(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)

(upbeat music) >> Hello, this is Dave Valente with theCUBE, and one of the most gratifying aspects of my role as a host of "theCUBE TV" is I get to cover a wide range of topics. And quite often, we're able to bring to our program a level of expertise that allows us to more deeply explore and unpack some of the topics that we cover throughout the year. And one of our favorite topics, of course, is data. Now, in 2021, after being in isolation for the better part of two years, a group of industry analysts met up at AWS re:Invent and started a collaboration to look at the trends in data and predict what some likely outcomes will be for the coming year. And it resulted in a very popular session that we had last year focused on the future of data management. And I'm very excited and pleased to tell you that the 2023 edition of that predictions episode is back, and with me are five outstanding market analyst, Sanjeev Mohan of SanjMo, Tony Baer of dbInsight, Carl Olofson from IDC, Dave Menninger from Ventana Research, and Doug Henschen, VP and Principal Analyst at Constellation Research. Now, what is it that we're calling you, guys? A data pack like the rat pack? No, no, no, no, that's not it. It's the data crowd, the data crowd, and the crowd includes some of the best minds in the data analyst community. They'll discuss how data management is evolving and what listeners should prepare for in 2023. Guys, welcome back. Great to see you. >> Good to be here. >> Thank you. >> Thanks, Dave. (Tony and Dave faintly speaks) >> All right, before we get into 2023 predictions, we thought it'd be good to do a look back at how we did in 2022 and give a transparent assessment of those predictions. So, let's get right into it. We're going to bring these up here, the predictions from 2022, they're color-coded red, yellow, and green to signify the degree of accuracy. And I'm pleased to report there's no red. Well, maybe some of you will want to debate that grading system. But as always, we want to be open, so you can decide for yourselves. So, we're going to ask each analyst to review their 2022 prediction and explain their rating and what evidence they have that led them to their conclusion. So, Sanjeev, please kick it off. Your prediction was data governance becomes key. I know that's going to knock you guys over, but elaborate, because you had more detail when you double click on that. >> Yeah, absolutely. Thank you so much, Dave, for having us on the show today. And we self-graded ourselves. I could have very easily made my prediction from last year green, but I mentioned why I left it as yellow. I totally fully believe that data governance was in a renaissance in 2022. And why do I say that? You have to look no further than AWS launching its own data catalog called DataZone. Before that, mid-year, we saw Unity Catalog from Databricks went GA. So, overall, I saw there was tremendous movement. When you see these big players launching a new data catalog, you know that they want to be in this space. And this space is highly critical to everything that I feel we will talk about in today's call. Also, if you look at established players, I spoke at Collibra's conference, data.world, work closely with Alation, Informatica, a bunch of other companies, they all added tremendous new capabilities. So, it did become key. The reason I left it as yellow is because I had made a prediction that Collibra would go IPO, and it did not. And I don't think anyone is going IPO right now. The market is really, really down, the funding in VC IPO market. But other than that, data governance had a banner year in 2022. >> Yeah. Well, thank you for that. And of course, you saw data clean rooms being announced at AWS re:Invent, so more evidence. And I like how the fact that you included in your predictions some things that were binary, so you dinged yourself there. So, good job. Okay, Tony Baer, you're up next. Data mesh hits reality check. As you see here, you've given yourself a bright green thumbs up. (Tony laughing) Okay. Let's hear why you feel that was the case. What do you mean by reality check? >> Okay. Thanks, Dave, for having us back again. This is something I just wrote and just tried to get away from, and this just a topic just won't go away. I did speak with a number of folks, early adopters and non-adopters during the year. And I did find that basically that it pretty much validated what I was expecting, which was that there was a lot more, this has now become a front burner issue. And if I had any doubt in my mind, the evidence I would point to is what was originally intended to be a throwaway post on LinkedIn, which I just quickly scribbled down the night before leaving for re:Invent. I was packing at the time, and for some reason, I was doing Google search on data mesh. And I happened to have tripped across this ridiculous article, I will not say where, because it doesn't deserve any publicity, about the eight (Dave laughing) best data mesh software companies of 2022. (Tony laughing) One of my predictions was that you'd see data mesh washing. And I just quickly just hopped on that maybe three sentences and wrote it at about a couple minutes saying this is hogwash, essentially. (laughs) And that just reun... And then, I left for re:Invent. And the next night, when I got into my Vegas hotel room, I clicked on my computer. I saw a 15,000 hits on that post, which was the most hits of any single post I put all year. And the responses were wildly pro and con. So, it pretty much validates my expectation in that data mesh really did hit a lot more scrutiny over this past year. >> Yeah, thank you for that. I remember that article. I remember rolling my eyes when I saw it, and then I recently, (Tony laughing) I talked to Walmart and they actually invoked Martin Fowler and they said that they're working through their data mesh. So, it takes a really lot of thought, and it really, as we've talked about, is really as much an organizational construct. You're not buying data mesh >> Bingo. >> to your point. Okay. Thank you, Tony. Carl Olofson, here we go. You've graded yourself a yellow in the prediction of graph databases. Take off. Please elaborate. >> Yeah, sure. So, I realized in looking at the prediction that it seemed to imply that graph databases could be a major factor in the data world in 2022, which obviously didn't become the case. It was an error on my part in that I should have said it in the right context. It's really a three to five-year time period that graph databases will really become significant, because they still need accepted methodologies that can be applied in a business context as well as proper tools in order for people to be able to use them seriously. But I stand by the idea that it is taking off, because for one thing, Neo4j, which is the leading independent graph database provider, had a very good year. And also, we're seeing interesting developments in terms of things like AWS with Neptune and with Oracle providing graph support in Oracle database this past year. Those things are, as I said, growing gradually. There are other companies like TigerGraph and so forth, that deserve watching as well. But as far as becoming mainstream, it's going to be a few years before we get all the elements together to make that happen. Like any new technology, you have to create an environment in which ordinary people without a whole ton of technical training can actually apply the technology to solve business problems. >> Yeah, thank you for that. These specialized databases, graph databases, time series databases, you see them embedded into mainstream data platforms, but there's a place for these specialized databases, I would suspect we're going to see new types of databases emerge with all this cloud sprawl that we have and maybe to the edge. >> Well, part of it is that it's not as specialized as you might think it. You can apply graphs to great many workloads and use cases. It's just that people have yet to fully explore and discover what those are. >> Yeah. >> And so, it's going to be a process. (laughs) >> All right, Dave Menninger, streaming data permeates the landscape. You gave yourself a yellow. Why? >> Well, I couldn't think of a appropriate combination of yellow and green. Maybe I should have used chartreuse, (Dave laughing) but I was probably a little hard on myself making it yellow. This is another type of specialized data processing like Carl was talking about graph databases is a stream processing, and nearly every data platform offers streaming capabilities now. Often, it's based on Kafka. If you look at Confluent, their revenues have grown at more than 50%, continue to grow at more than 50% a year. They're expected to do more than half a billion dollars in revenue this year. But the thing that hasn't happened yet, and to be honest, they didn't necessarily expect it to happen in one year, is that streaming hasn't become the default way in which we deal with data. It's still a sidecar to data at rest. And I do expect that we'll continue to see streaming become more and more mainstream. I do expect perhaps in the five-year timeframe that we will first deal with data as streaming and then at rest, but the worlds are starting to merge. And we even see some vendors bringing products to market, such as K2View, Hazelcast, and RisingWave Labs. So, in addition to all those core data platform vendors adding these capabilities, there are new vendors approaching this market as well. >> I like the tough grading system, and it's not trivial. And when you talk to practitioners doing this stuff, there's still some complications in the data pipeline. And so, but I think, you're right, it probably was a yellow plus. Doug Henschen, data lakehouses will emerge as dominant. When you talk to people about lakehouses, practitioners, they all use that term. They certainly use the term data lake, but now, they're using lakehouse more and more. What's your thoughts on here? Why the green? What's your evidence there? >> Well, I think, I was accurate. I spoke about it specifically as something that vendors would be pursuing. And we saw yet more lakehouse advocacy in 2022. Google introduced its BigLake service alongside BigQuery. Salesforce introduced Genie, which is really a lakehouse architecture. And it was a safe prediction to say vendors are going to be pursuing this in that AWS, Cloudera, Databricks, Microsoft, Oracle, SAP, Salesforce now, IBM, all advocate this idea of a single platform for all of your data. Now, the trend was also supported in 2023, in that we saw a big embrace of Apache Iceberg in 2022. That's a structured table format. It's used with these lakehouse platforms. It's open, so it ensures portability and it also ensures performance. And that's a structured table that helps with the warehouse side performance. But among those announcements, Snowflake, Google, Cloud Era, SAP, Salesforce, IBM, all embraced Iceberg. But keep in mind, again, I'm talking about this as something that vendors are pursuing as their approach. So, they're advocating end users. It's very cutting edge. I'd say the top, leading edge, 5% of of companies have really embraced the lakehouse. I think, we're now seeing the fast followers, the next 20 to 25% of firms embracing this idea and embracing a lakehouse architecture. I recall Christian Kleinerman at the big Snowflake event last summer, making the announcement about Iceberg, and he asked for a show of hands for any of you in the audience at the keynote, have you heard of Iceberg? And just a smattering of hands went up. So, the vendors are ahead of the curve. They're pushing this trend, and we're now seeing a little bit more mainstream uptake. >> Good. Doug, I was there. It was you, me, and I think, two other hands were up. That was just humorous. (Doug laughing) All right, well, so I liked the fact that we had some yellow and some green. When you think about these things, there's the prediction itself. Did it come true or not? There are the sub predictions that you guys make, and of course, the degree of difficulty. So, thank you for that open assessment. All right, let's get into the 2023 predictions. Let's bring up the predictions. Sanjeev, you're going first. You've got a prediction around unified metadata. What's the prediction, please? >> So, my prediction is that metadata space is currently a mess. It needs to get unified. There are too many use cases of metadata, which are being addressed by disparate systems. For example, data quality has become really big in the last couple of years, data observability, the whole catalog space is actually, people don't like to use the word data catalog anymore, because data catalog sounds like it's a catalog, a museum, if you may, of metadata that you go and admire. So, what I'm saying is that in 2023, we will see that metadata will become the driving force behind things like data ops, things like orchestration of tasks using metadata, not rules. Not saying that if this fails, then do this, if this succeeds, go do that. But it's like getting to the metadata level, and then making a decision as to what to orchestrate, what to automate, how to do data quality check, data observability. So, this space is starting to gel, and I see there'll be more maturation in the metadata space. Even security privacy, some of these topics, which are handled separately. And I'm just talking about data security and data privacy. I'm not talking about infrastructure security. These also need to merge into a unified metadata management piece with some knowledge graph, semantic layer on top, so you can do analytics on it. So, it's no longer something that sits on the side, it's limited in its scope. It is actually the very engine, the very glue that is going to connect data producers and consumers. >> Great. Thank you for that. Doug. Doug Henschen, any thoughts on what Sanjeev just said? Do you agree? Do you disagree? >> Well, I agree with many aspects of what he says. I think, there's a huge opportunity for consolidation and streamlining of these as aspects of governance. Last year, Sanjeev, you said something like, we'll see more people using catalogs than BI. And I have to disagree. I don't think this is a category that's headed for mainstream adoption. It's a behind the scenes activity for the wonky few, or better yet, companies want machine learning and automation to take care of these messy details. We've seen these waves of management technologies, some of the latest data observability, customer data platform, but they failed to sweep away all the earlier investments in data quality and master data management. So, yes, I hope the latest tech offers, glimmers that there's going to be a better, cleaner way of addressing these things. But to my mind, the business leaders, including the CIO, only want to spend as much time and effort and money and resources on these sorts of things to avoid getting breached, ending up in headlines, getting fired or going to jail. So, vendors bring on the ML and AI smarts and the automation of these sorts of activities. >> So, if I may say something, the reason why we have this dichotomy between data catalog and the BI vendors is because data catalogs are very soon, not going to be standalone products, in my opinion. They're going to get embedded. So, when you use a BI tool, you'll actually use the catalog to find out what is it that you want to do, whether you are looking for data or you're looking for an existing dashboard. So, the catalog becomes embedded into the BI tool. >> Hey, Dave Menninger, sometimes you have some data in your back pocket. Do you have any stats (chuckles) on this topic? >> No, I'm glad you asked, because I'm going to... Now, data catalogs are something that's interesting. Sanjeev made a statement that data catalogs are falling out of favor. I don't care what you call them. They're valuable to organizations. Our research shows that organizations that have adequate data catalog technologies are three times more likely to express satisfaction with their analytics for just the reasons that Sanjeev was talking about. You can find what you want, you know you're getting the right information, you know whether or not it's trusted. So, those are good things. So, we expect to see the capabilities, whether it's embedded or separate. We expect to see those capabilities continue to permeate the market. >> And a lot of those catalogs are driven now by machine learning and things. So, they're learning from those patterns of usage by people when people use the data. (airy laughs) >> All right. Okay. Thank you, guys. All right. Let's move on to the next one. Tony Bear, let's bring up the predictions. You got something in here about the modern data stack. We need to rethink it. Is the modern data stack getting long at the tooth? Is it not so modern anymore? >> I think, in a way, it's got almost too modern. It's gotten too, I don't know if it's being long in the tooth, but it is getting long. The modern data stack, it's traditionally been defined as basically you have the data platform, which would be the operational database and the data warehouse. And in between, you have all the tools that are necessary to essentially get that data from the operational realm or the streaming realm for that matter into basically the data warehouse, or as we might be seeing more and more, the data lakehouse. And I think, what's important here is that, or I think, we have seen a lot of progress, and this would be in the cloud, is with the SaaS services. And especially you see that in the modern data stack, which is like all these players, not just the MongoDBs or the Oracles or the Amazons have their database platforms. You see they have the Informatica's, and all the other players there in Fivetrans have their own SaaS services. And within those SaaS services, you get a certain degree of simplicity, which is it takes all the housekeeping off the shoulders of the customers. That's a good thing. The problem is that what we're getting to unfortunately is what I would call lots of islands of simplicity, which means that it leads it (Dave laughing) to the customer to have to integrate or put all that stuff together. It's a complex tool chain. And so, what we really need to think about here, we have too many pieces. And going back to the discussion of catalogs, it's like we have so many catalogs out there, which one do we use? 'Cause chances are of most organizations do not rely on a single catalog at this point. What I'm calling on all the data providers or all the SaaS service providers, is to literally get it together and essentially make this modern data stack less of a stack, make it more of a blending of an end-to-end solution. And that can come in a number of different ways. Part of it is that we're data platform providers have been adding services that are adjacent. And there's some very good examples of this. We've seen progress over the past year or so. For instance, MongoDB integrating search. It's a very common, I guess, sort of tool that basically, that the applications that are developed on MongoDB use, so MongoDB then built it into the database rather than requiring an extra elastic search or open search stack. Amazon just... AWS just did the zero-ETL, which is a first step towards simplifying the process from going from Aurora to Redshift. You've seen same thing with Google, BigQuery integrating basically streaming pipelines. And you're seeing also a lot of movement in database machine learning. So, there's some good moves in this direction. I expect to see more than this year. Part of it's from basically the SaaS platform is adding some functionality. But I also see more importantly, because you're never going to get... This is like asking your data team and your developers, herding cats to standardizing the same tool. In most organizations, that is not going to happen. So, take a look at the most popular combinations of tools and start to come up with some pre-built integrations and pre-built orchestrations, and offer some promotional pricing, maybe not quite two for, but in other words, get two products for the price of two services or for the price of one and a half. I see a lot of potential for this. And it's to me, if the class was to simplify things, this is the next logical step and I expect to see more of this here. >> Yeah, and you see in Oracle, MySQL heat wave, yet another example of eliminating that ETL. Carl Olofson, today, if you think about the data stack and the application stack, they're largely separate. Do you have any thoughts on how that's going to play out? Does that play into this prediction? What do you think? >> Well, I think, that the... I really like Tony's phrase, islands of simplification. It really says (Tony chuckles) what's going on here, which is that all these different vendors you ask about, about how these stacks work. All these different vendors have their own stack vision. And you can... One application group is going to use one, and another application group is going to use another. And some people will say, let's go to, like you go to a Informatica conference and they say, we should be the center of your universe, but you can't connect everything in your universe to Informatica, so you need to use other things. So, the challenge is how do we make those things work together? As Tony has said, and I totally agree, we're never going to get to the point where people standardize on one organizing system. So, the alternative is to have metadata that can be shared amongst those systems and protocols that allow those systems to coordinate their operations. This is standard stuff. It's not easy. But the motive for the vendors is that they can become more active critical players in the enterprise. And of course, the motive for the customer is that things will run better and more completely. So, I've been looking at this in terms of two kinds of metadata. One is the meaning metadata, which says what data can be put together. The other is the operational metadata, which says basically where did it come from? Who created it? What's its current state? What's the security level? Et cetera, et cetera, et cetera. The good news is the operational stuff can actually be done automatically, whereas the meaning stuff requires some human intervention. And as we've already heard from, was it Doug, I think, people are disinclined to put a lot of definition into meaning metadata. So, that may be the harder one, but coordination is key. This problem has been with us forever, but with the addition of new data sources, with streaming data with data in different formats, the whole thing has, it's been like what a customer of mine used to say, "I understand your product can make my system run faster, but right now I just feel I'm putting my problems on roller skates. (chuckles) I don't need that to accelerate what's already not working." >> Excellent. Okay, Carl, let's stay with you. I remember in the early days of the big data movement, Hadoop movement, NoSQL was the big thing. And I remember Amr Awadallah said to us in theCUBE that SQL is the killer app for big data. So, your prediction here, if we bring that up is SQL is back. Please elaborate. >> Yeah. So, of course, some people would say, well, it never left. Actually, that's probably closer to true, but in the perception of the marketplace, there's been all this noise about alternative ways of storing, retrieving data, whether it's in key value stores or document databases and so forth. We're getting a lot of messaging that for a while had persuaded people that, oh, we're not going to do analytics in SQL anymore. We're going to use Spark for everything, except that only a handful of people know how to use Spark. Oh, well, that's a problem. Well, how about, and for ordinary conventional business analytics, Spark is like an over-engineered solution to the problem. SQL works just great. What's happened in the past couple years, and what's going to continue to happen is that SQL is insinuating itself into everything we're seeing. We're seeing all the major data lake providers offering SQL support, whether it's Databricks or... And of course, Snowflake is loving this, because that is what they do, and their success is certainly points to the success of SQL, even MongoDB. And we were all, I think, at the MongoDB conference where on one day, we hear SQL is dead. They're not teaching SQL in schools anymore, and this kind of thing. And then, a couple days later at the same conference, they announced we're adding a new analytic capability-based on SQL. But didn't you just say SQL is dead? So, the reality is that SQL is better understood than most other methods of certainly of retrieving and finding data in a data collection, no matter whether it happens to be relational or non-relational. And even in systems that are very non-relational, such as graph and document databases, their query languages are being built or extended to resemble SQL, because SQL is something people understand. >> Now, you remember when we were in high school and you had had to take the... Your debating in the class and you were forced to take one side and defend it. So, I was was at a Vertica conference one time up on stage with Curt Monash, and I had to take the NoSQL, the world is changing paradigm shift. And so just to be controversial, I said to him, Curt Monash, I said, who really needs acid compliance anyway? Tony Baer. And so, (chuckles) of course, his head exploded, but what are your thoughts (guests laughing) on all this? >> Well, my first thought is congratulations, Dave, for surviving being up on stage with Curt Monash. >> Amen. (group laughing) >> I definitely would concur with Carl. We actually are definitely seeing a SQL renaissance and if there's any proof of the pudding here, I see lakehouse is being icing on the cake. As Doug had predicted last year, now, (clears throat) for the record, I think, Doug was about a year ahead of time in his predictions that this year is really the year that I see (clears throat) the lakehouse ecosystems really firming up. You saw the first shots last year. But anyway, on this, data lakes will not go away. I've actually, I'm on the home stretch of doing a market, a landscape on the lakehouse. And lakehouse will not replace data lakes in terms of that. There is the need for those, data scientists who do know Python, who knows Spark, to go in there and basically do their thing without all the restrictions or the constraints of a pre-built, pre-designed table structure. I get that. Same thing for developing models. But on the other hand, there is huge need. Basically, (clears throat) maybe MongoDB was saying that we're not teaching SQL anymore. Well, maybe we have an oversupply of SQL developers. Well, I'm being facetious there, but there is a huge skills based in SQL. Analytics have been built on SQL. They came with lakehouse and why this really helps to fuel a SQL revival is that the core need in the data lake, what brought on the lakehouse was not so much SQL, it was a need for acid. And what was the best way to do it? It was through a relational table structure. So, the whole idea of acid in the lakehouse was not to turn it into a transaction database, but to make the data trusted, secure, and more granularly governed, where you could govern down to column and row level, which you really could not do in a data lake or a file system. So, while lakehouse can be queried in a manner, you can go in there with Python or whatever, it's built on a relational table structure. And so, for that end, for those types of data lakes, it becomes the end state. You cannot bypass that table structure as I learned the hard way during my research. So, the bottom line I'd say here is that lakehouse is proof that we're starting to see the revenge of the SQL nerds. (Dave chuckles) >> Excellent. Okay, let's bring up back up the predictions. Dave Menninger, this one's really thought-provoking and interesting. We're hearing things like data as code, new data applications, machines actually generating plans with no human involvement. And your prediction is the definition of data is expanding. What do you mean by that? >> So, I think, for too long, we've thought about data as the, I would say facts that we collect the readings off of devices and things like that, but data on its own is really insufficient. Organizations need to manipulate that data and examine derivatives of the data to really understand what's happening in their organization, why has it happened, and to project what might happen in the future. And my comment is that these data derivatives need to be supported and managed just like the data needs to be managed. We can't treat this as entirely separate. Think about all the governance discussions we've had. Think about the metadata discussions we've had. If you separate these things, now you've got more moving parts. We're talking about simplicity and simplifying the stack. So, if these things are treated separately, it creates much more complexity. I also think it creates a little bit of a myopic view on the part of the IT organizations that are acquiring these technologies. They need to think more broadly. So, for instance, metrics. Metric stores are becoming much more common part of the tooling that's part of a data platform. Similarly, feature stores are gaining traction. So, those are designed to promote the reuse and consistency across the AI and ML initiatives. The elements that are used in developing an AI or ML model. And let me go back to metrics and just clarify what I mean by that. So, any type of formula involving the data points. I'm distinguishing metrics from features that are used in AI and ML models. And the data platforms themselves are increasingly managing the models as an element of data. So, just like figuring out how to calculate a metric. Well, if you're going to have the features associated with an AI and ML model, you probably need to be managing the model that's associated with those features. The other element where I see expansion is around external data. Organizations for decades have been focused on the data that they generate within their own organization. We see more and more of these platforms acquiring and publishing data to external third-party sources, whether they're within some sort of a partner ecosystem or whether it's a commercial distribution of that information. And our research shows that when organizations use external data, they derive even more benefits from the various analyses that they're conducting. And the last great frontier in my opinion on this expanding world of data is the world of driver-based planning. Very few of the major data platform providers provide these capabilities today. These are the types of things you would do in a spreadsheet. And we all know the issues associated with spreadsheets. They're hard to govern, they're error-prone. And so, if we can take that type of analysis, collecting the occupancy of a rental property, the projected rise in rental rates, the fluctuations perhaps in occupancy, the interest rates associated with financing that property, we can project forward. And that's a very common thing to do. What the income might look like from that property income, the expenses, we can plan and purchase things appropriately. So, I think, we need this broader purview and I'm beginning to see some of those things happen. And the evidence today I would say, is more focused around the metric stores and the feature stores starting to see vendors offer those capabilities. And we're starting to see the ML ops elements of managing the AI and ML models find their way closer to the data platforms as well. >> Very interesting. When I hear metrics, I think of KPIs, I think of data apps, orchestrate people and places and things to optimize around a set of KPIs. It sounds like a metadata challenge more... Somebody once predicted they'll have more metadata than data. Carl, what are your thoughts on this prediction? >> Yeah, I think that what Dave is describing as data derivatives is in a way, another word for what I was calling operational metadata, which not about the data itself, but how it's used, where it came from, what the rules are governing it, and that kind of thing. If you have a rich enough set of those things, then not only can you do a model of how well your vacation property rental may do in terms of income, but also how well your application that's measuring that is doing for you. In other words, how many times have I used it, how much data have I used and what is the relationship between the data that I've used and the benefits that I've derived from using it? Well, we don't have ways of doing that. What's interesting to me is that folks in the content world are way ahead of us here, because they have always tracked their content using these kinds of attributes. Where did it come from? When was it created, when was it modified? Who modified it? And so on and so forth. We need to do more of that with the structure data that we have, so that we can track what it's used. And also, it tells us how well we're doing with it. Is it really benefiting us? Are we being efficient? Are there improvements in processes that we need to consider? Because maybe data gets created and then it isn't used or it gets used, but it gets altered in some way that actually misleads people. (laughs) So, we need the mechanisms to be able to do that. So, I would say that that's... And I'd say that it's true that we need that stuff. I think, that starting to expand is probably the right way to put it. It's going to be expanding for some time. I think, we're still a distance from having all that stuff really working together. >> Maybe we should say it's gestating. (Dave and Carl laughing) >> Sorry, if I may- >> Sanjeev, yeah, I was going to say this... Sanjeev, please comment. This sounds to me like it supports Zhamak Dehghani's principles, but please. >> Absolutely. So, whether we call it data mesh or not, I'm not getting into that conversation, (Dave chuckles) but data (audio breaking) (Tony laughing) everything that I'm hearing what Dave is saying, Carl, this is the year when data products will start to take off. I'm not saying they'll become mainstream. They may take a couple of years to become so, but this is data products, all this thing about vacation rentals and how is it doing, that data is coming from different sources. I'm packaging it into our data product. And to Carl's point, there's a whole operational metadata associated with it. The idea is for organizations to see things like developer productivity, how many releases am I doing of this? What data products are most popular? I'm actually in right now in the process of formulating this concept that just like we had data catalogs, we are very soon going to be requiring data products catalog. So, I can discover these data products. I'm not just creating data products left, right, and center. I need to know, do they already exist? What is the usage? If no one is using a data product, maybe I want to retire and save cost. But this is a data product. Now, there's a associated thing that is also getting debated quite a bit called data contracts. And a data contract to me is literally just formalization of all these aspects of a product. How do you use it? What is the SLA on it, what is the quality that I am prescribing? So, data product, in my opinion, shifts the conversation to the consumers or to the business people. Up to this point when, Dave, you're talking about data and all of data discovery curation is a very data producer-centric. So, I think, we'll see a shift more into the consumer space. >> Yeah. Dave, can I just jump in there just very quickly there, which is that what Sanjeev has been saying there, this is really central to what Zhamak has been talking about. It's basically about making, one, data products are about the lifecycle management of data. Metadata is just elemental to that. And essentially, one of the things that she calls for is making data products discoverable. That's exactly what Sanjeev was talking about. >> By the way, did everyone just no notice how Sanjeev just snuck in another prediction there? So, we've got- >> Yeah. (group laughing) >> But you- >> Can we also say that he snuck in, I think, the term that we'll remember today, which is metadata museums. >> Yeah, but- >> Yeah. >> And also comment to, Tony, to your last year's prediction, you're really talking about it's not something that you're going to buy from a vendor. >> No. >> It's very specific >> Mm-hmm. >> to an organization, their own data product. So, touche on that one. Okay, last prediction. Let's bring them up. Doug Henschen, BI analytics is headed to embedding. What does that mean? >> Well, we all know that conventional BI dashboarding reporting is really commoditized from a vendor perspective. It never enjoyed truly mainstream adoption. Always that 25% of employees are really using these things. I'm seeing rising interest in embedding concise analytics at the point of decision or better still, using analytics as triggers for automation and workflows, and not even necessitating human interaction with visualizations, for example, if we have confidence in the analytics. So, leading companies are pushing for next generation applications, part of this low-code, no-code movement we've seen. And they want to build that decision support right into the app. So, the analytic is right there. Leading enterprise apps vendors, Salesforce, SAP, Microsoft, Oracle, they're all building smart apps with the analytics predictions, even recommendations built into these applications. And I think, the progressive BI analytics vendors are supporting this idea of driving insight to action, not necessarily necessitating humans interacting with it if there's confidence. So, we want prediction, we want embedding, we want automation. This low-code, no-code development movement is very important to bringing the analytics to where people are doing their work. We got to move beyond the, what I call swivel chair integration, between where people do their work and going off to separate reports and dashboards, and having to interpret and analyze before you can go back and do take action. >> And Dave Menninger, today, if you want, analytics or you want to absorb what's happening in the business, you typically got to go ask an expert, and then wait. So, what are your thoughts on Doug's prediction? >> I'm in total agreement with Doug. I'm going to say that collectively... So, how did we get here? I'm going to say collectively as an industry, we made a mistake. We made BI and analytics separate from the operational systems. Now, okay, it wasn't really a mistake. We were limited by the technology available at the time. Decades ago, we had to separate these two systems, so that the analytics didn't impact the operations. You don't want the operations preventing you from being able to do a transaction. But we've gone beyond that now. We can bring these two systems and worlds together and organizations recognize that need to change. As Doug said, the majority of the workforce and the majority of organizations doesn't have access to analytics. That's wrong. (chuckles) We've got to change that. And one of the ways that's going to change is with embedded analytics. 2/3 of organizations recognize that embedded analytics are important and it even ranks higher in importance than AI and ML in those organizations. So, it's interesting. This is a really important topic to the organizations that are consuming these technologies. The good news is it works. Organizations that have embraced embedded analytics are more comfortable with self-service than those that have not, as opposed to turning somebody loose, in the wild with the data. They're given a guided path to the data. And the research shows that 65% of organizations that have adopted embedded analytics are comfortable with self-service compared with just 40% of organizations that are turning people loose in an ad hoc way with the data. So, totally behind Doug's predictions. >> Can I just break in with something here, a comment on what Dave said about what Doug said, which (laughs) is that I totally agree with what you said about embedded analytics. And at IDC, we made a prediction in our future intelligence, future of intelligence service three years ago that this was going to happen. And the thing that we're waiting for is for developers to build... You have to write the applications to work that way. It just doesn't happen automagically. Developers have to write applications that reference analytic data and apply it while they're running. And that could involve simple things like complex queries against the live data, which is through something that I've been calling analytic transaction processing. Or it could be through something more sophisticated that involves AI operations as Doug has been suggesting, where the result is enacted pretty much automatically unless the scores are too low and you need to have a human being look at it. So, I think that that is definitely something we've been watching for. I'm not sure how soon it will come, because it seems to take a long time for people to change their thinking. But I think, as Dave was saying, once they do and they apply these principles in their application development, the rewards are great. >> Yeah, this is very much, I would say, very consistent with what we were talking about, I was talking about before, about basically rethinking the modern data stack and going into more of an end-to-end solution solution. I think, that what we're talking about clearly here is operational analytics. There'll still be a need for your data scientists to go offline just in their data lakes to do all that very exploratory and that deep modeling. But clearly, it just makes sense to bring operational analytics into where people work into their workspace and further flatten that modern data stack. >> But with all this metadata and all this intelligence, we're talking about injecting AI into applications, it does seem like we're entering a new era of not only data, but new era of apps. Today, most applications are about filling forms out or codifying processes and require a human input. And it seems like there's enough data now and enough intelligence in the system that the system can actually pull data from, whether it's the transaction system, e-commerce, the supply chain, ERP, and actually do something with that data without human involvement, present it to humans. Do you guys see this as a new frontier? >> I think, that's certainly- >> Very much so, but it's going to take a while, as Carl said. You have to design it, you have to get the prediction into the system, you have to get the analytics at the point of decision has to be relevant to that decision point. >> And I also recall basically a lot of the ERP vendors back like 10 years ago, we're promising that. And the fact that we're still looking at the promises shows just how difficult, how much of a challenge it is to get to what Doug's saying. >> One element that could be applied in this case is (indistinct) architecture. If applications are developed that are event-driven rather than following the script or sequence that some programmer or designer had preconceived, then you'll have much more flexible applications. You can inject decisions at various points using this technology much more easily. It's a completely different way of writing applications. And it actually involves a lot more data, which is why we should all like it. (laughs) But in the end (Tony laughing) it's more stable, it's easier to manage, easier to maintain, and it's actually more efficient, which is the result of an MIT study from about 10 years ago, and still, we are not seeing this come to fruition in most business applications. >> And do you think it's going to require a new type of data platform database? Today, data's all far-flung. We see that's all over the clouds and at the edge. Today, you cache- >> We need a super cloud. >> You cache that data, you're throwing into memory. I mentioned, MySQL heat wave. There are other examples where it's a brute force approach, but maybe we need new ways of laying data out on disk and new database architectures, and just when we thought we had it all figured out. >> Well, without referring to disk, which to my mind, is almost like talking about cave painting. I think, that (Dave laughing) all the things that have been mentioned by all of us today are elements of what I'm talking about. In other words, the whole improvement of the data mesh, the improvement of metadata across the board and improvement of the ability to track data and judge its freshness the way we judge the freshness of a melon or something like that, to determine whether we can still use it. Is it still good? That kind of thing. Bringing together data from multiple sources dynamically and real-time requires all the things we've been talking about. All the predictions that we've talked about today add up to elements that can make this happen. >> Well, guys, it's always tremendous to get these wonderful minds together and get your insights, and I love how it shapes the outcome here of the predictions, and let's see how we did. We're going to leave it there. I want to thank Sanjeev, Tony, Carl, David, and Doug. Really appreciate the collaboration and thought that you guys put into these sessions. Really, thank you. >> Thank you. >> Thanks, Dave. >> Thank you for having us. >> Thanks. >> Thank you. >> All right, this is Dave Valente for theCUBE, signing off for now. Follow these guys on social media. Look for coverage on siliconangle.com, theCUBE.net. Thank you for watching. (upbeat music)

(upbeat music) >> Announcer: theCUBE presents UiPath Forward5. Brought to you by UiPath. >> Hi everybody, we're back in Las Vegas. We're live with Cube's coverage of Forward 5 2022. Dave Vellante with Dave Nicholson Ted Kumer this year is the Executive Vice President, product and engineering at UiPath. Brought on to do a lot of the integration and bring on new capabilities for the platform and we've seen that over the last several years. And he's joined by Dr. Edward Challis, who's the co-founder of the recent acquisition that UiPath made, company called Re:infer. We're going to learn about those guys. Gents, welcome to theCUBE. Ted, good to see you again. Ed, welcome. >> Good to be here. >> First time. >> Thank you. >> Yeah, great to be here with you. >> Yeah, so we have seen, as I said, this platform expanding. I think you used the term business automation platform. It's kind of a new term you guys introduced at the conference. Where'd that come from? What is that? What are the characteristics that are salient to the platform? >> Well, I see the, the evolution of our platform in three chapters. You understand the first chapter, we call that the RPA chapter. And that's where we saw the power of UI automation applied to the old problems of how do I integrate apps? How do I automate processes? That was chapter one. You know, chapter two gets us to Forward3 in 2019, and the definition of this end-to-end automation platform you know, with the capabilities from discover to measure, and building out that core platform. And as the platform's progressed, what we've seen happen with our customers is the use of it goes from being very heavy in automating the repetitive and routine to being more balanced, to now where they're implementing new brought business process, new capability for their organization. So that's where the name, Business Automation Platform, came from. Reflecting now that it's got this central role, as a strategic tool, sitting between their application landscape, their processes, their people, helping that move forward at the rate that it needs to. >> And process mining and task mining, that was sort of the enabler of chapter two, is that right? >> Well, I'd say chapter two was, you know, first the robots got bigger in terms of what they could cover and do. API integration, long running workflows, AI and ML skills integrated document processing, citizen development in addition to professional development, engaging end users with things like user interfaces built with UiPath apps. And then the discovery. >> So, more robustness of the? Yeah, okay. >> Yeah. Just an expansion of the whole surface area which opened up a lot of things for our customers to do. That went much broader than where core RPA started. And so, and the other thing about this progression to the business automation platform is, you know, we see customers now talking more about outcomes. Early on they talk a lot about hours saved and that's great, but then what about the business outcomes it's enabling? The transformations in their business. And the other thing we're doing in the platform is thinking about, well, where can we land with solutions capabilities that more directly land on business, measurable business outcomes? And so we had started, for example, offering an email automation solution, big business problem for a lot of our customers last year. And we'd started encountering this company Re:infer as we were working with customers. And then, and we encountered Re:infer being used with our platform together. And we saw we can accelerate this. And what that is giving us now is a solution now that aligns with a very defined business outcome. And this way, you know, we can help you process communications and do it efficiently and provide better service for your customers. And that's beginning of another important progression for us in our platform. >> So that's a nice segue, Ed. Tell about Re:infer. Why did you start the company? >> Right, yeah, so my whole career has been in machine learning and AI and I finished my PhD around 2013, it was a very exciting time in AI. And me and my co-founders come from UCL, this university in London, and Deep Mind, this company which Google acquired a few years later, came from our same university. So very exciting time amongst the people that really knew about machine learning and AI. And everyone was thinking, you know, how do we, these are just really big breakthroughs. And you could just see there was going to be a whole bunch of subsequent breakthroughs and we thought NLP would be the next breakthrough. So we were really focused on machine reading problems. And, but we also knew as people that had like built machine learning production systems. 'Cause I'd also worked in industry that built that journey from having a hypothesis that machine learning can solve a problem to getting machine learning into production. That journey is of painful, painful journey and that, you know, you can see that you've got these advances, but getting into broad is just way too hard. >> So where do you fit in the platform? >> Yeah, so I think when you look in the enterprise just so many processes start with a message start with a no, start with a case ticket or, you know, some other kind of request from a colleague or a customer. And so it's super exciting to be able to, you know, take automation one step higher in that process chain. So, you could automatically read that request, interpret it, get all the structured data you need to drive that process forward. So it's about bringing automation into these human channels. >> So I want to give the audience a sense here. So we do a lot of events at the Venetian Conference Center, and it's usually very booth heavy, you know, brands and big giant booths. And here the booths are all very small. They're like kiosks, and they're all pretty much the same size. So it's not like one vendor trying to compete with the other. And there are all these elements, you know I feel like there's clouds and there's, you know, of course orange is the color here. And one of the spots is, it has this really kind of cool sitting area around customer stories. And I was in there last night reading about Deutsche Bank. Deutsche Bank was also up on stage. Deutsche Bank, you guys were talking about a Re:infer. So share with our audience what Deutsche Bank are doing with UiPath and Re:infer. >> Yeah, so I mean, you know, before we automate something, we often like to do what we call communications mining. Which is really understanding what all of these messages are about that might be hitting a part of the business. And at Deutsche Bank and in many, you know, like many large financial services businesses, huge volumes of messages coming in from the clients. We analyze those, interpret the high volume query types and then it's about automating against those to free up capacity. Which ultimately means you can provide faster, higher quality service because you've got more time to do it. And you're not dealing with all of those mundane tasks. So it's that whole journey of mining to automation of the coms that come into the corporate bank. >> So how do I invoke the service? So is it mother module or what's the customer onboarding experience like? >> So, I think the first thing that we do is we generate some understanding of actually the communications data they want to observe, right? And we call it mining, but you know, what we're trying to understand is like what are these communications about? What's the intent? What are they trying to accomplish? Tone can be interesting, like what's the sentiment of this customer? And once you understand that, you essentially then understand categories of conversations you're having and then you apply automations to that. And so then essentially those individual automations can be pointed to sets of emails for them to automate the processing of. And so what we've seen is customers go from things they're handling a hundred percent manual to now 95% of them are handled basically with completely automated processing. The other thing I think is super interesting here and why communications mining and automation are so powerful together is communications about your business can be very, very dynamic. So like, new conversations can emerge, something happens right in your business, you have an outage, whatever, and the automation platform, being a very rapid development platform, can help you adapt quickly to that in an automated way. Which is another reason why this is such a powerful thing to put the two things together. >> So, you can build that event into the automation very quickly you're saying? >> Speaker 1: Yeah. >> Speaker 2: That's totally right. >> Cool. >> So Ed, on the subject of natural language processing and machine learning versus machine teaching. If I text my wife and ask her would you like to go to an Italian restaurant tonight? And she replies, fine. Okay, how smart is your machine? And, of course, context usually literally denotes things within the text, and a short response like that's very difficult to do this. But how do you go through this process? Let's say you're implementing this for a given customer. And we were just talking about, you know, the specific customer requirements that they might have. What does that process look like? Do you have an auditor that goes through? And I mean do you get like 20% accuracy, and then you do a pass, and now you're at 80% accuracy, and you do a pass? What does that look? >> Yeah, so I mean, you know when I was talking about the pain of getting a machine learning model into production one of the principle drivers of that is this process of training the machine learning model. And so what we use is a technique called active learning which is effectively where the AI and ML model queries the user to say, teach me about this data point, teach me about this sentence. And that's a dynamic iterative process. And by doing it in that way you make that training process much, much faster. But critically that means that the user has, when you train the model the user defines how you want to encode that interpretation. So when you were training it you would say fine from my wife is not good, right? >> Sure, so it might be fine, do you have a better suggestion? >> Yeah, but that's actually a very serious point because one of the things we do is track the quality of service. Our customers use us to attract the quality of service they deliver to their clients. And in many industries people don't use flowery language, like, thank you so much, or you know, I'm upset with you, you know. What they might say is fine, and you know, the person that manages that client, that is not good, right? Or they might say I'd like to remind you that we've been late the last three times, you know. >> This is urgent. >> Yeah, you know, so it's important that the client, our client, the user of Re:infer, can encode what their notions of good and bad are. >> Sorry, quick follow up on that. Differences between British English and American English. In the U.K., if you're thinking about becoming an elected politician, you stand for office, right? Here in the U.S., you run for office. That's just the beginning of the vagaries and differences. >> Yeah, well, I've now got a lot more American colleagues and I realize my English phrasing often goes amiss. So I'm really aware of the problem. We have customers that have contact centers, some of them are in the U.K., some of them are in America, and they see big differences in the way that the customers get treated based on where the customer is based. So we've actually done analysis in Re:infer to look at how agents and customers interact and how you should route customers to the contact centers to be culturally matched. Because sometimes there can be a little bit of friction just for that cultural mapping. >> Ted, what's the what's the general philosophy when you make an acquisition like this and you bring in new features? Do you just wake up one day and all of a sudden there's this new capability? Is it a separate sort of for pay module? Does it depend? >> I think it depends. You know, in this case we were really led here by customers. We saw a very high value opportunity and the beginnings of a strategy and really being able to mine all forms of communication and drive automated processing of all forms of communication. And in this case we found a fantastic team and a fantastic piece of software that we can move very quickly to get in the hands of our customer's via UiPath. We're in private preview now, we're going to be GA in the cloud right after the first of the year and it's going to continue forward from there. But it's definitely not one size fits all. Every single one of 'em is different and it's important to approach 'em that way. >> Right, right. So some announcements, StudioWeb was one that I think you could. So I think it came out today. Can't remember what was today. I think we talked about it yesterday on the keynotes anyway. Why is that important? What is it all about? >> Well we talked, you know, at a very top level. I think every development platform thinks about two things for developers. They think, how do I make it more expressive so you can do other things, richer scenarios. And how do I make it simpler? 'Cause fast is always better, and lower learning curves is always better, and those sorts of things. So, Re:infer's a great example of look the runtime is becoming more and more expressive and now you can buy in communications state as part of your automation, which is super cool. And then, you know StudioWeb is about kind of that second point and Studios and Studio X are already low code visual, but they're desktop. And part of our strategy here is to elevate all of that experience into the web. Now we didn't elevate all of studio there, it's a subset. It is API integration and web based application automation, Which is a great foundation for a lot of apps. It's a complete reimagining of the studio user interface and most importantly it's our first cross-platform developer strategy. And so that's been another piece of our strategy, is to say to the customers we want to be everywhere you need us to be. We did cross-platform deployment with the automation suite. We got cross-platform robots with linear robots, serverless robots, Mac support and now we got a cross-platform devs story. So we're starting out with a subset of capabilities maybe oriented toward what you would associate with citizen scenarios. But you're going to see more roadmap, bringing more and more of that. But it's pretty exciting for us. We've been working on this thing for a couple years now and like this is a huge milestone for the team to get to this, this point. >> I think my first conversation on theCUBE with a customer was six years ago maybe at one of the earlier Forwards, I think Forward2. And the pattern that I saw was basically people taking existing processes and making them better, you know taking the mundane away. I remember asking customers, yeah, aren't you kind of paving the cow path? Aren't there sort of new things that you can do, new process? And they're like, yeah, that's sort of the next wave. So what are you seeing in terms of automating existing processes versus new processes? I would see Re:infer is going to open up a whole new vector of new processes. How should we think about that? >> Yeah, I think, you know, I mean in some ways RPA has this reputation because there's so much value that's been provided in the automating of the repetitive and routine. But I'd say in my whole time, I've been at the company now for two and a half years, I've seen lots of new novel stuff stood up. I mean just in Covid we saw the platform being used in PPP loan processing. We saw it in new clinical workflows for COVID testing. We see it and we've just seen more and more progression and it's been exciting that the conference, to see customers now talking about things they built with UiPath apps. So app experiences they've been delivering, you know. I talked about one in healthcare yesterday and basically how they've improved their patient intake processing and that sort of thing. And I think this is just the front end. I truly believe that we are seeing the convergence happen and it's happening already of categories we've talked about separately, iPass, BPM, low-code, RPA. It's happening and it's good for customers 'cause they want one thing to cover more stuff and you know, I think it just creates more opportunity for developers to do more things. >> Your background at Microsoft probably well prepared you for a company that you know, was born on-prem and then went all in on the cloud and had, you know, multiple code bases to deal with. UiPath has gone through a similar transformation and we talked to Daniel last night about this and you're now cloud first. So how is that going just in terms of managing multiple code bases? >> Well it's actually not multiple Code bases. >> Oh, it's the same one, Right, deployment models I should say. >> Is the first thing, Yeah, the deployment models. Another thing we did along the way was basically replatform at an infrastructure level. So we now can deploy into a Kubernetes Docker world, what you'd call the cloud native platform. And that allows us to have much more of a shared infrastructure layer as we look to deliver to the automation cloud. The same workload to the automation cloud that we now deliver in the automation suite for deployment on-prem or deploying a public cloud for a customer to manage. Interesting and enough, that's how Re:infer was built, which is it was built also in the cloud native platform. So it's going to be pretty easy. Well, pretty easy, there's some work to do, but it's going to be pretty easy for us to then bring that into the platform 'cause they're already working on that same platform and provide those same services both on premises and in the cloud without having your developers have to think too much about both. >> Okay, I got to ask you, so I could wrap my stack in a container and put it into AWS or Azure or Google and it'll run great. As well, I could tap some of the underlying primitives of those respective clouds, which are different and I could run them just fine. Or/and I could create an abstraction layer that could hide those underlying primitives and then take the best of each and create an automation cloud, my own cloud. Does that resonate? Is that what you're doing architecturally? Is that a roadmap, or? >> Certainly going forward, you know, in the automation cloud. The automation cloud, we announced a great partnership or a continued partnership with Microsoft. And just Azure and our platform. We obviously take advantage of anything we can to make that great and native capabilities. And I think you're going to see in the Automation Suite us doing more and more to be in a deployment model on Azure, be more and more optimized to using those infrastructure services. So if you deploy automation suite on-prem we'll use our embedded distro then when we deploy it say on Azure, we'll use some of their higher level managed services instead of our embedded distro. And that will just give customers a better optimized experience. >> Interesting to see how that'll develop. Last question is, you know what should we expect going forward? Can you show us a little leg on on the future? >> Well, we've talked about a number of directions. This idea of semantic automation is a place where you know, you're going to, I think, continue to see things, shoots, green shoots, come up in our platform. And you know, it's somewhat of an abstract idea but the idea that the platform is just going to become semantically smarter. You know, I had to serve Re:infer as a way, we're semantically smarter now about communications data and forms of communications data. We're getting semantically smarter about documents, screens you know, so developers aren't dealing with, like, this low level stuff. They can focus on business problem and get out of having to deal with all this lower level mechanism. That is one of many areas I'm excited about, but I think that's an area you're going to see a lot from us in the next coming years. >> All right guys, hey, thanks so much for coming to theCUBE. Really appreciate you taking us through this. Awesome >> Yeah Always a pleasure. >> Platform extension. Ed. All right, keep it right there, everybody. Dave Nicholson, I will be back right after this short break from UiPath Forward5, Las Vegas. (upbeat music)

>> Narrator: From theCUBE Studios in Palo Alto, in Boston bringing you data-driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> The next 10 years of cloud, they're going to differ dramatically from the past decade. The early days of cloud, deployed virtualization of standard off-the-shelf components, X86 microprocessors, disk drives et cetera, to then scale out and build a large distributed system. The coming decade is going to see a much more data-centric, real-time, intelligent, call it even hyper-decentralized cloud that will comprise on-prem, hybrid, cross-cloud and edge workloads with a services layer that will obstruct the underlying complexity of the infrastructure which will also comprise much more custom and varied components. This was a key takeaway of the guests from theCUBE on Cloud, an event hosted by SiliconANGLE on theCUBE. Welcome to this week's Wikibon CUBE Insights Powered by ETR. In this episode, we'll summarize the findings of our recent event and extract the signal from our great guests with a couple of series and comments and clips from the show. CUBE on Cloud is our very first virtual editorial event. It was designed to bring together our community in an open forum. We ran the day on our 365 software platform and had a great lineup of CEOs, CIOs, data practitioners technologists. We had cloud experts, analysts and many opinion leaders all brought together in a day long series of sessions that we developed in order to unpack the future of cloud computing in the coming decade. Let me briefly frame up the conversation and then turn it over to some of our guests. First, we put forth our view of how modern cloud has evolved and where it's headed. This graphic that we're showing here, talks about the progression of cloud innovation over time. A cloud like many innovations, it started as a novelty. When AWS announced S3 in March of 2006, nobody in the vendor or user communities really even in the trade press really paid too much attention to it. Then later that year, Amazon announced EC2 and people started to think about a new model of computing. But it was largely tire kickers, bleeding-edge developers that took notice and really leaned in. Now the financial crisis of 2007 to 2009, really created what we call a cloud awakening and it put cloud on the radar of many CFOs. Shadow IT emerged within departments that wanted to take IT in bite-sized chunks and along with the CFO wanted to take it as OPEX versus CAPEX. And then I teach transformation that really took hold. We came out of the financial crisis and we've been on an 11-year cloud boom. And it doesn't look like it's going to stop anytime soon, cloud has really disrupted the on-prem model as we've reported and completely transformed IT. Ironically, the pandemic hit at the beginning of this decade, and created a mandate to go digital. And so it accelerated the industry transformation that we're highlighting here, which probably would have taken several more years to mature but overnight the forced March to digital happened. And it looks like it's here to stay. Now the next wave, we think we'll be much more about business or industry transformation. We're seeing the first glimpses of that. Holger Mueller of Constellation Research summed it up at our event very well I thought, he basically said the cloud is the big winner of COVID. Of course we know that now normally we talk about seven-year economic cycles. He said he was talking about for planning and investment cycles. Now we operate in seven-day cycles. The examples he gave where do we open or close the store? How do we pivot to support remote workers without the burden of CAPEX? And we think that the things listed on this chart are going to be front and center in the coming years, data AI, a fully digitized and intelligence stack that will support next gen disruptions in autos, manufacturing, finance, farming and virtually every industry where the system will expand to the edge. And the underlying infrastructure across physical locations will be hidden. Many issues remain, not the least of which is latency which we talked about at the event in quite some detail. So let's talk about how the Big 3 cloud players are going to participate in this next era. Well, in short, the consensus from the event was that the rich get richer. Let's take a look at some data. This chart shows our most recent estimates of IaaS and PaaS spending for the Big 3. And we're going to update this after earning season but there's a couple of points stand out. First, we want to make the point that combined the Big 3 now account for almost $80 billion of infrastructure spend last year. That $80 billion, was not all incremental (laughs) No it's caused consolidation and disruption in the on-prem data center business and within IT shops companies like Dell, HPE, IBM, Oracle many others have felt the heat and have had to respond with hybrid and cross cloud strategies. Second while it's true that Azure and GCP they appear to be growing faster than AWS. We don't know really the exact numbers, of course because only AWS provides a clean view of IaaS and passwords, Microsoft and Google. They kind of hide them all ball on their numbers which by the way, I don't blame them but they do leave breadcrumbs and clues on growth rates. And we have other means of estimating through surveys and the like, but it's undeniable Azure is closing the revenue gap on AWS. The third is that I like the fact that Azure and Google are growing faster than AWS. AWS is the only company by our estimates to grow its business sequentially last quarter. And in and of itself, that's not really enough important. What is significant is that because AWS is so large now at 45 billion, even at their slower growth rates it grows much more in absolute terms than its competitors. So we think AWS is going to keep its lead for some time. We think Microsoft and AWS will continue to lead the pack. You know, they might converge maybe it will be a 200 just race in terms of who's first who's second in terms of cloud revenue and how it's counted depending on what they count in their numbers. And Google look with its balance sheet and global network. It's going to play the long game and virtually everyone else with the exception of perhaps Alibaba is going to be secondary players on these platforms. Now this next graphic underscores that reality and kind of lays out the competitive landscape. What we're showing here is survey data from ETR of more than 1400 CIOs and IT buyers and on the vertical axis is Net Score which measures spending momentum on the horizontal axis is so-called Market Share which is a measure of pervasiveness in the data set. The key points are AWS and Microsoft look at it. They stand alone so far ahead of the pack. I mean, they really literally, it would have to fall down to lose their lead high spending velocity and large share of the market or the hallmarks of these two companies. And we don't think that's going to change anytime soon. Now, Google, even though it's far behind they have the financial strength to continue to position themselves as an alternative to AWS. And of course, an analytics specialist. So it will continue to grow, but it will be challenged. We think to catch up to the leaders. Now take a look at the hybrid zone where the field is playing. These are companies that have a large on-prem presence and have been forced to initiate a coherent cloud strategy. And of course, including multicloud. And we include Google in this so pack because they're behind and they have to take a differentiated approach relative to AWS, and maybe cozy up to some of these traditional enterprise vendors to help Google get to the enterprise. And you can see from the on-prem crowd, VMware Cloud on AWS is stands out as having some, some momentum as does Red Hat OpenShift, which is it's cloudy, but it's really sort of an ingredient it's not really broad IaaS specifically but it's a component of cloud VMware cloud which includes VCF or VMware Cloud Foundation. And even Dell's cloud. We would expect HPE with its GreenLake strategy. Its financials is shoring up, should be picking up momentum in the future in terms of what the customers of this survey consider cloud. And then of course you could see IBM and Oracle you're in the game, but they don't have the spending momentum and they don't have the CAPEX chops to compete with the hyperscalers IBM's cloud revenue actually dropped 7% last quarter. So that highlights the challenges that that company facing Oracle's cloud business is growing in the single digits. It's kind of up and down, but again underscores these two companies are really about migrating their software install basis to their captive clouds and as well for IBM, for example it's launched a financial cloud as a way to differentiate and not take AWS head-on an infrastructure as a service. The bottom line is that other than the Big 3 in Alibaba the rest of the pack will be plugging into hybridizing and cross-clouding those platforms. And there are definitely opportunities there specifically related to creating that abstraction layer that we talked about earlier and hiding that underlying complexity and importantly creating incremental value good examples, snowfallLike what snowflake is doing with its data cloud, what the data protection guys are doing. A company like Loomio is headed in that direction as are others. So, you keep an eye on that and think about where the white space is and where the value can be across-clouds. That's where the opportunity is. So let's see, what is this all going to look like? How does the cube community think it's going to unfold? Let's hear from theCUBE Guests and theCUBE on Cloud speakers and some of those highlights. Now, unfortunately we don't have time to show you clips from every speaker. We are like 10-plus hours of video content but we've tried to pull together some comments that summarize the sentiment from the community. So I'm going to have John Furrier briefly explain what theCUBE on Cloud is all about and then let the guests speak for themselves. After John, Pradeep Sindhu is going to give a nice technical overview of how the cloud was built out and what's changing in the future. I'll give you a hint it has to do with data. And then speaking of data, Mai-Lan Bukovec, who heads up AWS is storage portfolio. She'll explain how she views the coming changes in cloud and how they look at storage. Again, no surprise, it's all about data. Now, one of the themes that you'll hear from guests is the notion of a distributed cloud model. And Zhamak Deghani, he was a data architect. She'll explain her view of the future of data architectures. We also have thoughts from analysts like Zeus Karavalla and Maribel Lopez, and some comments from both Microsoft and Google to compliment AWS's view of the world. In fact, we asked JG Chirapurath from Microsoft to comment on the common narrative that Microsoft products are not best-to-breed. They put out a one dot O and then they get better, or sometimes people say, well, they're just good enough. So we'll see what his response is to that. And Paul Gillin asks, Amit Zavery of Google his thoughts on the cloud leaderboard and how Google thinks about their third-place position. Dheeraj Pandey gives his perspective on how technology has progressed and been miniaturized over time. And what's coming in the future. And then Simon Crosby gives us a framework to think about the edge as the most logical opportunity to process data not necessarily a physical place. And this was echoed by John Roese, and Chris Wolf to experience CTOs who went into some great depth on this topic. Unfortunately, I don't have the clips of those two but their comments can be found on the CTO power panel the technical edge it's called that's the segment at theCUBE on Cloud events site which we'll share the URL later. Now, the highlight reel ends with CEO Joni Klippert she talks about the changes in securing the cloud from a developer angle. And finally, we wrap up with a CIO perspective, Dan Sheehan. He provides some practical advice on building on his experience as a CIO, COO and CTO specifically how do you as a business technology leader deal with the rapid pace of change and still be able to drive business results? Okay, so let's now hear from the community please run the highlights. >> Well, I think one of the things we talked about COVID is the personal impact to me but other people as well one of the things that people are craving right now is information, factual information, truth, textures that we call it. But here this event for us Dave is our first inaugural editorial event. Rob, both Kristen Nicole the entire cube team, SiliconANGLE on theCUBE we're really trying to put together more of a cadence. We're going to do more of these events where we can put out and feature the best people in our community that have great fresh voices. You know, we do interview the big names Andy Jassy, Michael Dell, the billionaires of people making things happen, but it's often the people under them that are the real Newsmakers. >> If you look at the architecture of cloud data centers the single most important invention was scale-out. Scale-out of identical or near identical servers all connected to a standard IP ethernet network. That's the architecture. Now the building blocks of this architecture is ethernet switches which make up the network, IP ethernet switches. And then the server is all built using general purpose x86 CPU's with DRAM, with SSD, with hard drives all connected to inside the CPU. Now, the fact that you scale these server nodes as they're called out was very, very important in addressing the problem of how do you build very large scale infrastructure using general purpose compute but this architecture, Dave is a compute centric architecture. And the reason it's a compute centric architecture is if you open this, is server node. What you see is a connection to the network typically with a simple network interface card. And then you have CPU's which are in the middle of the action. Not only are the CPU's processing the application workload but they're processing all of the IO workload what we call data centric workload. And so when you connect SSDs and hard drives and GPU is everything to the CPU, as well as to the network you can now imagine that the CPU is doing two functions. It's running the applications but it's also playing traffic cop for the IO. So every IO has to go to the CPU and you're executing instructions typically in the operating system. And you're interrupting the CPU many many millions of times a second. Now general purpose CPU and the architecture of the CPU's was never designed to play traffic cop because the traffic cop function is a function that requires you to be interrupted very, very frequently. So it's critical that in this new architecture where does a lot of data, a lot of these stress traffic the percentage of workload, which is data centric has gone from maybe one to 2% to 30 to 40%. >> The path to innovation is paved by data. If you don't have data, you don't have machine learning you don't have the next generation of analytics applications that helps you chart a path forward into a world that seems to be changing every week. And so in order to have that insight in order to have that predictive forecasting that every company needs, regardless of what industry that you're in today, it all starts from data. And I think the key shift that I've seen is how customers are thinking about that data, about being instantly usable. Whereas in the past, it might've been a backup. Now it's part of a data Lake. And if you can bring that data into a data lake you can have not just analytics or machine learning or auditing applications it's really what does your application do for your business and how can it take advantage of that vast amount of shared data set in your business? >> We are actually moving towards decentralization if we think today, like if it let's move data aside if we said is the only way web would work the only way we get access to various applications on the web or pages to centralize it We would laugh at that idea. But for some reason we don't question that when it comes to data, right? So I think it's time to embrace the complexity that comes with the growth of number of sources, the proliferation of sources and consumptions models, embrace the distribution of sources of data that they're not just within one part of organization. They're not just within even bounds of organizations that are beyond the bounds of organization. And then look back and say, okay, if that's the trend of our industry in general, given the fabric of compensation and data that we put in, you know, globally in place then how the architecture and technology and organizational structure incentives need to move to embrace that complexity. And to me that requires a paradigm shift a full stack from how we organize our organizations how we organize our teams, how we put a technology in place to look at it from a decentralized angle. >> I actually think we're in the midst of the transition to what's called a distributed cloud, where if you look at modernized cloud apps today they're actually made up of services from different clouds. And also distributed edge locations. And that's going to have a pretty profound impact on the way we go vast. >> We wake up every day, worrying about our customer and worrying about the customer condition and to absolutely make sure we dealt with the best in the first attempt that we do. So when you take the plethora of products we've dealt with in Azure, be it Azure SQL be it Azure cosmos DB, Synapse, Azure Databricks, which we did in partnership with Databricks Azure machine learning. And recently when we sort of offered the world's first comprehensive data governance solution and Azure overview, I would, I would humbly submit to you that we are leading the way. >> How important are rankings within the Google cloud team or are you focused mainly more on growth and just consistency? >> No, I don't think again, I'm not worried about we are not focused on ranking or any of that stuff. Typically I think we are worried about making sure customers are satisfied and the adding more and more customers. So if you look at the volume of customers we are signing up a lot of the large deals we did doing. If you look at the announcement we've made over the last year has been tremendous momentum around that. >> The thing that is really interesting about where we have been versus where we're going is we spend a lot of time talking about virtualizing hardware and moving that around. And what does that look like? And creating that as more of a software paradigm. And the thing we're talking about now is what does cloud as an operating model look like? What is the manageability of that? What is the security of that? What, you know, we've talked a lot about containers and moving into different, DevSecOps and all those different trends that we've been talking about. Like now we're doing them. So we've only gotten to the first crank of that. And I think every technology vendor we talked to now has to address how are they are going to do a highly distributed management insecurity landscape? Like, what are they going to layer on top of that? Because it's not just about, oh, I've taken a rack of something, server storage, compute, and virtualized it. I know have to create a new operating model around it in a way we're almost redoing what the OSI stack looks like and what the software and solutions are for that. >> And the whole idea of we in every recession we make things smaller. You know, in 91 we said we're going to go away from mainframes into Unix servers. And we made the unit of compute smaller. Then in the year, 2000 windows the next bubble burst and the recession afterwards we moved from Unix servers to Wintel windows and Intel x86 and eventually Linux as well. Again, we made things smaller going from million dollar servers to $5,000 servers, shorter lib servers. And that's what we did in 2008, 2009. I said, look, we don't even need to buy servers. We can do things with virtual machines which are servers that are an incarnation in the digital world. There's nothing in the physical world that actually even lives but we made it even smaller. And now with cloud in the last three, four years and what will happen in this coming decade. They're going to make it even smaller not just in space, which is size, with functions and containers and virtual machines, but also in time. >> So I think the right way to think about edges where can you reasonably process the data? And it obviously makes sense to process data at the first opportunity you have but much data is encrypted between the original device say and the application. And so edge as a place doesn't make as much sense as edge as an opportunity to decrypt and analyze it in the care. >> When I think of Shift-left, I think of that Mobius that we all look at all of the time and how we deliver and like plan, write code, deliver software, and then manage it, monitor it, right like that entire DevOps workflow. And today, when we think about where security lives, it either is a blocker to deploying production or most commonly it lives long after code has been deployed to production. And there's a security team constantly playing catch up trying to ensure that the development team whose job is to deliver value to their customers quickly, right? Deploy as fast as we can as many great customer facing features. They're then looking at it months after software has been deployed and then hurrying and trying to assess where the bugs are and trying to get that information back to software developers so that they can fix those issues. Shifting left to me means software engineers are finding those bugs as they're writing code or in the CIC CD pipeline long before code has been deployed to production. >> During this for quite a while now, it still comes down to the people. I can get the technology to do what it needs to do as long as they have the right requirements. So that goes back to people making sure we have the partnership that goes back to leadership and the people and then the change management aspects right out of the gate, you should be worrying about how this change is going to be how it's going to affect, and then the adoption and an engagement, because adoption is critical because you can go create the best thing you think from a technology perspective. But if it doesn't get used correctly, it's not worth the investment. So I agree, what is a digital transformation or innovation? It still comes down to understand the business model and injecting and utilizing technology to grow our reduce costs, grow the business or reduce costs. >> Okay, so look, there's so much other content on theCUBE on Cloud events site we'll put the link in the description below. We have other CEOs like Kathy Southwick and Ellen Nance. We have the CIO of UI path. Daniel Dienes talks about automation in the cloud and Appenzell from Anaplan. And a plan is not her company. By the way, Dave Humphrey from Bain also talks about his $750 million investment in Nutanix. Interesting, Rachel Stevens from red monk talks about the future of software development in the cloud and CTO, Hillary Hunter talks about the cloud going vertical into financial services. And of course, John Furrier and I along with special guests like Sergeant Joe Hall share our take on key trends, data and perspectives. So right here, you see the coupon cloud. There's a URL, check it out again. We'll, we'll pop this URL in the description of the video. So there's some great content there. I want to thank everybody who participated and thank you for watching this special episode of theCUBE Insights Powered by ETR. This is Dave Vellante and I'd appreciate any feedback you might have on how we can deliver better event content for you in the future. We'll be doing a number of these and we look forward to your participation and feedback. Thank you, all right, take care, we'll see you next time. (upbeat music)

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle. Okay, >>we're now going to explore the vision of the future of cloud computing From the perspective of one of the leaders in the field, J G >>Share >>a pure off is the vice president of As Your Data ai and Edge at Microsoft G. Welcome to the Cuban cloud. Thanks so much for participating. >>Well, thank you, Dave, and it's a real pleasure to be here with you. And I just wanna welcome the audience as well. >>Well, jg judging from your title, we have a lot of ground to cover, and our audience is definitely interested in all the topics that are implied there. So let's get right into it. You know, we've said many times in the Cube that the new innovation cocktail comprises machine intelligence or a I applied to troves of data. With the scale of the cloud. It's it's no longer, you know, we're driven by Moore's law. It's really those three factors, and those ingredients are gonna power the next wave of value creation and the economy. So, first, do you buy into that premise? >>Yes, absolutely. we do buy into it. And I think, you know, one of the reasons why we put Data Analytics and Ai together is because all of that really begins with the collection of data and managing it and governing it, unlocking analytics in it. And we tend to see things like AI, the value creation that comes from a I as being on that continues off, having started off with really things like analytics and proceeding toe. You know, machine learning and the use of data. Interesting breaks. Yes. >>I'd like to get some more thoughts around a data and how you see the future data and the role of cloud and maybe how >>Microsoft, you >>know, strategy fits in there. I mean, you, your portfolio, you got you got sequel server, Azure, Azure sequel. You got arc, which is kinda azure everywhere for people that aren't familiar with that. You've got synapse. Which course that's all the integration a data warehouse, and get things ready for B I and consumption by the business and and the whole data pipeline and a lot of other services as your data bricks you got You got cosmos in their, uh, Blockchain. You've got open source services like Post Dress and my sequel. So lots of choices there. And I'm wondering, you know, how do you think about the future of Of of Cloud data platforms? It looks like your strategies, right tool for the right job? Is that fair? >>It is fair, but it's also just to step back and look at it. It's fundamentally what we see in this market today is that customer was the Sikh really a comprehensive proposition? And when I say a comprehensive proposition, it is sometimes not just about saying that. Hey, listen way No, you're a sequel server company. We absolutely trust that you have the best Azure sequel database in the cloud, but tell us more. We've got data that's sitting in her group systems. We've got data that's sitting in Post Press in things like mongo DB, right? So that open source proposition today and data and data management and database management has become front and center, so are really sort of push. There is when it comes to migration management, modernization of data to present the broadest possible choice to our customers so we can meet them where they are. However, when it comes to analytics. One of the things they asked for is give us a lot more convergence use. You know it, really, it isn't about having 50 different services. It's really about having that one comprehensive service that is converged. That's where things like synapse Fitzer, where in just land any kind of data in the leg and then use any compute engine on top of it to drive insights from it. So, fundamentally, you know, it is that flexibility that we really sort of focus on to meet our customers where they are and really not pushing our dogma and our beliefs on it. But to meet our customers according to the way they have deployed stuff like this. >>So that's great. I want to stick on this for a minute because, you know, I know when when I have guests on like yourself, do you never want to talk about you know, the competition? But that's all we ever talk about. That's all your customers ever talk about, because because the counter to that right tool for the right job and that I would say, is really kind of Amazon's approach is is that you got the single unified data platform, the mega database that does it all. And that's kind of Oracle's approach. It sounds like you wanna have your cake and eat it, too, so you you got the right tool for the right job approach. But you've got an integration layer that allows you to have that converge database. I wonder if you could add color to that and you confirm or deny what I just said. >>No, that's a That's a very fair observation, but I I say there's a nuance in what I sort of describe when it comes to data management. When it comes to APS, we have them customers with the broadest choice. Even in that, even in that perspective, we also offer convergence. So, case in point, when you think about Cosmos TV under that one sort of service, you get multiple engines, but with the same properties, right global distribution, the five nines availability. It gives customers the ability to basically choose when they have to build that new cloud native AB toe, adopt cosmos Davey and adopted in a way that it's and choose an engine that is most flexible. Tow them, however you know when it comes to say, you know, writing a sequel server, for example from organizing it you know you want. Sometimes you just want to lift and shift it into things. Like I asked In other cases, you want to completely rewrite it, so you need to have the flexibility of choice there that is presented by a legacy off What's its on premises? When it moved into things like analytics, we absolutely believe in convergence, right? So we don't believe that look, you need to have a relation of data warehouse that is separate from a loop system that is separate from, say, a B I system. That is just, you know, it's a bolt on for us. We love the proposition off, really building things that are so integrated that once you land data, once you prep it inside the lake, you can use it for analytics. You can use it for being. You can use it for machine learning. So I think you know, are sort of differentiated. Approach speaks for itself there. Well, >>that's that's interesting, because essentially, again, you're not saying it's an either or, and you're seeing a lot of that in the marketplace. You got some companies say no, it's the Data Lake and others saying No, no put in the data warehouse and that causes confusion and complexity around the data pipeline and a lot of calls. And I'd love to get your thoughts on this. Ah, lot of customers struggled to get value out of data and and specifically data product builders of frustrated that it takes too long to go from. You know, this idea of Hey, I have an idea for a data service and it could drive monetization, but to get there, you gotta go through this complex data lifecycle on pipeline and beg people to add new data sources. And do you do you feel like we have to rethink the way that we approach data architectures? >>Look, I think we do in the cloud, and I think what's happening today and I think the place where I see the most amount of rethink the most amount of push from our customers to really rethink is the area of analytics in a I. It's almost as if what worked in the past will not work going forward. Right? So when you think about analytics on in the Enterprise today, you have relational systems, you have produced systems. You've got data marts. You've got data warehouses. You've got enterprise data warehouses. You know, those large honking databases that you use, uh, to close your books with right? But when you start to modernize it, what deep you are saying is that we don't want to simply take all of that complexity that we've built over say, you know, 34 decades and simply migrated on mass exactly as they are into the cloud. What they really want is a completely different way of looking at things. And I think this is where services like synapse completely provide a differentiated proposition to our customers. What we say there is land the data in any way you see shape or form inside the lake. Once you landed inside the lake, you can essentially use a synapse studio toe. Prep it in the way that you like, use any compute engine of your choice and and operate on this data in any way that you see fit. So, case in point, if you want to hydrate relation all data warehouse, you can do so if you want to do ad hoc analytics using something like spark. You can do so if you want to invoke power. Bi I on that data or b i on that data you can do so if you want to bring in a machine learning model on this breath data you can do so, so inherently. So when customers buy into this proposition, what it solves for them and what it gives them is complete simplicity, right? One way to land the data, multiple ways to use it. And it's all eso. >>Should we think of synapse as an abstraction layer that abstracts away the complexity of the underlying technology? Is that a fair way toe? Think about it. >>Yeah, you can think of it that way. It abstracts away, Dave a couple of things. It takes away the type of data, you know, sort of the complexities related to the type of data. It takes away the complexity related to the size of data. It takes away the complexity related to creating pipelines around all these different types of data and fundamentally puts it in a place where it can be now consumed by any sort of entity inside the actual proposition. And by that token, even data breaks. You know, you can, in fact, use data breaks in in sort off an integrated way with a synapse, Right, >>Well, so that leads me to this notion of and then wonder if you buy into it s Oh, my inference is that a data warehouse or a data lake >>could >>just be a node in inside of a global data >>mesh on. >>Then it's synapses sort of managing, uh, that technology on top. Do you buy into that that global data mesh concept >>we do. And we actually do see our customers using synapse and the value proposition that it brings together in that way. Now it's not where they start. Often times when a customer comes and says, Look, I've got an enterprise data warehouse, I want to migrate it or I have a group system. I want to migrate it. But from there, the evolution is absolutely interesting to see. I give you an example. You know, one of the customers that we're very proud off his FedEx And what FedEx is doing is it's completely reimagining its's logistics system that basically the system that delivers What is it? The three million packages a day on in doing so in this covert times, with the view of basically delivering our covert vaccines. One of the ways they're doing it is basically using synapse. Synapse is essentially that analytic hub where they can get complete view into their logistic processes. Way things are moving, understand things like delays and really put all that together in a way that they can essentially get our packages and these vaccines delivered as quickly as possible. Another example, you know, is one of my favorite, uh, we see once customers buy into it, they essentially can do other things with it. So an example of this is, uh is really my favorite story is Peace Parks Initiative. It is the premier Air White Rhino Conservancy in the world. They essentially are using data that has landed in azure images in particular. So, basically, you know, use drones over the vast area that they patrol and use machine learning on this data to really figure out where is an issue and where there isn't an issue so that this part with about 200 rangers can scramble surgically versus having to read range across the last area that they cover. So What do you see here is you know, the importance is really getting your data in order. Landed consistently. Whatever the kind of data ideas build the right pipelines and then the possibilities of transformation are just endless. >>Yeah, that's very nice how you worked in some of the customer examples. I appreciate that. I wanna ask you, though, that that some people might say that putting in that layer while it clearly adds simplification and e think a great thing that they're begins over time to be be a gap, if you will, between the ability of that layer to integrate all the primitives and all the peace parts on that, that you lose some of that fine grain control and it slows you down. What would you say to that? >>Look, I think that's what we excel at, and that's what we completely sort of buy into on. It's our job to basically provide that level off integration that granularity in the way that so it's an art, absolutely admit it's an art. There are areas where people create simplicity and not a lot of you know, sort of knobs and dials and things like that. But there are areas where customers want flexibility, right? So I think just to give you an example of both of them in landing the data inconsistency in building pipelines, they want simplicity. They don't want complexity. They don't want 50 different places to do this. Just 100 to do it. When it comes to computing and reducing this data analyzing this data, they want flexibility. This is one of the reasons why we say, Hey, listen, you want to use data breaks? If you're you're buying into that proposition and you're absolutely happy with them, you can plug plug it into it. You want to use B I and no, essentially do a small data mart. You can use B I If you say that. Look, I've landed in the lake. I really only want to use em melt, bringing your animal models and party on. So that's where the flexibility comes in. So that's sort of really sort of think about it. Well, >>I like the strategy because, you know, my one of our guest, Jim Octagon, e E. I think one of the foremost thinkers on this notion of off the data mesh and her premises that that that data builders, data product and service builders air frustrated because the the big data system is generic to context. There's no context in there. But by having context in the big data architecture and system, you could get products to market much, much, much faster. So but that seems to be your philosophy. But I'm gonna jump ahead to do my ecosystem question. You've mentioned data breaks a couple of times. There's another partner that you have, which is snowflake. They're kind of trying to build out their own, uh, data cloud, if you will, on global mesh in and the one hand, their partner. On the other hand, there are competitors. How do you sort of balance and square that circle? >>Look, when I see snowflake, I actually see a partner. You know that when we essentially you know, we are. When you think about as you know, this is where I sort of step back and look at Azure as a whole and in azure as a whole. Companies like snowflakes are vital in our ecosystem, right? I mean, there are places we compete, but you know, effectively by helping them build the best snowflake service on Asia. We essentially are able toe, you know, differentiate and offer a differentiated value proposition compared to, say, a Google or on AWS. In fact, that's being our approach with data breaks as well, where you know they are effectively on multiple club, and our opportunity with data breaks is toe essentially integrate them in a way where we offer the best experience. The best integrations on Azure Barna That's always been a focus. >>That's hard to argue with. Strategy. Our data with our data partner eat er, shows Microsoft is both pervasive and impressively having a lot of momentum spending velocity within the budget cycles. I wanna come back thio ai a little bit. It's obviously one of the fastest growing areas in our in our survey data. As I said, clearly, Microsoft is a leader in this space. What's your what's your vision of the future of machine intelligence and how Microsoft will will participate in that opportunity? >>Yeah, so fundamentally, you know, we've built on decades of research around, you know, around, you know, essentially, you know, vision, speech and language that's being the three core building blocks and for the for a really focused period of time we focused on essentially ensuring human parody. So if you ever wondered what the keys to the kingdom are it, czar, it's the most we built in ensuring that the research posture that we've taken there, what we then done is essentially a couple of things we focused on, essentially looking at the spectrum. That is a I both from saying that, Hollis and you know it's gotta work for data. Analysts were looking toe basically use machine learning techniques, toe developers who are essentially, you know, coding and building a machine learning models from scratch. So for that select proposition manifesto us, as you know, really a. I focused on all skill levels. The other court thing we've done is that we've also said, Look, it will only work as long as people trust their data and they can trust their AI models. So there's a tremendous body of work and research we do in things like responsibility. So if you ask me where we sort of push on is fundamentally to make sure that we never lose sight of the fact that the spectrum off a I, and you can sort of come together for any skill level, and we keep that responsibly. I proposition. Absolutely strong now against that canvas, Dave. I'll also tell you that you know, as edge devices get way more capable, right where they can input on the edge, see a camera or a mike or something like that, you will see us pushing a lot more of that capability onto the edge as well. But to me, that's sort of a modality. But the core really is all skill levels and that responsible denia. >>Yeah, So that that brings me to this notion of wanna bring an edge and and hybrid cloud Understand how you're thinking about hybrid cloud multi cloud. Obviously one of your competitors, Amazon won't even say the word multi cloud you guys have, Ah, you know, different approach there. But what's the strategy with regard? Toe, toe hybrid. You know, Do you see the cloud you bringing azure to the edge? Maybe you could talk about that and talk about how you're different from the competition. >>Yeah, I think in the edge from Annette, you know, I live in I'll be the first one to say that the word nge itself is conflated. Okay, It's, uh but I will tell you, just focusing on hybrid. This is one of the places where you know I would say the 2020 if I would have looked back from a corporate perspective. In particular, it has Bean the most informative because we absolutely saw customers digitizing moving to the cloud. And we really saw hybrid in action. 2020 was the year that hybrid sort of really became really from a cloud computing perspective and an example of this is we understood it's not all or nothing. So sometimes customers want azure consistency in their data centers. This is where things like Azure stack comes in. Sometimes they basically come to us and say, We want the flexibility of adopting flexible pattern, you know, platforms like, say, containers orchestra, Cuban Pettis, so that we can essentially deployed wherever you want. And so when we design things like art, it was built for that flexibility in mind. So here is the beauty of what's something like our can do for you. If you have a kubernetes endpoint anywhere we can deploy and as your service onto it, that is the promise, which means if for some reason, the customer says that. Hey, I've got this kubernetes endpoint in AWS and I love as your sequel. You will be able to run as your sequel inside AWS. There's nothing that stops you from doing it so inherently you remember. Our first principle is always to meet our customers where they are. So from that perspective, multi cloud is here to stay. You know, we're never going to be the people that says, I'm sorry, we will never see a But it is a reality for our customers. >>So I wonder if we could close. Thank you for that by looking, looking back and then and then ahead. And I wanna e wanna put forth. Maybe it's, Ah criticism, but maybe not. Maybe it's an art of Microsoft, but But first you know, you get Microsoft an incredible job of transitioning. It's business as your nominee president Azzawi said. Our data shows that so two part question First, Microsoft got there by investing in the cloud, really changing its mind set, I think, in leveraging its huge software state and customer base to put Azure at the center of its strategy, and many have said me included that you got there by creating products that air Good enough. You know, we do a 1.0, it's not that great. And the two Dato, and maybe not the best, but acceptable for your customers. And that's allowed you to grow very rapidly expanding market. >>How >>do you respond to that? Is that is that a fair comment? Ume or than good enough? I wonder if you could share your >>thoughts, gave you? You hurt my feelings with that question. I don't hate me, g getting >>it out there. >>So there was. First of all, thank you for asking me that. You know, I am absolutely the biggest cheerleader. You'll find a Microsoft. I absolutely believe you know that I represent the work off almost 9000 engineers and we wake up every day worrying about our customer and worrying about the customer condition and toe. Absolutely. Make sure we deliver the best in the first time that we do. So when you take the platter off products we've delivered in nausea, be it as your sequel, be it as your cosmos TV synapse as your data breaks, which we did in partnership with data breaks, a za machine learning and recently when we prevail, we sort off, you know, sort of offered the world's first comprehensive data government solution in azure purview. I would humbly submit to you that we're leading the way and we're essentially showing how the future off data ai and the actual work in the cloud. >>I'd be disappointed if you if you had If you didn't, if you capitulated in any way J g So so thank you for that. And the kind of last question is, is looking forward and how you're thinking about the future of cloud last decade. A lot about your cloud migration simplifying infrastructure management, deployment SAS if eyeing my enterprise, lot of simplification and cost savings. And, of course, the redeployment of resource is toward digital transformation. Other other other valuable activities. How >>do >>you think this coming decade will will be defined? Will it be sort of more of the same? Or is there Is there something else out there? >>I think I think that the coming decade will be one where customers start one law outside value out of this. You know what happened in the last decade when people leave the foundation and people essentially looked at the world and said, Look, we've got to make the move, you know, the largely hybrid, but we're going to start making steps to basically digitize and modernize our platforms. I would tell you that with the amount of data that people are moving to the cloud just as an example, you're going to see use of analytics ai for business outcomes explode. You're also going to see a huge sort of focus on things like governance. You know, people need to know where the data is, what the data catalog continues, how to govern it, how to trust this data and given all other privacy and compliance regulations out there. Essentially, they're complying this posture. So I think the unlocking of outcomes versus simply Hey, I've saved money Second, really putting this comprehensive sort off, you know, governance, regime in place. And then, finally, security and trust. It's going to be more paramount than ever before. Yeah, >>nobody's gonna use the data if they don't trust it. I'm glad you brought up your security. It's It's a topic that hits number one on the CEO list. J G. Great conversation. Obviously the strategy is working, and thanks so much for participating in Cuba on cloud. >>Thank you. Thank you, David. I appreciate it and thank you to. Everybody was tuning in today. >>All right? And keep it right there. I'll be back with our next guest right after this short break.

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle, everybody to Cuban cloud. My name is Dave Volonte, and I'll be here throughout the day with my co host, John Ferrier, who was quarantined in an undisclosed location in California. He's all good. Don't worry. Just precautionary. John, how are you doing? >>Hey, great to see you. John. Quarantine. My youngest daughter had covitz, so contact tracing. I was negative in quarantine at a friend's location. All good. >>Well, we wish you the best. Yeah, well, right. I mean, you know what's it like, John? I mean, you're away from your family. Your basically shut in, right? I mean, you go out for a walk, but you're really not in any contact with anybody. >>Correct? Yeah. I mean, basically just isolation, Um, pretty much what everyone's been kind of living on, kind of suffering through, but hopefully the vaccines are being distributed. You know, one of the things we talked about it reinvent the Amazon's cloud conference. Was the vaccine on, but just the whole workflow around that it's gonna get better. It's kind of really sucky. Here in the California area, they haven't done a good job, a lot of criticism around, how that's rolling out. And, you know, Amazon is now offering to help now that there's a new regime in the U. S. Government S o. You know, something to talk about, But certainly this has been a terrible time for Cove it and everyone in the deaths involved. But it's it's essentially pulled back the covers, if you will, on technology and you're seeing everything. Society. In fact, um, well, that's big tech MIT disinformation campaigns. All these vulnerabilities and cyber, um, accelerated digital transformation. We'll talk about a lot today, but yeah, it's totally changed the world. And I think we're in a new generation. I think this is a real inflection point, Dave. You know, modern society and the geo political impact of this is significant. You know, one of the benefits of being quarantined you'd be hanging out on these clubhouse APS, uh, late at night, listening to experts talk about what's going on, and it's interesting what's happening with with things like water and, you know, the island of Taiwan and China and U. S. Sovereignty, data, sovereignty, misinformation. So much going on to talk about. And, uh, meanwhile, companies like Mark injuries in BC firm starting a media company. What's going on? Hell freezing over. So >>we're gonna be talking about a lot of that stuff today. I mean, Cuba on cloud. It's our very first virtual editorial event we're trying to do is bring together our community. It's a it's an open forum and we're we're running the day on our 3 65 software platform. So we got a great lineup. We got CEO Seo's data Practitioners. We got a hard core technologies coming in, cloud experts, investors. We got some analysts coming in and we're creating this day long Siri's. And we've got a number of sessions that we've developed and we're gonna unpack. The future of Cloud computing in the coming decade is, John said, we're gonna talk about some of the public policy new administration. What does that mean for tech and for big tech in General? John, what can you add to that? >>Well, I think one of the things that we talked about Cove in this personal impact to me but other people as well. One of the things that people are craving right now is information factual information, truth texture that we call it. But hear this event for us, Davis, our first inaugural editorial event. Robbo, Kristen, Nicole, the entire Cube team Silicon angle, really trying to put together Morva cadence we're gonna doom or of these events where we can put out feature the best people in our community that have great fresh voices. You know, we do interview the big names Andy Jassy, Michael Dell, the billionaires with people making things happen. But it's often the people under there that are the rial newsmakers amid savory, for instance, that Google one of the most impressive technical people, he's gotta talk. He's gonna present democratization of software development in many Mawr riel people making things happen. And I think there's a communal element. We're going to do more of these. Obviously, we have, uh, no events to go to with the Cube. So we have the cube virtual software that we have been building and over years and now perfecting and we're gonna introduce that we're gonna put it to work, their dog footing it. We're gonna put that software toe work. We're gonna do a lot mawr virtual events like this Cuban cloud Cuban startup Cuban raising money. Cuban healthcare, Cuban venture capital. Always think we could do anything. Question is, what's the right story? What's the most important stories? Who's telling it and increase the aperture of the lens of the industry that we have and and expose that and fastest possible. That's what this software, you'll see more of it. So it's super exciting. We're gonna add new features like pulling people up on stage, Um, kind of bring on the clubhouse vibe and more of a community interaction with people to meet each other, and we'll roll those out. But the goal here is to just showcase it's cloud story in a way from people that are living it and providing value. So enjoy the day is gonna be chock full of presentations. We're gonna have moderated chat in these sessions, so it's an all day event so people can come in, drop out, and also that's everything's on demand immediately after the time slot. But you >>want to >>participate, come into the time slot into the cube room or breakout session. Whatever you wanna call it, it's a cube room, and the people in there chatting and having a watch party. So >>when you're in that home page when you're watching, there's a hero video there. Beneath that, there's a calendar, and you'll see that red line is that red horizontal line of vertical line is rather, it's a linear clock that will show you where we are in the day. If you click on any one of those sessions that will take you into the chat, we'll take you through those in a moment and share with you some of the guests that we have upcoming and and take you through the day what I wanted to do. John is trying to set the stage for the conversations that folks are gonna here today. And to do that, I wanna ask the guys to bring up a graphic. And I want to talk to you, John, about the progression of cloud over time and maybe go back to the beginning and review the evolution of cloud and then really talk a little bit about where we think it Z headed. So, guys, if you bring up that graphic when a W S announced s three, it was March of 2000 and six. And as you recall, John you know, nobody really. In the vendor and user community. They didn't really pay too much attention to that. And then later that year, in August, it announced E C two people really started. They started to think about a new model of computing, but they were largely, you know, chicken tires. And it was kind of bleeding edge developers that really leaned in. Um what? What were you thinking at the time? When when you saw, uh, s three e c to this retail company coming into the tech world? >>I mean, I thought it was totally crap. I'm like, this is terrible. But then at that time, I was thinking working on I was in between kind of start ups and I didn't have a lot of seed funding. And then I realized the C two was freaking awesome. But I'm like, Holy shit, this is really great because I don't need to pay a lot of cash, the Provisional Data center, or get a server. Or, you know, at that time, state of the art startup move was to buy a super micro box or some sort of power server. Um, it was well past the whole proprietary thing. But you have to assemble probably anyone with 5 to 8 grand box and go in, and we'll put a couple ghetto rack, which is basically, uh, you know, you put it into some coasting location. It's like with everybody else in the tech ghetto of hosting, still paying monthly fees and then maintaining it and provisioning that's just to get started. And then Amazon was just really easy. And then from there you just It was just awesome. I just knew Amazon would be great. They had a lot of things that they had to fix. You know, custom domains and user interface Council got better and better, but it was awesome. >>Well, what we really saw the cloud take hold from my perspective anyway, was the financial crisis in, you know, 709 It put cloud on the radar of a number of CFOs and, of course, shadow I T departments. They wanted to get stuff done and and take I t in in in, ah, pecs, bite sized chunks. So it really was. There's cloud awakening and we came out of that financial crisis, and this we're now in this 10 year plus boom um, you know, notwithstanding obviously the economic crisis with cove it. But much of it was powered by the cloud in the decade. I would say it was really about I t transformation. And it kind of ironic, if you will, because the pandemic it hits at the beginning of this decade, >>and it >>creates this mandate to go digital. So you've you've said a lot. John has pulled forward. It's accelerated this industry transformation. Everybody talks about that, but and we've highlighted it here in this graphic. It probably would have taken several more years to mature. But overnight you had this forced march to digital. And if you weren't a digital business, you were kind of out of business. And and so it's sort of here to stay. How do you see >>You >>know what this evolution and what we can expect in the coming decades? E think it's safe to say the last 10 years defined by you know, I t transformation. That's not gonna be the same in the coming years. How do you see it? >>It's interesting. I think the big tech companies are on, but I think this past election, the United States shows um, the power that technology has. And if you look at some of the main trends in the enterprise specifically around what clouds accelerating, I call the second wave of innovations coming where, um, it's different. It's not what people expect. Its edge edge computing, for instance, has talked about a lot. But industrial i o t. Is really where we've had a lot of problems lately in terms of hacks and malware and just just overall vulnerabilities, whether it's supply chain vulnerabilities, toe actual disinformation, you know, you know, vulnerabilities inside these networks s I think this network effects, it's gonna be a huge thing. I think the impact that tech will have on society and global society geopolitical things gonna be also another one. Um, I think the modern application development of how applications were written with data, you know, we always been saying this day from the beginning of the Cube data is his integral part of the development process. And I think more than ever, when you think about cloud and edge and this distributed computing paradigm, that cloud is now going next level with is the software and how it's written will be different. You gotta handle things like, where's the compute component? Is it gonna be at the edge with all the server chips, innovations that Amazon apple intel of doing, you're gonna have compute right at the edge, industrial and kind of human edge. How does that work? What's Leighton see to that? It's it really is an edge game. So to me, software has to be written holistically in a system's impact on the way. Now that's not necessarily nude in the computer science and in the tech field, it's just gonna be deployed differently. So that's a complete rewrite, in my opinion of the software applications. Which is why you're seeing Amazon Google VM Ware really pushing Cooper Netease and these service messes in the micro Services because super critical of this technology become smarter, automated, autonomous. And that's completely different paradigm in the old full stack developer, you know, kind of model. You know, the full stack developer, his ancient. There's no such thing as a full stack developer anymore, in my opinion, because it's a half a stack because the cloud takes up the other half. But no one wants to be called the half stack developer because it doesn't sound as good as Full Stack, but really Cloud has eliminated the technology complexity of what a full stack developer used to dio. Now you can manage it and do things with it, so you know, there's some work to done, but the heavy lifting but taking care of it's the top of the stack that I think is gonna be a really critical component. >>Yeah, and that that sort of automation and machine intelligence layer is really at the top of the stack. This this thing becomes ubiquitous, and we now start to build businesses and new processes on top of it. I wanna I wanna take a look at the Big Three and guys, Can we bring up the other The next graphic, which is an estimate of what the revenue looks like for the for the Big three. And John, this is I asked and past spend for the Big Three Cloud players. And it's It's an estimate that we're gonna update after earning seasons, and I wanna point a couple things out here. First is if you look at the combined revenue production of the Big Three last year, it's almost 80 billion in infrastructure spend. I mean, think about that. That Z was that incremental spend? No. It really has caused a lot of consolidation in the on Prem data center business for guys like Dell. And, you know, um, see, now, part of the LHP split up IBM Oracle. I mean, it's etcetera. They've all felt this sea change, and they had to respond to it. I think the second thing is you can see on this data. Um, it's true that azure and G C P they seem to be growing faster than a W s. We don't know the exact numbers >>because >>A W S is the only company that really provides a clean view of i s and pass. Whereas Microsoft and Google, they kind of hide the ball in their numbers. I mean, I don't blame them because they're behind, but they do leave breadcrumbs and clues about growth rates and so forth. And so we have other means of estimating, but it's it's undeniable that azure is catching up. I mean, it's still quite distance the third thing, and before I want to get your input here, John is this is nuanced. But despite the fact that Azure and Google the growing faster than a W s. You can see those growth rates. A W s I'll call this out is the only company by our estimates that grew its business sequentially last quarter. Now, in and of itself, that's not significant. But what is significant is because AWS is so large there $45 billion last year, even if the slower growth rates it's able to grow mawr and absolute terms than its competitors, who are basically flat to down sequentially by our estimates. Eso So that's something that I think is important to point out. Everybody focuses on the growth rates, but it's you gotta look at also the absolute dollars and, well, nonetheless, Microsoft in particular, they're they're closing the gap steadily, and and we should talk more about the competitive dynamics. But I'd love to get your take on on all this, John. >>Well, I mean, the clouds are gonna win right now. Big time with the one the political climate is gonna be favoring Big check. But more importantly, with just talking about covert impact and celebrating the digital transformation is gonna create a massive rising tide. It's already happening. It's happening it's happening. And again, this shift in programming, uh, models are gonna really kinda accelerating, create new great growth. So there's no doubt in my mind of all three you're gonna win big, uh, in the future, they're just different, You know, the way they're going to market position themselves, they have to be. Google has to be a little bit different than Amazon because they're smaller and they also have different capabilities, then trying to catch up. So if you're Google or Microsoft, you have to have a competitive strategy to decide. How do I wanna ride the tide If you will put the rising tide? Well, if I'm Amazon, I mean, if I'm Microsoft and Google, I'm not going to try to go frontal and try to copy Amazon because Amazon is just pounding lead of features and scale and they're different. They were, I would say, take advantage of the first mover of pure public cloud. They really awesome. It passed and I, as they've integrated in Gardner, now reports and integrated I as and passed components. So Gardner finally got their act together and said, Hey, this is really one thing. SAS is completely different animal now Microsoft Super Smart because they I think they played the right card. They have a huge installed base converted to keep office 3 65 and move sequel server and all their core jewels into the cloud as fast as possible, clarified while filling in the gaps on the product side to be cloud. So you know, as you're doing trends job, they're just it's just pedal as fast as you can. But Microsoft is really in. The strategy is just go faster trying. Keep pedaling fast, get the features, feature velocity and try to make it high quality. Google is a little bit different. They have a little power base in terms of their network of strong, and they have a lot of other big data capabilities, so they have to use those to their advantage. So there is. There is there is competitive strategy game application happening with these companies. It's not like apples, the apples, In my opinion, it never has been, and I think that's funny that people talk about it that way. >>Well, you're bringing up some great points. I want guys bring up the next graphic because a lot of things that John just said are really relevant here. And what we're showing is that's a survey. Data from E. T. R R Data partners, like 1400 plus CEOs and I T buyers and on the vertical axis is this thing called Net score, which is a measure of spending momentum. And the horizontal axis is is what's called market share. It's a measure of the pervasiveness or, you know, number of mentions in the data set. There's a couple of key points I wanna I wanna pick up on relative to what John just said. So you see A W S and Microsoft? They stand alone. I mean, they're the hyper scale er's. They're far ahead of the pack and frankly, they have fall down, toe, lose their lead. They spend a lot on Capex. They got the flywheel effects going. They got both spending velocity and large market shares, and so, but they're taking a different approach. John, you're right there living off of their SAS, the state, their software state, Andi, they're they're building that in to their cloud. So they got their sort of a captive base of Microsoft customers. So they've got that advantage. They also as we'll hear from from Microsoft today. They they're building mawr abstraction layers. Andy Jassy has said We don't wanna be in that abstraction layer business. We wanna have access to those, you know, fine grain primitives and eso at an AP level. So so we can move fast with the market. But but But so those air sort of different philosophies, John? >>Yeah. I mean, you know, people who know me know that I love Amazon. I think their product is superior at many levels on in its way that that has advantages again. They have a great sass and ecosystem. They don't really have their own SAS play, although they're trying to add some stuff on. I've been kind of critical of Microsoft in the past, but one thing I'm not critical of Microsoft, and people can get this wrong in the marketplace. Actually, in the journalism world and also in just some other analysts, Microsoft has always had large scale eso to say that Microsoft never had scale on that Amazon owned the monopoly on our franchise on scales wrong. Microsoft had scale from day one. Their business was always large scale global. They've always had infrastructure with MSN and their search and the distributive how they distribute browsers and multiple countries. Remember they had the lock on the operating system and the browser for until the government stepped in in 1997. And since 1997 Microsoft never ever not invested in infrastructure and scale. So that whole premise that they don't compete well there is wrong. And I think that chart demonstrates that there, in there in the hyper scale leadership category, hands down the question that I have. Is that there not as good and making that scale integrate in because they have that legacy cards. This is the classic innovator's dilemma. Clay Christensen, right? So I think they're doing a good job. I think their strategy sound. They're moving as fast as they can. But then you know they're not gonna come out and say We don't have the best cloud. Um, that's not a marketing strategy. Have to kind of hide in this and get better and then double down on where they're winning, which is. Clients are converting from their legacy at the speed of Microsoft, and they have a huge client base, So that's why they're stopping so high That's why they're so good. >>Well, I'm gonna I'm gonna give you a little preview. I talked to gear up your f Who's gonna come on today and you'll see I I asked him because the criticism of Microsoft is they're, you know, they're just good enough. And so I asked him, Are you better than good enough? You know, those are fighting words if you're inside of Microsoft, but so you'll you'll have to wait to see his answer. Now, if you guys, if you could bring that that graphic back up I wanted to get into the hybrid zone. You know where the field is. Always got >>some questions coming in on chat, Dave. So we'll get to those >>great Awesome. So just just real quick Here you see this hybrid zone, this the field is bunched up, and the other companies who have a large on Prem presence and have been forced to initiate some kind of coherent cloud strategy included. There is Michael Michael, multi Cloud, and Google's there, too, because they're far behind and they got to take a different approach than a W s. But as you can see, so there's some real progress here. VM ware cloud on AWS stands out, as does red hat open shift. You got VM Ware Cloud, which is a VCF Cloud Foundation, even Dell's cloud. And you'd expect HP with Green Lake to be picking up momentum in the future quarters. And you've got IBM and Oracle, which there you go with the innovator's dilemma. But there, at least in the cloud game, and we can talk about that. But so, John, you know, to your point, you've gotta have different strategies. You're you're not going to take out the big too. So you gotta play, connect your print your on Prem to your cloud, your hybrid multi cloud and try to create new opportunities and new value there. >>Yeah, I mean, I think we'll get to the question, but just that point. I think this Zeri Chen's come on the Cube many times. We're trying to get him to come on lunch today with Features startup, but he's always said on the Q B is a V C at Greylock great firm. Jerry's Cloud genius. He's been there, but he made a point many, many years ago. It's not a winner. Take all the winner. Take most, and the Big Three maybe put four or five in there. We'll take most of the markets here. But I think one of the things that people are missing and aren't talking about Dave is that there's going to be a second tier cloud, large scale model. I don't want to say tear to cloud. It's coming to sound like a sub sub cloud, but a new category of cloud on cloud, right? So meaning if you get a snowflake, did I think this is a tale? Sign to what's coming. VM Ware Cloud is a native has had huge success, mainly because Amazon is essentially enabling them to be successful. So I think is going to be a wave of a more of a channel model of indirect cloud build out where companies like the Cube, potentially for media or others, will build clouds on top of the cloud. So if Google, Microsoft and Amazon, whoever is the first one to really enable that okay, we'll do extremely well because that means you can compete with their scale and create differentiation on top. So what snowflake did is all on Amazon now. They kind of should go to azure because it's, you know, politically correct that have multiple clouds and distribution and business model shifts. But to get that kind of performance they just wrote on Amazon. So there's nothing wrong with that. Because you're getting paid is variable. It's cap ex op X nice categorization. So I think that's the way that we're watching. I think it's super valuable, I think will create some surprises in terms of who might come out of the woodwork on be a leader in a category. Well, >>your timing is perfect, John and we do have some questions in the chat. But before we get to that, I want to bring in Sargi Joe Hall, who's a contributor to to our community. Sargi. Can you hear us? All right, so we got, uh, while >>bringing in Sarpy. Let's go down from the questions. So the first question, Um, we'll still we'll get the student second. The first question. But Ronald ask, Can a vendor in 2021 exist without a hybrid cloud story? Well, story and capabilities. Yes, they could live with. They have to have a story. >>Well, And if they don't own a public cloud? No. No, they absolutely cannot. Uh hey, Sergey. How you doing, man? Good to see you. So, folks, let me let me bring in Sergeant Kohala. He's a He's a cloud architect. He's a practitioner, He's worked in as a technologist. And there's a frequent guest on on the Cube. Good to see you, my friend. Thanks for taking the time with us. >>And good to see you guys to >>us. So we were kind of riffing on the competitive landscape we got. We got so much to talk about this, like, it's a number of questions coming in. Um, but Sargi we wanna talk about you know, what's happening here in Cloud Land? Let's get right into it. I mean, what do you guys see? I mean, we got yesterday. New regime, new inaug inauguration. Do you do you expect public policy? You'll start with you Sargi to have What kind of effect do you think public policy will have on, you know, cloud generally specifically, the big tech companies, the tech lash. Is it gonna be more of the same? Or do you see a big difference coming? >>I think that there will be some changing narrative. I believe on that. is mainly, um, from the regulators side. A lot has happened in one month, right? So people, I think are losing faith in high tech in a certain way. I mean, it doesn't, uh, e think it matters with camp. You belong to left or right kind of thing. Right? But parlor getting booted out from Italy s. I think that was huge. Um, like, how do you know that if a cloud provider will not boot you out? Um, like, what is that line where you draw the line? What are the rules? I think that discussion has to take place. Another thing which has happened in the last 23 months is is the solar winds hack, right? So not us not sort acknowledging that I was Russia and then wish you watching it now, new administration might have a different sort of Boston on that. I think that's huge. I think public public private partnership in security arena will emerge this year. We have to address that. Yeah, I think it's not changing. Uh, >>economics economy >>will change gradually. You know, we're coming out off pandemic. The money is still cheap on debt will not be cheap. for long. I think m and a activity really will pick up. So those are my sort of high level, Uh, >>thank you. I wanna come back to them. And because there's a question that chat about him in a But, John, how do you see it? Do you think Amazon and Google on a slippery slope booting parlor off? I mean, how do they adjudicate between? Well, what's happening in parlor? Uh, anything could happen on clubhouse. Who knows? I mean, can you use a I to find that stuff? >>Well, that's I mean, the Amazons, right? Hiding right there bunkered in right now from that bad, bad situation. Because again, like people we said Amazon, these all three cloud players win in the current environment. Okay, Who wins with the U. S. With the way we are China, Russia, cloud players. Okay, let's face it, that's the reality. So if I wanted to reset the world stage, you know what better way than the, you know, change over the United States economy, put people out of work, make people scared, and then reset the entire global landscape and control all with cash? That's, you know, conspiracy theory. >>So you see the riches, you see the riches, get the rich, get richer. >>Yeah, well, that's well, that's that. That's kind of what's happening, right? So if you start getting into this idea that you can't actually have an app on site because the reason now I'm not gonna I don't know the particular parlor, but apparently there was a reason. But this is dangerous, right? So what? What that's gonna do is and whether it's right or wrong or not, whether political opinion is it means that they were essentially taken offline by people that weren't voted for that. Weren't that when people didn't vote for So that's not a democracy, right? So that's that's a different kind of regime. What it's also going to do is you also have this groundswell of decentralized thinking, right. So you have a whole wave of crypto and decentralized, um, cyber punks out there who want to decentralize it. So all of this stuff in January has created a huge counterculture, and I had predicted this so many times in the Cube. David counterculture is coming and and you already have this kind of counterculture between centralized and decentralized thinking and so I think the Amazon's move is dangerous at a fundamental level. Because if you can't get it, if you can't get buy domain names and you're completely blackballed by by organized players, that's a Mafia, in my opinion. So, uh, and that and it's also fuels the decentralized move because people say, Hey, if that could be done to them, it could be done to me. Just the fact that it could be done will promote a swing in the other direction. I >>mean, independent of of, you know, again, somebody said your political views. I mean Parlor would say, Hey, we're trying to clean this stuff up now. Maybe they didn't do it fast enough, but you think about how new parlor is. You think about the early days of Twitter and Facebook, so they were sort of at a disadvantage. Trying to >>have it was it was partly was what it was. It was a right wing stand up job of standing up something quick. Their security was terrible. If you look at me and Cory Quinn on be great to have him, and he did a great analysis on this, because if you look the lawsuit was just terrible. Security was just a half, asshole. >>Well, and the experience was horrible. I mean, it's not It was not a great app, but But, like you said, it was a quick stew. Hand up, you know, for an agenda. But nonetheless, you know, to start, get to your point earlier. It's like, you know, Are they gonna, you know, shut me down? If I say something that's, you know, out of line, or how do I control that? >>Yeah, I remember, like, 2019, we involved closing sort of remarks. I was there. I was saying that these companies are gonna be too big to fail. And also, they're too big for other nations to do business with. In a way, I think MNCs are running the show worldwide. They're running the government's. They are way. Have seen the proof of that in us this year. Late last year and this year, um, Twitter last night blocked Chinese Ambassador E in us. Um, from there, you know, platform last night and I was like, What? What's going on? So, like, we used to we used to say, like the Chinese company, tech companies are in bed with the Chinese government. Right. Remember that? And now and now, Actually, I think Chinese people can say the same thing about us companies. Uh, it's not a good thing. >>Well, let's >>get some question. >>Let's get some questions from the chat. Yeah. Thank you. One is on M and a subject you mentioned them in a Who do you see is possible emanate targets. I mean, I could throw a couple out there. Um, you know, some of the cdn players, maybe aka my You know, I like I like Hashi Corp. I think they're doing some really interesting things. What do you see? >>Nothing. Hashi Corp. And anybody who's doing things in the periphery is a candidate for many by the big guys, you know, by the hyper scholars and number two tier two or five hyper scholars. Right. Uh, that's why sales forces of the world and stuff like that. Um, some some companies, which I thought there will be a target, Sort of. I mean, they target they're getting too big, because off their evaluations, I think how she Corpuz one, um, >>and >>their bunch in the networking space. Uh, well, Tara, if I say the right that was acquired by at five this week, this week or last week, Actually, last week for $500 million. Um, I know they're founder. So, like I found that, Yeah, there's a lot going on on the on the network side on the anything to do with data. Uh, that those air too hard areas in the cloud arena >>data, data protection, John, any any anything you could adhere. >>And I think I mean, I think ej ej is gonna be where the gaps are. And I think m and a activity is gonna be where again, the bigger too big to fail would agree with you on that one. But we're gonna look at white Spaces and say a white space for Amazon is like a monster space for a start up. Right? So you're gonna have these huge white spaces opportunities, and I think it's gonna be an M and a opportunity big time start ups to get bought in. Given the speed on, I think you're gonna see it around databases and around some of these new service meshes and micro services. I mean, >>they there's a There's a question here, somebody's that dons asking why is Google who has the most pervasive tech infrastructure on the planet. Not at the same level of other to hyper scale is I'll give you my two cents is because it took him a long time to get their heads out of their ads. I wrote a piece of around that a while ago on they just they figured out how to learn the enterprise. I mean, John, you've made this point a number of times, but they just and I got a late start. >>Yeah, they're adding a lot of people. If you look at their who their hiring on the Google Cloud, they're adding a lot of enterprise chops in there. They realized this years ago, and we've talked to many of the top leaders, although Curry and hasn't yet sit down with us. Um, don't know what he's hiding or waiting for, but they're clearly not geared up to chicken Pete. You can see it with some some of the things that they're doing, but I mean competed the level of Amazon, but they have strength and they're playing their strength, but they definitely recognize that they didn't have the enterprise motions and people in the DNA and that David takes time people in the enterprise. It's not for the faint of heart. It's unique details that are different. You can't just, you know, swing the Google playbook and saying We're gonna home The enterprises are text grade. They knew that years ago. So I think you're going to see a good year for Google. I think you'll see a lot of change. Um, they got great people in there. On the product marketing side is Dev Solution Architects, and then the SRE model that they have perfected has been strong. And I think security is an area that they could really had a lot of value it. So, um always been a big fan of their huge network and all the intelligence they have that they could bring to bear on security. >>Yeah, I think Google's problem main problem that to actually there many, but one is that they don't They don't have the boots on the ground as compared to um, Microsoft, especially an Amazon actually had a similar problem, but they had a wide breath off their product portfolio. I always talk about feature proximity in cloud context, like if you're doing one thing. You wanna do another thing? And how do you go get that feature? Do you go to another cloud writer or it's right there where you are. So I think Amazon has the feature proximity and they also have, uh, aske Compared to Google, there's skills gravity. Larger people are trained on AWS. I think Google is trying there. So second problem Google is having is that that they're they're more focused on, I believe, um, on the data science part on their sort of skipping the cool components sort of off the cloud, if you will. The where the workloads needs, you know, basic stuff, right? That's like your compute storage and network. And that has to be well, talk through e think e think they will do good. >>Well, so later today, Paul Dillon sits down with Mids Avery of Google used to be in Oracle. He's with Google now, and he's gonna push him on on the numbers. You know, you're a distant third. Does that matter? And of course, you know, you're just a preview of it's gonna say, Well, no, we don't really pay attention to that stuff. But, John, you said something earlier that. I think Jerry Chen made this comment that, you know, Is it a winner? Take all? No, but it's a winner. Take a lot. You know the number two is going to get a big chunk of the pie. It appears that the markets big enough for three. But do you? Does Google have to really dramatically close the gap on be a much, much closer, you know, to the to the leaders in orderto to compete in this race? Or can they just kind of continue to bump along, siphon off the ad revenue? Put it out there? I mean, I >>definitely can compete. I think that's like Google's in it. Then it they're not. They're not caving, right? >>So But But I wrote I wrote recently that I thought they should even even put mawr oven emphasis on the cloud. I mean, maybe maybe they're already, you know, doubling down triple down. I just I think that is a multi trillion dollar, you know, future for the industry. And, you know, I think Google, believe it or not, could even do more. Now. Maybe there's just so much you could dio. >>There's a lot of challenges with these company, especially Google. They're in Silicon Valley. We have a big Social Justice warrior mentality. Um, there's a big debate going on the in the back channels of the tech scene here, and that is that if you want to be successful in cloud, you have to have a good edge strategy, and that involves surveillance, use of data and pushing the privacy limits. Right? So you know, Google has people within the country that will protest contract because AI is being used for war. Yet we have the most unstable geopolitical seen that I've ever witnessed in my lifetime going on right now. So, um, don't >>you think that's what happened with parlor? I mean, Rob Hope said, Hey, bar is pretty high to kick somebody off your platform. The parlor went over the line, but I would also think that a lot of the employees, whether it's Google AWS as well, said, Hey, why are we supporting you know this and so to your point about social justice, I mean, that's not something. That >>parlor was not just social justice. They were trying to throw the government. That's Rob e. I think they were in there to get selfies and being protesters. But apparently there was evidence from what I heard in some of these clubhouse, uh, private chats. Waas. There was overwhelming evidence on parlor. >>Yeah, but my point is that the employee backlash was also a factor. That's that's all I'm saying. >>Well, we have Google is your Google and you have employees to say we will boycott and walk out if you bid on that jet I contract for instance, right, But Microsoft one from maybe >>so. I mean, that's well, >>I think I think Tom Poole's making a really good point here, which is a Google is an alternative. Thio aws. The last Google cloud next that we were asked at they had is all virtual issue. But I saw a lot of I T practitioners in the audience looking around for an alternative to a W s just seeing, though, we could talk about Mano Cloud or Multi Cloud, and Andy Jassy has his his narrative around, and he's true when somebody goes multiple clouds, they put you know most of their eggs in one basket. Nonetheless, I think you know, Google's got a lot of people interested in, particularly in the analytic side, um, in in an alternative, hedging their bets eso and particularly use cases, so they should be able to do so. I guess my the bottom line here is the markets big enough to have Really? You don't have to be the Jack Welch. I gotta be number one and number two in the market. Is that the conclusion here? >>I think so. But the data gravity and the skills gravity are playing against them. Another problem, which I didn't want a couple of earlier was Google Eyes is that they have to boot out AWS wherever they go. Right? That is a huge challenge. Um, most off the most off the Fortune 2000 companies are already using AWS in one way or another. Right? So they are the multi cloud kind of player. Another one, you know, and just pure purely somebody going 200% Google Cloud. Uh, those cases are kind of pure, if you will. >>I think it's gonna be absolutely multi cloud. I think it's gonna be a time where you looked at the marketplace and you're gonna think in terms of disaster recovery, model of cloud or just fault tolerant capabilities or, you know, look at the parlor, the next parlor. Or what if Amazon wakes up one day and said, Hey, I don't like the cubes commentary on their virtual events, so shut them down. We should have a fail over to Google Cloud should Microsoft and Option. And one of people in Microsoft ecosystem wants to buy services from us. We have toe kind of co locate there. So these are all open questions that are gonna be the that will become certain pretty quickly, which is, you know, can a company diversify their computing An i t. In a way that works. And I think the momentum around Cooper Netease you're seeing as a great connective tissue between, you know, having applications work between clouds. Right? Well, directionally correct, in my opinion, because if I'm a company, why wouldn't I wanna have choice? So >>let's talk about this. The data is mixed on that. I'll share some data, meaty our data with you. About half the companies will say Yeah, we're spreading the wealth around to multiple clouds. Okay, That's one thing will come back to that. About the other half were saying, Yeah, we're predominantly mono cloud we didn't have. The resource is. But what I think going forward is that that what multi cloud really becomes. And I think John, you mentioned Snowflake before. I think that's an indicator of what what true multi cloud is going to look like. And what Snowflake is doing is they're building abstraction, layer across clouds. Ed Walsh would say, I'm standing on the shoulders of Giants, so they're basically following points of presence around the globe and building their own cloud. They call it a data cloud with a global mesh. We'll hear more about that later today, but you sign on to that cloud. So they're saying, Hey, we're gonna build value because so many of Amazon's not gonna build that abstraction layer across multi clouds, at least not in the near term. So that's a really opportunity for >>people. I mean, I don't want to sound like I'm dating myself, but you know the date ourselves, David. I remember back in the eighties, when you had open systems movement, right? The part of the whole Revolution OS I open systems interconnect model. At that time, the networking stacks for S N A. For IBM, decadent for deck we all know that was a proprietary stack and then incomes TCP I p Now os I never really happened on all seven layers, but the bottom layers standardized. Okay, that was huge. So I think if you look at a W s or some of the comments in the chat AWS is could be the s n a. Depends how you're looking at it, right? And you could say they're open. But in a way, they want more Amazon. So Amazon's not out there saying we love multi cloud. Why would they promote multi cloud? They are a one of the clouds they want. >>That's interesting, John. And then subject is a cloud architect. I mean, it's it is not trivial to make You're a data cloud. If you're snowflake, work on AWS work on Google. Work on Azure. Be seamless. I mean, certainly the marketing says that, but technically, that's not trivial. You know, there are latent see issues. Uh, you know, So that's gonna take a while to develop. What? Do your thoughts there? >>I think that multi cloud for for same workload and multi cloud for different workloads are two different things. Like we usually put multiple er in one bucket, right? So I think you're right. If you're trying to do multi cloud for the same workload, that's it. That's Ah, complex, uh, problem to solve architecturally, right. You have to have a common ap ice and common, you know, control playing, if you will. And we don't have that yet, and then we will not have that for a for at least one other couple of years. So, uh, if you if you want to do that, then you have to go to the lower, lowest common denominator in technical sort of stock, if you will. And then you're not leveraging the best of the breed technology off their from different vendors, right? I believe that's a hard problem to solve. And in another thing, is that that that I always say this? I'm always on the death side, you know, developer side, I think, uh, two deaths. Public cloud is a proxy for innovative culture. Right. So there's a catch phrase I have come up with today during shower eso. I think that is true. And then people who are companies who use the best of the breed technologies, they can attract the these developers and developers are the Mazen's off This digital sort of empires, amazingly, is happening there. Right there they are the Mazen's right. They head on the bricks. I think if you don't appeal to developers, if you don't but extensive for, like, force behind educating the market, you can't you can't >>put off. It's the same game Stepping story was seeing some check comments. Uh, guard. She's, uh, linked in friend of mine. She said, Microsoft, If you go back and look at the Microsoft early days to the developer Point they were, they made their phones with developers. They were a software company s Oh, hey, >>forget developers, developers, developers. >>You were if you were in the developer ecosystem, you were treated his gold. You were part of the family. If you were outside that world, you were competitors, and that was ruthless times back then. But they again they had. That was where it was today. Look at where the software defined businesses and starve it, saying it's all about being developer lead in this new way to program, right? So the cloud next Gen Cloud is going to look a lot like next Gen Developer and all the different tools and techniques they're gonna change. So I think, yes, this kind of developer ecosystem will be harnessed, and that's the power source. It's just gonna look different. So, >>Justin, Justin in the chat has a comment. I just want to answer the question about elastic thoughts on elastic. Um, I tell you, elastic has momentum uh, doing doing very well in the market place. Thea Elk Stack is a great alternative that people are looking thio relative to Splunk. Who people complain about the pricing. Of course it's plunks got the easy button, but it is getting increasingly expensive. The problem with elk stack is you know, it's open source. It gets complicated. You got a shard, the databases you gotta manage. It s Oh, that's what Ed Walsh's company chaos searches is all about. But elastic has some riel mo mentum in the marketplace right now. >>Yeah, you know, other things that coming on the chat understands what I was saying about the open systems is kubernetes. I always felt was that is a bad metaphor. But they're with me. That was the TCP I peep In this modern era, C t c p I p created that that the disruptor to the S N A s and the network protocols that were proprietary. So what KUBERNETES is doing is creating a connective tissue between clouds and letting the open source community fill in the gaps in the middle, where kind of way kind of probably a bad analogy. But that's where the disruption is. And if you look at what's happened since Kubernetes was put out there, what it's become kind of de facto and standard in the sense that everyone's rallying around it. Same exact thing happened with TCP was people were trashing it. It is terrible, you know it's not. Of course they were trashed because it was open. So I find that to be very interesting. >>Yeah, that's a good >>analogy. E. Thinks the R C a cable. I used the R C. A cable analogy like the VCRs. When they started, they, every VC had had their own cable, and they will work on Lee with that sort of plan of TV and the R C. A cable came and then now you can put any TV with any VCR, and the VCR industry took off. There's so many examples out there around, uh, standards And how standards can, you know, flair that fire, if you will, on dio for an industry to go sort of wild. And another trend guys I'm seeing is that from the consumer side. And let's talk a little bit on the consuming side. Um, is that the The difference wouldn't be to B and B to C is blood blurred because even the physical products are connected to the end user Like my door lock, the August door lock I didn't just put got get the door lock and forget about that. Like I I value the expedience it gives me or problems that gives me on daily basis. So I'm close to that vendor, right? So So the middle men, uh, middle people are getting removed from from the producer off the technology or the product to the consumer. Even even the sort of big grocery players they have their APs now, uh, how do you buy stuff and how it's delivered and all that stuff that experience matters in that context, I think, um, having, uh, to be able to sell to thes enterprises from the Cloud writer Breuder's. They have to have these case studies or all these sample sort off reference architectures and stuff like that. I think whoever has that mawr pushed that way, they are doing better like that. Amazon is Amazon. Because of that reason, I think they have lot off sort off use cases about on top of them. And they themselves do retail like crazy. Right? So and other things at all s. So I think that's a big trend. >>Great. Great points are being one of things. There's a question in there about from, uh, Yaden. Who says, uh, I like the developer Lead cloud movement, But what is the criticality of the executive audience when educating the marketplace? Um, this comes up a lot in some of my conversations around automation. So automation has been a big wave to automate this automate everything. And then everything is a service has become kind of kind of the the executive suite. Kind of like conversation we need to make everything is a service in our business. You seeing people move to that cloud model. Okay, so the executives think everything is a services business strategy, which it is on some level, but then, when they say Take that hill, do it. Developers. It's not that easy. And this is where a lot of our cube conversations over the past few months have been, especially during the cova with cute virtual. This has come up a lot, Dave this idea, and start being around. It's easy to say everything is a service but will implement it. It's really hard, and I think that's where the developer lead Connection is where the executive have to understand that in order to just say it and do it are two different things. That digital transformation. That's a big part of it. So I think that you're gonna see a lot of education this year around what it means to actually do that and how to implement it. >>I'd like to comment on the as a service and subject. Get your take on it. I mean, I think you're seeing, for instance, with HP Green Lake, Dell's come out with Apex. You know IBM as its utility model. These companies were basically taking a page out of what I what I would call a flawed SAS model. If you look at the SAS players, whether it's salesforce or workday, service now s a P oracle. These models are They're really They're not cloud pricing models. They're they're basically you got to commit to a term one year, two year, three year. We'll give you a discount if you commit to the longer term. But you're locked in on you. You probably pay upfront. Or maybe you pay quarterly. That's not a cloud pricing model. And that's why I mean, they're flawed. You're seeing companies like Data Dog, for example. Snowflake is another one, and they're beginning to price on a consumption basis. And that is, I think, one of the big changes that we're going to see this decade is that true cloud? You know, pay by the drink pricing model and to your point, john toe, actually implement. That is, you're gonna need a whole new layer across your company on it is quite complicated it not even to mention how you compensate salespeople, etcetera. The a p. I s of your product. I mean, it is that, but that is a big sea change that I see coming. Subject your >>thoughts. Yeah, I think like you couldn't see it. And like some things for this big tech exacts are hidden in the plain >>sight, right? >>They don't see it. They they have blind spots, like Look at that. Look at Amazon. They went from Melissa and 200 millisecond building on several s, Right, Right. And then here you are, like you're saying, pay us for the whole year. If you don't use the cloud, you lose it or will pay by month. Poor user and all that stuff like that that those a role models, I think these players will be forced to use that term pricing like poor minute or for a second, poor user. That way, I think the Salesforce moral is hybrid. They're struggling in a way. I think they're trying to bring the platform by doing, you know, acquisition after acquisition to be a platform for other people to build on top off. But they're having a little trouble there because because off there, such pricing and little closeness, if you will. And, uh, again, I'm coming, going, going back to developers like, if you are not appealing to developers who are writing the latest and greatest code and it is open enough, by the way open and open source are two different things that we all know that. So if your platform is not open enough, you will have you know, some problems in closing the deals. >>E. I want to just bring up a question on chat around from Justin didn't fitness. Who says can you touch on the vertical clouds? Has your offering this and great question Great CP announcing Retail cloud inventions IBM Athena Okay, I'm a huge on this point because I think this I'm not saying this for years. Cloud computing is about horizontal scalability and vertical specialization, and that's absolutely clear, and you see all the clouds doing it. The vertical rollouts is where the high fidelity data is, and with machine learning and AI efforts coming out, that's accelerated benefits. There you have tow, have the vertical focus. I think it's super smart that clouds will have some sort of vertical engine, if you will in the clouds and build on top of a control playing. Whether that's data or whatever, this is clearly the winning formula. If you look at all the successful kind of ai implementations, the ones that have access to the most data will get the most value. So, um if you're gonna have a data driven cloud you have tow, have this vertical feeling, Um, in terms of verticals, the data on DSO I think that's super important again, just generally is a strategy. I think Google doing a retail about a super smart because their whole pitches were not Amazon on. Some people say we're not Google, depending on where you look at. So every of these big players, they have dominance in the areas, and that's scarce. Companies and some companies will never go to Amazon for that reason. Or some people never go to Google for other reasons. I know people who are in the ad tech. This is a black and we're not. We're not going to Google. So again, it is what it is. But this idea of vertical specialization relevant in super >>forts, I want to bring to point out to sessions that are going on today on great points. I'm glad you asked that question. One is Alan. As he kicks off at 1 p.m. Eastern time in the transformation track, he's gonna talk a lot about the coming power of ecosystems and and we've talked about this a lot. That that that to compete with Amazon, Google Azure, you've gotta have some kind of specialization and vertical specialization is a good one. But of course, you see in the big Big three also get into that. But so he's talking at one o'clock and then it at 3 36 PM You know this times are strange, but e can explain that later Hillary Hunter is talking about she's the CTO IBM I B M's ah Financial Cloud, which is another really good example of specifying vertical requirements and serving. You know, an audience subject. I think you have some thoughts on this. >>Actually, I lost my thought. E >>think the other piece of that is data. I mean, to the extent that you could build an ecosystem coming back to Alan Nancy's premise around data that >>billions of dollars in >>their day there's billions of dollars and that's the title of the session. But we did the trillion dollar baby post with Jazzy and said Cloud is gonna be a trillion dollars right? >>And and the point of Alan Answer session is he's thinking from an individual firm. Forget the millions that you're gonna save shifting to the cloud on cost. There's billions in ecosystems and operating models. That's >>absolutely the business value. Now going back to my half stack full stack developer, is the business value. I've been talking about this on the clubhouses a lot this past month is for the entrepreneurs out there the the activity in the business value. That's the new the new intellectual property is the business logic, right? So if you could see innovations in how work streams and workflow is gonna be a configured differently, you have now large scale cloud specialization with data, you can move quickly and take territory. That's much different scenario than a decade ago, >>at the point I was trying to make earlier was which I know I remember, is that that having the horizontal sort of features is very important, as compared to having vertical focus. You know, you're you're more healthcare focused like you. You have that sort of needs, if you will, and you and our auto or financials and stuff like that. What Google is trying to do, I think that's it. That's a good thing. Do cook up the reference architectures, but it's a bad thing in a way that you drive drive away some developers who are most of the developers at 80 plus percent, developers are horizontal like you. Look at the look into the psyche of a developer like you move from company to company. And only few developers will say I will stay only in health care, right? So I will only stay in order or something of that, right? So they you have to have these horizontal capabilities which can be applied anywhere on then. On top >>of that, I think that's true. Sorry, but I'll take a little bit different. Take on that. I would say yes, that's true. But remember, remember the old school application developer Someone was just called in Application developer. All they did was develop applications, right? They pick the framework, they did it right? So I think we're going to see more of that is just now mawr of Under the Covers developers. You've got mawr suffer defined networking and software, defined storage servers and cloud kubernetes. And it's kind of like under the hood. But you got your, you know, classic application developer. I think you're gonna see him. A lot of that come back in a way that's like I don't care about anything else. And that's the promise of cloud infrastructure is code. So I think this both. >>Hey, I worked. >>I worked at people solved and and I still today I say into into this context, I say E r P s are the ultimate low code. No code sort of thing is right. And what the problem is, they couldn't evolve. They couldn't make it. Lightweight, right? Eso um I used to write applications with drag and drop, you know, stuff. Right? But But I was miserable as a developer. I didn't Didn't want to be in the applications division off PeopleSoft. I wanted to be on the tools division. There were two divisions in most of these big companies ASAP. Oracle. Uh, like companies that divisions right? One is the cooking up the tools. One is cooking up the applications. The basketball was always gonna go to the tooling. Hey, >>guys, I'm sorry. We're almost out of time. I always wanted to t some of the sections of the day. First of all, we got Holder Mueller coming on at lunch for a power half hour. Um, you'll you'll notice when you go back to the home page. You'll notice that calendar, that linear clock that we talked about that start times are kind of weird like, for instance, an appendix coming on at 1 24. And that's because these air prerecorded assets and rather than having a bunch of dead air, we're just streaming one to the other. So so she's gonna talk about people, process and technology. We got Kathy Southwick, whose uh, Silicon Valley CEO Dan Sheehan was the CEO of Dunkin Brands and and he was actually the c 00 So it's C A CEO connecting the dots to the business. Daniel Dienes is the CEO of you I path. He's coming on a 2:47 p.m. East Coast time one of the hottest companies, probably the fastest growing software company in history. We got a guy from Bain coming on Dave Humphrey, who invested $750 million in Nutanix. He'll explain why and then, ironically, Dheeraj Pandey stew, Minuteman. Our friend interviewed him. That's 3 35. 1 of the sessions are most excited about today is John McD agony at 403 p. M. East Coast time, she's gonna talk about how to fix broken data architectures, really forward thinking stuff. And then that's the So that's the transformation track on the future of cloud track. We start off with the Big Three Milan Thompson Bukovec. At one oclock, she runs a W s storage business. Then I mentioned gig therapy wrath at 1. 30. He runs Azure is analytics. Business is awesome. Paul Dillon then talks about, um, IDs Avery at 1 59. And then our friends to, um, talks about interview Simon Crosby. I think I think that's it. I think we're going on to our next session. All right, so keep it right there. Thanks for watching the Cuban cloud. Uh huh.

>> Okay, we're now going to explore the vision of the future of cloud computing from the perspective of one of the leaders in the field, JG Chirapurath is the Vice President of Azure Data AI and Edge at Microsoft. JG, welcome to theCUBE on Cloud, thanks so much for participating. >> Well, thank you, Dave. And it's a real pleasure to be here with you and just want to welcome the audience as well. >> Well, JG, judging from your title, we have a lot of ground to cover and our audience is definitely interested in all the topics that are implied there. So let's get right into it. We've said many times in theCUBE that the new innovation cocktail comprises machine intelligence or AI applied to troves of data with the scale of the cloud. It's no longer we're driven by Moore's law. It's really those three factors and those ingredients are going to power the next wave of value creation in the economy. So first, do you buy into that premise? >> Yes, absolutely. We do buy into it and I think one of the reasons why we put data analytics and AI together, is because all of that really begins with the collection of data and managing it and governing it, unlocking analytics in it. And we tend to see things like AI, the value creation that comes from AI as being on that continuum of having started off with really things like analytics and proceeding to be machine learning and the use of data in interesting ways. >> Yes, I'd like to get some more thoughts around data and how you see the future of data and the role of cloud and maybe how Microsoft strategy fits in there. I mean, your portfolio, you've got SQL Server, Azure SQL, you got Arc which is kind of Azure everywhere for people that aren't familiar with that you got Synapse which course does all the integration, the data warehouse and it gets things ready for BI and consumption by the business and the whole data pipeline. And then all the other services, Azure Databricks, you got you got Cosmos in there, you got Blockchain, you've got Open Source services like PostgreSQL and MySQL. So lots of choices there. And I'm wondering, how do you think about the future of cloud data platforms? It looks like your strategy is right tool for the right job. Is that fair? >> It is fair, but it's also just to step back and look at it. It's fundamentally what we see in this market today, is that customers they seek really a comprehensive proposition. And when I say a comprehensive proposition it is sometimes not just about saying that, "Hey, listen "we know you're a sequence of a company, "we absolutely trust that you have the best "Azure SQL database in the cloud. "But tell us more." We've got data that is sitting in Hadoop systems. We've got data that is sitting in PostgreSQL, in things like MongoDB. So that open source proposition today in data and data management and database management has become front and center. So our real sort of push there is when it comes to migration management modernization of data to present the broadest possible choice to our customers, so we can meet them where they are. However, when it comes to analytics, one of the things they ask for is give us lot more convergence use. It really, it isn't about having 50 different services. It's really about having that one comprehensive service that is converged. That's where things like Synapse fits in where you can just land any kind of data in the lake and then use any compute engine on top of it to drive insights from it. So fundamentally, it is that flexibility that we really sort of focus on to meet our customers where they are. And really not pushing our dogma and our beliefs on it but to meet our customers according to the way they've deployed stuff like this. >> So that's great. I want to stick on this for a minute because when I have guests on like yourself they never want to talk about the competition but that's all we ever talk about. And that's all your customers ever talk about. Because the counter to that right tool for the right job and that I would say is really kind of Amazon's approach is that you got the single unified data platform, the mega database. So it does it all. And that's kind of Oracle's approach. It sounds like you want to have your cake and eat it too. So you got the right tool with the right job approach but you've got an integration layer that allows you to have that converged database. I wonder if you could add color to that and confirm or deny what I just said. >> No, that's a very fair observation but I'd say there's a nuance in what I sort of described. When it comes to data management, when it comes to apps, we have then customers with the broadest choice. Even in that perspective, we also offer convergence. So case in point, when you think about cosmos DB under that one sort of service, you get multiple engines but with the same properties. Right, global distribution, the five nines availability. It gives customers the ability to basically choose when they have to build that new cloud native app to adopt cosmos DB and adopt it in a way that is an choose an engine that is most flexible to them. However, when it comes to say, writing a SequenceServer for example, if modernizing it, you want sometimes, you just want to lift and shift it into things like IS. In other cases, you want to completely rewrite it. So you need to have the flexibility of choice there that is presented by a legacy of what sits on premises. When you move into things like analytics, we absolutely believe in convergence. So we don't believe that look, you need to have a relational data warehouse that is separate from a Hadoop system that is separate from say a BI system that is just, it's a bolt-on. For us, we love the proposition of really building things that are so integrated that once you land data, once you prep it inside the Lake you can use it for analytics, you can use it for BI, you can use it for machine learning. So I think, our sort of differentiated approach speaks for itself there. >> Well, that's interesting because essentially again you're not saying it's an either or, and you see a lot of that in the marketplace. You got some companies you say, "No, it's the data lake." And others say "No, no, put it in the data warehouse." And that causes confusion and complexity around the data pipeline and a lot of cutting. And I'd love to get your thoughts on this. A lot of customers struggle to get value out of data and specifically data product builders are frustrated that it takes them too long to go from, this idea of, hey, I have an idea for a data service and it can drive monetization, but to get there you got to go through this complex data life cycle and pipeline and beg people to add new data sources and do you feel like we have to rethink the way that we approach data architecture? >> Look, I think we do in the cloud. And I think what's happening today and I think the place where I see the most amount of rethink and the most amount of push from our customers to really rethink is the area of analytics and AI. It's almost as if what worked in the past will not work going forward. So when you think about analytics only in the enterprise today, you have relational systems, you have Hadoop systems, you've got data marts, you've got data warehouses you've got enterprise data warehouse. So those large honking databases that you use to close your books with. But when you start to modernize it, what people are saying is that we don't want to simply take all of that complexity that we've built over, say three, four decades and simply migrate it en masse exactly as they are into the cloud. What they really want is a completely different way of looking at things. And I think this is where services like Synapse completely provide a differentiated proposition to our customers. What we say there is land the data in any way you see, shape or form inside the lake. Once you landed inside the lake, you can essentially use a Synapse Studio to prep it in the way that you like. Use any compute engine of your choice and operate on this data in any way that you see fit. So case in point, if you want to hydrate a relational data warehouse, you can do so. If you want to do ad hoc analytics using something like Spark, you can do so. If you want to invoke Power BI on that data or BI on that data, you can do so. If you want to bring in a machine learning model on this prep data, you can do so. So inherently, so when customers buy into this proposition, what it solves for them and what it gives to them is complete simplicity. One way to land the data multiple ways to use it. And it's all integrated. >> So should we think of Synapse as an abstraction layer that abstracts away the complexity of the underlying technology? Is that a fair way to think about it? >> Yeah, you can think of it that way. It abstracts away Dave, a couple of things. It takes away that type of data. Sort of complexities related to the type of data. It takes away the complexity related to the size of data. It takes away the complexity related to creating pipelines around all these different types of data. And fundamentally puts it in a place where it can be now consumed by any sort of entity inside the Azure proposition. And by that token, even Databricks. You can in fact use Databricks in sort of an integrated way with the Azure Synapse >> Right, well, so that leads me to this notion of and I wonder if you buy into it. So my inference is that a data warehouse or a data lake could just be a node inside of a global data mesh. And then it's Synapse is sort of managing that technology on top. Do you buy into that? That global data mesh concept? >> We do and we actually do see our customers using Synapse and the value proposition that it brings together in that way. Now it's not where they start, oftentimes when a customer comes and says, "Look, I've got an enterprise data warehouse, "I want to migrate it." Or "I have a Hadoop system, I want to migrate it." But from there, the evolution is absolutely interesting to see. I'll give you an example. One of the customers that we're very proud of is FedEx. And what FedEx is doing is it's completely re-imagining its logistics system. That basically the system that delivers, what is it? The 3 million packages a day. And in doing so, in this COVID times, with the view of basically delivering on COVID vaccines. One of the ways they're doing it, is basically using Synapse. Synapse is essentially that analytic hub where they can get complete view into the logistic processes, way things are moving, understand things like delays and really put all of that together in a way that they can essentially get our packages and these vaccines delivered as quickly as possible. Another example, it's one of my favorite. We see once customers buy into it, they essentially can do other things with it. So an example of this is really my favorite story is Peace Parks initiative. It is the premier of white rhino conservancy in the world. They essentially are using data that has landed in Azure, images in particular to basically use drones over the vast area that they patrol and use machine learning on this data to really figure out where is an issue and where there isn't an issue. So that this part with about 200 radios can scramble surgically versus having to range across the vast area that they cover. So, what you see here is, the importance is really getting your data in order, landing consistently whatever the kind of data it is, build the right pipelines, and then the possibilities of transformation are just endless. >> Yeah, that's very nice how you worked in some of the customer examples and I appreciate that. I want to ask you though that some people might say that putting in that layer while you clearly add simplification and is I think a great thing that there begins over time to be a gap, if you will, between the ability of that layer to integrate all the primitives and all the piece parts, and that you lose some of that fine grain control and it slows you down. What would you say to that? >> Look, I think that's what we excel at and that's what we completely sort of buy into. And it's our job to basically provide that level of integration and that granularity in the way that it's an art. I absolutely admit it's an art. There are areas where people crave simplicity and not a lot of sort of knobs and dials and things like that. But there are areas where customers want flexibility. And so I think just to give you an example of both of them, in landing the data, in consistency in building pipelines, they want simplicity. They don't want complexity. They don't want 50 different places to do this. There's one way to do it. When it comes to computing and reducing this data, analyzing this data, they want flexibility. This is one of the reasons why we say, "Hey, listen you want to use Databricks. "If you're buying into that proposition. "And you're absolutely happy with them, "you can plug it into it." You want to use BI and essentially do a small data model, you can use BI. If you say that, "Look, I've landed into the lake, "I really only want to use ML." Bring in your ML models and party on. So that's where the flexibility comes in. So that's sort of that we sort of think about it. >> Well, I like the strategy because one of our guests, Jumark Dehghani is I think one of the foremost thinkers on this notion of of the data mesh And her premise is that the data builders, data product and service builders are frustrated because the big data system is generic to context. There's no context in there. But by having context in the big data architecture and system you can get products to market much, much, much faster. So, and that seems to be your philosophy but I'm going to jump ahead to my ecosystem question. You've mentioned Databricks a couple of times. There's another partner that you have, which is Snowflake. They're kind of trying to build out their own DataCloud, if you will and GlobalMesh, and the one hand they're a partner on the other hand they're a competitor. How do you sort of balance and square that circle? >> Look, when I see Snowflake, I actually see a partner. When we see essentially we are when you think about Azure now this is where I sort of step back and look at Azure as a whole. And in Azure as a whole, companies like Snowflake are vital in our ecosystem. I mean, there are places we compete, but effectively by helping them build the best Snowflake service on Azure, we essentially are able to differentiate and offer a differentiated value proposition compared to say a Google or an AWS. In fact, that's been our approach with Databricks as well. Where they are effectively on multiple clouds and our opportunity with Databricks is to essentially integrate them in a way where we offer the best experience the best integrations on Azure Berna. That's always been our focus. >> Yeah, it's hard to argue with the strategy or data with our data partner and ETR shows Microsoft is both pervasive and impressively having a lot of momentum spending velocity within the budget cycles. I want to come back to AI a little bit. It's obviously one of the fastest growing areas in our survey data. As I said, clearly Microsoft is a leader in this space. What's your vision of the future of machine intelligence and how Microsoft will participate in that opportunity? >> Yeah, so fundamentally, we've built on decades of research around essentially vision, speech and language. That's been the three core building blocks and for a really focused period of time, we focused on essentially ensuring human parity. So if you ever wonder what the keys to the kingdom are, it's the boat we built in ensuring that the research or posture that we've taken there. What we've then done is essentially a couple of things. We've focused on essentially looking at the spectrum that is AI. Both from saying that, "Hey, listen, "it's got to work for data analysts." We're looking to basically use machine learning techniques to developers who are essentially, coding and building machine learning models from scratch. So for that select proposition manifest to us as really AI focused on all skill levels. The other core thing we've done is that we've also said, "Look, it'll only work as long "as people trust their data "and they can trust their AI models." So there's a tremendous body of work and research we do and things like responsible AI. So if you asked me where we sort of push on is fundamentally to make sure that we never lose sight of the fact that the spectrum of AI can sort of come together for any skill level. And we keep that responsible AI proposition absolutely strong. Now against that canvas Dave, I'll also tell you that as Edge devices get way more capable, where they can input on the Edge, say a camera or a mic or something like that. You will see us pushing a lot more of that capability onto the edge as well. But to me, that's sort of a modality but the core really is all skill levels and that responsibility in AI. >> Yeah, so that brings me to this notion of, I want to bring an Edge and hybrid cloud, understand how you're thinking about hybrid cloud, multicloud obviously one of your competitors Amazon won't even say the word multicloud. You guys have a different approach there but what's the strategy with regard to hybrid? Do you see the cloud, you're bringing Azure to the edge maybe you could talk about that and talk about how you're different from the competition. >> Yeah, I think in the Edge from an Edge and I even I'll be the first one to say that the word Edge itself is conflated. Okay, a little bit it's but I will tell you just focusing on hybrid, this is one of the places where, I would say 2020 if I were to look back from a COVID perspective in particular, it has been the most informative. Because we absolutely saw customers digitizing, moving to the cloud. And we really saw hybrid in action. 2020 was the year that hybrid sort of really became real from a cloud computing perspective. And an example of this is we understood that it's not all or nothing. So sometimes customers want Azure consistency in their data centers. This is where things like Azure Stack comes in. Sometimes they basically come to us and say, "We want the flexibility of adopting "flexible button of platforms let's say containers, "orchestrating Kubernetes "so that we can essentially deploy it wherever you want." And so when we designed things like Arc, it was built for that flexibility in mind. So, here's the beauty of what something like Arc can do for you. If you have a Kubernetes endpoint anywhere, we can deploy an Azure service onto it. That is the promise. Which means, if for some reason the customer says that, "Hey, I've got "this Kubernetes endpoint in AWS. And I love Azure SQL. You will be able to run Azure SQL inside AWS. There's nothing that stops you from doing it. So inherently, remember our first principle is always to meet our customers where they are. So from that perspective, multicloud is here to stay. We are never going to be the people that says, "I'm sorry." We will never say (speaks indistinctly) multicloud but it is a reality for our customers. >> So I wonder if we could close, thank you for that. By looking back and then ahead and I want to put forth, maybe it's a criticism, but maybe not. Maybe it's an art of Microsoft. But first, you did Microsoft an incredible job at transitioning its business. Azure is omnipresent, as we said our data shows that. So two-part question first, Microsoft got there by investing in the cloud, really changing its mindset, I think and leveraging its huge software estate and customer base to put Azure at the center of it's strategy. And many have said, me included, that you got there by creating products that are good enough. We do a one Datto, it's still not that great, then a two Datto and maybe not the best, but acceptable for your customers. And that's allowed you to grow very rapidly expand your market. How do you respond to that? Is that a fair comment? Are you more than good enough? I wonder if you could share your thoughts. >> Dave, you hurt my feelings with that question. >> Don't hate me JG. (both laugh) We're getting it out there all right, so. >> First of all, thank you for asking me that. I am absolutely the biggest cheerleader you'll find at Microsoft. I absolutely believe that I represent the work of almost 9,000 engineers. And we wake up every day worrying about our customer and worrying about the customer condition and to absolutely make sure we deliver the best in the first attempt that we do. So when you take the plethora of products we deliver in Azure, be it Azure SQL, be it Azure Cosmos DB, Synapse, Azure Databricks, which we did in partnership with Databricks, Azure Machine Learning. And recently when we premiered, we sort of offered the world's first comprehensive data governance solution in Azure Purview. I would humbly submit it to you that we are leading the way and we're essentially showing how the future of data, AI and the Edge should work in the cloud. >> Yeah, I'd be disappointed if you capitulated in any way, JG. So, thank you for that. And that's kind of last question is looking forward and how you're thinking about the future of cloud. Last decade, a lot about cloud migration, simplifying infrastructure to management and deployment. SaaSifying My Enterprise, a lot of simplification and cost savings and of course redeployment of resources toward digital transformation, other valuable activities. How do you think this coming decade will be defined? Will it be sort of more of the same or is there something else out there? >> I think that the coming decade will be one where customers start to unlock outsize value out of this. What happened to the last decade where people laid the foundation? And people essentially looked at the world and said, "Look, we've got to make a move. "They're largely hybrid, but you're going to start making "steps to basically digitize and modernize our platforms. I will tell you that with the amount of data that people are moving to the cloud, just as an example, you're going to see use of analytics, AI or business outcomes explode. You're also going to see a huge sort of focus on things like governance. People need to know where the data is, what the data catalog continues, how to govern it, how to trust this data and given all of the privacy and compliance regulations out there essentially their compliance posture. So I think the unlocking of outcomes versus simply, Hey, I've saved money. Second, really putting this comprehensive sort of governance regime in place and then finally security and trust. It's going to be more paramount than ever before. >> Yeah, nobody's going to use the data if they don't trust it, I'm glad you brought up security. It's a topic that is at number one on the CIO list. JG, great conversation. Obviously the strategy is working and thanks so much for participating in Cube on Cloud. >> Thank you, thank you, Dave and I appreciate it and thank you to everybody who's tuning into today. >> All right then keep it right there, I'll be back with our next guest right after this short break.

>>From around the globe. It's the queue with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS, and our community partners. >>Hello, and welcome back to the cube live coverage of reinvent 2020 virtual. We're not there this year. It's the cube virtual. We are the cube virtual. I'm your host, John fro with Dave Alante and analyzing our take on the partner day. Um, keynotes and leadership sessions today was AWS APN, which is Amazon partner network global partner network day, where all the content being featured today is all about the partners and what Amazon is doing to create an ecosystem, build the ecosystem, nurture the ecosystem and reinvent what it means to be a partner. Dave, thanks for joining me today on the analysis of Amazon's ecosystem and partner network and a great stuff today. Thanks for coming on. >>Yeah, you're welcome. I mean, watch the keynote this morning. I mean, partners are critical to AWS. Look, the fact is that when, when AWS was launched, it was the developers ate it up. You know, if you're a developer, you dive right in infrastructure is code beautiful. You know, if you're mainstream it, this thing's just got more complex with the cloud. And so there's, there's a big gap right between how I, where I am today and where I want to be. And partners are critical to help helping people get there. And we'll talk about the details of specifically what Amazon did, but I mean, especially when John, when you look at things like smaller outposts, you know, going hybrid, Andy Jassy redefining hybrid, you need partners to really help you plan design, implement, manage at scale. >>Yeah. You know, one of the things I'm always, um, you know, saying nice things about Amazon, but one of the things that they're vulnerable on in my opinion is how they balanced their own SAS offerings and with what they develop in the ecosystem. This has been a constant, um, challenge and, and they've balanced it very well. Um, so other vendors, they are very clear. They make their own software, right. And they have a channel and it's kind of the old playbook. Amazon's got to reinvent the playbook here. And I think that's, what's key today on stage Doug Yom. He's the, uh, the leader you had, um, also Dave McCann who heads up marketplace and Sandy Carter who heads up worldwide public sector partners. So Dave interesting combination of three different teams, you had the classic ISV partners in the ecosystem, the cohesiveness of the world, the EMCs and so on, you had the marketplace with Dave McCann. That's where the future of procurement is. That's where people are buying product and you had public sector, huge tsunami of innovation happening because of the pandemic and Sandy is highlighting their partners. So it's partner day it's partner ecosystem, but multiple elements. They're moving marketplace where you buy programs and competencies with public sector and then ISV, all of those three areas are changing. Um, I want to get your take because you've been following ecosystems years and you've been close to the enterprise and how they buy your, >>And I think, I think John, Oh, a couple of things. One is, you know, Dave McCann was talking a lot about how CIO is one of modernize applications and they have to rationalize, and it will save some of that talk for later on, you know, Tim prophet on. But there's no question that Amazon's out to reinvent, as you said, uh, the whole experience from procurement all the way through, and, you know, normally you had to, to acquire services outside of the marketplace. And now what they're doing is bundling the services and software together. You know, it's straightforward services, implementation services, but those are well understood. The processes are known. You can pretty much size them and price them. So I think that's a huge opportunity for partners and customers to reduce friction. I think the other thing I would say is ecosystems are, are critical. >>Uh, one of the themes that we've been talking about in the cube as we've gone from a product centric world in the old days of it to a platform centric world, which has really been the last decade has been about SAS platforms and cloud platforms. And I think ecosystems are going to be a really power, the new innovation in the coming decade. And what I mean by that is look, if you're just building a service and Amazon is going to do that same service, you know, you got to keep innovating. And one of the ways you can innovate is you can build on ecosystems. There's all this data within industries, across industries, and you can through the partner network and through customer networks within industry start building new innovation around ecosystems and partners or that glue, Amazon's not going to go in. And like Jandy Jesse even said in the, uh, in his fireside chat, you know, customers will ask us for our advice and we're happy to give it to them, but frankly partners are better at that nitty gritty hardcore stuff. They have closer relationships with the customers. And so that's a really important gap that Amazon has been closing for the last, you know, frankly 10 years. And I think that to your point, they've still got a long way to go, but that's a huge opportunity in that. >>A good call out on any Jess, I've got to mention that one of the highlights of today's keynote was on a scheduled, um, Andy Jassy fireside chat. Uh, normally Andy does his keynote and then he kind of talks to customers and does his thing normally at a normal re-invent this time he came out on stage. And I think what I found interesting was he was talking about this builder. You always use the word builder customer, um, solutions. And I think one of the things that's interesting about this partner network is, is that I think there's a huge opportunity for companies to be customer centric and build on top of Amazon. And what I mean by that is, is that Amazon is pretty cool with you doing things on top of their platform that does two things serves the customer's needs better than they do, and they can make more money on and other services look at snowflake as an example, um, that's a company built on AWS. I know they've got other clouds going on, but mainly Amazon Zoom's the same way. They're doing a great solution. They've got Redshift, Amazon, Amazon's got Redshift, Dave, but also they're a customer and a partner. So this is the dynamic. If you can be successful on Amazon serving customers better than Amazon does, that's the growth hack. That's the hack on Amazon's partner network. If you could. >>I think, I think Snowflake's a really good example. You snowflake you use new Relic as an example, I've heard Andy Jess in the past use cloud air as an example, I like snowflake better because they're, they're sort of thriving. And so, but, but I will say this there's a, they're a great example of that ecosystem that we just talked about because yes, not only are they building on AWS, they're connecting to other clouds and that is an ecosystem that they're building out. And Amazon's got a lot of snowflake, I guess, unless you're the Redshift team, but, but generally speaking, Snowflake's driving a lot of business for Amazon and Andy Jesse addressed that in that, uh, in that fireside chat, he's asked that question a lot. And he said, look, we, we, we have our primary services. And at the same time we want to enable our partners to be successful. And snowflake is a really good example of that. >>Yeah. I want to call out also, uh, yesterday. Um, I had our Monday, I should say Tuesday, December 1st, uh, Jesse's keynote. I did an interview with Jerry chin with gray lock. He's investing in startups and one of the things he observed and he pointed out Dave, is that with Amazon, if you're, if you're a full all-in in the cloud, you're going to take advantage of things that are just not available on say on premises that is data patterns, other integrations. And I think one of the things that Doug pointed out was with interoperability and integration with say things like the SAS factor that they put out there there's advantages for being in the cloud specifically with Amazon, that you can get on integrations. And I think Dave McCann teases that out with the marketplace when they talk about integrations. But the idea of being in the cloud with all these other partners makes integration and interoperability different and unique and better potentially a differentiator. This is going to become a huge deal. >>I didn't pick up on that because yesterday I thought I wasn't in the keynote. I think it was in the analyst one-on-one with, with Jesse, he talked about, you know, this notion that people, I think he was addressing multi-cloud he didn't use that term, but this notion of an abstraction layer and how it does simplify things in, in his basic, he basically said, look, our philosophy is we want to have, you know, the, the ability to go deep with the primitives and have that fine grain access, because that will give us control. A lot of times when you put in this abstraction layer, which people are trying to do across clouds, you know, it limits your ability to really move fast. And then of course it's big theme is, is this year, at the same time, if you look at a company who was called out today, like, like Octa, you know, when you do an identity management and single sign-on, you're, you're touching a lot of pieces, there's a lot of integration to your point. >>So you need partners to come in and be that glue that does a lot of that heavy lifting that needs to needs to be done. Amazon. What Jessie was essentially saying, I think to the partner network is, look, we're not going to put in that abstraction layer. You're going to you, you got to do that. We're going to do stuff maybe between our own own services like they did with the, you know, the glue between databases, but generally speaking, that's a giant white space for partner organizations. He mentioned Okta. He been talked about in for apt Aptio. This was Dave McCann, actually Cohesity came up a confluent doing fully managed Kafka. So that to me was a signal to the partners. Look, here's where you guys should be playing. This is what customers need. And this is where we're not going to, you know, eat your lunch. >>Yeah. And the other thing McCann pointed out was 200 new Dave McCann pointed out who leads these leader of the, of the marketplace. He pointed out 200 new ISP. ISV is out there, huge news, and they're going to turn already. He went, he talked with his manage entitlements, which got my attention. And this is kind of an, um, kind of one of those advantage points that it's kind of not sexy and mainstream to talk about, but it's really one of those details. That's the heavy lifting. That's a pain in the butt to deal with licensing and tracking all this compliance stuff that goes on under the covers and distribution of software. I think that's where the cloud could be really advantaged. And also the app service catalog registry that he talked about and the professional services. So these are areas that Amazon is going to kind of create automation around. >>And as Jassy always talks about that undifferentiated heavy lifting, they're going to take care of some of these plumbing issues. And I think you're right about this differentiation because if I'm a partner and I could build on top of Amazon and have my own cloud, I mean, let's face it. Snowflake is a born in the cloud, in the cloud only solution on Amazon. So they're essentially Amazon's cloud. So I think the thing that's not being talked about this year, that is probably my come up in future reinvents is that whoever can build their own cloud on top of Amazon's cloud will be a winner. And I, I talked about this years ago, data around this tier two, I call it tier two clouds. This new layer of cloud service provider is going to be kind of the, on the power law, the, the second wave of cloud. >>In other words, you're on top of Amazon differentiating with a modern application at scale inside the cloud with all the other people in there, a whole new ecosystem is going to emerge. And to me, I think this is something that is not yet baked out, but if I was a partner, I would be out there planning like hell right now to say, I'm going to build a cloud business on Amazon. I'm going to take advantage of the relationships and the heavy lifting and compete and win that way. I think that's a re redefining moment. And I think whoever does that will win >>And a big theme around reinventing everything, reinvent the industry. And one of the areas that's being reinvented as is the, you know, the VAR channel really well, consultancies, you know, smaller size for years, these companies made a ton of dough selling boxes, right? All the, all the Dell and the IBM and the EMC resellers, you know, they get big boats and big houses, but that business changed dramatically. They had to shift toward value, value, value add. So what did they do? They became VMware specialists. They came became SAP specialists. There's a couple of examples, maybe, you know, adding into security. The cloud was freaking them out, but the cloud is really an opportunity for them. And I'll give you an example. We've talked a lot about snowflake. The other is AWS glue elastic views. That's what the AWS announced to connect all their databases together. Think about a consultancy that is able to come in and totally rearchitect your big data life cycle and pipeline with the people, the processes, the skillsets, you know, Amazon's not going to do that work, but the upside value for the organizations is tremendous. So you're seeing consultancies becoming managed service providers and adding all kinds of value throughout the stack. That's really reinvention of the partnership. >>Yeah. I think it's a complete, um, channel strategy. That's different. It doesn't, it looks like other channels, but it's not, it's, it's, it's driven by value. And I think this idea of competing on value versus just being kind of a commodity play is shifting. I think the ISV and the VARs, those traditional markets, David, as you pointed out, are going to definitely go value oriented. And you can just own a specialty area because as data comes in and when, and this is interesting. And one of the key things that Andy Jassy said in his fireside chat want to ask directly, how do partners benefit when asked about his keynote, how that would translate to partners. He really kind of went in and he was kind of rambling, but he, he, he hit the chips. He said, well, we've got our own chips, which means compute. Then he went into purpose-built data store and data Lake data, elastic views SageMaker Q and QuickSight. He kind of went down the road of, we have the horsepower, we have the data Lake data, data, data. So he was kind of hinting at innovate on the data and you'll do okay. >>Well, and this is again, we kind of, I'm like a snowflake fan boy, you know, in the way you, you like AWS. But look, if you look at AWS glue elastic views, that to me is like snowflakes data cloud is different, a lot of pushing and moving a date, a lot of copying data. But, but this is a great example of where like, remember last year at reinvent, they said, Hey, we're separating compute from storage. Well, you know, of course, snowflake popularized that. So this is great example of two companies thriving that are both competitors and partners. >>Well, I've got to ask you, you know, you, you and I always say we kind of his stories, we've been around the block on the enterprise for years. Um, where do you Mark the, um, evolution of their partner? Because again, Amazon has been so explosive in their growth. The numbers have been off the charts and they've done it well with and pass. And now you have the pandemic which kind of puts on full display, digital transformation. And then Jassy telegraphing that the digital global it spend is their next kind of conquering ground, um, to take, and they got the edge exploding with 5g. So you have this kind of range and they doing all kinds of stuff with IOT, and they're doing stuff in you on earth and in space. So you have this huge growth and they still don't have their own fully oriented business model. They rely on people to build on top of Amazon. So how do you see that evolving in your opinion? Because they're trying to add their own Amazon only, we've got Redshift that competes with others. How do you see that playing out? >>So I think it's going to be specialized and, and something that, uh, that I've talked about is Amazon, you know, AWS in the old day, old days being last decade, they really weren't that solution focused. It was really, you know, serving the builders with tooling, with you, look at something like what they're doing in the call center and what they're doing at the edge and IOT there. I think they're, so I think their move up the stack is going to be very solution oriented, but not necessarily, you know, horizontal going after CRM or going after, you know, uh, supply chain management or ERP. I don't think that's going to be their play. I think their play is going to be to really focus on hard problems that they can automate through their tooling and bring special advantage. And that's what they'll SAS. And at the same time, they'll obviously allow SAS players. >>It's just reminds me of the early days when you and I first met, uh, VMware. Everybody had to work with VMware because they had a such big ecosystem. Well, the SAS players will run on top. Like Workday does like Salesforce does Infour et cetera. And then I think you and I, and Jerry Chen talked about this years ago, I think they're going to give tools to builders, to disrupt the service now is in the sales forces who are out buying companies like crazy to try to get a, you know, half, half a billion dollar, half a trillion dollar market caps. And that is a really interesting dynamic. And I think right now, they're, they're not even having to walk a fine line. I think the lines are reasonably clear. We're going up to database, we're going to do specialized solutions. We're going to enable SAS. We're going to compete where we compete, come on, partner ecosystem. And >>Yeah, I, I, I think that, you know, the Slack being bought by Salesforce is just going to be one of those. I think it's a web van moment, you know, um, you know, where it's like, okay, Slack is going to go die on Salesforce. Okay. I get that. Um, but it's, it's just, it's just, it's just, it's just old school thinking. And I think if you're an entrepreneur and if you're a developer or a partner, you could really reinvent the business model because if you're, dis-aggregating all these other services like you can compete with Salesforce, Slack has now taken out of the game with Salesforce, but what Amazon is doing with say connect, which they're promoting heavily at this conference. I mean, you hear it, you heard it on Andy Jessie's keynote, Sandy Carter. They've had huge success with AWS connect. It's a call center mindset, but it's not calling just on phones. >>It's contact that is descent, intermediating, the Salesforce model. And I think when you start getting into specialists and specialism in channels, you have customer opportunity to be valuable. And I think call center, these kinds of stories that you can stand up pretty quickly and then integrate into a business model is going to be game changing. And I think that's going to going to a lot of threat on these big incumbents, like Salesforce, like Slack, because let's face it. Bots is just the chat bot is just a call center front end. You can innovate on the audio, the transcriptions there's so much Amazon goodness there, that connect. Isn't just a call center that could level the playing field and every vertical >>Well, and SAS is getting disrupted, you know, to your, to your point. I mean, you think about what happened with, with Oracle and SAP. You had, you know, these new emerging players come up like, like Salesforce, like Workday, like service now, but their pricing model, it was all the same. We lock you in for a one-year two-year three-year term. A lot of times you have to pay up front. Now you look at guys like Datadog. Uh, you, you look at a snowflake, you look at elastic, they're disrupting the Splunks of the world. And that model, I think that SAS model is right for disruption with a consumption pricing, a true cloud pricing model. You combine that with new innovation that developers are going to attack. I mean, you know, people right now, they complain about service now pricing, they complain about Splunk pricing. They, you know, they talk about, Oh, elastic. We can get that for half the price Datadog. And so I'm not predicting that those companies service now Workday, the great companies, but they are going to have to respond much in the same way that Oracle and SAP had to respond to the disruption that they saw. >>Yeah. It's interesting. During the keynote, they'll talk about going out to the mainframes today, too. So you have Amazon going into Oracle and Microsoft, and now the mainframes. So you have Oracle database and SQL server and windows server all going to being old school technologies. And now mainframe very interesting. And I think the, this whole idea of this SAS factory, um, got my attention to Cohesity, which we've been covering Dave on the storage front, uh, Mo with the founder was on stage. I'm a data management as a service they're part of this new SAS factory thing that Amazon has. And what they talk about here is they're trying to turn ISV and VARs into full-on SAS providers. And I think if they get that right with the SAS factory, um, then that's going to be potentially game changing. And I'm gonna look at to see if what the successes are there, because if Amazon can create more SAS applications, then their Tam and the global it market is there is going to, it can be mopped up pretty quickly, but they got to enable it. They got to enable that quickly. Yeah. >>Enabling to me means not just, and I think, you know, when Jesse answered your question, I saw it in the article that you wrote about, you know, you asked them about multi-cloud and it, to me, it's not about running on AWS and being compatible with Azure and being compatible with Google. No, it's about that frankly abstraction layer that he talked about, and that's what Cohesity is trying to do. You see others trying to do it as well? Snowflake for sure. It's about abstracting that complexity away and adding value on top of the cloud. In other words, you're using the cloud for scale being really expert at taking advantage of the native cloud services, which requires is that Jessie was saying different API APIs, different control, plane, different data plane, but taking that complexity away and then adding new value on top that's white space for a lot of players there. And, and, and I'll tell you, it's not trivial. It takes a lot of R and D and it takes really smart people. And that's, what's going to be really interesting to see, shake out is, you know, can the Dell and HPE, can they go fast enough to compete with the, the Cohesity's you've got guys like CLU Mayo coming in that are, that are brand new. Obviously we talked about snowflake a lot and many others. >>I think there's going to be a huge change in expectations, experience, huge opportunity for people to come in with unique solutions. We're going to have specialty programming on the cube all day today. So if you're watching us here on the Amazon channel, you know that we're going to have an all of a sudden demand. There's a little link on our page. On the, on the, um, the Amazon reinvent virtual event platform, click here, the bottom, it's going to be a landing page, check out all the interviews as we roll them out all day. We got a great lineup, Dave, we got Nutanix pure storage, big ID, BMC, Amazon leaders, all coming in to talk today. Uh, chaos search ed Walsh, Rachel Rose, uh, Medicar Kumar, um, Mike Gill, flux, tons of great, great, uh, partners coming in and they're going to share their story and what's working for them and their new strategies. And all throughout the day, you're going to hear specific examples of how people are changing and reinventing their business development, their partnership strategies on the product, and go to market with Amazon. So really interesting learnings. We're going to have great conversations all throughout the day. So check it out. And again, everything's going to be on demand. And when in doubt, go to the cube.net, we have everything there and Silicon angle.com, uh, for all the great coverage. So >>I don't think John is, we're going to have a conversation with him. David McCann touched on this. You talked about the need for modernization and rationalization, Tim Crawford on, on later. And th this is, this is sort of the, the, uh, the call-out that Andy Jassy made in his keynote. He gave the story of that one. CIO is a good friend of his who said, Hey, I love what you're doing, but it's not going to happen on my watch. And, and so, you know, Jessie's sort of poking at that, that, uh, complacency saying, guys, you have to reinvent, you have to go fast, you have to keep moving. And so we're gonna talk a little bit about what, what does that mean to modernize applications, why the CIO is want to rationalize what is the role of AWS and its ecosystem and providing that, that, that level of innovation, and really try to understand what the next five to seven years are gonna look like in that regard. >>Funny, you mentioned, uh, Andy Jesuit that story. When I had my one-on-one conversation with them, uh, he was kind of talking about that anonymous CIO and I, if people don't know Andy, he's a big movie buff, too, right? He loves it goes to Sundance every year. Um, so I said to him, I said, this error of digital transformation, uh, is kind of like that scene in the godfather, Dave, where, um, Michael Corleone goes to Tom Hagen, Tom, you're not a wartime conciliary. And what he meant by that was is that, you know, they were going to war with the other five families. I think now I think this is what chassis pointed out is that, that this is such an interesting, important time in history. And he pointed this out. If you don't have the leadership chops to lean into this, you're going to get swept away. >>And that story about the CIO being complacent. Yeah. He didn't want to shift. And the new guy came in or gal and they, and they, and they lost three years, three years of innovation. And the time loss, you can't get that back. And during this time, I think you have to have the stomach for the digital transformation. You have to have the fortitude to go forward and face the truth. And the truth is you got to learn new stuff. So the old way of doing things, and he pointed that out very aggressively. And I think for the partners, that same thing is true. You got to look in the mirror and say, where are we? What's the opportunity. And you gotta gotta go there. If not, you can wait, be swept away, be driftwood as Pat Gelsinger would say, or lean in and pick up a, pick up a shovel and start digging the new solution. >>You know what the other interesting thing, I mean, every year when you listen to Jassy and his keynotes and you sort of experienced re-invent culture comes through and John you're live in Silicon Valley, you talked to leaders of Silicon Valley, you know, well, what's the secret of success though? Nine times out of 10, they'll talk about culture, maybe 10 times out of 10. And, and, and so that's, that comes through in Jesse's keynotes. But one of the things that was interesting this year, and it's been thematic, you know, Andy, you know, repetition is important, uh, to, to him because he wants to educate people and make sure it sticks. One of the things that's really been he's been focused on is you actually can change your culture. And there's a lot of inertia. People say, well, not on my watch. Well, it doesn't work that way around here. >>And then he'll share stories about how AWS encourages people to write papers. Anybody in the organization say we should do it differently. And, and you know, they have to follow their protocol and work backwards and all of those stuff. But I believe him when he says that they're open to what you have a great example today. He said, look, if somebody says, well, it's 10 feet and somebody else says, well, it's, it's five feet. He said, okay, let's compromise and say it's seven and a half feet. Well, we know it's not seven and a half feet. We don't want to compromise. We either want to be a 10, Oh, we want to be at five, which is the right answer. And they push that. And that that's, he gives examples like that for the AWS culture, the working backwards, the frequently asked questions, documents, and he's always pushing. And that to me is very, very important and fundamental to understanding AWS. >>It's no doubt that Andy Jassy is the best CEO in the business. These days. If you look at him compared to everyone else, he's hands down, more humble as keynote who does three hour keynotes, the way he does with no notes with no, he memorize it all. So he's competitive and he's open. And he's a good leader. I think he's a great CEO. And I think it will be written and then looked back at his story this time in history. The next, I think post COVID Dave is going to be an error. We're going to look back and say the digital transformation was accelerated. Yes, all that good stuff, people process technology. But I think we're gonna look at this time, this year and saying, this was the year that there was before COVID and after COVID and the people who change and modernize will build the winners and not, and the losers will, will be sitting still. So I think it's important. I think that was a great message by him. So great stuff. All right. We gotta leave it there. Dave, the analysis we're going to be back within the power panel. Two sessions from now, stay with us. We've got another great guest coming on next. And then we have a pair of lb talk about the marketplace pricing and how enterprises have CIO is going to be consuming the cloud in their ecosystem. This is the cube. Thanks for watching..

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation as we've been reporting the Koba 19 pandemic has created a bifurcated IT spending picture and over the last several weeks we've reported both on the macro and even some come at it from from a vendor and a sector view I mean for example we've reported on some of the companies that have really continued to thrive we look at the NASDAQ and its you know near at all-time highs companies like oh and in CrowdStrike we've reported on snowflake uipath the sectors are PA some of the analytic databases around AI maybe even to a lesser extent cloud but still has a lot of tailwind relative to some of those on-prem infrastructure plays even companies like Cisco bifurcated in and of themselves where you see this Meraki side of the house you know doing quite well the work from home stuff but maybe some of the traditional networking not as much well now what if you flip that to really try to understand what's going on with the shape of the recovery which is the main narrative right now is it a v-shape does it a u-shape what is what's that what do people expect and now you understand that you really have to look at different industries because different industries are going to come back at a different pace with me again is Sagar khadiyah who's the director of research at EGR Sagar you guys are all over this as usual timely information it's great to see you again hope all is well in New York City thanks so much David it's a pleasure to be back on again yeah so where are we in the cycle we give dividend a great job and very timely ETR was the first to really put out data on the koban impact with the survey that ran from mid-march to to mid-april and now everybody's attention sagar is focused on okay we're starting to come back stores are starting to open people are beginning to to go out again and everybody wants to know what the shape of the recovery looks like so where are we actually in that research cycle for you guys yeah no problem so like you said you know in that kind of march/april timeframe we really want to go out there and get an idea of what we're doing the budget impacts you know as it relates to IT because of kovat 19 right so we kind of ended off there around a decline of 5% and coming into the year the consensus was of growth of 4 or 5% right so we saw about a 900,000 basis points wing you know to the negative side and the public covered in March and April were you know which sectors and vendors were going to benefit as a result of work from home and so now as we kind of fast forward to the research cycle as we kind of go more into May and into the summer rather than asking those exact same question to get again because it's just been you know maybe 40 or 50 days we really want Singh on the recovery type as well as kind of more emerging private vendors right we want to understand what's gonna be the impact on on these vendors that typically rely on you know larger conferences more in-person meetings because these are younger technologies there's not a lot of information about them and so last Thursday we launched our biannual emerging technology study it covers roughly 300 private emerging technologies across maybe 60 sectors of technology and in tandem we've launched a co-ed flash poll right what we wanted to do was kind of twofold one really understand from CIOs the recovery type they had in mind as well as if they were seeing any any kind of permanent changes in their IT stacks IT spend because of koban 19 and so if we kind of look at the first chart here and kind of get more into that first question around recovery type what we asked CIOs and this kind of COBIT flash poll again we did it last Thursday was what type of recovery are you expecting is it v-shaped so kind of a brief decline you know maybe one quarter and then you're gonna start seeing growth in 2 to H 20 is it you shaped so two to three quarters of a decline or deceleration revenue and you're kind of forecasting that growth in revenue as an organization to come back in 2021 is it l-shaped right so maybe three four five quarters of a decline or deceleration and then you know very minimal to moderate growth or none of the above you know your organization is actually benefiting from from from koban 19 as you know we've seen some many reports so those are kind of the options that we gave CIOs and you kind of see it on that first chart here interesting and this is a survey a flash service 700 CIOs or approximately and the interesting thing I really want to point out here is this you know the koban pandemic was it didn't suppress you know all companies you know and in the return it's not going to be a rising tide lifts all ships you really got to do your research you have to understand the different sectors really try to peel back the onion skin and understand why there's certain momentum how certain organizations are accommodating the work from home we heard you know several weeks ago how there's a major change in in networking mindsets we're talking about how security is changing we're going to talk about some of the permanence but it's really really important to try to understand these different trends by different industries which you're going to talk about in a minute but if you take a look at this slide I mean obviously most people expect this u-shaped decline I mean a you know a u-shaped recovery rather so it's two or three quarters followed by some growth next year but as we'll see some of these industries are gonna really go deeper with an l-shape recovery and then it's really interesting that a pretty large and substantial portion see this as a tailwind presumably those with you know strong SAS models some annual recurring revenue models your thoughts if we kind of star on this kind of aggregate chart you know you're looking at about forty four percent of CIOs anticipated u-shaped recovery right that's the largest bucket and then you can see another 15 percent and to say an l-shape recovery 14 on the v-shaped and then 16 percent to your point that are kind of seeing this this tailwind but if we kind of focus on that largest bucket that you shaped you know one of the thing to remember and again when we asked is two CIOs within the within this kind of coded flash poll we also asked can you give us some commentary and so one of the things that or one of the themes that are kind of coming along with this u-shaped recovery is you know CIOs are cautiously optimistic about this u-shaped recovery you know they believe that they can get back on to a growth cycle into 2021 as long as there's a vaccine available we don't go into a second wave of lockdowns economic activity picks up a lot of the government actions you know become effective so there are some kind of let's call it qualifiers with this bucket of CIOs that are anticipating a u-shape recovery what they're saying is that look we are expecting these things to happen we're not expecting that our lock down we are expecting a vaccine and if that takes place then we do expect an uptick in growth or going back to kind of pre coded levels in in 2021 but you know I think it's fair to assume that if one or more of these are apps and and things do get worse as all these states are opening up maybe the recovery cycle gets pushed along so kind of at the aggregate this is where we are right now yeah so as I was saying and you really have to understand the different not only different sectors and all the different vendors but you got to look into the industries and then even within industries so if we pull up the next chart we have the industry to the breakdown and sort of the responses by the industries v-shape you shape or shape I had a conversation with a CIO of a major resort just the other day and even he was saying what was actually I'll tell you it was Windham Resorts public company I mean and obviously that business got a good crush they had their earnings call the other day they talked about how they cut their capex in half but the stock sagar since the March lows is more than doubled yeah and you know that's amazing and now but even there within that sector they're peeling that on you're saying well certain parts are going to come back sooner or certain parts are going to longer depending on you know what type of resort what type of hotel so it really is a complicated situation so take us through what you're seeing by industry sure so let's start with kind of the IT telco retail consumer space Dave to your point there's gonna be a tremendous amount of bifurcation within both of those verticals look if we start on the IT telco side you know you're seeing a very large bucket of individuals right over twenty percent that indicated they're seeing a tail with our additional revenue because of covin 19 and you know Dave we spoke about this all the way back in March right all these work from home vendors you know CIOs were doubling down on cloud and SAS and we've seen how some of these events have reported in April you know with this very good reports all the major cloud vendors right select security vendors and so that's why you're seeing on the kind of telco side definitely more positivity right as it relates to recovery type right some of them are not even going through recovery they're they're seeing an acceleration same thing on the retail consumer side you're seeing another large bucket of people who are indicating what we've benefited and again there's going to be a lot of bifurcation here there's been a lot of retail consumers you just mentioned with the hotel lines that are definitely hurting but you know if you have a good online presence as a retailer and you know you had essential goods or groceries you benefited and and those are the organizations that we're seeing you know really indicate that they saw an acceleration due to Koga 19 so I thought those two those two verticals between kind of the IT and retail side there was a big bucket or you know of people who indicated positivity so I thought that was kind of the first kind of you know I was talking about kind of peeling this onion back you know that was really interesting you know tech continues to power on and I think you know a lot of people try I think that somebody was saying that the record of the time in which we've developed a fit of vaccine previously was like mumps or something and it was I mean it was just like years but now today 2020 we've got a I we've got all this data you've got these great companies all working on this and so you know wow if we can compress that that's going to change the equation a couple other things sagar that jump out at me here in this chart I want to ask you about I mean the education you know colleges are really you know kind of freaking out right now some are coming back I know like for instance my daughter University Arizona they're coming back in the fall evidently others are saying and no you can clearly see the airlines and transportation as the biggest sort of l-shape which is the most negative I'm sure restaurants and hospitality are kind of similar and then you see energy you know which got crushed we had you know oil you know negative people paying it big barrels of oil but now look at that you know expectation of a pretty strong you know you shape recovery as people start driving again and the economy picks up so maybe you could give us some thoughts on on some of those sort of outliers yeah so I kind of bucket you know the the next two outliers as from an l-shaped in a u-shaped so on the l-shaped side like like you said education airlines transportation and probably to a little bit lesser extent industrials materials manufacturing services consulting these verticals are indicating the highest percentages from an l-shaped recovery right so three plus orders of revenue declines and deceleration followed by kind of you know minimal to moderate growth and look there's no surprise here those are the verticals that have been impacted the most by less demand from consumers and and businesses and then as you mentioned on the energy utility side and then I would probably bucket maybe healthcare Pharma those have some of the largest percentages of u-shaped recovery and it's funny like I read a lot of commentary from some of the energy in the healthcare CIOs and they were said they were very optimistic about a u-shaped type of recovery and so it kind of you know maybe with those two issues then you could even kind of lump them into you know probably to a lesser extent but you could probably open into the prior one with the airlines and the education and services consulting and IMM where you know these are definitely the verticals that are going to see the longest longest recoveries it's probably a little bit more uniform versus what we've kind of talked about a few minutes ago with you know IT and and retail consumer where it's definitely very bifurcated you know there's definitely winners and losers there yeah and again it's a very complicated situation a lot of people that I've talked to are saying look you know we really don't have a clear picture that's why all these companies have are not giving guidance many people however are optimistic not only for a vet a vaccine but but but also they're thinking as young people with disposable income they're gonna kind of say dorm damn the torpedoes I'm not really going to be exposed and you know they can come back much stronger you know there seems to be pent up demand for some of the things like elective surgery or even the weather is sort of more important health care needs so that obviously could be a snap back so you know obviously we're really closely looking at this one thing though is is certain is that people are expecting a permanent change and you've got data that really shows that on the on the next chart that's right so one of the one of the last questions that we asked on this you know quick coded flash poll was do you anticipate permanent changes to your kind of IT stack IT spend based on the last few months you know as everyone has been working remotely and you know rarely do you see results point this much in one direction but 92% of CIOs and and kind of IT you know high level ITN users indicated yes there are going to be permanent changes and you know one of the things we talked about in March and look we were really the first ones you know you know in our discussion where we were talking about work from home spend kind of negating or balancing out all these declines right we were saying look yes we are seeing a lot of budgets come down but surprisingly we're seeing 2030 percent of organizations accelerate spent and even the ones that are spending less they even then you know some of their some of their budgets are kind of being negated by this work from home spend right when you think about collaboration tool is an additional VPN and networking bandwidth in laptops and then security all that stuff CIOs now continue to spend on because what what CIO is now understand as productivity has remained at very high levels right in March CIOs were very with the catastrophe and productivity that has not come true so on the margin CIOs and organizations are probably much more positive on that front and so now because there is no vaccine where you know CIOs and just in general the population we don't know when one is coming and so remote work seems to be the new norm moving forward especially that productivity you know levels are are pretty good with people working from home so from that perspective everything that looked like it was maybe going to be temporary just for the next few months as people work from home that's how organizations are now moving forward well and we saw Twitter basically said we're gonna make work from home permanent that's probably cuz their CEO wants to you know live in Africa Google I think is going to the end of the year I think many companies are going to look at a hybrid and give employees a choice say look if you want to work from home and you can be productive you get your stuff done you know we're cool with that I think the other point is you know everybody talks about these digital transformations you know leading into Kovan and I got to tell you I think a lot of companies were sort of complacent they talked the talk but they weren't walking the walk meaning they really weren't becoming digital businesses they really weren't putting data at the core and I think now it's really becoming an imperative there's no question that that what we've been talking about and forecasting has been pulled forward and you you're either going to have to step up your digital game or you're going to be in big trouble and the other thing that's I'm really interested in is will companies sub optimize profitability in the near term in order to put better business resiliency in place and better flexibility will they make those investments and I think if they do you know longer term they're going to be in better shape you know if they don't they could maybe be okay in the near term but I'm gonna put a caution sign a little longer term no look I think everything that's been done in the last few months you know in terms of having those continuation plans because you know do two pandemics all that stuff that is now it look you got to have that in your playbook right and so to your point you know this is where CIOs are going and if you're not transforming yourself or you didn't or you know lesson learned because now you're probably having to move twice as fast to support all your employees so I think you know this pandemic really kind of sped up you know digital transformation initiatives which is why you know you're seeing some companies desks and cloud related companies with very good earnings reports that are guiding well and then you're seeing other companies that are pulling their guidance because of uncertainty but it's it's likely more on the side of they're just not seeing the same levels of spend because if they haven't oriented themselves on that digital transformation side so I think you know events like this they typically you know Showcase winners and losers then you know when when things are going well and you know everything is kind of going up well I think that - there's a big you know discussion around is the ESPY overvalued right now I won't make that call but I will say this then there's a lot of data out there there's data and earnings reports there's data about this pandemic which change continues to change maybe not so much daily but you're getting new information multiple times a week so you got to look to that data you got to make your call pick your spot so you talk about a stock pickers market I think it's very much true here there are some some gonna be really strong companies emerging out of this you know don't gamble but do your research and I think you'll you'll find some you know some Dems out there you know maybe Warren Buffett can't find them okay but the guys at Main Street I think you know the I am I'm optimistic I wonder how you feel about about the recovery I I think we may be tainted by tech you know I'm very much concerned about certain industries but I think the tech industry which is our business is gonna come out of this pretty strong yeah we look at the one thing we we should we should have stated this earlier the majority of organizations are not expecting a v-shaped recovery and yet I still think there's part of the consensus is expecting a v-shaped recovery you can see as we demonstrate in some of the earlier charts the you know almost the majority of organizations are expecting a u-shaped recovery and even then as we mentioned right that you shape there is some cautious up around there and I have it you probably have it where yes if everything goes well it looks like 2021 we can really get back on track but there's so much unknown and so yes that does give I think everyone pause when it comes from an investment perspective and even just bringing on technologies and into your organization right which ones are gonna work which ones are it so I'm definitely on the boat of this is a more u-shaped in a v-shaped recovery I think the data backs that up I think you know when it comes to cloud and SAS players those areas and I think you've seen this on the investment side a lot of money has come out of all these other sectors that we mentioned that are having these l-shaped recoveries a lot of it has gone into the tech space I imagine that will continue and so that might be kind of you know it's tough to sometimes balance what's going on on the investor in the stock market side with you know how organizations are recovering I think people are really looking out in two to three quarters and saying look you know to your point where you set up earlier is there a lot of that pent up demand are things gonna get right back to normal because I think you know a lot of people are anticipating that and if we don't see that I think you know the next time we do some of these kind of coded flash bolts you know I'm interested to see whether or not you know maybe towards the end of the summer these recovery cycles are actually longer because maybe we didn't see some of that stuff so there's still a lot of unknowns but what we do know right now is it's not a v-shaped recovery agree especially on the unknowns there's monetary policy there's fiscal policy there's an election coming up there's a third there's escalating tensions with China there's your thoughts on the efficacy of the vaccine what about therapeutics you know do people who have this yet immunity how many people actually have it what about testing so the point I'm making here is it's very very important that you update your forecast regularly that's why it's so great that I have this partnership with you guys because we you know you're constantly updating the numbers it's not just a one-shot deal so suck it you know thanks so much for coming on looking forward to having you on in in the coming weeks really appreciate it absolutely yeah well I will really start kind of digging into how a lot of these emerging technologies are faring because of kovat 19 so that's I'm actually interested to start thinking through the data myself so yeah well we'll do some reporting in the coming weeks about that as well well thanks everybody for watching this episode of the cube insights powered by ETR I'm Dave Volante for sauger kuraki check out ETR dot plus that's where all the ETR data lives i published weekly on wiki bon calm and silicon angle calm and reach me at evil on Tay we'll see you next time [Music]

(funky music) >> Announcer: From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE Conversation. >> Everyone, welcome to this special CUBE Conversation here in Palo Alto, California with CUBE Studios. I'm John Furrier, your host of this CUBE Conversation with Dheeraj Pandey, CEO of Nutanix. CUBE alumni, very special part of our community. Great to see you again, thanks for coming in. We're previewing your big show coming up, Nutanix NEXT in Europe. Thanks for joining me. >> It's an honor. >> It's always great to get you. I saw your interview on Bloomberg with Emily Chang. Kind of short interview, but still, you're putting the message out there. You've been talking software. We covered your show here in North America. Clearly moving to the subscription model, and I want to get into that conversation. I think there's some notable things to talk about now that we're in this cloud 2.0 era, as we're calling it, kind of a goof on web 2.0. But cloud 2.0 is a whole shift happening, and you've been on it for a while. But you got the event coming up in Europe, Nutanix NEXT. What's the focus? Give a quick plug for that event. Let's talk about that. >> Yeah, in fact, the reiteration of the message is a key part of any of our user conferences. We have 14,000 customers around the world now, across 150 countries. We've done almost more than $5 billion worth of just software business in the last six, seven years of selling. It's a billion six run rate. There's a lot going on in the business, but we need to take a step back and in our user conference talk about the vision. So what's the vision of Nutanix? And the best part is that it hasn't changed. It's basically one of those timeless things that hopefully will withstand the test of time in the future as well. Make computing invisible anywhere. People scratch their heads. What does computing mean? What does invisible mean? What does anywhere mean? And that's where we'll actually go to these user conferences, talk about what is computing for us. Is it just infrastructure? Is it infrastructure and platform? Now that we're getting into desktop delivery, is it also about business users and applications? The same thing about invisible, what's invisible? For us, it's always been a special word. It's a very esoteric word. If you think about the B2B world, it doesn't talk about the word invisible a lot. But for us it's a very profound word. It's about autonomous software. It's about continuous, virtues of continuous delivery, continuous consumption, continuous mobility. That's how you make things invisible. And subscription is a big part of that continuous delivery message and continuous consumption message. >> So the event is October 9th, around the first week of October. You got some time there, but getting geared up for that. I wanted to ask you what you've learned from the North America conference and going into the European conference. It's ultimately the same message, same vision, with a tweak, you got some time under your belt since then. The subscription model business, which you were talking in your Bloomberg interview, is in play. It is not a new thing. It's been in operation for a while. Could you talk about that specifically? Because I think most people would say, hey, hardware to software, hard to do. Software subscription, hard to maintain and grow. Where are you on that transition? Explain and clarify your mix of business, hardware, software. Where are you in the progress of that transformation? >> Well, you know, I have been a big student of history, and I can't think of a company that's gone from hardware to software and software subscription in such a short span. Actually, I don't know of any company. If you know of one, please let me know. But why? The why of subscription is to be frictionless. Hybrid is impossible without having the same kind of consumption model, both on-prem and off-prem. And if we didn't go through that, we would be hypocritical as a company to talk about cloud and hybrid itself. The next 10 years for this company is about hybrid, and doing it as if private and public are one in the same is basically the essence of Nutanix's architecture. >> Well, I can think of some hardware-software dynamics that, again, might not match your criteria, but some might say Apple. Is it a software or hardware company? Hardware drives the ecosystem, they commoditize it. Peloton bicycle is a bike, but it's mainly a software business and in-person business. So there's different models. Oracle has hardware, they have software. It doesn't always relate to the enterprise. What's the argument to say, hey, why don't you just create your own box and kick ass with that box, or is it just different dynamics? What's that? >> Well, there's a tension in the system. People want to buy experiences as opposed to buying things. They don't want to integrate things, like, oh, I need to actually now get a hardware vendor to behave as a software vendor when it comes to support issues and such. And at the same time, you want to be flexible and portable. How do you really work with the customer with their relationships that they have with their hardware vendors? So the word anywhere in our vision is exactly that. It's like, okay, we can work on multiple servers, multiple hypervisors, and multiple clouds. At the end of the day, the customer experience is king. And that's one thing that the last 10 years has taught us, John, if anything, is don't sell things to people. You know, Kubernetes is a thing. Cloud is a thing. Can you really go sell experiences? The biggest lesson in the last year for us has been integrate better. Not just with partners, but also within your own products. And now if you can do that well, customers will buy from you. >> I think you just kind of clarified where I was thinking out loud, because if you think about Apple, the hardware is part of the experience. So they have to have it. >> Mm-hmm. >> You don't have to have the hardware to create those experiences. Is that right? >> Absolutely, which is why it's now 2% of our business, and yet we are saying that we take the burden of responsibility of supporting it, integrating with it. One of the biggest issues with cloud is operations. What is operations? It's day two patching. How do you do day two patching? Intel is coming up with microcode upgrades every quarter now because of security reasons. If we are not doing an awesome job of one-click upgrade of firmware and microcode and BIOS, we don't belong in the hybrid cloud world. I think that's the level of mundaneness that we've gotten to with our software that makes us such a high NPS company with our customers. >> I want to just drill in on the notion of a thing versus experience. You mentioned Kubernetes is a thing. I would say Hadoop was a thing. But Hadoop was a great example. It was hard to do. Kubernetes, jury's still out. People love them. Kubernetes, we'll see how that goes. If it can be abstracted away, it's not a thing anymore. We'll see. But Hadoop was a great example. Unbelievable technology direction, big data, all the goodness of object storage and unstructured data. We knew that. Just hard to work with. Setting up clusters, managing clusters. And it ended up being the death of the sector, in my opinion. What is an experience? Define what does that mean. Is it frictionless only? Is there a trust equation? Just unpack your vision on what that means. A thing, which could be a box with software on it, and experience, which is something different. >> Yeah, I mean, now you start to unpeel the word experience. It's really about being frictionless, trusted, and invisible. If you can really do these things well, around the word, define frictionless. Well, it has to be consumer-grade. It has to be web scalable, 'cause customers are looking for the Amazon architecture inside, and aren't just going and renting it from Amazon, but also saying, can I get the same experience inside? So you've got to make it web scale. You've got to make it consumer-grade. Because our operators and users, talk about Hadoop, I mean, they struggled with the experience of Hadoop itself because it was a thing, it was a technology, as opposed to being something that was consumer-grade itself. And then finally, security. Trust is very important. We must secure always on resilient. The word resilience is very important. In fact, that's one of the things we'll actually talk about at our conference, is resilience. What does it mean, not just for Nutanix stock, to be where it is today from where it was six months ago. And that's what I'm most proud of, is you go through these transitions, you actually talk about resilience of software, resilience of systems, resilience of customer support, and resilience of companies. >> So you mentioned hybrid cloud. We were talking before we came on camera about hybrid cloud. But software's a two-way relationship. Talk about what you mean by that, and then I want to ask you a follow-up question of where hardware may or may be an opportunity or a problem in that construct. >> Yeah, I mean, look, in the world of hybrid, what's really important is delivering an experience that's really without silos. Ideally, on-prem infrastructure is an availability zone. How do you make it look like an availability zone that can stand up shoulder-to-shoulder with a public cloud availability zone? That's where you sell an experience. That's how you talk about a management plane where you can actually have a single pane of glass that really delivers a cloud experience both ways. >> You're kind of a contrarian. I always love interviewing you because you seem to be on the next wave before any realizes it. Right now everyone's trying to go on-premise and you're moving from on-premise to the cloud. Not you guys moving, but your whole vision is. You've been there, done that on premises. Now you've got to be where the customers are, which is where they need to be, which is the cloud. I heard you say that. It's interesting, you're going the other way, right? >> Mm-hmm. But you could look at the infrastructure and say, hey, there's a lot of hardware inside these clouds that have a lot of hardware-specific features like hardware assist that software or network latency might not be able to deliver. Is that a missed opportunity for you guys, or does your software leverage these trends? And even on premises, there's hardware offload-like features coming. How do you reconcile that? Because I would just argue inside of the company, say, hey, Dheeraj, let's not go all in on software. We can maximize this new technology, this thing, for our software. How do you-- >> Look, I think if you look at our features, like security, the way we use TPM, which is a piece of assist that you get from Intel's motherboards for doing key encryption management. What does it mean to really do encryption at scale using Intel's vectored instructions? How do you do RDMA? How do you look at InfiniBand? How do you look at Optane drives? We've been really good at that lowest level, but making sure that it's actually selling a solution that can then go drive SAP HANA and Oracle databases and GPU for graphics and desktops. So as a company, we don't talk about those things because they are the how of the business. You don't talk about the how. You'd rather talk about the why and the what, actually. >> So from a business strategy standpoint, I just want to get this clear because there's downfalls for getting into the hardware business. You know them. Inventory, all these hardware cycles are moving fast. You mentioned Intel shipping microcode for security reasons. So you're basically saying you'd rather optimize for decoupling hardware from the software and ride the innovation of the hardware guys, like Nvidia and Intel and others. >> Absolutely, and do it faster than anybody else, but more integrated than anybody else. You know, all together now is kind of our message for .NEXT. How do you bring it all together? Because the world is struggling with things, and that's the opportunity for Nutanix. >> Well, I would say making compute invisible is a great tagline. I would add storage and networking to that too. >> Yeah, computing, by the way. >> Computing. >> I said computing. >> Okay, computing. >> 'Cause computing is compute storage networking. Computing is infrastructure, platform, and apps. It's a very clever word, and it's a very profound word as well. >> Well, let's just throw Kubernetes in there too and move up the stack, because ultimately, we're writing a lot of stories on covering this editorially, is that the world's flipped upside down. It used to be the infrastructure. We're calling this cloud 2.0, like I said earlier. The world used to be the infrastructure enabled what the apps could do, and they were limited to the resources they had. Now the apps are in charge. They're dictating terms below the software line, if you want to call it the app line. So the apps are in charge now. Whoever can serve up the best infrastructure capability, which changes the entire computing industry because now the suppliers who can deliver that elastic or flexible capacity or resource, wins. >> Absolutely. >> And that's ultimately a complete shift. >> You know, I tell people, John, about the strategy of Nutanix because we have some apps now. Frame is an app for us. Beam is an app. Calm is an app. These are apps, they're drawn on the platform, which is the core platform of Nutanix, the core hyper-convergence innovation that we did. If you go back to the '90s, who was to say that Windows really fueled Office or Office fueled Windows? They had to work in conjunction, because without one, there would be no, the other, actually. So without Office there would be no Windows. Without Windows there would be no Office. How platforms and apps work with each other synergistically is at the core of delivering that experience. >> I want to add just you're a student of history. As an entrepreneur, you've been there through the many waves and you also invest a lot, and I want to ask you this question. It used to be that platforms was the holy grail. You'd go to a VC and say, hey, I'm building a platform. Big time investment. An entrepreneur will come back: I got a tool. You're a feature. You're a feature, not a platform. Platforms was the elite engineering position to come in to look for the big money. How would you define platforms now? Because with cloud, if apps are in charge, and there's potential features that are coming around the corner that no one's yet invented, what is this platform 2.0 world look like if you were coming out of grad school or you were a young engineer or a young entrepreneur? How do you think about that right now? >> Well, the biggest thing is around extensibility and openness. You know, we were talking about openness before, but the idea of APIs, where API is the new graphically why, because the developer is the builder. And how do you really go sell to them and still deliver a great experience? And not just from the point of view of, well, I've given you the best APIs, but the best SDKs. What does it mean to give them a development kit that gets them up and running in no time? And maybe even a graphical Kickstarter. We're working with our partners a lot, where it's not just about delivering APIs or raw APIs because they're not as consumable, but to deliver SDKs and to deliver graphical structural kits to them so that they can be up and running, building applications in two months rather than two years. I think that's at the core of what our platform is. >> And data and having an operating system thinking seems to be another common pattern. Understand the subsystems of data. Running and assembling things together. >> I think what is Nutanix, I mean, if people ask me what is Nutanix, I start with data. Data is the core of the company. We've done data for virtualization. We're now doing data for applications with Nutanix Files. We have object store data. We are doing Era, which is database as a service. Without data, we'd be dead as a company. That's how important it is. Now, how do you meld that with design and delivery is basically where the three Ds come together: data-- >> I wrote a blog post. Dave Vellante always laughs when I bring this up because he always references it too. In 2007 I said, data is the new development kit. 'Cause back then, development kits existed. SDKs, software development kits. MSDN was Microsoft's thing. You remember those glory days, Dheeraj, I know. But the thesis was, if data does actually come in, it's actually an input into the software. This is what I think you guys are doing that is clever that's not well understood, is data is an input, like a software library almost. A module, but it's dynamic and it's always changing. And writing software for that is a nouveau kind of thing. This is new. >> Yeah, I know, and delivered to the developer, because right now data and hardware data is sitting in silos which are mainframe-like systems. How do you deliver it where they can spin it up on their own? Making sure that we democratize data is the biggest challenge in most companies. >> We're in a new era, I think you just pointed that out, and we talk about it at CUBE all the time. We don't really talk about up-front. It used to be UI was the thing, user interface, ease of use. I think now the new table stake feature in all companies is if you can't show value instantly in any solution that has a thing or things in it, then it's pretty much not going to happen. I mean, this is the new expectation that becomes the experience for-- >> Yeah, I mean, millennials are the new developers, and they need to actually see instant gratification, many of these-- >> Well, cost too. I don't want to spend a million dollars to find out it didn't work. I want to maybe spend something variable. >> And look, agility, the cliched word, and I don't want to talk about agility per se, but at the end of the day it's all about, can we provide that experience where you don't have to really learn something over 18 months and provide it in the next three hours. >> Great conversation here with Dheeraj Pandey, CEO of Nutanix, about his vision. I always loved your software vision. You guys have smart engineers there. Let's talk about your company. I think a lot of people at your conference and your community and others want to know, is how you're doing and how the company's doing. Because I think you guys are in the midst of a major transition we talked about earlier, hardware to software, software to subscription, recurring revenue. I mean, it's pretty much a disruptive enabler for you guys at one level as an opportunity. It's changing how you do accounting. It's having product management. Your customers are going to consume it differently. It's been a big challenge. And stock's taken a little bit of a hit, but you're kind of playing the long game. Talk about the growth strategy as you guys go forward. This has been a struggle. There's been some personnel changes in the company. What's going on? Give us the straight scoop. >> Yeah, in fact the biggest thing is about the transformation for this coming decade. And there's fundamental things that need to change for the world of cloud. Otherwise, you're basically just talking the word rather than walking the walk itself. So this last quarter I was very pleased to announce that we finally showed the first strong point of this whole transformation. There's a really good data point coming out that the company is growing back again. We beat street estimates on pretty much every metric. Billings, revenue, gross margin. And we also guided above street estimates for billings, revenue, and gross margin, and I think that's probably one of the biggest things I'm proud of in the last six, nine months of this subscription transition. We're also telling the street about how to look at us from software and support billings point of view as opposed to looking at overall billings and revenue. If you take a step back into the company, I talk about this in our earnings call, 'til three years ago, we were a commercial company, also doing federal and some international. And the last three years we proved to ourselves and to the community that we can do enterprise, you know, high-end customers, upmarket, and also do a very good job of international. Now, the next three years is really about saying, can we do both enterprise and commercial together? All together now, which is also our, coincidentally, our .NEXT message, is the proof that we actually have to go and show that we can do federal, enterprise, and commercial to really build a very large business from it. >> Well, federal's got certification levels. We know that's different depending upon which agency you're talking to. Commercial, a little bit different ball game. SaaS becomes important, cloud becomes important. The big trend is on-premise hardware. Outposts for AWS, Azure Stack for Microsoft. How do you fit into that? Because you, again, you said you're both ways. >> Mm-hmm. >> So are you worried about that? Is that a headwind, tailwind for you? What's the impact for this now fashionable on-premises shift? Which I think is just a temporary thing as cloud continues to grow. But I still argue with Michael Dell about this. I think cloud is going to be a bigger TAM. Even though there's a huge total addressable market on enterprise, that's like saying there's a great TAM for horses and buggies when cars are coming out. It's different world between public cloud and on-premises. How does that impact Nutanix, this on-premise-- >> Well, remember I said about the word anywhere in our vision? Make computing invisible anywhere? With software you can actually reduce the tension between public and private. It's not this or that. It's this and that. Our software running on Outpost is a reality. It's not like we're saying, Outpost is one thing and Nutanix is another. And that's the value of software. It's so fungible, it's so portable, that you don't have to take sides between-- >> Are you guys at ISV inside Amazon Marketplace? >> No, but again, it's still a thing. Marketplace is still not where it should be, and it's hard to search and discover things from there. So we are saying, let's do it right. Remember, we were not the first hyper-convergence company. Right? We were probably the ninth one, like the way Google was as a search engine, actually. But we did it right, because the experience mattered. You know that search box that did everything? That's what Nutanix's overall experience is today. We will do the public cloud right with our software so that we can use the customer's credits with Amazon-- >> But you're still selling direct. And your partners. >> Well, everything is coming through partners, so at the end of the day we have to do an even better job of that, like what we're doing at HPE now. I think being able to go and find that common ground with partners is what commercial is all about. Commercial is a lot about distribution. As a company, we've done a really good job of enterprise and federal. But doing it with partners-- >> What are the biggest impact areas for your business and business model, elements with software transition that you're scaling up on the subscription side? What are the biggest areas? >> Well, one is just communication, 'cause obviously a lot is changing. At a private company, things change, nobody cares. The board just needs to know about it. But at a public company, we have investors in the public market. And many of them are in the nosebleeding section, actually, of this arena. So really, you're sitting in the arena, being the man in the arena, or the woman in the arena. How do you really take this message to the bleachers section is probably the biggest one, actually. >> Well, I think one of the things I've always speculated on, you look at the growth of, just pick some stocks that we all know. VMware, Microsoft. You look at the demarcation point where, right when the stock was low to high was the shift to cloud and software. With VMware, it was they had a failing strategy and they kill it and they do a deal with Amazon. Game has changed, now they're all in the software-defined data center. Microsoft, Satya Nadella comes in, boom, they're in cloud. Real commitment. And with Microsoft specifically, that was a real management commitment. They were committed to software. They were committed to the cloud business model, and took whatever medicine they needed to take. >> That's it. That's it, you take short-term pain for long-term gain, and look, anything that becomes large over time, to me it's all about long-term greed, and I use this word a lot. I want all our employees and our customers and our investors to really think about the word. There's greed, but it's long-term greed, and that's how most companies have become large over time. So I think for us to have done this right, to say, look, we are set for the next 10 years, was very important. >> It's interesting. Everyone wants to be like Jeff Bezos. Everyone wants to be like you guys now, because long-term greed or long-term thinking is the new fashion. It's the new standard and tack. >> Yeah, I mean, look the CEOs, the top 200 CEOs, came out and talked about, are we taking good care of main street, or are we just focused on this hamster wheel of three months reporting to Wall Street alone? And I think consensus is emerging that you got to take care of main street. You and I were talking about, that I look at investors as customers, and I look at customers as investors. Which is really kind of a contrarian way of thinking about it. >> It's interesting. We live in the world, we've seen many waves. I think the wave we're on now from an entrepreneurial and venture creation standpoint, whether you're public or private, is the long game is the new 3D chess. It's where the masters are playing their best game. You look at the results of the best companies. I just bought the book about Uber from Mike Isaac from the New York Times. Short-term thinking, win at all costs, that's not the 3D chess game that's going on with entrepreneurs these days. All the investment thesis is stay long-term. And certainly now, with this perceived bubble popping, or this downturn that may or may not happen, long-term game is more important than ever. Your thoughts on it? >> I think the word authenticity has never been more important, not just in the Valley, but around the world, actually. What you're seeing with all this Me Too movement and a lot of skeletons in the cupboard out there, I think at the end of the day, the word authentic cannot be artificially created. It has to come from within. What you talk about, Satya... I look at Shantanu Narayen, the Adobe CEO, and they're authentic CEOs. I mean, I look at Dara now, at Uber, he's talking about bringing authenticity to Uber. I think there's no shortcuts to success in this world. >> I think Adobe's a great example. What they've done has been amazing. I know you're on the board there, so congratulations. Final word, I'll let you get your plug in for the event and your customer base. Talk to your customers and investors out there that might watch this. From your state of mind, what's the state of the union for Nutanix? Speak directly to your customers and investors right now. >> Well, the tagline for .NEXT Copenhagen is all together now. We're bringing clouds together. We're bringing app infrastructure and data together. I think it's a really large opportunity for us to go sell an experience to our customers, rather than selling things. All these buzzwords that come up in technology, as a company, we've done a really good job of integrating them, and the next decade is about integrating the public cloud and the private cloud. And I look at investors and customers alike. I talk about long-term greed with them. Providing an experience to them is the core of our journey. >> Thanks for your insight, Dheeraj. This was a CUBE Conversation here in Palo Alto. I'm John Furrier, thanks for watching. (funky music)

>> Live from Seattle, Washington it's theCUBE covering KubeCon and CloudNativeCom North America 2018. Brought to you by Red Hat the cloud-native computing foundation and its ecosystem partner. >> Welcome back everyone, it's theCUBE's live coverage here in Seattle of KubeCon and CloudNativeCon 2018. I'm John Furrier, with Stu Miniman, host of theCUBE. Three days of live coverage. Wall to wall, 8000 people here. Doubled from the previous event in North America, expanding globally, we are here with Liz Rice, technology analyst, evangelist at Aqua Security and program co-chair here at KubeCon, CloudNativeCon. Liz, thanks for joining us. >> Thank you for having me. >> I know you had a busy day, keynotes and all. A lot of activity, a lot of hand shaking, walking around, very crowded. >> It is, we're packed. We're absolutely at capacity here and the event sold out and it's busy. >> A lot of energy, real quick, I know you guys did a lot of work, you guys always do a great job, exceptional performance again. >> Thank you. >> CNCF does a great job on the content programming. It's about the open source communities. That's fundamental, a lot of end users, both participating and consuming. Vendor list is expanding. Putting the program together gets challenging when you have these kind of numbers. What were the themes? How did you put it all together? What was resonating? What's the focus? >> Yeah, it was so hard, we had so many applications that we could only accept 13%, which makes it almost impossible some of the decisions you have to make. And some of the things that were coming out, were like Knative, a lot of submissions around Knative. Serverless in general obviously being quite a hot topic, I would say across our industry. Really great talks from end users and we've seen a few on the keynote stage. Where some brands that we're all aware of, people like Airbnb, sharing their stories of what they've done to make their deployments, their cloud-native deployments, their use of kubernetes successful. So it's not just working from the ties, and doing some experiments, they are telling us how they've done this for real. >> You had a very successful KubeCon in Copenhagen. And so how did you integrate from Copenhagen to here. What were some of the inefficiencies? Obviously, the bigger numbers here. You recently had China the success where, we've reported on SiliconANGLE, the open source consumption and contribution is off the charts. It's huge, it's growing and it's a new dynamic. So between China, and Copenhagen, here, interesting things happening. >> China was phenomenal for me. It was my first trip to China, so it was eye-opening in all sorts of respects. And one of the really interesting things there was the use of machine learning. The uses of kube flow, real life examples. Again I think there is something about how much data they've been able to collect in China. But we heard some really great stories of, for example, electricity companies using machine learning on kubernetes to predict demand. It was fascinating. >> It's a lot of adoption. >> Yes. >> They are at the front end, they are a mobile culture. IOT is booming over there, it's just massive. >> Absolutely. >> Alright here in Seattle, obviously Seattle home of AWS, and I was just talking to some folks here locally in Seattle, just this morning, they said they think this is the biggest conference of the year here in Seattle. Which is really telling where you guys have come from. Interesting dynamic. A lot of new ecosystem partners. What's happening? It seems to be energy, the buzz. There's a subtext here that's buzzing around the hallways. What's the most important thing that people should be taking away from this event this year? >> I think the scale of it is coming from real adoption and businesses that are moving their applications into the cloud. Public cloud and hybrid cloud and finding success through doing that with cloud native components. You mentioned the end users who want to be part of the community, and they actually wanted to contribute to the community. You can look around the hall and see booths from, like Uber's over there. They're really contributing to this community. It's not just a bunch of enthusiasts, it's for real. >> Problems being solved, real company end users. >> So Liz, one of the things we've been looking at this is not a monolith here. You've actually got a whole lot of communities. As I've been wandering the floor, if I'm talking to people. We had Matt come on to talk about Envoy and they had their own conference at the beginning of the week and they had 250 people. As I'm wandering around, you talk to a number and it's like oh, I'm here all about Helm. You know there's different service meshes all over the place that everybody is talking about. >> Yeah another big theme. >> You're heavily focused on the security aspects there. I believe you've got a project that Aqua has been involved in. It was kube-hunter if I've got it. Maybe before you talk about kube-hunter, maybe just talk about balancing, this isn't one community, it's gotten really big. Do we need to break this into a micro-services space show? We'll have the core, but lots of other things and spread it out all over the world. >> Sure, it's a real challenge as this community is growing so fast and trying to keep the community feel. Balancing what the contributors want to do and making sure they're getting value and having the conversations they want, but also enabling the vendors, and the end users, and every constituent part to get something good out of this conference. It's a challenge as this gets bigger. There's no kind of, if this doubles again, will it feel the same? That's hard to imagine. So we got to think carefully about how-- >> We've seen that happen and it would not, even from last year to this year was a big change for a lot of people. >> For sure. >> So kube-hunter tell us about that. >> Yeah, kube-hunter, yes, kube-hunter is one of our open source projects at Aqua. It's basically penetration testing for kubernetes clusters, so it's written in Python. It attempts to make network requests looking for things like the open ports. It will tell you if you got some misconfigurations, 'cause a lot of the security issues with kubernetes can come about through poor configuration. And the other thing you can do, you can run it from externally to your cluster. You can also run it inside a pod inside your cluster and then that's simulating what might happen if an attacker got into your cluster, what could they do from there. They compromised a pod which could happen to a software vulnerability. Once they're in the pod, how vulnerable are you? What's the blast radius of that attack? And kube-hunter can help you see whether it's a complete disaster or actually fairly contained. >> Alright, Liz how are we doing from a security standpoint? We've watched the rise of containers over the last few years. And it's like okay wait do I need to put in some kind of lightweight VM? Do I do something there? What can I trust? What do I do? At AWS Reinvent a couple of weeks ago, there's the whole container marketplace. Feels like we are making progress but still plenty of work to do. >> Right, right, container security has lots of parts to it as you go through the life cycle of a container. Actually at AWS Reinvent, Aqua was recognized as having, I think they called it competency. Which I think it's a bit better than competency in container security. >> That's a complement I believe. >> Yeah, really complement, really competent. I think as community on the open source level, there are lots of good things happening. For example, the defaults in kubernetes have been getting better and better. If you are an enterprise, and particularly if you're a financial user, or a media company, or a government organization, you have much stronger requirements from a security perspective and that's where the open source tooling on its own may not be sufficient, and you may need to plug in commercial solutions like Aqua to really beef that up. And also to provide that end to end security right from when you're building your image through to the run time protection which is really powerful. >> Security has got to be built in from the beginning. Let me get your thoughts on end user traction and the huge demand for what end users are doing. I know you guys are seeing on the program side, the Linux foundation, CNC was talking about trying to get more case studies. We're seeing the end users prominent here. You mentioned Uber, Apple's here. A bunch of other companies, they're here. So end users are not only just contributing, they are also consuming. How are the new enterprises that are coming in consuming and interacting and engaging with kubernetes? Where are they on the IQ, if you will, level and what are they engaging on? Kubernetes has matured a bit and ready. It's been deployed, people using it. People gathering around it, but now people are starting to consume and deploy it at different scales. What's the end user uptake? What's the hot areas? What do you see the most people digging in? >> Great question, so I think we are seeing a lot of, particularly, I want to say like mature start-ups, so the Ubers and the Airbnbs and the Lyfts. They've got these massive scaled technology problems, and kubernetes is giving them, and the whole cloud-native community around it, it's giving them the ability to do these kind of custom things that they need to do. The kind of weird and wonderful things. They can add whatever adaptations they need, that maybe they wouldn't get if they were in a traditional architecture. So they're kind of the prominent voices that we are hearing right now. But at Aqua we are seeing some of these, maybe what you might call more traditional businesses like banks. They want to replicate that. They want to shape functionality really quickly. They are seeing challenges from upstart and they want to compete. So they know they've got to shift functionality quickly. They've got to do continuous deployment. Containers enable that. The whole cloud-native world enables that and that's where the adoption's from. >> They can take the blueprints from the people who built it from the ground up, the large scale startups, cloud-native in the beginning, and kind of apply the traditional IT kind of approach with the same tooling and the same platform. >> And we are seeing some interesting things around making that easier. So things like the CNAB, the cloud-native application bundling, that is coming out at Microsoft and Docker are involved in that. I think that's all to do with making it easier for enterprises to just go, yeah, this is the application I want to run it in the cloud. >> So let me ask you a question around the customer end users that we see coming onboard, because you have the upstream kind of community, the downstream benefits are impacting certainly IT and then developers, right? The classic developers, IT is starting to reimagine their infrastructure. All the goodness with cloud, and machine learning, and application is being redefined. It's changing the investment. So in 2019, what's your view on how companies are shaping their investment strategy to IT investment or technology investment strategies with cloud-native? Because this is a real trend that you just pointed out. Okay I'm a big company and I've used the old way and now I want the new way. So there's a lot of okay, instant start. Turn the key, does it run? There's a lot of managed services here, so the new persona of customer. How does that impact their investment, IT investments in your mind? What are you seeing please share any color commentary around that? >> I'm sure we're all aware that we're seeing shifts away from the traditional data center into public cloud which has implications around opex rather than capex. And I guess following on from that people worrying about whether vendor lock-in is a thing. Should they be just adopting in one public cloud or perhaps putting their eggs across different baskets? Should they be using these managed platforms? We have all these different distributions, we have these different managed solutions for kubernetes, there's a lot of choice out there. I think it's going to be interesting to see how that shapes out over the next few years. Are all these different distributions going to find a niche or how's that going to work? >> Matt Klein had a great observation. He was on earlier today from Lyft. He says look to solve a problem, use the tech to solve a problem, and then iterate, build on that. It's iteration mull of dev, ops. I think that's a good starting point. There's no magic silver bullet here. There's no magic answer, I think it's more of just get in there and get it going. The other question I have for you is 2019 prediction for kubernetes. What's going to happen this coming year? We're seeing this picture now, 8000 people, diverse audience. >> Yeah. >> What's the prediction 2019 for kubernetes? >> Oh, great question. I think maybe broader than just kubernetes, but the kind of cloud-native. Because kubernetes is like Janet said in her keynote this morning it's essentially boring. It kind of does what it's supposed to do now. I think what's going to be interesting is seeing those other pieces around it and above it, the improved developer experiences making it easier for companies to adopt. Maybe some of these choices around things like what service mesh you're going to use. How you're going to implement your observability. How you're going to deploy all this stuff without needing to hire 20 super detailed experts. We've got all the experts in this stuff. They're kind of here. The early adopters, great. Maybe that next wave, how are they going to be able to take advantage of this cloud-native? >> I think the programmability is key. Well great to have-- >> I think a big part of that is actually is going to be serverless. The ease of using serverless rather than the flexibility you get out of-- >> The millisecond latency around compute, yeah it's great. Well thanks for coming on, really appreciate it. Final question for you, what surprised you this year? Is there one thing that jumped out at you that you didn't expect? Good, bad or ugly? Great show here, it was packed. The waiting list was like 1500. What was the surprise this year from a program standpoint? >> I think actually the nicest surprise was the contribution of Phippy and all those lovely characters from Phippy Goes to the Zoo and those characters being donated by Microsoft, Matt Butcher and Karen Chu's work, was terrific. And it's just beautiful, just lovely. >> That's awesome, thanks so much Liz. Appreciate Liz right here. Program co-chair at KubeCon, CloudNativeCon, also technology evangelist at Aqua Security. That's her day job and her other job, she's running the content programming which is very huge here. Congratulations, I know it's tough work, a great job. >> Thank you very much. >> It's theCUBE coverage, breaking down all the action here at KubeCon and CloudNativeCon. I'm John Furrier and Stu Miniman, stay with us. Three days of wall-to-wall coverage. We're only on day two, we've got a whole nother day. A lot of great stories coming out of here and great content. Stay with us for more after this short break. (upbeat music)

>> Live, from Washington, D.C., it's theCUBE! Covering Inforum DC 2018. Brought to you by Infor. >> Well, welcome back. We are here on theCUBE. Thanks for joining us here as we continue our coverage here at Inforum 18. We're in Washington D.C., at the Walter Washington Convention Center. I'm John Walls, with Dave Vellante and we're joined now by Rahul Pathak, who is the G.M. at Amazon Athena and Amazon EMR. >> Hey there. Rahul, nice to see you, sir. >> Nice to see you as well. Thanks for having me. >> Thank you for being with us, um, now you spoke earlier, at the executive forum, and, um, wanted to talk to you about the title of the presentation. It was Datalinks and Analytics: the Coming Wave of Brilliance. Alright, so tell me about the title, but more about the talk, too. >> Sure. Uh, so the talk was really about a set of components and a set of transdriving data lake adoption and then how we partner with Infor to allow Infor to provide a data lake that's customized for their vertical lines of business to their customers. And I think part of the notion is that we're coming from a world where customers had to decide what data they could keep, because their systems were expensive. Now, moving to a world of data lakes where storage and analytics is a much lower cost and so customers don't have to make decisions about what data to throw away. They can keep it all and then decide what's valuable later. So we believe we're in this transition, an inflection point where you'll see a lot more insights possible, with a lot of novel types of analytics, much more so than we could do, uh, to this point. >> That's the brilliance. That's the brilliance of it. >> Right. >> Right? Opportunity to leverage... >> To do more. >> Like, that you never could before. >> Exactly. >> I'm sorry, Dave. >> No, no. That's okay. So, if you think about the phases of so called 'big data,' you know, the.... We went from, sort of, EDW to cheaper... >> (laughs) Sure. >> Data warehouses that were distributed, right? And this guy always joked that the ROI of a dupe was reduction of investment, and that's what it became. And as a result, a lot of the so-called data lakes just became stagnant, and so then you had a whole slew of companies that emerged trying to get, sort of, clean up the swamp, so to speak. Um, you guys provide services and tools, so you're like "Okay guys, here it is. We're going to make it easier for you." One of the challenges that Hadoop and big data generally had was the complexity, and so, what we noticed was the cloud guys--not just AWS, but in particular AWS really started to bring in tooling that simplified the effort around big data. >> Right. >> So fast-forward to today, and now we're at the point of trying to get insights-- data's plentiful,insights aren't. Um, bring us up to speed on Amazon's big data strategy, the status, what customers are doing. Where are we at in those waves? >> Uh, it's a big question, but yeah, absolutely. So... >> It's a John Furrier question. (laughter) So what we're seeing is this transition from sort of classic EDW to S3 based data lakes. S3's our Amazon storage service, and it's really been foundational for customers. And what customers are doing is they're bringing their data to S3 and open data formats. EDWs still have a role to play. And then we offer services that make it easy to catalog and transform the data in S3, as well as the data in customer databases and data warehouses, and then make that available for systems to drive insight. And, when I talk about that, what I mean is, we have the classic reporting and visualization use cases, but increasingly we're seeing a lot more real time event processing, and so we have services like Kinesis Analytics that makes it easy to run real time queries on data as it's moving. And then we're seeing the integration of machine learning into the stacks. Once you've got data in S3, it's available to all of these different analytic services simultaneously, and so now you're able to run your reporting, your real time processing, but also now use machine learning to make predictive analytics and decisions. And then I would say a fourth piece of this is there's really been, with machine learning and deep learning and embedding them in developer services, there's now been a way to get at data that was historically opaque. So, if you had an audio recording of a social support call, you can now put it through a service that will actually transcribe it, tell you the sentiment in the call and that becomes data that you can then track and measure and report against. So, there's been this real explosion in capability and flexibility. And what we've tried to do at AWS is provide managed services to customers, so that they can assemble sophisticated applications out of building blocks that make each of these components easier, and, that focus on being best of breed in their particular use case. >> And you're responsible for EMR, correct? >> Uh, so I own a few of these, EMR, Athena and Glue. And, uh, really these are... EMR's Open Source, Spark and Hadoop, um, with customized clusters that upbraid directly against S3 data lakes, so no need to load in HDFS, so you avoid that staleness point that you mentioned. And then, Athena is a serverless sequel NS3, so you can let any analyst log in, just get a sequel prompt and run a query. And then Glue is for cataloging the data in your data lake and databases, and for running transformations to get data from raw form into an efficient form for querying, typically. >> So, EMR is really the first service, if I recall, right? The sort of first big data service-- >> That's right. >> -that you offered, right? And, as you say, you really begin to simplify for customers, because the dupe complexity was just unwieldy, and the momentum is still there with EMR? Are people looking for alternatives? Sounds like it's still a linchpin of the strategy? >> No, absolutely. I mean, I think what we've seen is, um, customers bring data to S3, they will then use a service, like Redshift, for petabyte scale data warehousing, they'll use EMR for really arbitrary analytics, using opensource technologies, and then they'll use Athena for broad data lake query and access. So these things are all very much complimentary, uh, to each other. >> How do you define, just the concept of data lakes, uh, versus other approaches to clients? And trying to explain to them, you know, the value and the use for them, uh, I guess ultimately how they can best leverage it for their purposes? How do you walk them through that? >> Yeah, absolutely. So, there's, um. You know, that starts from the principles around how data is changing. So before we used to have, typically, tabular data coming out of ERP systems, or CRM systems, going into data warehouses. Now we're seeing a lot more variety of data. So, you might have tweets, you might have JSON events, you might have log events, real time data. And these don't fit traditional... well into the traditional relational tabular model, ah, so what data lakes allow you to do is, you can actually keep both types of the data. You can keep your tabular data indirectly in your data lake and you can bring in these new types of data, the semi-structured or the unstructured data sets. And they can all live in the data lake. And the key is to catalog that all so you know what you have and then figure out how to get that catalog visible to the analytic layer. And so the value becomes you can actually now keep all your data. You don't have to make decisions about it a priori about what's going to be valuable or what format it's going to be useful in. And you don't have to throw away data, because it's expensive to store it in traditional systems. And this gives you the ability then to replay the past when you develop better ideas in the future about how to leverage that data. Ah, so there's a benefit to being able to store everything. And then I would say the third big benefit is around um, by placing data and data lakes in open data formats, whether that's CSV or JSON or a more efficient formats, that allows customers to take advantage of best of breed analytics technology at any point in time without having to replatform their data. So you get this technical agility that's really powerful for customers, because capabilities evolve over time, constantly, and so, being in a position to take advantage of them easily is a real competitive advantage for customers. >> I want to get to Infor, but this is so much fun, I have some other questions, because Amazon's such a force in this space. Um, when you think about things like Redshift, S3, Pedisys, DynamoDB...we're a customer, these are all tools we're using. Aurora. Um, the data pipeline starts to get very complex, and the great thing about AWS is I get, you know, API access to each of those and Primitive access. The drawback is, it starts to get complicated, my data pipeline gets elongated and I'm not sure whether I should run it on this service or that service until I get my bill at the end of the month. So, are there things you're doing to help... First of all, is that a valid concern of customers and what are you doing to help customers in that regard? >> Yeah, so, we do provide a lot of capability and I think our core idea is to provide the best tool for the job, with APIs to access them and combine them and compose them. So, what we're trying to do to help simplify this is A) build in more proscriptive guidance into our services about look, if you're trying to do x, here's the right way to do x, at least the right way to start with x and then we can evolve and adapt. Uh, we're also working hard with things like blogs and solution templates and cloud formation templates to automatically stand up environments, and then, the third piece is we're trying to bring in automation and machine learning to simplify the creation of these data pipelines. So, Glue for example. When you put data in S3, it will actually crawl it on your behalf and infer its structure and store that structure in a catalog and then once you've got a source table, and a destination table, you can point those out and Glue will then automatically generate a pipeline for you to go from A to B, that you can then edit or store in version control. So we're trying to make these capabilities easier to access and provide more guidance, so that you can actually get up and running more quickly, without giving up the power that comes from having the granular access. >> That's a great answer. Because the granularity's critical, because it allows you, as the market changes, it allows you... >> To adapt. To move fast, right? And so you don't want to give that up, but at the same time, you're bringing in complexity and you just, I think, answered it well, in terms of how you're trying to simplify that. The strategy's obviously worked very well. Okay, let's talk about Infor now. Here's a big ISP partner. They've got the engineering resources to deal with all this stuff, and they really seem to have taken advantage of it. We were talking earlier, that, I don't know if you heard Charles's keynote this morning, but he said, when we were an on prem software company, we didn't manage customer servers for them. Back then, the server was the server, uh software companies didn't care about the server infrastructure. Today it's different. It's like the cloud is giving Infor strategic advantage. The flywheel effect that you guys talk about spins off innovation that they can exploit in new ways. So talk about your relationship with Infor, and kind of the history of where it's come and where it's going. >> Sure. So, Infor's a great partner. We've been a partner for over four years, they're one of our first all-in partners, and we have a great working relationship with them. They're sophisticated. They understand our services well. And we collaborate on identifying ways that we can make our services better for their use cases. And what they've been able to do is take all of the years of industry and domain expertise that they've gained over time in their vertical segments, and with their customers, and bring that to bear by using the components that we provide in the cloud. So all these services that I mentioned, the global footprint, the security capabilities, the, um, all of the various compliance certifications that we offer act as accelerators for what Infor's trying to do, and then they're able to leverage their intellectual property and their relationships and experience they've built up over time to get this global footprint that they can deploy for their customers, that gets better over time as we add new capabilities, they can build that into the Infor platform, and then that rolls out to all of their customers much more quickly than it could before. >> And they seem to be really driving hard, I have not heard an enterprise software company talk so much about data, and how they're exploiting data, the way that I've heard Infor talk about it. So, data's obviously key, it's the lifeblood-- people say it's the new oil--I'm not sure that's the best analogy. I can only put oil in my house or my car, I can't put it in both. Data--I can do so many things with it, so, um... >> I suspect that analogy will evolve. >> I think it should. >> I'm already thinking about it now. >> You heard it here first in the Cube. >> You keep going, I'll come up with something >> Don't use that anymore. >> Scratch the oil. >> Okay, so, your perspectives on Infor, it's sort of use of data and what Amazon's role is in terms of facilitating that. >> So what we're providing is a platform, a set of services with powerful building blocks, that Infor can then combine into their applications that match the needs of their customers. And so what we're looking to do is give them a broad set of capabilities, that they can build into their offerings. So, CloudSuite is built entirely on us, and then Infor OS is a shared set of services and part of that is their data lake, which uses a number of our analytic services underneath. And so, what Infor's able to do for their customers is break down data silos within their customer organizations and provide a common way to think about data and machine learning and IoT applications across data in the data lake. And we view our role as really a supporting partner for them in providing a set of capabilities that they can then use to scale and grow and deploy their applications. >> I want to ask you about--I mean, security-- I've always been comfortable with cloud security, maybe I'm naive--but compliance is something that's interesting and something you said before... I think you said cataloging Glue allows you to essentially keep all the data, right? And my concern about that is, from a governance perspective, the legal counsel might say, "Well, I don't "want to keep all my data, if it's work in process, "I want to get rid of it "or if there's a smoking gun in there, "I want to get rid of it as soon as I can." Keep data as long as possible but no longer, to sort of paraphrase Einstein. So, what do you say to that? Do you have customers in the legal office that say, "Hey, we don't want to keep data forever, "and how can you help?" >> Yeah, so, just to refine the point on Glue. What Glue does is it gives you essentially a catalog, which is a map of all your data. Whether you choose to keep that data or not keep that data, that's a function of the application. So, absolutely >> Sure. Right. We have customers that say, "Look, here are my data sets for "whether it's new regulations, or I just don't want this "set of data to exist anymore, or this customer's no longer with us and we need to delete that," we provide all of those capabilities. So, our goal is to really give customers the set of features, functionality, and compliance certifications they need to express the enterprise security policies that they have, and ensure that they're complying with them. And, so, then if you have data sets that need to be deleted, we provide capabilities to do that. And then the other side of that is you want the audit capabilities, so we actually log every API access in the environment in a service called CloudTrail and then you can actually verify by going back and looking at CloudTrail that only the things that you wanted to have happen, actually did happen. >> So, you seem very relaxed. I have to ask you what life is like at Amazon, because when I was down at AWS's D.C. offices, and you walk in there, and there's this huge-- I don't know if you've seen it-- there's this giant graph of the services launched and announced, from 2006, when EC2 first came out, til today. And it's just this ridiculous set of services. I mean the line, the graph is amazing. So you're moving at this super, hyper pace. What's life like at AWS? >> You know, I've been there almost seven years. I love it. It's been fantastic. I was an entrepreneur and came out of startups before AWS, and when I joined, I found an environment where you can continue to be entrepreneurial and active on behalf of you customers, but you have the ability to have impact at a global scale. So it's been super fun. The pace is fast, but exhilarating. We're working on things we're excited about, and we're working on things that we believe matter, and make a difference to our customers. So, it's been really fun. >> Well, so you got--I mean, you're right at the heart of what I like to call the innovation sandwich. You've got data, tons of data, obviously, in the cloud. You're a leader and increasingly becoming sophisticated in machine intelligence. So you've got data, machine intelligence, or AI, applied to that data, and you've got cloud for scale, cloud for economics, cloud for innovation, you're able to attract startups--that's probably how you found AWS to begin with, right? >> That's right. >> All the startups, including ours, we want to be on AWS. That's where the developers want to be. And so, again, it's an overused word, but that flywheel of innovation occurs. And that to us is the innovation sandwich, it's not Moore's Law anymore, right? For decades this industry marched to the cadence of Moore's Law. Now it's a much more multi-dimensional matrix and it's exciting and sometimes scary. >> Yeah. No, I think you touched on a lot of great points. It's really fun. I mean, I think, for us, the core is, we want to put things together the customers want. We want to make them broadly available. We want to partner with our customers to understand what's working and what's not. We want to pass on efficiencies when we can and then that helps us speed up the cycle of learning. >> Well, Rahul, I actually was going to say, I think he's so relaxed because he's on theCUBE. >> Ah, could be. >> Right, that's it. We just like to do that with people. >> No, you're fantastic. >> Thanks for being with us. >> It's a pleasure. >> We appreciate the insights, and we certainly wish you well with the rest of the show here. >> Excellent. Thank you very much, it was great to be here. >> Thank you, sir. >> You're welcome. >> You're watching theCUBE. We are live here in Washington, D.C. at Inforum 18. (techno music)