me to answer that. It's, it's more that, I mean, for example, Envoy is used by basically every major internet company in China. I mean, like, wow. Everyone in China uses Envoy, like TikTok, like Alibaba. I mean like everyone, all >>The large sale, >>Everyone. You know, and it's used, it's used in the, I'm just, it's not just even the us. So I, I think the thing that has surprised me more than individual use cases is just the, the worldwide adoption. You know, that something could be be everywhere. And that I think, you know, when I open my phone and I'm opening all of these apps on my phone, 80 or 90% of them are going through Envoy in some form. Yeah. You know, it's, it's just that pervasive, I blow your mind a little bit sometimes >>That does, that's why you say plumber on your Twitter handle as your title. Cause you're working on all these things that are like really important substrate issues, Right. For scale, stability, growth. >>And, you know, to, I, I guess the only thing that I would add is, my goal for Envoy has always been that it is that boring, transparent piece of technology. Kind of similar to Linux. Linux is everywhere. Right? But no one really knows that they're using Linux. It's, it's justs like Intel inside, we're not paying attention. It's just there, there's >>A core group working on, if they have pride, they understand the mission, the importance of it, and they make their job is to make it invisible. >>Right. Exactly. >>And that's really ease of use. What's some of the ease of use sways and, and simplicity that you're working on, if you can talk about that. Because to be boring, you gotta be simpler and easier. All boring complex is unique is not boring. Complex is stressful. No, >>I I think we approach it in a couple different ways. One of them is that because we view Envoy as a, as a base technology in the ecosystem, we're starting to see, you know, not only vendors, but other open source projects that are being built on top of Envoy. So things like API Gateway, sorry, Envoy Gateway or you know, projects like Istio or all the other projects that are out there. They use Envoy as a component, but in some sense Envoy is a, as a transparent piece of that system. Yeah. So I'm a big believer in the ecosystem that we need to continue to make cloud native easier for, for end users. I still think it's too complicated. And so I think we're there, we're, we're pushing up the stack a bit. >>Yeah. And that brings up a good point. When you start seeing people building on top of things, right? That's enabling. So as you look at the enablement of Envoy, what are some of the things you see out on the horizon if you got the 20 mile stare out as you check these boring boxes, make it more plumbing, Right? Stable. You'll have a disruptive enabling platform. Yeah. What do you see out there? >>I am, you know, I, again, I'm not a big buzzword person, but, so some people call it serverless functions as a service, whatever. I'm a big believer in platforms in the sense that I really believe in the next 10 to 15 years, developers, they want to provide code. You know, they want to call APIs, they want to use pub subsystems, they want to use cas and databases. And honestly, they don't care about container scheduling or networking or load balancing or any of >>These things. It's handled in the os >>They just want it to be part of the operating system. Yeah, exactly. So I, I really believe that whether it's an open source or in cloud provider, you know, package solutions, that we're going to be just moving increasingly towards systems likes Lambda and Fargate and Google Cloud Run and Azure functions and all those kinds of things. And I think that when you do that much of the functionality that has historically powered this conference like Kubernetes and Onvoy, these become critical but transparent components that people don't, they're not really aware of >>At that point. Yeah. And I think that's a great call out because one of the things we're seeing is the market forces of, of this evolution, what you just said is what has to happen Yep. For digital transformation to, to get to its conclusion. Yep. Which means that everything doesn't have to serve the business, it is the business. Right. You know it in the old days. Yep. Engineers, they serve the business. Like what does that even mean? Yep. Now, right. Developers are the business, so they need that coding environment. So for your statement to happen, that simplicity in visibility calling is invisible os has to happen. So it brings up the question in open source, the trend is things always work itself out on the wash, as we say. So when you start having these debates and the alignment has to come at some point, you can't get to those that stay without some sort of defacto or consensus. Yep. And even standards, I'm not a big be around hardcore standards, but we can all agree and have consensus Sure. That will align behind, say Kubernetes, It's Kubernetes a standard. It's not like an i e you know, but this next, what, what's your reaction to this? Because this alignment has to come after debate. So all the process contending for I am the this of that. >>Yeah. I'm a look, I mean, I totally see the value in like i e e standards and, and there's a place for that. At the same time, for me personally as a technologist, as an engineer, I prefer to let the, the market as it were sort out what are the defacto standards. So for example, at least with Envoy, Envoy has an API that we call Xds. Xds is now used beyond Envoy. It's used by gc, it's used by proprietary systems. And I'm a big believer that actually Envoy in its form is probably gonna go away before Xds goes away. So in some ways Xds has become a defacto standard. It's not an i e e standard. Yeah. We, we, we have been asked about whether we should do that. Yeah. But I just, I I think the >>It becomes a component. >>It becomes a component. Yeah. And then I think people gravitate towards these things that become de facto standards. And I guess I would rather let the people on the show floor decide what are the standards than have, you know, 10 people sitting in a room figure out >>The community define standards versus organizational institutional defined standards. >>And they both have places a >>Hundred percent. Yeah, sure. And, and there's social proof in both of them. Yep. >>Frankly, >>And we were saying on the cube that we believe that the developers will decide the standard. Sure. Because that's what you're basically saying. They're deciding what they do with their code. Right. And over time, as people realize the trade of, hey, if everyone's coding this right. And makes my life easier to get to that state of nirvana and enlightenment, as we would say. Yeah. Yeah. >>Starting strong this morning. John, I I love this. I'm curious, you mentioned Backstage by Spotify wonderful example. Do you think that this is a trend we're gonna see with more end users >>Creating open source projects? Like I, you know, I hope so. The flip side of that, and as we all know, we're entering an uncertain economic time and it can be hard to justify the effort that it takes to do it well. And what I typically counsel people when they are about to open source something is don't do it unless you're ready to commit the resources. Because opensourcing something and not supporting it. Yeah. I actually can be think, I think it'd be worse. >>It's an, it's insult that people, you're asking to commit to something. Exactly. Needs of time, need the money investment, you gotta go all in and push. >>So I, so I very much want to see it and, and I want to encourage that here, but it's hard for me to look into the crystal ball and know, you know, whether it's gonna happen more >>Or less at what point there were, are there too many projects? You know, I mean, but I'm not, I mean this in, in a, in a negative way. I mean it more in the way of, you know, you mentioned supply chain. We were riffing on the cube about at some point there's gonna be so much code open source continuing thundering away with, with the value that you're just gluing things. Right. I don't need the code, this code there. Okay. What's in the code? Okay. Maybe automation can help out on supply chain. Yeah. But ultimately composability is the new >>Right? It is. Yeah. And, and I think that's always going to be the case. Case. Good thing. It is good thing. And I, I think that's just, that's just the way of things for sure. >>So no code will be, >>I think, I think we're seeing a lot of no code situations that are working great for people. And, and, but this is actually really no different than my, than my serverless arguing from before. Just as a, as a, a slight digression. I'm building something new right now and you know, we're using cloud native technologies and all this stuff and it's still, >>What are you building? >>Even as a I'm, I'm gonna keep that, I'm gonna keep that secret. I know I'm, but >>We'll find out on Twitter. We're gonna find out now that we know it. Okay. Keep on mystery. You open that door. We're going down see in a couple weeks. >>Front >>Page is still an angle. >>But I, I was just gonna say that, you know, and I consider myself, you know, you're building something, I'm, I see myself an expert in the cloud native space. It's still difficult, It's difficult to, to pull together these technologies and I think that we will continue to make it easier for people. >>What's the biggest difficulties? Can you give us some examples? >>Well, just, I mean, we still live in a big mess of yammel, right? Is a, there's a, there's a lot of yaml out there. And I think just wrangling all of that in these systems, there's still a lot of cobbling together where I think that there can be unified platforms that make it easier for us to focus on our application logic. >>Yeah. I gotta ask you a question cuz I've talked to college kids all the time. My son's a junior in CS and he's, you know, he's coding away. What would you, how does a student or someone who's learning figure out where, who they are? Because there's now, you know, you're either into the infrastructure under the hood Yeah. Or you're, cuz that's coding there option now coding the way your infrastructure people are working on say the boring stuff so everyone else can have ease of use. And then what is just, I wanna just code, there's two types of personas. How does someone know who they are? >>My, when I give people career advice, my biggest piece of advice to them is in the first five to seven to 10 years of their career, I encourage people to do different things like every say one to two to three years. And that doesn't mean like quitting companies and changing companies, it could mean, you know, within a company that they join doing different teams, you know, working on front end versus back end. Because honestly I think people don't know. I think it's actually very, Yeah. Our industry is so broad. Yeah. That I think it's almost impossible to >>Know. You gotta get your hands dirty to jump >>In order to know what you like. And for me, in my career, you know, I've dabbled in different areas, but I've always come back to infrastructure, you know, that that's what I enjoy >>The most. Okay. You gotta, you gotta taste everything. See what you, what >>You like. Exactly. >>Right. Last question for you, Matt. It's been three years since you were here. Yep. What do you hope that we're able to say next year? That we can't say this year? Hmm. Beyond the secrets of your project, which hopefully we will definitely be discussing then. >>You know, I I, I don't have anything in particular. I would just say that I would like to see more movement towards projects that are synthesizing and making it easier to use a lot of the existing projects that we have today. So for example, I'm, I'm very bullish on backstage. Like I, I've, I've always said that we need better developer UIs that are not CLIs. Like I know it's a general perception among many people. Totally agree with you. Frankly, you're not a real systems engineer unless you type on the command line. I, I think better user interfaces are better for humans. Yep. So just for a project like Backstage to be more integrated with the rest of the projects, whether that be Envo or Kubernete or Argo or Flagger. I, I just, I think there's tremendous potential for further integration of some >>Of these projects. It just composability That makes total sense. Yep. Yep. You're, you're op you're operating and composing. >>Yep. And there's no reason that user experience can't be better. And then more people can create and build. So I think it's awesome. Matt, thank you so much. Thank you. Yeah, this has been fantastic. Be sure and check out Matt on Twitter to find out what that next secret project is. John, thank you for joining me this morning. My name is Savannah Peterson and we'll be here all day live from the cube. We hope you'll be joining us throughout the evening until a happy hour today. Thanks for coming. Thanks for coming. Thanks for watching.

(upbeat techno music) >> Hello, everyone. Welcome to theCUBE here live in Detroit for KubeCon + CloudNativeCon 2022. I'm John Furrier, host of theCUBE. This is our seventh consecutive KubeCon + CloudNativeCon. Since inception, theCube's been there every year. And of course, theCUBE continues to grow. So does the community as well as our host roster. I'm here with my co-host, Lisa Martin. Lisa, great to see you. And our new theCube host, Savannah Peterson. Savannah, welcome to theCUBE. >> Thanks, John. >> Welcome. >> Welcome to the team. >> Thanks, team. It's so wonderful to be here. I met you all last KubeCon and to be sitting on this stage in your company is honestly an honor. >> Well, great to have you. Lisa and I have done a lot of shows together and it's great to have more cadence around. You know, more fluid around the content, and also the people. And I would like you to take a minute to tell people your background. You know the community here. What's the roots? You know the Cloud Native world pretty well. >> I know it as well as someone my age can. As we know, the tools and the tech is always changing. So hello, everyone. I'm Savannah Peterson. You can find me on the internet @SavIsSavvy. Would love to hear from you during the show. Big fan of this space and very passionate about DevOps. I've been working in the Silicon Valley and the Silicon Alley for a long time, helping companies scale internationally as a community builder as well as a international public speaker. And honestly, this is just such a fun evolution for my career and I'm grateful to be here with you both. >> We're looking forward to having you on theCUBE. Appreciate it. Lisa? >> Yes. >> KubeCon. Amazing again this year. Just keeps growing bigger and bigger. >> Yes. >> Keynote review, you were in there. >> Yup. >> I had a chance to peek in a little bit, but you were there and got most of the news. What was the action? >> You know, the action was really a big focus around the maintainers, what they're doing, giving them the props and the kudos and the support that they deserve. Not just physically, but mentally as well. That was a really big focus. It was also a big focus on mentoring and really encouraging more people- >> Love that. >> I did, too. I thought that was fantastic to get involved to help others. And then they showed some folks that had great experiences, really kind of growing up within the community. Probably half of the keynote focus this morning was on that. And then looking at some of the other projects that have graduated from CNCF, some of these successful projects, what they're doing, what folks are doing. Cruise, one of the ones that was featured. You've probably seen their driverless cars around San Francisco. So it was great to see that, the successes that they've had and where that's going. >> Yeah. Lisa, we've done how many shows? Hundreds of shows together. When you see a show like this grow and continue to mature, what's your observation? You've seen many shows we've hosted together. What jumps out this year? Is it just that level of maturization? What's your take on this? >> The maturization of the community and the collaboration of the community. I think those two things jumped out at me even more than last year. Last year, obviously a little bit smaller event in North America. It was Los Angeles. This year you got a much stronger sense of the community, the support that they have for each other. There were a lot of standing ovations particularly when the community came out and talked about what they were doing in Ukraine to support fellow community members in Ukraine and also to support other Ukrainians in terms of getting in to tech. Lot of standing ovations. Lot of- >> Savannah: Love that, yeah. >> Real authenticity around the community. >> Yeah, Savannah, we talked on our intro prior to the event about how inclusive this community is. They are really all in on inclusivity. And the Ukraine highlight, this community is together and they're open. They're open to everybody. >> Absolutely. >> And they're also focused on growing the educational knowledge. >> Yeah, I think there's a real celebration of curiosity within this community that we don't find in certain other sectors. And we saw it at dinner last night. I mean, I was struck just like you Lisa walking in today. The energy in that room is palpably different from last year. I saw on Twitter this morning, people are very excited. Many people, their first KubeCon. And I'm sure we're going to be feeding off of that, that kind of energy and that... Just a general enthusiasm and excitement to be here in Detroit all week. It's a treat. >> Yeah, I even saw Stu Miniman earlier, former theCube host. He's at Red Hat. We were talking on the way in and he made an observation I thought was interesting I'll bring up because this show, it's a lot "What is this show? What isn't this show?" And I think this show is about developers. What it isn't is not a business show. It's not about business. It's not about industry kind of posturing or marketing. All the heavy hitters on the dev side are here and you don't see the big execs. I mean, you got the CEOs of startups here but not the CEOs of the big public companies. We see the doers. So, I mean, I think my take is this show's about creating products for builders and creating products that people can consume. And I think that is the Cloud Native lanes that are starting to form. You're either creating something for builders to build stuff with or you're creating stuff that could be consumed. And that seems for applications. So the whole app side and services seem to be huge. >> They also did a great job this morning of showcasing some of the big companies that we all know and love. Spotify. Obviously, I don't think a day goes by where I don't turn on Spotify. And what it's done- >> Me neither. >> What it's done for the community... Same with Intuit, I'm a user of both. Intuit was given an End User Award this morning during the keynote for their contributions, what they're doing. But it was nice to see some just everyday companies, Cloud Native companies that we all know and love, and to understand their contributions to the community and how those contributions are affecting all of us as end users. >> Yeah, and I think those companies like Intuit... Argo's been popular, Arlo now new, seeing those services, and even enterprises are contributing. You know, Lyft is always here, popular with Envoy. The community isn't just vendors and that's the interesting thing. >> I think that's why it works. To me, this event is really about the celebration of developer relations. I mean, every DevRel from every single one of these companies is here. Like you said, in lieu of the executive, that's essentially who we're attracting. And if you look out over the show floor here, I mean, we've probably got, I don't know, three to four extra vendors that we had last year. It totally is a different tone. This community doesn't like to be sold to. This community likes to be collaborative. They like to learn and they like to help. And I think we see that within the ecosystem inside the room today. >> It's not a top down sales pitch. It's really consensus. >> No. >> Do it out in the open transparency. Don't sell me stuff. And I think the other thing I like about this community is that we're starting to see that... And then we've said this in theCube before. We'll say it again. Maybe be more controversial. Digital transformation is about the developer, right? And I think the power is going to shift in every company to the developer because if you take digital transformation to completion, everything happens the way it's happening, the company is the application. It's not IT who serves the organization- >> I love thinking about it like that. That's a great point, John. >> The old phase was IT was a department that served the business. Well, the business is IT now. So that means developer community is going to grow like crazy and they're going to be in the front lines driving all the change. In my opinion, you going to see this developer community grow like crazy and then the business side on industry will match up with that. I think that's what's going to happen. >> So, the developers are becoming the influencers? >> Developers are the power source for all companies. They're in charge. They're going to dictate terms to how businesses will run because that's going to be natural 'cause digital transformation's about the app and the business is the app. So that mean it has to be coded. So I think you're going to see a lot of innovation around app server-like experiences where the the apps are just being developed faster than the infrastructures enabling that completely invisible. And I think you're going to see this kind of architecture-less, I'll put it out there that term architecture-less, environment where you don't need an architecture. It's just you code away. >> Yeah, yeah. We saw GitHub's mentioned in the keynote this morning. And I mean, low code, no code. I think your fingers right on the pulse there. >> Yeah. What did you guys see? Anything else you see? >> I think just the overall... To your point, Savannah, the energy. Definitely higher than last year. When I saw those standing ovations, people really come in together around the sense of community and what they've accomplished especially in the last two plus years of being remote. They did a great job of involving a lot of folks, some of whom are going to be on the program with us this week that did remote parts of the keynote. One of our guests on today from Vitess was talking about the successes and the graduation of their program so that the sense of community, but also not just the sense of it, the actual demonstration of it was also quite palpable this morning, and I think that's something that I'm excited for us to hear about with our guests on the program this week. >> Yeah, and I think the big story coming out so far as the show starts is the developers are in charge. They're going to set the pace for all the ops, data ops, security ops, all operations. And then the co-located events that were held Monday and Tuesday prior to kickoff today. You saw WebAssembly's come out of the woodwork as it got a lot of attention. Two startups got funded heavily on Series A. You're starting to see that project really work well. That's going to be an additional to the container market. So, interesting to see how Docker reacts to that. Red Hat's doing great. ServiceMeshCon was phenomenal. I saw Solo.iOS got massive traction with those guys. So like Service Mesh, WebAssembly, you can start to see the dots connecting. You're starting to see this layer below Kubernetes and then a layer above Kubernetes developing. So I think it's going to be great for applications and great for the infrastructure. I think we'll see how it comes out and all these companies we have on here are all about faster, more integrated, some very, very interesting to see. So far, so good. >> You guys talked about in your highlight session last week or so. Excited to hear about the end users, the customer stories. That's what I'm interested in understanding as well. It's why it resonates with me when I see brands that I recognize. Well, I use it every day. How are they using containers and Kubernetes? How are they actually not just using it to deploy their app, their technologies, that we all expect are going to be up 24/7, but how are they also contributing to the development of it? So I'm really excited to hear those end users. >> We're going to have Lockheed Martin. And we wrote a story on SiliconANGLE, the Red Hat, Lockheed Martin, real innovation on the edge. You're starting to see educate with the edge. It's really the industrial edge coming to be big. It'd be very interesting to see. >> Absolutely, we got Ford Motor Company coming on as well. I always loved stories, Savannah, that are history of companies. Ford's been around since 1903. How is a company that- >> Well, we're in the home of Ford- as well here. >> We are. How they evolved digitally? What are they doing to enable the developers to be those influencers that John says? It's going to be them. >> They're a great example of a company that's always been on the forefront, too. I mean, they had a head of VRs 25 years ago when most people didn't even know what VR was going to stand for. So, I can't wait for that one. You tease the Docker interview coming up very well, John. I'm excited for that one. One last thing I want to bring up that I think is really refreshing and it's reflected right here on this stage is you talked about the inclusion. I think there's a real commitment to diversity here. You can see the diversity stats on CNCF's website. It's right there on KubeCon. At the bottom, there's a link in every email I've gotten highlighting that. We've got two women on this stage all week which is very exciting. And the opening keynote was a woman. So quite frankly, I am happy as a female in this industry to see a bit more representation. And I do appreciate just on the note of being inclusive, it's not just about gender or age, it's also about the way that CNCF thinks about your experience since we're in this kind of pandemic transitional period. They've got little pins. Last year, we had bracelets depending on your level of comfort. Equivocally like a stoplight which is... I just think it's really nice and sensitive and that attention to detail makes people feel comfortable. Which is why we have the community energy that we have. >> Yeah, and being 12 years in the business... With theCUBE, we've been 12 years in the business, seven years with KubeCon and Cloud Native, I really appreciate the Linux Foundation including me as I get older. (Lisa and Savannah laugh) >> Savannah: That's a good point. >> Ageism were, "Hey!" Thank you. >> There was a lot of representation. You talked about females and so often we go to shows and there's very few females. Some companies are excellent at it. But from an optics perspective, to me it stands out. There was great representation across. There was disabled people on stage, people of color, women, men of all ages. It was very well-orchestrated. >> On the demographic- >> And sincere. >> Yeah, yeah. >> And the demographics, too. On the age side, it's lower too. You're starting to see younger... I mean, high school, college representation. I saw a lot of college students last night. I saw on the agenda sessions targeting universities. I mean, I'm telling you this is reaching down. Open source now is so great. It's growing so fast. It's continuing to thunder away. And with success, it's just getting better and better. In fact, we were talking last night about at some point we might not have to write code. Just glue it together. And that's why I think the supply chain and security thing is an issue. But this is why it's so great. Anyone can code and I think there's a lot of learning to have. So, I think we'll continue to do our job to extract the signal from the noise. So, thanks for the kickoff. Good commentary. Thank you. All right. >> Of course. >> Let's get started. Day one of three days of live coverage here at KubeCon + CloudNativeCon. I'm John Furrier with Lisa Martin, and Savannah Peterson. Be back with more coverage starting right now. (gentle upbeat music)

(upbeat music) >> Hey guys, welcome back to the show floor of KubeCon + CloudNativeCon '22 North America from Detroit, Michigan. Lisa Martin here with John Furrier. This is day one, John at theCUBE's coverage. >> CUBE's coverage. >> theCUBE's coverage of KubeCon. Try saying that five times fast. Day one, we have three wall-to-wall days. We've been talking about Kubernetes, containers, adoption, cloud adoption, app modernization all morning. We can't talk about those things without addressing security. >> Yeah, this segment we're going to hear container and Kubernetes security for modern application 'cause the enterprise are moving there. And this segment with Red Hat's going to be important because they are the leader in the enterprise when it comes to open source in Linux. So this is going to be a very fun segment. >> Very fun segment. Two guests from Red Hat join us. Please welcome Doron Caspin, Senior Principal Product Manager at Red Hat. Michael Foster joins us as well, Principal Product Marketing Manager and StackRox Community Lead at Red Hat. Guys, great to have you on the program. >> Thanks for having us. >> Thank you for having us. >> It's awesome. So Michael StackRox acquisition's been about a year. You got some news? >> Yeah, 18 months. >> Unpack that for us. >> It's been 18 months, yeah. So StackRox in 2017, originally we shifted to be the Kubernetes-native security platform. That was our goal, that was our vision. Red Hat obviously saw a lot of powerful, let's say, mission statement in that, and they bought us in 2021. Pre-acquisition we were looking to create a cloud service. Originally we ran on Kubernetes platforms, we had an operator and things like that. Now we are looking to basically bring customers in into our service preview for ACS as a cloud service. That's very exciting. Security conversation is top notch right now. It's an all time high. You can't go with anywhere without talking about security. And specifically in the code, we were talking before we came on camera, the software supply chain is real. It's not just about verification. Where do you guys see the challenges right now? Containers having, even scanning them is not good enough. First of all, you got to scan them and that may not be good enough. Where's the security challenges and where's the opportunity? >> I think a little bit of it is a new way of thinking. The speed of security is actually does make you secure. We want to keep our images up and fresh and updated and we also want to make sure that we're keeping the open source and the different images that we're bringing in secure. Doron, I know you have some things to say about that too. He's been working tirelessly on the cloud service. >> Yeah, I think that one thing, you need to trust your sources. Even if in the open source world, you don't want to copy paste libraries from the web. And most of our customers using third party vendors and getting images from different location, we need to trust our sources and we have a really good, even if you have really good scanning solution, you not always can trust it. You need to have a good solution for that. >> And you guys are having news, you're announcing the Red Hat Advanced Cluster Security Cloud Service. >> Yes. >> What is that? >> So we took StackRox and we took the opportunity to make it as a cloud services so customer can consume the product as a cloud services as a start offering and customer can buy it through for Amazon Marketplace and in the future Azure Marketplace. So customer can use it for the AKS and EKS and AKS and also of course OpenShift. So we are not specifically for OpenShift. We're not just OpenShift. We also provide support for EKS and AKS. So we provided the capability to secure the whole cloud posture. We know customer are not only OpenShift or not only EKS. We have both. We have free cloud or full cloud. So we have open. >> So it's not just OpenShift, it's Kubernetes, environments, all together. >> Doron: All together, yeah. >> Lisa: Meeting customers where they are. >> Yeah, exactly. And we focus on, we are not trying to boil the ocean or solve the whole cloud security posture. We try to solve the Kubernetes security cluster. It's very unique and very need unique solution for that. It's not just added value in our cloud security solution. We think it's something special for Kubernetes and this is what Red that is aiming to. To solve this issue. >> And the ACS platform really doesn't change at all. It's just how they're consuming it. It's a lot quicker in the cloud. Time to value is right there. As soon as you start up a Kubernetes cluster, you can get started with ACS cloud service and get going really quickly. >> I'm going to ask you guys a very simple question, but I heard it in the bar in the lobby last night. Practitioners talking and they were excited about the Red Hat opportunity. They actually asked a question, where do I go and get some free Red Hat to test some Kubernetes out and run helm or whatever. They want to play around. And do you guys have a program for someone to get start for free? >> Yeah, so the cloud service specifically, we're going to service preview. So if people sign up, they'll be able to test it out and give us feedback. That's what we're looking for. >> John: Is that a Sandbox or is that going to be in the cloud? >> They can run it in their own environment. So they can sign up. >> John: Free. >> Doron: Yeah, free. >> For the service preview. All we're asking for is for customer feedback. And I know it's actually getting busy there. It's starting December. So the quicker people are, the better. >> So my friend at the lobby I was talking to, I told you it was free. I gave you the sandbox, but check out your cloud too. >> And we also have the open source version so you can download it and use it. >> Yeah, people want to know how to get involved. I'm getting a lot more folks coming to Red Hat from the open source side that want to get their feet wet. That's been a lot of people rarely interested. That's a real testament to the product leadership. Congratulations. >> Yeah, thank you. >> So what are the key challenges that you have on your roadmap right now? You got the products out there, what's the current stake? Can you scope the adoption? Can you share where we're at? What people are doing specifically and the real challenges? >> I think one of the biggest challenges is talking with customers with a slightly, I don't want to say outdated, but an older approach to security. You hear things like malware pop up and it's like, well, really what we should be doing is keeping things into low and medium vulnerabilities, looking at the configuration, managing risk accordingly. Having disparate security tools or different teams doing various things, it's really hard to get a security picture of what's going on in the cluster. That's some of the biggest challenges that we talk with customers about. >> And in terms of resolving those challenges, you mentioned malware, we talk about ransomware. It's a household word these days. It's no longer, are we going to get hit? It's when? It's what's the severity? It's how often? How are you guys helping customers to dial down some of the risk that's inherent and only growing these days? >> Yeah, risk, it's a tough word to generalize, but our whole goal is to give you as much security information in a way that's consumable so that you can evaluate your risk, set policies, and then enforce them early on in the cluster or early on in the development pipeline so that your developers get the security information they need, hopefully asynchronously. That's the best way to do it. It's nice and quick, but yeah. I don't know if Doron you want to add to that? >> Yeah, so I think, yeah, we know that ransomware, again, it's a big world for everyone and we understand the area of the boundaries where we want to, what we want to protect. And we think it's about policies and where we enforce it. So, and if you can enforce it on, we know that as we discussed before that you can scan the image, but we never know what is in it until you really run it. So one of the thing that we we provide is runtime scanning. So you can scan and you can have policy in runtime. So enforce things in runtime. But even if one image got in a way and get to your cluster and run on somewhere, we can stop it in runtime. >> Yeah. And even with the runtime enforcement, the biggest thing we have to educate customers on is that's the last-ditch effort. We want to get these security controls as early as possible. That's where the value's going to be. So we don't want to be blocking things from getting to staging six weeks after developers have been working on a project. >> I want to get you guys thoughts on developer productivity. Had Docker CEO on earlier and since then I had a couple people messaging me. Love the vision of Docker, but Docker Hub has some legacy and it might not, has does something kind of adoption that some people think it does. Are people moving 'cause there times they want to have these their own places? No one place or maybe there is, or how do you guys see the movement of say Docker Hub to just using containers? I don't need to be Docker Hub. What's the vis-a-vis competition? >> I mean working with open source with Red Hat, you have to meet the developers where they are. If your tool isn't cutting it for developers, they're going to find a new tool and really they're the engine, the growth engine of a lot of these technologies. So again, if Docker, I don't want to speak about Docker or what they're doing specifically, but I know that they pretty much kicked off the container revolution and got this whole thing started. >> A lot of people are using your environment too. We're hearing a lot of uptake on the Red Hat side too. So, this is open source help, it all sorts stuff out in the end, like you said, but you guys are getting a lot of traction there. Can you share what's happening there? >> I think one of the biggest things from a developer experience that I've seen is the universal base image that people are using. I can speak from a security standpoint, it's awesome that you have a base image where you can make one change or one issue and it can impact a lot of different applications. That's one of the big benefits that I see in adoption. >> What are some of the business, I'm curious what some of the business outcomes are. You talked about faster time to value obviously being able to get security shifted left and from a control perspective. but what are some of the, if I'm a business, if I'm a telco or a healthcare organization or a financial organization, what are some of the top line benefits that this can bubble up to impact? >> I mean for me, with those two providers, compliance is a massive one. And just having an overall look at what's going on in your clusters, in your environments so that when audit time comes, you're prepared. You can get through that extremely quickly. And then as well, when something inevitably does happen, you can get a good image of all of like, let's say a Log4Shell happens, you know exactly what clusters are affected. The triage time is a lot quicker. Developers can get back to developing and then yeah, you can get through it. >> One thing that we see that customers compliance is huge. >> Yes. And we don't want to, the old way was that, okay, I will provision a cluster and I will do scans and find things, but I need to do for PCI DSS for example. Today the customer want to provision in advance a PCI DSS cluster. So you need to do the compliance before you provision the cluster and make all the configuration already baked for PCI DSS or HIPAA compliance or FedRAMP. And this is where we try to use our compliance, we have tools for compliance today on OpenShift and other clusters and other distribution, but you can do this in advance before you even provision the cluster. And we also have tools to enforce it after that, after your provision, but you have to do it again before and after to make it more feasible. >> Advanced cluster management and the compliance operator really help with that. That's why OpenShift Platform Plus as a bundle is so popular. Just being able to know that when a cluster gets provision, it's going to be in compliance with whatever the healthcare provider is using. And then you can automatically have ACS as well pop up so you know exactly what applications are running, you know it's in compliance. I mean that's the speed. >> You mentioned the word operator, I get triggering word now for me because operator role is changing significantly on this next wave coming because of the automation. They're operating, but they're also devs too. They're developing and composing. It's almost like a dashboard, Lego blocks. The operator's not just manually racking and stacking like the old days, I'm oversimplifying it, but the new operators running stuff, they got observability, they got coding, their servicing policy. There's a lot going on. There's a lot of knobs. Is it going to get simpler? How do you guys see the org structures changing to fill the gap on what should be a very simple, turn some knobs, operate at scale? >> Well, when StackRox originally got acquired, one of the first things we did was put ACS into an operator and it actually made the application life cycle so much easier. It was very easy in the console to go and say, Hey yeah, I want ACS my cluster, click it. It would get provisioned. New clusters would get provisioned automatically. So underneath it might get more complicated. But in terms of the application lifecycle, operators make things so much easier. >> And of course I saw, I was lucky enough with Lisa to see Project Wisdom in AnsibleFest. You going to say, Hey, Red Hat, spin up the clusters and just magically will be voice activated. Starting to see AI come in. So again, operations operator is got to dev vibe and an SRE vibe, but it's not that direct. Something's happening there. We're trying to put our finger on. What do you guys think is happening? What's the real? What's the action? What's transforming? >> That's a good question. I think in general, things just move to the developers all the time. I mean, we talk about shift left security, everything's always going that way. Developers how they're handing everything. I'm not sure exactly. Doron, do you have any thoughts on that. >> Doron, what's your reaction? You can just, it's okay, say what you want. >> So I spoke with one of our customers yesterday and they say that in the last years, we developed tons of code just to operate their infrastructure. That if developers, so five or six years ago when a developer wanted VM, it will take him a week to get a VM because they need all their approval and someone need to actually provision this VM on VMware. And today they automate all the way end-to-end and it take two minutes to get a VM for developer. So operators are becoming developers as you said, and they develop code and they make the infrastructure as code and infrastructure as operator to make it more easy for the business to run. >> And then also if you add in DataOps, AIOps, DataOps, Security Ops, that's the new IT. It seems to be the new IT is the stuff that's scaling, a lot of data's coming in, you got security. So all that's got to be brought in. How do you guys view that into the equation? >> Oh, I mean you become big generalists. I think there's a reason why those cloud security or cloud professional certificates are becoming so popular. You have to know a lot about all the different applications, be able to code it, automate it, like you said, hopefully everything as code. And then it also makes it easy for security tools to come in and look and examine where the vulnerabilities are when those things are as code. So because you're going and developing all this automation, you do become, let's say a generalist. >> We've been hearing on theCUBE here and we've been hearing the industry, burnout, associated with security professionals and some DataOps because the tsunami of data, tsunami of breaches, a lot of engineers getting called in the middle of the night. So that's not automated. So this got to get solved quickly, scaled up quickly. >> Yes. There's two part question there. I think in terms of the burnout aspect, you better send some love to your security team because they only get called when things get broken and when they're doing a great job you never hear about them. So I think that's one of the things, it's a thankless profession. From the second part, if you have the right tools in place so that when something does hit the fan and does break, then you can make an automated or a specific decision upstream to change that, then things become easy. It's when the tools aren't in place and you have desperate environments so that when a Log4Shell or something like that comes in, you're scrambling trying to figure out what clusters are where and where you're impacted. >> Point of attack, remediate fast. That seems to be the new move. >> Yeah. And you do need to know exactly what's going on in your clusters and how to remediate it quickly, how to get the most impact with one change. >> And that makes sense. The service area is expanding. More things are being pushed. So things will, whether it's a zero day vulnerability or just attack. >> Just mix, yeah. Customer automate their all of things, but it's good and bad. Some customer told us they, I think Spotify lost the whole a full zone because of one mistake of a customer because they automate everything and you make one mistake. >> It scale the failure really. >> Exactly. Scaled the failure really fast. >> That was actually few contact I think four years ago. They talked about it. It was a great learning experience. >> It worked double edge sword there. >> Yeah. So definitely we need to, again, scale automation, test automation way too, you need to hold the drills around data. >> Yeah, you have to know the impact. There's a lot of talk in the security space about what you can and can't automate. And by default when you install ACS, everything is non-enforced. You have to have an admission control. >> How are you guys seeing your customers? Obviously Red Hat's got a great customer base. How are they adopting to the managed service wave that's coming? People are liking the managed services now because they maybe have skills gap issues. So managed service is becoming a big part of the portfolio. What's your guys' take on the managed services piece? >> It's just time to value. You're developing a new application, you need to get it out there quick. If somebody, your competitor gets out there a month before you do, that's a huge market advantage. >> So you care how you got there. >> Exactly. And so we've had so much Kubernetes expertise over the last 10 or so, 10 plus year or well, Kubernetes for seven plus years at Red Hat, that why wouldn't you leverage that knowledge internally so you can get your application. >> Why change your toolchain and your workflows go faster and take advantage of the managed service because it's just about getting from point A to point B. >> Exactly. >> Well, in time to value is, you mentioned that it's not a trivial term, it's not a marketing term. There's a lot of impact that can be made. Organizations that can move faster, that can iterate faster, develop what their customers are looking for so that they have that competitive advantage. It's definitely not something that's trivial. >> Yeah. And working in marketing, whenever you get that new feature out and I can go and chat about it online, it's always awesome. You always get customers interests. >> Pushing new code, being secure. What's next for you guys? What's on the agenda? What's around the corner? We'll see a lot of Red Hat at re:Invent. Obviously your relationship with AWS as strong as a company. Multi-cloud is here. Supercloud as we've been saying. Supercloud is a thing. What's next for you guys? >> So we launch the cloud services and the idea that we will get feedback from customers. We are not going GA. We're not going to sell it for now. We want to get customers, we want to get feedback to make the product as best what we can sell and best we can give for our customers and get feedback. And when we go GA and we start selling this product, we will get the best product in the market. So this is our goal. We want to get the customer in the loop and get as much as feedback as we can. And also we working very closely with our customers, our existing customers to announce the product to add more and more features what the customer needs. It's all about supply chain. I don't like it, but we have to say, it's all about making things more automated and make things more easy for our customer to use to have security in the Kubernetes environment. >> So where can your customers go? Clearly, you've made a big impact on our viewers with your conversation today. Where are they going to be able to go to get their hands on the release? >> So you can find it on online. We have a website to sign up for this program. It's on my blog. We have a blog out there for ACS cloud services. You can just go there, sign up, and we will contact the customer. >> Yeah. And there's another way, if you ever want to get your hands on it and you can do it for free, Open Source StackRox. The product is open source completely. And I would love feedback in Slack channel. It's one of the, we also get a ton of feedback from people who aren't actually paying customers and they contribute upstream. So that's an awesome way to get started. But like you said, you go to, if you search ACS cloud service and service preview. Don't have to be a Red Hat customer. Just if you're running a CNCF compliant Kubernetes version. we'd love to hear from you. >> All open source, all out in the open. >> Yep. >> Getting it available to the customers, the non-customers, they hopefully pending customers. Guys, thank you so much for joining John and me talking about the new release, the evolution of StackRox in the last season of 18 months. Lot of good stuff here. I think you've done a great job of getting the audience excited about what you're releasing. Thank you for your time. >> Thank you. >> Thank you. >> For our guest and for John Furrier, Lisa Martin here in Detroit, KubeCon + CloudNativeCon North America. Coming to you live, we'll be back with our next guest in just a minute. (gentle music)

>>Keon Cloud Native Con kicks off in Detroit on October 24th, and we're pleased to have Stewart Miniman, who's the director of Market Insights, hi, at, for hybrid platforms at Red Hat back in the studio to help us understand the key trends to look for at the events. Do welcome back, like old, old, old >>Home. Thank you, David. It's great to, great to see you and always love doing these previews, even though Dave, come on. How many years have I told you Cloud native con, It's a hoodie crowd. They're gonna totally call you out for where in a tie and things like that. I, I know you want to be an ESPN sportscaster, but you know, I I, I, I still don't think even after, you know, this show's been around for so many years that there's gonna be too many ties into Troy. I >>Know I left the hoodie in my off, I'm sorry folks, but hey, we'll just have to go for it. Okay. Containers generally, and Kubernetes specifically continue to show very strong spending momentum in the ETR survey data. So let's bring up this slide that shows the ETR sectors, all the sectors in the tax taxonomy with net score or spending velocity in the vertical axis and pervasiveness on the horizontal axis. Now, that red dotted line that you see, that marks the elevated 40% mark, anything above that is considered highly elevated in terms of momentum. Now, for years, the big four areas of momentum that shine above all the rest have been cloud containers, rpa, and ML slash ai for the first time in 10 quarters, ML and AI and RPA have dropped below the 40% line, leaving only cloud and containers in rarefied air. Now, Stu, I'm sure this data doesn't surprise you, but what do you make of this? >>Yeah, well, well, Dave, I, I did an interview with at Deepak who owns all the container and open source activity at Amazon earlier this year, and his comment was, the default deployment mechanism in Amazon is containers. So when I look at your data and I see containers and cloud going in sync, yeah, that, that's, that's how we see things. We're helping lots of customers in their overall adoption. And this cloud native ecosystem is still, you know, we're still in that Cambridge explosion of new projects, new opportunities, AI's a great workload for these type type of technologies. So it's really becoming pervasive in the marketplace. >>And, and I feel like the cloud and containers go hand in hand, so it's not surprising to see those two above >>The 40%. You know, there, there's nothing to say that, Look, can I run my containers in my data center and not do the public cloud? Sure. But in the public cloud, the default is the container. And one of the hot discussions we've been having in this ecosystem for a number of years is edge computing. And of course, you know, I want something that that's small and lightweight and can do things really fast. A lot of times it's an AI workload out there, and containers is a great fit at the edge too. So wherever it goes, containers is a good fit, which has been keeping my group at Red Hat pretty busy. >>So let's talk about some of those high level stats that we put together and preview for the event. So it's really around the adoption of open source software and Kubernetes. Here's, you know, a few fun facts. So according to the state of enterprise open source report, which was published by Red Hat, although it was based on a blind survey, nobody knew that that Red Hat was, you know, initiating it. 80% of IT execs expect to increase their use of enterprise open source software. Now, the CNCF community has currently more than 120,000 developers. That's insane when you think about that developer resource. 73% of organizations in the most recent CNCF annual survey are using Kubernetes. Now, despite the momentum, according to that same Red Hat survey, adoption barriers remain for some organizations. Stu, I'd love you to talk about this specifically around skill sets, and then we've highlighted some of the other trends that we expect to see at the event around Stu. I'd love to, again, your, get your thoughts on the preview. You've done a number of these events, automation, security, governance, governance at scale, edge deployments, which you just mentioned among others. Now Kubernetes is eight years old, and I always hear people talking about there's something coming beyond Kubernetes, but it looks like we're just getting started. Yeah, >>Dave, It, it is still relatively early days. The CMC F survey, I think said, you know, 96% of companies when they, when CMC F surveyed them last year, were either deploying Kubernetes or had plans to deploy it. But when I talked to enterprises, nobody has said like, Hey, we've got every group on board and all of our applications are on. It is a multi-year journey for most companies and plenty of them. If you, you look at the general adoption of technology, we're still working through kind of that early majority. We, you know, passed the, the chasm a couple of years ago. But to a point, you and I we're talking about this ecosystem, there are plenty of people in this ecosystem that could care less about containers and Kubernetes. Lots of conversations at this show won't even talk about Kubernetes. You've got, you know, big security group that's in there. >>You've got, you know, certain workloads like we talked about, you know, AI and ml and that are in there. And automation absolutely is playing a, a good role in what's going on here. So in some ways, Kubernetes kind of takes a, a backseat because it is table stakes at this point. So lots of people involved in it, lots of activities still going on. I mean, we're still at a cadence of three times a year now. We slowed it down from four times a year as an industry, but there's, there's still lots of innovation happening, lots of adoption, and oh my gosh, Dave, I mean, there's just no shortage of new projects and new people getting involved. And what's phenomenal about it is there's, you know, end user practitioners that aren't just contributing. But many of the projects were spawned out of work by the likes of Intuit and Spotify and, and many others that created some of the projects that sit alongside or above the, the, you know, the container orchestration itself. >>So before we talked about some of that, it's, it's kind of interesting. It's like Kubernetes is the big dog, right? And it's, it's kind of maturing after, you know, eight years, but it's still important. I wanna share another data point that underscores the traction that containers generally are getting in Kubernetes specifically have, So this is data from the latest ETR survey and shows the spending breakdown for Kubernetes in the ETR data set for it's cut for respondents with 50 or more citations in, in by the IT practitioners that lime green is new adoptions, the forest green is spending 6% or more relative to last year. The gray is flat spending year on year, and those little pink bars, that's 6% or down spending, and the bright red is retirements. So they're leaving the platform. And the blue dots are net score, which is derived by subtracting the reds from the greens. And the yellow dots are pervasiveness in the survey relative to the sector. So the big takeaway here is that there is virtually no red, essentially zero churn across all sectors, large companies, public companies, private firms, telcos, finance, insurance, et cetera. So again, sometimes I hear this things beyond Kubernetes, you've mentioned several, but it feels like Kubernetes is still a driving force, but a lot of other projects around Kubernetes, which we're gonna hear about at the show. >>Yeah. So, so, so Dave, right? First of all, there was for a number of years, like, oh wait, you know, don't waste your time on, on containers because serverless is gonna rule the world. Well, serverless is now a little bit of a broader term. Can I do a serverless viewpoint for my developers that they don't need to think about the infrastructure but still have containers underneath it? Absolutely. So our friends at Amazon have a solution called Fargate, their proprietary offering to kind of hide that piece of it. And in the open source world, there's a project called Can Native, I think it's the second or third can Native Con's gonna happen at the cncf. And even if you use this, I can still call things over on Lambda and use some of those functions. So we know Dave, it is additive and nothing ever dominates the entire world and nothing ever dies. >>So we have, we have a long runway of activities still to go on in containers and Kubernetes. We're always looking for what that next thing is. And what's great about this ecosystem is most of it tends to be additive and plug into the pieces there, there's certain tools that, you know, span beyond what can happen in the container world and aren't limited to it. And there's others that are specific for it. And to talk about the industries, Dave, you know, I love, we we have, we have a community event that we run that's gonna happen at Cubans called OpenShift Commons. And when you look at like, who's speaking there? Oh, we've got, you know, for Lockheed Martin, University of Michigan and I g Bank all speaking there. So you look and it's like, okay, cool, I've got automotive, I've got, you know, public sector, I've got, you know, university education and I've got finance. So all of you know, there is not an industry that is not touched by this. And the general wave of software adoption is the reason why, you know, not just adoption, but the creation of new software is one of the differentiators for companies. And that is what, that's the reason why I do containers, isn't because it's some cool technology and Kubernetes is great to put on my resume, but that it can actually accelerate my developers and help me create technology that makes me respond to my business and my ultimate end users. Well, >>And you know, as you know, we've been talking about the Supercloud a lot and the Kubernetes is clearly enabler to, to Supercloud, but I wanted to go back, you and John Furrier have done so many of, you know, the, the cube cons, but but go back to Docker con before Kubernetes was even a thing. And so you sort of saw this, you know, grow. I think there's what, how many projects are in CNCF now? I mean, hundreds. Hundreds, okay. And so you're, Will we hear things in Detroit, things like, you know, new projects like, you know, Argo and capabilities around SI store and things like that? Well, you're gonna hear a lot about that. Or is it just too much to cover? >>So I, I mean the, the good news, Dave, is that the CNCF really is, is a good steward for this community and new things got in get in. So there's so much going on with the existing projects that some of the new ones sometimes have a little bit of a harder time making a little bit of buzz. One of the more interesting ones is a project that's been around for a while that I think back to the first couple of Cube Cuban that John and I did service Mesh and Istio, which was created by Google, but lived under basically a, I guess you would say a Google dominated governance for a number of years is now finally under the CNCF Foundation. So I talked to a number of companies over the years and definitely many of the contributors over the years that didn't love that it was a Google Run thing, and now it is finally part. >>So just like Kubernetes is, we have SEO and also can Native that I mentioned before also came outta Google and those are all in the cncf. So will there be new projects? Yes. The CNCF is sometimes they, they do matchmaking. So in some of the observability space, there were a couple of projects that they said, Hey, maybe you can go merge down the road. And they ended up doing that. So there's still you, you look at all these projects and if I was an end user saying, Oh my God, there is so much change and so many projects, you know, I can't spend the time in the effort to learn about all of these. And that's one of the challenges and something obviously at Red Hat, we spend a lot of time figuring out, you know, not to make winners, but which are the things that customers need, Where can we help make them run in production for our, our customers and, and help bring some stability and a little bit of security for the overall ecosystem. >>Well, speaking of security, security and, and skill sets, we've talked about those two things and they sort of go hand in hand when I go to security events. I mean, we're at reinforced last summer, we were just recently at the CrowdStrike event. A lot of the discussion is sort of best practice because it's so complicated. And, and, and will you, I presume you're gonna hear a lot of that here because security securing containers now, you know, the whole shift left thing and shield right is, is a complicated matter, especially when you saw with the earlier data from the Red Hat survey, the the gaps are around skill sets. People don't have the skill. So should we expect to hear a lot about that, A lot of sort of how to, how to take advantage of some of these new capabilities? >>Yeah, Dave, absolutely. So, you know, one of the conversations going on in the community right now is, you know, has DevOps maybe played out as we expect to see it? There's a newer term called platform engineering, and how much do I need to do there? Something that I, I know your, your team's written a lot about Dave, is how much do you need to know versus what can you shift to just a platform or a service that I can consume? I've talked a number of times with you since I've been at Red Hat about the cloud services that we offer. So you want to use our offering in the public cloud. Our first recommendation is, hey, we've got cloud services, how much Kubernetes do you really want to learn versus you want to do what you can build on top of it, modernize the pieces and have less running the plumbing and electric and more, you know, taking advantage of the, the technologies there. So that's a big thing we've seen, you know, we've got a big SRE team that can manage that for use so that you have to spend less time worrying about what really is un differentiated heavy lifting and spend more time on what's important to your business and your >>Customers. So, and that's, and that's through a managed service. >>Yeah, absolutely. >>That whole space is just taken off. All right, Stu I'll give you the final word. You know, what are you excited about for, for, for this upcoming event and Detroit? Interesting choice of venue? Yeah, >>Look, first of off, easy flight. I've, I've never been to Detroit, so I'm, I'm willing to give it a shot and hopefully, you know, that awesome airport. There's some, some, some good things there to learn. The show itself is really a choose your own adventure because there's so much going on. The main show of QAN and cloud Native Con is Wednesday through Friday, but a lot of a really interesting stuff happens on Monday and Tuesday. So we talked about things like OpenShift Commons in the security space. There's cloud Native Security Day, which is actually two days and a SIG store event. There, there's a get up show, there's, you know, k native day. There's so many things that if you want to go deep on a topic, you can go spend like a workshop in some of those you can get hands on to. And then at the show itself, there's so much, and again, you can learn from your peers. >>So it was good to see we had, during the pandemic, it tilted a little bit more vendor heavy because I think most practitioners were pretty busy focused on what they could work on and less, okay, hey, I'm gonna put together a presentation and maybe I'm restricted at going to a show. Yeah, not, we definitely saw that last year when I went to LA I was disappointed how few customer sessions there were. It, it's back when I go look through the schedule now there's way more end users sharing their stories and it, it's phenomenal to see that. And the hallway track, Dave, I didn't go to Valencia, but I hear it was really hopping felt way more like it was pre pandemic. And while there's a few people that probably won't come because Detroit, we think there's, what we've heard and what I've heard from the CNCF team is they are expecting a sizable group up there. I know a lot of the hotels right near the, where it's being held are all sold out. So it should be, should be a lot of fun. Good thing I'm speaking on an edge panel. First time I get to be a speaker at the show, Dave, it's kind of interesting to be a little bit of a different role at the show. >>So yeah, Detroit's super convenient, as I said. Awesome. Airports too. Good luck at the show. So it's a full week. The cube will be there for three days, Tuesday, Wednesday, Thursday. Thanks for coming. >>Wednesday, Thursday, Friday, sorry, >>Wednesday, Thursday, Friday is the cube, right? So thank you for that. >>And, and no ties from the host, >>No ties, only hoodies. All right Stu, thanks. Appreciate you coming in. Awesome. And thank you for watching this preview of CubeCon plus cloud Native Con with at Stu, which again starts the 24th of October, three days of broadcasting. Go to the cube.net and you can see all the action. We'll see you there.

(soft music) >> Welcome back, everyone. theCube's live coverage. Day two here at VMware Explore. Our 12th year covering VMware's annual conference formally called Vmworld, now it's VMware Explore. Exploring new frontiers multi-cloud and also bearing some of the fruit from all the investments in cloud native Tanzu and others. I'm John Furrier with Dave Vellante. We have the man who's in charge of a lot of that business and a lot of stuff coming out of the oven and hitting the market. Ajay Patel, senior vice president and general manager of the modern applications and management group at VMware, basically the modern apps. >> Absolutely. >> That's Tanzu. All the good stuff. >> And Aria now. >> And Aria, the management platform, which got social graph and all kinds of graph databases. Welcome back. >> Oh, thank you so much. Thanks for having me. >> Great to see you in person, been since 2019 when you were on. So, a lot's happened since 2019 in your area. Again, things get, the way VMware does it as we all know, they announce something and then you build it and then you ship it and then you announce it. >> I don't think that's true, but okay. (laughs) >> You guys had announced a lot of cool stuff. You bought Heptio, we saw that Kubernetes investment and all the cloud native goodness around it. Bearing fruit now, what's the status? Give us the update on the modern applications of the management, obviously the areas, the big announcement here on the management side, but in general holistically, what's the update? >> I think the first update is just the speed and momentum that containers and Kubernetes are getting in the marketplace. So if you take the market context, over 70% of organizations now have Kubernetes in production, not one or two clusters, but hundreds of clusters, sometimes tens of clusters. So, to me, that is a market opportunity that's coming to fruition. Sometimes people will come and say, Ajay, aren't you late to the market? I say, no, I'm just perfectly timing it. 'Cause where does our value come in? It's enterprise readiness. We're the company that people look to when you have complexity, you have scale, you need performance, you need security, you need the robustness. And so, Tanzu is really about making modern applications real, helping you design, develop, build and run these applications. And with Aria, we're fundamentally changing the game around multicloud management. So the one-two punch of Tanzu and Aria is I'm most excited about. >> Isn't it true that most of the Kubernetes, you know, today is people pulling down open source and banging away. And now, they're looking for, you know, like you say, more of a robust management capability. >> You know, last two years when I would go to many of the largest customers, like, you know, we're doing good. We've got a DIY platform, we're building this. And then you go to the customer a year later, he's got knocked 30, 40 teams and he has Log4j happen. And all of a sudden he is like, oh, I don't want to be in the business of patching this thing or updating it. And, you know, when's the next shoe going to fall? So, that maturity curve is what I was talking about. >> Yeah. Free like a puppy. >> Ajay, you know, mentioned readiness, enterprise readiness and the timing's perfect. You kind of included, not your exact words, but I'm paraphrasing. That's a lot to do with what's going on. I mean, I'll say Cloud Native, IWS, think of the hyper scale partner, big partner and Google and even Google said it today. You know, the market world's spinning in their direction. Especially with respect to VMware. You get the relationship with the hyperscalers. Cloud's been on everyone's agenda for a long time. So, it's always been ready. But enterprise, you are customer base at VMware, very cloud savvy in the sense they know it's there, there's some dabbling, there's some endeavors in the cloud, no problem. But from a business perspective and truly transforming the VMware value proposition, is already, they're ready and it's already time now for them, like, you can see the movement. And so, can you explain the timing of that? I mean, I get enterprise readiness, so we're ready to scale all that good stuff. But the timing of product market fit is important here. >> I think when Raghu talks about that cloud first to cloud chaos, to cloud smart, that's the transition we're seeing. And what I mean by that is, they're hitting that inflection point where it's not just about a single team. One of the guys, basically I talked to the CIO, he was like, look, let's assume hypothetically I have thousand developers. Hundred can talk about microservices, maybe 50 has built a microservice and three are really good at it. So how do I get my thousand developers productive? Right? And the other CIO says, this team comes to me and says, I should be able develop directly to the public cloud. And he goes, absolutely you can do that. You don't have to come through IT. But here's the book of security and compliance that you need to enforce to get that thing in production. >> Go for it. >> Go for it. >> Good luck with that. >> So that reality of how do I scale my dev developers is turning into a developer experience problem. We now have titles which says, head of developer experience. Imagine that two years ago. We didn't talk about it. People start, hey, containers Kubernetes. I'm good to go. I can go get all the open source technology you talked about. And now they're saying no. >> And also software supply chains, another board that you're think. This is a symptom of the growth. I mean, open source is the software industry. That is, I don't think debatable. >> Right. >> Okay. That's cool. But now integration becomes vetting, trust, trusting codes. It's very interesting software time right now. >> That's right. >> And how is that impacting the cloud native momentum in your mind? Accelerating it? What inning are we in? How would you peg the progress? >> You know, on that scale of 1 to 10, I think we're halfway marked now. And that moved pretty quickly. >> It really did. >> And if you sit back today, the kinds of applications we're involved in, I have a Chicago wealth management company. We're building the next generation wealth management application. It's a fundamental refactoring of the legacy application. If you go to a prescription company, they're building a brand new prescription platform. These are not just trivial. What they're learning is the lift and shift. Doesn't work for these major applications. They're having to refactor them which is the modernization. >> So how specifically, are they putting some kind of abstraction layer on that? Are they actually gutting it and rewriting it? >> There's always going to be brownfield. Remember the old days of SOA? >> Yeah, yeah. >> They are putting APIs in front of their main systems. They're not rewriting the core banking or the core platform, but the user experience, the business logic, the AIML capability to bring intelligence in the platform. It's surrounding the capability to make it much more intuitive, much more usable, much more declarative. That's where things are going. And so I'm seeing this mix of integration all over again. Showing my age now. But, you know, the new EAI so is now microservices and messaging and events with the same patterns. But again, being much more accelerated with cloud native services. >> And it is to the point, it's accelerated today. They're not having to freeze the code for six months or nine months and that which would kill the whole recipe for failure. So they're able to now to fast track their modernization. They have to prioritize 'cause they got limited resources. But how are you guys coming up to that? >> But the practice is changing as well, right? Well, the old days, it was 12, 18 months cycle or anything software. If you heard the CVS CIO, Rohan. >> Yeah. >> Three months where they started to engage with us in getting an app in production, right? If you look at the COVID, 10 days to get kind of a new application for getting small loans going with Pfizer, right? These are dramatically short term, but it's not rewriting the entire app. It's just putting these newer experiences, newer capability in front with newer modern developer practices. And they're saying, I need to do it not just once, but for 100, 200, 5,000 members. JPMC has 50,000 developers. Fifty thousand. They're not a bank anymore. >> We just have thousands of apps. >> Exactly. >> Ajay, I want to get your thoughts on something that we've been talking about on our super cloud event. I know we had an event a couple weeks ago, you guys were one of our sponsors, VMware was. It was called super cloud where we're defining that this next gen environment's a super cloud and every company will have a super cloud capability. And underneath that is cross cloud capabilities. So, super cloud is like a super set on top of a multi-cloud. And little word play or play on words is, ecosystem partners versus partners in the ecosystem. Because if you're coming down to the integration side of things, it's about knowing what goes what, it's almost like building an OS if you're a coder or an operating systems person. You got to put the pieces together right, not just go to the directory and say, okay, who's got the cheapest price in DR or air gaping or something or some solution. So ecosystem partners are truly partners. Partners in the ecosystem are a bunch of people out on a list. How do you see that? Because the trend we're seeing is, the development process includes partners at day one. >> That's right. Not bolt-on. >> Completely agree. >> Share your thoughts on that. >> So let's look at that. The first thing I'm hearing from my customers is, they're trying to use all the public clouds as a new IS. That's the first API or contract infrastructures code IS. From then on they're saying, I want more and more portable services. And if you see the success of some of the data vendors and the messaging vendors, you're starting to see best of breed becoming part of the platform. So you are to identify which of these are truly, you know, getting market momentum and are becoming kind of defacto leaders. So, Kafka goes hand in hand with streaming. RabbitMQ from my portfolio goes with messaging. Postgres for database. So these are the, in your definition, ecosystem partners, they're foundational. In the security space, you know, Snyk is a common player in terms of scanning or Aqua and Prisma even though we have Carbon Black. Those become partners from a container security perspective. So, what's happening is the industry stabilizing a handful of critical players that are becoming multi-cloud preference of choice in this. And our job is to bring it all together in a all coordinated, orchestrated manner to give them a platform. >> I mean, you guys always had ecosystem, but I think that priority more than ever. It wasn't really your job at VMware, even, Dave, 10 years ago to say, hey, this is the strategic role that you might play one partner. It was pretty much the partners all kind of fed off the momentum of VMware. Virtualization. And there's not a lot of nuance there. There's pretty much they plug in and you got. >> So what we're doing here is, since we're not the center of the universe, unfortunately, for the application world, things like Backstage is a developer portal from Spotify that became open source. That's becoming the place where everyone wants to provide a plugin. And so we took Backstage, we said, let's provide enterprise support for Backstage. If you take a technology like, you know, what we have with Spring. Every job where developer uses Spring, how do we make it modern with Spring cloud. We work with Microsoft to launch a service with Azure Spring Enterprise for Spring. So you're starting to see us taking communities where they have momentum and bringing the ecosystem around those technologies. Cluster API for Kubernetes, for have you managed stuff. >> Yeah. >> So it's about standard. >> Because the developers are voting with their clicks and their code repos. And so you're identifying the patterns that they like. >> That's right. >> And aligning with them and connecting with them rather than trying to sell against it. >> Exactly. It's the end story with everyone. I say stop competing. So people used to think Tanzu is Kubernetes. It's really Tanzu is the modern application platform that runs on any Kubernetes. So I've changed the narrative. When Heptio was here, we were trying to be a Kubernetes player. I'm like, Kubernetes is just another dial tone. You can use mine, you can use OpenShift. So this week we announced support for OpenShift by Tanzu application platform. The values moving up, it's around outcomes. So industry standards, taking lead and solving the problem. >> You know, we had a panel at super cloud. Dave, I know you got a question. I'll get to you in a second. But the panel was the innovator's dilemma. And then during the event, one of the panelists, Chris Hoff knows VMware very well, Beaker on Twitter, said it should be called the integrators dilemma. Because the innovations here, >> How do you put it all together? >> But the integration of the, putting the piece parts together, building the thing is the innovation. >> And we come back and say, it's a secure software supply chain. It starts with great content. Did you know, I published most of the open source content on every hyperscaler through my Bitnami acquisition. So I start with great content that's curated. Then I allow you to create your own golden images. Then I have a build service that secures and so on and so forth and we bring the part. So, that opinionated solution, but batteries included but you can change it is been one of our key differentiator. We recognize the roles is going to be modular, come back and solve for it. >> So I want to understand sort of relationship Tanzu and Aria, John was talking about, you know, super cloud before we had our event. We had an earlier session where we help people understand that Aria was not, you know, vRealize renamed. >> It's rebranded. >> And reason I bring that up is because we had said it around super cloud, that one of the defining characteristics was, sorry, super PaaS, which is a specific purpose built PaaS layer designed to support your objective for multi-cloud. And speaking to a lot of people this week, there's a federated architecture, there's graph relationships, there's real time ability to ingest and analyze. That's unique. And that's IP that is purpose built for what you're doing. >> Absolutely. When I think what came out of all that learning is after 20 years of Pivotal and BA and what we learned that you still need some abstraction layer. Kubernetes is too low level. So what are the developer problems? What are the delivery problems? What are the operations and management problems? Aria solves all the operations and management problem. Tanzu solves a super PaaS problems. >> Yes. Right. >> Of providing a consistent way to build great software and the secure software supply chain to run on any infrastructure. So the combination of Tanzu and Aria complete the value chain. >> And it's different. Again, we get a lot of heat for this, but we're saying, look, we're trying to describe, it's not just IAS, PaaS, and SaaS of last decade. There's something new that's happening. And we chose the name super cloud. >> And what's the difference? It's modular. It's pluggable. It fits into the way you operate. >> Whereas PaaS was very prescriptive. If you couldn't fit, you couldn't jump down to the next level. This is very much, you can stay at the abstraction level or go lower level. >> Oh, we got to add that to the attribute. >> We're recruiting him right now. (laughs) >> We'll give you credit. >> I mean, funny all the web service's background. Look at an app server. You well knew all about app servers. Basically the company is an app. So, if you believe that, say, Capital One is an application as a company and Amazon's providing all the CapEx, >> That's it. >> Okay. And they run all their quote, old IT spend millions, billions of dollars on operating expenses that's going to translate to the top line called the income statement. So, Dave always says, oh, it's on the balance sheet, but now they're going to go to the top line. So we're seeing dynamic. Ajay, I want to get your reaction to this where the business model shift if everything's tech enabled, the company is like an app server. >> Correct. >> So therefore, the revenue that's generated from the technology, making the app work has to get recognized in the income. Okay. But Amazon's doing all, or the cloud hyperscale is doing all the heavy lifting on the CapEx. So technically it's the cloud on top of a cloud. >> Yes and no. The way I look at it, >> I call that a super cloud. >> So I like the idea of super cloud, but I think we're mixing two different constructs. One is, the cloud is a new hardware, right? In terms of dynamic, elastic, always available, et cetera. And I believe when more and more customer I talk about, there's a service catalog of infrastructure services. That's emerging. This super cloud is the next set of PaaS super PaaS services. And the management service is to use the cloud. We spend so much time as VMware building clouds, the problem seems, how do you effectively use the cloud? What problems do we solve around digital where every company is a digital company and the product is this application, as you said. So everything starts with an application. And you look at from the lens of how you run the application, what it costs the application, what impact it's driving. And I think that's the change. So I agree with you in some way. That is a digital strategy. >> And that's the company. >> That's the company. The application is the company. >> That's the t-shirt. >> And API is the currency. >> So, Ajay, first of all, we love having you in theCube 'cause you're like a masterclass in multiple dimensions. So, I want to get your thoughts on the abstraction layer. 'Cause we were also talking earlier in theCube here as well as before. But abstraction layers happen when you have major movements in markets that are game changing or major inflection points because you've reached a complexity point where it's working so great, this new thing, that's too complex to reign it in. And we were quoting Andy Grove by saying, "let chaos reign then reign in the chaos". So, all major industry moments go back 30, 40 years happen with abstractions. So the question is is that, you can't be a vendor, we've observed you can't be a vendor and be the abstraction. Like, if Cisco's running routers, they can't be the abstraction layer. They have to be the benefit of the abstraction layer. And if you're on the other side of the abstraction layer, you can't be running that either. >> I like the way you're thinking about it. Yeah. Do you agree? >> I completely agree. And, you know, I'm an old middleware guy. And when I used to say this to my CEO, he's like, no, it's not middleware, it's just a new middleware. And what's middleware, right? It's a thing between app and infrastructure. You could define it whatever we want, right? And so this is the new distributed middleware. >> It's a metaphor and it's a good one because it does a purpose. >> It's a purpose. >> It creates a separation but then you have, it's like a DMZ zone or whatever you want to call it. It's an area that things happen. >> But the difference before last time was, you could always deploy it to a thing. The thing is now the cloud. The thing is a set of services. So now it's as much of a networking problem at the application layer is as much as security problem. It's how you build software, how we design. So APIs, become part of your development. You can't think of APIs after the fact, right? When you build an API, you got to publish API because the minute you publish it and if you change it, the API's out of. So you can't have it as a documentation process. So, the way you build software, you use software consume is all about it. So to me, digital product with an API as a currency is where we're headed towards. >> Yeah, that's a great observation. Want to make a mental note of that and make that a clip. I want to get your thoughts on software development. You mentioned that, obviously software development life cycles are changing. I'll say open sources now. I mean, it's unlimited codes, supply chain issue. What's in the code, I get that verified codes going to happen. Is software development coding as much or is coding changing the notion of writing code? Or is it more glue layer you're writing. >> I think you're onto something. I call software developments composition now. My son's at Facebook or Google. They have so many libraries. So you don't no longer start with the very similar primitive, you start with building blocks, components, services, libraries, open source technology. What are you really doing? You're composing these things from multiple artifacts. And how do you make sure those artifacts are good artifacts? So someone's not sticking in security in a vulnerability into it. So, the world is moving towards composition and there are few experts who build the core components. Most of the time we're just using those to build solutions. And so, the art here is, how do you provide that set of best practices? We call them patterns or building blocks or services that you can compose to build these next generation (indistinct) >> It's interesting. >> Cooking meals. >> I agree with you a hundred percent what you're thinking. I agree about that worldview. Here's a dilemma that I'm seeing. In the security world, you've got zero trust. You know, Which is, I don't know you, I don't trust you at all. And if you're going to go down this composed, we're going to have an orchestra of players with instruments, say to speak, Dave, metaphor. That's trust involved. >> Yes. >> So you have two spectrums of issues. >> Yes. >> If software's going trust and you're seeing Docker containers getting more verifications, software supply chain, and then you got hardware I call network guys, love zero trust. Where's the balance? How do you reconcile that? Is it just decoupled? Nuance? I mean, what's the point? >> No, no. I think it all comes together. And what I mean by that is, it starts with left shifting it all the way to hands of the developers, right? So, are you starting with good content? You have providence of the stuff you're using. Are you building it correctly? So you're not introducing bad things like solar winds along the process. Are you testing it along the way of the development process? And then once in production, do you know, half the time it's configurations of where you're running the stuff versus the software itself. So you can think of the two coming together. And the network security is protecting people from going laterally once they've got in there. So, a whole security solution requires all of the above, a secure software supply chain, the way to kind of monitor and look at configuration, we call posture management or workload management and the network security of SaaS-e for zero trust. That's a hard thing. And the boundary is the application. >> All right. >> So is it earned trust model sort of over time? >> No, it's designed in, it's been a thing. >> Okay. So it's not a, >> Because it developed. >> You can bolt in afterwards. >> Because the developers are driving it. They got to know what they're doing. >> And it's changing every week. If I'm putting a new code out every week. You can't, it can be changed to something else. >> Well, you guys got guardrails. The guardrails constant is a good example. >> It stops on the configuration side, but I also need the software. So, Tanzu is all about, the secure chain is about the development side of the house. Guardrails are on the operational side of the house. >> To make sure the developers don't stop. >> That's right. >> Things will always get out there. And I find out there's a CV that I use a library, I found after the fact. >> Okay. So again, while I got here again, this is great. I want to get test this thesis. So, we've been saying on theCube, talking about the new ops, the new kind of ops that emerging. DevOps, which we believe is cloud native. So DevOps moving infrastructure's code, that's happened, it's all good. Open source is growing. DevOps is done deal. It's done deal. Developers are doing that. That ops was IT. Then don't need the server, clouds my hardware. Check. That balances. The new ops is data and security which has to match up to the velocity of the developers. Do you believe that? >> Completely. That's why we call it DevSecOps. And the Sec is where all the action is. >> And data. And data too. >> And data is about making the data available where the app meets. So the problem was, you know, we had to move the logic to where the data is or you're going to move the data where the logic is. So data fabrics are going to become more and more interesting. I'll give you a simple example. I publish content today in a service catalog. My customer's saying, but my content catalog needs to be in 300 locations. How do I get the content to each of the repos that are running in 300 location? So I have a content distribution problem. So you call it a data problem. Yes, it's about getting the right data. Whether it's simple as even content, images available for use for deployment. >> So you think when I think about the application development stack and the analytics stack, the data stack, if I can call it that, they're separate, right? Are those worlds, I mean, people say, I want to inject data and AI intelligence into apps. Those worlds have deployment? I think about the insight from the historical being projected in the operational versus they all coming together. I have a Greenplum platform, it's a great analytics platform. I have a transactional platform. Do my customers buy the same? No, they're different buyers, they're different users. But the insight from that is being now plugged in so that at real time I can ask the question. So even this information is being made available on demand. So that's where I see it. And that's most coming together, but the insight is being incorporated in the operational use. So I can say, do I give the risk score? Do I give you credit? It's based on a whole bunch of historical analytics done. And at the real time, processing is happening, but the intelligence is behind it. >> It's a mind shift for sure because the old model was, I have a database, we're good. Now you have time series database, you got graphs. Each one has a role in the overall construct of the new thing. >> But it's about at the end. How do I make use of it? Someone built a smart AI model. I don't know how it was built, but I want to apply it for that particular purpose. >> Okay. So the final question for you, at least from my standpoint is, here at VMware Explore, you have a lot of the customers and so new people coming in that we've heard about, what's their core order of operations right now? Get on the bandwagon for modern apps. How do you see their world unfolding as they go back to the ranch, their places, and go back to their boss? Okay. We got the modern application. We're on the right track boss, full steam ahead. Or what change do they make? >> I think the biggest thing I saw was with some of the branding changes well and some of the new offerings. The same leader had two teams, the VMware team and the public cloud team. And they're saying, hey, maybe VMware's going to be the answer for both. And that's the world model. That's the biggest change I'm seeing. They were only thinking of us on the left column. Now they see us as a unifying player to play across cloud native and VMware, the uniquely set up to bring it all together. That's been really exciting this week. >> All right, Ajay, great to have you on. Great perspective. Worthy of great stuff. Congratulations on the success of all that investment coming to bear. >> Thank you. >> And on the new management platform. >> Yeah. Thank you. And thanks always for giving us all the support we need. It's always great. >> All right Cube coverage here. Getting all the data, getting inside the heads, getting all the specifics and all the new trends and actually connecting the dots here on theCube. I'm John Furrier with Dave Vellante. Stay tuned for more coverage from day two. Two sets, three days, Cube at VMware Explore. We'll be right back. (gentle music)

(upbeat music) >> Hey, welcome back everyone to theCube's live coverage of the Monaco Crypto Summit here in Monaco. I'm John Furrier, host of theCube, and Lauren Bissell here, founder and CEO of Immutable Industries, focused on the advancement of technologies in art, entertainment, blockchain across multiple sectors. Great background in entertainment music, complying that into the convergence and to crypto. Welcome to theCube. I appreciate it. >> Thank you so much. Thank you guys for having me. It's been an incredible day so far. >> So we were just talking before we came on camera, your background and just the people you've worked with in the music industry. You've been there for a very long part of your career, from the beginning. Now you're on the wave of Web3, crypto, DeFi. There's a confluence of refactoring businesses. We're seeing that impact. And I think a lot of people, finance and entrepreneurial, the best brains are coming into the sector because it's an opportunity, clearly, to reset and refactor old antiquated business models and practices, in a new way to achieve the same things. Better, faster, cheaper >> Exactly. Better, faster, cheaper, is good sometimes, other times that's... We will see. But I think for me, coming in from the music industry was something that, I honestly never expected to be involved in blockchain and futuristic tech. It's always something that I admired, but I didn't really see, "Okay. Here's how I can be involved in that." I was obsessed with it. But as I was sort of progressing my career as a music producer, I saw so many issues with the industry. The way capital came in, the way that it was distributed. I mean, these things are still happening today. But I was just constantly looking around for better solutions and how to make this work in a better way. In 2017, when I started really diving into crypto, that was something where I saw a huge opportunity for the entire industry. The music industry is notorious for just sort of being behind the curve when it comes to new tech. And it's a shame. When you're in an industry that's full of art and innovation, you would think that it's something... It's an industry that would embrace this position. Maybe some people do this, and I applaud those people very much. But in general, the music industry is kind of behind. We live a little bit in the Wild West. Not in the futures way, but kind of in the old way. I'm just really excited to be able to bring these things into the industry. >> It's interesting. I'm not in the industry, in the music side, but I've been on the software industry, where you had the proprietary software, the rights, and people used to build software. And then when the company went under, the software was gone, lost forever. And in around the late eighties, nineties, open source movement happened, and it just changed everything. And I think, to me, I feel like this is a similar structural inflection point in change, where rights are changing. People are still holding onto like, "He can't use the copyright." And I even saw a stat that said, with AI now, you can actually copyright every single melody, every single note in music. So that means like, "Who the hell's going to develop anything?" So are even rights even matter? So rights, ownership, art, mixing. Funny story of my son, a year and a half ago, mixed an old song from a band that wasn't around, and it became a TikTok sensation. Hundreds of millions of listens, and then the Spotify and Apple account was making like 20,000 a week, and DistroKid cut him off. Because someone went back and claimed the copyrights. But it was a mix of a couple of different pieces of the song for a new melody. But because that wasn't his work, the middle man killed the account. >> Right. But if there had been maybe an easier solution for him to go get those rights. So I actually used to be a rights and royalties negotiation specialist. I was on the phone with labels, every second of every day. From a producer standpoint, you're trying to find something that works for the artist, something that works for the label, something that you can arrange in perpetuity, if possible. But it's just... Again, there's so many people that have to just get on the phone- >> Like a busy gen system of like- >> Yeah. >> Weirdness >> Right. >> What's the solution? >> I mean, right now one of the favorite... It's super simple. Smart contracts related to publishing and royalties. Now you still need, probably in the interim, someone to go out and... The old school job for someone in rights and royalties is sitting in a restaurant and listening to see if the music is being played, and then you write it all down on a piece of paper. I mean, that's quite old school, but that still happens in a lot of places. So we can kind of move into smart contracts for the payment systems, and eventually we can move into AI, to actually detect what music is being played where. Just to go, not really on a tangent, but it's like, "Okay. Well, are we taking a job away from someone who's supposed to sit in a restaurant and listen to the music?" Well, I think we're developing a lot of new jobs by needing to generate this software. This is more- >> I've heard that. We've heard that argument before, "Oh! Bank tellers are going to be put out of business by the ATM machine." Turns out there's more branches now. >> Right. >> Okay. There's a total waste there. I mean, people say that are like... I mean, but it does bring up the next gen, the creator, the young artist, the ability to collaborate with smart contracts, the removal of the middle person in all this, the intermediaries. That's really the key, right? >> I think it is the key. And like I said, before removal of the middle person, some people would look down on that. I think it's more efficient systems. When you have more efficient systems, you have more efficient societies, you can create bigger and better things. So is there a change process that has to happen there? Yeah, of course. But this is humanity, this is history, this is what happens. >> Okay. So you're a pro, you've been through- >> I just embrace that. >> You've been through the business, you got the scar tissue, you got the experience, you got the brains. Now you're here in the front of a new generation, a lot of pioneering going on, a lot of chaos, a lot of confusion. Some people... Blood's spilling on the ground. There's a lot of stuff going on, that is opportunity. What are you up to? How are you attacking this market, how do you look at it, what's on your mind? >> Yeah. I mean, so what's funny, I've actually been spending the last few years, sort of directly advising individuals and companies in the music industry. So everyone from artists to label executives, content distribution executives, licensing teams and publishers, and sort of explaining, "Here's how things work. Here's how we think they're going to go. And here's how, instead of running away from that and trying to block your artists from using that system, we can actually use this to enhance the financial pie of the music industry, instead of just trying to steal a piece of everyone else's pie." That's what I really want to do, is, the industry pie can get bigger. We don't need to steal your blueberries. It's just- >> They're picking up crumbs and fighting over crumbs >> Exactly. The industry changed, and I understand why it's scary. I really, really do. I've lived through this. But it's going to be- >> What do they say? What's your advice to them, and what's their reaction? Is it like, "Yeah, you said that you'd get lip service." Or like, "Yeah, we're trying my best. I'll stop drinking, I promise." I mean, I've heard... I tried last week. I mean, are they actually getting it done, or they don't know what to do? >> Yeah. Well, I think it starts with individuals. I actually spent a lot of time working with individuals on education and how they can take that information to their companies or implement that in their companies. It's on sort of a corporate level. It is slower. That's okay. That's expected. But educating sort of individuals, like I said, that's what I've been doing for the past few years, is what's really been helpful. Because if you just kind of do this overnight, I understand it's not going to happen overnight. But being able, like I said, to figure out, "Okay. We grow the financial pie for the whole industry." This accumulates, this helps the health of the industry. Like I said, I grew up in the industry. I care a lot about the industry. I actually want to see good things happen- >> Positive change. >> It's in my heart, in my soul, to make the music industry- >> So Lauren, I got to ask you. So as you see the industry changing, and it's going to be hard to get people to go through transformation. >> Yeah. >> They have to get there. Otherwise, they'll be extinct. And we kind of see that. Is there new brands emerging that have a clean sheet of paper? Because I'm a far young artist, I'm saying to myself, "Okay. If I can write my own ticket..." And by the way, brands become platforms is a big trend you're seeing with NFTs and- >> Yeah. >> And these great Web3 platforms. So I got more social power, I got collective intelligence, I got network effect, I got fans. All that's tappable now from a monetization standpoint. >> Yeah. >> Are there new agencies, new brands, emerging that's artists friendly like this? >> I mean, that's one of the reasons we're here, to begin with. I'm obviously just going to mention Digital Bits, because they're literally creating NFTs for brands. I'm here because I believe in what they're building. Their model is applicable to brands, it's applicable to artists and athletes. I actually truly believe in what they're building and how they're doing it. NFTs is a faster way to achieve what we thought we were going to achieve with sort of the tokenization of a person or an individual brand. NFTs, I think, is a better way to do that. Obviously NFTs are tokens as well, but it's a different type of thing than an ICO. >> It has more versatility and it's got the same kind of characteristics- >> Yeah. I think you can build more community with it, you can maintain the value of the token itself, the non-punchable token itself, a little bit better, and you can build community around it. >> What are some of the companies you're advising and people you're advising? Are they record labels, are they executive, like an executive coach on one end, business consultant on the other? >> Yeah. >> What's some of the range of... >> So I actually advise a couple of brands, I can't completely speak about in the music industry, but from the executive position, I do advise individual executives from the label and the content distribution side, on sort of how to implement futurist tech into their company a little bit better, and sort of what the real things that are going on, the new things that are going on. I actually just took on a role for a company called Cyber Yachts, which I'm really excited about. This one's just going to be fun. International music, entertainment, fun. >> Do you need some media up there? We'll have to do interviews on both- >> Yeah. You can come on the metaverse yacht and the physical yacht, if you want to. But- >> Monaco's a great place for that. >> We will be here. >> All right. >> Absolutely. >> So tell me about the future of some of these big agencies you mentioned? Because if you look at the market right now, if you zoom out, content is king, distribution is Kong. That's what they say. There's a lot more distribution now more than, it seems, content. That's maybe on some perspectives. But it seems like there's a lot more outlets looking for better content. >> Always. >> Do you agree that distribution's hungry for the content, or is there more content than distribution? >> I think it just depends on the type of content. If you look at the content that's being distributed over, say social media, for example, there's a plethora of content. >> Yeah. I guess I'm not- >> There's actually, now, this new hierarchy there, where you have to really scrap to get to the top. So in a weird way, you're seeing that sort of mimic. We see how societies work. So now that's become very hierarchical, and that's almost mimicking the way the traditional industry has been developed. So we go through these cycles. >> It must be hard for a record label to try to do the A and R job, when you have more artists emerging from TikTok, Instagram, the social networks, or- >> I would say their job's probably gotten easier. >> Do you think because of the filtering? >> Well, yeah. Now you can view so much talent in a tiny amount of time online. Now, do I know what they are like lives, do I know how they perform? No, I got to go figure that out. But before you had to go to clubs and sit in there, and run around a city. You can only be in so many places at one time. >> You got to chase content down, look it down. >> Yeah. >> All right, so what's the most exciting thing that you think is happening in the whole crypto world, that's people should pay attention to, that's going to impact some of the mainstream? What's the most important things, do you think? >> Well, something that's actually, somewhat unrelated to music, which is government adoption. Sorry, but hands down, that is the most exciting and important thing that's going on right now. >> Adopting it and embracing it is important. >> Adopting it, embracing it, new regulations coming out. >> Are you happy with the progress? >> Yeah. I mean, it takes time. But right now we're the biggest sort of country that sun is, El Salvador. >> And now Monaco's leaning in. >> Now Monaco is obviously leaning in, that's... It's exciting. It's really exciting. >> Well, to me, I think Digital Bits, so when you climbed in earlier, is that, there's a legitimate crossover between the physical asset, digital asset world, and now the kind of the tough parts, the in between the details and the gaps, the contracts, the royalties. >> Yeah. >> Compliance. What does that even mean? >> Right. >> How is that going to get sorted out? Do you think this is going to settle itself out on its own or self govern, a little bit of a iron hand in there, or... >> It'll be a mix. I mean, there's a lot of trial and error going on right now, as far as governments. Like I said, there's really only a few places in the world that are doing it. I applaud these places for their bravery because... Don't get me wrong. It's going to be a struggle. There's going to be failures and successes, and being willing to be one of the countries that does that, that shows some grit. I really respect it. >> And the upside is if they get it right, it's huge. Lauren, final question. What are you up to next, what's on your mind? What are you working on beyond this consultancy? What's around the corner for you? Where do you see the self dots connecting in the future? >> Well, I'm really... Right now I travel quite a bit. I spend a lot of different... A lot of time at different conferences. I spoke earlier a little bit about an education program that I'm developing with an alliance with Draper University in El Salvador. So I want to finish the programming for that. We're going to scale that out across multiple countries. And that's everything from education for governments and education for people that, maybe just recently heard of Bitcoin and they don't even know how to go about seeing what it is. >> 5G in emerging countries is pretty potential there. >> It is. Absolutely. >> Great stuff. Lauren, thanks for coming on theCube sharing. >> Thank you so much. >> I appreciate it. Lauren Bissell here on theCube, I'm John Furrier, live in Monaco, for the Monaco Crypto Summit, Digital Bits. We got a big gala event tonight with Prince Albert in attendance. A lot of action, a lot of big news happening here. All the players are gathered for the inaugural Monaco Crypto Summit. I'm John Furrier. We'll have more live coverage after this short break. (upbeat music)

(upbeat music) >> Hello, everyone. Welcome to the theCUBE's presentation of the AWS Startup Showcase. The theme, this showcase is MarTech, the emerging cloud scale customer experiences. Season two of episode three, the ongoing series covering the startups, the hot startups, talking about analytics, data, all things MarTech. I'm your host, John Furrier, here joined by Christian Wiklund, founder and CEO of unitQ here, talk about harnessing the power of user feedback to empower marketing. Thanks for joining us today. >> Thank you so much, John. Happy to be here. >> In these new shifts in the market, when you got cloud scale, open source software is completely changing the software business. We know that. There's no longer a software category. It's cloud, integration, data. That's the new normal. That's the new category, right? So as companies are building their products, and want to do a good job, it used to be, you send out surveys, you try to get the product market fit. And if you were smart, you got it right the third, fourth, 10th time. If you were lucky, like some companies, you get it right the first time. But the holy grail is to get it right the first time. And now, this new data acquisition opportunities that you guys in the middle of that can tap customers or prospects or end users to get data before things are shipped, or built, or to iterate on products. This is the customer feedback loop or data, voice of the customer journey. It's a gold mine. And it's you guys, it's your secret weapon. Take us through what this is about now. I mean, it's not just surveys. What's different? >> So yeah, if we go back to why are we building unitQ? Which is we want to build a quality company. Which is basically, how do we enable other companies to build higher quality experiences by tapping into all of the existing data assets? And the one we are in particularly excited about is user feedback. So me and my co-founder, Nik, and we're doing now the second company together. We spent 14 years. So we're like an old married couple. We accept each other, and we don't fight anymore, which is great. We did a consumer company called Skout, which was sold five years ago. And Skout was kind of early in the whole mobile first. I guess, we were actually mobile first company. And when we launched this one, we immediately had the entire world as our marketplace, right? Like any modern company. We launch a product, we have support for many languages. It's multiple platforms. We have Android, iOS, web, big screens, small screens, and that brings some complexities as it relates to staying on top of the quality of the experience because how do I test everything? >> John: Yeah. >> Pre-production. How do I make sure that our Polish Android users are having a good day? And we found at Skout, personally, like I could discover million dollar bugs by just drinking coffee and reading feedback. And we're like, "Well, there's got to be a better way to actually harness the end user feedback. That they are leaving in so many different places." So, you know what, what unitQ does is that we basically aggregate all different sources of user feedback, which can be app store reviews, Reddit posts, Tweets, comments on your Facebook ads. It can be better Business Bureau Reports. We don't like to get to many of those, of course. But really, anything on the public domain that mentions or refers to your product, we want to ingest that data in this machine, and then all the private sources. So you probably have a support system deployed, a Zendesk, or an Intercom. You might have a chatbot like an Ada, or and so forth. And your end user is going to leave a lot of feedback there as well. So we take all of these channels, plug it into the machine, and then we're able to take this qualitative data. Which and I actually think like, when an end user leaves a piece of feedback, it's an act of love. They took time out of the day, and they're going to tell you, "Hey, this is not working for me," or, "Hey, this is working for me," and they're giving you feedback. But how do we package these very messy, multi-channel, multiple languages, all over the place data? How can we distill it into something that's quantifiable? Because I want to be able to monitor these different signals. So I want to turn user feedback into time series. 'Cause with time series, I can now treat this the same way as Datadog treats machine logs. I want to be able to see anomalies, and I want to know when something breaks. So what we do here is that we break down your data in something called quality monitors, which is basically machine learning models that can aggregate the same type of feedback data in this very fine grained and discrete buckets. And we deploy up to a thousand of these quality monitors per product. And so we can get down to the root cause. Let's say, passive reset link is not working. And it's in that root cause, the granularity that we see that companies take action on the data. And I think historically, there has been like the workflow between marketing and support, and engineering and product has been a bit broken. They've been siloed from a data perspective. They've been siloed from a workflow perspective, where support will get a bunch of tickets around some issue in production. And they're trained to copy and paste some examples, and throw it over the wall, file a Jira ticket, and then they don't know what happens. So what we see with the platform we built is that these teams are able to rally around the single source of troop or like, yes, passive recent link seems to have broken. This is not a user error. It's not a fix later, or I can't reproduce. We're looking at the data, and yes, something broke. We need to fix it. >> I mean, the data silos a huge issue. Different channels, omnichannel. Now, there's more and more channels that people are talking in. So that's huge. I want to get to that. But also, you said that it's a labor of love to leave a comment or a feedback. But also, I remember from my early days, breaking into the business at IBM and Hewlett-Packard, where I worked. People who complain are the most loyal customers, if you service them. So it's complaints. >> Christian: Yeah. >> It's leaving feedback. And then, there's also reading between the lines with app errors or potentially what's going on under the covers that people may not be complaining about, but they're leaving maybe gesture data or some sort of digital trail. >> Yeah. >> So this is the confluence of the multitude of data sources. And then you got the siloed locations. >> Siloed locations. >> It's complicated problem. >> It's very complicated. And when you think about, so I started, I came to Bay Area in 2005. My dream was to be a quant analyst on Wall Street, and I ended up in QA at VMware. So I started at VMware in Palo Alto, and didn't have a driver's license. I had to bike around, which was super exciting. And we were shipping box software, right? This was literally a box with a DVD that's been burned, and if that DVD had bugs in it, guess what it'll be very costly to then have to ship out, and everything. So I love the VMware example because the test cycles were long and brutal. It was like a six month deal to get through all these different cases, and they couldn't be any bugs. But then as the industry moved into the cloud, CI/CD, ship at will. And if you look at the modern company, you'll have at least 20 plus integrations into your product. Analytics, add that's the case, authentication, that's the case, and so forth. And these integrations, they morph, and they break. And you have connectivity issues. Is your product working as well on Caltrain, when you're driving up and down, versus wifi? You have language specific bugs that happen. Android is also quite a fragmented market. The binary may not perform as well on that device, or is that device. So how do we make sure that we test everything before we ship? The answer is, we can't. There's no company today that can test everything before the ship. In particular, in consumer. And the epiphany we had at our last company, Skout, was that, "Hey, wait a minute. The end user, they're testing every configuration." They're sitting on the latest device, the oldest device. They're sitting on Japanese language, on Swedish language. >> John: Yeah. >> They are in different code paths because our product executed differently, depending on if you were a paid user, or a freemium user, or if you were certain demographical data. There's so many ways that you would have to test. And PagerDuty actually had a study they came out with recently, where they said 51% of all end user impacting issues are discovered first by the end user, when they serve with a bunch of customers. And again, like the cool part is, they will tell you what's not working. So now, how do we tap into that? >> Yeah. >> So what I'd like to say is, "Hey, your end user is like your ultimate test group, and unitQ is the layer that converts them into your extended test team." Now, the signals they're producing, it's making it through to the different teams in the organization. >> I think that's the script that you guys are flipping. If I could just interject. Because to me, when I hear you talking, I hear, "Okay, you're letting the customers be an input into the product development process." And there's many different pipelines of that development. And that could be whether you're iterating, or geography, releases, all kinds of different pipelines to get to the market. But in the old days, it was like just customer satisfaction. Complain in a call center. >> Christian: Yeah. >> Or I'm complaining, how do I get support? Nothing made itself into the product improvement, except for slow moving, waterfall-based processes. And then, maybe six months later, a small tweak could be improved. >> Yes. >> Here, you're taking direct input from collective intelligence. Okay. >> Is that have input and on timing is very important here, right? So how do you know if the product is working as it should in all these different flavors and configurations right now? How do you know if it's working well? And how do you know if you're improving or not improving over time? And I think the industry, what can we look at, as far as when it relates to quality? So I can look at star ratings, right? So what's the star rating in the app store? Well, star ratings, that's an average over time. So that's something that you may have a lot of issues in production today, and you're going to get dinged on star ratings over the next few months. And then, it brings down the score. NPS is another one, where we're not going to run NPS surveys every day. We're going to run it once a quarter, maybe once a month, if we're really, really aggressive. That's also a snapshot in time. And we need to have the finger on the pulse of product quality today. I need to know if this release is good or not good. I need to know if anything broke. And I think that real time aspect, what we see as stuff sort of bubbles up the stack, and not into production, we see up to a 50% reduction in time to fix these end user impacting issues. And I think, we also need to appreciate when someone takes time out of the day to write an app review, or email support, or write that Reddit post, it's pretty serious. It's not going to be like, "Oh, I don't like the shade of blue on this button." It's going to be something like, "I got double billed," or "Hey, someone took over my account," or, "I can't reset my password anymore. The CAPTCHA, I'm solving it, but I can't get through to the next phase." And we see a lot of these trajectory impacting bugs and quality issues in these work, these flows in the product that you're not testing every day. So if you work at Snapchat, your employees probably going to use Snapchat every day. Are they going to sign up every day? No. Are they going to do passive reset every day? No. And these things are very hard to instrument, lower in the stack. >> Yeah, I think this is, and again, back to these big problems. It's smoke before fire, and you're essentially seeing it early with your process. Can you give an example of how this new focus or new mindset of user feedback data can help customers increase their experience? Can you give some examples, 'cause folks watching and be like, "Okay, I love this value. Sell me on this idea, I'm sold. Okay, I want to tap into my prospects, and my customers, my end users to help me improve my product." 'Cause again, we can measure everything now with data. >> Yeah. We can measure everything. we can even measure quality these days. So when we started this company, I went out to talk to a bunch of friends, who are entrepreneurs, and VCs, and board members, and I asked them this very simple question. So in your board meetings, or on all hands, how do you talk about quality of the product? Do you have a metric? And everyone said, no. Okay. So are you data driven company? Yes, we're very data driven. >> John: Yeah. Go data driven. >> But you're not really sure if quality, how do you compare against competition? Are you doing as good as them, worse, better? Are you improving over time, and how do you measure it? And they're like, "Well, it's kind of like a blind spot of the company." And then you ask, "Well, do you think quality of experience is important?" And they say, "Yeah." "Well, why?" "Well, top of fund and growth. Higher quality products going to spread faster organically, we're going to make better store ratings. We're going to have the storefronts going to look better." And of course, more importantly, they said the different conversion cycles in the product box itself. That if you have bugs and friction, or an interface that's hard to use, then the inputs, the signups, it's not going to convert as well. So you're going to get dinged on retention, engagement, conversion to paid, and so forth. And that's what we've seen with the companies we work with. It is that poor quality acts as a filter function for the entire business, if you're a product led company. So if you think about product led company, where the product is really the centerpiece. And if it performs really, really well, then it allows you to hire more engineers, you can spend more on marketing. Everything is fed by this product at them in the middle, and then quality can make that thing perform worse or better. And we developed a metric actually called the unitQ Score. So if you go to our website, unitq.com, we have indexed the 5,000 largest apps in the world. And we're able to then, on a daily basis, update the score. Because the score is not something you do once a month or once a quarter. It's something that changes continuously. So now, you can get a score between zero and 100. If you get the score 100, that means that our AI doesn't find any quality issues reported in that data set. And if your score is 90, that means that 10% will be a quality issue. So now you can do a lot of fun stuff. You can start benchmarking against competition. So you can see, "Well, I'm Spotify. How do I rank against Deezer, or SoundCloud, or others in my space?" And what we've seen is that as the score goes up, we see this real big impact on KPI, such as conversion, organic growth, retention, ultimately, revenue, right? And so that was very satisfying for us, when we launched it. quality actually still really, really matters. >> Yeah. >> And I think we all agree at test, but how do we make a science out of it? And that's so what we've done. And when we were very lucky early on to get some incredible brands that we work with. So Pinterest is a big customer of ours. We have Spotify. We just signed new bank, Chime. So like we even signed BetterHelp recently, and the world's largest Bible app. So when you look at the types of businesses that we work with, it's truly a universal, very broad field, where if you have a digital exhaust or feedback, I can guarantee you, there are insights in there that are being neglected. >> John: So Chris, I got to. >> So these manual workflows. Yeah, please go ahead. >> I got to ask you, because this is a really great example of this new shift, right? The new shift of leveraging data, flipping the script. Everything's flipping the script here, right? >> Yeah. >> So you're talking about, what the value proposition is? "Hey, board example's a good one. How do you measure quality? There's no KPI for that." So it's almost category creating in its own way. In that, this net new things, it's okay to be new, it's just new. So the question is, if I'm a customer, I buy it. I can see my product teams engaging with this. I can see how it can changes my marketing, and customer experience teams. How do I operationalize this? Okay. So what do I do? So do I reorganize my marketing team? So take me through the impact to the customer that you're seeing. What are they resonating towards? Obviously, getting that data is key, and that's holy gray, we all know that. But what do I got to do to change my environment? What's my operationalization piece of it? >> Yeah, and that's one of the coolest parts I think, and that is, let's start with your user base. We're not going to ask your users to ask your users to do something differently. They're already producing this data every day. They are tweeting about it. They're putting in app produce. They're emailing support. They're engaging with your support chatbot. They're already doing it. And every day that you're not leveraging that data, the data that was produced today is less valuable tomorrow. And in 30 days, I would argue, it's probably useless. >> John: Unless it's same guy commenting. >> Yeah. (Christian and John laughing) The first, we need to make everyone understand. Well, yeah, the data is there, and we don't need to do anything differently with the end user. And then, what we do is we ask the customer to tell us, "Where should we listen in the public domain? So do you want the Reddit post, the Trustpilot? What channels should we listen to?" And then, our machine basically starts ingesting that data. So we have integration with all these different sites. And then, to get access to private data, it'll be, if you're on Zendesk, you have to issue a Zendesk token, right? So you don't need any engineering hours, except your IT person will have to grant us access to the data source. And then, when we go live. We basically build up this taxonomy with the customers. So we don't we don't want to try and impose our view of the world, of how do you describe the product with these buckets, these quality monitors? So we work with the company to then build out this taxonomy. So it's almost like a bespoke solution that we can bootstrap with previous work we've done, where you don't have these very, very fine buckets of where stuff could go wrong. And then what we do is there are different ways to hook this into the workflow. So one is just to use our products. It's a SaaS product as anything else. So you log in, and you can then get this overview of how is quality trending in different markets, on different platforms, different languages, and what is impacting them? What is driving this unitQ Score that's not good enough? And all of these different signals, we can then hook into Jira for instance. We have a Jira integration. We have a PagerDuty integration. We can wake up engineers if certain things break. We also tag tickets in your support system, which is actually quite cool. Where, let's say, you have 200 people, who wrote into support, saying, "I got double billed on Android." It turns out, there are some bugs that double billed them. Well, now we can tag all of these users in Zendesk, and then the support team can then reach out to that segment of users and say, "Hey, we heard that you had this bug with double billing. We're so sorry. We're working on it." And then when we push fix, we can then email the same group again, and maybe give them a little gift card or something, for the thank you. So you can have, even big companies can have that small company experience. So, so it's groups that use us, like at Pinterest, we have 800 accounts. So it's really through marketing has vested interest because they want to know what is impacting the end user. Because brand and product, the lines are basically gone, right? >> John: Yeah. >> So if the product is not working, then my spend into this machine is going to be less efficient. The reputation of our company is going to be worse. And the challenge for marketers before unitQ was, how do I engage with engineering and product? I'm dealing with anecdotal data, and my own experience of like, "Hey, I've never seen these type of complaints before. I think something is going on." >> John: Yeah. >> And then engineering will be like, "Ah, you know, well, I have 5,000 bugs in Jira. Why does this one matter? When did it start? Is this a growing issue?" >> John: You have to replicate the problem, right? >> Replicate it then. >> And then it goes on and on and on. >> And a lot of times, reproducing bugs, it's really hard because it works on my device. Because you don't sit on that device that it happened on. >> Yup. >> So now, when marketing can come with indisputable data, and say, "Hey, something broke here." And we see the same with support. Product engineering, of course, for them, we talk about, "Hey, listen, you you've invested a lot in observability of your stack, haven't you?" "Yeah, yeah, yeah." "So you have a Datadog in the bottom?" "Absolutely." "And you have an APP D on the client?" "Absolutely." "Well, what about the last mile? How the product manifests itself? Shouldn't you monitor that as well using machines?" They're like, "Yeah, that'd be really cool." (John laughs) And we see this. There's no way to instrument everything, lowering the stack to capture these bugs that leak out. So it resonates really well there. And even for the engineers who's going to fix it. >> Yeah. >> I call it like empathy data. >> Yup. >> Where I get assigned a bug to fix. Well, now, I can read all the feedback. I can actually see, and I can see the feedback coming in. >> Yeah. >> Oh, there's users out there, suffering from this bug. And then when I fix it and I deploy the fix, and I see the trend go down to zero, and then I can celebrate it. So that whole feedback loop is (indistinct). >> And that's real time. It's usually missed too. This is the power of user feedback. You guys got a great product, unitQ. Great to have you on. Founder and CEO, Christian Wiklund. Thanks for coming on and sharing, and showcase. >> Thank you, John. For the last 30 seconds, the minute we have left, put a plug in for the company. What are you guys looking for? Give a quick pitch for the company, real quick, for the folks out there. Looking for more people, funding status, number of employees. Give a quick plug. >> Yes. So we raised our A Round from Google, and then we raised our B from Excel that we closed late last year. So we're not raising money. We are hiring across go-to-markets, engineering. And we love to work with people, who are passionate about quality and data. We're always, of course, looking for customers, who are interested in upping their game. And hey, listen, competing with features is really hard because you can copy features very quickly. Competing with content. Content is commodity. You're going to get the same movies more or less on all these different providers. And competing on price, we're not willing to do. You're going to pay 10 bucks a month for music. So how do you compete today? And if your competitor has a better fine tuned piano than your competitor will have better efficiencies, and they're going to retain customers and users better. And you don't want to lose on quality because it is actually a deterministic and fixable problem. So yeah, come talk to us if you want to up the game there. >> Great stuff. The iteration lean startup model, some say took craft out of building the product. But this is now bringing the craftsmanship into the product cycle, when you can get that data from customers and users. >> Yeah. >> Who are going to be happy that you fixed it, that you're listening. >> Yeah. >> And that the product got better. So it's a flywheel of loyalty, quality, brand, all off you can figure it out. It's the holy grail. >> I think it is. It's a gold mine. And every day you're not leveraging this assets, your use of feedback that's there, is a missed opportunity. >> Christian, thanks so much for coming on. Congratulations to you and your startup. You guys back together. The band is back together, up into the right, doing well. >> Yeah. We we'll check in with you later. Thanks for coming on this showcase. Appreciate it. >> Thank you, John. Appreciate it very much. >> Okay. AWS Startup Showcase. This is season two, episode three, the ongoing series. This one's about MarTech, cloud experiences are scaling. I'm John Furrier, your host. Thanks for watching. (upbeat music)