(upbeat music) >> Hello and welcome to theCube's coverage of International Women's Day. I'm John Furrier, your host of "theCUBE." We've got great two remote guests coming into our Palo Alto Studios, some tech athletes, as we say, people that've been in the trenches, years of experience, Lena Smart, CISO at MongoDB, Cube alumni, and Tara Hernandez, VP of Developer Productivity at MongoDB as well. Thanks for coming in to this program and supporting our efforts today. Thanks so much. >> Thanks for having us. >> Yeah, everyone talk about the journey in tech, where it all started. Before we get there, talk about what you guys are doing at MongoDB specifically. MongoDB is kind of gone the next level as a platform. You have your own ecosystem, lot of developers, very technical crowd, but it's changing the business transformation. What do you guys do at Mongo? We'll start with you, Lena. >> So I'm the CISO, so all security goes through me. I like to say, well, I don't like to say, I'm described as the ones throat to choke. So anything to do with security basically starts and ends with me. We do have a fantastic Cloud engineering security team and a product security team, and they don't report directly to me, but obviously we have very close relationships. I like to keep that kind of church and state separate and I know I've spoken about that before. And we just recently set up a physical security team with an amazing gentleman who left the FBI and he came to join us after 26 years for the agency. So, really starting to look at the physical aspects of what we offer as well. >> I interviewed a CISO the other day and she said, "Every day is day zero for me." Kind of goofing on the Amazon Day one thing, but Tara, go ahead. Tara, go ahead. What's your role there, developer productivity? What are you focusing on? >> Sure. Developer productivity is kind of the latest description for things that we've described over the years as, you know, DevOps oriented engineering or platform engineering or build and release engineering development infrastructure. It's all part and parcel, which is how do we actually get our code from developer to customer, you know, and all the mechanics that go into that. It's been something I discovered from my first job way back in the early '90s at Borland. And the art has just evolved enormously ever since, so. >> Yeah, this is a very great conversation both of you guys, right in the middle of all the action and data infrastructures changing, exploding, and involving big time AI and data tsunami and security never stops. Well, let's get into, we'll talk about that later, but let's get into what motivated you guys to pursue a career in tech and what were some of the challenges that you faced along the way? >> I'll go first. The fact of the matter was I intended to be a double major in history and literature when I went off to university, but I was informed that I had to do a math or a science degree or else the university would not be paid for. At the time, UC Santa Cruz had a policy that called Open Access Computing. This is, you know, the late '80s, early '90s. And anybody at the university could get an email account and that was unusual at the time if you were, those of us who remember, you used to have to pay for that CompuServe or AOL or, there's another one, I forget what it was called, but if a student at Santa Cruz could have an email account. And because of that email account, I met people who were computer science majors and I'm like, "Okay, I'll try that." That seems good. And it was a little bit of a struggle for me, a lot I won't lie, but I can't complain with how it ended up. And certainly once I found my niche, which was development infrastructure, I found my true love and I've been doing it for almost 30 years now. >> Awesome. Great story. Can't wait to ask a few questions on that. We'll go back to that late '80s, early '90s. Lena, your journey, how you got into it. >> So slightly different start. I did not go to university. I had to leave school when I was 16, got a job, had to help support my family. Worked a bunch of various jobs till I was about 21 and then computers became more, I think, I wouldn't say they were ubiquitous, but they were certainly out there. And I'd also been saving up every penny I could earn to buy my own computer and bought an Amstrad 1640, 20 meg hard drive. It rocked. And kind of took that apart, put it back together again, and thought that could be money in this. And so basically just teaching myself about computers any job that I got. 'Cause most of my jobs were like clerical work and secretary at that point. But any job that had a computer in front of that, I would make it my business to go find the guy who did computing 'cause it was always a guy. And I would say, you know, I want to learn how these work. Let, you know, show me. And, you know, I would take my lunch hour and after work and anytime I could with these people and they were very kind with their time and I just kept learning, so yep. >> Yeah, those early days remind me of the inflection point we're going through now. This major C change coming. Back then, if you had a computer, you had to kind of be your own internal engineer to fix things. Remember back on the systems revolution, late '80s, Tara, when, you know, your career started, those were major inflection points. Now we're seeing a similar wave right now, security, infrastructure. It feels like it's going to a whole nother level. At Mongo, you guys certainly see this as well, with this AI surge coming in. A lot more action is coming in. And so there's a lot of parallels between these inflection points. How do you guys see this next wave of change? Obviously, the AI stuff's blowing everyone away. Oh, new user interface. It's been called the browser moment, the mobile iPhone moment, kind of for this generation. There's a lot of people out there who are watching that are young in their careers, what's your take on this? How would you talk to those folks around how important this wave is? >> It, you know, it's funny, I've been having this conversation quite a bit recently in part because, you know, to me AI in a lot of ways is very similar to, you know, back in the '90s when we were talking about bringing in the worldwide web to the forefront of the world, right. And we tended to think in terms of all the optimistic benefits that would come of it. You know, free passing of information, availability to anyone, anywhere. You just needed an internet connection, which back then of course meant a modem. >> John: Not everyone had though. >> Exactly. But what we found in the subsequent years is that human beings are what they are and we bring ourselves to whatever platforms that are there, right. And so, you know, as much as it was amazing to have this freely available HTML based internet experience, it also meant that the negatives came to the forefront quite quickly. And there were ramifications of that. And so to me, when I look at AI, we're already seeing the ramifications to that. Yes, are there these amazing, optimistic, wonderful things that can be done? Yes. >> Yeah. >> But we're also human and the bad stuff's going to come out too. And how do we- >> Yeah. >> How do we as an industry, as a community, you know, understand and mitigate those ramifications so that we can benefit more from the positive than the negative. So it is interesting that it comes kind of full circle in really interesting ways. >> Yeah. The underbelly takes place first, gets it in the early adopter mode. Normally industries with, you know, money involved arbitrage, no standards. But we've seen this movie before. Is there hope, Lena, that we can have a more secure environment? >> I would hope so. (Lena laughs) Although depressingly, we've been in this well for 30 years now and we're, at the end of the day, still telling people not to click links on emails. So yeah, that kind of still keeps me awake at night a wee bit. The whole thing about AI, I mean, it's, obviously I am not an expert by any stretch of the imagination in AI. I did read (indistinct) book recently about AI and that was kind of interesting. And I'm just trying to teach myself as much as I can about it to the extent of even buying the "Dummies Guide to AI." Just because, it's actually not a dummies guide. It's actually fairly interesting, but I'm always thinking about it from a security standpoint. So it's kind of my worst nightmare and the best thing that could ever happen in the same dream. You know, you've got this technology where I can ask it a question and you know, it spits out generally a reasonable answer. And my team are working on with Mark Porter our CTO and his team on almost like an incubation of AI link. What would it look like from MongoDB? What's the legal ramifications? 'Cause there will be legal ramifications even though it's the wild, wild west just now, I think. Regulation's going to catch up to us pretty quickly, I would think. >> John: Yeah, yeah. >> And so I think, you know, as long as companies have a seat at the table and governments perhaps don't become too dictatorial over this, then hopefully we'll be in a good place. But we'll see. I think it's a really interest, there's that curse, we're living in interesting times. I think that's where we are. >> It's interesting just to stay on this tech trend for a minute. The standards bodies are different now. Back in the old days there were, you know, IEEE standards, ITF standards. >> Tara: TPC. >> The developers are the new standard. I mean, now you're seeing open source completely different where it was in the '90s to here beginning, that was gen one, some say gen two, but I say gen one, now we're exploding with open source. You have kind of developers setting the standards. If developers like it in droves, it becomes defacto, which then kind of rolls into implementation. >> Yeah, I mean I think if you don't have developer input, and this is why I love working with Tara and her team so much is 'cause they get it. If we don't have input from developers, it's not going to get used. There's going to be ways of of working around it, especially when it comes to security. If they don't, you know, if you're a developer and you're sat at your screen and you don't want to do that particular thing, you're going to find a way around it. You're a smart person. >> Yeah. >> So. >> Developers on the front lines now versus, even back in the '90s, they're like, "Okay, consider the dev's, got a QA team." Everything was Waterfall, now it's Cloud, and developers are on the front lines of everything. Tara, I mean, this is where the standards are being met. What's your reaction to that? >> Well, I think it's outstanding. I mean, you know, like I was at Netscape and part of the crowd that released the browser as open source and we founded mozilla.org, right. And that was, you know, in many ways kind of the birth of the modern open source movement beyond what we used to have, what was basically free software foundation was sort of the only game in town. And I think it is so incredibly valuable. I want to emphasize, you know, and pile onto what Lena was saying, it's not just that the developers are having input on a sort of company by company basis. Open source to me is like a checks and balance, where it allows us as a broader community to be able to agree on and enforce certain standards in order to try and keep the technology platforms as accessible as possible. I think Kubernetes is a great example of that, right. If we didn't have Kubernetes, that would've really changed the nature of how we think about container orchestration. But even before that, Linux, right. Linux allowed us as an industry to end the Unix Wars and as someone who was on the front lines of that as well and having to support 42 different operating systems with our product, you know, that was a huge win. And it allowed us to stop arguing about operating systems and start arguing about software or not arguing, but developing it in positive ways. So with, you know, with Kubernetes, with container orchestration, we all agree, okay, that's just how we're going to orchestrate. Now we can build up this huge ecosystem, everybody gets taken along, right. And now it changes the game for what we're defining as business differentials, right. And so when we talk about crypto, that's a little bit harder, but certainly with AI, right, you know, what are the checks and balances that as an industry and as the developers around this, that we can in, you know, enforce to make sure that no one company or no one body is able to overly control how these things are managed, how it's defined. And I think that is only for the benefit in the industry as a whole, particularly when we think about the only other option is it gets regulated in ways that do not involve the people who actually know the details of what they're talking about. >> Regulated and or thrown away or bankrupt or- >> Driven underground. >> Yeah. >> Which would be even worse actually. >> Yeah, that's a really interesting, the checks and balances. I love that call out. And I was just talking with another interview part of the series around women being represented in the 51% ratio. Software is for everybody. So that we believe that open source movement around the collective intelligence of the participants in the industry and independent of gender, this is going to be the next wave. You're starting to see these videos really have impact because there are a lot more leaders now at the table in companies developing software systems and with AI, the aperture increases for applications. And this is the new dynamic. What's your guys view on this dynamic? How does this go forward in a positive way? Is there a certain trajectory you see? For women in the industry? >> I mean, I think some of the states are trying to, again, from the government angle, some of the states are trying to force women into the boardroom, for example, California, which can be no bad thing, but I don't know, sometimes I feel a bit iffy about all this kind of forced- >> John: Yeah. >> You know, making, I don't even know how to say it properly so you can cut this part of the interview. (John laughs) >> Tara: Well, and I think that they're >> I'll say it's not organic. >> No, and I think they're already pulling it out, right. It's already been challenged so they're in the process- >> Well, this is the open source angle, Tara, you are getting at it. The change agent is open, right? So to me, the history of the proven model is openness drives transparency drives progress. >> No, it's- >> If you believe that to be true, this could have another impact. >> Yeah, it's so interesting, right. Because if you look at McKinsey Consulting or Boston Consulting or some of the other, I'm blocking on all of the names. There has been a decade or more of research that shows that a non homogeneous employee base, be it gender or ethnicity or whatever, generates more revenue, right? There's dollar signs that can be attached to this, but it's not enough for all companies to want to invest in that way. And it's not enough for all, you know, venture firms or investment firms to grant that seed money or do those seed rounds. I think it's getting better very slowly, but socialization is a much harder thing to overcome over time. Particularly, when you're not just talking about one country like the United States in our case, but around the world. You know, tech centers now exist all over the world, including places that even 10 years ago we might not have expected like Nairobi, right. Which I think is amazing, but you have to factor in the cultural implications of that as well, right. So yes, the openness is important and we have, it's important that we have those voices, but I don't think it's a panacea solution, right. It's just one more piece. I think honestly that one of the most important opportunities has been with Cloud computing and Cloud's been around for a while. So why would I say that? It's because if you think about like everybody holds up the Steve Jobs, Steve Wozniak, back in the '70s, or Sergey and Larry for Google, you know, you had to have access to enough credit card limit to go to Fry's and buy your servers and then access to somebody like Susan Wojcicki to borrow the garage or whatever. But there was still a certain amount of upfrontness that you had to be able to commit to, whereas now, and we've, I think, seen a really good evidence of this being able to lease server resources by the second and have development platforms that you can do on your phone. I mean, for a while I think Africa, that the majority of development happened on mobile devices because there wasn't a sufficient supply chain of laptops yet. And that's no longer true now as far as I know. But like the power that that enables for people who would otherwise be underrepresented in our industry instantly opens it up, right? And so to me that's I think probably the biggest opportunity that we've seen from an industry on how to make more availability in underrepresented representation for entrepreneurship. >> Yeah. >> Something like AI, I think that's actually going to take us backwards if we're not careful. >> Yeah. >> Because of we're reinforcing that socialization. >> Well, also the bias. A lot of people commenting on the biases of the large language inherently built in are also problem. Lena, I want you to weigh on this too, because I think the skills question comes up here and I've been advocating that you don't need the pedigree, college pedigree, to get into a certain jobs, you mentioned Cloud computing. I mean, it's been around for you think a long time, but not really, really think about it. The ability to level up, okay, if you're going to join something new and half the jobs in cybersecurity are created in the past year, right? So, you have this what used to be a barrier, your degree, your pedigree, your certification would take years, would be a blocker. Now that's gone. >> Lena: Yeah, it's the opposite. >> That's, in fact, psychology. >> I think so, but the people who I, by and large, who I interview for jobs, they have, I think security people and also I work with our compliance folks and I can't forget them, but let's talk about security just now. I've always found a particular kind of mindset with security folks. We're very curious, not very good at following rules a lot of the time, and we'd love to teach others. I mean, that's one of the big things stem from the start of my career. People were always interested in teaching and I was interested in learning. So it was perfect. And I think also having, you know, strong women leaders at MongoDB allows other underrepresented groups to actually apply to the company 'cause they see that we're kind of talking the talk. And that's been important. I think it's really important. You know, you've got Tara and I on here today. There's obviously other senior women at MongoDB that you can talk to as well. There's a bunch of us. There's not a whole ton of us, but there's a bunch of us. And it's good. It's definitely growing. I've been there for four years now and I've seen a growth in women in senior leadership positions. And I think having that kind of track record of getting really good quality underrepresented candidates to not just interview, but come and join us, it's seen. And it's seen in the industry and people take notice and they're like, "Oh, okay, well if that person's working, you know, if Tara Hernandez is working there, I'm going to apply for that." And that in itself I think can really, you know, reap the rewards. But it's getting started. It's like how do you get your first strong female into that position or your first strong underrepresented person into that position? It's hard. I get it. If it was easy, we would've sold already. >> It's like anything. I want to see people like me, my friends in there. Am I going to be alone? Am I going to be of a group? It's a group psychology. Why wouldn't? So getting it out there is key. Is there skills that you think that people should pay attention to? One's come up as curiosity, learning. What are some of the best practices for folks trying to get into the tech field or that's in the tech field and advancing through? What advice are you guys- >> I mean, yeah, definitely, what I say to my team is within my budget, we try and give every at least one training course a year. And there's so much free stuff out there as well. But, you know, keep learning. And even if it's not right in your wheelhouse, don't pick about it. Don't, you know, take a look at what else could be out there that could interest you and then go for it. You know, what does it take you few minutes each night to read a book on something that might change your entire career? You know, be enthusiastic about the opportunities out there. And there's so many opportunities in security. Just so many. >> Tara, what's your advice for folks out there? Tons of stuff to taste, taste test, try things. >> Absolutely. I mean, I always say, you know, my primary qualifications for people, I'm looking for them to be smart and motivated, right. Because the industry changes so quickly. What we're doing now versus what we did even last year versus five years ago, you know, is completely different though themes are certainly the same. You know, we still have to code and we still have to compile that code or package the code and ship the code so, you know, how well can we adapt to these new things instead of creating floppy disks, which was my first job. Five and a quarters, even. The big ones. >> That's old school, OG. There it is. Well done. >> And now it's, you know, containers, you know, (indistinct) image containers. And so, you know, I've gotten a lot of really great success hiring boot campers, you know, career transitioners. Because they bring a lot experience in addition to the technical skills. I think the most important thing is to experiment and figuring out what do you like, because, you know, maybe you are really into security or maybe you're really into like deep level coding and you want to go back, you know, try to go to school to get a degree where you would actually want that level of learning. Or maybe you're a front end engineer, you want to be full stacked. Like there's so many different things, data science, right. Maybe you want to go learn R right. You know, I think it's like figure out what you like because once you find that, that in turn is going to energize you 'cause you're going to feel motivated. I think the worst thing you could do is try to force yourself to learn something that you really could not care less about. That's just the worst. You're going in handicapped. >> Yeah and there's choices now versus when we were breaking into the business. It was like, okay, you software engineer. They call it software engineering, that's all it was. You were that or you were in sales. Like, you know, some sort of systems engineer or sales and now it's,- >> I had never heard of my job when I was in school, right. I didn't even know it was a possibility. But there's so many different types of technical roles, you know, absolutely. >> It's so exciting. I wish I was young again. >> One of the- >> Me too. (Lena laughs) >> I don't. I like the age I am. So one of the things that I did to kind of harness that curiosity is we've set up a security champions programs. About 120, I guess, volunteers globally. And these are people from all different backgrounds and all genders, diversity groups, underrepresented groups, we feel are now represented within this champions program. And people basically give up about an hour or two of their time each week, with their supervisors permission, and we basically teach them different things about security. And we've now had seven full-time people move from different areas within MongoDB into my team as a result of that program. So, you know, monetarily and time, yeah, saved us both. But also we're showing people that there is a path, you know, if you start off in Tara's team, for example, doing X, you join the champions program, you're like, "You know, I'd really like to get into red teaming. That would be so cool." If it fits, then we make that happen. And that has been really important for me, especially to give, you know, the women in the underrepresented groups within MongoDB just that window into something they might never have seen otherwise. >> That's a great common fit is fit matters. Also that getting access to what you fit is also access to either mentoring or sponsorship or some sort of, at least some navigation. Like what's out there and not being afraid to like, you know, just ask. >> Yeah, we just actually kicked off our big mentor program last week, so I'm the executive sponsor of that. I know Tara is part of it, which is fantastic. >> We'll put a plug in for it. Go ahead. >> Yeah, no, it's amazing. There's, gosh, I don't even know the numbers anymore, but there's a lot of people involved in this and so much so that we've had to set up mentoring groups rather than one-on-one. And I think it was 45% of the mentors are actually male, which is quite incredible for a program called Mentor Her. And then what we want to do in the future is actually create a program called Mentor Them so that it's not, you know, not just on the female and so that we can live other groups represented and, you know, kind of break down those groups a wee bit more and have some more granularity in the offering. >> Tara, talk about mentoring and sponsorship. Open source has been there for a long time. People help each other. It's community-oriented. What's your view of how to work with mentors and sponsors if someone's moving through ranks? >> You know, one of the things that was really interesting, unfortunately, in some of the earliest open source communities is there was a lot of pervasive misogyny to be perfectly honest. >> Yeah. >> And one of the important adaptations that we made as an open source community was the idea, an introduction of code of conducts. And so when I'm talking to women who are thinking about expanding their skills, I encourage them to join open source communities to have opportunity, even if they're not getting paid for it, you know, to develop their skills to work with people to get those code reviews, right. I'm like, "Whatever you join, make sure they have a code of conduct and a good leadership team. It's very important." And there are plenty, right. And then that idea has come into, you know, conferences now. So now conferences have codes of contact, if there are any good, and maybe not all of them, but most of them, right. And the ideas of expanding that idea of intentional healthy culture. >> John: Yeah. >> As a business goal and business differentiator. I mean, I won't lie, when I was recruited to come to MongoDB, the culture that I was able to discern through talking to people, in addition to seeing that there was actually women in senior leadership roles like Lena, like Kayla Nelson, that was a huge win. And so it just builds on momentum. And so now, you know, those of us who are in that are now representing. And so that kind of reinforces, but it's all ties together, right. As the open source world goes, particularly for a company like MongoDB, which has an open source product, you know, and our community builds. You know, it's a good thing to be mindful of for us, how we interact with the community and you know, because that could also become an opportunity for recruiting. >> John: Yeah. >> Right. So we, in addition to people who might become advocates on Mongo's behalf in their own company as a solution for themselves, so. >> You guys had great successful company and great leadership there. I mean, I can't tell you how many times someone's told me "MongoDB doesn't scale. It's going to be dead next year." I mean, I was going back 10 years. It's like, just keeps getting better and better. You guys do a great job. So it's so fun to see the success of developers. Really appreciate you guys coming on the program. Final question, what are you guys excited about to end the segment? We'll give you guys the last word. Lena will start with you and Tara, you can wrap us up. What are you excited about? >> I'm excited to see what this year brings. I think with ChatGPT and its copycats, I think it'll be a very interesting year when it comes to AI and always in the lookout for the authentic deep fakes that we see coming out. So just trying to make people aware that this is a real thing. It's not just pretend. And then of course, our old friend ransomware, let's see where that's going to go. >> John: Yeah. >> And let's see where we get to and just genuine hygiene and housekeeping when it comes to security. >> Excellent. Tara. >> Ah, well for us, you know, we're always constantly trying to up our game from a security perspective in the software development life cycle. But also, you know, what can we do? You know, one interesting application of AI that maybe Google doesn't like to talk about is it is really cool as an addendum to search and you know, how we might incorporate that as far as our learning environment and developer productivity, and how can we enable our developers to be more efficient, productive in their day-to-day work. So, I don't know, there's all kinds of opportunities that we're looking at for how we might improve that process here at MongoDB and then maybe be able to share it with the world. One of the things I love about working at MongoDB is we get to use our own products, right. And so being able to have this interesting document database in order to put information and then maybe apply some sort of AI to get it out again, is something that we may well be looking at, if not this year, then certainly in the coming year. >> Awesome. Lena Smart, the chief information security officer. Tara Hernandez, vice president developer of productivity from MongoDB. Thank you so much for sharing here on International Women's Day. We're going to do this quarterly every year. We're going to do it and then we're going to do quarterly updates. Thank you so much for being part of this program. >> Thank you. >> Thanks for having us. >> Okay, this is theCube's coverage of International Women's Day. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>>Welcome back to super cloud 22, our inaugural event. It's a pilot event here in the cube studios we're live and streaming virtually until we do it in person. Maybe next year. I'm John fury, host of the cube with Dave Lon two great guests, distinguished engineers managers, CTOs investors. Mariana Tessel is a CTO of Intuit ins Ray founder of vertex ventures. Both have a lot of DNA. Founder allow cloud here with mark Andre and Ben Horowitz, a variety of other great ventures you've done. And now you're an investor. Yep. Maria, you've been a seasoned CTO, VP of engineering, VMware Docker Intuit. Now thanks for joining us. >>Absolutely. >>So super cloud is a, is a thing. And apparently it's got a lot of momentum and you guys got stats over there at, at Intuit in, so you're investing and we were challenged on super cloud. Our initial thesis was you build on the clouds, get all that leverage like snowflake, you get a good differentiation and then you compete and then move to other clouds. Now it's becoming a thing where I can do this. Every enterprise could possibly do it. So I want to get your guys thoughts on what you think of super cloud concept and where are the holes in it, what needs to be defined. And so we'll start with you. You've done a lot of cloud things in your day. What >>Do you think? Yeah, it's the whole cloud journey started with a desire to consolidate and desire to actually provide uniformity and, and standards driven ways of doing things. And I think Amazon was a leader there. They helped kind of teach everybody else. You know, when I was in loud cloud, we were trying to do it with proprietary stacks just wouldn't work. But once everyone standardized upon Unix and you know, the chip sets no longer became as relevant. They did a lot of good things there, but what's happened since then is now you've got competing standards at the API layer at the interface layer no longer at the chip set layer, no longer at the operating system layer. Right? So the evolution of the, the, the battles are still there. When you talk about multicloud and super cloud, though, like one of the big things you have to keep in mind is latency is not free. Latency is very expensive and it's getting even more expensive now with, with multi-cloud. So you have to really understand where the separations of boundaries are between your data, your compute, and, and the network is just there as a facilitator to help binding compute and data. Right? And I think there's a lot of bets being made across different vendors like CloudFlare Akamai, as well as Amazon Google Microsoft in terms of how they think we should take computing either to the edge, from the core or back and forth. >>These, this is structural change. I mean, this is structural, >>It's desired by incumbents, but it's not something that I'm seeing from the consumption. I'd love to hear, hear from our end's per perspective, from a consumption point of view, like how much edge computing really matters. Right. >>Mario. >>So I think there's like, there's kind of a, a story of like two, like it's kind of, you can cut it for both edges. No, no pun intended on one end. It is really simplifying to actually go into like a single cloud and standardize on it and just have everything there. But I think what over time companies find is that they end up in multiple clouds, whether like, you know, through acquisitions or through like needing to use a service in another cloud. So you do find yourself in a situation where you have multi multi-cloud and you have to kind of work through it and understand how to make it all like work and latency is an issue, but also for many, many workloads, you can work around it and you can make it work where you have workloads that actually span multiple vendors and clouds. You know, again, having said that, I would say the world is such, that is still a simplifying assumption. When if you go to a single cloud, it's much easier to just go and, and bet on that >>Easier in terms of everything's integrated, IAS works with SAS, they solve a lot of problems. >>Correct. And you can do like for your developers, you can actually provide an environment that's super homogenous, simple. You can use services easily up and down the stack. And, you know, we, we actually made that deliberate decision. When we started migrating to the cloud at the beginning, it was like, oh, let's do like hybrid we'll, you know, make it, so it work anywhere. It was so complicated. It was not worth it. >>When was the, when did you give up, what was the moment? Was there a flash point where you said, oh, this is terrible. This is >>Dead. Yeah. When, when we started to try to make it interoperable and you just see what it requires to do that and the complexity of the architecture that it just became not worth it for the gains you have. >>So speaking obviously as a SAS provider, right. So it just doesn't, it didn't make business case sense for you guys to do that. So it was super cloud. Then an infrastructure thing we just heard from Ben wa deja VI that they're not, they're going beyond instantiating their, their data cloud. They're actually running, you know, their own little snow grid. They called it. And, and then when I asked him, well, what about latency? He said, well, we copied data over, you know, so, okay. That's you have to do, but that's a singular experience with the same governance or the same security. Just wasn't worth it for you guys is what I'm hearing. >>Correct. But again, like for some workload or for some services that we want to use, we are gonna go there and we are gonna then figure out what is the work around the latency issue, whether it's like copy or, you know, redundancy. >>Well, the question I have Dave on snowflake is maybe the question for you and in the panel is snowflake a tan expansion opportunity, or is there a technical reason to go to other clouds? >>I think they wanted to leverage the hyperscale infrastructure globally. And they said that they're out there, it's a free gift. We're gonna go take it. I, I think it started with we're on AWS. Do you think? And then we're on Azure and then we're on Google. And then they said, why don't we just connect all these and make it a singular experience? And yeah, I guess it's a TA expansion as a differentiator and it's, it adds value. Right. If I can share data across that global network, >>We have customers on Azure now, >>Right? Yeah. Yeah. Of course. >>You guys don't need to go CP. What do you think about that? >>Well, I think Snowflake's in a good position cuz they work mostly with analytical workloads and you have capacity. That's always gonna increase like no one subtracts, their analytical workload like ever, right. So there was just compounded growth is like 50% or 80% for, you know, many enterprises despite their best intentions, not to collect more data, they just can't stop doing it. So it's different than if you're like an Oracle or a transactional database where you don't have those, you know, like kind of infinite growth paths. So Snowflake's gonna continue to expand footprint their customers. They don't mind as long as you, they can figure out the, the lowest cost on denominator for, for that. >>Yeah. So it makes sense to be in all the clouds >>For them, for, for them, for sure. Yeah. >>But, but, but Oracle just announced with Microsoft what I would call super cloud, a, a cross cloud database service running on OCI and Azure with very low latency and a database that looks like a, the singular experience. Yeah. With, with a PAs layers >>That lost me after OCI that's >>Okay. You know, but that's the, that's the, the BS answer for all U VCs. The do nobody develops on Oracle? Well, it's a 240 billion market cap company. Show me who you all want be. >>We're gonna talk about SRDF and em C next, you >>All want Oracle. So there we go. You throw that into, you all want Oracle to buy your companies, your funding, you know, cause, cause we all wanna be like Oracle with that kinda cash flow. But, but anyway, >>Here's, here's one thing that I'm noticing that is gonna be really practical. I think for companies that do run SA is because like, you know, you have all these solutions, whether it's like analytics or like monitoring or logging or whatever. And each one of them is very data hungry and all of them have like SAS solutions that end up copy the data, moving data to their cloud, and then they might charge you by the size of your data. It does become kind of overwhelming for companies to use that many tools and basically maybe have that data kind of charge for it, multiple places because you use it for different purposes or just in general, if you have a lot of data, you know, that that is becoming an issue. So that's something that I've noticed in our, in our own kind of, you know, a world, but it's just something that I think companies need to think about how they solve because eventually a lot of companies will say, I cannot have all these solutions, so there's no way I'm gonna be willing to have so many copies of the data and actually pay for that. >>So many times, just something to think about. >>But one of the criticisms of the super cloud concept is that it's just SAS. If I'm running workload on prem and I, and I've got, you know, a connection to the cloud, which you probably do, that's, that's SAS, what's, what's the big deal and that's not anything new or different. So I'd love to get your thoughts on that. But Goldman Sachs, for instance, just announced the service last reinvent with AWS, connecting their tools, their data, and their software from on-prem to AWS, they're offering it as a service. I'm like, Hmm. Kind of looking like Supercloud, but maybe it's just SAS. >>It could be. And like, what I'm talking about is not so much like, you know, like what you wanna connect your data. But the idea is like a lot of the providers of different services, like in the past and, and like higher layer, they're actually COPI the data. They need the data in their cloud or their solution. And it just becomes complicated and expensive is, is kind of like my point. So yes, connecting it like for you to have the data in one place and then be able to connect to it. I think that is a valid, if, if that's kinda what you think about as a super cloud, that is a valid need, I think that companies will >>Have where developers actually want access to tools that might exist. >>Also the key is developers, right? Yeah. Developers decide all decisions, not database on administrators, not, you know, a hundred percent security engineers, not admins. So what's really interesting is where are the developers going next? If you look at the current winners in the current ecosystem, companies like MongoDB, I mean, they capture the minds of yeah. The JavaScript, you know, no JS developers absolutely very early on. And I started catch base and I could tell you like the difference was that capture motion was so important. So developers are basically used to this game-like experience now where they want to see tools that are free, whether it's open source or not, they actually don't care. They just want, and they want it SAS. They want it SAS delivered on demand. Right. And pay as you go. And so there's a lot of these different frameworks coming out next generation, no code, low code, whether it's Java, JavaScript, rust, you know, whatever, you know, go Lang. And there's a lot of people fighting religious wars about how to develop the next kind of modern pattern design pattern. Okay. And that's where a lot of excitement is how we look at like investment opportunities. Like where are those big bets who are, you know, frustrated developers, who are they frustrated, what's wrong with their current environment? You know, do they really enjoy using Kubernetes or trying to use Kubernetes? Yeah. Right. Like developers have a very different view than operator, >>But you mentioned couch base. I mean, I look at couch base what they're doing with Capellas as a form of Supercloud. I mean, I think that's an excellent, they're bringing that out to the edge. We're gonna hear later on from someone from couch base. That's gonna talk about that now. It's kind of a lightweight, you know, sort of, it's gonna be a, a synchronization, but it's the beginning >>A cool new venture deal that I'm not in, but was like duck DB. I'm like, what's duck DB like, well, it's an Emory database that has like this like remote store thing. I'm like, okay, that sounds interesting. Like let's call Mike Olson cuz that sounds like sleepy cat redone red distributed world. But like it's, it's like there's a lot of people refactoring design patterns that we're all grew up with since the popup days of, you know, typical round. Right? >>Yeah. That's the refactory I think that's the big pattern. So I have to ask you guys, what are you guys investing in? We've got a couple minutes left to chat about that. What are you investing at into it from a, from a, a CTO engineering perspective and what are you investing in that feels super cloud like to you? >>Well, the, the thing that like I'm focused on is to make sure that we have absolutely best in the world development environment for our engineers, where it's modern, it's easy to use and it incorporates as many things as we can into that environment. So the engineers don't have to think about it. Like one big example would be security and how we incorporated that into development environment. So again, the engineers don't have to bother with trying to think through how they secure their workloads and every step of the way their other things that we incorporated, whether it's like rollbacks or monitoring or, you know, like baly enough other things. But I think that's really an investment that has panned off for us. We actually started investing in development environment several years ago. We started measure our development velocity and we, it actually went up by six X justly investing. So >>User experience, developer experience and productivity pretty much right. >>Yeah. AB absolutely. Yeah. That's like a big investment area for us that, you know, cloud cloud >>Sounds like super cloudlike factor and I'm assuming it's you're on AWS. >>We are mostly on AWS. Yes. >>And so what are you investing in that from a VC money doling out standpoint? That feels super cloudlike >>So very similar to what we just touched on a lot of developer tool experiences. We have a company that we've invested in called ops level that the service catalogs it's, it's helping, you know, understand your, where your services live and how they could be accessed and, and you know, enterprise kind of that come with that. And then we have a company called Lugo that helps you do serverless debugging container debugging, cuz it turns out debugging distributed, you know, applications is a real problem right now just you can only do so much by log tracing, right? We have a company haven't announced yet that's in the web assembly space. So we're looking at modernizing the next generation past stack and throwing everything out the window, including Java and all of the, you know, current prebuilt components because turns out 90% of enterprise workloads are actually not used. They're they're just policy code. You compiled with they're sitting there as vulnerabilities that no one's actually accessing, but you still have to compile with all of it. So we have a lot of bloatware happening in the enterprise. So we're thinking about how do you skinny that up with the next generation paths that's enterprise capable with security context and frameworks >>Super pass. >>Well, yeah, super pass. That's a kind of good way to, well, is >>It, is it a consistent developer experience across clouds? >>It is. And, and, and, and web assembly is a very raw standard if you can call it that. I mean it's, but it's supported by every modern browser, every major platform, vendor cloud, and Adobe and others, and are using it for their uses. And it's not just about your edge browser compute. It's really, you can take the same framework and compile it down to server side as well as client site, just like JavaScript was a client side tool before it became node. Right. Right. So we're looking at that as a very interesting opportunity. It's very nascent. Yeah. >>Great patterns. Yeah. Well, thanks so much for spending the time outta your busy day. Ariana. Thanks for your commentary. Appreciate your coming on the cubes first in IGUR super cloud event, pilot. Thanks for, for sharing. Thanks for having, thanks for having us. Okay. More coverage here. Super cloud 2022. I'm Jeff David Alane stay with us. We got our cloud ARA panel coming up next.

>>Welcome back everyone to Supercloud 22. This is the Cube's live presentation streaming out virtually our inaugural event, kind of a pilot I'm John Furo of the cube with Dave ante. Got a great panel here to discuss the enablers and blockers question mark for superclouds. We got, we got kit Culbert, CTO of VMware basketball, Gor CEO platform nine, and has Pani who is the CEO of RA systems. We got a mix of the big leader, VMware and the upstart companies growing into the same space, all cloud native friends of the cube. Great to see you guys. Thanks for coming on. Thank >>You. >>Start. All right. So there's no debate cloud native is booming. We see that clearly Kubernetes became a unifying force. It's an ops layer kind of almost like a kind of a midline between dev and ops DevSecOps is happening at scale. What are the blockers and what are the enablers for super cloud? What do we need? Let's see what do get your take? >>Sure. So UN I spoke about this a little bit in, at New York summit, the big trend I'm seeing, and it's, it's a blocker that's being sort of taken care of by enterprises, which is, you know, until very recently, Kubernetes was effectively a project that NA would take on. They'd try things out, they'd go to the cloud, they'd spin things up. And then the next team would come and they'd do the same things. And there was no consistency. There was no ization, it's a mess, right? It's all over the place. Some things are moving fast. Some things are not going fast and this is not how enterprises do business, right? That's not how things work. Traditionally enterprises have had it organizations that create standards, right? So those it organizations now kind of are starting to think like a platform organization. So centrally come up with the right framework for all application teams to consume infrastructure, modern infrastructure. So I'm not using the word Kubernetes here because Kubernetes is an enabler. We are a Kubernetes company, obviously, but it's about modern applications, modern infrastructure. So stepping back and thinking about it as to how an enterprise will do this across the board is the right answer. And I'm seeing this happen in a pretty significant way across all the large enterprises I talked to. >>That's why you've had a great career. And we talked before you came on Opia you did a turnaround there, we, you even go back to the old days of the web web 1.0 and early software. You've seen the movie before. >>Yes. >>You know, complexity is not solved way more complexity. This is kind of the old enterprise way. And they don't want that. They've seen the benefits of self-service. They see architecture and standards as being an enabler. Where are we in here in the market? Is, are we positioned in your opinion for customers to get the value of a super cloud? >>Absolutely. So if you think about, first of all, I think the topic of cloud native developers and app developers picking containers and Kubernetes, that's a done deal, right? That has already happened. So every cloud native developer is already using these tools. Now, I think as has been discussed today in you, in the earlier sessions, is, are the operations and infrastructure catching up or they're lagging behind, right? As more and more developers are using multi-cloud technologies, enterprises are creating a choice, I think operations and what we also strongly believe that's actually part of the name of our company is, is a platform. The platform of which a company uses to transform itself to be cloud native is the big opportunity. I don't think it's a blocker, but it's a huge opportunity. And I think this is where, you know, as you can't stop developers from developing on different clouds, private, public, multi edge, that's gonna happen. Innovation is gonna continue. But then how does the infrastructure in the platform make it seamless? Right? And almost treat all these different clouds as a single pan super cloud platform. That's I think is the >>Opportunity. So we in a platform more than with other companies, or is there one unified platform called cloud native? We know customers been buying tools from security they're they got so many tools in, in their tools shed, so to speak. What is that platform? I mean, is it more unique, fragmentation? Is it unified? >>I mean, if you think about it, a couple of it's a combination of tools that are stitched together to reach a purpose, right? So if you think about, you know, APIs continued APIs that's been discussed earlier today, I think that's, that should be standardized. The other thing is always on monitoring because I think that's a very key aspect. Once you build it, then as the enterprises are using it, the always on monitoring becomes. So I think it's a combination of capabilities that are stitched together to enable the acceleration for companies to become cloud native. >>I, I have a thought on a blocker. None of you guys are gonna like it. Oh, maybe you can come. Maybe some of you guys probably won't but comment, but maybe John will. I think AWS is a blocker to Supercloud cuz they, they don't want those cross cloud service. It's like they, they, for years they wouldn't even say multicloud. The first time I heard it was in Boston three weeks ago, I actually heard it. So Hey, you see, >>You know, I'm gonna disagree with that. Okay. >>But, but okay, go ahead. All >>So we'll get their reaction. So my, we just heard from the last panel that the security should be leading the consortium. Yeah. Because they're, they're not the enemy they're actually, >>Maybe they should be >>Well back in the old web days, when standards were driving things, you had a common enemy, proprietary NASAs, proprietary networking stack. So the evil empire was at and T that's owned Unix. If you remember, they copyright that. >>So you think they're greasing the skids for, >>I think Supercloud, I think the hyperscalers could cuz they're driving the CapEx, they're providing the value. So in my opinion, Amazon and Azure, whoever does the right thing first can win every, maybe >>This is how Google could catch up >>It. It could be a, it could be a Slingshot move. It could, you know, boomerang, someone to the front of the line or extend. Amazon's already huge lead. So if I'm AWS, if I'm Adam Slosky and I'm talking to Andy Jassy, he says, how am I gonna differentiate myself? I'd say, I'm gonna come in and own multicloud. I'm gonna own Supercloud we are the Supercloud and you work with AWS's primitives in a way that makes services work. I would go for that. I'd be like, okay, show me more. What do you >>Think? I, I, I don't think think any one company is going to be a super cloud because I think yes, there is going to be a lot of workloads on public clouds, but there's a huge amount of workloads at the enterprise at the edge at the store. I think those will continue for various reasons, whether it's data, sovereignty regulations. So I think it's going to be a combination. Everybody's not gonna go to one, you know, cloud, it's going to be an amalgamation. >>Okay. But I I've argued that snowflake is a form of a super data cloud and a very specific use case, you know, Aviatrix is trying to be a network, you know, layer and you know, sneak in a security, let me on and on, on a lot of small you get, you get super cloud stove pipes, but, but nonetheless you're, you're still abstracting. I mean, we've this industry attractions, right? >>Well this, this concept I completely agree with, right? This idea that, so, so one of the, my is that right now enterprises buy 500 different technologies and they have to become PhDs in 500 different things. It's just never gonna happen skills issue, which is no way. Right. So what's gonna happen is all of these providers are gonna essentially become managed service providers. Cloud is in manifestation of that. Snowflake is a ation data breaks is a manifestation of that. Right? So in our general industry, there's gonna be a handful of platforms. Right. And they're gonna work across these clouds. Amazon may have one too. Right? Look, they, they, they, for the longest time sort of ignored OnPrem, but now they have something called SSA, which runs on Preem. Right. Why, why would they bother? Because, well, obviously there's a lot of money to be made in a data center as well. >>So I, my sense is they get it completely understand and appreciate that there's other things outside of Amazon. But in terms of what Bosco was talking about, my sense is, you know, these multiple platforms will come about. And to the point we were making earlier about standardization and I, I mean, is it gonna be one company or is it gonna be standards that everybody will else will adopt? There's a topic that the three of us have talked about before, which is this vCenter for Kubernetes. Right. And all due respect to kit. Right. My sense is that there there's gonna be multiple companies that are gonna start working towards a vCenter for Kubernetes. And it is right. I mean, that's how I've, I mean, I've been thinking about this before and a half years, including >>VMware. >>Yeah. And you know, and we, we should compare notes. Right. But what's gonna happen is there was a, there was a distinct advantage VMware had back in the day because ESX was their product. Right. And that was a standard right now. What's the ESX in the new it's sort of Kubernetes, right. I mean, it's on bare metal for the most part or whatever VMs. So that's a standard, that's got standardized APIs, the things around it are standardized APIs. So what is the unfair advantage that one company has other than execution? >>Nothing. Well also composability if you over rotate on Kubernetes, for example, and not take advantage of say C two, for instance. Totally, >>Totally. >>It's a mix and match. >>Yeah. But I think, I think if you get too focused on Kubernetes, it's a means to an end. Yeah. But at the end of the day, it it's a mean to end end. And I think all these tools, there's a lot of standardization happening that's gonna happen. Right. And no one vendor is gonna control that. Right. It's it's going to be, it's gonna continue. I think how you bring these together and orchestrate right. And manage the service. Because I think that if you think about the lack of skills to keep up with the operations and platforms is one of the largest inhibitors right now for enterprises to move as fast as they want to become cloud native. >>And you have the shiny new toy problem kit where people just go and grab it. You know, Keith Townsend has a, as a quote, he says, look, we essentially move at the speed of the CIO or else we're going too fast or too slow. So, so the, to, to the point about the new toy now I've got new skills. >>Yep. Well, so this has been a really good discussion. And I think so there's a couple of things, right. Going back to the, the paper that we wrote, right. How we have these different sort of layers of multi-cloud services or, or categories of multi-cloud services. And it's exactly to capture some of the ex different examples you just mentioned. And yeah, the challenge is that each of them by themselves are a little bit of an island today. Like you don't have that extra level of integration. And so what the platform teams typically do is try to add that extra glue to make the experience more seamless for the, the, the, you know, developers at that company. And so like, you know, for instance, things like identity. So the nice thing about going to a single public cloud is that there's one, usually one identity system for everything. And that's great. All the different services roles are, you know, are back all that. Stuff's all centralized, but you don't have that when you're going across many different multicloud services. So what does that look like? So I think there's some of these different crosscutting concerns that we need to look at how we standardize on as an industry. And that's, again, one of the things >>You felt that part. And I think, I think also the other key thing is yes, you can always say I'll put everything in one world, world garden and I'm done. Yeah. Okay. But that's not the reality because at some point you need, the flexibility and cost comes into play and flexibility to move comes into play. And I think that is a key factor. Yep. Right. >>Yeah. And so like, so then the question is, what degrees of freedom do you give yourself there? And I think that's the architectural question is how you, how do you design it? What sort of abstractions do you leverage? And I think that goes back to some of our discussion before, which is, do you directly go on top of a native cloud service or do you use a multi-cloud service? >>But I think it's a combination of, I don't think it's either or no, it's not, it's not an either or you have to have the ability to choose a public cloud or do it private. Yeah. At the same time you don't change. It's like a common dictionary, right. You're not gonna change every time the accent changes, you know? So that's, >>So here's a question for you guys. So what has to happen for super clouds, be existing assume that AWS and Azure and Google, aren't gonna sit still assume that maybe they normalize into some sort of swim lane or position that they have to rationalize. What, assuming they're not gonna sit still, what has to happen for super clouds to, to actually work >>Well? Well, I think, you know, really quick going back to the platform team point, I would say that the platform teams at various companies, and we got one at VMware two, they're creating a rudimentary form of a super cloud. Right. Cause they, you know, absolutely like if, if they are supporting multiple clouds, like all the things they're stitching together and all that work, that is a super cloud. The problem is that there's not really a standard approach or architecture or reusable things to enable that. I think that's really what's missing. >>Yeah. But I think the key here is standard us reusable. Because for example, we have customers who are in doesn't matter where they are, some of their loads are in public cloud. Some are in private, some are at the edge, but they're still using the same platform. Yeah. Right. So it is a standard open source based technology. So it is standard. There's no lock in for them from an infrastructure point of view. Yep. And it gives them the flexibility because certain apps, you wanna put it on the public cloud, certain apps, you do not, you need the, I mean, for example, some of the AI, I think earlier discussion that was going on about chips and AI and ML workloads. I mean, think about moving all of that to a public cloud, to, and I think a lot of machine learning and AI applications are going to happen where the data is getting created at the edge. Yeah. At the edge >>Public cloud. It's not gonna happen cloud. It's gonna be real time in, >>It's gonna the end time. And so therefore you have to decide based on your workload, what are you gonna move all the way to a public cloud? And what are you need to do to make business decisions at this spot where the data is created? >>That's a huge disruptor potentially to Supercloud. This is a whole new architecture that emerges at the edge with a whole new set of economics. I >>Think the edge is gonna be like massively disruptive. >>I think it's gonna think about, if you think about the edge, go beyond just the classic definition of edge. Think about branches in stores, retail stores. Yeah. Right. I mean, you cannot shut down retail store because you lost connectivity to the network or something you still have to serve your company >>Edge is a disruptive enabler. I think it's gonna change potentially change the position of the players in the business. Whoever embraces the edge. >>Yeah. Maybe going back to the question that you had asked before, which is what is, what is a framework for a super cloud? So you said something that is important, which is your team's burning one. Yeah. I met that team. Actually. They seem to be very sharp guys. >>They're they're mine. They're my are great. They're awesome. >>We got a deal going on here. Yeah. >>I tried. We have >>It. >>So this is the interesting part, right? So I will pause it that the super cloud of the future will be a company that owns zero servers and no network. >>Okay. >>That's gonna happen. Okay. So I just kind of it's >>Full point you >>Made before I made that point just about the public cloud, just so Mr. >>Yeah, yeah, yeah, yeah. No, that really interesting. Not >>We that, so I've thought about this a long time that in my opinion, and I've, I'm, I'm sure I've said this to you, John, that, you know, the one company that I've always believed has the best shot at doing this well is actually VMware because that's the one company that's, you know, that there's, there's no, you know, infrastructure back haul. Right. You know, that you're carrying, but, but in terms of thinking and getting there, you know, being, being a company that can do it is not the same as being the company who has done it. That's a, there's a distance, but >>I have to defend that now because hyperscalers are not gonna be able to super cloud. They're not now it's hype. See, agreed, great point. Public clouds will be part of the super cloud. Yeah, totally. But they will not, the hyperscalers are not building super clouds. Totally. They're blocking it. Right. Yeah. >>They're enabling it. >>We agree on >>No, they're enabling >>Because it's, it's not in there to their advantage. Right. Look, the, the snowflake example you gave is the pivotal example in this conversation. Yep. Right. Why does snowflake exist at all when Redshift exists and all these other things exist because they provide value that is beyond a single clouds purview. Right. And at that point, just step back from our platforms and what we sell. Forget about that for a minute. Right. It's it's about, look, I think, I think this, we are, this market is early, we're out early, right. 10 years from now, what will a company look like? That actually solves a superly problem they're gonna solve for yeah. Kubernetes, whatever. Right. But they're gonna solve for truly modern applications. >>Yeah. They're gonna refactor application that has new economics new value, right. >>At that point, this idea of edge and cloud, forget about it. Right. This is all distribution issues, right. It doesn't really matter. Is it retail or not? Yeah, absolutely. These are places, but, but the way, the right way to think about this is not about edge versus cloud, right? This is about an app. Sometimes it needs to run in one location and it's good enough. Sometimes it needs to run in 10,000 locations and, and it's a distribution issue. I've always believed there's this idea of edge versus cloud. This is BS, right? Because it, it is a cloud over a different size. Sure. But, but I'm making a slightly different point. Sure. Which is, it's a distribution problem. Right. If you step back and think about distribution, my app could run in Azure or AWS or in a retail store, in a branch or whatever. Right. >>And once that is done, the question is, how am I in, in making all this happen? There was a point made in the prior conversation, in the, in the session about a database kind of popping up in the place where I needed to run. Okay. Nobody does that today, by the way. Right. At least truly well right about that, sir, that will come. Right? Yeah. But when that comes, my application is a conglomerate of compute data. I don't know a, a service bus and network and all these things and they will all kind of pop together. That company does not exist >>Today. Well, we'll, we will be documenting which we have more time. We're gonna document it. We have to unfortunately stop this panel because it's awesome. We can go for another hour. Sure. Let's bring you guys back, but that's it. The super cloud of the future will look like something and we're gonna debate it. And speaking of snowflake, we have the co-founder here next to sit down with us to talk about what he thinks about this super cloud. He, he probably heard the comment, come back more coverage. This break with the co-founder of snowflake after the short break. >>Do thank you.

>>We're back with Jess Borgman of Starburst and Richard Jarvis of EVAs health. Okay. We're gonna get into lie. Number two, and that is this an open source based platform cannot give you the performance and control that you can get with a proprietary system. Is that a lie? Justin, the enterprise data warehouse has been pretty dominant and has evolved and matured. Its stack has mature over the years. Why is it not the default platform for data? >>Yeah, well, I think that's become a lie over time. So I, I think, you know, if we go back 10 or 12 years ago with the advent of the first data lake really around Hudu, that probably was true that you couldn't get the performance that you needed to run fast, interactive, SQL queries in a data lake. Now a lot's changed in 10 or 12 years. I remember in the very early days, people would say, you'll, you'll never get performance because you need to be column. You need to store data in a column format. And then, you know, column formats were introduced to, to data lake. You have Parque ORC file in aro that were created to ultimately deliver performance out of that. So, okay. We got, you know, largely over the performance hurdle, you know, more recently people will say, well, you don't have the ability to do updates and deletes like a traditional data warehouse. >>And now we've got the creation of new data formats, again, like iceberg and Delta and hoote that do allow for updates and delete. So I think the data lake has continued to mature. And I remember a quote from, you know, Kurt Monash many years ago where he said, you know, it takes six or seven years to build a functional database. I think that's that's right. And now we've had almost a decade go by. So, you know, these technologies have matured to really deliver very, very close to the same level performance and functionality of, of cloud data warehouses. So I think the, the reality is that's become a lie and now we have large giant hyperscale internet companies that, you know, don't have the traditional data warehouse at all. They do all of their analytics in a data lake. So I think we've, we've proven that it's very much possible today. >>Thank you for that. And so Richard, talk about your perspective as a practitioner in terms of what open brings you versus, I mean, the clothes is it's open as a moving target. I remember Unix used to be open systems and so it's, it is an evolving, you know, spectrum, but, but from your perspective, what does open give you that you can't get from a proprietary system where you are fearful of in a proprietary system? >>I, I suppose for me open buys us the ability to be unsure about the future, because one thing that's always true about technology is it evolves in a, a direction, slightly different to what people expect and what you don't want to end up done is backed itself into a corner that then prevents it from innovating. So if you have chosen the technology and you've stored trillions of records in that technology and suddenly a new way of processing or machine learning comes out, you wanna be able to take advantage your competitive edge might depend upon it. And so I suppose for us, we acknowledge that we don't have perfect vision of what the future might be. And so by backing open storage technologies, we can apply a number of different technologies to the processing of that data. And that gives us the ability to remain relevant, innovate on our data storage. And we have bought our way out of the, any performance concerns because we can use cloud scale infrastructure to scale up and scale down as we need. And so we don't have the concerns that we don't have enough hardware today to process what we want to do, want to achieve. We can just scale up when we need it and scale back down. So open source has really allowed us to maintain the being at the cutting edge. >>So Jess, let me play devil's advocate here a little bit, and I've talked to JAK about this and you know, obviously her vision is there's an open source that, that data mesh is open source, an open source tooling, and it's not a proprietary, you know, you're not gonna buy a data mesh. You're gonna build it with, with open source toolings and, and vendors like you are gonna support it, but come back to sort of today, you can get to market with a proprietary solution faster. I'm gonna make that statement. You tell me if it's a lie and then you can say, okay, we support Apache iceberg. We're gonna support open source tooling, take a company like VMware, not really in the data business, but how, the way they embraced Kubernetes and, and you know, every new open source thing that comes along, they say, we do that too. Why can't proprietary systems do that and be as effective? >>Yeah, well I think at least with the, within the data landscape saying that you can access open data formats like iceberg or, or others is, is a bit dis disingenuous because really what you're selling to your customer is a certain degree of performance, a certain SLA, and you know, those cloud data warehouses that can reach beyond their own proprietary storage drop all the performance that they were able to provide. So it is, it reminds me kind of, of, again, going back 10 or 12 years ago when everybody had a connector to hit and that they thought that was the solution, right? But the reality was, you know, a connector was not the same as running workloads in hit back then. And I think similarly, you know, being able to connect to an external table that lives in an open data format, you know, you're, you're not going to give it the performance that your customers are accustomed to. And at the end of the day, they're always going to be predisposed. They're always going to be incentivized to get that data ingested into the data warehouse, cuz that's where they have control. And you know, the bottom line is the database industry has really been built around vendor lockin. I mean, from the start, how, how many people love Oracle today, but our customers, nonetheless, I think, you know, lockin is, is, is part of this industry. And I think that's really what we're trying to change with open data formats. >>Well, it's interesting remind of when I, you know, I see the, the gas price, the TSR gas price I, I drive up and then I say, oh, that's the cash price credit card. I gotta pay 20 cents more, but okay. But so the, the argument then, so let me, let me come back to you, Justin. So what's wrong with saying, Hey, we support open data formats, but yeah, you're gonna get better performance if you, if you, you keep it into our closed system, are you saying that long term that's gonna come back and bite you cuz you're gonna end up, you mentioned Oracle, you mentioned Teradata. Yeah. That's by, by implication, you're saying that's where snowflake customers are headed. >>Yeah, absolutely. I think this is a movie that, you know, we've all seen before. At least those of us who've been in the industry long enough to, to see this movie play over a couple times. So I do think that's the future. And I think, you know, I loved what Richard said. I actually wrote it down. Cause I thought it was an amazing quote. He said, it buys us the ability to be unsure of the future. That that pretty much says it all the, the future is unknowable and the reality is using open data formats. You remain interoperable with any technology you want to utilize. If you want to use spark to train a machine learning model and you wanna use Starbust to query via sequel, that's totally cool. They can both work off the same exact, you know, data, data sets by contrast, if you're, you know, focused on a proprietary model, then you're kind of locked in again to that model. I think the same applies to data, sharing to data products, to a wide variety of, of aspects of the data landscape that a proprietary approach kind of closes you and, and locks you in. >>So I, I would say this Richard, I'd love to get your thoughts on it. Cause I talked to a lot of Oracle customers, not as many te data customers there, but, but a lot of Oracle customers and they, you know, they'll admit yeah, you know, the Jammin us on price and the license cost, but we do get value out of it. And so my question to you, Richard, is, is do the, let's call it data warehouse systems or the proprietary systems. Are they gonna deliver a greater ROI sooner? And is that in allure of, of that customers, you know, are attracted to, or can open platforms deliver as fast an ROI? >>I think the answer to that is it can depend a bit. It depends on your business's skillset. So we are lucky that we have a number of proprietary teams that work in databases that provide our operational data capability. And we have teams of analytics and big data experts who can work with open data sets and open data formats. And so for those different teams, they can get to an ROI more quickly with different technologies for the business though, we can't do better for our operational data stores than proprietary databases. Today we can back off very tight SLAs to them. We can demonstrate reliability from millions of hours of those databases being run at enterprise scale, but for an analytics workload where increasing our business is growing in that direction, we can't do better than open data formats with cloud-based data mesh type technologies. And so it's not a simple answer. That one will always be the right answer for our business. We definitely have times when proprietary databases provide a capability that we couldn't easily represent or replicate with open technologies. >>Yeah. Richard, stay with you. You mentioned, you know, you know, some things before that, that strike me, you know, the data brick snowflake, you know, thing is always a lot of fun for analysts like me. You've got data bricks coming at it. Richard, you mentioned you have a lot of rockstar, data engineers, data bricks coming at it from a data engineering heritage. You get snowflake coming at it from an analytics heritage. Those two worlds are, are colliding people like PJI Mohan said, you know what? I think it's actually harder to play in the data engineering. So IE, it's easier to for data engineering world to go into the analytics world versus the reverse, but thinking about up and coming engineers and developers preparing for this future of data engineering and data analytics, how, how should they be thinking about the future? What, what's your advice to those young people? >>So I think I'd probably fall back on general programming skill sets. So the advice that I saw years ago was if you have open source technologies, the pythons and Javas on your CV, you command a 20% pay, hike over people who can only do proprietary programming languages. And I think that's true of data technologies as well. And from a business point of view, that makes sense. I'd rather spend the money that I save on proprietary licenses on better engineers, because they can provide more value to the business that can innovate us beyond our competitors. So I think I would my advice to people who are starting here or trying to build teams to capitalize on data assets is begin with open license, free capabilities because they're very cheap to experiment with. And they generate a lot of interest from people who want to join you as a business. And you can make them very successful early, early doors with, with your analytics journey. >>It's interesting. Again, analysts like myself, we do a lot of TCO work and have over the last 20 plus years and in the world of Oracle, you know, normally it's the staff, that's the biggest nut in total cost of ownership, not an Oracle. It's the it's the license cost is by far the biggest component in the, in the blame pie. All right, Justin, help us close out this segment. We've been talking about this sort of data mesh open, closed snowflake data bricks. Where does Starburst sort of as this engine for the data lake data lake house, the data warehouse, it, it fit in this, in this world. >>Yeah. So our view on how the future ultimately unfolds is we think that data lakes will be a natural center of gravity for a lot of the reasons that we described open data formats, lowest total cost of ownership, because you get to choose the cheapest storage available to you. Maybe that's S3 or Azure data lake storage or Google cloud storage, or maybe it's on-prem object storage that you bought at a, at a really good price. So ultimately storing a lot of data in a data lake makes a lot of sense, but I think what makes our perspective unique is we still don't think you're gonna get everything there either. We think that basically centralization of all your data assets is just an impossible endeavor. And so you wanna be able to access data that lives outside of the lake as well. So we kind of think of the lake as maybe the biggest place by volume in terms of how much data you have, but to, to have comprehensive analytics and to truly understand your business and understanding holistically, you need to be able to go access other data sources as well. And so that's the role that we wanna play is to be a single point of access for our customers, provide the right level of fine grained access controls so that the right people have access to the right data and ultimately make it easy to discover and consume via, you know, the creation of data products as well. >>Great. Okay. Thanks guys. Right after this quick break, we're gonna be back to debate whether the cloud data model that we see emerging and the so-called modern data stack is really modern or is it the same wine new bottle when it comes to data architectures, you're watching the cube, the leader in enterprise and emerging tech coverage.

>>In 2011, early Facebook employee and Cloudera co-founder Jeff Ocker famously said the best minds of my generation are thinking about how to get people to click on ads. And that sucks. Let's face it more than a decade later organizations continue to be frustrated with how difficult it is to get value from data and build a truly agile data-driven enterprise. What does that even mean? You ask? Well, it means that everyone in the organization has the data they need when they need it. In a context that's relevant to advance the mission of an organization. Now that could mean cutting cost could mean increasing profits, driving productivity, saving lives, accelerating drug discovery, making better diagnoses, solving, supply chain problems, predicting weather disasters, simplifying processes, and thousands of other examples where data can completely transform people's lives beyond manipulating internet users to behave a certain way. We've heard the prognostications about the possibilities of data before and in fairness we've made progress, but the hard truth is the original promises of master data management, enterprise data, warehouses, data marts, data hubs, and yes, even data lakes were broken and left us wanting from more welcome to the data doesn't lie, or doesn't a series of conversations produced by the cube and made possible by Starburst data. >>I'm your host, Dave Lanta and joining me today are three industry experts. Justin Borgman is this co-founder and CEO of Starburst. Richard Jarvis is the CTO at EMI health and Theresa tongue is cloud first technologist at Accenture. Today we're gonna have a candid discussion that will expose the unfulfilled and yes, broken promises of a data past we'll expose data lies, big lies, little lies, white lies, and hidden truths. And we'll challenge, age old data conventions and bust some data myths. We're debating questions like is the demise of a single source of truth. Inevitable will the data warehouse ever have featured parody with the data lake or vice versa is the so-called modern data stack, simply centralization in the cloud, AKA the old guards model in new cloud close. How can organizations rethink their data architectures and regimes to realize the true promises of data can and will and open ecosystem deliver on these promises in our lifetimes, we're spanning much of the Western world today. Richard is in the UK. Teresa is on the west coast and Justin is in Massachusetts with me. I'm in the cube studios about 30 miles outside of Boston folks. Welcome to the program. Thanks for coming on. Thanks for having us. Let's get right into it. You're very welcome. Now here's the first lie. The most effective data architecture is one that is centralized with a team of data specialists serving various lines of business. What do you think Justin? >>Yeah, definitely a lie. My first startup was a company called hit adapt, which was an early SQL engine for hit that was acquired by Teradata. And when I got to Teradata, of course, Teradata is the pioneer of that central enterprise data warehouse model. One of the things that I found fascinating was that not one of their customers had actually lived up to that vision of centralizing all of their data into one place. They all had data silos. They all had data in different systems. They had data on prem data in the cloud. You know, those companies were acquiring other companies and inheriting their data architecture. So, you know, despite being the industry leader for 40 years, not one of their customers truly had everything in one place. So I think definitely history has proven that to be a lie. >>So Richard, from a practitioner's point of view, you know, what, what are your thoughts? I mean, there, there's a lot of pressure to cut cost, keep things centralized, you know, serve the business as best as possible from that standpoint. What, what is your experience show? >>Yeah, I mean, I think I would echo Justin's experience really that we, as a business have grown up through acquisition, through storing data in different places sometimes to do information governance in different ways to store data in, in a platform that's close to data experts, people who really understand healthcare data from pharmacies or from, from doctors. And so, although if you were starting from a Greenfield site and you were building something brand new, you might be able to centralize all the data and all of the tooling and teams in one place. The reality is that that businesses just don't grow up like that. And, and it's just really impossible to get that academic perfection of, of storing everything in one place. >>Y you know, Theresa, I feel like Sarbanes Oxley kinda saved the data warehouse, you know, right. You actually did have to have a single version of the truth for certain financial data, but really for those, some of those other use cases, I, I mentioned, I, I do feel like the industry has kinda let us down. What's your take on this? Where does it make sense to have that sort of centralized approach versus where does it make sense to maybe decentralized? >>I, I think you gotta have centralized governance, right? So from the central team, for things like star Oxley, for things like security for certainly very core data sets, having a centralized set of roles, responsibilities to really QA, right. To serve as a design authority for your entire data estate, just like you might with security, but how it's implemented has to be distributed. Otherwise you're not gonna be able to scale. Right? So being able to have different parts of the business really make the right data investments for their needs. And then ultimately you're gonna collaborate with your partners. So partners that are not within the company, right. External partners, we're gonna see a lot more data sharing and model creation. And so you're definitely going to be decentralized. >>So, you know, Justin, you guys last, geez, I think it was about a year ago, had a session on, on data mesh. It was a great program. You invited Jamma, Dani, of course, she's the creator of the data mesh. And her one of our fundamental premises is that you've got this hyper specialized team that you've gotta go through. And if you want anything, but at the same time, these, these individuals actually become a bottleneck, even though they're some of the most talented people in the organization. So I guess question for you, Richard, how do you deal with that? Do you, do you organize so that there are a few sort of rock stars that, that, you know, build cubes and, and the like, and, and, and, or have you had any success in sort of decentralizing with, you know, your, your constituencies, that data model? >>Yeah. So, so we absolutely have got rockstar, data scientists and data guardians. If you like people who understand what it means to use this data, particularly as the data that we use at emos is very private it's healthcare information. And some of the, the rules and regulations around using the data are very complex and, and strict. So we have to have people who understand the usage of the data, then people who understand how to build models, how to process the data effectively. And you can think of them like consultants to the wider business, because a pharmacist might not understand how to structure a SQL query, but they do understand how they want to process medication information to improve patient lives. And so that becomes a, a consulting type experience from a, a set of rock stars to help a, a more decentralized business who needs to, to understand the data and to generate some valuable output. >>Justin, what do you say to a, to a customer or prospect that says, look, Justin, I'm gonna, I got a centralized team and that's the most cost effective way to serve the business. Otherwise I got, I got duplication. What do you say to that? >>Well, I, I would argue it's probably not the most cost effective and, and the reason being really twofold. I think, first of all, when you are deploying a enterprise data warehouse model, the, the data warehouse itself is very expensive, generally speaking. And so you're putting all of your most valuable data in the hands of one vendor who now has tremendous leverage over you, you know, for many, many years to come. I think that's the story at Oracle or Terra data or other proprietary database systems. But the other aspect I think is that the reality is those central data warehouse teams is as much as they are experts in the technology. They don't necessarily understand the data itself. And this is one of the core tenants of data mash that that jam writes about is this idea of the domain owners actually know the data the best. >>And so by, you know, not only acknowledging that data is generally decentralized and to your earlier point about SAR, brain Oxley, maybe saving the data warehouse, I would argue maybe GDPR and data sovereignty will destroy it because data has to be decentralized for, for those laws to be compliant. But I think the reality is, you know, the data mesh model basically says, data's decentralized, and we're gonna turn that into an asset rather than a liability. And we're gonna turn that into an asset by empowering the people that know the data, the best to participate in the process of, you know, curating and creating data products for, for consumption. So I think when you think about it, that way, you're going to get higher quality data and faster time to insight, which is ultimately going to drive more revenue for your business and reduce costs. So I think that that's the way I see the two, the two models comparing and contrasting. >>So do you think the demise of the data warehouse is inevitable? I mean, I mean, you know, there Theresa you work with a lot of clients, they're not just gonna rip and replace their existing infrastructure. Maybe they're gonna build on top of it, but what does that mean? Does that mean the E D w just becomes, you know, less and less valuable over time, or it's maybe just isolated to specific use cases. What's your take on that? >>Listen, I still would love all my data within a data warehouse would love it. Mastered would love it owned by essential team. Right? I think that's still what I would love to have. That's just not the reality, right? The investment to actually migrate and keep that up to date. I would say it's a losing battle. Like we've been trying to do it for a long time. Nobody has the budgets and then data changes, right? There's gonna be a new technology. That's gonna emerge that we're gonna wanna tap into. There's going to be not enough investment to bring all the legacy, but still very useful systems into that centralized view. So you keep the data warehouse. I think it's a very, very valuable, very high performance tool for what it's there for, but you could have this, you know, new mesh layer that still takes advantage of the things. I mentioned, the data products in the systems that are meaningful today and the data products that actually might span a number of systems, maybe either those that either source systems for the domains that know it best, or the consumer based systems and products that need to be packaged in a way that be really meaningful for that end user, right? Each of those are useful for a different part of the business and making sure that the mesh actually allows you to use all of them. >>So, Richard, let me ask you, you take, take Gemma's principles back to those. You got to, you know, domain ownership and, and, and data as product. Okay, great. Sounds good. But it creates what I would argue are two, you know, challenges, self-serve infrastructure let's park that for a second. And then in your industry, the one of the high, most regulated, most sensitive computational governance, how do you automate and ensure federated governance in that mesh model that Theresa was just talking about? >>Well, it absolutely depends on some of the tooling and processes that you put in place around those tools to be, to centralize the security and the governance of the data. And I think, although a data warehouse makes that very simple, cause it's a single tool, it's not impossible with some of the data mesh technologies that are available. And so what we've done at emus is we have a single security layer that sits on top of our data match, which means that no matter which user is accessing, which data source, we go through a well audited well understood security layer. That means that we know exactly who's got access to which data field, which data tables. And then everything that they do is, is audited in a very kind of standard way, regardless of the underlying data storage technology. So for me, although storing the data in one place might not be possible understanding where your source of truth is and securing that in a common way is still a valuable approach and you can do it without having to bring all that data into a single bucket so that it's all in one place. And, and so having done that and investing quite heavily in making that possible has paid dividends in terms of giving wider access to the platform and ensuring that only data that's available under GDPR and other regulations is being used by, by the data users. >>Yeah. So Justin, I mean, Democrat, we always talk about data democratization and you know, up until recently, they really haven't been line of sight as to how to get there. But do you have anything to add to this because you're essentially taking, you know, do an analytic queries and with data that's all dispersed all over the, how are you seeing your customers handle this, this challenge? >>Yeah. I mean, I think data products is a really interesting aspect of the answer to that. It allows you to, again, leverage the data domain owners, people know the data, the best to, to create, you know, data as a product ultimately to be consumed. And we try to represent that in our product as effectively a almost eCommerce like experience where you go and discover and look for the data products that have been created in your organization. And then you can start to consume them as, as you'd like. And so really trying to build on that notion of, you know, data democratization and self-service, and making it very easy to discover and, and start to use with whatever BI tool you, you may like, or even just running, you know, SQL queries yourself, >>Okay. G guys grab a sip of water. After this short break, we'll be back to debate whether proprietary or open platforms are the best path to the future of data excellence, keep it right there. >>Your company has more data than ever, and more people trying to understand it, but there's a problem. Your data is stored across multiple systems. It's hard to access and that delays analytics and ultimately decisions. The old method of moving all of your data into a single source of truth is slow and definitely not built for the volume of data we have today or where we are headed while your data engineers spent over half their time, moving data, your analysts and data scientists are left, waiting, feeling frustrated, unproductive, and unable to move the needle for your business. But what if you could spend less time moving or copying data? What if your data consumers could analyze all your data quickly? >>Starburst helps your teams run fast queries on any data source. We help you create a single point of access to your data, no matter where it's stored. And we support high concurrency, we solve for speed and scale, whether it's fast, SQL queries on your data lake or faster queries across multiple data sets, Starburst helps your teams run analytics anywhere you can't afford to wait for data to be available. Your team has questions that need answers. Now with Starburst, the wait is over. You'll have faster access to data with enterprise level security, easy connectivity, and 24 7 support from experts, organizations like Zolando Comcast and FINRA rely on Starburst to move their businesses forward. Contact our Trino experts to get started. >>We're back with Jess Borgman of Starburst and Richard Jarvis of EVAs health. Okay, we're gonna get to lie. Number two, and that is this an open source based platform cannot give you the performance and control that you can get with a proprietary system. Is that a lie? Justin, the enterprise data warehouse has been pretty dominant and has evolved and matured. Its stack has mature over the years. Why is it not the default platform for data? >>Yeah, well, I think that's become a lie over time. So I, I think, you know, if we go back 10 or 12 years ago with the advent of the first data lake really around Hudu, that probably was true that you couldn't get the performance that you needed to run fast, interactive, SQL queries in a data lake. Now a lot's changed in 10 or 12 years. I remember in the very early days, people would say, you you'll never get performance because you need to be column there. You need to store data in a column format. And then, you know, column formats we're introduced to, to data apes, you have Parque ORC file in aro that were created to ultimately deliver performance out of that. So, okay. We got, you know, largely over the performance hurdle, you know, more recently people will say, well, you don't have the ability to do updates and deletes like a traditional data warehouse. >>And now we've got the creation of new data formats, again like iceberg and Delta and Hodi that do allow for updates and delete. So I think the data lake has continued to mature. And I remember a, a quote from, you know, Kurt Monash many years ago where he said, you know, know it takes six or seven years to build a functional database. I think that's that's right. And now we've had almost a decade go by. So, you know, these technologies have matured to really deliver very, very close to the same level performance and functionality of, of cloud data warehouses. So I think the, the reality is that's become a line and now we have large giant hyperscale internet companies that, you know, don't have the traditional data warehouse at all. They do all of their analytics in a data lake. So I think we've, we've proven that it's very much possible today. >>Thank you for that. And so Richard, talk about your perspective as a practitioner in terms of what open brings you versus, I mean, look closed is it's open as a moving target. I remember Unix used to be open systems and so it's, it is an evolving, you know, spectrum, but, but from your perspective, what does open give you that you can't get from a proprietary system where you are fearful of in a proprietary system? >>I, I suppose for me open buys us the ability to be unsure about the future, because one thing that's always true about technology is it evolves in a, a direction, slightly different to what people expect. And what you don't want to end up is done is backed itself into a corner that then prevents it from innovating. So if you have chosen a technology and you've stored trillions of records in that technology and suddenly a new way of processing or machine learning comes out, you wanna be able to take advantage and your competitive edge might depend upon it. And so I suppose for us, we acknowledge that we don't have perfect vision of what the future might be. And so by backing open storage technologies, we can apply a number of different technologies to the processing of that data. And that gives us the ability to remain relevant, innovate on our data storage. And we have bought our way out of the, any performance concerns because we can use cloud scale infrastructure to scale up and scale down as we need. And so we don't have the concerns that we don't have enough hardware today to process what we want to do, want to achieve. We can just scale up when we need it and scale back down. So open source has really allowed us to maintain the being at the cutting edge. >>So Jess, let me play devil's advocate here a little bit, and I've talked to Shaak about this and you know, obviously her vision is there's an open source that, that the data meshes open source, an open source tooling, and it's not a proprietary, you know, you're not gonna buy a data mesh. You're gonna build it with, with open source toolings and, and vendors like you are gonna support it, but to come back to sort of today, you can get to market with a proprietary solution faster. I'm gonna make that statement. You tell me if it's a lie and then you can say, okay, we support Apache iceberg. We're gonna support open source tooling, take a company like VMware, not really in the data business, but how, the way they embraced Kubernetes and, and you know, every new open source thing that comes along, they say, we do that too. Why can't proprietary systems do that and be as effective? >>Yeah, well, I think at least with the, within the data landscape saying that you can access open data formats like iceberg or, or others is, is a bit dis disingenuous because really what you're selling to your customer is a certain degree of performance, a certain SLA, and you know, those cloud data warehouses that can reach beyond their own proprietary storage drop all the performance that they were able to provide. So it is, it reminds me kind of, of, again, going back 10 or 12 years ago when everybody had a connector to Haddo and that they thought that was the solution, right? But the reality was, you know, a connector was not the same as running workloads in Haddo back then. And I think similarly, you know, being able to connect to an external table that lives in an open data format, you know, you're, you're not going to give it the performance that your customers are accustomed to. And at the end of the day, they're always going to be predisposed. They're always going to be incentivized to get that data ingested into the data warehouse, cuz that's where they have control. And you know, the bottom line is the database industry has really been built around vendor lockin. I mean, from the start, how, how many people love Oracle today, but our customers, nonetheless, I think, you know, lockin is, is, is part of this industry. And I think that's really what we're trying to change with open data formats. >>Well, that's interesting reminded when I, you know, I see the, the gas price, the tees or gas price I, I drive up and then I say, oh, that's the cash price credit card. I gotta pay 20 cents more, but okay. But so the, the argument then, so let me, let me come back to you, Justin. So what's wrong with saying, Hey, we support open data formats, but yeah, you're gonna get better performance if you, if you keep it into our closed system, are you saying that long term that's gonna come back and bite you cuz you're gonna end up, you mentioned Oracle, you mentioned Teradata. Yeah. That's by, by implication, you're saying that's where snowflake customers are headed. >>Yeah, absolutely. I think this is a movie that, you know, we've all seen before. At least those of us who've been in the industry long enough to, to see this movie play over a couple times. So I do think that's the future. And I think, you know, I loved what Richard said. I actually wrote it down. Cause I thought it was an amazing quote. He said, it buys us the ability to be unsure of the future. Th that that pretty much says it all the, the future is unknowable and the reality is using open data formats. You remain interoperable with any technology you want to utilize. If you want to use spark to train a machine learning model and you want to use Starbust to query via sequel, that's totally cool. They can both work off the same exact, you know, data, data sets by contrast, if you're, you know, focused on a proprietary model, then you're kind of locked in again to that model. I think the same applies to data, sharing to data products, to a wide variety of, of aspects of the data landscape that a proprietary approach kind of closes you in and locks you in. >>So I, I would say this Richard, I'd love to get your thoughts on it. Cause I talked to a lot of Oracle customers, not as many te data customers, but, but a lot of Oracle customers and they, you know, they'll admit, yeah, you know, they're jamming us on price and the license cost they give, but we do get value out of it. And so my question to you, Richard, is, is do the, let's call it data warehouse systems or the proprietary systems. Are they gonna deliver a greater ROI sooner? And is that in allure of, of that customers, you know, are attracted to, or can open platforms deliver as fast in ROI? >>I think the answer to that is it can depend a bit. It depends on your businesses skillset. So we are lucky that we have a number of proprietary teams that work in databases that provide our operational data capability. And we have teams of analytics and big data experts who can work with open data sets and open data formats. And so for those different teams, they can get to an ROI more quickly with different technologies for the business though, we can't do better for our operational data stores than proprietary databases. Today we can back off very tight SLAs to them. We can demonstrate reliability from millions of hours of those databases being run at enterprise scale, but for an analytics workload where increasing our business is growing in that direction, we can't do better than open data formats with cloud-based data mesh type technologies. And so it's not a simple answer. That one will always be the right answer for our business. We definitely have times when proprietary databases provide a capability that we couldn't easily represent or replicate with open technologies. >>Yeah. Richard, stay with you. You mentioned, you know, you know, some things before that, that strike me, you know, the data brick snowflake, you know, thing is, oh, is a lot of fun for analysts like me. You've got data bricks coming at it. Richard, you mentioned you have a lot of rockstar, data engineers, data bricks coming at it from a data engineering heritage. You get snowflake coming at it from an analytics heritage. Those two worlds are, are colliding people like PJI Mohan said, you know what? I think it's actually harder to play in the data engineering. So I E it's easier to for data engineering world to go into the analytics world versus the reverse, but thinking about up and coming engineers and developers preparing for this future of data engineering and data analytics, how, how should they be thinking about the future? What, what's your advice to those young people? >>So I think I'd probably fall back on general programming skill sets. So the advice that I saw years ago was if you have open source technologies, the pythons and Javas on your CV, you commander 20% pay, hike over people who can only do proprietary programming languages. And I think that's true of data technologies as well. And from a business point of view, that makes sense. I'd rather spend the money that I save on proprietary licenses on better engineers, because they can provide more value to the business that can innovate us beyond our competitors. So I think I would my advice to people who are starting here or trying to build teams to capitalize on data assets is begin with open license, free capabilities, because they're very cheap to experiment with. And they generate a lot of interest from people who want to join you as a business. And you can make them very successful early, early doors with, with your analytics journey. >>It's interesting. Again, analysts like myself, we do a lot of TCO work and have over the last 20 plus years. And in world of Oracle, you know, normally it's the staff, that's the biggest nut in total cost of ownership, not an Oracle. It's the it's the license cost is by far the biggest component in the, in the blame pie. All right, Justin, help us close out this segment. We've been talking about this sort of data mesh open, closed snowflake data bricks. Where does Starburst sort of as this engine for the data lake data lake house, the data warehouse fit in this, in this world? >>Yeah. So our view on how the future ultimately unfolds is we think that data lakes will be a natural center of gravity for a lot of the reasons that we described open data formats, lowest total cost of ownership, because you get to choose the cheapest storage available to you. Maybe that's S3 or Azure data lake storage, or Google cloud storage, or maybe it's on-prem object storage that you bought at a, at a really good price. So ultimately storing a lot of data in a deal lake makes a lot of sense, but I think what makes our perspective unique is we still don't think you're gonna get everything there either. We think that basically centralization of all your data assets is just an impossible endeavor. And so you wanna be able to access data that lives outside of the lake as well. So we kind of think of the lake as maybe the biggest place by volume in terms of how much data you have, but to, to have comprehensive analytics and to truly understand your business and understand it holistically, you need to be able to go access other data sources as well. And so that's the role that we wanna play is to be a single point of access for our customers, provide the right level of fine grained access controls so that the right people have access to the right data and ultimately make it easy to discover and consume via, you know, the creation of data products as well. >>Great. Okay. Thanks guys. Right after this quick break, we're gonna be back to debate whether the cloud data model that we see emerging and the so-called modern data stack is really modern, or is it the same wine new bottle? When it comes to data architectures, you're watching the cube, the leader in enterprise and emerging tech coverage. >>Your data is capable of producing incredible results, but data consumers are often left in the dark without fast access to the data they need. Starers makes your data visible from wherever it lives. Your company is acquiring more data in more places, more rapidly than ever to rely solely on a data centralization strategy. Whether it's in a lake or a warehouse is unrealistic. A single source of truth approach is no longer viable, but disconnected data silos are often left untapped. We need a new approach. One that embraces distributed data. One that enables fast and secure access to any of your data from anywhere with Starburst, you'll have the fastest query engine for the data lake that allows you to connect and analyze your disparate data sources no matter where they live Starburst provides the foundational technology required for you to build towards the vision of a decentralized data mesh Starburst enterprise and Starburst galaxy offer enterprise ready, connectivity, interoperability, and security features for multiple regions, multiple clouds and everchanging global regulatory requirements. The data is yours. And with Starburst, you can perform analytics anywhere in light of your world. >>Okay. We're back with Justin Boardman. CEO of Starbust Richard Jarvis is the CTO of EMI health and Theresa tongue is the cloud first technologist from Accenture. We're on July number three. And that is the claim that today's modern data stack is actually modern. So I guess that's the lie it's it is it's is that it's not modern. Justin, what do you say? >>Yeah. I mean, I think new isn't modern, right? I think it's the, it's the new data stack. It's the cloud data stack, but that doesn't necessarily mean it's modern. I think a lot of the components actually are exactly the same as what we've had for 40 years, rather than Terra data. You have snowflake rather than Informatica you have five trend. So it's the same general stack, just, you know, a cloud version of it. And I think a lot of the challenges that it plagued us for 40 years still maintain. >>So lemme come back to you just, but okay. But, but there are differences, right? I mean, you can scale, you can throw resources at the problem. You can separate compute from storage. You really, you know, there's a lot of money being thrown at that by venture capitalists and snowflake, you mentioned it's competitors. So that's different. Is it not, is that not at least an aspect of, of modern dial it up, dial it down. So what, what do you say to that? >>Well, it, it is, it's certainly taking, you know, what the cloud offers and taking advantage of that, but it's important to note that the cloud data warehouses out there are really just separating their compute from their storage. So it's allowing them to scale up and down, but your data still stored in a proprietary format. You're still locked in. You still have to ingest the data to get it even prepared for analysis. So a lot of the same sort of structural constraints that exist with the old enterprise data warehouse model OnPrem still exist just yes, a little bit more elastic now because the cloud offers that. >>So Theresa, let me go to you cuz you have cloud first in your, in your, your title. So what's what say you to this conversation? >>Well, even the cloud providers are looking towards more of a cloud continuum, right? So the centralized cloud, as we know it, maybe data lake data warehouse in the central place, that's not even how the cloud providers are looking at it. They have news query services. Every provider has one that really expands those queries to be beyond a single location. And if we look at a lot of where our, the future goes, right, that that's gonna very much fall the same thing. There was gonna be more edge. There's gonna be more on premise because of data sovereignty, data gravity, because you're working with different parts of the business that have already made major cloud investments in different cloud providers. Right? So there's a lot of reasons why the modern, I guess, the next modern generation of the data staff needs to be much more federated. >>Okay. So Richard, how do you deal with this? You you've obviously got, you know, the technical debt, the existing infrastructure it's on the books. You don't wanna just throw it out. A lot of, lot of conversation about modernizing applications, which a lot of times is a, you know, a microservices layer on top of leg legacy apps. How do you think about the modern data stack? >>Well, I think probably the first thing to say is that the stack really has to include the processes and people around the data as well is all well and good changing the technology. But if you don't modernize how people use that technology, then you're not going to be able to, to scale because just cuz you can scale CPU and storage doesn't mean you can get more people to use your data, to generate you more, more value for the business. And so what we've been looking at is really changing in very much aligned to data products and, and data mesh. How do you enable more people to consume the service and have the stack respond in a way that keeps costs low? Because that's important for our customers consuming this data, but also allows people to occasionally run enormous queries and then tick along with smaller ones when required. And it's a good job we did because during COVID all of a sudden we had enormous pressures on our data platform to answer really important life threatening queries. And if we couldn't scale both our data stack and our teams, we wouldn't have been able to answer those as quickly as we had. So I think the stack needs to support a scalable business, not just the technology itself. >>Well thank you for that. So Justin let's, let's try to break down what the critical aspects are of the modern data stack. So you think about the past, you know, five, seven years cloud obviously has given a different pricing model. De-risked experimentation, you know that we talked about the ability to scale up scale down, but it's, I'm, I'm taking away that that's not enough based on what Richard just said. The modern data stack has to serve the business and enable the business to build data products. I, I buy that. I'm a big fan of the data mesh concepts, even though we're early days. So what are the critical aspects if you had to think about, you know, paying, maybe putting some guardrails and definitions around the modern data stack, what does that look like? What are some of the attributes and, and principles there >>Of, of how it should look like or, or how >>It's yeah. What it should be. >>Yeah. Yeah. Well, I think, you know, in, in Theresa mentioned this in, in a previous segment about the data warehouse is not necessarily going to disappear. It just becomes one node, one element of the overall data mesh. And I, I certainly agree with that. So by no means, are we suggesting that, you know, snowflake or Redshift or whatever cloud data warehouse you may be using is going to disappear, but it's, it's not going to become the end all be all. It's not the, the central single source of truth. And I think that's the paradigm shift that needs to occur. And I think it's also worth noting that those who were the early adopters of the modern data stack were primarily digital, native born in the cloud young companies who had the benefit of, of idealism. They had the benefit of it was starting with a clean slate that does not reflect the vast majority of enterprises. >>And even those companies, as they grow up mature out of that ideal state, they go buy a business. Now they've got something on another cloud provider that has a different data stack and they have to deal with that heterogeneity that is just change and change is a part of life. And so I think there is an element here that is almost philosophical. It's like, do you believe in an absolute ideal where I can just fit everything into one place or do I believe in reality? And I think the far more pragmatic approach is really what data mesh represents. So to answer your question directly, I think it's adding, you know, the ability to access data that lives outside of the data warehouse, maybe living in open data formats in a data lake or accessing operational systems as well. Maybe you want to directly access data that lives in an Oracle database or a Mongo database or, or what have you. So creating that flexibility to really Futureproof yourself from the inevitable change that you will, you won't encounter over time. >>So thank you. So there, based on what Justin just said, I, my takeaway there is it's inclusive, whether it's a data Mar data hub, data lake data warehouse, it's a, just a node on the mesh. Okay. I get that. Does that include there on Preem data? O obviously it has to, what are you seeing in terms of the ability to, to take that data mesh concept on Preem? I mean, most implementations I've seen in data mesh, frankly really aren't, you know, adhering to the philosophy. They're maybe, maybe it's data lake and maybe it's using glue. You look at what JPMC is doing. Hello, fresh, a lot of stuff happening on the AWS cloud in that, you know, closed stack, if you will. What's the answer to that Theresa? >>I mean, I, I think it's a killer case for data. Me, the fact that you have valuable data sources, OnPrem, and then yet you still wanna modernize and take the best of cloud cloud is still, like we mentioned, there's a lot of great reasons for it around the economics and the way ability to tap into the innovation that the cloud providers are giving around data and AI architecture. It's an easy button. So the mesh allows you to have the best of both worlds. You can start using the data products on-prem or in the existing systems that are working already. It's meaningful for the business. At the same time, you can modernize the ones that make business sense because it needs better performance. It needs, you know, something that is, is cheaper or, or maybe just tap into better analytics to get better insights, right? So you're gonna be able to stretch and really have the best of both worlds. That, again, going back to Richard's point, that is meaningful by the business. Not everything has to have that one size fits all set a tool. >>Okay. Thank you. So Richard, you know, talking about data as product, wonder if we could give us your perspectives here, what are the advantages of treating data as a product? What, what role do data products have in the modern data stack? We talk about monetizing data. What are your thoughts on data products? >>So for us, one of the most important data products that we've been creating is taking data that is healthcare data across a wide variety of different settings. So information about patients' demographics about their, their treatment, about their medications and so on, and taking that into a standards format that can be utilized by a wide variety of different researchers because misinterpreting that data or having the data not presented in the way that the user is expecting means that you generate the wrong insight. And in any business, that's clearly not a desirable outcome, but when that insight is so critical, as it might be in healthcare or some security settings, you really have to have gone to the trouble of understanding the data, presenting it in a format that everyone can clearly agree on. And then letting people consume in a very structured, managed way, even if that data comes from a variety of different sources in, in, in the first place. And so our data product journey has really begun by standardizing data across a number of different silos through the data mesh. So we can present out both internally and through the right governance externally to, to researchers. >>So that data product through whatever APIs is, is accessible, it's discoverable, but it's obviously gotta be governed as well. You mentioned you, you appropriately provided to internally. Yeah. But also, you know, external folks as well. So the, so you've, you've architected that capability today >>We have, and because the data is standard, it can generate value much more quickly and we can be sure of the security and, and, and value that that's providing because the data product isn't just about formatting the data into the correct tables, it's understanding what it means to redact the data or to remove certain rows from it or to interpret what a date actually means. Is it the start of the contract or the start of the treatment or the date of birth of a patient? These things can be lost in the data storage without having the proper product management around the data to say in a very clear business context, what does this data mean? And what does it mean to process this data for a particular use case? >>Yeah, it makes sense. It's got the context. If the, if the domains own the data, you, you gotta cut through a lot of the, the, the centralized teams, the technical teams that, that data agnostic, they don't really have that context. All right. Let's send Justin, how does Starburst fit into this modern data stack? Bring us home. >>Yeah. So I think for us, it's really providing our customers with, you know, the flexibility to operate and analyze data that lives in a wide variety of different systems. Ultimately giving them that optionality, you know, and optionality provides the ability to reduce costs, store more in a data lake rather than data warehouse. It provides the ability for the fastest time to insight to access the data directly where it lives. And ultimately with this concept of data products that we've now, you know, incorporated into our offering as well, you can really create and, and curate, you know, data as a product to be shared and consumed. So we're trying to help enable the data mesh, you know, model and make that an appropriate compliment to, you know, the, the, the modern data stack that people have today. >>Excellent. Hey, I wanna thank Justin Theresa and Richard for joining us today. You guys are great. I big believers in the, in the data mesh concept, and I think, you know, we're seeing the future of data architecture. So thank you. Now, remember, all these conversations are gonna be available on the cube.net for on-demand viewing. You can also go to starburst.io. They have some great content on the website and they host some really thought provoking interviews and, and, and they have awesome resources, lots of data mesh conversations over there, and really good stuff in, in the resource section. So check that out. Thanks for watching the data doesn't lie or does it made possible by Starburst data? This is Dave Valante for the cube, and we'll see you next time. >>The explosion of data sources has forced organizations to modernize their systems and architecture and come to terms with one size does not fit all for data management today. Your teams are constantly moving and copying data, which requires time management. And in some cases, double paying for compute resources. Instead, what if you could access all your data anywhere using the BI tools and SQL skills your users already have. And what if this also included enterprise security and fast performance with Starburst enterprise, you can provide your data consumers with a single point of secure access to all of your data, no matter where it lives with features like strict, fine grained, access control, end to end data encryption and data masking Starburst meets the security standards of the largest companies. Starburst enterprise can easily be deployed anywhere and managed with insights where data teams holistically view their clusters operation and query execution. So they can reach meaningful business decisions faster, all this with the support of the largest team of Trino experts in the world, delivering fully tested stable releases and available to support you 24 7 to unlock the value in all of your data. You need a solution that easily fits with what you have today and can adapt to your architecture. Tomorrow. 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>>In 2011, early Facebook employee and Cloudera co-founder Jeff Ocker famously said the best minds of my generation are thinking about how to get people to click on ads. And that sucks. Let's face it more than a decade later organizations continue to be frustrated with how difficult it is to get value from data and build a truly agile data-driven enterprise. What does that even mean? You ask? Well, it means that everyone in the organization has the data they need when they need it. In a context that's relevant to advance the mission of an organization. Now that could mean cutting cost could mean increasing profits, driving productivity, saving lives, accelerating drug discovery, making better diagnoses, solving, supply chain problems, predicting weather disasters, simplifying processes, and thousands of other examples where data can completely transform people's lives beyond manipulating internet users to behave a certain way. We've heard the prognostications about the possibilities of data before and in fairness we've made progress, but the hard truth is the original promises of master data management, enterprise data, warehouses, data marts, data hubs, and yes, even data lakes were broken and left us wanting from more welcome to the data doesn't lie, or doesn't a series of conversations produced by the cube and made possible by Starburst data. >>I'm your host, Dave Lanta and joining me today are three industry experts. Justin Borgman is this co-founder and CEO of Starburst. Richard Jarvis is the CTO at EMI health and Theresa tongue is cloud first technologist at Accenture. Today we're gonna have a candid discussion that will expose the unfulfilled and yes, broken promises of a data past we'll expose data lies, big lies, little lies, white lies, and hidden truths. And we'll challenge, age old data conventions and bust some data myths. We're debating questions like is the demise of a single source of truth. Inevitable will the data warehouse ever have featured parody with the data lake or vice versa is the so-called modern data stack, simply centralization in the cloud, AKA the old guards model in new cloud close. How can organizations rethink their data architectures and regimes to realize the true promises of data can and will and open ecosystem deliver on these promises in our lifetimes, we're spanning much of the Western world today. Richard is in the UK. Teresa is on the west coast and Justin is in Massachusetts with me. I'm in the cube studios about 30 miles outside of Boston folks. Welcome to the program. Thanks for coming on. Thanks for having us. Let's get right into it. You're very welcome. Now here's the first lie. The most effective data architecture is one that is centralized with a team of data specialists serving various lines of business. What do you think Justin? >>Yeah, definitely a lie. My first startup was a company called hit adapt, which was an early SQL engine for hit that was acquired by Teradata. And when I got to Teradata, of course, Teradata is the pioneer of that central enterprise data warehouse model. One of the things that I found fascinating was that not one of their customers had actually lived up to that vision of centralizing all of their data into one place. They all had data silos. They all had data in different systems. They had data on prem data in the cloud. You know, those companies were acquiring other companies and inheriting their data architecture. So, you know, despite being the industry leader for 40 years, not one of their customers truly had everything in one place. So I think definitely history has proven that to be a lie. >>So Richard, from a practitioner's point of view, you know, what, what are your thoughts? I mean, there, there's a lot of pressure to cut cost, keep things centralized, you know, serve the business as best as possible from that standpoint. What, what is your experience show? >>Yeah, I mean, I think I would echo Justin's experience really that we, as a business have grown up through acquisition, through storing data in different places sometimes to do information governance in different ways to store data in, in a platform that's close to data experts, people who really understand healthcare data from pharmacies or from, from doctors. And so, although if you were starting from a Greenfield site and you were building something brand new, you might be able to centralize all the data and all of the tooling and teams in one place. The reality is that that businesses just don't grow up like that. And, and it's just really impossible to get that academic perfection of, of storing everything in one place. >>Y you know, Theresa, I feel like Sarbanes Oxley kinda saved the data warehouse, you know, right. You actually did have to have a single version of the truth for certain financial data, but really for those, some of those other use cases, I, I mentioned, I, I do feel like the industry has kinda let us down. What's your take on this? Where does it make sense to have that sort of centralized approach versus where does it make sense to maybe decentralized? >>I, I think you gotta have centralized governance, right? So from the central team, for things like star Oxley, for things like security for certainly very core data sets, having a centralized set of roles, responsibilities to really QA, right. To serve as a design authority for your entire data estate, just like you might with security, but how it's implemented has to be distributed. Otherwise you're not gonna be able to scale. Right? So being able to have different parts of the business really make the right data investments for their needs. And then ultimately you're gonna collaborate with your partners. So partners that are not within the company, right. External partners, we're gonna see a lot more data sharing and model creation. And so you're definitely going to be decentralized. >>So, you know, Justin, you guys last, geez, I think it was about a year ago, had a session on, on data mesh. It was a great program. You invited Jamma, Dani, of course, she's the creator of the data mesh. And her one of our fundamental premises is that you've got this hyper specialized team that you've gotta go through. And if you want anything, but at the same time, these, these individuals actually become a bottleneck, even though they're some of the most talented people in the organization. So I guess question for you, Richard, how do you deal with that? Do you, do you organize so that there are a few sort of rock stars that, that, you know, build cubes and, and the like, and, and, and, or have you had any success in sort of decentralizing with, you know, your, your constituencies, that data model? >>Yeah. So, so we absolutely have got rockstar, data scientists and data guardians. If you like people who understand what it means to use this data, particularly as the data that we use at emos is very private it's healthcare information. And some of the, the rules and regulations around using the data are very complex and, and strict. So we have to have people who understand the usage of the data, then people who understand how to build models, how to process the data effectively. And you can think of them like consultants to the wider business, because a pharmacist might not understand how to structure a SQL query, but they do understand how they want to process medication information to improve patient lives. And so that becomes a, a consulting type experience from a, a set of rock stars to help a, a more decentralized business who needs to, to understand the data and to generate some valuable output. >>Justin, what do you say to a, to a customer or prospect that says, look, Justin, I'm gonna, I got a centralized team and that's the most cost effective way to serve the business. Otherwise I got, I got duplication. What do you say to that? >>Well, I, I would argue it's probably not the most cost effective and, and the reason being really twofold. I think, first of all, when you are deploying a enterprise data warehouse model, the, the data warehouse itself is very expensive, generally speaking. And so you're putting all of your most valuable data in the hands of one vendor who now has tremendous leverage over you, you know, for many, many years to come. I think that's the story at Oracle or Terra data or other proprietary database systems. But the other aspect I think is that the reality is those central data warehouse teams is as much as they are experts in the technology. They don't necessarily understand the data itself. And this is one of the core tenants of data mash that that jam writes about is this idea of the domain owners actually know the data the best. >>And so by, you know, not only acknowledging that data is generally decentralized and to your earlier point about SAR, brain Oxley, maybe saving the data warehouse, I would argue maybe GDPR and data sovereignty will destroy it because data has to be decentralized for, for those laws to be compliant. But I think the reality is, you know, the data mesh model basically says, data's decentralized, and we're gonna turn that into an asset rather than a liability. And we're gonna turn that into an asset by empowering the people that know the data, the best to participate in the process of, you know, curating and creating data products for, for consumption. So I think when you think about it, that way, you're going to get higher quality data and faster time to insight, which is ultimately going to drive more revenue for your business and reduce costs. So I think that that's the way I see the two, the two models comparing and contrasting. >>So do you think the demise of the data warehouse is inevitable? I mean, I mean, you know, there Theresa you work with a lot of clients, they're not just gonna rip and replace their existing infrastructure. Maybe they're gonna build on top of it, but what does that mean? Does that mean the E D w just becomes, you know, less and less valuable over time, or it's maybe just isolated to specific use cases. What's your take on that? >>Listen, I still would love all my data within a data warehouse would love it. Mastered would love it owned by essential team. Right? I think that's still what I would love to have. That's just not the reality, right? The investment to actually migrate and keep that up to date. I would say it's a losing battle. Like we've been trying to do it for a long time. Nobody has the budgets and then data changes, right? There's gonna be a new technology. That's gonna emerge that we're gonna wanna tap into. There's going to be not enough investment to bring all the legacy, but still very useful systems into that centralized view. So you keep the data warehouse. I think it's a very, very valuable, very high performance tool for what it's there for, but you could have this, you know, new mesh layer that still takes advantage of the things. I mentioned, the data products in the systems that are meaningful today and the data products that actually might span a number of systems, maybe either those that either source systems for the domains that know it best, or the consumer based systems and products that need to be packaged in a way that be really meaningful for that end user, right? Each of those are useful for a different part of the business and making sure that the mesh actually allows you to use all of them. >>So, Richard, let me ask you, you take, take Gemma's principles back to those. You got to, you know, domain ownership and, and, and data as product. Okay, great. Sounds good. But it creates what I would argue are two, you know, challenges, self-serve infrastructure let's park that for a second. And then in your industry, the one of the high, most regulated, most sensitive computational governance, how do you automate and ensure federated governance in that mesh model that Theresa was just talking about? >>Well, it absolutely depends on some of the tooling and processes that you put in place around those tools to be, to centralize the security and the governance of the data. And I think, although a data warehouse makes that very simple, cause it's a single tool, it's not impossible with some of the data mesh technologies that are available. And so what we've done at emus is we have a single security layer that sits on top of our data match, which means that no matter which user is accessing, which data source, we go through a well audited well understood security layer. That means that we know exactly who's got access to which data field, which data tables. And then everything that they do is, is audited in a very kind of standard way, regardless of the underlying data storage technology. So for me, although storing the data in one place might not be possible understanding where your source of truth is and securing that in a common way is still a valuable approach and you can do it without having to bring all that data into a single bucket so that it's all in one place. And, and so having done that and investing quite heavily in making that possible has paid dividends in terms of giving wider access to the platform and ensuring that only data that's available under GDPR and other regulations is being used by, by the data users. >>Yeah. So Justin, I mean, Democrat, we always talk about data democratization and you know, up until recently, they really haven't been line of sight as to how to get there. But do you have anything to add to this because you're essentially taking, you know, do an analytic queries and with data that's all dispersed all over the, how are you seeing your customers handle this, this challenge? >>Yeah. I mean, I think data products is a really interesting aspect of the answer to that. It allows you to, again, leverage the data domain owners, people know the data, the best to, to create, you know, data as a product ultimately to be consumed. And we try to represent that in our product as effectively a almost eCommerce like experience where you go and discover and look for the data products that have been created in your organization. And then you can start to consume them as, as you'd like. And so really trying to build on that notion of, you know, data democratization and self-service, and making it very easy to discover and, and start to use with whatever BI tool you, you may like, or even just running, you know, SQL queries yourself, >>Okay. G guys grab a sip of water. After this short break, we'll be back to debate whether proprietary or open platforms are the best path to the future of data excellence, keep it right there. >>Your company has more data than ever, and more people trying to understand it, but there's a problem. Your data is stored across multiple systems. It's hard to access and that delays analytics and ultimately decisions. The old method of moving all of your data into a single source of truth is slow and definitely not built for the volume of data we have today or where we are headed while your data engineers spent over half their time, moving data, your analysts and data scientists are left, waiting, feeling frustrated, unproductive, and unable to move the needle for your business. But what if you could spend less time moving or copying data? What if your data consumers could analyze all your data quickly? >>Starburst helps your teams run fast queries on any data source. We help you create a single point of access to your data, no matter where it's stored. And we support high concurrency, we solve for speed and scale, whether it's fast, SQL queries on your data lake or faster queries across multiple data sets, Starburst helps your teams run analytics anywhere you can't afford to wait for data to be available. Your team has questions that need answers. Now with Starburst, the wait is over. You'll have faster access to data with enterprise level security, easy connectivity, and 24 7 support from experts, organizations like Zolando Comcast and FINRA rely on Starburst to move their businesses forward. Contact our Trino experts to get started. >>We're back with Jess Borgman of Starburst and Richard Jarvis of EVAs health. Okay, we're gonna get to lie. Number two, and that is this an open source based platform cannot give you the performance and control that you can get with a proprietary system. Is that a lie? Justin, the enterprise data warehouse has been pretty dominant and has evolved and matured. Its stack has mature over the years. Why is it not the default platform for data? >>Yeah, well, I think that's become a lie over time. So I, I think, you know, if we go back 10 or 12 years ago with the advent of the first data lake really around Hudu, that probably was true that you couldn't get the performance that you needed to run fast, interactive, SQL queries in a data lake. Now a lot's changed in 10 or 12 years. I remember in the very early days, people would say, you you'll never get performance because you need to be column there. You need to store data in a column format. And then, you know, column formats we're introduced to, to data apes, you have Parque ORC file in aro that were created to ultimately deliver performance out of that. So, okay. We got, you know, largely over the performance hurdle, you know, more recently people will say, well, you don't have the ability to do updates and deletes like a traditional data warehouse. >>And now we've got the creation of new data formats, again like iceberg and Delta and Hodi that do allow for updates and delete. So I think the data lake has continued to mature. And I remember a, a quote from, you know, Kurt Monash many years ago where he said, you know, know it takes six or seven years to build a functional database. I think that's that's right. And now we've had almost a decade go by. So, you know, these technologies have matured to really deliver very, very close to the same level performance and functionality of, of cloud data warehouses. So I think the, the reality is that's become a line and now we have large giant hyperscale internet companies that, you know, don't have the traditional data warehouse at all. They do all of their analytics in a data lake. So I think we've, we've proven that it's very much possible today. >>Thank you for that. And so Richard, talk about your perspective as a practitioner in terms of what open brings you versus, I mean, look closed is it's open as a moving target. I remember Unix used to be open systems and so it's, it is an evolving, you know, spectrum, but, but from your perspective, what does open give you that you can't get from a proprietary system where you are fearful of in a proprietary system? >>I, I suppose for me open buys us the ability to be unsure about the future, because one thing that's always true about technology is it evolves in a, a direction, slightly different to what people expect. And what you don't want to end up is done is backed itself into a corner that then prevents it from innovating. So if you have chosen a technology and you've stored trillions of records in that technology and suddenly a new way of processing or machine learning comes out, you wanna be able to take advantage and your competitive edge might depend upon it. And so I suppose for us, we acknowledge that we don't have perfect vision of what the future might be. And so by backing open storage technologies, we can apply a number of different technologies to the processing of that data. And that gives us the ability to remain relevant, innovate on our data storage. And we have bought our way out of the, any performance concerns because we can use cloud scale infrastructure to scale up and scale down as we need. And so we don't have the concerns that we don't have enough hardware today to process what we want to do, want to achieve. We can just scale up when we need it and scale back down. So open source has really allowed us to maintain the being at the cutting edge. >>So Jess, let me play devil's advocate here a little bit, and I've talked to Shaak about this and you know, obviously her vision is there's an open source that, that the data meshes open source, an open source tooling, and it's not a proprietary, you know, you're not gonna buy a data mesh. You're gonna build it with, with open source toolings and, and vendors like you are gonna support it, but to come back to sort of today, you can get to market with a proprietary solution faster. I'm gonna make that statement. You tell me if it's a lie and then you can say, okay, we support Apache iceberg. We're gonna support open source tooling, take a company like VMware, not really in the data business, but how, the way they embraced Kubernetes and, and you know, every new open source thing that comes along, they say, we do that too. Why can't proprietary systems do that and be as effective? >>Yeah, well, I think at least with the, within the data landscape saying that you can access open data formats like iceberg or, or others is, is a bit dis disingenuous because really what you're selling to your customer is a certain degree of performance, a certain SLA, and you know, those cloud data warehouses that can reach beyond their own proprietary storage drop all the performance that they were able to provide. So it is, it reminds me kind of, of, again, going back 10 or 12 years ago when everybody had a connector to Haddo and that they thought that was the solution, right? But the reality was, you know, a connector was not the same as running workloads in Haddo back then. And I think similarly, you know, being able to connect to an external table that lives in an open data format, you know, you're, you're not going to give it the performance that your customers are accustomed to. And at the end of the day, they're always going to be predisposed. They're always going to be incentivized to get that data ingested into the data warehouse, cuz that's where they have control. And you know, the bottom line is the database industry has really been built around vendor lockin. I mean, from the start, how, how many people love Oracle today, but our customers, nonetheless, I think, you know, lockin is, is, is part of this industry. And I think that's really what we're trying to change with open data formats. >>Well, that's interesting reminded when I, you know, I see the, the gas price, the tees or gas price I, I drive up and then I say, oh, that's the cash price credit card. I gotta pay 20 cents more, but okay. But so the, the argument then, so let me, let me come back to you, Justin. So what's wrong with saying, Hey, we support open data formats, but yeah, you're gonna get better performance if you, if you keep it into our closed system, are you saying that long term that's gonna come back and bite you cuz you're gonna end up, you mentioned Oracle, you mentioned Teradata. Yeah. That's by, by implication, you're saying that's where snowflake customers are headed. >>Yeah, absolutely. I think this is a movie that, you know, we've all seen before. At least those of us who've been in the industry long enough to, to see this movie play over a couple times. So I do think that's the future. And I think, you know, I loved what Richard said. I actually wrote it down. Cause I thought it was an amazing quote. He said, it buys us the ability to be unsure of the future. Th that that pretty much says it all the, the future is unknowable and the reality is using open data formats. You remain interoperable with any technology you want to utilize. If you want to use spark to train a machine learning model and you want to use Starbust to query via sequel, that's totally cool. They can both work off the same exact, you know, data, data sets by contrast, if you're, you know, focused on a proprietary model, then you're kind of locked in again to that model. I think the same applies to data, sharing to data products, to a wide variety of, of aspects of the data landscape that a proprietary approach kind of closes you in and locks you in. >>So I, I would say this Richard, I'd love to get your thoughts on it. Cause I talked to a lot of Oracle customers, not as many te data customers, but, but a lot of Oracle customers and they, you know, they'll admit, yeah, you know, they're jamming us on price and the license cost they give, but we do get value out of it. And so my question to you, Richard, is, is do the, let's call it data warehouse systems or the proprietary systems. Are they gonna deliver a greater ROI sooner? And is that in allure of, of that customers, you know, are attracted to, or can open platforms deliver as fast in ROI? >>I think the answer to that is it can depend a bit. It depends on your businesses skillset. So we are lucky that we have a number of proprietary teams that work in databases that provide our operational data capability. And we have teams of analytics and big data experts who can work with open data sets and open data formats. And so for those different teams, they can get to an ROI more quickly with different technologies for the business though, we can't do better for our operational data stores than proprietary databases. Today we can back off very tight SLAs to them. We can demonstrate reliability from millions of hours of those databases being run at enterprise scale, but for an analytics workload where increasing our business is growing in that direction, we can't do better than open data formats with cloud-based data mesh type technologies. And so it's not a simple answer. That one will always be the right answer for our business. We definitely have times when proprietary databases provide a capability that we couldn't easily represent or replicate with open technologies. >>Yeah. Richard, stay with you. You mentioned, you know, you know, some things before that, that strike me, you know, the data brick snowflake, you know, thing is, oh, is a lot of fun for analysts like me. You've got data bricks coming at it. Richard, you mentioned you have a lot of rockstar, data engineers, data bricks coming at it from a data engineering heritage. You get snowflake coming at it from an analytics heritage. Those two worlds are, are colliding people like PJI Mohan said, you know what? I think it's actually harder to play in the data engineering. So I E it's easier to for data engineering world to go into the analytics world versus the reverse, but thinking about up and coming engineers and developers preparing for this future of data engineering and data analytics, how, how should they be thinking about the future? What, what's your advice to those young people? >>So I think I'd probably fall back on general programming skill sets. So the advice that I saw years ago was if you have open source technologies, the pythons and Javas on your CV, you commander 20% pay, hike over people who can only do proprietary programming languages. And I think that's true of data technologies as well. And from a business point of view, that makes sense. I'd rather spend the money that I save on proprietary licenses on better engineers, because they can provide more value to the business that can innovate us beyond our competitors. So I think I would my advice to people who are starting here or trying to build teams to capitalize on data assets is begin with open license, free capabilities, because they're very cheap to experiment with. And they generate a lot of interest from people who want to join you as a business. And you can make them very successful early, early doors with, with your analytics journey. >>It's interesting. Again, analysts like myself, we do a lot of TCO work and have over the last 20 plus years. And in world of Oracle, you know, normally it's the staff, that's the biggest nut in total cost of ownership, not an Oracle. It's the it's the license cost is by far the biggest component in the, in the blame pie. All right, Justin, help us close out this segment. We've been talking about this sort of data mesh open, closed snowflake data bricks. Where does Starburst sort of as this engine for the data lake data lake house, the data warehouse fit in this, in this world? >>Yeah. So our view on how the future ultimately unfolds is we think that data lakes will be a natural center of gravity for a lot of the reasons that we described open data formats, lowest total cost of ownership, because you get to choose the cheapest storage available to you. Maybe that's S3 or Azure data lake storage, or Google cloud storage, or maybe it's on-prem object storage that you bought at a, at a really good price. So ultimately storing a lot of data in a deal lake makes a lot of sense, but I think what makes our perspective unique is we still don't think you're gonna get everything there either. We think that basically centralization of all your data assets is just an impossible endeavor. And so you wanna be able to access data that lives outside of the lake as well. So we kind of think of the lake as maybe the biggest place by volume in terms of how much data you have, but to, to have comprehensive analytics and to truly understand your business and understand it holistically, you need to be able to go access other data sources as well. And so that's the role that we wanna play is to be a single point of access for our customers, provide the right level of fine grained access controls so that the right people have access to the right data and ultimately make it easy to discover and consume via, you know, the creation of data products as well. >>Great. Okay. Thanks guys. Right after this quick break, we're gonna be back to debate whether the cloud data model that we see emerging and the so-called modern data stack is really modern, or is it the same wine new bottle? When it comes to data architectures, you're watching the cube, the leader in enterprise and emerging tech coverage. >>Your data is capable of producing incredible results, but data consumers are often left in the dark without fast access to the data they need. Starers makes your data visible from wherever it lives. Your company is acquiring more data in more places, more rapidly than ever to rely solely on a data centralization strategy. Whether it's in a lake or a warehouse is unrealistic. A single source of truth approach is no longer viable, but disconnected data silos are often left untapped. We need a new approach. One that embraces distributed data. One that enables fast and secure access to any of your data from anywhere with Starburst, you'll have the fastest query engine for the data lake that allows you to connect and analyze your disparate data sources no matter where they live Starburst provides the foundational technology required for you to build towards the vision of a decentralized data mesh Starburst enterprise and Starburst galaxy offer enterprise ready, connectivity, interoperability, and security features for multiple regions, multiple clouds and everchanging global regulatory requirements. The data is yours. And with Starburst, you can perform analytics anywhere in light of your world. >>Okay. We're back with Justin Boardman. CEO of Starbust Richard Jarvis is the CTO of EMI health and Theresa tongue is the cloud first technologist from Accenture. We're on July number three. And that is the claim that today's modern data stack is actually modern. So I guess that's the lie it's it is it's is that it's not modern. Justin, what do you say? >>Yeah. I mean, I think new isn't modern, right? I think it's the, it's the new data stack. It's the cloud data stack, but that doesn't necessarily mean it's modern. I think a lot of the components actually are exactly the same as what we've had for 40 years, rather than Terra data. You have snowflake rather than Informatica you have five trend. So it's the same general stack, just, you know, a cloud version of it. And I think a lot of the challenges that it plagued us for 40 years still maintain. >>So lemme come back to you just, but okay. But, but there are differences, right? I mean, you can scale, you can throw resources at the problem. You can separate compute from storage. You really, you know, there's a lot of money being thrown at that by venture capitalists and snowflake, you mentioned it's competitors. So that's different. Is it not, is that not at least an aspect of, of modern dial it up, dial it down. So what, what do you say to that? >>Well, it, it is, it's certainly taking, you know, what the cloud offers and taking advantage of that, but it's important to note that the cloud data warehouses out there are really just separating their compute from their storage. So it's allowing them to scale up and down, but your data still stored in a proprietary format. You're still locked in. You still have to ingest the data to get it even prepared for analysis. So a lot of the same sort of structural constraints that exist with the old enterprise data warehouse model OnPrem still exist just yes, a little bit more elastic now because the cloud offers that. >>So Theresa, let me go to you cuz you have cloud first in your, in your, your title. So what's what say you to this conversation? >>Well, even the cloud providers are looking towards more of a cloud continuum, right? So the centralized cloud, as we know it, maybe data lake data warehouse in the central place, that's not even how the cloud providers are looking at it. They have news query services. Every provider has one that really expands those queries to be beyond a single location. And if we look at a lot of where our, the future goes, right, that that's gonna very much fall the same thing. There was gonna be more edge. There's gonna be more on premise because of data sovereignty, data gravity, because you're working with different parts of the business that have already made major cloud investments in different cloud providers. Right? So there's a lot of reasons why the modern, I guess, the next modern generation of the data staff needs to be much more federated. >>Okay. So Richard, how do you deal with this? You you've obviously got, you know, the technical debt, the existing infrastructure it's on the books. You don't wanna just throw it out. A lot of, lot of conversation about modernizing applications, which a lot of times is a, you know, a microservices layer on top of leg legacy apps. How do you think about the modern data stack? >>Well, I think probably the first thing to say is that the stack really has to include the processes and people around the data as well is all well and good changing the technology. But if you don't modernize how people use that technology, then you're not going to be able to, to scale because just cuz you can scale CPU and storage doesn't mean you can get more people to use your data, to generate you more, more value for the business. And so what we've been looking at is really changing in very much aligned to data products and, and data mesh. How do you enable more people to consume the service and have the stack respond in a way that keeps costs low? Because that's important for our customers consuming this data, but also allows people to occasionally run enormous queries and then tick along with smaller ones when required. And it's a good job we did because during COVID all of a sudden we had enormous pressures on our data platform to answer really important life threatening queries. And if we couldn't scale both our data stack and our teams, we wouldn't have been able to answer those as quickly as we had. So I think the stack needs to support a scalable business, not just the technology itself. >>Well thank you for that. So Justin let's, let's try to break down what the critical aspects are of the modern data stack. So you think about the past, you know, five, seven years cloud obviously has given a different pricing model. De-risked experimentation, you know that we talked about the ability to scale up scale down, but it's, I'm, I'm taking away that that's not enough based on what Richard just said. The modern data stack has to serve the business and enable the business to build data products. I, I buy that. I'm a big fan of the data mesh concepts, even though we're early days. So what are the critical aspects if you had to think about, you know, paying, maybe putting some guardrails and definitions around the modern data stack, what does that look like? What are some of the attributes and, and principles there >>Of, of how it should look like or, or how >>It's yeah. What it should be. >>Yeah. Yeah. Well, I think, you know, in, in Theresa mentioned this in, in a previous segment about the data warehouse is not necessarily going to disappear. It just becomes one node, one element of the overall data mesh. And I, I certainly agree with that. So by no means, are we suggesting that, you know, snowflake or Redshift or whatever cloud data warehouse you may be using is going to disappear, but it's, it's not going to become the end all be all. It's not the, the central single source of truth. And I think that's the paradigm shift that needs to occur. And I think it's also worth noting that those who were the early adopters of the modern data stack were primarily digital, native born in the cloud young companies who had the benefit of, of idealism. They had the benefit of it was starting with a clean slate that does not reflect the vast majority of enterprises. >>And even those companies, as they grow up mature out of that ideal state, they go buy a business. Now they've got something on another cloud provider that has a different data stack and they have to deal with that heterogeneity that is just change and change is a part of life. And so I think there is an element here that is almost philosophical. It's like, do you believe in an absolute ideal where I can just fit everything into one place or do I believe in reality? And I think the far more pragmatic approach is really what data mesh represents. So to answer your question directly, I think it's adding, you know, the ability to access data that lives outside of the data warehouse, maybe living in open data formats in a data lake or accessing operational systems as well. Maybe you want to directly access data that lives in an Oracle database or a Mongo database or, or what have you. So creating that flexibility to really Futureproof yourself from the inevitable change that you will, you won't encounter over time. >>So thank you. So there, based on what Justin just said, I, my takeaway there is it's inclusive, whether it's a data Mar data hub, data lake data warehouse, it's a, just a node on the mesh. Okay. I get that. Does that include there on Preem data? O obviously it has to, what are you seeing in terms of the ability to, to take that data mesh concept on Preem? I mean, most implementations I've seen in data mesh, frankly really aren't, you know, adhering to the philosophy. They're maybe, maybe it's data lake and maybe it's using glue. You look at what JPMC is doing. Hello, fresh, a lot of stuff happening on the AWS cloud in that, you know, closed stack, if you will. What's the answer to that Theresa? >>I mean, I, I think it's a killer case for data. Me, the fact that you have valuable data sources, OnPrem, and then yet you still wanna modernize and take the best of cloud cloud is still, like we mentioned, there's a lot of great reasons for it around the economics and the way ability to tap into the innovation that the cloud providers are giving around data and AI architecture. It's an easy button. So the mesh allows you to have the best of both worlds. You can start using the data products on-prem or in the existing systems that are working already. It's meaningful for the business. At the same time, you can modernize the ones that make business sense because it needs better performance. It needs, you know, something that is, is cheaper or, or maybe just tap into better analytics to get better insights, right? So you're gonna be able to stretch and really have the best of both worlds. That, again, going back to Richard's point, that is meaningful by the business. Not everything has to have that one size fits all set a tool. >>Okay. Thank you. So Richard, you know, talking about data as product, wonder if we could give us your perspectives here, what are the advantages of treating data as a product? What, what role do data products have in the modern data stack? We talk about monetizing data. What are your thoughts on data products? >>So for us, one of the most important data products that we've been creating is taking data that is healthcare data across a wide variety of different settings. So information about patients' demographics about their, their treatment, about their medications and so on, and taking that into a standards format that can be utilized by a wide variety of different researchers because misinterpreting that data or having the data not presented in the way that the user is expecting means that you generate the wrong insight. And in any business, that's clearly not a desirable outcome, but when that insight is so critical, as it might be in healthcare or some security settings, you really have to have gone to the trouble of understanding the data, presenting it in a format that everyone can clearly agree on. And then letting people consume in a very structured, managed way, even if that data comes from a variety of different sources in, in, in the first place. And so our data product journey has really begun by standardizing data across a number of different silos through the data mesh. So we can present out both internally and through the right governance externally to, to researchers. >>So that data product through whatever APIs is, is accessible, it's discoverable, but it's obviously gotta be governed as well. You mentioned you, you appropriately provided to internally. Yeah. But also, you know, external folks as well. So the, so you've, you've architected that capability today >>We have, and because the data is standard, it can generate value much more quickly and we can be sure of the security and, and, and value that that's providing because the data product isn't just about formatting the data into the correct tables, it's understanding what it means to redact the data or to remove certain rows from it or to interpret what a date actually means. Is it the start of the contract or the start of the treatment or the date of birth of a patient? These things can be lost in the data storage without having the proper product management around the data to say in a very clear business context, what does this data mean? And what does it mean to process this data for a particular use case? >>Yeah, it makes sense. It's got the context. If the, if the domains own the data, you, you gotta cut through a lot of the, the, the centralized teams, the technical teams that, that data agnostic, they don't really have that context. All right. Let's send Justin, how does Starburst fit into this modern data stack? Bring us home. >>Yeah. So I think for us, it's really providing our customers with, you know, the flexibility to operate and analyze data that lives in a wide variety of different systems. Ultimately giving them that optionality, you know, and optionality provides the ability to reduce costs, store more in a data lake rather than data warehouse. It provides the ability for the fastest time to insight to access the data directly where it lives. And ultimately with this concept of data products that we've now, you know, incorporated into our offering as well, you can really create and, and curate, you know, data as a product to be shared and consumed. So we're trying to help enable the data mesh, you know, model and make that an appropriate compliment to, you know, the, the, the modern data stack that people have today. >>Excellent. Hey, I wanna thank Justin Theresa and Richard for joining us today. You guys are great. I big believers in the, in the data mesh concept, and I think, you know, we're seeing the future of data architecture. So thank you. Now, remember, all these conversations are gonna be available on the cube.net for on-demand viewing. You can also go to starburst.io. They have some great content on the website and they host some really thought provoking interviews and, and, and they have awesome resources, lots of data mesh conversations over there, and really good stuff in, in the resource section. So check that out. Thanks for watching the data doesn't lie or does it made possible by Starburst data? This is Dave Valante for the cube, and we'll see you next time. >>The explosion of data sources has forced organizations to modernize their systems and architecture and come to terms with one size does not fit all for data management today. Your teams are constantly moving and copying data, which requires time management. And in some cases, double paying for compute resources. Instead, what if you could access all your data anywhere using the BI tools and SQL skills your users already have. 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from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante at our inaugural super cloud 22 event we further refined the concept of a super cloud iterating on the definition the salient attributes and some examples of what is and what is not a super cloud welcome to this week's wikibon cube insights powered by etr you know snowflake has always been what we feel is one of the strongest examples of a super cloud and in this breaking analysis from our studios in palo alto we unpack our interview with benoit de javille co-founder and president of products at snowflake and we test our super cloud definition on the company's data cloud platform and we're really looking forward to your feedback first let's examine how we defl find super cloudant very importantly one of the goals of super cloud 22 was to get the community's input on the definition and iterate on previous work super cloud is an emerging computing architecture that comprises a set of services which are abstracted from the underlying primitives of hyperscale clouds we're talking about services such as compute storage networking security and other native tooling like machine learning and developer tools to create a global system that spans more than one cloud super cloud as shown on this slide has five essential properties x number of deployment models and y number of service models we're looking for community input on x and y and on the first point as well so please weigh in and contribute now we've identified these five essential elements of a super cloud let's talk about these first the super cloud has to run its services on more than one cloud leveraging the cloud native tools offered by each of the cloud providers the builder of the super cloud platform is responsible for optimizing the underlying primitives of each cloud and optimizing for the specific needs be it cost or performance or latency or governance data sharing security etc but those primitives must be abstracted such that a common experience is delivered across the clouds for both users and developers the super cloud has a metadata intelligence layer that can maximize efficiency for the specific purpose of the super cloud i.e the purpose that the super cloud is intended for and it does so in a federated model and it includes what we call a super pass this is a prerequisite that is a purpose-built component and enables ecosystem partners to customize and monetize incremental services while at the same time ensuring that the common experiences exist across clouds now in terms of deployment models we'd really like to get more feedback on this piece but here's where we are so far based on the feedback we got at super cloud 22. we see three deployment models the first is one where a control plane may run on one cloud but supports data plane interactions with more than one other cloud the second model instantiates the super cloud services on each individual cloud and within regions and can support interactions across more than one cloud with a unified interface connecting those instantiations those instances to create a common experience and the third model superimposes its services as a layer or in the case of snowflake they call it a mesh on top of the cloud on top of the cloud providers region or regions with a single global instantiation a single global instantiation of those services which spans multiple cloud providers this is our understanding from a comfort the conversation with benoit dejaville as to how snowflake approaches its solutions and for now we're going to park the service models we need to more time to flesh that out and we'll propose something shortly for you to comment on now we peppered benoit dejaville at super cloud 22 to test how the snowflake data cloud aligns to our concepts and our definition let me also say that snowflake doesn't use the term data cloud they really want to respect and they want to denigrate the importance of their hyperscale partners nor do we but we do think the hyperscalers today anyway are building or not building what we call super clouds but they are but but people who bar are building super clouds are building on top of hyperscale clouds that is a prerequisite so here are the questions that we tested with snowflake first question how does snowflake architect its data cloud and what is its deployment model listen to deja ville talk about how snowflake has architected a single system play the clip there are several ways to do this you know uh super cloud as as you name them the way we we we picked is is to create you know one single system and that's very important right the the the um [Music] there are several ways right you can instantiate you know your solution uh in every region of a cloud and and you know potentially that region could be a ws that region could be gcp so you are indeed a multi-cloud solution but snowflake we did it differently we are really creating cloud regions which are superposed on top of the cloud provider you know region infrastructure region so we are building our regions but but where where it's very different is that each region of snowflake is not one in instantiation of our service our service is global by nature we can move data from one region to the other when you land in snowflake you land into one region but but you can grow from there and you can you know exist in multiple clouds at the same time and that's very important right it's not one single i mean different instantiation of a system is one single instantiation which covers many cloud regions and many cloud providers snowflake chose the most advanced level of our three deployment models dodgeville talked about too presumably so it could maintain maximum control and ensure that common experience like the iphone model next we probed about the technical enablers of the data cloud listen to deja ville talk about snow grid he uses the term mesh and then this can get confusing with the jamaicani's data mesh concept but listen to benoit's explanation well as i said you know first we start by building you know snowflake regions we have today furry region that spawn you know the world so it's a worldwide worldwide system with many regions but all these regions are connected together they are you know meshed together with our technology we name it snow grid and that makes it hard because you know regions you know azure region can talk to a ws region or gcp regions and and as a as a user of our cloud you you don't see really these regional differences that you know regions are in different you know potentially clown when you use snowflake you can exist your your presence as an organization can be in several regions several clouds if you want geographic and and and both geographic and cloud provider so i can share data irrespective of the the cloud and i'm in the snowflake data cloud is that correct i can do that today exactly and and that's very critical right what we wanted is to remove data silos and and when you instantiate a system in one single region and that system is locked in that region you cannot communicate with other parts of the world you are locking the data in one region right and we didn't want to do that we wanted you know data to be distributed the way customer wants it to be distributed across the world and potentially sharing data at world scale now maybe there are many ways to skin the other cat meaning perhaps if a platform does instantiate in multiple places there are ways to share data but this is how snowflake chose to approach the problem next question how do you deal with latency in this big global system this is really important to us because while snowflake has some really smart people working as engineers and and the like we don't think they've solved for the speed of light problem the best people working on it as we often joke listen to benoit deja ville's comments on this topic so yes and no the the way we do it it's very expensive to do that because generally if you want to join you know data which is in which are in different regions and different cloud it's going to be very expensive because you need to move you know data every time you join it so the way we do it is that you replicate the subset of data that you want to access from one region from other regions so you can create this data mesh but data is replicated to make it very cheap and very performant too and is the snow grid does that have the metadata intelligence yes to actually can you describe that a little bit yeah snow grid is both uh a way to to exchange you know metadata about so each region of snowflake knows about all the other regions of snowflake every time we create a new region diary you know the metadata is distributed over our data cloud not only you know region knows all the regions but knows you know every organization that exists in our clouds where this organization is where data can be replicated by this organization and then of course it's it's also used as a way to uh uh exchange data right so you can exchange you know beta by scale of data size and we just had i was just receiving an email from one of our customers who moved more than four petabytes of data cross-region cross you know cloud providers in you know few days and you know it's a lot of data so it takes you know some time to move but they were able to do that online completely online and and switch over you know to the diff to the other region which is failover is very important also so yes and no probably means typically no he says yes and no probably means no so it sounds like snowflake is selectively pulling small amounts of data and replicating it where necessary but you also heard him talk about the metadata layer which is one of the essential aspects of super cloud okay next we dug into security it's one of the most important issues and we think one of the hardest parts related to deploying super cloud so we've talked about how the cloud has become the first line of defense for the cso but now with multi-cloud you have multiple first lines of defense and that means multiple shared responsibility models and multiple tool sets from different cloud providers and an expanded threat surface so listen to benoit's explanation here please play the clip this is a great question uh security has always been the most important aspect of snowflake since day one right this is the question that every customer of ours has you know how you can you guarantee the security of my data and so we secure data really tightly in region we have several layers of security it starts by by encrypting it every data at rest and that's very important a lot of customers are not doing that right you hear these attacks for example on on cloud you know where someone left you know their buckets uh uh open and then you know you can access the data because it's a non-encrypted uh so we are encrypting everything at rest we are encrypting everything in transit so a region is very secure now you know you never from one region you never access data from another region in snowflake that's why also we replicate data now the replication of that data across region or the metadata for that matter is is really highly secure so snow grits ensure that everything is encrypted everything is you know we have multiple you know encryption keys and it's you know stored in hardware you know secure modules so we we we built you know snow grids such that it's secure and it allows very secure movement of data so when we heard this explanation we immediately went to the lowest common denominator question meaning when you think about how aws for instance deals with data in motion or data and rest it might be different from how another cloud provider deals with it so how does aws uh uh uh differences for example in the aws maturity model for various you know cloud capabilities you know let's say they've got a faster nitro or graviton does it do do you have to how does snowflake deal with that do they have to slow everything else down like imagine a caravan cruising you know across the desert so you know every truck can keep up let's listen it's a great question i mean of course our software is abstracting you know all the cloud providers you know infrastructure so that when you run in one region let's say aws or azure it doesn't make any difference as far as the applications are concerned and and this abstraction of course is a lot of work i mean really really a lot of work because it needs to be secure it needs to be performance and you know every cloud and it has you know to expose apis which are uniform and and you know cloud providers even though they have potentially the same concept let's say blob storage apis are completely different the way you know these systems are secure it's completely different the errors that you can get and and the retry you know mechanism is very different from one cloud to the other performance is also different we discovered that when we were starting to port our software and and and you know we had to completely rethink how to leverage blob storage in that cloud versus that cloud because just of performance too so we had you know for example to you know stripe data so all this work is work that's you know you don't need as an application because our vision really is that applications which are running in our data cloud can you know be abstracted of all this difference and and we provide all the services all the workload that this application need whether it's transactional access to data analytical access to data you know managing you know logs managing you know metrics all of these is abstracted too such that they are not you know tied to one you know particular service of one cloud and and distributing this application across you know many regions many cloud is very seamless so from that answer we know that snowflake takes care of everything but we really don't understand the performance implications in you know in that specific case but we feel pretty certain that the promises that snowflake makes around governance and security within their data sharing construct construct will be kept now another criterion that we've proposed for super cloud is a super pass layer to create a common developer experience and an enabler for ecosystem partners to monetize please play the clip let's listen we build it you know a custom build because because as you said you know what exists in one cloud might not exist in another cloud provider right so so we have to build you know on this all these this components that modern application mode and that application need and and and and that you know goes to machine learning as i say transactional uh analytical system and the entire thing so such that they can run in isolation basically and the objective is the developer experience will be identical across those clouds yes right the developers doesn't need to worry about cloud provider and actually our system we have we didn't talk about it but the marketplace that we have which allows actually to deliver we're getting there yeah okay now we're not going to go deep into ecosystem today we've talked about snowflakes strengths in this regard but snowflake they pretty much ticked all the boxes on our super cloud attributes and definition we asked benoit dejaville to confirm that this is all shipping and available today and he also gave us a glimpse of the future play the clip and we are still developing it you know the transactional you know unistore as we call it was announced in last summit so so they are still you know working properly but but but that's the vision right and and and that's important because we talk about the infrastructure right you mentioned a lot about storage and compute but it's not only that right when you think about application they need to use the transactional database they need to use an analytical system they need to use you know machine learning so you need to provide also all these services which are consistent across all the cloud providers so you can hear deja ville talking about expanding beyond taking advantage of the core infrastructure storage and networking et cetera and bringing intelligence to the data through machine learning and ai so of course there's more to come and there better be at this company's valuation despite the recent sharp pullback in a tightening fed environment okay so i know it's cliche but everyone's comparing snowflakes and data bricks databricks has been pretty vocal about its open source posture compared to snowflakes and it just so happens that we had aligotsy on at super cloud 22 as well he wasn't in studio he had to do remote because i guess he's presenting at an investor conference this week so we had to bring him in remotely now i didn't get to do this interview john furrier did but i listened to it and captured this clip about how data bricks sees super cloud and the importance of open source take a listen to goatzee yeah i mean let me start by saying we just we're big fans of open source we think that open source is a force in software that's going to continue for you know decades hundreds of years and it's going to slowly replace all proprietary code in its way we saw that you know it could do that with the most advanced technology windows you know proprietary operating system very complicated got replaced with linux so open source can pretty much do anything and what we're seeing with the data lake house is that slowly the open source community is building a replacement for the proprietary data warehouse you know data lake machine learning real-time stack in open source and we're excited to be part of it for us delta lake is a very important project that really helps you standardize how you lay out your data in the cloud and with it comes a really important protocol called delta sharing that enables you in an open way actually for the first time ever share large data sets between organizations but it uses an open protocol so the great thing about that is you don't need to be a database customer you don't even like databricks you just need to use this open source project and you can now securely share data sets between organizations across clouds and it actually does so really efficiently just one copy of the data so you don't have to copy it if you're within the same cloud so the implication of ellie gotzi's comments is that databricks with delta sharing as john implied is playing a long game now i don't know if enough about the databricks architecture to comment in detail i got to do more research there so i reached out to my two analyst friends tony bear and sanji mohan to see what they thought because they cover these companies pretty closely here's what tony bear said quote i've viewed the divergent lake house strategies of data bricks and snowflake in the context of their roots prior to delta lake databrick's prime focus was the compute not the storage layer and more specifically they were a compute engine not a database snowflake approached from the opposite end of the pool as they originally fit the mold of the classic database company rather than a specific compute engine per se the lake house pushes both companies outside of their original comfort zones data bricks to storage snowflake to compute engine so it makes perfect sense for databricks to embrace the open source narrative at the storage layer and for snowflake to continue its walled garden approach but in the long run their strategies are already overlapping databricks is not a 100 open source company its practitioner experience has always been proprietary and now so is its sql query engine likewise snowflake has had to open up with the support of iceberg for open data lake format the question really becomes how serious snowflake will be in making iceberg a first-class citizen in its environment that is not necessarily officially branding a lake house but effectively is and likewise can databricks deliver the service levels associated with walled gardens through a more brute force approach that relies heavily on the query engine at the end of the day those are the key requirements that will matter to data bricks and snowflake customers end quote that was some deep thought by by tony thank you for that sanjay mohan added the following quote open source is a slippery slope people buy mobile phones based on open source android but it's not fully open similarly databricks delta lake was not originally fully open source and even today its photon execution engine is not we are always going to live in a hybrid world snowflake and databricks will support whatever model works best for them and their customers the big question is do customers care as deeply about which vendor has a higher degree of openness as we technology people do i believe customers evaluation criteria is far more nuanced than just to decipher each vendor's open source claims end quote okay so i had to ask dodgeville about their so-called wall garden approach and what their strategy is with apache iceberg here's what he said iceberg is is very important so just to to give some context iceberg is an open you know table format right which was you know first you know developed by netflix and netflix you know put it open source in the apache community so we embrace that's that open source standard because because it's widely used by by many um many you know companies and also many companies have you know really invested a lot of effort in building you know big data hadoop solution or data like solution and they want to use snowflake and they couldn't really use snowflake because all their data were in open you know formats so we are embracing icebergs to help these companies move through the cloud but why we have been relentless with direct access to data direct access to data is a little bit of a problem for us and and the reason is when you direct access to data now you have direct access to storage now you have to understand for example the specificity of one cloud versus the other so as soon as you start to have direct access to data you lose your you know your cloud diagnostic layer you don't access data with api when you have direct access to data it's very hard to secure data because you need to grant access direct access to tools which are not you know protected and you see a lot of you know hacking of of data you know because of that so so that was not you know direct access to data is not serving well our customers and that's why we have been relented to do that because it's it's cr it's it's not cloud diagnostic it's it's you you have to code that you have to you you you need a lot of intelligence while apis access so we want open apis that's that's i guess the way we embrace you know openness is is by open api versus you know you access directly data here's my take snowflake is hedging its bets because enough people care about open source that they have to have some open data format options and it's good optics and you heard benoit deja ville talk about the risks of directly accessing the data and the complexities it brings now is that maybe a little fud against databricks maybe but same can be said for ollie's comments maybe flooding the proprietaryness of snowflake but as both analysts pointed out open is a spectrum hey i remember unix used to equal open systems okay let's end with some etr spending data and why not compare snowflake and data bricks spending profiles this is an xy graph with net score or spending momentum on the y-axis and pervasiveness or overlap in the data set on the x-axis this is data from the january survey when snowflake was holding above 80 percent net score off the charts databricks was also very strong in the upper 60s now let's fast forward to this next chart and show you the july etr survey data and you can see snowflake has come back down to earth now remember anything above 40 net score is highly elevated so both companies are doing well but snowflake is well off its highs and data bricks has come down somewhat as well databricks is inching to the right snowflake rocketed to the right post its ipo and as we know databricks wasn't able to get to ipo during the covet bubble ali gotzi is at the morgan stanley ceo conference this week they got plenty of cash to withstand a long-term recession i'm told and they've started the message that they're a billion dollars in annualized revenue i'm not sure exactly what that means i've seen some numbers on their gross margins i'm not sure what that means i've seen some numbers on their net retention revenue or net revenue retention again i'll reserve judgment until we see an s1 but it's clear both of these companies have momentum and they're out competing in the market well as always be the ultimate arbiter different philosophies perhaps is it like democrats and republicans well it could be but they're both going after a solving data problem both companies are trying to help customers get more value out of their data and both companies are highly valued so they have to perform for their investors to paraphrase ralph nader the similarities may be greater than the differences okay that's it for today thanks to the team from palo alto for this awesome super cloud studio build alex myerson and ken shiffman are on production in the palo alto studios today kristin martin and sheryl knight get the word out to our community rob hoff is our editor-in-chief over at siliconangle thanks to all please check out etr.ai for all the survey data remember these episodes are all available as podcasts wherever you listen just search breaking analysis podcasts i publish each week on wikibon.com and siliconangle.com and you can email me at david.vellante at siliconangle.com or dm me at devellante or comment on my linkedin posts and please as i say etr has got some of the best survey data in the business we track it every quarter and really excited to be partners with them this is dave vellante for the cube insights powered by etr thanks for watching and we'll see you next time on breaking analysis [Music] you

>>Okay, welcome back everyone to Supercloud 22, this is the cube studio's live performance. We streaming virtually@siliconangledotcomandthecube.net. I'm John for host the cube at Dave Alane with a distinguished panel talking about securing the Supercloud all cube alumni G written house was the CEO of Skyhigh security, Peter Sharma founder of, of QX sold to tenable and Tony qua who's investor. Co-founder former head of product at VMware chance. Thanks for coming on and to our, in all girls super cloud pilot event. >>Good to see you guys big topic. >>Okay. So before we get into secure in the cloud, one of the things that we were discussing before we came on camera was how cloud, the relationship between cloud and on premise and multi-cloud and how Supercloud fits into that. At the end of the day, security's driving a lot of the conversations at the op side and dev shift left is happening. We see that out there. So before we get into it, how do you guys see super cloud Tony? We'll start with you. We'll go down the line. What is Supercloud to you? >>Well, to me, super cloud is really the next evolution, the culmination of the services coming all together, right? As a application developer today, you really don't need to worry about where this thing is. Sit sitting or what's the latency cuz cuz the internet is fast enough. Now I really wanna know what services something provides. What, how do I get access to it now? Security. We'll talk about that later. That that becomes a, a big issue because of the fragmentation of how security is implemented across all the different vendors. So to me it's an IP address I program to it and you know, off we go, but there's a lot of >>You like that pipe happens >>Iceberg chart, right? Like I'm the developer touching the APIs up there. There's a bunch of other things. BU service. >>Okay. Looking forward again. Gee, what's your take? Obviously we've had many conversations on the cube. What's your super cloud update. >>Yeah, so I, I view it as just an extension of what we see today before like maybe 10 years ago we were mashing up applications built on other SAS applications and whatnot. Now we're just extending that down to further primitives, not, we don't really care where our mashup resides, what cloud platform, where it sits to Tony's point, as long as you have an IP address. But beyond that, we're just gonna start to get little micro services and deeper into the applications. >>BP, what should you take? >>I think, I think super cloud to me is something that don't don't exist. It exists only on my laptop. That's the super cloud means to me. I know it takes a lot behind the scene to get that working of and running. But, but essentially, essentially that the everything having be able to touch physically versus not being able to touch anything is super cloud to me. >>So we, what Victoria was saying. Yeah, we see serverless out there, all these cool things happening. Exactly. And you look at the, some of the successful companies that have come in, I call V two cloud. Some are, some are saying the next gen, they're all building on top of the CapEx. I mean, if, why would you not wanna leverage all that work AWS is doing and now Azure, and obviously Google's out there and you got other, other, other clouds out there. But in terms of AWS as a hyperscaler, they're spending all the money and they're getting better. They're getting lower level. We're talking about some of that yesterday, data bricks, snowflake, Goldman Sachs there's industry clouds that could be powerhouse service providers to themselves and their vertical. Then you got specialty clouds. Like there could be a data cloud, there could be an identity cloud. So yeah. How does this sort itself out? How do you guys see that? Because can they coexist? >>But I think they have to right, because I, I think, you know, eventually organizations will get big enough where they can be strong and really market leading in multiple segments. But if you think about what it takes to really build a massive scaled out database company that, that DNA doesn't just overnight translate to identity or translate to video, it takes years to build that up. So in the meantime, all these guys have to understand that they are one part of the service stack to power the next gen solutions. And if they don't play well with each other, then you're gonna have a problem. >>So security, I think is one of the hardest problems of, of super cloud. And not only do you have too many tools and a lack of talent, but you've now got this new first line of defense, which is the cloud. And the problem is you've got multiple clouds. So you've got multiple first lines of defense with multiple cloud provider tools. And then the CISO, I guess, is the next line of defense with the application development team. You know, there to be the pivot point between strategy and execution. And I guess audit is the third line of the defense. So it's an even more complicated environment. So gee, how do you see that CSO role changing and, and can there actually be a unified security layer in Supercloud? >>Yeah, so I believe that that they can be, the role is definitely changing because now a CSO actually has to have a basic understanding of how clouds work, the dependency of clouds on the, on the business that they serve. And, and this is to your point, not only do we have these new lines and opening up in a tax surface, but they're coupled together. So we have supply chain type connections between this. So there's a coherence across these systems that a CISO has to kind of think about not only these Bo cloud boundaries, but the trust boundaries between them. So classic example visibility, wh what, where are these things and what are the dependencies in my business then of course you mentioned compliance. Am I regulatory? And then of course protecting and responding to this, >>You know? Yeah. The, the, the supply chain piece that you just mentioned. I mean, I feel like there's like these milestones stocks, net was a milestone, you know, obvious obviously log four J was another one, the supply chain hack with solar winds. Yep. You know, it's just, the adversary just keeps getting stronger and stronger and, and, and more agile. So, so is this a data? Do we solve this as a data problem? Is it, you know, you can't just throw more infrastructure at it. What are your thoughts >>For it? I think, you know, great, great point that you're brought up. We need to look at things very fundamentally. What is happening is security has the most difficult job in the cloud, especially super cloud. The poor guys are managing some, managing something or securing something that they can't govern, right? Your, your custodian of the cloud as your developers and DevOps, they are the ones who are defining, creating, destroying things in the cloud. And that guy sitting at the end of the tunnel, looking at things that what he gets and he has to immediately respond. That's why it has to be fundamentally solve. Number one, we talked about supply chain. We talked about the, the, the stuck net to wanna cry, to sort of wins, to know the most recent one on the pipeline. Once the interesting phenomena is that the way industry has moved super cloud, the attackers are also moving them super attackers, right? They have stopped. They have not stopped, but they have started slowly moving to the left, which is the governance part. So they have started attacking your source code, you know, impersonating the codes, replacing the binary, finding one is there. So if they can, if the cloud is built so early, why can't I go early and, and, and inject myself. >>So super hackers is coming to super thinking Hollywood right now. I mean, that brings up a good point. I mean, this whole trust thing is huge. I mean, I hear zero trust. I think, wait a minute, that's not the conference I was just at, we went to, we managed, we work with DockerCon and they were talking about trust services. Yeah. So supply chain source code has trust brokering going on. And yet you got zero trust, which is which are they contextually different? I mean, what, what, >>What, from my perspective, though, the same in that zero trust is a framework that starts with minimum privileges and then build up those privileges over time. Normally in today's dialogue, zero trust is around access. I'm not having a broad access. I'm having a narrow access around an application, but you can also extend those principles to usage. What can, how much privilege do I have within an application? I have to build up my trust to enhance and, and get extended privileges within an application. Of course you can then extend this naturally to applications, APIs, applications, talking with each other. And so by you, you have to restrict the attack surface that is based on a trust model fundamentally. And then to your point, I mean, there's always this residual that you have to deal with afterwards. >>So, so super cloud implies more surface area. You're talking about private. So here we go. So how, and by the way, the AWS was supposed to be at this conference. They said they couldn't make it. They had a schedule issue, but they wanted to be here, but I would ask them, how do you differentiate AWS going forward? Do you go IAS all the way? Do you release the pass layer up? How does this solve? Because you have native clouds that are doing great, the complexity on super cloud, and multi-cloud has to be solved. >>Let me offer maybe a different argument. So if you think about we're all old enough to see the history sort of re pendulum shift and it shifting back in a way, if you're arguing that this culmination of all these services in the form of cloud today, essentially moving up stack, then really this is a architectural pattern that's emerging, right? And therefore there needs to be a super cloud, almost operating system. So operating systems, if you build one before you need a scheduler, you need process handler, you need process isolation, you need memory storage, compute all that together. Now that is our sitting in different parts of the internet. And, and there is no operating system. Yes. And that's the gap, right? And so if you don't even have an operating system, how do you implement security? And that's the pain. Yeah, because today it's one off, directly from service to service. Like how many times can you set up SAML orchestration? You can have an entire team doing that, right. If that's, that's what you have to do. So I think that's ultimately the gap and, and we're sort of just revolving around this concept that there's missing an operating system for superpower. >>It's like Maribel Lopez said in the previous panel that Lord of the rings, there will be no one ring rule the ball. Right. Probably there is needs one. Oh yeah. But, but, but, so what happens? So again, security's the hardest problem. So Snowflake's gotta implement its security, you know, data bricks with an open source model has to implement its security. So there's these multiple security models. You talk about zero trust, which I, if, if I infer what you said, gee, it's essentially, if you don't have privilege access, you don't get access. Yeah. Right. If you, okay. Okay. So that's the framework. Fine. And then you gotta earn it over time. Yeah. Now companies like Amazon, they have the, the talent and the skills to implement that zero trust framework. Exactly. So, so the, the industry, you, you guys with the R and D have to actually ultimately build that, that super cloud framework, don't you? >>Yeah. But I would just look all of the major cloud providers, the ones you mentioned and more will have their own framework within their own environment. Right? Yeah. The problem is with super cloud, you're extending it across multiple ones. There's no standards. There's no easy way to integrate that. So now all of that is left to the developer who is like throwing out code as fast as they can >>Is their, their job is to abstract that, I mean, they've gotta secure the, the run time, they gotta secure the container. >>You have to >>Abstract it. Right. Okay. But, but they're not security pros or ops. >>Exactly. They're haves. >>But to, but to G's point, right. If everyone's implementing their own little Z TNA, then inherently, there's a blind trust between two vendors. Right. That has to >>Be, >>That has to be >>Established. That's implicit. You're saying, >>Yeah. But, but it's, it's contractual, it's not technology. Right. Because I'm turning something out in my cloud, you're turning out something in your cloud that says we've got something, some token exchange, which gives us trust. But what happens if that breaks down and whatever happens to the third party comes in? I think that's the problem. >>Yeah. In fact, in fact, the, if I put the, you know, combine one of those commons, the zero trust was build, keeping identity authentication, then authorization in mind, right? Yeah. This needs to be extended because the zero test definition now probably go into integrity. Yeah, exactly. Right. Yeah. I authenticated. I worked well with Tony in the past, but how do I know that something has changed on the Tony's side? Yeah, exactly. Right, right. That, that integrity is going to be very, very foundational. Given developers are building those third party libraries, those source code pumping stuff. The only way I can validate is, Hey, what has changed? >>And then throw edge into the equation, John and IOT and machine to machine. Exactly. It's just, >>Well, >>Yeah. I think, I think we have another example to build on Tony's operating system model. Okay. And that is the cloud access service broker model for SAS. So we, we have these services sitting out there, we've brokered them together. They're normally on user policies. What I can have access to what I can do, what I can't do, but that can be extended down to services and have the same kind of broker arrangement all through APIs. You have to establish that trust and the, and the policies there, and they can be dynamic and all of this stuff. But you can from an, either an operating system or a SAS interaction and integration model come to these same kind of points. So who >>Builds the, the, the secure Supercloud? Is it new guys like you? Is it your old company giants like Palo Alto? Who, who actually builds the and secures the Supercloud it sounds like it's an ecosystem. >>Yeah. It is an ecosystem. Absolutely. It's an ecosystem. >>Yeah. There's no one security Supercloud >>As well. No, but I, I do think there's one, there's one difference in that historically security has always focused on that shiny object. The, the, the, a particular solution to a particular threat when you're dealing with a, a cloud or super cloud, like the number of that is incalculable. So you have to come into some sort of platform. And so you will see if it's not one, you know, a finite number of platform type solutions that are trying to solve this on behalf of the >>Customer. That to your point, then get connected. >>I think it's gonna be like Unix, right? Like how many flavors of Unix were there out there? All of them 'em had a scheduler. All of them had these processes. All of them had their little compilers. You can compile to that system, target to that system. And for a while, it's gonna be very fragmented until multiple parties decide to converge. >>Right? Well, this is, this is the final question we have one minute left. I wish we had more time. This is a great panel. We'll we'll bring you guys back for sure. After the event, what one thing needs to happen to unify or get through the other side of this fragmentation than the challenges for Supercloud. Because remember the enterprise equation is solve complexity with more complexity. Well, that's not what the market wants. They want simplicity. They want SA they want ease of use. They want infrastructure risk code. What has to happen? What do you think each of you? >>So I, I can start and extending to the previous conversation. I think we need a consortium. We need, we need a framework that defines that if you really want to operate in super cloud, these are the 10 things that you must follow. It doesn't matter whether you take AWS slash or GCP, or you have all, and you will have the on-prem also, which means that it has to follow a pattern. And that pattern is what is required for super cloud. In my opinion, otherwise security is going everywhere. They're like they have to fix everything, find everything and so on. So forth, it's not gonna be possible. So they need a, they need a framework. They need a consortium. And it, this consortium needs to be, I think, needs to led by the cloud providers, because they're the ones who have these foundational infrastructure elements and the security vendor should contribute on providing more severe detections or findings. So that's, in my opinion is, should be the model. >>Well, thank you G >>Yeah, I would think it's more along the lines of a business model we've seen in cloud that the scale matters. And once you're big, you get bigger. We haven't seen that coals around either a vendor, a business model, whatnot, to bring all of this and connect it all together yet. So that value proposition in the industry I think is missing, but there's elements of it already available. >>I, I think there needs to be a mindset. If you look again, history repeating itself, the internet sort of came together around set of I ETF, RSC standards, everybody embraced and extended it. Right. But still there was at least a baseline. Yeah. And I think at that time, the, the largest and most innovative vendors understood that they couldn't do it by themselves. Right. And so I think what we need is a mindset where these big guys like Google, let's take an example. They're not gonna win at all, but they can have a substantial share. So how do they collaborate with the ecosystem around a set of standards so that they can bring, bring their differentiation and then embrace everybody >>Together. Guys, this has been fantastic. I mean, I would just chime in back in the day, those was proprietary nosis proprietary network protocols. You had kind of an enemy to rally around. I'm not sure. I see an enemy out here right now. So the clouds are doing great. Right? So it's a tough one, but I think super OS super consortiums, super business models are gonna emerge. Thanks so much for spending the time. Great conversation. Thank you for having us to bring, keep going hour superclouds here in Palo Alto, live coverage stream virtually I'm John with Dave. Thanks for watching. Stay with us for more coverage. This break.

>>We're back with Jess Borgman of Starburst and Richard Jarvis of emus health. Okay. We're gonna get into lie. Number two, and that is this an open source based platform cannot give you the performance and control that you can get with a proprietary system. Is that a lie? Justin, the enterprise data warehouse has been pretty dominant and has evolved and matured. Its stack has mature over the years. Why is it not the default platform for data? >>Yeah, well, I think that's become a lie over time. So I, I think, you know, if we go back 10 or 12 years ago with the advent of the first data lake really around Hudu, that probably was true that you couldn't get the performance that you needed to run fast, interactive, SQL queries in a data lake. Now a lot's changed in 10 or 12 years. I remember in the very early days, people would say, you'll, you'll never get performance because you need to be column. You need to store data in a column format. And then, you know, column formats were introduced to, to data lakes. You have Parque ORC file in aro that were created to ultimately deliver performance out of that. So, okay. We got, you know, largely over the performance hurdle, you know, more recently people will say, well, you don't have the ability to do updates and deletes like a traditional data warehouse. >>And now we've got the creation of new data formats, again like iceberg and Delta and DY that do allow for updates and delete. So I think the data lake has continued to mature. And I remember a, a quote from, you know, Kurt Monash many years ago where he said, you know, it takes six or seven years to build a functional database. I think that's that's right. And now we've had almost a decade go by. So, you know, these technologies have matured to really deliver very, very close to the same level performance and functionality of, of cloud data warehouses. So I think the, the reality is that's become a lie and now we have large giant hyperscale internet companies that, you know, don't have the traditional data warehouse at all. They do all of their analytics in a data lake. So I think we've, we've proven that it's very much possible today. >>Thank you for that. And so Richard, talk about your perspective as a practitioner in terms of what open brings you versus, I mean, the closed is it's open as a moving target. I remember Unix used to be open systems and so it's, it is an evolving, you know, spectrum, but, but from your perspective, what does open give you that you can't get from a proprietary system where you are fearful of in a proprietary system? >>I, I suppose for me open buys us the ability to be unsure about the future, because one thing that's always true about technology is it evolves in a, a direction, slightly different to what people expect. And what you don't want to end up is done is backed itself into a corner that then prevents it from innovating. So if you have chosen the technology and you've stored trillions of records in that technology and suddenly a new way of processing or machine learning comes out, you wanna be able to take advantage and your competitive edge might depend upon it. And so I suppose for us, we acknowledge that we don't have perfect vision of what the future might be. And so by backing open storage technologies, we can apply a number of different technologies to the processing of that data. And that gives us the ability to remain relevant, innovate on our data storage. And we have bought our way out of the, any performance concerns because we can use cloud scale infrastructure to scale up and scale down as we need. And so we don't have the concerns that we don't have enough hardware today to process what we want to do, but want to achieve. We can just scale up when we need it and scale back down. So open source has really allowed us to maintain the being at the cutting edge. >>So Justin, let me play devil's advocate here a little bit, and I've talked to JAK about this and you know, obviously her vision is there's an open source that, that data mesh is open source, an open source tooling, and it's not a proprietary, you know, you're not gonna buy a data mesh. You're gonna build it with, with open source toolings and, and vendors like you are gonna support it, but come back to sort of today, you can get to market with a proprietary solution faster. I'm gonna make that statement. You tell me if it's a lie and then you can say, okay, we support Apache iceberg. We're gonna support open source tooling, take a company like VMware, not really in the data business, but how, the way they embraced Kubernetes and, and you know, every new open source thing that comes along, they say, we do that too. Why can't proprietary systems do that and be as effective? >>Yeah, well, I think at least with the, within the data landscape saying that you can access open data formats like iceberg or, or others is, is a bit dis disingenuous because really what you're selling to your customer is a certain degree of performance, a certain SLA, and you know, those cloud data warehouses that can reach beyond their own proprietary storage drop all the performance that they were able to provide. So it is, it reminds me kind of, of, again, going back 10 or 12 years ago when everybody had a connector to hit and that they thought that was the solution, right? But the reality was, you know, a connector was not the same as running workloads in had back then. And I think, think similarly, you know, being able to connect to an external table that lives in an open data format, you know, you're, you're not going to give it the performance that your customers are accustomed to. And at the end of the day, they're always going to be predisposed. They're always going to be incentivized to get that data ingested into the data warehouse, cuz that's where they have control. And you know, the bottom line is the database industry has really been built around vendor lockin. I mean, from the start, how, how many people love Oracle today, but our customers, nonetheless, I think, you know, lockin is, is, is part of this industry. And I think that's really what we're trying to change with open data formats. >>Well, it's interesting reminded when I, you know, I see the, the gas price, the TSR gas price I, I drive up and then I say, oh, that's the cash price credit card. I gotta pay 20 cents more, but okay. But so the, the argument then, so let me, let me come back to you, Justin. So what's wrong with saying, Hey, we support open data formats, but yeah, you're gonna get better performance if you, if you, you keep it into our closed system, are you saying that long term that's gonna come back and bite you cuz you're gonna end up. You mentioned Oracle, you mentioned Teradata. Yeah. That's by, by implication, you're saying that's where snowflake customers are headed. >>Yeah, absolutely. I think this is a movie that, you know, we've all seen before. At least those of us who've been in the industry long enough to, to see this movie play over a couple times. So I do think that's the future. And I think, you know, I loved what Richard said. I actually wrote it down cause I thought it was amazing quote. He said, it buys us the ability to be unsure of the future. That that pretty much says it all the, the future is unknowable and the reality is using open data formats. You remain interoperable with any technology you want to utilize. If you want to use smart to train a machine learning model and you wanna use Starbust to query be a sequel, that's totally cool. They can both work off the same exact, you know, data, data sets by contrast, if you're, you know, focused on a proprietary model, then you're kind of locked in again to that model. I think the same applies to data, sharing to data products, to a wide variety of, of aspects of the data landscape that a proprietary approach kind of closes you and, and locks you in. >>So I would say this Richard, I'd love to get your thoughts on it. Cause I talked to a lot of Oracle customers, not as many te data customers, but, but a lot of Oracle customers and they, you know, they'll admit yeah, you know, they Jimin some price and the license cost they give, but we do get value out of it. And so my question to you, Richard, is, is do the, let's call it data warehouse systems or the proprietary systems. Are they gonna deliver a greater ROI sooner? And is that in allure of, of that customers, you know, are attracted to, or can open platforms deliver as fast an ROI? >>I think the answer to that is it can depend a bit. It depends on your business's skillset. So we are lucky that we have a number of proprietary teams that work in databases that provide our operational data capability. And we have teams of analytics and big data experts who can work with open data sets and open data formats. And so for those different teams, they can get to an ROI more quickly with different technologies for the business though, we can't do better for our operational data stores than proprietary databases. Today we can back off very tight SLAs to them. We can demonstrate reliability from millions of hours of those databases being run enterprise scale, but for an analytics workload where increasing our business is growing in that direction, we can't do better than open data formats with cloud based data mesh type technologies. And so it's not a simple answer. That one will always be the right answer for our business. We definitely have times when proprietary databases provide a capability that we couldn't easily represent or replicate with open technologies. >>Yeah. Richard, stay with you. You mentioned, you know, you know, some things before that, that strike me, you know, the data brick snowflake, you know, thing is a lot of fun for analysts like me. You've got data bricks coming at it. Richard, you mentioned you have a lot of rockstar, data engineers, data bricks coming at it from a data engineering heritage. You get snowflake coming at it from an analytics heritage. Those two worlds are, are colliding people like P Sanji Mohan said, you know what? I think it's actually harder to play in the data engineering. So I E it's easier to for data engineering world to go into the analytics world versus the reverse, but thinking about up and coming engineers and developers preparing for this future of data engineering and data analytics, how, how should they be thinking about the future? What, what's your advice to those young people? >>So I think I'd probably fall back on general programming skill sets. So the advice that I saw years ago was if you have open source technologies, the pythons and Javas on your CV, you command a 20% pay, hike over people who can only do proprietary programming languages. And I think that's true of data technologies as well. And from a business point of view, that makes sense. I'd rather spend the money that I save on proprietary licenses on better engineers, because they can provide more value to the business that can innovate us beyond our competitors. So I think I would my advice to people who are starting here or trying to build teams to capitalize on data assets is begin with open license, free capabilities, because they're very cheap to experiment with. And they generate a lot of interest from people who want to join you as a business. And you can make them very successful early, early doors with, with your analytics journey. >>It's interesting. Again, analysts like myself, we do a lot of TCO work and have over the last 20 plus years and in the world of Oracle, you know, normally it's the staff, that's the biggest nut in total cost of ownership, not an Oracle. It's the it's the license cost is by far the biggest component in the, in the blame pie. All right, Justin, help us close out this segment. We've been talking about this sort of data mesh open, closed snowflake data bricks. Where does Starburst sort of as this engine for the data lake data lake house, the data warehouse, it fit in this, in this world. >>Yeah. So our view on how the future ultimately unfolds is we think that data lakes will be a natural center of gravity for a lot of the reasons that we described open data formats, lowest total cost of ownership, because you get to choose the cheapest storage available to you. Maybe that's S3 or Azure data lake storage, or Google cloud storage, or maybe it's on-prem object storage that you bought at a, at a really good price. So ultimately storing a lot of data in a data lake makes a lot of sense, but I think what makes our perspective unique is we still don't think you're gonna get everything there either. We think that basically centralization of all your data assets is just an impossible endeavor. And so you wanna be able to access data that lives outside of the lake as well. So we kind of think of the lake as maybe the biggest place by volume in terms of how much data you have, but to, to have comprehensive analytics and to truly understand your business and understand it holistically, you need to be able to go access other data sources as well. And so that's the role that we wanna play is to be a single point of access for our customers, provide the right level of fine grained access control so that the right people have access to the right data and ultimately make it easy to discover and consume via, you know, the creation of data products as well. >>Great. Okay. Thanks guys. Right after this quick break, we're gonna be back to debate whether the cloud data model that we see emerging and the so-called modern data stack is really modern, or is it the same wine new bottle when it comes to data architectures, you're watching the cube, the leader in enterprise and emerging tech coverage.

>>Hey everyone. Welcome back to the Cube's live coverage of snowflake summit 22. We are live at Caesar's forum in Vegas, Lisa Martin, with Dave ante, excited to welcome a VIP fresh from the keynote stage, the SAP, a product at snowflake Christian C Claman Christian. Thank you so much for joining us on the queue today. >>Thank you for having me very exciting. >>And thanks for bringing your energy, loved your keynote. I thought, wow. He is really excited about all of the announcements jam packed. We, and we didn't even get to see the entire keynote talk to us about, and, and for the audience, some of the things going on the product revenue in Q1 fiscal 23, 390 4 million, 85% growth, lot of momentum at snowflake. No doubt. >>So I think that the, the punch line is our innovation is if anything, gaining speed. Uh, we were over the moon excited to share many of these projects with customers and partners, cuz some of these efforts have been going on for multiple years. So, um, lots of interesting announcements across the board from making the existing workloads faster, but also we announced some new workloads getting into cyber security, getting into more transactional workloads with uni store. Um, so we're very excited. >>Well first time being back, this is the fourth summit, but the first time being back since 2019 a tremendous amount has changed for snowflake in that time, the IPO, the massive growth in customers, the massive growth in growth in customers with over 1 million in ARR, you talked about one of the things that clearly did not slow down during the last two years is innovation at snowflake. >>Yeah, that, that, that for, for sure, like, um, even though we, we had a, um, highly in the office culture, we did not miss a beat the moment that we said, Hey, let's all start doing zoom based calls. We, we did. So, uh, I dunno if you saw the, the first five minute minutes of my section in the keynote. Yeah. We, we originally talked about summarizing it and no we're gonna spend 40 minutes here. So we did a one minute clip and whatever gets flashed there. So no, the, the pace of innovation, I think it's second to none and maybe I'll highlight the something that we're very proud of. Snowflake is a single product, a single engine. So if we're making a query performance enhancement, it will help the cyber security workload and the low high concurrency, low latency workload. And eventually we're starting to see some of those enhancements all the way to uni store. So, so we get a lot of leverage out of our investments. What's >>Your favorite announcement? >>That's like picking children. Of course. Um, I think the native applications is the one that looks like, eh, I don't know about it on the surface, but it has the biggest potential to change everything like create an entire ecosystem of solutions for within a company or across companies that I don't know that we know what's possible. >>Well, I I've been saying for a while now that you have this application development stack over here, the database is kind of here and then you have the analytics and data pipeline stack. Those are those separate worlds. We, we talk about bringing data and AI and machine intelligence into applications. The only way that that is actually gonna move forward is if you bring those worlds together is a good example of that happening, um, within a proprietary framework, uh, it's probably gonna happen open source organically and you can sort of roll your own. Is that by design or is it just sort of happening? Well, >>The, the, they bring it all into a single platform obviously by design, cuz there is so much friction today on making all the pieces work together, which database do I use for transactions and how do I move data to my analytics system? And how do I keep system, uh, reference data in sync between the two? So, so it's complicated and our mission was remove all of this friction from, from, from the equation. Uh, the open source versus not the way we think about it is opensourcing open formats or even open APIs it's does it help us deliver the solution that we want for our customer? Does it help us solve their problems? In certain instances, it has done in the past and we've opened source frameworks in, in others. We mentioned at the keynote today, the, the integration of iceberg tables, that's an strong embrace of open technologies, but that does not mean that we want to continue to innovate in our formats. A lot of what you see in the open formats is because snowflake proprietary, uh, innovation. So, uh, we have a very clear philosophy around this. Well >>Like any cloud player, you have to bring open source tools in and make them available for your application developers. But take us through an example of, of uni store and specifically how you're embracing transaction data. What's a customer gonna actually do take us paint a picture >>For us. I I'm gonna give you a very simple use case, but I love it because it, it shows the power of the scenario today. When people are ingesting data into snowflake, you wanna do some book capping associating with those loads. So imagine I have, I dunno, a million files. How many of those files have I loaded? Imagine that one of those loads fail, how do you keep in sync? Whether the data made or not with your bookkeeping today, if you had to do it with a separate transactional database for the bookkeeping and the loading in, in snowflake, it is a lot of complexity for you to know what's where with uni store, you can just say, I'm gonna do the bookkeeping with these new table. It's called hybrid tables. The lows are transactional and all of this is a single transaction. So for, for anyone that has dealt with inconsistencies in database world, this is like a godsend. >>Okay. So my interpretation of that's all about what happens when something goes wrong >><laugh> which is a lot of the, everything about transactions. Yeah. It's what happens when goes wrong and goes wrong. Doesn't mean failures like goes wrong is when you're debiting money from your bank account, not having enough balance that counts as go wrong and the transactions should be aborted. So yes, transactions are all about conflict management and we're simplifying that in a broader set of use cases >>And, and in recovery. So you're, you're in fast recovery. So you're, you're the, the business impact of what you're doing is to sort of simplify that process. Is that the easy way to >>Boil down? Pretty much everything we do is about simplification. Like we, we we've seen organizations are large focusing on wrestling infrastructure as opposed to what are the business problems for a Frank who reference something that, that, that I believe very much in like, which is mission alignment. We are working on helping our customers achieve what they're set out to achieve, not giving them more technology for them to their goal to become, to wrestle the infrastructure. So it's all about ease of use all about simplification removal, friction, >>Just so if I may, so mission alignment, you know, you always hear about technology companies that, you know, provide infrastructure or a service, and then the customer takes that and, and, you know, monetizes it pretty much on their own. What the big change that I'm discerning from these announcements is you're talking about directly monetizing and participating in that monetization as a technology partner, but also the marketplace as well. >>Correct. And I would say in some ways this is not new. This has been happening for the last couple of years with data. Like if you just saw our industry data cloud launches, the financial services cloud, it comes with data providers that help you achieve specific outcomes on a specific industry. Mm-hmm <affirmative> what we're doing now is saying, it's not just data. Maybe it's some business logic, maybe it's some machine learning, maybe it's some user interface. So I think we're just turning the knob on collaboration and it's a continuation of what we've been doing. >>Talk a little bit more about mission alignment. When I heard Frank, Sweetman talk about that this morning. I always love that when I hear cultural alignment with organizations, but as you just said, it's really about enabling our customers to deliver outcomes to their customers as the SVP product. Can you, uh, talk a little bit about how the customers are influencing the product roadmap, the innovations and the speed with which things are coming out at snowflake? >>Yeah, so great question. We have several organizations at snowflake that are organized by vertical by industry. So the, the major sales organization is part of ed that the marketplace business development team is organized like that. We have a separate team that provides top leadership by industry vertical, um, globally. And then even within our solution engineering, there is verticals. So we have a longitudinal view of all the different functions and what do we need to do to achieve a set of use cases in a vertical? And all of those functions are in con constant communication with us on this is where the product is, um, seeing an opportunity or could do better for that vertical. So yeah, I can tell you, and obviously we love when, when there's alignment between those, but that's not always the case. You heard us talk about clean rooms now for some time, clean rooms are applicable to almost any industry, but it's red hot for media and advertising, third party, cookie deprecation, and all of that. So we, we get to, to see that lens, that our innovation is informed by industries. >>So we, we're seeing, obviously the evolution of snowflake we talked about in the keynotes today, you guys talked about 2019 and, you know, pre 2019, even it was to me anyway, your first phase was, Hey, we got a simpler EDW. You know, we're gonna pick that off and put it in the cloud and make it elastic and separate compute from storage, all that kind of cool stuff. And then during the pandemic, it was really IPO, but also the data cloud concept, you sort of laid that vision out. And now you're talking about application development, monetization, what I call the super cloud that layer. Right. Okay. So I, are >>You determin it best? >>Yes. You talk about this, uh, these announcements, how they fit into that larger vision where you're >>Going. Great question. The, the, the notion of the data cloud has not changed one bit. The data cloud thesis is that we want to provide amazing technology for our customers, but also facilitate collaboration and content exchange VR platform. And all that we did today is expand what that content can be. It's not just data or little helper function, it's entire applications, entire experiences. That is the, the summing up the, the, the impact of our announcements today. That, that that's the end of it. So it's still about the data cloud. >>So what is impressive to me is that you guys wouldn't couldn't have a company without the hyperscalers, right? It would be a lot different, right? So you built on top of that and, and now you have your customers building their own super clouds. I call it, I get a lot of grief for that term it's but the, the, the big area of criticism I get is, ah, that's just SAS. And I'm like, no, it's not, no, uh, I, I is everybody public who's announcing stuff. I, I better be careful, but you have customers that are actually building services, taking their data, their tooling, their proprietary information, and putting it on the snowflake data cloud and building their own clouds. Yeah. That's different. Then that's not multi-cloud, which is I can run on a different cloud and it's not, is it sass? If it feels like it's something new from a, from your perspective, is, is it different? >>I, I, I love that you called out that running on all clouds is not what we do right. This days, everyone is multi-cloud, you, you run on a VM or a container, and I multi-cloud check, no, we have a single platform that does multi-region multi-cloud but also cross region cross cloud globally, that that is the essence of what we're doing. So it, it is enabling new capabilities. >>I've I've also said, you know, in many respects, the super cloud hides, the underlying complexity, you think about things like exploiting graviton and a developer. Doesn't need to worry about that. You're gonna worry about that. Uh, but at the same time, they, the, as you get into the develop, the world of application development, some of your developers may want access to some of those cloud primitives. Are you providing both? What's the strategy there? >>Generally not in some areas, we, we, we, I would say bleed through some details that are material, but think of the reality of someone that wants to build a solution, it's really difficult to build an awesome solution in one cloud, Hey, you need to do this. What's the latest instance, and is gravity tank gonna help you or not all of that. Now do it for another one and then do it for another one. And I can tell you it's really difficult because we go through that exercise. Snowflake pouring to a new cloud is somewhere between one and two years of effort and not, not a small number of people because you're looking at security models and storage models. So that's the value that we give to anyone know, wants to build a solution and target customers in all three clouds. I >>Mean, people are still gonna do it themselves, but they're gonna spend a lot more and they're gonna lose their focus on what their real business is. And there'll still be that. I think that D DIY market is enormous for you guys, huge >>Opportunity. And there's also the question on what is the cost of that analysis and that effort. And can we amortize it on behalf of all of our customers? Like we talk about graviton, we have not talked about the many things that we evaluated that were not better price performance for our customers. That evaluation happened. That value was delivered by not moving there. >>And when you do it yourself, the curve looks like, okay, Hey, we can do it ourselves. We can make it pretty Inex. And then, and then the costs are gonna decline, but what really happens, like developing a mobile app, you gotta maintain it. And then if you don't have the scale and you don't have the engineering resources, you're just, the, the costs are gonna continue to go through the roof. I, >>I, I love that you compare it to mobile apps. Like, yeah. I still don't understand why every company that wants to build an app has to build two <laugh>. They got it. Yeah. There is no super cloud for the phone. >>Right. >>That's sort of our, our, our broad vision. Not yet. Not, not the phone, but the super cloud. Yeah, >>Yeah, absolutely. >>You >>Get it. This is, and you look out the ecosystem here. I mean, what a difference that you've been pointing this out, Lisa from, from, from 2019, a lot of buzz, it's all about innovation. You see this at, at thing at the reinvent is like the super bowl obviously. And you see that and it used to be, oh, how is, how is AWS gonna compete with snowflake and separate compute with stores? That's I, I feel like in a large way, that's all gone. It's like, okay, how do we like rise the whole, the whole industry? And that's really where the innovation is. >>We have an amazing partnership with AWS and they benefit from what we do. Yes. There's some competitive elements, but we're changing so many things creating so much opportunity that we're more aligned than not. Yeah. >>Last question for you is continuing on the part AWS partnership front, how does a partner like AWS and other partners, how do they fit into the data cloud narrative that you're talking about to customers? >>I would say that other than the one or two teams that are directly competitive, the rest of their teams are part of in data cloud. Like, uh, our relationship with SageMaker as an example is amazing. And a lot of what we wanna deliver to our customers is choice around machine learning, frameworks and tools. And they're part of the data cloud. We're working with them on how do you push down computation to avoid getting data out, to reinforce governance? So I, I would say that and, and go look at it that they have a hundred and something teams. So if two teams out of hundreds, uh, are, are the competitive element, we are largely aligned. And they're part of data cloud. >>Yeah. I mean, you, your customers consume a lot of compute and storage for, >>For a lot. Yes. >>AWS and, and also, you know, increasingly Azure and, and Google. I mean, it's, um, pretty amazing times, uh, Christian, I want to ask you about, um, couple of terms. Uh, one term that came up a couple of times today in Frank's keynote, he said, I'm not gonna call it a data mesh out kind of out of respect for the purists, which is cool, I thought, but then you had a customer stand up Geico and said, we're building a data. Mesh JPMC is, is speaking at this event, building a data mesh. And I look at things through that prism and say, okay, data mesh is about, you know, decentralization. Some, I I'd be curious as to whether or not you tick that box, but it's about building data products. It's about, uh, uh, self-service infrastructure. And it's about automated computational governance. You are actually tipping a lot of the ticking, a lot of those boxes and, and Mike, I guess the big one is, are, are you building a bigger walled garden? But I, I think you would say, no, it's a, it's a giant distributed network, but, but what, what, what do you say to that? We, >>The latter, the latter, yeah, giant distributed, open cloud and open in the sense that we want anyone to plug in and, and someone can say, well, but I cannot read your file formats. Sure. You can with what we announced today, but it's not about that. Our APIs are open. We have rest APIs. We have JDC ODC, probably most popular interfaces ever. Um, and we want everyone to be part of it. If anything, there's lots of areas that we would not want to go into ourselves cause we want partners and customers to go in there. So, no, we we're looking at a very broad ecosystem. We win based on the value created on top of the platform. Yeah. >>And I makes total sense to me. I mean, I think the imaculate conception of data mesh might be a purely open source version of snowflake. I just don't see that happening anytime soon. And so I, I think you're gonna, you are, I wrote about this creating a defacto standard and >>Exactly, and, and I don't like to get into the terminology that, oh, is the data measure? Not, no go look at the concepts like people used to say, but snowflake is not a data lake. Okay. What is the data lake? It's just a pattern. And if you follow the pattern and you can do it, that's fine. Then there's the, uh, emotional quasi-religious overlay open versus not, I think that's a choice. Not necessarily the concept, >>It's a moving target. I mean, I Unix used to be open. You know, that was the, I agree. Now, the reason why I do think the data mesh conversation is important is because Shaak Dani, when she defined data mesh, she pointed out in my view. Anyway, the problems of getting value outta data is that you go through these hyper specialized teams and they're they're blockers in the organization. And I think you in many respects are attacking that. And it's an organizational issue. >>The, the insights in the pattern are a hundred percent value and aligned with what we do, which is they, you want some amount of centralization, some amount of decentralization living in harmony. Uh, yeah. I have no problem with, with terminology. >>And the governance piece is, is, is massive. Especially it's the, the picture's becoming much more clear. Um, whatever's in the data cloud is a first class citizen, right? And you give all these wonderful benefits. I mean, the interesting thing, what you're doing with Dell and, and pure, I, I asked you that on the analyst call, it's a start. You know, I, I, I mean, >>And I said it briefly in, in, in the keynote this morning, we're publishing a set of standard conformance tests. So any storage system can plug into data cloud. >>Yeah. >>And by the way, it's based on S three APIs, another defect of standard. Like it's not a standard, but everyone is emulating that. And we're plugging >>Into that. Yeah. Nobody's complaining against, against S3 API >>About it is a, oh, it's not a Apache project. We shouldn't, who cares. Everyone has standard horizon net. That's it? >>Well, we've seen the mistakes of the past with this. I mean, look at, look at Hadoop, right? There was this huge battle between, you know, Cloudera and Horton works and map, oh, map bar is proprietary. Oh, Horton works is purely open. Cloudera is open. They're, they're all gone now. I mean, not gone, but they're just, they didn't have it. Right. You know, they, they got unfocused. I go back to Frank's book. They were trying to do too much to, to too many of those, the, the, the zoo animals and you can't fund it all >>To be effective for us. It's very important. I can give you, I don't know, 20 announcements or 50 announcements from the conference, but they're all going a singular goal. And it's, this do not trade off governance of data with the ability to get value out of data. That's everything we do. >>And that's critical for every company in every industry these days that has to be a data company to be, to survive, to be competitive, to be able to extract value from data. If data's currency, how do I leverage a tool like snowflake to be able to extract insights from it that I can act on and create value for my organization, Geico was on stage this morning. Everyone knows Geico and their beloved, um, gecko. Yeah. Is there another customer that you had that you think really articulates the value of the data cloud and to Dave's point how snowflake is becoming that defacto standard data platform? >>Well, we had Goldman Goldman Sachs on stage as well today. And he, he, he, he mentioned it that people think of Goldman as investment banking and all of that, but no, at the heart of what they do, there's a lot of data. And how do they make better decisions? So I think we could run through 20 different examples cuz your premise is the most important. Everything is a data problem. If it is not a data problem, you're not collecting the right data and getting the sense that you could be getting. >>These guys are public, right. >>Adobe. >>Yeah. Right. Adobe's doing it. Yeah. I dunno if the other one is, I don't wanna say, I'll have to ask you off camera, but the other financial firm building a super cloud, right. <laugh> yeah. I call it super cloud. So let be taking advantage of uni store. Yeah. To bring different data types in and monetize it. That's to me, that's the future of data. That's that's been the holy grail, right. >>We, we tried to emphasize that this is, is not a, Hey six, six months ago. We decided to do this. No, this is years in the making mm-hmm <affirmative>, which is why we were so excited to finally share it. Cuz you don't wanna say three years from now, we're gonna have something. No, it was the, now we have it. We have it in preview and it's working at it is as close to the holy grail as it gets. >>Yeah. I mean, look, pressure's on Kristin. Let's face it. Enterprise data warehouse failed to live up to the promises. Uh, certainly the data lakes fail to deliver master data management, all that's a Hadoop, all that stuff. There was a lot of hype around that. And a lot of us got really excited. Me included and then customers spent and they were underwhelmed. Yeah. So you know, you, you, you gotta deliver, you say it, you gotta do it. >>And correct. And then the, the other thing is I would say all of those waves of technology, there was no real better choice. >>Right. They added value. I wouldn't >>Debate that. You have to give it a shot. Like when you've bought 20 different appliances and you have all these silos and someone sells you, Hey, Hadoop will unify it. It sounds good. Just didn't do it. >>Yeah. And no debate that it brought some value for those that were agree. Sophisticated enough to deploy it. And I agree. Yeah. But, but this is a whole different ball game. >>Oh, everything we want to do is democratize and simplify mm-hmm <affirmative> yeah. We could go build something that I don't know. 10 companies in the world could use. That's not the sweet spot. Like how do we advance like the, the state of value generation in the world? That's the scale that we're talking about is go make it easy, accessible for everyone. >>Governed >>Governance and imperative this these days it's law. Yes. So >>Yeah, you have to, but it's not, it's, that's a, that's a ch really difficult challenge to create what I'll call automated or computational governance in a federated manner. That's not trivial. >>And that's our thesis. Everything we're doing is snow park, big announcement today. Python. I I've had people tell me well, but Python should be easy to host the Python run time. Like you can do it. Like I think in a week it took us years. Why? Oh, secure. Oh, details a lot. And <inaudible> mentioned it like securing. That is no easy, uh, feed >>Christian. Thank you so much for joining Dave and me bringing your energy from the keynote stage to the cube, set, breaking down some of the major announcements that have come out today. There's no doubt that the flywheel of innovation at snowflake is alive well and moving quickly, >>Innovation is, uh, at an all time hat snowflake. Thank you for having me. All >>Right. Our pleasure Christian from our guest, Dave ante, Lisa Martin here live in Las Vegas at Caesar's forum covering snowflake summit 22. We right back with our next guest.

>>The queue presents Coon and cloud native con Europe, 2022, brought to you by red hat, the cloud native computing foundation and its ecosystem partners. >>Welcome to Licia Spain, a Coon cloud native con Europe, 2022. I'm your host, Keith Townson, along with Paul Gillon senior editor, enterprise architecture for Silicon angle. Paul, we're gonna talk to some amazing people this week. Coon, what the energy here, what, what, what, what would you say about >>It? I'd say it's reminiscent of, of early year, uh, early stage conferences I've seen with other technologies. There is a lot of startup activity. Here's a lot of money in the market, despite the sell off in the stock market lately. Uh, a lot of anticipation that there are, there could be big exits. There could be big things ahead for these companies. You don't see that when you go to the big established conferences, uh, you see just, uh, anticipation here that I don't think you see, uh, you you'll see maybe in a couple years, so it's fun to be here right now. I'm sure it'll be a very different experience in two or three years. >>So welcome to our guest cube alum. Batam Tobar the founder and CEO of Upbound. Welcome back. >>Thank you. Yeah, pleasure to be on, on the show again. >>So Paul, tell us the we're in this phase of migrations and, and moving to cloud native stacks. Are we another replatforming generation? I mean, we've done, the enterprise has done this, you know, time and time again, whether it's from Java to.net or do net to Java or from bare metal to VMs, but are we in another age of replatforming? >>You know, it's interesting. Every company has now become a tech company and every tech company needs to build a very model, you know, modern digital platform for them to actually run their business. And if they don't do that, then they'll probably be out of business. And, um, it is interesting to think about how companies are platforming and replatforming. Like, you know, as you said, just a, a few years back, you know, we were on people using cloud Foundry or using Heroku, you hear Heroku a lot, or, you know, now it's cloud native and Kubernetes and, and it, it begs the question, you know, is this the end? That to your point, is this, you know, do we have a, you know, what, what makes us sure that this is the, you know, the last platform or the future proof platform that, that people are building, >>There's never a last platform, right? There's always something around the core. The question is, is Kubernetes Linux, or is it windows? >>That, that's a good question. Um, it's more like more like Linux. I think, um, you know, the, you know, you've heard this before, but people talk about Kubernetes as a platform off platforms. Um, you can use it to build other platforms and if you know what you're doing, you can probably put, assemble a set of pieces around it and arrive at something that looks and can work for your business, but it requires a ton of talent. It requires a lot of people that actually can act, you know, know how to put this stick together to, to work for your business. It is, there's not a lot of guidance. I, we were, I think we were chatting earlier about the CSCF landscape and, and, um, how there are all these different projects and companies around it, but, but they don't come together in meaningful ways that you have, they act the enterprise itself has to figure out how to bring them together. Right. And that's the combination of what they do there organically or not is their platform. Right. And that changes. It can change over time. >>Do you think they really do. They really want to put these things together? I mean, there's, that's not what enterprise is like to do. They want to find someone who's gonna come in and, uh, turnkey do it all for them. >>Yeah. And, and if there was, this is the, this is the things like EV every week now you hear about another platform that says, this is the new Heroku. This is the new cloud Foundry, this replaces every, you know, some vendor has, and you can see them all around here. You know, companies that are basically selling platform solutions, um, that do put 'em together. And the problem with it is that you typically outgrow these, like you are, um, it might solve 80% of the use cases you care about, but the other 20% are not represented. And so you end up outgrowing the platform itself, right? And the, the choice has been mostly around, you know, do you buy something off the shelf that solves 80% of your use cases, or do you build something on your own? And then you have to spend all your resources actually going through and building all of it. And that's been the dilemma, you know, people who talk about this as a platform dilemma, but it's been, it's been the way for a long time. Like you, every, we go through this cycle every few years and, you know, people end up essentially oscillating between buying something off the, you know, that's off the shelf or building it, building it themselves. >>So what's the payoff. If I'm a CIO and I'm looking at the landscape, I don't need to understand, you know, I don't know to know what a pod is to know that looking at 200 plus projects in co and at, in cloud native, uh, foundation and the bevy of, of co-located projects and, and conferences before they, even the start of this, what's the payoff >>Increasing the pace of innovation. I mean, that literally is when we talk to customers, they all say roughly the same thing. They want something that works for their business. They want something that helps them take their, you know, line of business applications to production in a much quicker way, lets them innovate, lets them create higher engineers that can, don't have to understand everything about every system, but can actually specialize and focus on the, the parts that they sh they care about. Um, but it's all in the context of, you know, people want to be able to innovate at a very high pace, otherwise they get disrupted. >>So I was at the, you know, my favorite part of, of Coon in general is the hallway track and talking to people on the ground, doing cool things. I was talking to a engineer who was able to take their Java, stack their, their, uh, net stack and start to create APIs between and break 'em into microservices. Now teams are working across from one another realizing that, that, that promise of innovation, but that was the end point. They they're there. Yeah. As companies are thinking about replatforming where like, where do we start? I mean, looking at the, the CNCF, the, the map and it's 200 plus projects, where do I start? >>Do you typically today start with Kubernetes and, and um, a lot of companies have now deployed Kubernetes to production as a container orchestrator, whether they're going through a vendor or not, but now you are seeing all the things around it, whether it's C I C D or GI ops that they're looking at, you know, or the starting to build consoles around, you know, their, their platforms or looking at managing more than just containers. And that's a theme that, you know, we're seeing a lot now, people want, people want to actually bring this modern stack to manage, not just container workloads, but start looking at databases and cloud workloads and everything else that they're doing around it. Honestly, everybody's trying to do the same thing. They're trying to arrive at a single point of control, a single, you know, a platform that can do it all that they can centralize policy centralized controls to compliance governance, cost controls, and then expose a self-service experience to developers. Like they're all trying to build what we probably call an internal cloud platform. They don't know, they talk about it in different ways, but almost everyone is trying to build some internal platform that sits on top of, on premises. And on top of cloud, depending on their scenarios, >>You make an interesting point, which is that everyone here is to some extent trying to do the same thing. And there's fine points of granularity between now they're approaching it as you walk around this floor. Do you understand what all of these companies are doing? >>I'm not sure I understand all of them, but I, I do. I do recognize a lot of them. Yes. >>And in terms of your approach, you, you use the term control plane, uh, what is distinctive about your approach? >>Very good question. So, you know, we, we end up out take a, um, we we're trying to solve, uh, this problem as well. We're trying to help people build their own platforms. Um, but let me, let me, you know, there's a lot to it. So let me actually step back and talk about the architecture of this. But if you were to look at any cloud platform, let's take the largest one. AWS, if you peek behind the scenes at AWS, you know, um, it's basically a set of independent services, EC two S3 databases, et cetera, um, that are, you know, essentially working on different parts of, you know, like offer completely different pricing, different services, et cetera. They come together because they all integrate into a control plan. >>It's the thing that serves an API. It's the thing that gives it all a common field. It's where you do access control. It's where you do, um, billing, metering, cost control policy, et cetera. Right? And so our realization was if the enterprises are platforming and replatforming, why shouldn't they build their platform in the same way that the cloud vendors build theirs? And so we started this project almost four years ago, now three and a half years, um, called cross plain, which is a, essentially an open source control plane that can become the integration point for all services. And essentially gives you a universal control plane for cloud. >>So you mentioned the idea of the orchestrating or managing stuff other than containers, as I think about companies that built amazing platforms, enterprise companies, building amazing applications on AWS 10 years ago, and they're adopting the AWS control plane. And now I'm looking at Kubernetes is Kubernetes the way to multi-cloud to be able to control those discrete applic, uh, services in a AWS or Google cloud Azure or Oracle cloud is cetera. >>We kind of have the tease it, the parts. So there are really two parts to Kubernetes and everybody thinks of Kubernetes as a container orchestration platform. Right? And, um, you know, there is a sense that people say, if I was to run Kubernetes on everywhere and can build everything on top of containers, that I get some kind of portability across clouds, right. That I can put things in containers. And then they magically run, you know, in different environments. Um, in reality, what we've seen is not everything fits in containers. It's not gonna be the world is not gonna look like containers on the bottom. Everything else is on top. Instead, what we're gonna see is essentially a set of services that people are using across the different vendors. So if you look at like, you could be at AWS shop primarily, but I bet you're using confluent or elastic or data breaks or snowflake or Mongo or other services. >>I bet you're using things that are on premises, right? And so when you look at that and you say to build my platform as an enterprise, I have to consume services from multiple vendors. Even it's just one major cloud vendor, but I'm consuming services from others. How do I bring them together in meaningful ways so that I can, you know, build my platform on top of the collection of them and offer something that my developers can consume. And self-service on. That's not a, that's not just containers. What's interesting though, is if you look at Kubernetes and, you know, look inside it, Kubernetes built a control plane. That's actually quite useful and applicable outside of container scenarios. So this whole notion of CRDs and controllers, if you've heard that term, um, the ability, you know, like there are two parts to Kubernetes, there is the control plane, and then there's the container container, uh, workloads and the control plane is generic. >>It could be used literally across, you know, you can use it to manage things that are completely outside of container workloads. And that's what we did with cross plain. We took the control plane of Kubernetes and then built bindings providers that connected to AWS, to Google, to Azure, to digital ocean, to all these different environments. So you can bring the way of managing, you know, the style of managing that Kubernetes invented to more than just containers. You can now manage cloud services, using the same approach that you are now using with Kubernetes and using the entire ecosystem of tooling around it. >>Enterprise have been under pressure replatform for a long time. It was first go to Unix then to Linux and virtualize then to move to the cloud. Now, Kubernetes, do you think that this is the stack that enterprises can finally commit to? >>I think if you take the orientation of your deploying a control plane within your enterprise, that is extensible, that enables you to actually connect it to all the things that are under your domain, um, that that actually can be a Futureproof way of doing a platform. And, you know, if you look at the largest cloud platforms, AWS has been around for at least 15 years now, uh, and they really haven't changed the architecture of AWS significantly. It's still a control plane, a set of control planes that are managing services. >>It's a legacy >>They've added a lot of services. They've have a ton of diversity. They've added so many different things, but the architecture is still a hub and spoke that they've built, right? And if the enterprise can take the same orientation, put a control plane, let it manage all the things that are, you know, about today, arrive at a single point of control, have a single point where you can enforce policy compliance, cost controls, et cetera, mm-hmm <affirmative>, and then expose a self-service experience to your developers that actually can become future proof. >>So we've heard this promise before the cloud of clouds, basically. Yes, the, the, to be able to manage everything, what we find is the devils in the details. The being able to say, you know, a load balancer issuing a, a command to, to deploy a load balancer in AWS is different than it is in Azure, which is different than it is in GCP. How do, how do enterprises know that we can talk to a single control plane to do that? I mean, that just seems extremely difficult to manage. Oh >>Yeah. That, um, the approach is not, you're not trying to create a lowest common denominator between clouds. That's a really, really hard problem. And in fact, you get relegated to just using this, you know, really shallow features of each, if you're, if you're gonna do that, like your, your example of load balancers, load balances look completely different between between cloud vendors. Um, the approach that we kind of advocate for is that you shouldn't think of them as you shouldn't try to unify them in a way that makes them, you know, there's a, uh, there's a global abstraction that says, oh, there's a load balancer. And it somehow magically works across the different cloud vendors. I think that's a really, really hard thing to say, to do as you point out. However, if you bring them all under a same control plane, As different as they are, you're able to now apply policies. You're able to set cost controls. You're able to expose a self-service experience on top of them, even, even if they are very different. And that's, that's something that I think is, you know, been hard to do in the past. >>So BAAM, we'll love to dig deeper into this in future segments. And I'm gonna take a look at the, the, the product and project <laugh> and see where you folks land in this conversation from Valencia Spain, I'm Keith towns. And along with Paul Gillon, and you're watching the leader in high tech.

(upbeat music) >> theCUBE presents KubeCon and CloudNativeCon Europe 22, brought to you by the Cloud Native Computing Foundation. >> Valencia, Spain, a KubeCon, CloudNativeCon Europe 2022. I'm Keith Towns along with Paul Gillon, Senior Editor Enterprise Architecture for Silicon Angle. Welcome Paul. >> Thank you Keith, pleasure to work with you. >> We're going to have some amazing people this week. I think I saw stat this morning, 65% of the attendees, 7,500 folks. First time KubeCon attendees, is this your first conference? >> It is my first KubeCon and it is amazing to see how many people are here and to think of just a couple of years ago, three years ago, we were still talking about, what the Cloud was, what the Cloud was going to do and how we were going to integrate multiple Clouds. And now we have this whole new framework for computing that is just rifled out of nowhere. And as we can see by the number of people who are here this has become the dominant trend in Enterprise Architecture right now how to adopt Kubernetes and containers, build microservices based applications, and really get to that transparent Cloud that has been so elusive. >> It has been elusive. And we are seeing vendors from startups with just a few dozen people, to some of the traditional players we see in the enterprise space with 1000s of employees looking to capture kind of lightning in a bottle so to speak, this elusive concept of multicloud. >> And what we're seeing here is very typical of an early stage conference. I've seen many times over the years where the floor is really dominated by companies, frankly, I've never heard of that. The many of them are only two or three years old, you don't see the big dominant computing players with the presence here that these smaller companies have. That's very typical. We saw that in the PC age, we saw it in the early days of Unix and it's happening again. And what will happen over time is that a lot of these companies will be acquired, there'll be some consolidation. And the nature of this show will change, I think dramatically over the next couple or three years but there is an excitement and an energy in this auditorium today that is really a lot of fun and very reminiscent of other new technologies just as they requested. >> Well, speaking of new technologies, we have Dave Cole, CRO, Chief Revenue Officer. >> That's right. >> Chief Marketing Officer of Spectrum Cloud. Welcome to the show. >> Thank you. It's great to be here. >> So let's talk about this big ecosystem, Kubernetes. >> Yes. >> Solve problem? >> Well the dream is... Well, first of all applications are really the lifeblood of a company, whether it's our phone or whether it's a big company trying to connect with its customers about applications. And so the whole idea today is how do I build these applications to build that tight relationship with my customers? And how do I reinvent these applications rapidly in along comes containerization which helps you innovate more quickly? And certainly a dominant technology there is Kubernetes. And the question is, how do you get Kubernetes to help you build applications that can be born anywhere and live anywhere and take advantage of the places that it's running? Because everywhere has pluses and minuses. >> So you know what, the promise of Kubernetes from when I first read about it years ago is, runs on my laptop? >> Yeah. >> I can push it to any Cloud, any platforms. >> That's right, that's right. >> Where's the gap? Where are we in that phase? Like talk to me about scale? Is it that simple? >> Well, that is actually the problem is that today, while the technology is the dominant containerization technology in orchestration technology, it really still takes a power user, it really hasn't been very approachable to the masses. And so was these very expensive highly skilled resources that sit in a dark corner that have focused on Kubernetes, but that now is trying to evolve to make it more accessible to the masses. It's not about sort of hand wiring together, what is a typical 20 layer stack, to really manage Kubernetes and then have your engineers manually can reconfigure it and make sure everything works together. Now it's about how do I create these stacks, make it easy to deploy and manage at scale? So we've gone from sort of DIY Developer Centric to all right, now how do I manage this at scale? >> Now this is a point that is important, I think is often overlooked. This is not just about Kubernetes. This is about a whole stack of Cloud Native Technologies. And you who is going to integrate that all that stuff, piece that stuff together? Obviously, you have a role in that. But in the enterprise, what is the awareness level of how complex this stack is and how difficult it is to assemble? >> We see a recognition of that we've had developers working on Kubernetes and applications, but now when we say, how do we weave it into our production environments? How do we ensure things like scalability and governance? How do we have this sort of interesting mix of innovation, flexibility, but with control? And that's sort of an interesting combination where you want developers to be able to run fast and use the latest tools, but you need to create these guardrails to deploy it at scale. >> So where do the developers fit in that operation stack then? Is Kubernetes an AIOps or an ops task or is it sort of a shared task across the development spectrum? >> Well, I think there's a desire to allow application developers to just focus on the application and have a Kubernetes related technology that ensures that all of the infrastructure and related application services are just there to support them. And because the typical stack from the operating system to the application can be up to 20 different layers, components, you just want all those components to work together, you don't want application developers to worry about those things. And the latest technologies like Spectra Cloud there's others are making that easy application engineers focus on their apps, all of the infrastructure and the services are taken care of. And those apps can then live natively on any environment. >> So help paint this picture for us. I get AKS, EKS, Anthos, all of these distributions OpenShift, the Tanzu, where's Spectra Cloud helping me to kind of cobble together all these different distros, I thought distro was the thing just like Linux has different distros, Randy said different distros. >> That actually is the irony, is that sort of the age of debating the distros largely is over. There are a lot of distros and if you look at them there are largely shades of gray in being different from each other. But the Kubernetes distribution is just one element of like 20 elements that all have to work together. So right now what's happening is that it's not about the distribution it's now how do I again, sorry to repeat myself, but move this into scale? How do I move it into deploy at scale to be able to manage ongoing at scale to be able to innovate at-scale, to allow engineers as I said, use the coolest tools but still have technical guardrails that the enterprise knows, they'll be in control of. >> What does at-scale mean to the enterprise customers you're talking to now? What do they mean when they say that? >> Well, I think it's interesting because we think scale's different because we've all been in the industry and it's frankly, sort of boring old word. But today it means different things, like how do I automate the deployment at-scale? How do I be able to make it really easy to provision resources for applications on any environment, from either a virtualized or bare metal data center, Cloud, or today Edge is really big, where people are trying to push applications out to be closer to the source of the data. And so you want to be able to deploy it-scale, you want to manage at-scale, you want to make it easy to, as I said earlier, allow application developers to build their applications, but ITOps wants the ability to ensure security and governance and all of that. And then finally innovate at-scale. If you look at this show, it's interesting, three years ago when we started Spectra Cloud, there are about 1400 businesses or technologies in the Kubernetes ecosystem, today there's over 1800 and all of these technologies made up of open source and commercial all version in a different rates, it becomes an insurmountable problem, unless you can set those guardrails sort of that balance between flexibility, control, let developers access the technologies. But again, manage it as a part of your normal processes of a scaled operation. >> So Dave, I'm a little challenged here, because I'm hearing two where I typically consider conflicting terms. Flexibility, control. >> Yes. >> In order to achieve control, I need complexity, in order to choose flexibility, I need t-shirt, one t-shirt fits all and I get simplicity. How can I get both that just doesn't compute. >> Well, that's the opportunity and the challenge at the same time. So you're right. So developers want choice, good developers want the ability to choose the latest technology so they can innovate rapidly. And yet ITOps, wants to be able to make sure that there are guardrails. And so with some of today's technologies, like Spectra Cloud, it is, you have the ability to get both. We actually worked with dimensional research, and we sponsor an annual state of Kubernetes survey. We found this last summer, that two out of three IT executives said, you could not have both flexibility and control together, but in fact they want it. And so it is this interesting balance, how do I give engineers the ability to get anything they want, but ITOps the ability to establish control. And that's why Kubernetes is really at its next inflection point. Whereas I mentioned, it's not debates about the distro or DIY projects. It's not big incumbents creating siloed Kubernetes solutions, but in fact it's about allowing all these technologies to work together and be able to establish these controls. And that's really where the industry is today. >> Enterprise , enterprise CIOs, do not typically like to take chances. Now we were talking about the growth in the market that you described from 1400, 1800 vendors, most of these companies, very small startups, our enterprises are you seeing them willing to take a leap with these unproven companies? Or are they holding back and waiting for the IBMs, the HPS, the MicrosoftS to come in with the VMwares with whatever they solution they have? >> I think so. I mean, we sell to the global 2000. We had yesterday, as a part of Edge day here at the event, we had GE Healthcare as one of our customers telling their story, and they're a market share leader in medical imaging equipment, X-rays, MRIs, CAT scans, and they're starting to treat those as Edge devices. And so here is a very large established company, a leader in their industry, working with people like Spectra Cloud, realizing that Kubernetes is interesting technology. The Edge is an interesting thought but how do I marry the two together? So we are seeing large corporations seeing so much of an opportunity that they're working with the smaller companies, the latest technology. >> So let's talk about the Edge a little, you kind of opened it up there. How should customers think about the Edge versus the Cloud Data Center or even bare metal? >> Actually it's a... Well bare metal is fairly easy is that many people are looking to reduce some of the overhead or inefficiencies of the virtualized environment. But we've had really sort of parallel little white tornadoes, we've had bare metal as infrastructure that's been developing, and then we've had orchestration developing but they haven't really come together very well. Lately, we're finally starting to see that come together. Spectra Cloud contributed to open source a metal as a service technology that finally brings these two worlds together, making bare metal much more approachable to the enterprise. Edge is interesting, because it seems pretty obvious, you want to push your application out closer to your source of data, whether it's AI inferencing, or IoT or anything like that, you don't want to worry about intermittent connectivity or latency or anything like that. But people have wanted to be able to treat the Edge as if it's almost like a Cloud, where all I worry about is the app. So really, the Edge to us is just the next extension in a multi-Cloud sort of motif where I want these Edge devices to require low IT resources, to automate the provisioning, automate the ongoing version management, patch management, really act like a Cloud. And we're seeing this as very popular now. And I just used the GE Healthcare example of that, imagine a CAT scan machine, I'm making this part up in China and that's just an Edge device and it's doing medical imagery which is very intense in terms of data, you want to be able to process it quickly and accurately, as close to the endpoint, the healthcare provider is possible. >> So let's talk about that in some level of details, we think about kind of Edge and these fixed devices such as imaging device, are we putting agents on there, or we looking at something talking back to the Cloud? Where does special Cloud inject and help make that simple, that problem of just having dispersed endpoints all over the world simpler? >> Sure. Well we announced our Edge Kubernetes, Edge solution at a big medical conference called HIMMS, months ago. And what we allow you to do is we allow the application engineers to develop their application, and then you can de you can design this declarative model this cluster API, but beyond Cluster profile which determines which additional application services you need and the Edge device, all the person has to do with the endpoint is plug in the power, plug in the communications, it registers the Edge device, it automates the deployment of the full stack and then it does the ongoing versioning and patch management, sort of a self-driving Edge device running Kubernetes. And we make it just very easy. No IT resources required at the endpoint, no expensive field engineering resources to go to these endpoints twice a year to apply new patches and things like that, all automated. >> But there's so many different types of Edge devices with different capabilities, different operating systems, some have no operating system. I mean that seems, like a much more complex environment, just calling it the Edge is simple, but what you're really talking about is 1000s of different devices, that you have to run your applications on how are you dealing with that? >> So one of the ways is that we're really unbiased. In other words, we're OS and distro agnostic. So we don't want to debate about which distribution you like, we don't want to debate about which OS you want to use. The truth is, you're right. There's different environments and different choices that you'll want to make. And so the key is, how do you incorporate those and also recognize everything beyond those, OS and Kubernetes and all of that and manage that full stack. So that's what we do, is we allow you to choose which tools you want to use and let it be deployed and managed on any environment. >> And who's... >> So... >> I'm sorry Keith, who's responsible for making Kubernetes run on the Edge device. >> We do. We provision the entire stack. I mean, of course the company does using our product, but we provision the entire Kubernetes infrastructure stack, all the application services and the application itself on that device. >> So I would love to dig into like where pods happen and all that. But, provisioning is getting to the point that is a solve problem. Day two. >> Yes. >> Like you just mentioned HIMMS, highly regulated environments. How does Spectra Cloud helping with configuration management, change control, audit, compliance, et cetera, the hard stuff. >> Yep. And one of the things we do, you bring up a good point is we manage the full life cycle from day zero, which is sort of create, deploy, all the way to day two, which is about access control, security, it's about ongoing versioning in a patch management. It's all of that built into the platform. But you're right, like the medical industry has a lot of regulations. And so you need to be able to make sure that everything works, it's always up to the latest level have the highest level of security. And so all that's built into the platform. It's not just a fire and forget it really is about that full life cycle of deploying, managing on an ongoing basis. >> Well, Dave, I'd love to go into a great deal of detail with you about kind of this day two ops and I think we'll be covering a lot more of that topic, Paul, throughout the week, as we talk about just as we've gotten past, how do I deploy Kubernetes pod, to how do I actually operate IT? >> Absolutely, absolutely. The devil is in the details as they say. >> Well, and also too, you have to recognize that the Edge has some very unique requirements, you want very small form factors, typically, you want low IT resources, it has to be sort of zero touch or low touch because if you're a large food provider with 20,000 store locations, you don't want to send out field engineers two or three times a year to update them. So it really is an interesting beast and we have some exciting technology and people like GE are using that. >> Well, Dave, thanks a lot for coming on theCUBE, you're now KubeCon, you've not been on before? >> I have actually, yes its... But I always enjoy it. >> Great conversation. From Valencia, Spain. I'm Keith Towns, along with Paul Gillon and you're watching theCUBE, the leader in high tech coverage. (upbeat music)

>>The cube presents, Coon and cloud native con Europe 22 brought to you by the cloud native computing foundation. >>Welcome to Licia Spain in Coon cloud native con Europe, 2022. I'm your host, Keith Townson, along with Paul Gillon senior editor, enterprise architecture for Silicon angle. Paul, we're gonna talk to some amazing people this week. Coon, what the energy here, what, what, what would you say about >>It? I'd say it's reminiscent of, of early year, early stage conferences I've seen with other technologies. There is a lot of startup activity. Here's a lot of money in the market, despite the selloff in the stock market lately, a lot of anticipation that there are, there could be big exits. There could be big things ahead for these companies. You don't see that when you go to the big established conferences, you see just anticipation here that I don't think you see you you'll see maybe in a couple of years. So it's fun to be here right now. I'm sure it'll be a very different experience in two or three years. >>So welcome to our guest Q alum. BAAM Tobar the founder and CEO of Upbound. Welcome back. >>Thank you. Yeah, pleasure to be on, on the show again. >>So Paul, tell us the we're in this phase of migrations and, and moving to cloud native stacks. Are we another re-platforming generation? I mean, we've done, the enterprise has done this, you know, time and time again, and whether it's from Java to.net or net to Java or from bare metal to VMs, but are we in another age of replatforming? >>You know, it's interesting. Every company has now become a tech company and every tech company needs to build a very model, you know, modern digital platform for them to actually run their business. And if they don't do that, then they'll probably be out of business. And it is interesting to think about how companies are platforming and replatforming. Like, you know, as you said, just a, a few years back, you know, we were on people using cloud Foundry or using Heroku, you hear Heroku a lot, or, you know, now it's cloud native and Kubernetes and, and it, it begs the question, you know, is this the end that the tr point is this, you know, do we have a, you know, what, what makes us sure that this is the, you know, the last platform or the future proof platform that, that people are building, >>There's never a last platform, right? There's always something around the core. The question is, is Kubernetes Linux, or is it windows? >>That, that's a good question. It's more like more like Linux. I think, you know, the, you know, you've heard this before, but people talk about Kubernetes as a platform off platforms, you can use it to build other platforms. And if you know what you're doing, you can probably put, assemble a set of pieces around it and arrive at something that looks and can work for your business. But it requires a ton of talent. It requires a lot of people that actually can act, you know, know how to put the stick together to, to work for your business. It is, there's not a lot of guidance. I, we were, I think we were chatting earlier about the CSCF landscape and, and how there all these different projects and companies around it. But, but they don't come together in meaningful ways that you have, they act the enterprise itself has to figure out how to bring them together. Right. And that's the combination of what they do there organically or not is their platform. Right. And that changes. It can change over time. >>Do you think they really do. They really want to put these things together? I mean, there's, that's not what enterprise is like to do. They want to find someone who's gonna come in and turnkey do it all for >>Them. Yeah. And, and if there were, this is the, this is the things like EV every week now you hear about another platform that says, this is the new Heroku. This is the new cloud Foundry. This replaces every, you know, some vendor has, and you can see them all around here. You know, companies that are basically selling platform solutions that do put 'em together. And the problem with it is that you typically outgrow these, like you are, it might solve 80% of the use cases you care about, but the other 20% are not represented. And so you end up outgrowing the platform itself, right? And the, the choice has been mostly around, you know, do you buy something off the shelf that solves 80% of your use cases? Or do you build something on your own? And then you have to spend all your resources actually going through and building all of it. And that's been the dilemma, you know, people who talk about this as a platform dilemma, but it's been, it's been the way for a long time. Like you, every, we go through this cycle every few years and, you know, people end up essentially oscillating between buying something off the, you know, that's off the shelf or building it, building it themselves. >>So what's the payoff. If I'm a CIO and I'm looking at the landscape, I don't need to understand, you know, I don't know what a pod is to know that looking at 200 plus projects in co and at, in cloud native foundation and the bevy of, of co-located projects and, and conferences before the, even the start of this, what's the payoff >>Increasing the pace of innovation. I mean, that literally is when we talk to customers, they all say roughly the same thing. They want something that works for their business. They want something that helps them take their, you know, line of business applications to production in a much quicker way, lets them innovate, lets them create higher engineers that can, don't have to understand everything about every system, but can actually specialize and focus on the, the parts that they sh they care about. But it's all in the context of, you know, people want to be able to innovate at a very high pace. Otherwise they get disrupted. >>So I was at the, you know, my favorite part of coan in general is the hallway track and talking to people on the ground, doing cool things. I was talking to a engineer who was able to take their Java, stack their, their.net stack and start to create APIs between and break 'em into microservices. Now teams are working across from one another realizing that, that, that promise of innovation, but that was the end point. They they're there. Yeah. As companies are thinking about replatforming where like, where do we start? I mean, I'm looking at the, the C CNCF, the, the map and it's 200 plus projects. Where, where do I start? >>You typically today start with Kubernetes. And, and a lot of companies have now deployed Kubernetes to production as a container orchestrator, whether they're going through a vendor or not. But now you're seeing all the things around it, whether it's C I C D or GI ops that they're looking at, you know, or they're starting to build consoles around, you know, their, their platforms or looking at managing more than just containers. And that's a theme that, you know, we're seeing a lot now, people want, people want to actually bring this modern stack to manage, not just container workloads, but start looking at databases and cloud workloads and everything else that they're doing around it. Honestly, everybody's trying to do the same thing. They're trying to arrive at a single point of control, a single, you know, a platform that can do it all that they can centralize policies, centralized controls to compliance governance, cost controls, and then expose a self-service experience to the developers. Like they're all trying to build what we probably call an internal cloud platform. They don't know, they talk about it in different ways, but almost everyone is trying to build some internal platform that sits on top of, on premises. And on top of cloud, depending on their scenarios, >>You make an interesting point, which is that everyone here is to some extent trying to do the same thing. And there's fine points of granularity between now they're approaching it as you walk around this floor. Do you understand what all of these companies are doing? >>I'm not sure I understand all of them, but I, I do. I do recognize a lot of them. Yes. >>And in terms of your approach, you, you use the term control plane. What is distinctive about your approach? >>Very good question. So, you know, we, we end, Upbound take a, we we're trying to solve this problem as well. We're trying to help people build their own platforms, but let me, let me, you know, there's a lot to it. So let me actually step back and, and talk about the architecture of this. But if you were to look at any cloud platform, let's take the largest one. AWS, if you peek behind the scenes at AWS, you know, it's basically a set of independent services, EC two S three databases, et cetera, that are, you know, essentially working on different parts of, you know, like offer completely different pricing, different services, et cetera. They come together because they all integrate into a control plan. >>It's the thing that serves an API. It's the thing that gives it all a common feel. It's where you do access control. It's where you do billing metering, cost control policy, et cetera. Right? And so our realization was if the enterprises are platforming and replatforming, why shouldn't they build their platform in the same way that the cloud vendors build theirs? And so we started this project almost four years ago, now three and a half years called cross plain, which is a, essentially an open source control plane that can become the integration point for all services. And essentially gives you a universal control plane for cloud. >>So you mentioned the idea of if orchestrating or managing stuff other than containers, as I think about companies that built amazing platforms, enterprise companies, building amazing applications on AWS 10 years ago, and they're adopting the AWS control plane. And now I'm looking at Kubernetes is Kubernetes the way to multi-cloud to be able to control those discrete services in a AWS or Google cloud Azure or Oracle cloud, is that true? >>We kind have the tease it, the parts. So there are really two parts to Kubernetes and everybody thinks of Kubernetes as a container orchestration platform. Right? And you know, there is a sense that people say, if I was to run Kubernetes on everywhere and can build everything on top of containers, that I get some kind of portability across clouds, right. That I can put things in containers. And then they magically run, you know, in different environments. In reality, what we've seen is not everything fits in containers. It's not gonna be the world is not gonna look like containers on the bottom. Everything else is on top. Instead, what we're gonna see is essentially a set of services that people are using across the different vendors. So if you look at like, you could be at AWS shop primarily, but I bet you're using confluent or elastic or data breaks or snowflake or Mongo or other services. >>I bet you're using things that are on premises, right? And so when you look at that and you say to build my platform as an enterprise, I have to consume services from multiple vendors. Even if it's just one major cloud vendor, but I'm consuming services from others. How do I bring them together in meaningful ways so that I can, you know, build my platform on top of the collection of them and offer something that my developers can consume. And self-service on. That's not a, that's not just containers. What's interesting though, is if you look at Kubernetes and, you know, look inside it, Kubernetes built a control plane. That's actually quite useful and applicable outside of container scenarios. So this whole notion of CRDs and controllers, if you've heard that term, the ability, you know, like there are two parts to Kubernetes, there is a control plane, and then there's the container container workloads. >>And the control plane is generic. It could be used literally across, you know, you can use it to manage things that are completely outside of container workloads. And that's what we did with cross mind. We took the control plane of Kubernetes and then built bindings providers that connected to AWS, to Google, to Azure, to digital ocean, to all these different environments. So you can bring the way of managing, you know, the style of managing that Kubernetes invented to more than just containers. You can now manage cloud services, using the same approach that you are now using with Kubernetes and using the entire ecosystem of tooling around it. >>Enterprise has been under pressure to replatform for a long time. It was first go to Unix then to Linux and virtualize then to move to the cloud. Now, Kubernetes, do you think that this is the stack that enterprises can finally commit to? >>I think if you take the orientation of your deploying a control plane within your enterprise, that is extensible, that enables you to actually connect it to all the things that are under your domain, that that actually can be a Futureproof way of doing a platform. And, you know, if you look at the largest cloud platforms, AWS has been around for at least 15 years now, and they really haven't changed the architecture of AWS significantly. It's still a control plane, a set of control planes that are managing services. >>It's a legacy >>They've added a lot of services. They've have a ton of diversity. They've added so many different things, but the architecture is still a hub and spoke that they've built, right? And if the enterprise can take the same orientation, put a control plane, let it manage all the things that are, you know, about today, arrive at a single point of control, have a single point where you can enforce policy compliance, cost controls, et cetera, and then expose a self-service experience to your developers that actually can become future proof. >>So we've heard this promise before the cloud of clouds, basically, yes, the, the, to be able to manage everything, what we find is the devils in the details. The being able to say, you know, a load balancer issuing a, a command to, to deploy a load balancer in AWS is different than it is in Azure, which is different than it is in GCP. How do, how do enterprises know that we can talk to a single control plane to do that? I mean, that just seems extremely difficult to manage. >>Oh yeah. That the approach is not, you're not trying to create a lowest common denominator between clouds. That's a really, really hard problem. And in fact, you get relegated to just using this, you know, really shallow features of each, if you're, if you're gonna do that, like your, your example of load balancers, load balances look completely different between between cloud vendors, the approach that we kind of advocate for is that you shouldn't think of them as you shouldn't try to unify them in a way that makes them, you know, there's a, there's a global abstraction that says, oh, there's a load balancer. And it somehow magically works across the different cloud vendors. I think that's a really, really hard thing to say, to do as you pointed out. However, if you bring them all under a same control plane, as different as they are, you're able to now apply policies. You're able to set cost controls. You're able to expose a self-service experience on top of them, even, even if they are very different. And that's, that's something that I think is, you know, been hard to do in the past. >>So BAAM, we'll love to dig deeper into this in future segments. And I'm gonna take a look at the, the, the product and project and see where you folks land in this conversation from Valencia Spain, I'm Keith towns, along with Paul Gillon and you're watching the leader in high tech coverage.

>>The cube presents, Coon and cloud native con Europe 22 brought to you by the cloud native computing foundation. >>Lisia Spain, a cuon cloud native con Europe 2022. I'm Keith towns, along with Paul Gillon, senior editor, enterprise architecture for Silicon angle. Welcome Paul, >>Thank you, Keith pleasure to work >>With you. You know, we're gonna have some amazing people this week. I think I saw stat this morning, 65% of the attendees, 7,500 folks. First time Q con attendees. This is your first conference. >>It is my first cubic con and it is amazing to see how many people are here and to think of, you know, just a couple of years ago, three years ago, we were still talking about what the cloud was and what the cloud was gonna do and how we were gonna integrate multiple clouds. And now we have this whole new framework for computing that is just rifled out of, out of nowhere. And as we can see by the number of people who are here, this has become a, a, this is the dominant trend in enterprise architecture right now, how to adopt Kubernetes and containers, build microservices based applications, and really get to that, that transparent cloud that has been so elusive. >>It has been elusive. And we are seeing vendors from startups with just a, a few dozen people to some of the traditional players we see in the enterprise space with thousands of employees looking to capture kind of lightning in a bottle, so to speak this elusive concept of multi-cloud. >>And what we're seeing here is very typical of an early stage conference. I've seen many times over the years where the, the floor is really dominated by companies, frankly, I've never heard of that. Many of them are only two or three years old, and you don't see the big, the big dominant computing players with, with the presence here that these smaller companies have. That's very typical. We saw that in the PC age, we saw it in the early days of Unix and, and it's happening again. And what will happen over time is that a lot of these companies will be acquired. There'll be some consolidation. And the nature of this show will change, I think, dramatically over the next couple or three years, but there is an excitement and an energy in this auditorium today that is, is really a lot of fun and very reminiscent of other new technologies just as they press it. >>Well, speaking of new technologies, we have Dave Cole, CR O chief revenue officer that's right. Chief marketing officer that's right of spec cloud. Welcome to the show. Thank >>You. It's great to be here. >>So let's talk about this big ecosystem. Okay. Kubernetes. Yes. Solve problem. >>Well, you know, the, the dream is, well, first of all, applications are really the lifeblood of a company, whether it's our phone or whether it's a big company trying to connect with its customer, it's about applications. And so the whole idea today is how do I build these applications to build that tight relationship with my customers? And how do I reinvent these applications rapidly in, along comes containerization, which helps you innovate more quickly. And certainly a dominant technology. There is Kubernetes. And the, the question is how do you get Kubernetes to help you build applications that can be born anywhere and live anywhere and take advantage of the places that it's running, cuz everywhere has pluses and minuses. >>So you know what the promise of Kubernetes from when I first read about it years ago is runs on my laptop. Yep. I can push it to any cloud, any platform that's that's right. Where's the gap. Where are we in that, in that phase? Like talk to me about scale. Is that, is that, is it that simple? >>Well, that act is actually the problem is that date while the technology is the dominant containerization technology and orchestration technology, it really still takes a power user. It really hasn't been very approachable to the masses. And so it was these very expensive, highly skilled resources that sit in a dark corner that have focused on Kubernetes, but that, that now is trying to evolve to make it more accessible to the masses. It's not about sort of hand wiring together. What is a typical 20 layer stack to really manage Kubernetes and then have your engineers manually can reconfigure it and make sure everything works together. Now it's about how do I create these stacks, make it easy to deploy and manage at scale. So we've gone from sort of DIY developer centric to all right, now, how do I manage this at scale? >>Now this is a point that is important, I think is often overlooked. This is not just about Kubernetes. This is about a whole stack of cloud native technologies. Yes. And you who is going to, who is going to integrate that, all that stuff, piece that stuff together, right? Obviously you have a, a role in that. Yes. But in the enterprise, what is the awareness level of how complex this stack is and how difficult it is to assemble? >>We, we see a recognition of that, that we've had developers working on Kubernetes and applications, but now when we say, how do we weave it into our production environments? How do we ensure things like scalability and governance? How do we have this sort of interesting mix of innovation, flexibility, but with control. And that's sort of an interesting combination where you want developers to be able to run fast and use the latest tools, but you need to create these guardrails to deploy it at scale. >>So where do the developers fit in that operation stack then? Is this, is Kubernetes an AI ops or an ops a task, or is it sort of a shared task across the development spectrum? >>Well, I think there's a desire to allow application developers, to just focus on the application and have a Kubernetes related technology that ensures that all of the infrastructure and related application services are just there to support them. And because the typical stack from the operating system to the application can be up to 20 different layers components. You just want all those components to work together. You don't want application developers to worry about those things. And the latest technologies like spectra cloud there's others are making that easy application engineers focus on their apps, all of the infrastructure and the services are taken care of. And those apps can then live natively on any environment. >>So help paint this picture for us. You know, I get got AKs ETS and those, all of these distributions OpenShift, the tan zoo, where is spec cloud helping me to kind of cobble together all these different distros I thought distro was the, was the thing like, just like Lennox has different distros, you know, right. Randy said different distros >>That actually is the irony. Is that sort of the age of debating, the distros largely is over. There are a lot of distros and if you look at them, there are largely shades of gray in being different from each other. But the Kubernetes distribution is just one element of like 20 elements that all have to work together. So right now what's what's happening is that it's not about the distribution it's now, how do I, again, sorry to repeat myself, but move this into a, into scale. How do I move it into deploy at scale, to be able to manage ongoing at scale, to be able to innovate at scale, to allow engineers, as I said, use the coolest tools, but still have technical guardrails that the, the enterprise knows they'll be in control of what, >>What does at scale mean to the enterprise customers you're talking to now? What do they mean when they say that? >>Well, I think it's interesting cuz we think scale's different cuz we've all been in the industry and it's frankly sort of boring old wor word, but today it means different things. Like how do I automate the deployment at scale? How do I be able to make it really easy to provision resources for applications on any environment from either a virtualized or bare metal data center cloud or today edge is really big where people are trying to push applications out to be closer to this source of the data. And so you want to be able to deploy it scale you wanna manage at scale, you wanna make it easy to, as I said earlier, allow application developers to build their applications, but it ops wants the ability to ensure security and governance and all of that. And then finally innovate at scale. If you look at this show, it's interesting, three years ago, when we started spectra cloud, there are about 1400 businesses or technologies in the Kubernetes ecosystem today there's over 1800 and all of these technologies made up of open source and commercial, all versioning at different rates. It becomes an insurmountable problem unless you can set those guardrails sort of that balance between flexibility and control, let developers access the technologies. But again, manage it as a part of your normal processes of a, of a scale of operation. >>So, so Dave, I'm a little challenged here cuz I'm hearing two where I typically consider conflicting terms. Okay. Flexibility control. Yes. In order to achieve control, I need complexity in order to choose flexibility. I need t-shirt one t-shirt fits all right. To and I, and I, and I get simplicity. How can I get both that just doesn't you know, compute >>Well thus the opportunity and the challenge at the same time. So you're right. So developers want choice, good developers want the ability to choose the latest technology so they can innovate rapidly. And yet it ops wants to be able to make sure that there are guard rails. And so with some of today's technologies like spectral cloud, it is you have the ability to get both. We actually worked with dimensional research and we sponsor an annual state of Kubernetes survey. We found this last summer, that two out of three, it executives said you could not have both flexibility and control together, but in fact they want it. And so it is this interesting balance. How do I give engineers the ability to get anything they want, but it ops the ability to establish control. And that's why Kubernetes is really at its next inflection point. Whereas I mentioned, it's not debates about the distro or DIY projects. It's not big incumbents creating siloed Kubernetes solutions. But in fact it's about allowing all these technologies to work together and be able to establish these controls. And that's, that's really where the industry is today. >>Enterprise enterprise CIOs do not typically like to take chances. Now we were talking about the growth in the market that you described from 1400, 1800 vendors. Most of these companies, very small startups are, are enterprises. Are you seeing them willing to take a leap with these unproven companies or are they holding back and waiting for the IBMs, the HPS, the Microsofts to come in with the VMwares with whatever they solution they have? >>I, I think so. I mean, we sell to the global 2000. We had yesterday as a part of edge day here at the event, we had GE healthcare as one of our customers telling their story. And they're a market share leader in medical imaging equipment. X-rays MRIs, cat scans, and they're, they're starting to treat those as edge devices. And so here is a very large established company, a leader in their industry, working with people like spectral cloud, realizing that Kubernetes is interesting technology. The edge is an interesting thought, but how do I marry the two together? So we are seeing large corporations seeing so much of an opportunity that they're working with the smaller companies, the latest technology. >>So let's talk about the edge a little. You kind of opened it up there. Yeah. How should customers think about the edge versus the cloud data center or even bare metal? >>Actually it's a well bare bare metal is fairly easy is that many people are looking to reduce some of the overhead or inefficiencies of the virtualized environment. And, but we've had really sort of parallel little white tornadoes. We've had bare metal as infrastructure that's been developing and then we've had orchestration technology's developing, but they haven't really come together very well lately. We're finally starting to see that come together. Spectra cloud contributed to open source a metal as a service technology that finally brings these two worlds together. Making bare metal much more approachable to the inters enterprise edge is interesting because it seems pretty obvious. You wanna push your application out closer to your source of data, whether it's AI in fencing or O T or anything like that, you don't wanna worry about intermittent connectivity or latency or anything like that. But people have wanted to be able to treat the edge as if it's almost like a cloud where all I worry about is the app. >>So really the edge to us is just the next extension in a multi-cloud sort of motif where I want these edge devices to require low it resources to automate the provisioning, automate the ongoing version management patch management really act like a cloud. And we're seeing this as very, very popular now. And I just used the GE healthcare example of that. Imagine a cat scan machine, I'm making this part up in China and that's just an edge device. And it's, it's doing medical imagery, which is very intense in terms of data. You want to be able to process it quickly and accurately as close to the endpoint, the healthcare provider as possible. >>So let's talk about that in some level of detail, as we think about kind of edge and you know, these fixed devices such as imaging device, are we putting agents on there? Are we looking at something talking back to the cloud, where does special cloud inject and help make that simple, that problem of just having dispersed endpoints all over the world? Simpler? >>Sure. Well we announced our edge Kubernetes edge solution at a big medical conference called, called hymns months ago. And what we allow you to do is we allow the application engineers to develop their application. And then you can de you can design this declarative model, this cluster API, but beyond cluster profile, which determines which additional application services you need and the edge device, all the person has to do with the endpoint is plug in the power plug in the communications. It registers the edge device. It automates the deployment of the full stack. And then it does the ongoing versioning and patch management, sort of a self-driving edge device running Kubernetes. And we make it just very, very easy. No, it resources required at the endpoint, no expensive field engineering resources to go to these endpoints twice a year to apply new patches and things like that, all >>Automated, but there's so many different types of edge devices with different capabilities, different operating systems, some have no operating system. Yeah. I mean, what, that seems like a much more complex environment, just calling it, the edge is simple, but what you're really talking about is thousands of different devices, right? That you have to run your applications on how, how are you dealing with that? >>So one of the ways is that we're really unbiased. In other words, we're OS and distro agnostic. So we don't want to debate about which distribution you like. We don't want to debate about, you know, which OS you want to use. The truth is you're right. There's different environments and different choices that you'll wanna make. And so the key is, is how do you incorporate those and also recognize everything beyond those, you know, OS and Kubernetes and all of that and manage that full stack. So that's what we do is we allow you to choose which tools you want to use and let it be deployed and managed on any environment. >>And who's respo, I'm sorry, key. Who's responsible for making Kubernetes run on the edge device. >>We do. We provision the entire stack. I mean, of course the company does using our product, but we provision the entire Kubernetes infrastructure stack all the application services and the application itself on that device. >>So I would love to dig into like where pods happen and all that, but provisioning is getting to the point that it's a solve problem. Day two. Yes. Like we, you know, you just mentioned hymns, highly regulated environments. How does spec cloud helping with configuration management change control, audit, compliance, et cetera, the hard stuff. >>Yep. And one of the things we do, you bring up a good point is we manage the full life cycle from day zero, which is sort of create, deploy all the way to day two, which is about, you know, access control, security. It's about ongoing versioning and patch management. It's all of that built into the platform. And, but you're right. Like the medical industry has a lot of regulations. And so you need to be able to make sure that everything works. It's always up to the latest level, have the highest level of security. And so all that's built into the platform. It's not just a fire and forget it really is about that full life cycle of deploying, managing on an ongoing basis. >>Well, Dave, I'd love to go into a great deal of detail with you about kind of this day two option. I think we'll be covering a lot more of that topic, Paul, throughout the week, as we talk about just, you know, as we've gotten past, you know, how do I deploy Kubernetes pod to how do I actually operate it? >>Absolutely, absolutely. The devil is in the details as they say, >>Well, and also too, you have to recognize that the edge has some very unique requirements. You want very small form factors. Typically you want low it resources. It has to be sort of zero touch or low touch because if you're a large food provider with 20,000 store locations, you don't wanna send out field engineers two or three times a year to update them. So it really is an interesting beast and we have some exciting technology and people like GE are using that. >>Well, Dave, thanks a lot for coming on to Q you're now Cub Alon. You've not been on before. >>I have actually. Yes. Oh. But I always enjoy it. >>It's great conversation. Foria Spain. I'm Keith towns along with Paul Gillon and you're watching the cue, the leader in high tech coverage.