>>from around the globe. It's the Cube with coverage of Coop Con and Cloud, Native Con Europe 2020 Virtual brought to You by Red Hat, The Cloud Native Computing Foundation and its ecosystem Partners. Hi, I'm stupid, man. And this is the Cube's coverage of Cube con Cloud Native Con Europe event, which, of course, this year has gone virtual, really lets us be able to talk to those guests where they are around the globe. Really happy to welcome back to the program. Liz Rice. First of all, she is the vice president of Open Source Engineering at Aqua Security. She's also the chair of the Technical Oversight Committee has part of Ah CN cf. Liz, it is great to see you. Unfortunately, it's remote, but ah, great to catch up with you. Thanks for joining. >>Yeah, Thanks for having me. Nice to see you if you know across the ocean. >>So, uh, you know, one of the one of the big things? Of course, for the Cube Con show. It's the rallying point for the community. There are so many people participating. One of the things we always love to highlight its not only the the vendor ecosystem. But there is a very robust, engaged community of end users that participate in it. And as I mentioned, you're the chair of that technology oversight committee. So maybe just give our audience a little bit of, you know, in case they're not familiar with the TOC does. And let's talk about the latest pieces there. >>Yes, say the TOC is really hit. C can qualify the different projects that want to join the CNC F. So we're assessing whether or not they're cloud native. We're assessing whether they could joined at sandbox or incubation or graduation levels. Which of the different maturity levels that we have for for project within the CN CF yeah, we're really there, Teoh also provide it steering around the What does cloud native mean and what does it mean to be a project inside the CN CF community? We're also a voice for all of the projects. We're not the only voice, but, you know, part >>of our role >>really is to make sure the projects are getting what they need in order to be successful. So it's it's really around the technology and the projects that we call cloud native >>Yeah, and and obliges Cloud Native because when people first heard of the show, of course, Kubernetes and Cube Con was the big discussion point. But as you said, Cloud native, there's a lot of projects there. I just glanced at the sandbox page and I think there's over 30 in the sandbox category on and you know they move along their process until they're, you know, fully mature and reach that, you know, 1.0 state, which is the stamp of approval that, you know, this could be used in production. I understand there's been some updates for the sandbox process, so help us understand you know where that is and what's the new piece of that? >>Yeah. So it's really been because of the growth off cloud native in general, the popularity off the CN CF and so much innovation happening in our space. So there's been so many projects who want Teoh become hard off the CNC f family on and we used to have a sponsorship model where members of the TOC would essentially back projects that they wanted to see joining at the sandbox level. But we ran into a number of issues with that process on and also dealing with the scale, the number of applications that have come in. So we've revamped the process. We made it much easier for projects to apply as much simpler form where really not making so much judgment we're really saying is it's a cloud native project and we have some requirements in terms off some governance features that we need from a project. And it's worth mentioning that when a project joins the CN CF, they are donating the intellectual property and the trademark off that project into the foundation. So it's not something that people should take lightly. But we have tried to make it easier and therefore much smoother. We're able Teoh assess the applications much more quickly, which I think everyone, the community, the projects, those of us on the TOC We're all pretty happy that we can make that a much faster process. >>Yeah, I actually, it brings up An interesting point is so you know, I've got a little bit of background in standards committees. A swell as I've been involved in open source for a couple of decades now some people don't understand. You know, when you talk about bringing a project under a foundation. You talked about things like trademarks and the like. There are more than one foundation out there for CN CF Falls under the Linux Foundation. Google, of course, brought Kubernetes in fully to be supported. There's been some rumblings I've heard for the last couple of years about SDO and K Native and I know about a month before the show there was some changes along SDO and what Google was doing there may be without trying to pass too many judgments in getting into some of the political arguments, help us understand. You know what Google did and you know where that kind of comparison the projects that sit in the CN cf themselves. >>Yeah, So I e I guess two years ago around two years ago, Stu was very much the new kid in the cloud native block. So much excitement about the project. And it was actually when I was a program co chair that we had a lot of talks about sdo at Cube Con cloud native bomb, particularly in Copenhagen, I'm recalling. And, uh, I think everyone I just saw a natural fit between that project on the CN, CF and There was an assumption from a lot of people across the community that it would eventually become part of the CNC f. That was it's natural home. And one of the things that we saw in recent weeks was a very clear statement from IBM, who were one off the Uh huh, yeah, big contributing companies towards that project that that was also their expectation. They were very much under the impression that Stu would be donated to the CN CF at an appropriate point of maturity, and unfortunately, that didn't happen. From my point of view, I think that has sown a lot of confusion amongst the community because we've seen so much. It's very much a project of fits. Service mesh designed to work with kubernetes is it really does. You're fit naturally in with the other CN CF projects. So it's created confusion for end users who, many of whom assume that it was called the CN CF, and that it has the neutral governance that the other projects. It's part of the requirements that we have on those projects. They have to have an open governance that they're not controlled by a single vendor, Uh, and we've seen that you know that confusion, Andi. Frustration around that confusion being expressed by more and more end users as well as other people across the community. And yeah, the door is still open, you know, we would still love to see SDO join the community. Clearly there are different opinions within the SD wan maintainers. I will have to see what happens. >>Yeah, lets you bring up some really good points. You know, absolutely some of some of that confusion out there. Absolutely. I've heard from customers that if they're making a decision point, they might say, Hey, maybe I'm not going to go down that maybe choose something else because I'm concerned about that. Um, you know, I sdo front and center k native, another project currently under Google that has, you know, a number of other big vendors in the community that aiding in that So hopefully we will see some progress on that, you know, going forward. But, you know, back to you talked about, You know, the TOC doesn't make judgements as to you know which project and how they are. One of the really nice things out there in the CN CF, it's like the landscape just for you to help, understand? Okay, here's all of these projects. Here's the different categories they fit in. Here is where they are along that maturity. There's another tool that I read. Cheryl Hung blogged about the technology radar. I believe for continuous delivery is the first technology radar. Help us understand how that is, you know, not telling customers what to do but giving them a little guidance that you know where some of these projects projects fit. In a certain segment, >>Yeah, the technology radar is a really great initiative. I'm really excited about it because we have increasing numbers or end users who are using these different projects it both inside the CN CF and projects that are outside of the CNC F family. Your end users are building stacks. They're solving real problems in the real world and with the technology radar. What Cheryl's been able to facilitate is having the end you to the end user community share with us. What tools? They're actually using what they actually believe are the right hammers for specific nails. And, you know, it's it's one thing for us as it's more on the developer or vendor side Teoh look at different projects and say what we think are the better solutions for solving different problems. Actually hearing from the horse's mouth from the end users who are doing it in the real world is super valuable. And I think that is a really useful input to help us understand. What are the problems that the end user is still a challenge by what are the gaps that we still need to fail more input we can get from the end user community, the more will be solving real problems and no necessarily academic problems that we haven't sorry discovered in >>the real world. Alright, well is, you know, teeing up a discussion about challenges that users still have in the world. If we go to your primary jobs, Main hat is you live in the security world and you know, we know security is still something, you know, front and center. It is something that has never done lots of discussion about the shared responsibility model and how cloud native in security fit together and all that. So maybe I know there's some new projects there, but love to just give me a snap shot as where we are in the security space. As I said, Overall, it's been, you know, super important topic for years. This year, with a global pandemic going on, security seems to be raised even more. We've seen a couple of acquisitions in the space, of course. Aqua Security helping customers along their security journey. So what do you seeing out there in the marketplace today and hear from your custom? >>Yeah, I Every business this year has, you know, look at what's going on and you know, it's been crazy time for everyone, but we've been pleasantly surprised at how, you know, in relative terms, our business has been able to. It's been strong, you know. And I think you know what you're touching on the fact that people are working remotely. People are doing so many things online. Security is evermore online. Cloud security's evermore part off what people need to pay attention to. We're doing more and more business online. So, actually, for those of us in the security business, it has bean, you know that there have been some silver linings to this this pandemic cloud? Um, yes. So many times in technology. The open source projects and in particularly defaults in kubernetes. Things are improving its long Bina thing that I've you know, I wished for and talked about that. You know, some of the default settings has always been the most secure they could be. We've seen a lot of improvements over the last 23 years we're seeing continuing to see innovation in the open source world as well as you know, on the commercial side and products that vendors like Akwa, you know, we continue to innovate, continue to write you ways for customers to validate that the application workloads that they're going to run are going to run securely in the cloud. >>Alright and lives. There's a new project that I know. Ah, you know, you Aqua are participating in Tell us a little bit about Starbird. You know what's what's the problem? It's helping solve and you know where that budget >>Yes, So stockholders, one of our open source initiatives coming out of my team are equal on, and the idea is to take security reporting information and turn it into a kubernetes native, uh, resources custom resources. And then that means the security information, your current security status could be queried over the kubernetes AP I, as you're querying the status or the deployment, say you can also be clearing to see whether it's passing configuration audits or it's passing vulnerability scans for the application containers inside that deployment. So that information is available through the same AP eyes through the queue control interface through dashboards like Octane, which is a nice dashboard viewer for kubernetes. And starboard brings security information not just from acquittals but from other vendor tools as well front and center into that kubernetes experience. So I'm really excited about Star Border. It's gonna be a great way of getting security visibility, Teoh more kubernetes use it >>all right. And we were talking earlier about just the maturity of projects and how they get into the sandbox. Is is this still pretty sandbox for >>this? OK, we're still very much in the early phases and you know it. I think in the open source world, we have the ability to share what we're doing early so that we can get feedback. We can see how it resonates with with real users. We've had some great feedback from partners that we've worked with and some actual customers who actually collaborated with When we're going through the initial design, some great feedback. There's still lots of work to do. But, yeah, the initial feedback has been really positive. >>Yeah, is usually the event is one of those places where you can help try toe, recruit some other people that might have tools as well as educate customers about what's going on. So is that part of the call to action on this is, you know, what are you looking for for kind of the rest of 2020 when it when it comes to this project? >>Yeah, absolutely. So internally, we're working on an operator which will automate some of the work that's double does in the background in terms off getting more collaboration. We would love to see integrations from or security tooling. We're talking with some people across the community about the resource definition, so we've come up with some custom resource definitions, but we'd love them to be applicable it to a variety of different tools. So we want to get feedback on on those definitions of people are interested in collaborating on that absolutely do come and talk to me and my team are reluctant. >>Great. Listen, and I'll give you the final word. Obviously, we're getting the community together while we're part So you know any other you know, engagement opportunities, you get togethers. Things that you want people to know about the European show this year. >>Well, it's gonna be really you know, I'm on tenterhooks to see whether or not we can recreate the same atmosphere as we would have in Q con. I mean, it won't be exactly the same, but I really hope that people will engage online. Do come and, you know, ask questions of the speakers. Come and talk to the vendors, get into slack channels with the community. You know, this is an opportunity to pretend we're in the same room. Let's let's let's do what we can Teoh recreate as close as we can. That community experience that you keep corn is famous for >>Yeah, absolutely. That whole way track is something that is super challenging to recreate. And there's no way that I am getting the Indonesian food that I was so looking forward to in Amsterdam just such a great culinary and cultural city. So hopefully sometime in the future will be able to be back there. Liz Rice. Always pleasure catching up with you. Thanks so much for all the work you're doing on the TOC. And always a pleasure talking to you. >>Thanks for having me. >>All right, Lots more coverage from Cube Con Cloud, Native con the European 2020 show, Of course. Virtual I'm stew minimum. And thank you for watching the Cube. Yeah, yeah, yeah, yeah.

>> Live from Barcelona, Spain it's theCUBE. Covering KubeCon + CloudNativeCon Europe 2019. Brought to you by Red Hat, the Cloud Native Computing Foundation and ecosystem partners. >> Welcome back to theCUBE here in Barcelona, Spain at the Fira, it's KubeCon + CloudNativeCon 2019. I'm Stu Miniman and my co-hosts for two days of live wall-to-wall coverage is Corey Quinn. Joining us back, we have two CUBE alums, Liz Rice, right to my right here who is a Technology Evangelist with Aqua security. Liz, thank you so much welcome back. >> Pleasure to be here. >> And Jeff Brewer, Vice President and Chief Architect, Small Business & Self-Employed Group, of Intuit. A CUBE alum since a few hours ago this morning. >> Yes, yes, thank you. >> Jeff, welcome back. >> Thank you. >> So, we've got you back with a different hat. Everybody in our industry can definitely recognize we wear lots of different hats we have lots of jobs thrown at us. Both of you are in the Technical Oversight Committee and Liz is not only a member but also the Chairperson, President. (people laughing) >> President is definitely a promotion. But, yeah, I'm Chair of the committee. >> Maybe, as it's known, the TOC. Liz, before we get there, your shirt says +1 binding. You have to explain for us and did not get a preview before the interview, so we'll see where this goes. >> It's one of the perks of being on the TOC. When we have something that comes to a vote we want to get input from the community so we ask anyone in the community to vote. But unless you're a member of the TOC your vote is non-binding. As a member of the committee, we have binding votes. And the traditional thing you write on the voting email is +1 binding. So, it's a nice surprise to get a t-shirt when I joined the TOC. >> Very nice. Can you just give us, our audience, that might not be familiar with the TOC, give us some of the key things about it. >> It's the Technical Oversight Committee for the CNCF. We are, really, the technical curation of the projects that come in to the CNCF. Which projects will get support and at what level because we have the sandbox experimentation stage then incubation and then finally graduation for the really established and kind of, de-risked projects. So, we're really evaluating the projects and kind of making a decision collaboratively on which ones we want the CNCF to support. >> All right. So Jeff, we had a great conversation with you about Intuit's cloud journey. Tell us how you got involved in the TOC. We always love the end users, not just using but participating in and helping to give some governance over what the community is doing. >> Yeah, so, about a year and a half ago we made a decision to acquire a small company called Applatix. Who was, actually, already in the end user community. And also contributors as well. Through that acquisition, I was part of that acquisition, I led that acquisition from the Intuit side and really got excited about the Kubernetes and the KubeCon story overall. Through the Kubernetes experts, I met them at a KubeCon and they introduced me to a whole lot more of the community. Just through some overall partnerships with AWS and also spending a lot of time with end-users that's how I really got to know the community a little bit. And then, was voted onto the CNCF as an end user representative in January. >> Wonderful. As far as you're concerned, as you go through this, do you find it challenging at times to separate your roles professionally from working for a large company, to whom many things matter incredibly. Again, as mentioned earlier, I am one of your customers. I care very much about technical excellence, coming out of Intuit, versus your involvement with the larger project. >> Yeah, so like most people in technology companies I'm extremely busy and I would love to spend, I would love to clone myself and spend more (laughing) more time. >> Everybody wants to submit a client project to the TOC we will prioritize that one. >> Exactly, exactly. >> The way I really balance it is that I make an explicit time carve out for those two activities. And most importantly, I attend the meetings. The TOC meetings that we have, those are extremely important. We get a lot of project reviews in those meetings. Liz chairs those meetings. That's where I always make sure that my schedule is cleared for that. >> Taking it, I guess, one step further. Do you find it challenging at all to separate out, in fact, when you're making decisions and making votes, for example, that are presumably binding, +1 binding as we've learned now, is the terminology. Do you find that you are often pulled between trying to advocate for your company and advocating for the community or are they invariably aligned in your mind? >> I mean, my job's the easiest because I come from an end user. So what I use and what I consume is likely what the community at large. There might be some niches and stuff like that. But I usually don't have that conflict. I don't know, as more of a vendor, you might have more of a conflict. >> It's something that I have be conscious of. I just try to mentally separate. I have a role with a company that pays my salary but when I'm doing open-source things if I feel conflicted about. This hasn't really come up yet, but if I do feel that there's some kind of conflict of interest I will always recuse myself. Actually, in my previous role, as the Co-Chair for the Program Committee for the KubeCon and CloudNativeCon Conference, on a couple of occasions we had competitors submit, and I would always just step back from those. Because it's the right thing to do. >> All right. So Liz, there's quite a few projects now, under the umbrella of CNCF. If I've go it right, it was like, 38 different ones. When Brian went on the stage this morning, 16 in the sandbox, 16 incubating and six have graduated now. How do you manage that? You know, there's some in the community they're like, oh my gosh, reminds us of like, big tent, from some initiatives. Some other things here, how much is too much? How do you balance that and what's the input of the TOC? >> Yeah, so one of the things that we're doing with the TOC is we've just established a thing called the SIGs, the special interest groups. Very much following the same model of Kubernetes SIGs. But the idea here is that we can, kind of formalize getting experts in the community to help us with particular kind of areas. So, we've already got a storage and security SIG set up. We expect there will be probably four to six more coming on board during the year. And that helps us with things like the project reviews and the due diligence to just be able to say, we would really appreciate some help. Those groups are also really enthusiastic about kind of sharing knowledge in the form of things like white papers. I think it will be really important for end-users to be able to navigate their way around these projects. Quite often there is more than one solution for a particular thing. And being able to, in a non-vendor way, in a neutral way, express why project X is good in one circumstance and project Y would be better in a different environment. There's work to be done there and I'm hoping to see that come out. >> This is one of my passions as the end user representative, is that trail map or that road map. That's one of the reasons why we really have invested at Intuit, in the Kubernetes technology and the Cloud Native technology. We didn't just roll them out as is. We actually curate them and create, really, a paved road for our developers to navigate that space. >> Yeah, and as we heard from your story it's not always, well, if there's some overlap you use SDO and Hellman. >> Yeah. >> That there's a fit for both of those in your environment, right. >> Yeah. >> From a, I guess, an end user perspective is there a waiting difference between someone like Intuit and someone like Twitter for pets, where there's a slight revenue scale, a slight revenue difference, like scale difference, like everything difference. >> Yes. >> Certainly, there is. I think that, but that's one of the beautiful things about the Cloud Native technologies. You can consume what you need and what you want, right. It's not one size fits all. A lot of people talk about, oh, there's a paradox of choice, there's so many projects, right. Actually, that's a benefit. Really, all you need is that road map to navigate your way through that, rather than just adopting a paved road that might not work for everybody. >> It almost feels, to some extent, almost like the AWS Service Catalog. Whenever you wind up looking at all the things they offer. It feels like going out to eat at the Cheesecake Factory. Where there is 80 pages of menu to flip through with some advertisements, great. And reminding yourself, at time, that they are not Pokemon, you do not need to catch them all. It's, sometimes, a necessary step, as you start to contextualize this. >> That's one of the great things about having over 80 members in the end user is. You can find a buddy, you can find a company like you. Talk to them, get connected with them and figure out what they're doing and learn from them. The community is broad enough to be able to do that. >> All right, so Liz, let's talk about security. >> Okay. (people laughing) >> You said there's a SIG that started up. Where are we, how are things going and you can you share about where we're going in the near future? >> The SIG came together from a group of people who really wanted to make it easier for end-users to roll out their Cloud Native stacks in a secure fashion. We don't always, as a community, speak the same language about security, we don't always have the most secure settings by default. They really came together around this common interest of just making it easier for people to secure. I think a big part of that will be looking at how the different projects, are they applying best practices from a security perspective? Is there more they should do to document how to operate their particular project more securely? I think that whole initiative and that group of people who've come together for SIG security, I'm so impressed and so pleased that they have come together with that enthusiasm to help on that front. >> Any commentary on what you're seeing in this space? >> Yeah, so as an almost, a fintech company, with a lot of fintech and, you know, we're not quite a bank, but we have a lot of the same security and compliance things. That SIG is so, so important to us. And having a roadmap. I found a education is really, really a big part of it of the security experts, right. Because this is somewhat newer technology. Even though it's been in use at Google for a long time the regulator's, the compliance people, don't totally understand it, right. So you have to have a way to explain to them what's going on. So things like, open policy agent, something that we've adopted, helps us explain what's going on in our system. Once they get it, they're like, this is awesome and our end users can now, really, our end users, meaning the people that use QuickBooks and TurboTax can really trust that we have those guardrails in place. >> At Aqua, it's a huge concern from a lot of our customers. Many of whom, coming from that kind of finance industry. That they're coming to us and saying, well, how can I be PCI compliant or GDPR. How do I manage these requirements with my container based stack, with my Cloud Native stack. That's why there is this huge ecosystem quite a lot of effort around security, compliance, policy. >> It feels very much like it's two problems rolled into one. First, how do you make sure that data is secure in these things? Secondly, how do you effectively and responsibly communicate that to a regulator, who expects to be taken on a tour of a data center when they show up on site? (people laughing) I checked, they won't let you. >> There are definitely two sets of security people in my experience. There are a set of people who care about how will I get attacked. How will breaches happen. And there are other people who go, I have a checklist and I need to check the boxes in the checklist, tell me how. Sometimes those two things overlap, but not always. >> All right, Liz, lot of updates, as always. Jeff, I really appreciate your commentary there. Well, there's the paradox of choice but we have a lot of customers out there and therefore we do. (people chuckling) Any highlights you want to share with our audience? >> I think one thing that happens every year is we see more. Well, we saw Kubernetes graduate, I think, early last year, end of the previous year. Now we've got six projects into graduation. From my perspective, that says something about how mature this whole set of projects, this whole platform is becoming. Because graduation is a pretty high bar. Not least in terms of the number of end users that have to be using it in production. This is solid technology. >> Yeah, any highlights from you? >> I think, like we might have touched on a little bit this morning. But I think that usually the technologies that where you're facing the big problems is pretty obvious which one to use, right. Like serverless, you're going to go look at something like Knative or whatnot. Functions as a service. There's some open fast projects, whatnot, like that. SDO services mesh is another one where it's getting mature and it's getting to the point where you can have these ubiquitous service meshes throughout it. So, those are the areas that we're most looking at right now. >> Great, all right. Well, Liz and Jeff, thank you so much for joining us. Thanks for all the work you do on the Oversight Committee and appreciate you sharing the updates with our community. >> Thank you for having us. >> Thank you. >> For Cory Quinn, I'm Stu Miniman. We'll be back more, with theCUBE here at KubeCon + CloudNativeCon 2019. Thanks for watching. (upbeat music)

>> Announcer: Live from Copenhagen, Denmark, it's theCUBE. Covering KubeCon and CloudNativeCon Europe 2018. Brought to you by the Cloud Native Computing Foundation and its ecosystem partners. >> Hello, everyone. Welcome back to theCUBE's exclusive coverage here in Copenhagen, Denmark for KubeCon 2018, part of the CNCF Cloud Native Compute Foundation, which is part of the Linux Foundation. I'm John Furrier, your host. We've got two great guests here, we've got Liz Rice, the co-chair of KubeCon and CloudNativeCon, kind of a dual naming because it's Kubernetes and it's Cloud Native and also technology evangelist at Aqua Security. She's co-chairing with Kelsey Hightower who will be on later today, and CUBE alumni as well, and Janet Kuo who is a software engineer at Google. Welcome to theCUBE, thanks for coming on. >> Yeah, thanks for inviting us. >> Super excited, we have a lot of energy even though we've got interviews all day and it's kind of, we're holding the line here. It's almost a celebration but also not a celebration because there's more work to do with Kubernetes. Just the growth of the CNCF continues to hit some interesting good performance KPIs on metrics. Growth's up on the membership, satisfaction is high, Kubernetes is being called a de facto standard. So by all kind of general qualitative metrics and quantitative, it's doing well. >> Lauren: It's doing great. >> But it's just the beginning. >> Yeah, yeah. I talked yesterday a little bit in, in the keynote, about project updates, about how Kubernetes has graduated. That's a real signal of maturity. It's a signal to the end-user companies out there that you know, the risk, nothing is ever risk-free, but you know, Kubernetes is here to stay. It's stable, it's got stable governance model, you know, it's not going away. >> John: It's working. >> It's going to continue to evolve and improve. But it's really working, and we've got end users, you know, not only happy and using it, they're prepared to come to this conference and share their stories, share their learnings, it's brilliant. >> Yeah, and Janet also, you know, you talk about China, we have announcement that, I don't know if it's formally announced, but Shanghai, is it out there now? >> Lauren: It is. >> Okay, so Shanghai in, I think November 14th, let me get the dates here, 14th and 15th in Shanghai, China. >> Janet: Yeah. >> Where it's going to be presented in either English or in Chinese, so it's going to be fully translated. Give us the update. >> Yeah, it will be fully translated, and we'll have a CFP coming soon, and people will be submitting their talks in English but they can choose to present either in English or Chinese. >> Can you help us get a CUBE host that can translate theCUBE for us? We need some, if you're out there watching, we need some hosts in China. But in all seriousness, this is a global framework, and this is again the theme of Cloud Native, you know. Being my age, I've seen the lift and shift IT world go from awesome greatness to consolidation to VMwares. I've seen the waves. But this is a different phenomenon with Cloud Native. Take a minute to share your perspectives on the global phenomenon of Cloud Native. It's a global platform, it's not just IT, it's a global platform, the cloud, and what that brings to the table for end users. >> I think for end users, if we're talking about consumers, it actually is, well what it's doing is allowing businesses to develop applications more quickly, to respond to their market needs more quickly. And end users are seeing that in more responsive applications, more responsive services, improved delivery of tech. >> And the businesses, too, have engineers on the front lines now. >> Absolutely, and there's a lot of work going on here, I think, to basically, we were talking earlier about making technology boring, you know, this Kubernetes level is really an abstraction that most application developers don't really need to know about. And making their experience easier, they can just write their code and it runs. >> So if it's invisible to the application developer, that's the success. >> That's a really helpful thing. They shouldn't have to worry about where their code is running. >> John: That's DevOps. >> Yeah, yeah. >> I think the container in Kubernetes technology or this Cloud Native technology that brings developer the ability to, you know, move fast and give them the agility to react to the business needs very quickly. And also users benefit from that and operators also, you know, can manage their applications much more easily. >> Yeah, when you have that abstraction layer, when you have that infrastructure as code, or even this new abstraction layer which is not just infrastructure, it's services, micro-services, growth has been phenomenal. You're bringing the application developer into an efficiency productivity mode where they're dictating the business model through software of the companies. So it's not just, "Hey build me something "and let's go sell it." They're on the front lines, writing the business logic of businesses and their customers. So you're seeing it's super important for them to have that ability to either double down or abandon quickly. This is what agile is. Now it's going from software to business. This, to me, I think is the highlight for me on this show. You see the dots connecting where the developers are truly in charge of actually being a business impact because they now have more capability. As you guys put this together and do the co-chair, do you and Kelsey, what do you guys do in the room, the secret room, you like, "Well let's do this on the content." I mean, 'cause there's so much to do. Take us through the process. >> So, a little bit of insight into how that whole process works. So we had well over 1,000 submissions, which, you know, there's no, I think there's like 150 slots, something like that. So that's a pretty small percentage that we can actually accept. We had an amazing program committee, I think there were around 60 people who reviewed, every individual reviewer looked at a subset. We didn't ask them to look at all thousand, that would be crazy. They scored them, that gave us a kind of first pass, like a sort of an ability to say, "Well, anything that was below average, "we can only take the top 15%, "so anything that's below average "is not going to make the cut." And then we could start looking at trying to balance, say, for example, there's been a lot of talk about were there too many Istio talks? Well, there were a lot of Istio talks because there were a lot of Istio submissions. And that says to us that the community wants to talk about Istio. >> And then number of stars, that's the number one project on the new list. I mean, Kubeflow and Istio are super hot. >> Yeah, yeah, Kubeflow's another great example, there are lots of submissions around it. We can't take them all but we can use the ratings and the advice from the program committee to try and assemble, you know, the best talks to try and bring different voices in, you know, we want to have subject matter experts and new voices. We want to have the big name companies and start-ups, we wanted to try and get a mix, you know. A diversity of opinion, really. >> And you're a membership organization so you have to balance the membership needs with the content program so, challenging with given the growth. I mean, I can only imagine. >> Yeah, so as program co-chairs, we actually have a really free hand over the content, so it's one of the really, I think, nice things about this conference. You know, sponsors do get to stand on stage and deliver their message, but they don't get to influence the actual program. The program is put together for the community, and by doing things like looking at the number of submissions, using those signals that the community want to talk about, I hope we can carry on giving the attendees that format. >> I would just say from an outsider perspective, I think that's something you want to preserve because if you look at the success of the CNCF, one thing I'm impressed by is they've really allowed a commercial environment to be fostered and enabled. But they didn't compromise the technical. >> Lauren: Yeah. >> And the content to me, content and technical tracks are super important because content, they all work together, right? So as long as there's no meddling, stay in your swim lane, whatever, whatever it is. Content is really important. >> Absolutely, yeah. >> Because that's the learning. >> Yeah, yeah. >> Okay, so what's on the cut list that you wish you could have put back on stage? Or is that too risque? You'll come back to that. >> Yeah. >> China, talk about China. Because obviously, we were super impressed last year when we went to go visit Alibaba just to the order of magnitude to the cultural mindset for their thinking around Cloud Native. And what I was most impressed with was Dr. Wong was talking about artistry. They just don't look at it as just technology, although they are nerdy and geeky like us in Silicon Valley. But they really were thinking about the artistry 'cause the app side of it has kind of a, not just design element to the user perspective. And they're very mobile-centric in China, so they're like, they were like, "This is what we want to do." So they were very advanced in my mind on this. Does that change the program in China vis a vis Seattle and here, is there any stark differences between Shanghai and Copenhagen and Seattle in terms of the program? Is there a certain focus? What's the insight into China? >> I think it's a little early to say 'cause we haven't yet opened the CFP. It'll be opening soon but I'm fully expecting that there will be, you know, some differences. I think the, you know, we're hoping to have speakers, a lot more speakers from China, from Asia, because it's local to them. So, like here, we tried to have a European flavor. You'll see a lot of innovators from Europe, like Spotify and the Financial Times, Monzo Bank. You know, they've all been able to share their stories with us. And I think we're hoping to get the same kind of thing in China, hear local stories as well. >> I mean that's a good call. I think conferences that do the rinse and repeat from North America and just slap it down in different regions aren't as effective as making it localized, in a way. >> Yeah. >> That's super important. >> I know that a lot of China companies, they are pretty invested pretty heavily into Kubernetes and Cloud Native technology and they are very innovative. So I actually joined a project in 2015 and I've been collaborating with a lot of Chinese contributors from China remotely on GitHub. For example, the contributors from Huawei and they've been invested a lot in this. >> And they have some contributors in the core. >> Yeah, so we are expecting to see submissions from those contributors and companies and users. >> Well, that's super exciting. We look forward to being there, and it should be excellent. We always have a fun time. The question that I want to ask you guys now, just to switch gears is, for the people watching who couldn't make it or might watch it on YouTube on Demand who didn't make the trip. What surprised you here? What's new, I'm asking, you have a view as the co-chair, you've seen it. But was there anything that surprised you, or did it go right? Nothing goes perfect. I mean, it's like my wedding, everything happens, didn't happen the way you planned it. There's always a surprise. Any wild cards, any x-factors, anything that stands out to you guys? >> So what I see from, so I attend, I think around five KubeCons. So from the first one it's only 550 people, only the small community, the contributors from Google and Red Hat and CoreOS and growing from now. We are growing from the inner circle to the outside circle, from the just contributors to also the users of it, like and also the ecosystem. Everyone that's building the technology around Cloud Native, and I see that growth and it's very surprising to me. We have a keynote yesterday from CERN and everyone is talking about their keynote, like they have I think 200 clusters, and that's amazing. And they said because of Kubernetes they can just focus on physics. >> Yeah, and that's a testimonial right there. >> Yeah. >> That was really good stories to hear, and I think maybe one of the things that surprises me, it sort of continues to surprise me is how collaborative, it's something about this kind of organization, this conference, this whole kind of movement, if you like. Where companies are coming in and sharing their learnings, and we've seen that, we've seen that a lot through the keynotes. And I think we see it on the conference floor, we see it in the hallway chat. And I think we see it in the way that the different SIGs and working groups and projects are all, kind of, collaborating on problem solving. And that's really exciting. >> That's why I was saying earlier in the beginning that there's a celebration amongst ourselves and the community. But also a realization that this is just the beginning, it's not a, it's kind of like when you get venture funding if you're a start-up. That's really when it begins, you don't celebrate, but you take a little bit of a pause. Now my personal take only to all of the hundreds of events we do a year is that I that think this community here has fought the hard DevOps battle. If you go back to 2008 timeframe, and '08, '09, '10, '11, '12, those years were, those were hyper scale years. Look at Google, Facebook, all the original DevOps engineers, they were eating glass and spitting nails. It was hard work. And it was really build your own, a lot of engineering, not just software development. So I think this, kind of like, camaraderie amongst the DevOps community saying, "Look, this is a really big "step up function with Kubernetes." Everyone's had some scar tissue. >> Yeah, I think a lot of people have learned from previous, you know, even other open source projects that they've worked on. And you see some of the amazing work that goes into the kind of, like, community governance side. The things that, you know, Paris Pittman does around contributor experience. It's so good to see people who are experts in helping developers engage, helping engineers engage, really getting to play that role. >> There's a lot of common experiences for people who have never met each other because there's people who have seen the hard work pay with scale and leverage and benefits. They see it, this is amazing. We had Sheryl from Google on saying, "When I left Google and I went out into the real world, "I was like, oh my God, "they don't actually use Borg," like, what? "What do they, how do they actually write software?" I mean, so she's a fish out of water and that, it's like, so again I think there's a lot of commonality, and it's a super great opportunity and a great community and you guys have done a great job, CNCF. And we hope to nurture that, the principles, and looking forward to China. Thanks for coming on theCUBE, we appreciate it. >> Yeah. >> Okay we're here at CNCF's KubeCon 2018, I'm John Furrier, more live coverage. Stay with us, day two of two days of CUBE coverage. Go to thecube.net, siliconangle.com for all the coverage. We'll be back, stay with us after this short break.

>>From the Cube Studios in Palo Alto in Boston, bringing you data-driven insights from the cube and E T R. This is breaking analysis with Dave Valante. >>Making technology predictions in 2022 was tricky business, especially if you were projecting the performance of markets or identifying I P O prospects and making binary forecast on data AI and the macro spending climate and other related topics in enterprise tech 2022, of course was characterized by a seesaw economy where central banks were restructuring their balance sheets. The war on Ukraine fueled inflation supply chains were a mess. And the unintended consequences of of forced march to digital and the acceleration still being sorted out. Hello and welcome to this week's weekly on Cube Insights powered by E T R. In this breaking analysis, we continue our annual tradition of transparently grading last year's enterprise tech predictions. And you may or may not agree with our self grading system, but look, we're gonna give you the data and you can draw your own conclusions and tell you what, tell us what you think. >>All right, let's get right to it. So our first prediction was tech spending increases by 8% in 2022. And as we exited 2021 CIOs, they were optimistic about their digital transformation plans. You know, they rushed to make changes to their business and were eager to sharpen their focus and continue to iterate on their digital business models and plug the holes that they, the, in the learnings that they had. And so we predicted that 8% rise in enterprise tech spending, which looked pretty good until Ukraine and the Fed decided that, you know, had to rush and make up for lost time. We kind of nailed the momentum in the energy sector, but we can't give ourselves too much credit for that layup. And as of October, Gartner had it spending growing at just over 5%. I think it was 5.1%. So we're gonna take a C plus on this one and, and move on. >>Our next prediction was basically kind of a slow ground ball. The second base, if I have to be honest, but we felt it was important to highlight that security would remain front and center as the number one priority for organizations in 2022. As is our tradition, you know, we try to up the degree of difficulty by specifically identifying companies that are gonna benefit from these trends. So we highlighted some possible I P O candidates, which of course didn't pan out. S NQ was on our radar. The company had just had to do another raise and they recently took a valuation hit and it was a down round. They raised 196 million. So good chunk of cash, but, but not the i p O that we had predicted Aqua Securities focus on containers and cloud native. That was a trendy call and we thought maybe an M SS P or multiple managed security service providers like Arctic Wolf would I p o, but no way that was happening in the crummy market. >>Nonetheless, we think these types of companies, they're still faring well as the talent shortage in security remains really acute, particularly in the sort of mid-size and small businesses that often don't have a sock Lacework laid off 20% of its workforce in 2022. And CO C e o Dave Hatfield left the company. So that I p o didn't, didn't happen. It was probably too early for Lacework. Anyway, meanwhile you got Netscope, which we've cited as strong in the E T R data as particularly in the emerging technology survey. And then, you know, I lumia holding its own, you know, we never liked that 7 billion price tag that Okta paid for auth zero, but we loved the TAM expansion strategy to target developers beyond sort of Okta's enterprise strength. But we gotta take some points off of the failure thus far of, of Okta to really nail the integration and the go to market model with azero and build, you know, bring that into the, the, the core Okta. >>So the focus on endpoint security that was a winner in 2022 is CrowdStrike led that charge with others holding their own, not the least of which was Palo Alto Networks as it continued to expand beyond its core network security and firewall business, you know, through acquisition. So overall we're gonna give ourselves an A minus for this relatively easy call, but again, we had some specifics associated with it to make it a little tougher. And of course we're watching ve very closely this this coming year in 2023. The vendor consolidation trend. You know, according to a recent Palo Alto network survey with 1300 SecOps pros on average organizations have more than 30 tools to manage security tools. So this is a logical way to optimize cost consolidating vendors and consolidating redundant vendors. The E T R data shows that's clearly a trend that's on the upswing. >>Now moving on, a big theme of 2020 and 2021 of course was remote work and hybrid work and new ways to work and return to work. So we predicted in 2022 that hybrid work models would become the dominant protocol, which clearly is the case. We predicted that about 33% of the workforce would come back to the office in 2022 in September. The E T R data showed that figure was at 29%, but organizations expected that 32% would be in the office, you know, pretty much full-time by year end. That hasn't quite happened, but we were pretty close with the projection, so we're gonna take an A minus on this one. Now, supply chain disruption was another big theme that we felt would carry through 2022. And sure that sounds like another easy one, but as is our tradition, again we try to put some binary metrics around our predictions to put some meat in the bone, so to speak, and and allow us than you to say, okay, did it come true or not? >>So we had some data that we presented last year and supply chain issues impacting hardware spend. We said at the time, you can see this on the left hand side of this chart, the PC laptop demand would remain above pre covid levels, which would reverse a decade of year on year declines, which I think started in around 2011, 2012. Now, while demand is down this year pretty substantially relative to 2021, I D C has worldwide unit shipments for PCs at just over 300 million for 22. If you go back to 2019 and you're looking at around let's say 260 million units shipped globally, you know, roughly, so, you know, pretty good call there. Definitely much higher than pre covid levels. But so what you might be asking why the B, well, we projected that 30% of customers would replace security appliances with cloud-based services and that more than a third would replace their internal data center server and storage hardware with cloud services like 30 and 40% respectively. >>And we don't have explicit survey data on exactly these metrics, but anecdotally we see this happening in earnest. And we do have some data that we're showing here on cloud adoption from ET R'S October survey where the midpoint of workloads running in the cloud is around 34% and forecast, as you can see, to grow steadily over the next three years. So this, well look, this is not, we understand it's not a one-to-one correlation with our prediction, but it's a pretty good bet that we were right, but we gotta take some points off, we think for the lack of unequivocal proof. Cause again, we always strive to make our predictions in ways that can be measured as accurate or not. Is it binary? Did it happen, did it not? Kind of like an O K R and you know, we strive to provide data as proof and in this case it's a bit fuzzy. >>We have to admit that although we're pretty comfortable that the prediction was accurate. And look, when you make an hard forecast, sometimes you gotta pay the price. All right, next, we said in 2022 that the big four cloud players would generate 167 billion in IS and PaaS revenue combining for 38% market growth. And our current forecasts are shown here with a comparison to our January, 2022 figures. So coming into this year now where we are today, so currently we expect 162 billion in total revenue and a 33% growth rate. Still very healthy, but not on our mark. So we think a w s is gonna miss our predictions by about a billion dollars, not, you know, not bad for an 80 billion company. So they're not gonna hit that expectation though of getting really close to a hundred billion run rate. We thought they'd exit the year, you know, closer to, you know, 25 billion a quarter and we don't think they're gonna get there. >>Look, we pretty much nailed Azure even though our prediction W was was correct about g Google Cloud platform surpassing Alibaba, Alibaba, we way overestimated the performance of both of those companies. So we're gonna give ourselves a C plus here and we think, yeah, you might think it's a little bit harsh, we could argue for a B minus to the professor, but the misses on GCP and Alibaba we think warrant a a self penalty on this one. All right, let's move on to our prediction about Supercloud. We said it becomes a thing in 2022 and we think by many accounts it has, despite the naysayers, we're seeing clear evidence that the concept of a layer of value add that sits above and across clouds is taking shape. And on this slide we showed just some of the pickup in the industry. I mean one of the most interesting is CloudFlare, the biggest supercloud antagonist. >>Charles Fitzgerald even predicted that no vendor would ever use the term in their marketing. And that would be proof if that happened that Supercloud was a thing and he said it would never happen. Well CloudFlare has, and they launched their version of Supercloud at their developer week. Chris Miller of the register put out a Supercloud block diagram, something else that Charles Fitzgerald was, it was was pushing us for, which is rightly so, it was a good call on his part. And Chris Miller actually came up with one that's pretty good at David Linthicum also has produced a a a A block diagram, kind of similar, David uses the term metacloud and he uses the term supercloud kind of interchangeably to describe that trend. And so we we're aligned on that front. Brian Gracely has covered the concept on the popular cloud podcast. Berkeley launched the Sky computing initiative. >>You read through that white paper and many of the concepts highlighted in the Supercloud 3.0 community developed definition align with that. Walmart launched a platform with many of the supercloud salient attributes. So did Goldman Sachs, so did Capital One, so did nasdaq. So you know, sorry you can hate the term, but very clearly the evidence is gathering for the super cloud storm. We're gonna take an a plus on this one. Sorry, haters. Alright, let's talk about data mesh in our 21 predictions posts. We said that in the 2020s, 75% of large organizations are gonna re-architect their big data platforms. So kind of a decade long prediction. We don't like to do that always, but sometimes it's warranted. And because it was a longer term prediction, we, at the time in, in coming into 22 when we were evaluating our 21 predictions, we took a grade of incomplete because the sort of decade long or majority of the decade better part of the decade prediction. >>So last year, earlier this year, we said our number seven prediction was data mesh gains momentum in 22. But it's largely confined and narrow data problems with limited scope as you can see here with some of the key bullets. So there's a lot of discussion in the data community about data mesh and while there are an increasing number of examples, JP Morgan Chase, Intuit, H S P C, HelloFresh, and others that are completely rearchitecting parts of their data platform completely rearchitecting entire data platforms is non-trivial. There are organizational challenges, there're data, data ownership, debates, technical considerations, and in particular two of the four fundamental data mesh principles that the, the need for a self-service infrastructure and federated computational governance are challenging. Look, democratizing data and facilitating data sharing creates conflicts with regulatory requirements around data privacy. As such many organizations are being really selective with their data mesh implementations and hence our prediction of narrowing the scope of data mesh initiatives. >>I think that was right on J P M C is a good example of this, where you got a single group within a, within a division narrowly implementing the data mesh architecture. They're using a w s, they're using data lakes, they're using Amazon Glue, creating a catalog and a variety of other techniques to meet their objectives. They kind of automating data quality and it was pretty well thought out and interesting approach and I think it's gonna be made easier by some of the announcements that Amazon made at the recent, you know, reinvent, particularly trying to eliminate ET t l, better connections between Aurora and Redshift and, and, and better data sharing the data clean room. So a lot of that is gonna help. Of course, snowflake has been on this for a while now. Many other companies are facing, you know, limitations as we said here and this slide with their Hadoop data platforms. They need to do new, some new thinking around that to scale. HelloFresh is a really good example of this. Look, the bottom line is that organizations want to get more value from data and having a centralized, highly specialized teams that own the data problem, it's been a barrier and a blocker to success. The data mesh starts with organizational considerations as described in great detail by Ash Nair of Warner Brothers. So take a listen to this clip. >>Yeah, so when people think of Warner Brothers, you always think of like the movie studio, but we're more than that, right? I mean, you think of H B O, you think of t n t, you think of C N N. We have 30 plus brands in our portfolio and each have their own needs. So the, the idea of a data mesh really helps us because what we can do is we can federate access across the company so that, you know, CNN can work at their own pace. You know, when there's election season, they can ingest their own data and they don't have to, you know, bump up against, as an example, HBO if Game of Thrones is going on. >>So it's often the case that data mesh is in the eyes of the implementer. And while a company's implementation may not strictly adhere to Jamma Dani's vision of data mesh, and that's okay, the goal is to use data more effectively. And despite Gartner's attempts to deposition data mesh in favor of the somewhat confusing or frankly far more confusing data fabric concept that they stole from NetApp data mesh is taking hold in organizations globally today. So we're gonna take a B on this one. The prediction is shaping up the way we envision, but as we previously reported, it's gonna take some time. The better part of a decade in our view, new standards have to emerge to make this vision become reality and they'll come in the form of both open and de facto approaches. Okay, our eighth prediction last year focused on the face off between Snowflake and Databricks. >>And we realized this popular topic, and maybe one that's getting a little overplayed, but these are two companies that initially, you know, looked like they were shaping up as partners and they, by the way, they are still partnering in the field. But you go back a couple years ago, the idea of using an AW w s infrastructure, Databricks machine intelligence and applying that on top of Snowflake as a facile data warehouse, still very viable. But both of these companies, they have much larger ambitions. They got big total available markets to chase and large valuations that they have to justify. So what's happening is, as we've previously reported, each of these companies is moving toward the other firm's core domain and they're building out an ecosystem that'll be critical for their future. So as part of that effort, we said each is gonna become aggressive investors and maybe start doing some m and a and they have in various companies. >>And on this chart that we produced last year, we studied some of the companies that were targets and we've added some recent investments of both Snowflake and Databricks. As you can see, they've both, for example, invested in elation snowflake's, put money into Lacework, the Secur security firm, ThoughtSpot, which is trying to democratize data with ai. Collibra is a governance platform and you can see Databricks investments in data transformation with D B T labs, Matillion doing simplified business intelligence hunters. So that's, you know, they're security investment and so forth. So other than our thought that we'd see Databricks I p o last year, this prediction been pretty spot on. So we'll give ourselves an A on that one. Now observability has been a hot topic and we've been covering it for a while with our friends at E T R, particularly Eric Bradley. Our number nine prediction last year was basically that if you're not cloud native and observability, you are gonna be in big trouble. >>So everything guys gotta go cloud native. And that's clearly been the case. Splunk, the big player in the space has been transitioning to the cloud, hasn't always been pretty, as we reported, Datadog real momentum, the elk stack, that's open source model. You got new entrants that we've cited before, like observe, honeycomb, chaos search and others that we've, we've reported on, they're all born in the cloud. So we're gonna take another a on this one, admittedly, yeah, it's a re reasonably easy call, but you gotta have a few of those in the mix. Okay, our last prediction, our number 10 was around events. Something the cube knows a little bit about. We said that a new category of events would emerge as hybrid and that for the most part is happened. So that's gonna be the mainstay is what we said. That pure play virtual events are gonna give way to hi hybrid. >>And the narrative is that virtual only events are, you know, they're good for quick hits, but lousy replacements for in-person events. And you know that said, organizations of all shapes and sizes, they learn how to create better virtual content and support remote audiences during the pandemic. So when we set at pure play is gonna give way to hybrid, we said we, we i we implied or specific or specified that the physical event that v i p experience is going defined. That overall experience and those v i p events would create a little fomo, fear of, of missing out in a virtual component would overlay that serves an audience 10 x the size of the physical. We saw that really two really good examples. Red Hat Summit in Boston, small event, couple thousand people served tens of thousands, you know, online. Second was Google Cloud next v i p event in, in New York City. >>Everything else was, was, was, was virtual. You know, even examples of our prediction of metaverse like immersion have popped up and, and and, and you know, other companies are doing roadshow as we predicted like a lot of companies are doing it. You're seeing that as a major trend where organizations are going with their sales teams out into the regions and doing a little belly to belly action as opposed to the big giant event. That's a definitely a, a trend that we're seeing. So in reviewing this prediction, the grade we gave ourselves is, you know, maybe a bit unfair, it should be, you could argue for a higher grade, but the, but the organization still haven't figured it out. They have hybrid experiences but they generally do a really poor job of leveraging the afterglow and of event of an event. It still tends to be one and done, let's move on to the next event or the next city. >>Let the sales team pick up the pieces if they were paying attention. So because of that, we're only taking a B plus on this one. Okay, so that's the review of last year's predictions. You know, overall if you average out our grade on the 10 predictions that come out to a b plus, I dunno why we can't seem to get that elusive a, but we're gonna keep trying our friends at E T R and we are starting to look at the data for 2023 from the surveys and all the work that we've done on the cube and our, our analysis and we're gonna put together our predictions. We've had literally hundreds of inbounds from PR pros pitching us. We've got this huge thick folder that we've started to review with our yellow highlighter. And our plan is to review it this month, take a look at all the data, get some ideas from the inbounds and then the e t R of January surveys in the field. >>It's probably got a little over a thousand responses right now. You know, they'll get up to, you know, 1400 or so. And once we've digested all that, we're gonna go back and publish our predictions for 2023 sometime in January. So stay tuned for that. All right, we're gonna leave it there for today. You wanna thank Alex Myerson who's on production and he manages the podcast, Ken Schiffman as well out of our, our Boston studio. I gotta really heartfelt thank you to Kristen Martin and Cheryl Knight and their team. They helped get the word out on social and in our newsletters. Rob Ho is our editor in chief over at Silicon Angle who does some great editing for us. Thank you all. Remember all these podcasts are available or all these episodes are available is podcasts. Wherever you listen, just all you do Search Breaking analysis podcast, really getting some great traction there. Appreciate you guys subscribing. I published each week on wikibon.com, silicon angle.com or you can email me directly at david dot valante silicon angle.com or dm me Dante, or you can comment on my LinkedIn post. And please check out ETR AI for the very best survey data in the enterprise tech business. Some awesome stuff in there. This is Dante for the Cube Insights powered by etr. Thanks for watching and we'll see you next time on breaking analysis.

>>Good afternoon and welcome back to Detroit, Lisa Martin here with John Furrier. We are live day two of our coverage of Coan Cloud Native Con North America. John, we've had great conversations. Yeah. All day yesterday. Half a day today. So far we're talking all things, Well, not all things Kubernetes so much more than that. We also have to talk about storage and data management solutions for Kubernetes projects, cuz that's obviously critical. >>Yeah, I mean the big trend here is Kubernetes going mainstream has been for a while. The adopt is crossing over, it's crossing the CADs and with that you're seeing security concerns. You're seeing things being gaps being filled. But enterprise grade is really the, the, the story. It's going enterprise, that's managed services, that's professional service, that's basically making things work at scale. This next segment hits that part and we are gonna talk about it in grade length >>With one of our alumni. Moral morale to Molly is back DP and GM of Port Work's Peer Storage. Great to have you back really? >>Yeah, absolutely. Delightful >>To be here. So I was looking on the website, number one in Kubernetes storage. Three years in a row. Yep. Awesome. What's Coworks doing here at KU Con? >>Well, I'll tell you, we, our engineering crew has been so productive and hard at work that I almost can't decide what to kind of tell you. But I thought what, what, what I thought I would do is kind of tell you that we are in forefront of two major trends in the world of Kubernetes. Right? And the, the two trends that I see are one is as a service, so is trend number one. So it's not software eating the world anymore. That's, that's old, old, old news. It's as a service unifying the world. The world wants easy, We all are, you know, subscribers to things like Netflix. We've been using Salesforce or other HR functions. Everything is as a service. And in the world of Kubernetes, it's a sign of that maturity that John was talking about as a platform that now as a service is the big trend. >>And so headline number one, if you will, is that Port Works is leading in the data management world for Kubernetes by providing, we're going all in on easy on as a service. So everything we do, we are satisfying it, right? So if you think, if you think about, if you think about this, that, that there are really, most of the people who are consuming Kubernetes are people who are building platforms for their dev users. And dev users want self service. That's one of the advantages of, of, of Kubernetes. And the more it is service size and made as a service, the more ready to consume it is. And so we are announcing at the show that we have, you know, the basic Kubernetes data management as a service, ha d r as a service. We have backup as a service and we have database as a service. So these are the three major components of data. And all of those are being made available as a service. And in fact, we're offering and announcing at the show our backup as a service freemium version where you can get free forever a terabyte of, of, you know, stuff to do for Kubernetes for forever. >>Congratulations on the announcement. Totally. In line with what the market wants. Developers want Selfer, they wanna also want simplicity by the way they'll leave if they don't like the service. Correct. So that you, you know that before we get into some more specifics, I want Yeah. Ask you on the industry and some of the point solutions you have, what, it's been two years since the acquisition with Pure Storage. Can you just give an update on how it's gone? Obviously as a service, you guys are hitting all your Marks, developers love it. Storage are big part of the game right now as well as these environments. Yeah. What's the update post acquisition two years. You had a great offering Stay right In >>Point Works. Yeah. So look, John, you're, you're, you're a veteran of the industry and have seen lots of acquisitions, right? And I've been acquired twice before myself. So, you know, there's, there's always best practices and poor practices in terms of acquisitions and I'm, you know, really delighted to say I think this, this acquisition has had some of the best practices. Let me just name a couple of them, right? One of them is just cultural fit, right? Cultural fit is great. Entrepreneurs, anybody, it's not just entrepreneurs. Everybody loves to work in a place they enjoy working with, with people that they, you know, thrive when they, when they interact with. And so the cultural fit with, with Pure is fantastic. The other one is the strategic intent that Pure had when they acquired us is still true. And so that goes a long way, you know, in terms of an investment profile, in terms of the ability to kind of leverage assets within the company. So Pure had kind of disrupted the world of storage using Flash and they wanted to disrupt higher up the stack using Kubernetes. And that's kind of been our role inside their strategy. And it's, it's still true. >>So culture, strategic intent. Yeah. Product market fit as well. You were, you weren't just an asset for customers or acquisition and then let the founders go through their next thing. You are part of their growth play. >>Absolutely. Right. The, the beauty of, of the kind of product market fit is, let's talk about the market is we have been always focused on the global two k and that is at the heart of, you know, purest 10,000 strong customer base, right? They have very strong presence in the, in the global two k. And we, we allow them to kind of go to those same folks with, with the offering. >>So satisfying everything that you do. What's for me as a business, whether I'm a financial services organization, I'm a hospital, I'm a retailer, what's in it for me >>As a customer? Yeah. So the, the what's in it for, for me is two things. It's speed and ease of use, which in a way are related. But, but, but you know, one is when something is provided as a service, it's much more consumable. It's instantly ready. It's like instant oatmeal, right? You just get it just ad hot water and it's there. Yep. So the world of of it has moved from owning large data centers, right? That used to be like 25 years ago and running those data centers better than everybody else to move to let me just consume a data center in the form of a cloud, right? So satisfying the cloud part of the data center. Now people are saying, well I expect that for software and services and I don't want it just from the public cloud, I want it from my own IT department. >>This is old news. And so the, the, the big news here is how fast Kubernetes has kind of moved everything. You know, you take a lot of these changes, Kubernetes is a poster child for things happening faster than the last wave. And in the last couple of years I would say that as a service model has really kind of thrived in the world of Kubernetes. And developers want to be able to get it fast. And the second thing is they want to be able to operate it fast. Self-service is the other benefit. Yeah. So speed and self-service are both benefits of, of >>This. Yeah. And, and the thing that's come up clearly in the cube, this is gonna be part of the headlines we'll probably end up getting a lot of highlights from telling my team to make a note of this, is that developers are gonna be be the, the business if you, if you take digital transformation to its conclusion, they're not a department that serves the business, they are the business that means Exactly. They have to be more productive. So developer productivity has been the top story. Yes. Security as a serves all these things. These are, these are examples to make developers more productive. But one of the things that came up and I wanna get your reaction to is, is that when you have disruption and, and the storage vision, you know what disruption it means. Cuz there's been a whole discussion around disruptive operations. When storage goes down, you have back m dr and failover. If there's a disruption that changes the nature of invisible infrastructure, developers want invisible infrastructure. That's the future steady state. So if there's a disruption in storage >>Yeah. It >>Can't affect the productivity and the tool chains and the workflows of developers. Yep. Right? So how do you guys look at that? Cuz you're a critical component. Storage is a service is a huge thing. Yeah. Storage has to, has to work seamlessly. And let's keep the developers out of the weeds. >>John. I think what, what what you put your finger on is another huge trend in the world of Kubernetes where at Cube Con, after all, which is really where, where all the leading practitioners both come and the leading vendors are. So here's the second trend that we are leading and, and actually I think it's happening not just with us, but with other, for folks in the industry. And that is, you know, the world of DevOps. Like DevOps has been such a catchphrase for all, all of us in the industry last five years. And it's been both a combination of cultural change as well as technology change. Here's what the latest is on the, in the world of DevOps. DevOps is now crystallized. It's not some kind of mysterious art form that you read about how people are practicing. DevOps is, it's broken into two, two things now. >>There is the platform part. So DevOps is now a bunch of platforms. And the other part of DevOps is a bunch of practices. So a little bit on both these, the platforms in the world of es there's only three platforms, right? There's the orchestration platforms, the, you know, eks, the open ships of the world and so on. There are the data management platforms, pro people like Port Works. And the third is security platforms, right? You know, Palo Alto Networks, others Aqua or all in this. So these are the three platforms and there are platform engineering teams now that many of our largest customers, some of the largest banks, the largest service providers, they're all operating as a ES platform engineering team. And then now developers, to your point, developers are in the practice of being able to use these platforms to launch new services. So the, the actual IT ops, the ops are run by developers now and they can do it on these platforms. And the platform engineering team provide that as an ease of use and they're there to troubleshoot when problems happen. So the idea of DevOps as a ops practice and a platform is the newest thing. E and, and ports and pure storage leading in the world of data management platforms >>There. Talk about a customer example that you think really articulates the value that Port Works and Pure Storage delivers from a data management perspective. >>Yeah, so there's so many examples. One of the, one of the longest running examples we have is a very, very large service provider that, you know, you all know and probably use, and they have been using us in the cable kinda set box or cable box business. They get streams of data from, from cable boxes all over the world. They collected all in a centralized large kind of thing and run elastic search and analytics on it. Now what they have done is they couldn't keep up with this at the scale and the depth, right? The speed of, of activity and the distributed nature of the activity. The only way to solve this was to use something like Kubernetes manage with Spark coming, bringing all the data in to deep, deep, deep silos of storage, which are all running not even on a sand, but on kind of, you know, very deep terabytes and terabytes of, of storage. So all of this is orchestrated with the Heco coworks and there's a platform engineering team. We are building that platform for them with some of these other components that allows them to kind of do analytics and, and make some changes in real time. Huge kind of setup for, for >>That. Yeah. Well, you guys have the right architecture. I love the vision. I love what you guys are doing. I think this is right in line with Pures. They've always been disruptors. I remember when we first interviewed the CEO when they started Yep. They, they stayed on path. They didn't waiver. EMC was the big player. They ended up taking their lunch and dinner as well and they beat 'em in the marketplace. But now you got this traction here. So I have to ask you, how's the business, what's the results look like? Either GM cloud native business unit of a storage company that's transformed and transforming? >>Yeah, you know, it's interesting, we just hit the two year anniversary, right John? And so what we did was just kind of like step back and hey, you know, we're running so hard, you just take a step back. And we've tripled the business in the two years since the acquisition, the two years before and, and we were growing through proven. So, you know, that that's quite a fe and we've tripled the number of people, the amount of engineering investments we have, the number of go to market investments have, have been, have been awesome. So business is going really well though, I will say. But I think, you know, we have, we can't be, we we're watching the market closely. You know, as a former ceo, I, you have to kind of learn to read the tea leaves when you invest. And I think, you know, what I would say is we're proceeding with caution in the next two quarters. I view business transformation as not a cancelable activity. So that's the, that's the good news, right? Our customers are large, it's, >>It's >>Right. All they're gonna do is say, Hey, they're gonna put their hand, their hand was always going right on the dial. Now they're kind of putting their hand on the dial going, hey, where, what is happening? But my, my own sense of this is that people will continue to invest through it. The question is at what level? And I also think that this is a six month kind of watch, the watch where, where we put the dial. So Q4 and q1 I think are kind of, you know, we have our, our watch kind of watch the market sign. But I have the highest confidence. What >>Does your gut tell you? You're an entrepreneur, >>Which my, my gut says that we'll go through a little bit of a cautious investment period in the next six months. And after that I think we're gonna be back in, back full, full in the crazy growth that we've always been. We're gonna grow by the way, in the next think >>It's core style. I think I'm, I'm more bullish. I think there's gonna be some, you know, weeding out of some overinvestment pre C or pre bubble. But I think tech's gonna continue to grow. I don't see >>It's stopping. Yeah. And, and the investment is gonna be on these core platforms. See, back to the platform story, it's gonna be in these core platforms and on unifying everything, let's consume it better rather than let's go kind of experiment with a whole bunch of things all over the map, right? So you'll see less experimentation and more kind of, let's harvest some of the investments we've made in the last couple >>Of years and actually be able to, to enable companies in any industry to truly be data companies. Because absolutely. We talked about as a service, we all have these expectations that any service we want, we can get it. Yes. There's no delay because patients has gone Yeah. From the pandemic. >>So it is kind of, you know, tightening up the screws on what they've built. They, you know, adding some polish to it, adding some more capability, like I said, a a a, a combination of harvesting and new investing. It's a combination I think is what we're gonna see. >>Yeah. What are some of the things that you're looking forward to? You talked about some of the, the growth things in the investment, but as we round out Q4 and head into a new year, what are you excited about? >>Yeah, so you know, I mentioned our, as a service kind of platform, the global two K for us has been a set of customers who we co-create stuff with. And so one of the other set of things that we are very excited about and announcing is because we're deployed at scale, we're, we're, we have upgraded our backend. So we have now the ability to go to million IOPS and more and, and for, for the right backends. And so Kubernetes is a add-on which will not slow down your, your core base infrastructure. Second thing that that we, we have is added a bunch of capability in the disaster recovery business continuity front, you know, we always had like metro kind of distance dr. We had long distance dr. We've added a near sync Dr. So now we can provide disaster recovery and business continuity for metro distances across continents and across the planet. Right? That's kind of a major change that we've done. The third thing is we've added the capability for file block and Object. So now by adding object, we're really a complete solution. So it is really that maturity of the business Yeah. That you start seeing as enterprises move to embracing a platform approach, deploying it much more widely. You talked about the early majority. Yeah. Right. And so what they require is more enterprise class capability and those are all the things that we've been adding and we're really looking forward >>To it. Well it sounds like tremendous evolution and maturation of Port Works in the two years since it's been with Pure Storage. You talked about the cultural alignment, great stuff that you're achieving. Congratulations on that. Yeah. Great stuff >>Ahead and having fun. Let's not forget that, that's too life's too short to do. It is right. >>You're right. Thank you. We will definitely, as always on the cube, keep our eyes on this space. Mur. Meley, it's been great to have you back on the program. Thank you for joining, John. >>Thank you so much. It's pleasure. Our, >>For our guests and John Furrier, Lisa Martin here live in Detroit with the cube about Coan Cloud Native Con at 22. We'll be back after a short break.

>>Good afternoon and welcome back to Detroit, Lisa Martin here with John Furrier. We are live day two of our coverage of Coan Cloud Native, Con North America. John, we've had great conversations. Yeah. All day yesterday. Half a day today. So far we're talking all things, Well, not all things Kubernetes so much more than that. We also have to talk about storage and data management solutions for Kubernetes projects, cuz that's obviously critical. >>Yeah, I mean the big trend here is Kubernetes going mainstream has been for a while. The adopt is crossing over, it's crossing the CADs and with that you're seeing security concerns. You're seeing things being gaps being filled. But enterprise grade is really the, the, the story. It's going enterprise, that's managed services, that's professional service, that's basically making things work at scale. This next segment hits that, that part, and we're gonna talk about it in grade length >>With one of our alumni morale to Molly is back VP and GM of Port Work's peer Storage. Great to have you back really? >>Yeah, absolutely. Delightful to >>Be here. So I was looking on the website, number one in Kubernetes storage. Three years in a row. Yep. Awesome. What's Coworks doing here at KU Con? >>Well, I'll tell you, we, our engineering crew has been so productive and hard at work that I almost can't decide what to kind of tell you. But I thought what, what, what I thought I would do is kind of tell you that we are in forefront of two major trends in the world of es. Right? And the, the two trends that I see are one is as a service, so is trend number one. So it's not software eating the world anymore. That's, that's old, old, old news. It's as a service, unifying the world. The world wants easy, We all are, you know, subscribers to things like Netflix. We've been using Salesforce or other HR functions. Everything is as a service. And in the world of Kubernetes, it's a sign of that maturity that John was talking about as a platform that now as a service is the big trend. >>And so headline number one, if you will, is that Port Works is leading in the data management world for the Kubernetes by providing, we're going all in on easy on as a service. So everything we do, we are satisfying it, right? So if you think, if you think about, if you think about this, that, that there are really, most of the people who are consuming Kubernetes are people who are building platforms for their dev users and their users want self service. That's one of the advantages of, of, of Kubernetes. And the more it is service size and made as a service, the more ready to consume it is. And so we are announcing at the show that we have, you know, the basic Kubernetes data management as a service, ha d r as a service. We have backup as a service and we have database as a service. So these are the three major components of data. And all of those are being made available as a service. And in fact, we're offering and announcing at the show our backup as a service freemium version where you can get free forever a terabyte of, of, you know, stuff to do for Kubernetes for forever. >>Congratulations on the announcement. Totally. In line with what the market wants. Developers want self serve, they wanna also want simplicity by the way they'll leave if they don't like the service. Correct. So that you, you know, that before we get into some more specifics, I want to Yeah. Ask you on the industry and some of the point solutions you have, what, it's been two years since the acquisition with Pure Storage. Can you just give an update on how it's gone? Obviously as a service, you guys are hitting all your Marks, developers love it. Storage a big part of the game right now as well as these environments. Yeah. What's the update post acquisition two years, You had a great offering Stay >>Right In Point Works. Yeah. So look, John, you're, you're, you're a veteran of the industry and have seen lots of acquisitions, right? And I've been acquired twice before myself. So, you know, there's, there's always best practices and poor practices in terms of acquisitions and I'm, you know, really delighted to say I think this, this acquisition has had some of the best practices. Let me just name a couple of them, right? One of them is just cultural fit, right? Cultural fit is great. Entrepreneurs, anybody, it's not just entrepreneurs. Everybody loves to work in a place they enjoy working with, with people that they, you know, thrive when they, when they interact with. And so the cultural fit with, with Pure is fantastic. The other one is the strategic intent that Pure had when they acquired us is still true. And so that goes a long way, you know, in terms of an investment profile, in terms of the ability to kind of leverage assets within the company. So Pure had kind of disrupted the world of storage using Flash and they wanted to disrupt higher up the stack using Kubernetes. And that's kind of been our role inside their strategy. And it's, it's still true. >>So culture, strategic intent. Yeah. Product market fit as well. You were, you weren't just an asset for customers or acquisition and then let the founders go through their next thing. You are part of their growth play. >>Absolutely. Right. The, the beauty of, of the kind of product market fit is, let's talk about the market is we have been always focused on the global two k and that is at the heart of, you know, purest 10,000 strong customer base, right? They have very strong presence in the, in the global two k. And we, we allow them to kind of go to those same folks with, with the offering. >>So satisfying everything that you do. What's for me as a business, whether I'm a financial services organization, I'm a hospital, I'm a retailer, what's in it for me >>As a customer? Yeah. So the, the what's in it for, for me is two things. It's speed and ease of use, which in a way are related. But, but, but you know, one is when something is provided as a service, it's much more consumable. It's instantly ready. It's like instant oatmeal, right? You just get it just adho water and it's there. Yep. So the world of of IT has moved from owning large data centers, right? That used to be like 25 years ago and running those data centers better than everybody else to move to let me just consume a data center in the form of a cloud, right? So satisfying the cloud part of the data center. Now people are saying, well I expect that for software and services and I don't want it just from the public cloud, I want it from my own IT department. >>This is old news. And so the, the, the big news here is how fast Kubernetes has kind of moved everything. You know, you take a lot of these changes, Kubernetes is a poster child for things happening faster than the last wave. And in the last couple of years I would say that as a service model has really kind of thrived in the world of Kubernetes. And developers want to be able to get it fast. And the second thing is they wanna be able to operate it fast. Self-service is the other benefit. Yeah. So speed and self-service are both benefits of, of >>This. Yeah. And, and the thing that's come up clearly in the cube, and this is gonna be part of the headlines, we'll probably end up getting a lot of highlights from telling my team to make a note of this, is that developers are gonna be be the business if you, if you take digital transformation to its conclusion, they're not a department that serves the business, they are the business that means Exactly. They have to be more productive. So developer productivity has been the top story. Yes. Security as a services, all these things. These are, these are examples to make developers more productive. But one of the things that came up and I wanna get your reaction to Yeah. Is, is that when you have disruption and, and the storage vision, you know what disruption it means. Cuz there's been a whole discussion around disruptive operations. When storage goes down, you have back DR. And failover. If there's a disruption that changes the nature of invisible infrastructure, developers want invisible infrastructure. That's the future steady state. So if there's a disruption in storage >>Yeah. It >>Can't affect the productivity and the tool chains and the workflows of developers. Yep. Right? So how do you guys look at that? Cause you're a critical component. Storage is a service, it's a huge thing. Yeah. Storage has to, has to work seamlessly. And let's keep the developers out of the weeds. >>John. I think what, what what you put your finger on is another huge trend in the world of Kubernetes where Atan after all, which is really where, where all the leading practitioners both come and the leading vendors are. So here's the second trend that we are leading and, and actually I think it's happening not just with us, but with other, for folks in the industry. And that is, you know, the world of DevOps. Like DevOps has been such a catchphrase for all of of us in the industry last five years. And it's been both a combination of cultural change as well as technology change. Here's what the latest is on the, in the world of DevOps. DevOps is now crystallized. It's not some kind of mysterious art form that you read about. Okay. How people are practicing. DevOps is, it's broken into two, two things now. >>There is the platform part. So DevOps is now a bunch of platforms. And the other part of DevOps is a bunch of practices. So a little bit on both these, the platforms in the world of es there's only three platforms, right? There's the orchestration platforms, the, you know, eks, the open ships of the world and so on. There are the data management platforms, pro people like Port Works. And the third is security platforms, right? You know, Palo Alto Networks, others Aqua are all in this. So these are the three platforms and there are platform engineering teams now that many of our largest customers, some of the largest banks, the largest service providers, they're all operating as a ES platform engineering team. And then now developers, to your point, developers are in the practice of being able to use these platforms to launch new services. So the, the actual IT ops, the ops are run by developers now and they can do it on these platforms. And the platform engineering team provide that as an ease of use and they're there to troubleshoot when problems happen. So the idea of DevOps as a ops practice and a platform is the newest thing. And, and ports and pure storage leading in the world of data management >>Platforms there. Talk about a customer example that you think really articulates the value that Port Works and Pure Storage delivers from a data management >>Perspective. Yeah, so there's so many examples. One of the, one of the longest running examples we have is a very, very large service provider that, you know, you all know and probably use, and they have been using us in the cable kind of set box or cable box business. They get streams of data from, from cable boxes all over the world. They collected all in a centralized large kind of thing and run elastic search and analytics on it. Now what they have done is they couldn't keep up with this at the scale and the depth, right? The speed of, of activity and the distributed nature of the activity. The only way to solve this was to use something like Kubernetes manage with Spark coming, bringing all the data in into deep, deep, deep silos of storage, which are all running not even on a sand, but on kind of, you know, very deep terabytes and terabytes of, of storage. So all of this is orchestrated with the he of Coworks and there's a platform engineering team. We are building that platform for them, them with some of these other components that allows them to kind of do analytics and, and make some changes in real time. Huge kind of setup for, for >>That. Yeah. Well, you guys have the right architecture. I love the vision. I love what you guys are doing. I think this is right in line with Pures. They've always been disruptors. I remember when we first interviewed the CEO and they started Yep. They, they stayed on path. They didn't waver. EMC was the big player. They ended up taking their lunch and dinner as well and they beat 'em in the marketplace. But now you got this traction here. So I have to ask you, how's the business, what's the results look like? You're a GM cloud native business unit of a storage company that's transformed and transforming. >>Yeah, you know, it's interesting, we just hit the two year anniversary, right John? And so what we did was just kind of like step back and hey to, you know, we're running so hard, you just take a step back and we've tripled the business in the two years since the acquisition, the two years before and, and we were growing through proven. So, you know, that that's quite a fee. And we've tripled the number of people, the amount of engineering investments we have, the number of go to market investments have been, have been awesome. So business is going really well though, I will say. But I think, you know, we have, we can't be, we're watching the market closely. You know, as a former ceo, I, you have to kind of learn to read the tea leaves when you invest. And I think, you know, what I would say is we're proceeding with caution in the next two quarters. I view business transformation as not a cancelable activity. So that's the, that's the good news, right? Our customers are large, >>It's >>Right. Never gonna stop prices, right? All they're gonna do is say, Hey, they're gonna put their hand, their hand was always going right on the dial. Now they're kind of putting their hand on the dial going, hey, where, what is happening? But my, my own sense of this is that people who continue to invest through it, the question is at what level? And I also think that this is a six month kind of watch, the watch where, where we put the dial. So Q4 and q1 I think are kind of, you know, we have our, our watch kind of watch the market sign. But I have the highest confidence. What >>Does your gut tell you? You're an >>Entrepreneur. My, my gut says that we'll go through a little bit of a cautious investment period in the next six months. And after that I think we're gonna be back in, back full, full in the crazy growth that we've always been. Yeah. We're gonna grow by the way, in the next, I think >>It's corn style. I think I'm, I'm more bullish. I think it's gonna be some, you know, weeding out of some overinvestment, pre covid or pre bubble. But I think tech's gonna continue to grow. I don't see >>It's stopping. Yeah. And, and the investment is gonna be on these core platforms. See, back to the platform story, it's gonna be in these lower platforms and on unifying everything, let's consume it better rather than let's go kind of experiment with a whole bunch of things all over the map, right? So you'll see less experimentation and more kind of, let's harvest some of the investments we've made in the last couple >>Of years and actually be able to, to enable companies in, in the industry to truly be data companies because absolutely. We talked about as a service, we all have these expectations that any service we want, we can get it. Yes. There's no delay because patients has gone Yeah. From the pandemic. >>So it is kind of, you know, tightening up the screws on what they've built. They, you know, adding some polish to it, adding some more capability, like I said, a, a a, a combination of harvesting and new investing. It's a combination I think is what we're gonna see. >>Yeah. What are some of the things that you're looking forward to? You talked about some of the, the growth things in the investment, but as we round out Q4 and head into a new year, what are you excited about? >>Yeah, so, you know, I mentioned our, as a service kind of platform. The global two K for us has been a set of customers who we co-create stuff with. And so one of the other set of things that we are very excited about and announcing is because we're deployed at scale, we're, we're, we have upgraded our backend. So we have now the ability to go to million IOPS and more and, and for, for the right backends. And so Kubernetes is a add-on, which will not slow down your, your core base infrastructure. Second thing that that we, we have is added a bunch of capability in the disaster recovery business continuity front, you know, we always had like metro kind of distance Dr. We had long distance dr. We've added a near sync Dr. So now we can provide disaster recovery and business continuity for metro distances across continents and across the planet. Right? That's kind of a major change that we've done. The third thing is we've added the capability for file block and Object. So now by adding object, we're really a complete solution. So it is really that maturity of the business Yeah. That you start seeing as enterprises move to embracing a platform approach, deploying it much more widely. You talked about the early majority. Yeah. Right. And so what they require is more enterprise class capability and those are all the things that we've been adding and we're really looking forward to it. >>Well it sounds like tremendous evolution and maturation of Port Works in the two years since it's been with Pure Storage. You talked about the cultural alignment, Great stuff that you are achieving. Congratulations on that. Great stuff >>Ahead and having fun. Let's not forget that that's too life's too short to do. It is. You're right. >>Right. Thank you. We will definitely, as always on the cube, keep our eyes on this space. Mur. Meley, it's been great to have you back on the program. Thank you for joining, John. >>Great. Thank you so much. It's a pleasure. Our, >>For our guests and John Furrier, Lisa Martin here live in Detroit with the cube about Cob Con Cloud native Con at 22. We'll be back after a short break.

>> From The Cube Studios in Palo Alto and Boston, bringing you data driven insights from The Cube at ETR. This is "Breaking Analysis" with Dave Vellante. >> The reverse momentum in tech stocks caused by rising interest rates, less attractive discounted cash flow models, and more tepid forward guidance, can be easily measured by public market valuations. And while there's lots of discussion about the impact on private companies and cash runway and 409A valuations, measuring the performance of non-public companies isn't as easy. IPOs have dried up and public statements by private companies, of course, they accentuate the good and they kind of hide the bad. Real data, unless you're an insider, is hard to find. Hello and welcome to this week's "Wikibon Cube Insights" powered by ETR. In this "Breaking Analysis", we unlock some of the secrets that non-public, emerging tech companies may or may not be sharing. And we do this by introducing you to a capability from ETR that we've not exposed you to over the past couple of years, it's called the Emerging Technologies Survey, and it is packed with sentiment data and performance data based on surveys of more than a thousand CIOs and IT buyers covering more than 400 companies. And we've invited back our colleague, Erik Bradley of ETR to help explain the survey and the data that we're going to cover today. Erik, this survey is something that I've not personally spent much time on, but I'm blown away at the data. It's really unique and detailed. First of all, welcome. Good to see you again. >> Great to see you too, Dave, and I'm really happy to be talking about the ETS or the Emerging Technology Survey. Even our own clients of constituents probably don't spend as much time in here as they should. >> Yeah, because there's so much in the mainstream, but let's pull up a slide to bring out the survey composition. Tell us about the study. How often do you run it? What's the background and the methodology? >> Yeah, you were just spot on the way you were talking about the private tech companies out there. So what we did is we decided to take all the vendors that we track that are not yet public and move 'em over to the ETS. And there isn't a lot of information out there. If you're not in Silicon (indistinct), you're not going to get this stuff. So PitchBook and Tech Crunch are two out there that gives some data on these guys. But what we really wanted to do was go out to our community. We have 6,000, ITDMs in our community. We wanted to ask them, "Are you aware of these companies? And if so, are you allocating any resources to them? Are you planning to evaluate them," and really just kind of figure out what we can do. So this particular survey, as you can see, 1000 plus responses, over 450 vendors that we track. And essentially what we're trying to do here is talk about your evaluation and awareness of these companies and also your utilization. And also if you're not utilizing 'em, then we can also figure out your sales conversion or churn. So this is interesting, not only for the ITDMs themselves to figure out what their peers are evaluating and what they should put in POCs against the big guys when contracts come up. But it's also really interesting for the tech vendors themselves to see how they're performing. >> And you can see 2/3 of the respondents are director level of above. You got 28% is C-suite. There is of course a North America bias, 70, 75% is North America. But these smaller companies, you know, that's when they start doing business. So, okay. We're going to do a couple of things here today. First, we're going to give you the big picture across the sectors that ETR covers within the ETS survey. And then we're going to look at the high and low sentiment for the larger private companies. And then we're going to do the same for the smaller private companies, the ones that don't have as much mindshare. And then I'm going to put those two groups together and we're going to look at two dimensions, actually three dimensions, which companies are being evaluated the most. Second, companies are getting the most usage and adoption of their offerings. And then third, which companies are seeing the highest churn rates, which of course is a silent killer of companies. And then finally, we're going to look at the sentiment and mindshare for two key areas that we like to cover often here on "Breaking Analysis", security and data. And data comprises database, including data warehousing, and then big data analytics is the second part of data. And then machine learning and AI is the third section within data that we're going to look at. Now, one other thing before we get into it, ETR very often will include open source offerings in the mix, even though they're not companies like TensorFlow or Kubernetes, for example. And we'll call that out during this discussion. The reason this is done is for context, because everyone is using open source. It is the heart of innovation and many business models are super glued to an open source offering, like take MariaDB, for example. There's the foundation and then there's with the open source code and then there, of course, the company that sells services around the offering. Okay, so let's first look at the highest and lowest sentiment among these private firms, the ones that have the highest mindshare. So they're naturally going to be somewhat larger. And we do this on two dimensions, sentiment on the vertical axis and mindshare on the horizontal axis and note the open source tool, see Kubernetes, Postgres, Kafka, TensorFlow, Jenkins, Grafana, et cetera. So Erik, please explain what we're looking at here, how it's derived and what the data tells us. >> Certainly, so there is a lot here, so we're going to break it down first of all by explaining just what mindshare and net sentiment is. You explain the axis. We have so many evaluation metrics, but we need to aggregate them into one so that way we can rank against each other. Net sentiment is really the aggregation of all the positive and subtracting out the negative. So the net sentiment is a very quick way of looking at where these companies stand versus their peers in their sectors and sub sectors. Mindshare is basically the awareness of them, which is good for very early stage companies. And you'll see some names on here that are obviously been around for a very long time. And they're clearly be the bigger on the axis on the outside. Kubernetes, for instance, as you mentioned, is open source. This de facto standard for all container orchestration, and it should be that far up into the right, because that's what everyone's using. In fact, the open source leaders are so prevalent in the emerging technology survey that we break them out later in our analysis, 'cause it's really not fair to include them and compare them to the actual companies that are providing the support and the security around that open source technology. But no survey, no analysis, no research would be complete without including these open source tech. So what we're looking at here, if I can just get away from the open source names, we see other things like Databricks and OneTrust . They're repeating as top net sentiment performers here. And then also the design vendors. People don't spend a lot of time on 'em, but Miro and Figma. This is their third survey in a row where they're just dominating that sentiment overall. And Adobe should probably take note of that because they're really coming after them. But Databricks, we all know probably would've been a public company by now if the market hadn't turned, but you can see just how dominant they are in a survey of nothing but private companies. And we'll see that again when we talk about the database later. >> And I'll just add, so you see automation anywhere on there, the big UiPath competitor company that was not able to get to the public markets. They've been trying. Snyk, Peter McKay's company, they've raised a bunch of money, big security player. They're doing some really interesting things in developer security, helping developers secure the data flow, H2O.ai, Dataiku AI company. We saw them at the Snowflake Summit. Redis Labs, Netskope and security. So a lot of names that we know that ultimately we think are probably going to be hitting the public market. Okay, here's the same view for private companies with less mindshare, Erik. Take us through this one. >> On the previous slide too real quickly, I wanted to pull that security scorecard and we'll get back into it. But this is a newcomer, that I couldn't believe how strong their data was, but we'll bring that up in a second. Now, when we go to the ones of lower mindshare, it's interesting to talk about open source, right? Kubernetes was all the way on the top right. Everyone uses containers. Here we see Istio up there. Not everyone is using service mesh as much. And that's why Istio is in the smaller breakout. But still when you talk about net sentiment, it's about the leader, it's the highest one there is. So really interesting to point out. Then we see other names like Collibra in the data side really performing well. And again, as always security, very well represented here. We have Aqua, Wiz, Armis, which is a standout in this survey this time around. They do IoT security. I hadn't even heard of them until I started digging into the data here. And I couldn't believe how well they were doing. And then of course you have AnyScale, which is doing a second best in this and the best name in the survey Hugging Face, which is a machine learning AI tool. Also doing really well on a net sentiment, but they're not as far along on that access of mindshare just yet. So these are again, emerging companies that might not be as well represented in the enterprise as they will be in a couple of years. >> Hugging Face sounds like something you do with your two year old. Like you said, you see high performers, AnyScale do machine learning and you mentioned them. They came out of Berkeley. Collibra Governance, InfluxData is on there. InfluxDB's a time series database. And yeah, of course, Alex, if you bring that back up, you get a big group of red dots, right? That's the bad zone, I guess, which Sisense does vis, Yellowbrick Data is a NPP database. How should we interpret the red dots, Erik? I mean, is it necessarily a bad thing? Could it be misinterpreted? What's your take on that? >> Sure, well, let me just explain the definition of it first from a data science perspective, right? We're a data company first. So the gray dots that you're seeing that aren't named, that's the mean that's the average. So in order for you to be on this chart, you have to be at least one standard deviation above or below that average. So that gray is where we're saying, "Hey, this is where the lump of average comes in. This is where everyone normally stands." So you either have to be an outperformer or an underperformer to even show up in this analysis. So by definition, yes, the red dots are bad. You're at least one standard deviation below the average of your peers. It's not where you want to be. And if you're on the lower left, not only are you not performing well from a utilization or an actual usage rate, but people don't even know who you are. So that's a problem, obviously. And the VCs and the PEs out there that are backing these companies, they're the ones who mostly are interested in this data. >> Yeah. Oh, that's great explanation. Thank you for that. No, nice benchmarking there and yeah, you don't want to be in the red. All right, let's get into the next segment here. Here going to look at evaluation rates, adoption and the all important churn. First new evaluations. Let's bring up that slide. And Erik, take us through this. >> So essentially I just want to explain what evaluation means is that people will cite that they either plan to evaluate the company or they're currently evaluating. So that means we're aware of 'em and we are choosing to do a POC of them. And then we'll see later how that turns into utilization, which is what a company wants to see, awareness, evaluation, and then actually utilizing them. That's sort of the life cycle for these emerging companies. So what we're seeing here, again, with very high evaluation rates. H2O, we mentioned. SecurityScorecard jumped up again. Chargebee, Snyk, Salt Security, Armis. A lot of security names are up here, Aqua, Netskope, which God has been around forever. I still can't believe it's in an Emerging Technology Survey But so many of these names fall in data and security again, which is why we decided to pick those out Dave. And on the lower side, Vena, Acton, those unfortunately took the dubious award of the lowest evaluations in our survey, but I prefer to focus on the positive. So SecurityScorecard, again, real standout in this one, they're in a security assessment space, basically. They'll come in and assess for you how your security hygiene is. And it's an area of a real interest right now amongst our ITDM community. >> Yeah, I mean, I think those, and then Arctic Wolf is up there too. They're doing managed services. You had mentioned Netskope. Yeah, okay. All right, let's look at now adoption. These are the companies whose offerings are being used the most and are above that standard deviation in the green. Take us through this, Erik. >> Sure, yet again, what we're looking at is, okay, we went from awareness, we went to evaluation. Now it's about utilization, which means a survey respondent's going to state "Yes, we evaluated and we plan to utilize it" or "It's already in our enterprise and we're actually allocating further resources to it." Not surprising, again, a lot of open source, the reason why, it's free. So it's really easy to grow your utilization on something that's free. But as you and I both know, as Red Hat proved, there's a lot of money to be made once the open source is adopted, right? You need the governance, you need the security, you need the support wrapped around it. So here we're seeing Kubernetes, Postgres, Apache Kafka, Jenkins, Grafana. These are all open source based names. But if we're looking at names that are non open source, we're going to see Databricks, Automation Anywhere, Rubrik all have the highest mindshare. So these are the names, not surprisingly, all names that probably should have been public by now. Everyone's expecting an IPO imminently. These are the names that have the highest mindshare. If we talk about the highest utilization rates, again, Miro and Figma pop up, and I know they're not household names, but they are just dominant in this survey. These are applications that are meant for design software and, again, they're going after an Autodesk or a CAD or Adobe type of thing. It is just dominant how high the utilization rates are here, which again is something Adobe should be paying attention to. And then you'll see a little bit lower, but also interesting, we see Collibra again, we see Hugging Face again. And these are names that are obviously in the data governance, ML, AI side. So we're seeing a ton of data, a ton of security and Rubrik was interesting in this one, too, high utilization and high mindshare. We know how pervasive they are in the enterprise already. >> Erik, Alex, keep that up for a second, if you would. So yeah, you mentioned Rubrik. Cohesity's not on there. They're sort of the big one. We're going to talk about them in a moment. Puppet is interesting to me because you remember the early days of that sort of space, you had Puppet and Chef and then you had Ansible. Red Hat bought Ansible and then Ansible really took off. So it's interesting to see Puppet on there as well. Okay. So now let's look at the churn because this one is where you don't want to be. It's, of course, all red 'cause churn is bad. Take us through this, Erik. >> Yeah, definitely don't want to be here and I don't love to dwell on the negative. So we won't spend as much time. But to your point, there's one thing I want to point out that think it's important. So you see Rubrik in the same spot, but Rubrik has so many citations in our survey that it actually would make sense that they're both being high utilization and churn just because they're so well represented. They have such a high overall representation in our survey. And the reason I call that out is Cohesity. Cohesity has an extremely high churn rate here about 17% and unlike Rubrik, they were not on the utilization side. So Rubrik is seeing both, Cohesity is not. It's not being utilized, but it's seeing a high churn. So that's the way you can look at this data and say, "Hm." Same thing with Puppet. You noticed that it was on the other slide. It's also on this one. So basically what it means is a lot of people are giving Puppet a shot, but it's starting to churn, which means it's not as sticky as we would like. One that was surprising on here for me was Tanium. It's kind of jumbled in there. It's hard to see in the middle, but Tanium, I was very surprised to see as high of a churn because what I do hear from our end user community is that people that use it, like it. It really kind of spreads into not only vulnerability management, but also that endpoint detection and response side. So I was surprised by that one, mostly to see Tanium in here. Mural, again, was another one of those application design softwares that's seeing a very high churn as well. >> So you're saying if you're in both... Alex, bring that back up if you would. So if you're in both like MariaDB is for example, I think, yeah, they're in both. They're both green in the previous one and red here, that's not as bad. You mentioned Rubrik is going to be in both. Cohesity is a bit of a concern. Cohesity just brought on Sanjay Poonen. So this could be a go to market issue, right? I mean, 'cause Cohesity has got a great product and they got really happy customers. So they're just maybe having to figure out, okay, what's the right ideal customer profile and Sanjay Poonen, I guarantee, is going to have that company cranking. I mean they had been doing very well on the surveys and had fallen off of a bit. The other interesting things wondering the previous survey I saw Cvent, which is an event platform. My only reason I pay attention to that is 'cause we actually have an event platform. We don't sell it separately. We bundle it as part of our offerings. And you see Hopin on here. Hopin raised a billion dollars during the pandemic. And we were like, "Wow, that's going to blow up." And so you see Hopin on the churn and you didn't see 'em in the previous chart, but that's sort of interesting. Like you said, let's not kind of dwell on the negative, but you really don't. You know, churn is a real big concern. Okay, now we're going to drill down into two sectors, security and data. Where data comprises three areas, database and data warehousing, machine learning and AI and big data analytics. So first let's take a look at the security sector. Now this is interesting because not only is it a sector drill down, but also gives an indicator of how much money the firm has raised, which is the size of that bubble. And to tell us if a company is punching above its weight and efficiently using its venture capital. Erik, take us through this slide. Explain the dots, the size of the dots. Set this up please. >> Yeah. So again, the axis is still the same, net sentiment and mindshare, but what we've done this time is we've taken publicly available information on how much capital company is raised and that'll be the size of the circle you see around the name. And then whether it's green or red is basically saying relative to the amount of money they've raised, how are they doing in our data? So when you see a Netskope, which has been around forever, raised a lot of money, that's why you're going to see them more leading towards red, 'cause it's just been around forever and kind of would expect it. Versus a name like SecurityScorecard, which is only raised a little bit of money and it's actually performing just as well, if not better than a name, like a Netskope. OneTrust doing absolutely incredible right now. BeyondTrust. We've seen the issues with Okta, right. So those are two names that play in that space that obviously are probably getting some looks about what's going on right now. Wiz, we've all heard about right? So raised a ton of money. It's doing well on net sentiment, but the mindshare isn't as well as you'd want, which is why you're going to see a little bit of that red versus a name like Aqua, which is doing container and application security. And hasn't raised as much money, but is really neck and neck with a name like Wiz. So that is why on a relative basis, you'll see that more green. As we all know, information security is never going away. But as we'll get to later in the program, Dave, I'm not sure in this current market environment, if people are as willing to do POCs and switch away from their security provider, right. There's a little bit of tepidness out there, a little trepidation. So right now we're seeing overall a slight pause, a slight cooling in overall evaluations on the security side versus historical levels a year ago. >> Now let's stay on here for a second. So a couple things I want to point out. So it's interesting. Now Snyk has raised over, I think $800 million but you can see them, they're high on the vertical and the horizontal, but now compare that to Lacework. It's hard to see, but they're kind of buried in the middle there. That's the biggest dot in this whole thing. I think I'm interpreting this correctly. They've raised over a billion dollars. It's a Mike Speiser company. He was the founding investor in Snowflake. So people watch that very closely, but that's an example of where they're not punching above their weight. They recently had a layoff and they got to fine tune things, but I'm still confident they they're going to do well. 'Cause they're approaching security as a data problem, which is probably people having trouble getting their arms around that. And then again, I see Arctic Wolf. They're not red, they're not green, but they've raised fair amount of money, but it's showing up to the right and decent level there. And a couple of the other ones that you mentioned, Netskope. Yeah, they've raised a lot of money, but they're actually performing where you want. What you don't want is where Lacework is, right. They've got some work to do to really take advantage of the money that they raised last November and prior to that. >> Yeah, if you're seeing that more neutral color, like you're calling out with an Arctic Wolf, like that means relative to their peers, this is where they should be. It's when you're seeing that red on a Lacework where we all know, wow, you raised a ton of money and your mindshare isn't where it should be. Your net sentiment is not where it should be comparatively. And then you see these great standouts, like Salt Security and SecurityScorecard and Abnormal. You know they haven't raised that much money yet, but their net sentiment's higher and their mindshare's doing well. So those basically in a nutshell, if you're a PE or a VC and you see a small green circle, then you're doing well, then it means you made a good investment. >> Some of these guys, I don't know, but you see these small green circles. Those are the ones you want to start digging into and maybe help them catch a wave. Okay, let's get into the data discussion. And again, three areas, database slash data warehousing, big data analytics and ML AI. First, we're going to look at the database sector. So Alex, thank you for bringing that up. Alright, take us through this, Erik. Actually, let me just say Postgres SQL. I got to ask you about this. It shows some funding, but that actually could be a mix of EDB, the company that commercializes Postgres and Postgres the open source database, which is a transaction system and kind of an open source Oracle. You see MariaDB is a database, but open source database. But the companies they've raised over $200 million and they filed an S-4. So Erik looks like this might be a little bit of mashup of companies and open source products. Help us understand this. >> Yeah, it's tough when you start dealing with the open source side and I'll be honest with you, there is a little bit of a mashup here. There are certain names here that are a hundred percent for profit companies. And then there are others that are obviously open source based like Redis is open source, but Redis Labs is the one trying to monetize the support around it. So you're a hundred percent accurate on this slide. I think one of the things here that's important to note though, is just how important open source is to data. If you're going to be going to any of these areas, it's going to be open source based to begin with. And Neo4j is one I want to call out here. It's not one everyone's familiar with, but it's basically geographical charting database, which is a name that we're seeing on a net sentiment side actually really, really high. When you think about it's the third overall net sentiment for a niche database play. It's not as big on the mindshare 'cause it's use cases aren't as often, but third biggest play on net sentiment. I found really interesting on this slide. >> And again, so MariaDB, as I said, they filed an S-4 I think $50 million in revenue, that might even be ARR. So they're not huge, but they're getting there. And by the way, MariaDB, if you don't know, was the company that was formed the day that Oracle bought Sun in which they got MySQL and MariaDB has done a really good job of replacing a lot of MySQL instances. Oracle has responded with MySQL HeatWave, which was kind of the Oracle version of MySQL. So there's some interesting battles going on there. If you think about the LAMP stack, the M in the LAMP stack was MySQL. And so now it's all MariaDB replacing that MySQL for a large part. And then you see again, the red, you know, you got to have some concerns about there. Aerospike's been around for a long time. SingleStore changed their name a couple years ago, last year. Yellowbrick Data, Fire Bolt was kind of going after Snowflake for a while, but yeah, you want to get out of that red zone. So they got some work to do. >> And Dave, real quick for the people that aren't aware, I just want to let them know that we can cut this data with the public company data as well. So we can cross over this with that because some of these names are competing with the larger public company names as well. So we can go ahead and cross reference like a MariaDB with a Mongo, for instance, or of something of that nature. So it's not in this slide, but at another point we can certainly explain on a relative basis how these private names are doing compared to the other ones as well. >> All right, let's take a quick look at analytics. Alex, bring that up if you would. Go ahead, Erik. >> Yeah, I mean, essentially here, I can't see it on my screen, my apologies. I just kind of went to blank on that. So gimme one second to catch up. >> So I could set it up while you're doing that. You got Grafana up and to the right. I mean, this is huge right. >> Got it thank you. I lost my screen there for a second. Yep. Again, open source name Grafana, absolutely up and to the right. But as we know, Grafana Labs is actually picking up a lot of speed based on Grafana, of course. And I think we might actually hear some noise from them coming this year. The names that are actually a little bit more disappointing than I want to call out are names like ThoughtSpot. It's been around forever. Their mindshare of course is second best here but based on the amount of time they've been around and the amount of money they've raised, it's not actually outperforming the way it should be. We're seeing Moogsoft obviously make some waves. That's very high net sentiment for that company. It's, you know, what, third, fourth position overall in this entire area, Another name like Fivetran, Matillion is doing well. Fivetran, even though it's got a high net sentiment, again, it's raised so much money that we would've expected a little bit more at this point. I know you know this space extremely well, but basically what we're looking at here and to the bottom left, you're going to see some names with a lot of red, large circles that really just aren't performing that well. InfluxData, however, second highest net sentiment. And it's really pretty early on in this stage and the feedback we're getting on this name is the use cases are great, the efficacy's great. And I think it's one to watch out for. >> InfluxData, time series database. The other interesting things I just noticed here, you got Tamer on here, which is that little small green. Those are the ones we were saying before, look for those guys. They might be some of the interesting companies out there and then observe Jeremy Burton's company. They do observability on top of Snowflake, not green, but kind of in that gray. So that's kind of cool. Monte Carlo is another one, they're sort of slightly green. They are doing some really interesting things in data and data mesh. So yeah, okay. So I can spend all day on this stuff, Erik, phenomenal data. I got to get back and really dig in. Let's end with machine learning and AI. Now this chart it's similar in its dimensions, of course, except for the money raised. We're not showing that size of the bubble, but AI is so hot. We wanted to cover that here, Erik, explain this please. Why TensorFlow is highlighted and walk us through this chart. >> Yeah, it's funny yet again, right? Another open source name, TensorFlow being up there. And I just want to explain, we do break out machine learning, AI is its own sector. A lot of this of course really is intertwined with the data side, but it is on its own area. And one of the things I think that's most important here to break out is Databricks. We started to cover Databricks in machine learning, AI. That company has grown into much, much more than that. So I do want to state to you Dave, and also the audience out there that moving forward, we're going to be moving Databricks out of only the MA/AI into other sectors. So we can kind of value them against their peers a little bit better. But in this instance, you could just see how dominant they are in this area. And one thing that's not here, but I do want to point out is that we have the ability to break this down by industry vertical, organization size. And when I break this down into Fortune 500 and Fortune 1000, both Databricks and Tensorflow are even better than you see here. So it's quite interesting to see that the names that are succeeding are also succeeding with the largest organizations in the world. And as we know, large organizations means large budgets. So this is one area that I just thought was really interesting to point out that as we break it down, the data by vertical, these two names still are the outstanding players. >> I just also want to call it H2O.ai. They're getting a lot of buzz in the marketplace and I'm seeing them a lot more. Anaconda, another one. Dataiku consistently popping up. DataRobot is also interesting because all the kerfuffle that's going on there. The Cube guy, Cube alum, Chris Lynch stepped down as executive chairman. All this stuff came out about how the executives were taking money off the table and didn't allow the employees to participate in that money raising deal. So that's pissed a lot of people off. And so they're now going through some kind of uncomfortable things, which is unfortunate because DataRobot, I noticed, we haven't covered them that much in "Breaking Analysis", but I've noticed them oftentimes, Erik, in the surveys doing really well. So you would think that company has a lot of potential. But yeah, it's an important space that we're going to continue to watch. Let me ask you Erik, can you contextualize this from a time series standpoint? I mean, how is this changed over time? >> Yeah, again, not show here, but in the data. I'm sorry, go ahead. >> No, I'm sorry. What I meant, I should have interjected. In other words, you would think in a downturn that these emerging companies would be less interesting to buyers 'cause they're more risky. What have you seen? >> Yeah, and it was interesting before we went live, you and I were having this conversation about "Is the downturn stopping people from evaluating these private companies or not," right. In a larger sense, that's really what we're doing here. How are these private companies doing when it comes down to the actual practitioners? The people with the budget, the people with the decision making. And so what I did is, we have historical data as you know, I went back to the Emerging Technology Survey we did in November of 21, right at the crest right before the market started to really fall and everything kind of started to fall apart there. And what I noticed is on the security side, very much so, we're seeing less evaluations than we were in November 21. So I broke it down. On cloud security, net sentiment went from 21% to 16% from November '21. That's a pretty big drop. And again, that sentiment is our one aggregate metric for overall positivity, meaning utilization and actual evaluation of the name. Again in database, we saw it drop a little bit from 19% to 13%. However, in analytics we actually saw it stay steady. So it's pretty interesting that yes, cloud security and security in general is always going to be important. But right now we're seeing less overall net sentiment in that space. But within analytics, we're seeing steady with growing mindshare. And also to your point earlier in machine learning, AI, we're seeing steady net sentiment and mindshare has grown a whopping 25% to 30%. So despite the downturn, we're seeing more awareness of these companies in analytics and machine learning and a steady, actual utilization of them. I can't say the same in security and database. They're actually shrinking a little bit since the end of last year. >> You know it's interesting, we were on a round table, Erik does these round tables with CISOs and CIOs, and I remember one time you had asked the question, "How do you think about some of these emerging tech companies?" And one of the executives said, "I always include somebody in the bottom left of the Gartner Magic Quadrant in my RFPs. I think he said, "That's how I found," I don't know, it was Zscaler or something like that years before anybody ever knew of them "Because they're going to help me get to the next level." So it's interesting to see Erik in these sectors, how they're holding up in many cases. >> Yeah. It's a very important part for the actual IT practitioners themselves. There's always contracts coming up and you always have to worry about your next round of negotiations. And that's one of the roles these guys play. You have to do a POC when contracts come up, but it's also their job to stay on top of the new technology. You can't fall behind. Like everyone's a software company. Now everyone's a tech company, no matter what you're doing. So these guys have to stay in on top of it. And that's what this ETS can do. You can go in here and look and say, "All right, I'm going to evaluate their technology," and it could be twofold. It might be that you're ready to upgrade your technology and they're actually pushing the envelope or it simply might be I'm using them as a negotiation ploy. So when I go back to the big guy who I have full intentions of writing that contract to, at least I have some negotiation leverage. >> Erik, we got to leave it there. I could spend all day. I'm going to definitely dig into this on my own time. Thank you for introducing this, really appreciate your time today. >> I always enjoy it, Dave and I hope everyone out there has a great holiday weekend. Enjoy the rest of the summer. And, you know, I love to talk data. So anytime you want, just point the camera on me and I'll start talking data. >> You got it. I also want to thank the team at ETR, not only Erik, but Darren Bramen who's a data scientist, really helped prepare this data, the entire team over at ETR. I cannot tell you how much additional data there is. We are just scratching the surface in this "Breaking Analysis". So great job guys. I want to thank Alex Myerson. Who's on production and he manages the podcast. Ken Shifman as well, who's just coming back from VMware Explore. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE. Does some great editing for us. Thank you. All of you guys. Remember these episodes, they're all available as podcast, wherever you listen. All you got to do is just search "Breaking Analysis" podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me to get in touch david.vellante@siliconangle.com. You can DM me at dvellante or comment on my LinkedIn posts and please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for Erik Bradley and The Cube Insights powered by ETR. Thanks for watching. Be well. And we'll see you next time on "Breaking Analysis". (upbeat music)

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

>> Announcer: 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 rapid pace of cloud adoption has changed the way organizations approach cybersecurity. Specifically, the cloud is increasingly becoming the first line of cyber defense. As such, along with communicating to the board and creating a security aware culture, the chief information security officer must ensure that the shared responsibility model is being applied properly. Meanwhile, the DevSecOps team has emerged as the critical link between strategy and execution, while audit becomes the free safety, if you will, in the equation, i.e., the last line of defense. Hello, and welcome to this week's, we keep on CUBE Insights, powered by ETR. In this "Breaking Analysis", we'll share the latest data on hyperscale, IaaS, and PaaS market performance, along with some fresh ETR survey data. And we'll share some highlights and the puts and takes from the recent AWS re:Inforce event in Boston. But first, the macro. It's earning season, and that's what many people want to talk about, including us. As we reported last week, the macro spending picture is very mixed and weird. Think back to a week ago when SNAP reported. A player like SNAP misses and the Nasdaq drops 300 points. Meanwhile, Intel, the great semiconductor hope for America misses by a mile, cuts its revenue outlook by 15% for the year, and the Nasdaq was up nearly 250 points just ahead of the close, go figure. Earnings reports from Meta, Google, Microsoft, ServiceNow, and some others underscored cautious outlooks, especially those exposed to the advertising revenue sector. But at the same time, Apple, Microsoft, and Google, were, let's say less bad than expected. And that brought a sigh of relief. And then there's Amazon, which beat on revenue, it beat on cloud revenue, and it gave positive guidance. The Nasdaq has seen this month best month since the isolation economy, which "Breaking Analysis" contributor, Chip Symington, attributes to what he calls an oversold rally. But there are many unknowns that remain. How bad will inflation be? Will the fed really stop tightening after September? The Senate just approved a big spending bill along with corporate tax hikes, which generally don't favor the economy. And on Monday, August 1st, the market will likely realize that we are in the summer quarter, and there's some work to be done. Which is why it's not surprising that investors sold the Nasdaq at the close today on Friday. Are people ready to call the bottom? Hmm, some maybe, but there's still lots of uncertainty. However, the cloud continues its march, despite some very slight deceleration in growth rates from the two leaders. Here's an update of our big four IaaS quarterly revenue data. The big four hyperscalers will account for $165 billion in revenue this year, slightly lower than what we had last quarter. We expect AWS to surpass 83 billion this year in revenue. Azure will be more than 2/3rds the size of AWS, a milestone from Microsoft. Both AWS and Azure came in slightly below our expectations, but still very solid growth at 33% and 46% respectively. GCP, Google Cloud Platform is the big concern. By our estimates GCP's growth rate decelerated from 47% in Q1, and was 38% this past quarter. The company is struggling to keep up with the two giants. Remember, both GCP and Azure, they play a shell game and hide the ball on their IaaS numbers, so we have to use a survey data and other means of estimating. But this is how we see the market shaping up in 2022. Now, before we leave the overall cloud discussion, here's some ETR data that shows the net score or spending momentum granularity for each of the hyperscalers. These bars show the breakdown for each company, with net score on the right and in parenthesis, net score from last quarter. lime green is new adoptions, forest green is spending up 6% or more, the gray is flat, pink is spending at 6% down or worse, and the bright red is replacement or churn. Subtract the reds from the greens and you get net score. One note is this is for each company's overall portfolio. So it's not just cloud. So it's a bit of a mixed bag, but there are a couple points worth noting. First, anything above 40% or 40, here as shown in the chart, is considered elevated. AWS, as you can see, is well above that 40% mark, as is Microsoft. And if you isolate Microsoft's Azure, only Azure, it jumps above AWS's momentum. Google is just barely hanging on to that 40 line, and Alibaba is well below, with both Google and Alibaba showing much higher replacements, that bright red. But here's the key point. AWS and Azure have virtually no churn, no replacements in that bright red. And all four companies are experiencing single-digit numbers in terms of decreased spending within customer accounts. People may be moving some workloads back on-prem selectively, but repatriation is definitely not a trend to bet the house on, in our view. Okay, let's get to the main subject of this "Breaking Analysis". TheCube was at AWS re:Inforce in Boston this week, and we have some observations to share. First, we had keynotes from Steven Schmidt who used to be the chief information security officer at Amazon on Web Services, now he's the CSO, the chief security officer of Amazon. Overall, he dropped the I in his title. CJ Moses is the CISO for AWS. Kurt Kufeld of AWS also spoke, as did Lena Smart, who's the MongoDB CISO, and she keynoted and also came on theCUBE. We'll go back to her in a moment. The key point Schmidt made, one of them anyway, was that Amazon sees more data points in a day than most organizations see in a lifetime. Actually, it adds up to quadrillions over a fairly short period of time, I think, it was within a month. That's quadrillion, it's 15 zeros, by the way. Now, there was drill down focus on data protection and privacy, governance, risk, and compliance, GRC, identity, big, big topic, both within AWS and the ecosystem, network security, and threat detection. Those are the five really highlighted areas. Re:Inforce is really about bringing a lot of best practice guidance to security practitioners, like how to get the most out of AWS tooling. Schmidt had a very strong statement saying, he said, "I can assure you with a 100% certainty that single controls and binary states will absolutely positively fail." Hence, the importance of course, of layered security. We heard a little bit of chat about getting ready for the future and skating to the security puck where quantum computing threatens to hack all of the existing cryptographic algorithms, and how AWS is trying to get in front of all that, and a new set of algorithms came out, AWS is testing. And, you know, we'll talk about that maybe in the future, but that's a ways off. And by its prominent presence, the ecosystem was there enforced, to talk about their role and filling the gaps and picking up where AWS leaves off. We heard a little bit about ransomware defense, but surprisingly, at least in the keynotes, no discussion about air gaps, which we've talked about in previous "Breaking Analysis", is a key factor. We heard a lot about services to help with threat detection and container security and DevOps, et cetera, but there really wasn't a lot of specific talk about how AWS is simplifying the life of the CISO. Now, maybe it's inherently assumed as AWS did a good job stressing that security is job number one, very credible and believable in that front. But you have to wonder if the world is getting simpler or more complex with cloud. And, you know, you might say, "Well, Dave, come on, of course it's better with cloud." But look, attacks are up, the threat surface is expanding, and new exfiltration records are being set every day. I think the hard truth is, the cloud is driving businesses forward and accelerating digital, and those businesses are now exposed more than ever. And that's why security has become such an important topic to boards and throughout the entire organization. Now, the other epiphany that we had at re:Inforce is that there are new layers and a new trust framework emerging in cyber. Roles are shifting, and as a direct result of the cloud, things are changing within organizations. And this first hit me in a conversation with long-time cyber practitioner and Wikibon colleague from our early Wikibon days, and friend, Mike Versace. And I spent two days testing the premise that Michael and I talked about. And here's an attempt to put that conversation into a graphic. The cloud is now the first line of defense. AWS specifically, but hyperscalers generally provide the services, the talent, the best practices, and automation tools to secure infrastructure and their physical data centers. And they're really good at it. The security inside of hyperscaler clouds is best of breed, it's world class. And that first line of defense does take some of the responsibility off of CISOs, but they have to understand and apply the shared responsibility model, where the cloud provider leaves it to the customer, of course, to make sure that the infrastructure they're deploying is properly configured. So in addition to creating a cyber aware culture and communicating up to the board, the CISO has to ensure compliance with and adherence to the model. That includes attracting and retaining the talent necessary to succeed. Now, on the subject of building a security culture, listen to this clip on one of the techniques that Lena Smart, remember, she's the CISO of MongoDB, one of the techniques she uses to foster awareness and build security cultures in her organization. Play the clip >> Having the Security Champion program, so that's just, it's like one of my babies. That and helping underrepresented groups in MongoDB kind of get on in the tech world are both really important to me. And so the Security Champion program is purely purely voluntary. We have over 100 members. And these are people, there's no bar to join, you don't have to be technical. If you're an executive assistant who wants to learn more about security, like my assistant does, you're more than welcome. Up to, we actually, people grade themselves when they join us. We give them a little tick box, like five is, I walk on security water, one is I can spell security, but I'd like to learn more. Mixing those groups together has been game-changing for us. >> Now, the next layer is really where it gets interesting. DevSecOps, you know, we hear about it all the time, shifting left. It implies designing security into the code at the dev level. Shift left and shield right is the kind of buzz phrase. But it's getting more and more complicated. So there are layers within the development cycle, i.e., securing the container. So the app code can't be threatened by backdoors or weaknesses in the containers. Then, securing the runtime to make sure the code is maintained and compliant. Then, the DevOps platform so that change management doesn't create gaps and exposures, and screw things up. And this is just for the application security side of the equation. What about the network and implementing zero trust principles, and securing endpoints, and machine to machine, and human to app communication? So there's a lot of burden being placed on the DevOps team, and they have to partner with the SecOps team to succeed. Those guys are not security experts. And finally, there's audit, which is the last line of defense or what I called at the open, the free safety, for you football fans. They have to do more than just tick the box for the board. That doesn't cut it anymore. They really have to know their stuff and make sure that what they sign off on is real. And then you throw ESG into the mix is becoming more important, making sure the supply chain is green and also secure. So you can see, while much of this stuff has been around for a long, long time, the cloud is accelerating innovation in the pace of delivery. And so much is changing as a result. Now, next, I want to share a graphic that we shared last week, but a little different twist. It's an XY graphic with net score or spending velocity in the vertical axis and overlap or presence in the dataset on the horizontal. With that magic 40% red line as shown. Okay, I won't dig into the data and draw conclusions 'cause we did that last week, but two points I want to make. First, look at Microsoft in the upper-right hand corner. They are big in security and they're attracting a lot of dollars in the space. We've reported on this for a while. They're a five-star security company. And every time, from a spending standpoint in ETR data, that little methodology we use, every time I've run this chart, I've wondered, where the heck is AWS? Why aren't they showing up there? If security is so important to AWS, which it is, and its customers, why aren't they spending money with Amazon on security? And I asked this very question to Merrit Baer, who resides in the office of the CISO at AWS. Listen to her answer. >> It doesn't mean don't spend on security. There is a lot of goodness that we have to offer in ESS, external security services. But I think one of the unique parts of AWS is that we don't believe that security is something you should buy, it's something that you get from us. It's something that we do for you a lot of the time. I mean, this is the definition of the shared responsibility model, right? >> Now, maybe that's good messaging to the market. Merritt, you know, didn't say it outright, but essentially, Microsoft they charge for security. At AWS, it comes with the package. But it does answer my question. And, of course, the fact is that AWS can subsidize all this with egress charges. Now, on the flip side of that, (chuckles) you got Microsoft, you know, they're both, they're competing now. We can take CrowdStrike for instance. Microsoft and CrowdStrike, they compete with each other head to head. So it's an interesting dynamic within the ecosystem. Okay, but I want to turn to a powerful example of how AWS designs in security. And that is the idea of confidential computing. Of course, AWS is not the only one, but we're coming off of re:Inforce, and I really want to dig into something that David Floyer and I have talked about in previous episodes. And we had an opportunity to sit down with Arvind Raghu and J.D. Bean, two security experts from AWS, to talk about this subject. And let's share what we learned and why we think it matters. First, what is confidential computing? That's what this slide is designed to convey. To AWS, they would describe it this way. It's the use of special hardware and the associated firmware that protects customer code and data from any unauthorized access while the data is in use, i.e., while it's being processed. That's oftentimes a security gap. And there are two dimensions here. One is protecting the data and the code from operators on the cloud provider, i.e, in this case, AWS, and protecting the data and code from the customers themselves. In other words, from admin level users are possible malicious actors on the customer side where the code and data is being processed. And there are three capabilities that enable this. First, the AWS Nitro System, which is the foundation for virtualization. The second is Nitro Enclaves, which isolate environments, and then third, the Nitro Trusted Platform Module, TPM, which enables cryptographic assurances of the integrity of the Nitro instances. Now, we've talked about Nitro in the past, and we think it's a revolutionary innovation, so let's dig into that a bit. This is an AWS slide that was shared about how they protect and isolate data and code. On the left-hand side is a classical view of a virtualized architecture. You have a single host or a single server, and those white boxes represent processes on the main board, X86, or could be Intel, or AMD, or alternative architectures. And you have the hypervisor at the bottom which translates instructions to the CPU, allowing direct execution from a virtual machine into the CPU. But notice, you also have blocks for networking, and storage, and security. And the hypervisor emulates or translates IOS between the physical resources and the virtual machines. And it creates some overhead. Now, companies like VMware have done a great job, and others, of stripping out some of that overhead, but there's still an overhead there. That's why people still like to run on bare metal. Now, and while it's not shown in the graphic, there's an operating system in there somewhere, which is privileged, so it's got access to these resources, and it provides the services to the VMs. Now, on the right-hand side, you have the Nitro system. And you can see immediately the differences between the left and right, because the networking, the storage, and the security, the management, et cetera, they've been separated from the hypervisor and that main board, which has the Intel, AMD, throw in Graviton and Trainium, you know, whatever XPUs are in use in the cloud. And you can see that orange Nitro hypervisor. That is a purpose-built lightweight component for this system. And all the other functions are separated in isolated domains. So very strong isolation between the cloud software and the physical hardware running workloads, i.e., those white boxes on the main board. Now, this will run at practically bare metal speeds, and there are other benefits as well. One of the biggest is security. As we've previously reported, this came out of AWS's acquisition of Annapurna Labs, which we've estimated was picked up for a measly $350 million, which is a drop in the bucket for AWS to get such a strategic asset. And there are three enablers on this side. One is the Nitro cards, which are accelerators to offload that wasted work that's done in traditional architectures by typically the X86. We've estimated 25% to 30% of core capacity and cycles is wasted on those offloads. The second is the Nitro security chip, which is embedded and extends the root of trust to the main board hardware. And finally, the Nitro hypervisor, which allocates memory and CPU resources. So the Nitro cards communicate directly with the VMs without the hypervisors getting in the way, and they're not in the path. And all that data is encrypted while it's in motion, and of course, encryption at rest has been around for a while. We asked AWS, is this an, we presumed it was an Arm-based architecture. We wanted to confirm that. Or is it some other type of maybe hybrid using X86 and Arm? They told us the following, and quote, "The SoC, system on chips, for these hardware components are purpose-built and custom designed in-house by Amazon and Annapurna Labs. The same group responsible for other silicon innovations such as Graviton, Inferentia, Trainium, and AQUA. Now, the Nitro cards are Arm-based and do not use any X86 or X86/64 bit CPUs. Okay, so it confirms what we thought. So you may say, "Why should we even care about all this technical mumbo jumbo, Dave?" Well, a year ago, David Floyer and I published this piece explaining why Nitro and Graviton are secret weapons of Amazon that have been a decade in the making, and why everybody needs some type of Nitro to compete in the future. This is enabled, this Nitro innovations and the custom silicon enabled by the Annapurna acquisition. And AWS has the volume economics to make custom silicon. Not everybody can do it. And it's leveraging the Arm ecosystem, the standard software, and the fabrication volume, the manufacturing volume to revolutionize enterprise computing. Nitro, with the alternative processor, architectures like Graviton and others, enables AWS to be on a performance, cost, and power consumption curve that blows away anything we've ever seen from Intel. And Intel's disastrous earnings results that we saw this past week are a symptom of this mega trend that we've been talking about for years. In the same way that Intel and X86 destroyed the market for RISC chips, thanks to PC volumes, Arm is blowing away X86 with volume economics that cannot be matched by Intel. Thanks to, of course, to mobile and edge. Our prediction is that these innovations and the Arm ecosystem are migrating and will migrate further into enterprise computing, which is Intel's stronghold. Now, that stronghold is getting eaten away by the likes of AMD, Nvidia, and of course, Arm in the form of Graviton and other Arm-based alternatives. Apple, Tesla, Amazon, Google, Microsoft, Alibaba, and others are all designing custom silicon, and doing so much faster than Intel can go from design to tape out, roughly cutting that time in half. And the premise of this piece is that every company needs a Nitro to enable alternatives to the X86 in order to support emergent workloads that are data rich and AI-based, and to compete from an economic standpoint. So while at re:Inforce, we heard that the impetus for Nitro was security. Of course, the Arm ecosystem, and its ascendancy has enabled, in our view, AWS to create a platform that will set the enterprise computing market this decade and beyond. Okay, that's it for today. Thanks to Alex Morrison, who is on production. And he does the podcast. And Ken Schiffman, our newest member of our Boston Studio team is also on production. Kristen Martin and Cheryl Knight help spread the word on social media and in the community. And Rob Hof is our editor in chief over at SiliconANGLE. He does some great, great work for us. Remember, all these episodes are available as podcast. Wherever you listen, just search "Breaking Analysis" podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me directly at David.Vellante@siliconangle.com or DM me @dvellante, comment on my LinkedIn post. And please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching. Be well, and we'll see you next time on "Breaking Analysis." (upbeat theme music)

(upbeat music) >> Hello, and welcome to this startup showcase. It's great to be here and talk about some of the innovations we are doing at AWS, how we work with our partner community, especially our open source partners. My name is Deepak Singh. I run our compute services organization, which is a very vague way of saying that I run a number of things that are connected together through compute. Very specifically, I run a container services organization. So for those of you who are into containers, ECS, EKS, fargate, ECR, App Runner Those are all teams that are within my org. I also run the Amazon Linux and BottleRocketing. So anything AWS does with Linux, both externally and internally, as well as our high-performance computing team. And perhaps very relevant to this discussion, I run the Amazon open source program office. Serving at AWS for over 13 years, almost 14, involved with compute in various ways, including EC2. What that has done has given me a vantage point of seeing how our customers use the services that we build for them, how they leverage various partner solutions, and along the way, how AWS itself has gotten involved with opensource. And I'll try and talk to you about some of those factors and how they impact, how you consume our services. So why don't we get started? So for many of you, you know, one of the things, there's two ways to look at AWS and open-source and Amazon in general. One is the number of contributors you may have. And the number of repositories that contribute to. Those are just a couple of measures. There are people that I work with on a regular basis, who will remind you that, those are not perfect measures. Sometimes you could just contribute to one thing and have outsized impact because of the nature of that thing. But it address being what it is, increasingly we'll look at different ways in which we can help contribute and enhance open source 'cause we consume a lot of it as well. I'll talk about it very specifically from the space that I work in the container space in particular, where we've worked a lot with people in the Kubernetes community. We've worked a lot with people in the broader CNCF community, as well as, you know, small projects that our customers might have got started off with. For example, I want to like talking about is Argo CD from Intuit. We were very actively involved with helping them figure out what to do with it. And it was great to see how into it. And we worked, etc, came together to think about get-ups at the Kubernetes level. And while those are their projects, we've always been involved with them. So we try and figure out what's important to our customers, how we can help and then take because of that. Well, let's talk about a little bit more, here's some examples of the kinds of open source projects that Amazon and AWS contribute to. They arranged from the open JDK. I think we even now have our own implementation of Java, the Corretto open source project. We contribute to projects like rust, where we are very active in the rest foundation from a leadership role as well, the robot operating system, just to pick some, we collaborate with Facebook and actively involved with the pirates project. And there's many others. You can see all the logos in here where we participate either because they're important to us as AWS in the services that we run or they're important to our customers and the services that they consume or the open source projects they care about and how we get to those. How we get and make those decisions is often depends on the importance of that particular project. At that point in time, how much impact they're having to AWS customers, or sometimes very feel that us contributing to that project is super critical because it helps us build more robust services. I'll talk about it in a completely, you know, somewhat different basis. You may have heard of us talk about our new next generation of Amazon Linux 2022, which is based on fedora as its sub stream. One of the reasons we made this decision was it allows us to go and participate in the preneurial project and make sure that the upstream project is robust, stays robust. And that, that what that ends up being is that Amazon Linux 2022 will be a robust operating system with the kinds of capabilities that our customers are asking for. That's just one example of how we think about it. So for example, you know, the Python software foundation is something that we work with very closely because so many of our customers use Python. So we help run something like PyPy which is many, you know, if you're a Python developer, I happened to be a Ruby one, but lots of our customers use Python and helping the Python project be robust by making sure PyPy is available to everybody is something that we help provide credits for help support in other ways. So it's not just code. It can mean many different ways of contributing as well, but in the end code and operations is where we hang our happens. Good examples of this is projects that we will create an open source because it makes sense to make sure that we open source some of the core primitives or foundations that are part of our own services. A great example of that, whether this be things that we open source or things that we contribute to. And I'll talk about both and I'll talk about things near and dear to my heart. There's many examples I've picked the two that I like talking about. The first of these is firecracker. Many of you have heard about it, a firecracker for those of you who don't know is a very lightweight virtual machine manager, which allows you to run these micro VMs. And why was this important many years ago when we started Lambda and quite honestly, Fugate and foggy, it still runs quite a bit in that mode, we used to have to run on VMs like everything else and finding the right VM for the size of tasks that somebody asks for the size of function that somebody asks for is requires us to provision capacity ahead of time. And it also wastes a lot of capacity because Lambda function is small. You won't even if you find the smallest VM possible, those can be a little that can be challenging. And you know, there's a lot of resources that are being wasted. VM start at a particular speed because they have to do a whole bunch of things before the operating system spins up and the virtual machine spins up and we asked ourselves, can we do better? come up with something that allows us to create right size, very lightweight, very fast booting. What's your machines, micro virtual machine that we ended up calling them. That's what led to firecracker. And we open source the project. And today firecrackers use, not just by AWS Lambda or foggy, but by a number of other folks, there's companies like fly IO that are using it. We know people using firecracker to run Kubernetes on prem on bare metal as an example. So we've seen a lot of other folks embrace it and use it as the foundation for building their own serverless services, their own container services. And we think there's a lot of value and learnings that we can bring to the table because we get the experience of operating at scale, but other people can bring to the table cause they may have specific requirements that we may not find it as important from an AWS perspective. So that's firecracker an example of a project where we contribute because we feel it's fundamentally important to us as continually. We were found, you know, we've been involved with continuity from the beginning. Today, we are a whole team that does nothing else, but contribute to container D because container D underlies foggy. It underlies our Kubernetes offerings. And it's increasingly being used by customers directly by their placement. You know, where they're running container D instead of running a full on Docker or similar container engine, what it has allowed us to do is focus on what's important so that we can operate continuously at scale, keep it robust and secure, add capabilities to it that AWS customers need manifested often through foggy Kubernetes, but in the end, it's a win-win for everybody. It makes continuously better. If you want to use containers for yourself on AWS, that's a great way to you. You know, you still, you still benefit from all the work that we're doing. The decision we took was since it's so important to us and our customers, we wanted a team that lived in breathed container D and made sure a super robust and there's many, many examples like that. No, that we ended up participating in, either by taking a project that exists or open sourcing our own. Here's an example of some of the open source projects that we have done from an AWS on Amazon perspective. And there's quite a few when I was looking at this list, I was quite surprised, not quite surprised I've seen the reports before, but every time I do, I have to recount and say, that's a lot more than one would have thought, even though I'd been looking at it for such a long time, examples of this in my world alone are things like, you know, what work had to do with Amazon Linux BottleRocket, which is a container host operating system. That's been open-sourced from day one. Firecracker is something we talked about. We have a project called AWS peril cluster, which allows you to spin up high performance computing clusters on AWS using the kind of schedulers you may use to use like slum. And that's an open source project. We have plenty of source projects in the web development space, in the security space. And more recently things like the open 3d engine, which is something that we are very excited about and that'd be open sourced a few months ago. And so there's a number of these projects that cover everything from tooling to developer, application frameworks, all the way to database and analytics and machine learning. And you'll notice that in a few areas, containers, as an example, machine learning as an example, our default is to go with open source option is where we can open source. And it makes sense for us to do so where we feel the product community might benefit from it. That's our default stance. The CNCF, the cloud native computing foundation is something that we've been involved with quite a bit. You know, we contribute to Kubernetes, be contribute to Envoy. I talked about continuity a bit. We've also contributed projects like CDK 8, which marries the AWS cloud development kit with Kubernetes. It's now a sandbox project in Kubernetes, and those are some of the areas. CNCF is such a wide surface area. We don't contribute to everything, but we definitely participate actively in CNCF with projects like HCB that are critical to eat for us. We are very, very active in just how the project evolves, but also try and see which of the projects that are important to our customers who are running Kubernetes maybe by themselves or some other project on AWS. Envoy is a good example. Kubernetes itself is a good example because in the end, we want to make sure that people running Kubernetes on AWS, even if they are not using our services are successful and we can help them, or we can work on the projects that are important to them. That's kind of how we think about the world. And it's worked pretty well for us. We've done a bunch of work on the Kubernetes side to make sure that we can integrate and solve a customer problem. We've, you know, from everything from models to work that we have done with gravity on our arm processor to a virtual GPU plugin that allows you to share and media GPU resources to the elastic fabric adapter, which are the network device for high performance computing that it can use at Kubernetes on AWS, along with things that directly impact Kubernetes customers like the CDKs project. I talked about work that we do with the container networking interface to the Amazon control of a Kubernetes, which is an open source project that allows you to use other AWS services directly from Kubernetes clusters. Again, you notice success, Kubernetes, not EKS, which is a managed Kubernetes service, because if we want you to be successful with Kubernetes and AWS, whether using our managed service or running your own, or some third party service. Similarly, we worked with premetheus. We now have a managed premetheus service. And at reinvent last year, we announced the general availability of this thing called carpenter, which is a provisioning and auto-scaling engine for Kubernetes, which is also an open source project. But here's the beauty of carpenter. You don't have to be using EKS to use it. Anyone running Kubernetes on AWS can leverage it. We focus on the AWS provider, but we've built it in such a way that if you wanted to take carpenter and implemented on prem or another cloud provider, that'd be completely okay. That's how it's designed and what we anticipated people may want to do. I talked a little bit about BottleRocket it's our Linux-based open-source operating system. And the thing that we have done with BottleRocket is make sure that we focus on security and the needs of customers who want to run orchestrated container, very focused on that problem. So for example, BottleRocket only has essential software needed to run containers, se Linux. I just notice it says that's the lineups, but I'm sure that, you know, Lena Torvalds will be pretty happy. And seeing that SE linux is enabled by default, we use things like DM Verity, and it has a read only root file system, no shell, you can assess it. You can install it if you wanted to. We allowed it to create different bill types, variants as we call them, you can create a variant for a non AWS resource as well. If you have your own homegrown container orchestrator, you can create a variant for that. It's designed to be used in many different contexts and all of that is open sourced. And then we use the update framework to publish and secure repository and kind of how this transactional system way of updating the software. And it's something that we didn't invent, but we have embraced wholeheartedly. It's a bottle rockets, completely open source, you know, have partners like Aqua, where who develop security tools for containers. And for them, you know, something I bought in rocket is a natural partnership because people are running a container host operating system. You can use Aqua tooling to make sure that they have a secure Indiana environment. And we see many more examples like that. You may think so over us, it's all about AWS proprietary technology because Lambda is a proprietary service. But you know, if you look peek under the covers, that's not necessarily true. Lambda runs on top of firecracker, as we've talked about fact crackers and open-source projects. So the foundation of Lambda in many ways is open source. What it also allows people to do is because Lambda runs at such extreme scale. One of the things that firecracker is really good for is running at scale. So if you want to build your own firecracker base at scale service, you can have most of the confidence that as long as your workload fits the design parameters, a firecracker, the battle hardening the robustness is being proved out day-to-day by services at scale like Lambda and foggy. For those of you who don't know service support services, you know, in the end, our goal with serverless is to make sure that you don't think about all the infrastructure that your applications run on. We focus on business logic as much as you can. That's how we think about it. And serverless has become its own quote-unquote "Sort of environment." The number of partners and open-source frameworks and tools that are spun up around serverless. In which case mostly, I mean, Lambda, API gateway. So it says like that is pretty high. So, you know, number of open source projects like Zappa server serverless framework, there's so many that have come up that make it easier for our customers to consume AWS services like Lambda and API gateway. We've also done some of our own tooling and frameworks, a serverless application model, AWS jealous. If you're a Python developer, we have these open service runtimes for Lambda, rust dot other options. We have amount of number of tools that we opened source. So in general, you'll find that tooling that we do runtime will tend to be always be open-sourced. We will often take some of the guts of the things that we use to build our systems like firecracker and open-source them while the control plane, etc, AWS services may end up staying proprietary, which is the case in Lambda. Increasingly our customers build their applications and leverage the broader AWS partner network. The AWS partner network is a network of partnerships that we've built of trusted partners. when you go to the APN website and find a partner, they know that that partner meets a certain set of criteria that AWS has developed, and you can rely on those partners for your own business. So whether you're a little tiny business that wants some function fulfill that you don't have the resources for or large enterprise that wants all these applications that you've been using on prem for a long time, and want to keep leveraging them in the cloud, you can go to APN and find that partner and then bring their solution on as part of your cloud infrastructure and could even be a systems integrator, for example, to help you solve this specific development problem that you may have a need for. Increasingly, you know, one of the things we like to do is work with an apartment community that is full of open-source providers. So a great one, there's so many, and you have, we have a panel discussion with many other partners as well, who make it easier for you to build applications on AWS, all open source and built on open source. But I like to call it a couple of them. The first one of them is TIDELIFT. TIDELIFT, For those of you who don't know is a company that provides SAS based tools to curate track, manage open source catalogs. You know, they have a whole network of maintainers and providers. They help, if you're an independent open developer, or a smart team should probably get to know TIDELIFT. They provide you benefits and, you know, capabilities as a developer and maintainer that are pretty unique and really help. And I've seen a number of our open source community embraced TIDELIFT quite honestly, even before they were part of the APN. But as part of the partner network, they get to participate in things like ISP accelerate and they get to they're officially an advanced tier partner because they are, they migrated the SAS offering onto AWS. But in the end, if you're part of the open source supply chain, you're a maintainer, you are a developer. I would recommend working with TIDELIFT because their goal is making all of you who are developing open source solutions, especially on AWS, more successful. And that's why I enjoy this partnership with them. And I'm looking to do a lot more because I think as a company, we want to make sure that open source developers don't feel like they are not supported because all you have to do is read various forums. It's challenging often to be a maintainer, especially of a small project. So I think with helping with licensing license management, security identification remediation, helping these maintainers is a big part of what TIDELIFT to us and it was great to see them as part of a partner network. Another partner that I like to call sysdig. I actually got introduced to them many years ago when they first launched. And one of the things that happened where they were super interested in some of our serverless stuff. And we've been trying to figure out how we can work together because all of our customers are interested in the capabilities that cystic provides. And over the last few years, he found a number of areas where we can collaborate. So sysdig, I know them primarily in a security company. So people use cystic to secure the bills, detect, you know, do threat response, threat detection, completely continuously validate their posture, get this continuous analytics signal on how they're doing and monitor performance. At the end of it, it's a SAS platform. They have a very nice open source security stack. The one I'm most familiar with. And I think most of you are probably familiar with is Falco. You know, sysdig, a CNCF project has been super popular. It's just to go SSS what 3, 37, 40 million downloads by now. So that's pretty, pretty cool. And they have been a great partner because we've had to do make sure that their solution works at target, which is not a natural place for their software to run, but there was enough demand and interest from our customers that, you know, or both companies leaned in to make sure they can be successful. So last year sister got a security competency. We have a number of specific competencies that we for our partners, they have integration and security hub is great. partners are lean in the way cystic has onto making our customer successful. And working with us are the best partners that we have. And there's a number of open source companies out there built on open source where their entire portfolio is built on open source software or the active participants like we are that we love working with on a day to day basis. So, you know, I think the thing I would like to, as we wind this out in this presentation is, you know, AWS is constantly looking for partnerships because our partners enable our customers. They could be with companies like Redis with Mongo, confluent with Databricks customers. Your default reaction might be, "Hey, these are companies that maybe compete with AWS." but no, I mean, I think we are partners as well, like from somebody at the lower end of the spectrum where people run on top of the services that I own on Linux and containers are SE 2, For us, these partners are just as important customers as any AWS service or any third party, 20 external customer. And so it's not a zero sum game. We look forward to working with all these companies and open source projects from an AWS perspective, a big part of how, where my open source program spends its time is making it easy for our developers to contribute, to open source, making it easy for AWS teams to decide when to open source software or participate in open source projects. Over the last few years, we've made significant changes in how we reduce the friction. And I think you can see it in the results that I showed you earlier in this stock. And the last one is one of the most important things that I say and I'll keep saying that, that we do as AWS is carry the pager. There's a lot of open source projects out there, operationalizing them, running them at scale is not easy. It's not all for whatever reason. It may not have anything to do with the software itself. But our core competency is taking that and being really good at operating it and becoming experts at operating it. And then ideally taking that expertise and experience and operating that project, that software and contributing back upstream. Cause that makes it better for everybody. And I think you'll see us do a lot more of that going forward. We've been doing that for the last few years, you know, in the container space, we do it every day. And I'm excited about the possibilities. With that. Thank you very much. And I hope you enjoy the rest of the showcase. >> Okay. Welcome back. We have Deepak sing here. We just had the keynote closing keynote vice-president of compute services. Deepak. Great to a great keynote, great wisdom and insight from that session. A very notable highlights and cutting edge trends and product information. Thanks for sharing. >> No, anytime it's always good to be here. It's too bad that we still doing this virtually, but always good to talk to you, John. >> We'll get hopefully through this way pretty quickly, I want to jump right in. Cause we don't have a lot of time. I want to get some quick question. You've brought up a good things. Open source innovation. Okay. Going next level. You've seen the rise of super clouds and super apps developing at open source. You're seeing big companies contributing, you know, you mentioned Argo into it. You're seeing that dynamic where companies are forming around this. This is a rising tide. This is, this is actually real. It's not the old school of, okay, here's a project. And then someone manages support and commercialization of it. It's actually platform in cloud scale. This is next gen. >> Yeah. And actually I think it started a few years ago. We can talk about a company that, you know, you're very familiar with as part of this event, which is armory many years ago, Netflix spun off this project called Spinnaker. A Spinnaker is CISED you know, CSED system that was developed at Netflix for their own purposes, but they chose to open solicit. And since then, it's become very popular with customers who want to use it even on prem. And you have a company that spun up on it. I think what's making this world very unique is you have very large companies like Facebook that will build things for themselves like VITAS or Netflix with Spinnaker and open source them. And you can have a lot of discussion about why they chose to do so, etc. But increasingly that's becoming the default when Amazon or Netflix or Facebook or Mehta, I guess you call them these days, build something for themselves for their own needs. The first question we ask ourselves is, should it be opensource? And increasingly we are all saying yes. And here's what happens because of that. It gives an opportunity depending on how you open source it for innovation through commercial deployments, so that you get SaaS companies, you know, that are going to take that product and make it relevant and useful to a very broad number of customers. You build partnerships with cloud providers like AWS, because our customers love this open source project and they need help. And they may choose an AWS managed service, or they may end up working with this partner on a day-to-day basis. And we want to work with that partner because they're making our customers successful, which is one reason all of us are here. So you're having this set of innovation from large companies from, you know, whether they are just consumer companies like Metta infrastructure companies like us, or just random innovation that's happening in an open source project that which ends up in companies being spun up and that foster that innovative innovation and that flywheel that's happening right now. And I think you said that like, this is unique. I mean, you never saw this happen before from so many different directions. >> It really is a nice progression on the business model side as well. You mentioned Argo, which is a great organic thing that was Intuit developed. We just interviewed code fresh. They just presented here in the showcase as well. You seeing the formation around these projects develop now in the community at a different scale. I mean, look at code fresh. I mean, Intuit did it Argo and they're not just supporting it. They're building a platform. So you seeing the dynamics of tools and now emerging the platforms, you mentioned Lambda, okay. Which is proprietary for AWS and your talk powered by open source. So again, open source combined with cloud scale allows for new potential super applications or super clouds that are developing. This is a new phenomenon. This isn't just lift and shift and host on the cloud. This is actually a construction production developer workflow. >> Yeah. And you are seeing consumers, large companies, enterprises, startups, you know, it used to be that startups would be comfortable adopting some of these solutions, but now you see companies of all sizes doing so. And I said, it's not just software it's software, the services increasingly becoming the way these are given, delivered to customers. I actually think the innovation is just getting going, which is why we have this. We have so many partners here who are all in inventing and innovating on top of open source, whether it's developed by them or a broader community. >> Yeah. I liked, I liked the represent container. Do you guys have, did that drove that you've seen a lot of changes and again, with cloud scale and open source, you seeing the dynamics change, whether you're enabling that, and then you see kind of like real big change. So let's take snowflake, a big customer of AWS. They started out as a startup too, but they weren't a data warehouse. They were bringing data warehouse like functionality and then changing everything differently and making it consumable for the cloud. And hence they're huge. So that's a disruption into an incumbent leader or sector. Then you've got new capabilities emerging. What's your thoughts, Deepak? Can you share your vision on how you have the disruption to existing leaders, old guard, if you will, as you guys call them and then new capabilities as these new platforms emerge at a net new functionality, how do you see that emerging? >> Yeah. So I speak from my side of the world. I've lived in over the last few years, which has containers and serverless, right? There's a lot of, if you go to any enterprise and ask them, do you want to modernize the infrastructure? Do you want to take advantage of automated software delivery, continuous delivery infrastructure as code modern observability, all of them will say yes, but they also are still a large enterprise, which has these enterprise level requirements. I'm using the word enterprise a lot. And I usually it's a trigger word for me because so many customers have similar requirements, but I'm using it here as large company with a lot of existing software and existing practices. I think the innovation that's coming and I see a lot of companies doing that is saying, "Hey, we understand the problems you want to solve. We understand the world where you live in, which could be regulated." You want to use all these new modalities. How do we allow you to use all of them? Keep the advantages of switching to a Lambda or switching to, and a service running on far gate, but give you the same capabilities. And I think I'll bring up cystic here because we work so closely with them on Falco. As an example, I just talked about them in my keynote. They could have just said, "Oh no, we'll just support the SE2 and be done with it." They said, "No, we're going to make sure that serverless containers in particular are something that you're going to be really good at because our customers want to use them, but requires us to think differently. And then they ended up developing new things like Falco that are born in this new world, but understand the requirements of the old world. If you get what I'm saying. And I think that a real example. >> Yeah. Oh, well, I mean, first of all, they're smart. So that was pretty obvious for most people that know, sees that you can connect the dots on serverless, which is a great point, but not everyone can see that again, this is what's new and and systig was just found in his backyard. As I found out on my interview, a great, great founder, they would do a new thing. So it was a very easy to connect the dots there again, that's the trend. Well, I got to ask if they're doing that for serverless, you mentioned graviton in your speech and what came out of you mentioned graviton in your speech and what came out of re-invent this past year was all the innovation going on at the compute level with gravitron at many levels in the Silicon. How should companies and open source developers think about how to innovate with graviton? >> Yeah, I mean, you've seen examples from people blogging and tweeting about how fast their applications run and grab it on the price performance benefits that they get, whether it's on, you know, whether it's an observability or other places. something that AWS is going to embrace across a compute something that AWS is going to embrace across a compute portfolio. Obviously you can go find EC2 instances, the gravitron two instances and run on them and that'll be great. But we know that most of our customers, many of our customers are building new applications on serverless containers and serveless than even as containers increasingly with things like foggy, where they don't want to operate the underlying infrastructure. A big part of what we're doing is to make sure that graviton is available to you on every compute modality. You can run it on a C2 forever. You've been running, being able to use ECS and EKS and run and grab it on almost since launch. What do you want me to take it a step further? You elastic Beanstalk customers, elastic Beanstalk has been around for a decade, but you can now use it with graviton. people running ECS on for gate can now use graviton. Lambda customers can pick graviton as well. So we're taking this price performance benefits that you get So we're taking this price performance benefits that you get from graviton and basically putting it across the entire compute portfolio. What it means is every high level service that gets built on compute infrastructure. And you get the price performance benefits, you get the price performance benefits of the lower power consumption of arm processes. So I'm personally excited like crazy. And you know, this has graviton 2 graviton 3 is coming. >> That's incredible. It's an opportunity like serverless was it's pretty obvious. And I think hopefully everyone will jump on that final question as the time's ticking here. I want to get your thoughts quickly. If you look at what's happened with containers over the past say eight years since the original founding of the first Docker instance, if you will, to how that's evolved and then the introduction of Kubernetes and the cloud native wave we're seeing now, what is, how would you describe the relationship between the success Docker, seeing now with Kubernetes in the cloud native construct what's different and why is this combination so successful? >> Yeah. I often say that containers would have, let me rephrase that. what I say is that people would have adopted sort of the modern way of running applications, whether containers came around or not. But the fact that containers came around made that migration and that journey is so much more efficient for people. So right from, I still remember the first doc that Solomon gave Billy announced DACA and starting to use it on customers, starting to get interested all the way to the more sort of advanced orchestration that we have now for containers across the board. And there's so many examples of the way you can do that. Kubernetes being the most, most well-known one. Here's the thing that I think has changed. I think what Kubernetes or Docker, or the whole sort of modern way of building applications has done is it's taken people who would have taken years adopting these practices and by bringing it right to the fingertips and rebuilding it into the APIs. And in the case of Kubernetes building an entire sort of software world around it, the number of, I would say number of decisions people have to take has gone smaller in many ways. There's so many options, the number of decisions that become higher, but the com the speed at which they can get to a result and a production version of an application that works for them is way low. I have not seen anything like what I've seen in the last 6, 7, 8 years of how quickly the most you know, the most I would say is, you know, a company that you would think would never adopt modern technology has been able to go from, this is interesting to getting a production really quickly. And I think it's because the tooling makes it So, and the fact that you see the adoption that you see right and the fact that you see the adoption that you see right from the fact that you could do Docker run Docker, build Docker, you know, so easily back in the day, all the way to all the advanced orchestration you can do with container orchestrator is today. sort of taking all of that away as well. there's never been a better time to be a developer independent of whatever you're trying to build. And I think containers are a big central part of why that's happened. >> Like the recipe, the combination of cloud-scale, the timing of Kubernetes and the containerization concepts just explode as a beautiful thing. And it creates more opportunities and will challenges, which are opportunities that are net new, but it solves the automation piece that we're seeing this again, it's only makes things go faster. >> Yes. >> And that's the key trend. Deepak, thank you so much for coming on. We're seeing tons of open cloud innovations, thanks to the success of your team at AWS and being great participants in the community. We're seeing innovations from startups. You guys are helping enabling that. Of course, they want to live on their own and be successful and build their super clouds and super app. So thank you for spending the time with us. Appreciate. >> Yeah. Anytime. And thank you. And you know, this is a great event. So I look forward to people running software and building applications, using AWS services and all these wonderful partners that we have. >> Awesome, great stuff. Great startups, great next generation leaders emerging. When you see startups, when they get successful, they become the modern software applications platforms out there powering business and changing the world. This is the cube you're watching the AWS startup showcase. Season two episode one open cloud innovations on John Furrier your host, see you next time.

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> The pandemic has changed the way we think about and predict the future. As we enter the third year of a global pandemic, we see the significant impact that it's had on technology strategy, spending patterns, and company fortunes Much has changed. And while many of these changes were forced reactions to a new abnormal, the trends that we've seen over the past 24 months have become more entrenched, and point to the way that's coming ahead in the technology business. Hello and welcome to this week's Wikibon CUBE Insights powered by ETR. In this Breaking Analysis, we welcome our partner and colleague and business friend, Erik Porter Bradley, as we deliver what's becoming an annual tradition for Erik and me, our predictions for Enterprise Technology in 2022 and beyond Erik, welcome. Thanks for taking some time out. >> Thank you, Dave. Luckily we did pretty well last year, so we were able to do this again. So hopefully we can keep that momentum going. >> Yeah, you know, I want to mention that, you know, we get a lot of inbound predictions from companies and PR firms that help shape our thinking. But one of the main objectives that we have is we try to make predictions that can be measured. That's why we use a lot of data. Now not all will necessarily fit that parameter, but if you've seen the grading of our 2021 predictions that Erik and I did, you'll see we do a pretty good job of trying to put forth prognostications that can be declared correct or not, you know, as black and white as possible. Now let's get right into it. Our first prediction, we're going to go run into spending, something that ETR surveys for quarterly. And we've reported extensively on this. We're calling for tech spending to increase somewhere around 8% in 2022, we can see there on the slide, Erik, we predicted spending last year would increase by 4% IDC. Last check was came in at five and a half percent. Gardner was somewhat higher, but in general, you know, not too bad, but looking ahead, we're seeing an acceleration from the ETR September surveys, as you can see in the yellow versus the blue bar in this chart, many of the SMBs that were hard hit by the pandemic are picking up spending again. And the ETR data is showing acceleration above the mean for industries like energy, utilities, retail, and services, and also, notably, in the Forbes largest 225 private companies. These are companies like Mars or Koch industries. They're predicting well above average spending for 2022. So Erik, please weigh in here. >> Yeah, a lot to bring up on this one, I'm going to be quick. So 1200 respondents on this, over a third of which were at the C-suite level. So really good data that we brought in, the usual bucket of, you know, fortune 500, global 2000 make up the meat of that median, but it's 8.3% and rising with momentum as we see. What's really interesting right now is that energy and utilities. This is usually like, you know, an orphan stock dividend type of play. You don't see them at the highest point of tech spending. And the reason why right now is really because this state of tech infrastructure in our energy infrastructure needs help. And it's obvious, remember the Florida municipality break reach last year? When they took over the water systems or they had the ability to? And this is a real issue, you know, there's bad nation state actors out there, and I'm no alarmist, but the energy and utility has to spend this money to keep up. It's really important. And then you also hit on the retail consumer. Obviously what's happened, the work from home shift created a shop from home shift, and the trends that are happening right now in retail. If you don't spend and keep up, you're not going to be around much longer. So I think the really two interesting things here to call out are energy utilities, usually a laggard in IT spend and it's leading, and also retail consumer, a lot of changes happening. >> Yeah. Great stuff. I mean, I recall when we entered the pandemic, really ETR was the first to emphasize the impact that work from home was going to have, so I really put a lot of weight on this data. Okay. Our next prediction is we're going to get into security, it's one of our favorite topics. And that is that the number one priority that needs to be addressed by organizations in 2022 is security and you can see, in this slide, the degree to which security is top of mind, relative to some other pretty important areas like cloud, productivity, data, and automation, and some others. Now people may say, "Oh, this is obvious." But I'm going to add some context here, Erik, and then bring you in. First, organizations, they don't have unlimited budgets. And there are a lot of competing priorities for dollars, especially with the digital transformation mandate. And depending on the size of the company, this data will vary. For example, while security is still number one at the largest public companies, and those are of course of the biggest spenders, it's not nearly as pronounced as it is on average, or in, for example, mid-sized companies and government agencies. And this is because midsized companies or smaller companies, they don't have the resources that larger companies do. Larger companies have done a better job of securing their infrastructure. So these mid-size firms are playing catch up and the data suggests cyber is even a bigger priority there, gaps that they have to fill, you know, going forward. And that's why we think there's going to be more demand for MSSPs, managed security service providers. And we may even see some IPO action there. And then of course, Erik, you and I have talked about events like the SolarWinds Hack, there's more ransomware attacks, other vulnerabilities. Just recently, like Log4j in December. All of this has heightened concerns. Now I want to talk a little bit more about how we measure this, you know, relatively, okay, it's an obvious prediction, but let's stick our necks out a little bit. And so in addition to the rise of managed security services, we're calling for M&A and/or IPOs, we've specified some names here on this chart, and we're also pointing to the digital supply chain as an area of emphasis. Again, Log4j really shone that under a light. And this is going to help the likes of Auth0, which is now Okta, SailPoint, which is called out on this chart, and some others. We're calling some winners in end point security. Erik, you're going to talk about sort of that lifecycle, that transformation that we're seeing, that migration to new endpoint technologies that are going to benefit from this reset refresh cycle. So Erik, weigh in here, let's talk about some of the elements of this prediction and some of the names on that chart. >> Yeah, certainly. I'm going to start right with Log4j top of mind. And the reason why is because we're seeing a real paradigm shift here where things are no longer being attacked at the network layer, they're being attacked at the application layer, and in the application stack itself. And that is a huge shift left. And that's taking in DevSecOps now as a real priority in 2022. That's a real paradigm shift over the last 20 years. That's not where attacks used to come from. And this is going to have a lot of changes. You called out a bunch of names in there that are, they're either going to work. I would add to that list Wiz. I would add Orca Security. Two names in our emerging technology study, in addition to the ones you added that are involved in cloud security and container security. These names are either going to get gobbled up. So the traditional legacy names are going to have to start writing checks and, you know, legacy is not fair, but they're in the data center, right? They're, on-prem, they're not cloud native. So these are the names that money is going to be flowing to. So they're either going to get gobbled up, or we're going to see some IPO's. And on the other thing I want to talk about too, is what you mentioned. We have CrowdStrike on that list, We have SentinalOne on the list. Everyone knows them. Our data was so strong on Tanium that we actually went positive for the first time just today, just this morning, where that was released. The trifecta of these are so important because of what you mentioned, under resourcing. We can't have security just tell us when something happens, it has to automate, and it has to respond. So in this next generation of EDR and XDR, an automated response has to happen because people are under-resourced, salaries are really high, there's a skill shortage out there. Security has to become responsive. It can't just monitor anymore. >> Yeah. Great. And we should call out too. So we named some names, Snyk, Aqua, Arctic Wolf, Lacework, Netskope, Illumio. These are all sort of IPO, or possibly even M&A candidates. All right. Our next prediction goes right to the way we work. Again, something that ETR has been on for awhile. We're calling for a major rethink in remote work for 2022. We had predicted last year that by the end of 2021, there'd be a larger return to the office with the norm being around a third of workers permanently remote. And of course the variants changed that equation and, you know, gave more time for people to think about this idea of hybrid work and that's really come in to focus. So we're predicting that is going to overtake fully remote as the dominant work model with only about a third of the workers back in the office full-time. And Erik, we expect a somewhat lower percentage to be fully remote. It's now sort of dipped under 30%, at around 29%, but it's still significantly higher than the historical average of around 15 to 16%. So still a major change, but this idea of hybrid and getting hybrid right, has really come into focus. Hasn't it? >> Yeah. It's here to stay. There's no doubt about it. We started this in March of 2020, as soon as the virus hit. This is the 10th iteration of the survey. No one, no one ever thought we'd see a number where only 34% of people were going to be in office permanently. That's a permanent number. They're expecting only a third of the workers to ever come back fully in office. And against that, there's 63% that are saying their permanent workforce is going to be either fully remote or hybrid. And this, I can't really explain how big of a paradigm shift this is. Since the start of the industrial revolution, people leave their house and go to work. Now they're saying that's not going to happen. The economic impact here is so broad, on so many different areas And, you know, the reason is like, why not? Right? The productivity increase is real. We're seeing the productivity increase. Enterprises are spending on collaboration tools, productivity tools, We're seeing an increased perception in productivity of their workforce. And the CFOs can cut down an expense item. I just don't see a reason why this would end, you know, I think it's going to continue. And I also want to point out these results, as high as they are, were before the Omicron wave hit us. I can only imagine what these results would have been if we had sent the survey out just two or three weeks later. >> Yeah. That's a great point. Okay. Next prediction, we're going to look at the supply chain, specifically in how it's affecting some of the hardware spending and cloud strategies in the future. So in this chart, ETRS buyers, have you experienced problems procuring hardware as a result of supply chain issues? And, you know, despite the fact that some companies are, you know, I would call out Dell, for example, doing really well in terms of delivering, you can see that in the numbers, it's pretty clear, there's been an impact. And that's not not an across the board, you know, thing where vendors are able to deliver, especially acute in PCs, but also pronounced in networking, also in firewall servers and storage. And what's interesting is how companies are responding and reacting. So first, you know, I'm going to call the laptop and PC demand staying well above pre-COVID norms. It had peaked in 2012. Pre-pandemic it kept dropping and dropping and dropping, in terms of, you know, unit volume, where the market was contracting. And we think can continue to grow this year in double digits in 2022. But what's interesting, Erik, is when you survey customers, is despite the difficulty they're having in procuring network hardware, there's as much of a migration away from existing networks to the cloud. You could probably comment on that. Their networks are more fossilized, but when it comes to firewalls and servers and storage, there's a much higher propensity to move to the cloud. 30% of customers that ETR surveyed will replace security appliances with cloud services and 41% and 34% respectively will move to cloud compute and storage in 2022. So cloud's relentless march on traditional on-prem models continues. Erik, what do you make of this data? Please weigh in on this prediction. >> As if we needed another reason to go to the cloud. Right here, here it is yet again. So this was added to the survey by client demand. They were asking about the procurement difficulties, the supply chain issues, and how it was impacting our community. So this is the first time we ran it. And it really was interesting to see, you know, the move there. And storage particularly I found interesting because it correlated with a huge jump that we saw on one of our vendor names, which was Rubrik, had the highest net score that it's ever had. So clearly we're seeing some correlation with some of these names that are there, you know, really well positioned to take storage, to take data into the cloud. So again, you didn't need another reason to, you know, hasten this digital transformation, but here we are, we have it yet again, and I don't see it slowing down anytime soon. >> You know, that's a really good point. I mean, it's not necessarily bad news for the... I mean, obviously you wish that it had no change, would be great, but things, you know, always going to change. So we'll talk about this a little bit later when we get into the Supercloud conversation, but this is an opportunity for people who embrace the cloud. So we'll come back to that. And I want to hang on cloud a bit and share some recent projections that we've made. The next prediction is the big four cloud players are going to surpass 167 billion, an IaaS and PaaS revenue in 2022. We track this. Observers of this program know that we try to create an apples to apples comparison between AWS, Azure, GCP and Alibaba in IaaS and PaaS. So we're calling for 38% revenue growth in 2022, which is astounding for such a massive market. You know, AWS is probably not going to hit a hundred billion dollar run rate, but they're going to be close this year. And we're going to get there by 2023, you know they're going to surpass that. Azure continues to close the gap. Now they're about two thirds of the size of AWS and Google, we think is going to surpass Alibaba and take the number three spot. Erik, anything you'd like to add here? >> Yeah, first of all, just on a sector level, we saw our sector, new survey net score on cloud jumped another 10%. It was already really high at 48. Went up to 53. This train is not slowing down anytime soon. And we even added an edge compute type of player, like CloudFlare into our cloud bucket this year. And it debuted with a net score of almost 60. So this is really an area that's expanding, not just the big three, but everywhere. We even saw Oracle and IBM jump up. So even they're having success, taking some of their on-prem customers and then selling them to their cloud services. This is a massive opportunity and it's not changing anytime soon, it's going to continue. >> And I think the operative word there is opportunity. So, you know, the next prediction is something that we've been having fun with and that's this Supercloud becomes a thing. Now, the reason I say we've been having fun is we put this concept of Supercloud out and it's become a bit of a controversy. First, you know, what the heck's the Supercloud right? It's sort of a buzz-wordy term, but there really is, we believe, a thing here. We think there needs to be a rethinking or at least an evolution of the term multi-cloud. And what we mean is that in our view, you know, multicloud from a vendor perspective was really cloud compatibility. It wasn't marketed that way, but that's what it was. Either a vendor would containerize its legacy stack, shove it into the cloud, or a company, you know, they'd do the work, they'd build a cloud native service on one of the big clouds and they did do it for AWS, and then Azure, and then Google. But there really wasn't much, if any, leverage across clouds. Now from a buyer perspective, we've always said multicloud was a symptom of multi-vendor, meaning I got different workloads, running in different clouds, or I bought a company and they run on Azure, and I do a lot of work on AWS, but generally it wasn't necessarily a prescribed strategy to build value on top of hyperscale infrastructure. There certainly was somewhat of a, you know, reducing lock-in and hedging the risk. But we're talking about something more here. We're talking about building value on top of the hyperscale gift of hundreds of billions of dollars in CapEx. So in addition, we're not just talking about transforming IT, which is what the last 10 years of cloud have been like. And, you know, doing work in the cloud because it's cheaper or simpler or more agile, all of those things. So that's beginning to change. And this chart shows some of the technology vendors that are leaning toward this Supercloud vision, in our view, building on top of the hyperscalers that are highlighted in red. Now, Jerry Chan at Greylock, they wrote a piece called Castles in the Cloud. It got our thinking going, and he and the team at Greylock, they're building out a database of all the cloud services and all the sub-markets in cloud. And that got us thinking that there's a higher level of abstraction coalescing in the market, where there's tight integration of services across clouds, but the underlying complexity is hidden, and there's an identical experience across clouds, and even, in my dreams, on-prem for some platforms, so what's new or new-ish and evolving are things like location independence, you've got to include the edge on that, metadata services to optimize locality of reference and data source awareness, governance, privacy, you know, application independent and dependent, actually, recovery across clouds. So we're seeing this evolve. And in our view, the two biggest things that are new are the technology is evolving, where you're seeing services truly integrate cross-cloud. And the other big change is digital transformation, where there's this new innovation curve developing, and it's not just about making your IT better. It's about SaaS-ifying and automating your entire company workflows. So Supercloud, it's not just a vendor thing to us. It's the evolution of, you know, the, the Marc Andreessen quote, "Every company will be a SaaS company." Every company will deliver capabilities that can be consumed as cloud services. So Erik, the chart shows spending momentum on the y-axis and net score, or presence in the ETR data center, or market share on the x-axis. We've talked about snowflake as the poster child for this concept where the vision is you're in their cloud and sharing data in that safe place. Maybe you could make some comments, you know, what do you think of this Supercloud concept and this change that we're sensing in the market? >> Well, I think you did a great job describing the concept. So maybe I'll support it a little bit on the vendor level and then kind of give examples of the ones that are doing it. You stole the lead there with Snowflake, right? There is no better example than what we've seen with what Snowflake can do. Cross-portability in the cloud, the ability to be able to be, you know, completely agnostic, but then build those services on top. They're better than anything they could offer. And it's not just there. I mean, you mentioned edge compute, that's a whole nother layer where this is coming in. And CloudFlare, the momentum there is out of control. I mean, this is a company that started off just doing CDN and trying to compete with Okta Mite. And now they're giving you a full soup to nuts with security and actual edge compute layer, but it's a fantastic company. What they're doing, it's another great example of what you're seeing here. I'm going to call out HashiCorp as well. They're more of an infrastructure services, a little bit more of an open-source freemium model, but what they're doing as well is completely cloud agnostic. It's dynamic. It doesn't care if you're in a container, it doesn't matter where you are. They recently IPO'd and they're down 25%, but their data looks so good across both of our emerging technology and TISA survey. It's certainly another name that's playing on this. And another one that we mentioned as well is Rubrik. If you need storage, compute, and in the cloud layer and you need to be agnostic to it, they're another one that's really playing in this space. So I think it's a great concept you're bringing up. I think it's one that's here to stay and there's certainly a lot of vendors that fit into what you're describing. >> Excellent. Thank you. All right, let's shift to data. The next prediction, it might be a little tough to measure. Before I said we're trying to be a little black and white here, but it relates to Data Mesh, which is, the ideas behind that term were created by Zhamak Dehghani of ThoughtWorks. And we see Data Mesh is really gaining momentum in 2022, but it's largely going to be, we think, confined to a more narrow scope. Now, the impetus for change in data architecture in many companies really stems from the fact that their Hadoop infrastructure really didn't solve their data problems and they struggle to get more value out of their data investments. Data Mesh prescribes a shift to a decentralized architecture in domain ownership of data and a shift to data product thinking, beyond data for analytics, but data products and services that can be monetized. Now this a very powerful in our view, but they're difficult for organizations to get their heads around and further decentralization creates the need for a self-service platform and federated data governance that can be automated. And not a lot of standards around this. So it's going to take some time. At our power panel a couple of weeks ago on data management, Tony Baer predicted a backlash on Data Mesh. And I don't think it's going to be so much of a backlash, but rather the adoption will be more limited. Most implementations we think are going to use a starting point of AWS and they'll enable domains to access and control their own data lakes. And while that is a very small slice of the Data Mesh vision, I think it's going to be a starting point. And the last thing I'll say is, this is going to take a decade to evolve, but I think it's the right direction. And whether it's a data lake or a data warehouse or a data hub or an S3 bucket, these are really, the concept is, they'll eventually just become nodes on the data mesh that are discoverable and access is governed. And so the idea is that the stranglehold that the data pipeline and process and hyper-specialized roles that they have on data agility is going to evolve. And decentralized architectures and the democratization of data will eventually become a norm for a lot of different use cases. And Erik, I wonder if you'd add anything to this. >> Yeah. There's a lot to add there. The first thing that jumped out to me was that that mention of the word backlash you said, and you said it's not really a backlash, but what it could be is these are new words trying to solve an old problem. And I do think sometimes the industry will notice that right away and maybe that'll be a little pushback. And the problems are what you already mentioned, right? We're trying to get to an area where we can have more assets in our data site, more deliverable, and more usable and relevant to the business. And you mentioned that as self-service with governance laid on top. And that's really what we're trying to get to. Now, there's a lot of ways you can get there. Data fabric is really the technical aspect and data mesh is really more about the people, the process, and the governance, but the two of those need to meet, in order to make that happen. And as far as tools, you know, there's even cataloging names like Informatica that play in this, right? Istio plays in this, Snowflake plays in this. So there's a lot of different tools that will support it. But I think you're right in calling out AWS, right? They have AWS Lake, they have AWS Glue. They have so much that's trying to drive this. But I think the really important thing to keep here is what you said. It's going to be a decade long journey. And by the way, we're on the shoulders of giants a decade ago that have even gotten us to this point to talk about these new words because this has been an ongoing type of issue, but ultimately, no matter which vendors you use, this is going to come down to your data governance plan and the data literacy in your business. This is really about workflows and people as much as it is tools. So, you know, the new term of data mesh is wonderful, but you still have to have the people and the governance and the processes in place to get there. >> Great, thank you for that, Erik. Some great points. All right, for the next prediction, we're going to shine the spotlight on two of our favorite topics, Snowflake and Databricks, and the prediction here is that, of course, Databricks is going to IPO this year, as expected. Everybody sort of expects that. And while, but the prediction really is, well, while these two companies are facing off already in the market, they're also going to compete with each other for M&A, especially as Databricks, you know, after the IPO, you're going to have, you know, more prominence and a war chest. So first, these companies, they're both looking pretty good, the same XY graph with spending velocity and presence and market share on the horizontal axis. And both Snowflake and Databricks are well above that magic 40% red dotted line, the elevated line, to us. And for context, we've included a few other firms. So you can see kind of what a good position these two companies are really in, especially, I mean, Snowflake, wow, it just keeps moving to the right on this horizontal picture, but maintaining the next net score in the Y axis. Amazing. So, but here's the thing, Databricks is using the term Lakehouse implying that it has the best of data lakes and data warehouses. And Snowflake has the vision of the data cloud and data sharing. And Snowflake, they've nailed analytics, and now they're moving into data science in the domain of Databricks. Databricks, on the other hand, has nailed data science and is moving into the domain of Snowflake, in the data warehouse and analytics space. But to really make this seamless, there has to be a semantic layer between these two worlds and they're either going to build it or buy it or both. And there are other areas like data clean rooms and privacy and data prep and governance and machine learning tooling and AI, all that stuff. So the prediction is they'll not only compete in the market, but they'll step up and in their competition for M&A, especially after the Databricks IPO. We've listed some target names here, like Atscale, you know, Iguazio, Infosum, Habu, Immuta, and I'm sure there are many, many others. Erik, you care to comment? >> Yeah. I remember a year ago when we were talking Snowflake when they first came out and you, and I said, "I'm shocked if they don't use this war chest of money" "and start going after more" "because we know Slootman, we have so much respect for him." "We've seen his playbook." And I'm actually a little bit surprised that here we are, at 12 months later, and he hasn't spent that money yet. So I think this prediction's just spot on. To talk a little bit about the data side, Snowflake is in rarefied air. It's all by itself. It is the number one net score in our entire TISA universe. It is absolutely incredible. There's almost no negative intentions. Global 2000 organizations are increasing their spend on it. We maintain our positive outlook. It's really just, you know, stands alone. Databricks, however, also has one of the highest overall net sentiments in the entire universe, not just its area. And this is the first time we're coming up positive on this name as well. It looks like it's not slowing down. Really interesting comment you made though that we normally hear from our end-user commentary in our panels and our interviews. Databricks is really more used for the data science side. The MLAI is where it's best positioned in our survey. So it might still have some catching up to do to really have that caliber of usability that you know Snowflake is seeing right now. That's snowflake having its own marketplace. There's just a lot more to Snowflake right now than there is Databricks. But I do think you're right. These two massive vendors are sort of heading towards a collision course, and it'll be very interesting to see how they deploy their cash. I think Snowflake, with their incredible management and leadership, probably will make the first move. >> Well, I think you're right on that. And by the way, I'll just add, you know, Databricks has basically said, hey, it's going to be easier for us to come from data lakes into data warehouse. I'm not sure I buy that. I think, again, that semantic layer is a missing ingredient. So it's going to be really interesting to see how this plays out. And to your point, you know, Snowflake's got the war chest, they got the momentum, they've got the public presence now since November, 2020. And so, you know, they're probably going to start making some aggressive moves. Anyway, next prediction is something, Erik, that you and I have talked about many, many times, and that is observability. I know it's one of your favorite topics. And we see this world screaming for more consolidation it's going all in on cloud native. These legacy stacks, they're fighting to stay relevant, but the direction is pretty clear. And the same XY graph lays out the players in the field, with some of the new entrants that we've also highlighted, like Observe and Honeycomb and ChaosSearch that we've talked about. Erik, we put a big red target around Splunk because everyone wants their gold. So please give us your thoughts. >> Oh man, I feel like I've been saying negative things about Splunk for too long. I've got a bad rap on this name. The Splunk shareholders come after me all the time. Listen, it really comes down to this. They're a fantastic company that was designed to do logging and monitoring and had some great tool sets around what you could do with it. But they were designed for the data center. They were designed for prem. The world we're in now is so dynamic. Everything I hear from our end user community is that all net new workloads will be going to cloud native players. It's that simple. So Splunk has entrenched. It's going to continue doing what it's doing and it does it really, really well. But if you're doing something new, the new workloads are going to be in a dynamic environment and that's going to go to the cloud native players. And in our data, it is extremely clear that that means Datadog and Elastic. They are by far number one and two in net score, increase rates, adoption rates. It's not even close. Even New Relic actually is starting to, you know, entrench itself really well. We saw New Relic's adoption's going up, which is super important because they went to that freemium model, you know, to try to get their little bit of an entrenched customer base and that's working as well. And then you made a great list here, of all the new entrants, but it goes beyond this. There's so many more. In our emerging technology survey, we're seeing Century, Catchpoint, Securonix, Lucid Works. There are so many options in this space. And let's not forget, the biggest data that we're seeing is with Grafana. And Grafana labs as yet to turn on their enterprise. Elastic did it, why can't Grafana labs do it? They have an enterprise stack. So when you look at how crowded this space is, there has to be consolidation. I recently hosted a panel and every single guy on that panel said, "Please give me a consolidation." Because they're the end users trying to actually deploy these and it's getting a little bit confusing. >> Great. Thank you for that. Okay. Last prediction. Erik, might be a little out of your wheelhouse, but you know, you might have some thoughts on it. And that's a hybrid events become the new digital model and a new category in 2022. You got these pure play digital or virtual events. They're going to take a back seat to in-person hybrids. The virtual experience will eventually give way to metaverse experiences and that's going to take some time, but the physical hybrid is going to drive it. And metaverse is ultimately going to define the virtual experience because the virtual experience today is not great. Nobody likes virtual. And hybrid is going to become the business model. Today's pure virtual experience has to evolve, you know, theCUBE first delivered hybrid mid last decade, but nobody really wanted it. We did Mobile World Congress last summer in Barcelona in an amazing hybrid model, which we're showing in some of the pictures here. Alex, if you don't mind bringing that back up. And every physical event that we're we're doing now has a hybrid and virtual component, including the pre-records. You can see in our studios, you see that the green screen. I don't know. Erik, what do you think about, you know, the Zoom fatigue and all this. I know you host regular events with your round tables, but what are your thoughts? >> Well, first of all, I think you and your company here have just done an amazing job on this. So that's really your expertise. I spent 20 years of my career hosting intimate wall street idea dinners. So I'm better at navigating a wine list than I am navigating a conference floor. But I will say that, you know, the trend just goes along with what we saw. If 35% are going to be fully remote. If 70% are going to be hybrid, then our events are going to be as well. I used to host round table dinners on, you know, one or two nights a week. Now those have gone virtual. They're now panels. They're now one-on-one interviews. You know, we do chats. We do submitted questions. We do what we can, but there's no reason that this is going to change anytime soon. I think you're spot on here. >> Yeah. Great. All right. So there you have it, Erik and I, Listen, we always love the feedback. Love to know what you think. Thank you, Erik, for your partnership, your collaboration, and love doing these predictions with you. >> Yeah. I always enjoy them too. And I'm actually happy. Last year you made us do a baker's dozen, so thanks for keeping it to 10 this year. >> (laughs) We've got a lot to say. I know, you know, we cut out. We didn't do much on crypto. We didn't really talk about SaaS. I mean, I got some thoughts there. We didn't really do much on containers and AI. >> You want to keep going? I've got another 10 for you. >> RPA...All right, we'll have you back and then let's do that. All right. All right. Don't forget, these episodes are all available as podcasts, wherever you listen, all you can do is search Breaking Analysis podcast. Check out ETR's website at etr.plus, they've got a new website out. It's the best data in the industry, and we publish a full report every week on wikibon.com and siliconangle.com. You can always reach out on email, David.Vellante@siliconangle.com I'm @DVellante on Twitter. Comment on our LinkedIn posts. This is Dave Vellante for the Cube Insights powered by ETR. Have a great week, stay safe, be well. And we'll see you next time. (mellow music)

(bright music) >> Surprising many people, including myself, Oracle last year began investing pretty heavily in the MySQL space. Now those investments continue today. Let me give you a brief history. Last December, Oracle made its first HeatWave announcement. Where converged OLTP and OLAP together in a single MySQL database. Now, what wasn't surprising was the approach Oracle took. They leveraged hardware to improve the performance and lower the cost. You see when Oracle acquired Sun more than a decade ago, rather than rely on loosely coupled partnerships with hardware vendors to speed up its databases. Oracle set out on a path to tightly integrate hardware and software innovations using its own in-house engineering. So with his first, MySQL HeatWave announcement, Oracle leaned heavily on developing software on top of an in-memory database technology to create an embedded OLAP capability that eliminates the need for ETL and data from a transaction system into a separate analytics database. Now in doing so, Oracle is taking a similar approach with its MySQL today, as it does for its, or back then, whereas it does for its mainstream Oracle database. And today extends that. And what I mean by that is it's converging capabilities in a single platform. So the argument is this simplifies and accelerates analytics that lowers the costs and allows analytics, things like analytics to be run on data that is more fresh. Now, as many of you know, this is a different strategy than how, for example, an AWS approaches database where it creates purpose-built database services, targeted at specific workloads. These are philosophical design decisions made for a variety of reasons, but it's very clear which direction Oracle is headed in. Today, Oracle continues its HeatWave announcement cadence with a focus on increased automation as well. The company is continuing the trend of using clustering technology to scale out for both performance and capacity. And again, that theme of marrying hardware with software Oracle is also making announcements that focus on security. Hello everyone and welcome to this video exclusive. This is Dave Vellante. We're going to dig into these capabilities, Nipun Agarwal here. He's VP of MySQL HeatWave and advanced development in Oracle. Nipun has been leading the MySQL and HeatWave development effort for nearly a decade. He's got 180 patents to his name about half of which are associated with HeatWave. Nipun, welcome back to the show. Great to have you. >> Thank you, Dave. >> So before we get into the new news, if we could, maybe you could give us all a quick overview of HeatWave again, and what problems you originally set out to solve with it? >> Sure. So HeatWave is a in-memory query accelerator for MySQL. Now, as most people are aware, MySQL was originally designed and optimized for transactional processing. So when customers had the need to run analytics, they would need to extract data from the, MySQL database into another database and run analytics. With MySQL HeatWave, customers get a single database, which can be used both for transactional processing and for analytics. There's no need to move the data from one database to another database and all existing tools and applications, which are compatible with MySQL, continue to work as is. So in-memory query accelerator for MySQL and this is significantly faster than any version of MySQL database. And also it's much faster than specialized databases for analytics. >> Yeah, we're going to talk about that. And so obviously when you made the announcement last December, you had, I'm sure, a core group of, of early customers and beta customers, but then you opened it up to the world. So what was the reaction once you expose that to customers? >> The reaction has been very positive, Dave. So initially we're thinking that they're going to be a lot of customers who are on premise users of MySQL, who are going to migrate to the service. And surely that was the case. But the part which was very interesting and surprising is that we see many customers who are migrating from other cloud vendors or migrating from other cloud services to MySQL HeatWave. And most notably the biggest number of migrations we are seeing are from AWS Aurora and AWS RDS. >> Interesting. Okay. I wonder if you've got other feedback you're obviously responding in a pretty, pretty fast cadence here, you know, seven, eight month cadence. What are the feedback that you get, were there gaps that customers wanted you to to close? >> Sure. Yes. So as customers starting moving in to HeatWave they found that HeatWave is much faster, much cheaper. And when it's so much faster, they told us that there are some classes of queries, which could just not run earlier, which they can now with HeatWave. So it makes the applications richer because they can write new classes of queries with which they could not in the past. But in terms of the feedback or enhancement requests we got, I would say they will categorize the number one was automation. There've been customers move their database from on-premise to the cloud. They expect more automation. So that was the number one thing. The second thing was people wanted the ability to run analytics on larger sizes of data with MySQL HeatWave because they like what they saw and they wanted us to increase the data size limit, which can be processed by HeatWave. Third one was they wanted more classes of queries to be accessed with HeatWave. Initially, when we went out, HeatWave was designed to be an accelerator for analytic queries but more and more customers started seeing the benefit of beyond just analytics. More towards mixed workloads. So that was a third request. And then finally they wanted us to scale to a larger cluster size. And that's what we have done over the last several months that incorporating this feedback, which you've gotten from customers. >> So you're addressing those, those, those gaps. And thank you for sharing that with us. I got the press release here. I wonder if we could kind of go through these. Let's start with AutoPilot, you know, what's, what's that all about? What's different about AutoPilot? >> That's right. So MySQL AutoPilot provides machine learning based automation. So the first difference is that not only is it automating things, where and as a cloud provider as a service provider, we feel there are a lot of opportunities for us to automate, but the big difference about the approach we've taken with MySQL AutoPilot is that it's all driven based on the data and the queries. It's machine learning based automation. That's the first aspect. The second thing is this is all done natively in the server, right? So we are enhancing the, MySQL engine. We're enhancing the HeatWave engine and that's where all the logic and all the processing resides. In order to do this, we have had to collect new kinds of data. So for instance, in the past, people would collect statistics, which are based on just the data. Now we also collect statistics based on queries, for instance, what is the compilation time? What is the execution time? And we have augmented this with new machine learning models. And finally we have made a lot of innovations, a lot of inventions in the process where we collect data in a smart way. We process data in a smart way and the machine learning models we are talking about, also have a lot of innovation. And that's what gives us an edge over what other vendors may try to do. >> Yeah. I mean, I'm just, again, I'm looking at this meat, this pretty meaty preference, press release. Auto-provisioning, auto parallel load, auto data placement, auto encoding, auto error, auto recovery, auto scheduling, and you know, using a lot of, you know, computer science techniques that are well-known, first in first out, auto change propagation. So really focusing on, on driving that automation for customers. The other piece of it that struck me, and I said this in my intro is, you know, using clustering technology, clustering technology has been around for a long time as, as in-memory database, but applying it and integrating it. My sense is that's really about scale and performance and taking advantage of course, cloud being able to drive that scale instantaneously, but talk about scale a little bit in your philosophy there and why so much emphasis on scalability? >> Right. So what we want to do is to provide the fastest engine for running analytics. And that's why we do the processing in memory. Now, one of the issues with in process, in-memory processing is that the amount of data which you're processing has to reside in memory. So when we went out in the version one, given the footprint of the MySQL customers we spoke to, we thought 12 terabytes of processing at any given point in time, would be adequate. In the very first month, we got feedback that customers wanted us to process larger amounts of data with HeatWave, because they really like what they saw and they wanted us to increase. So if we have increased deployment from 12 terabytes to 32 terabytes and in order to do so, we now have a HeatWave cluster, which can be up to 64 nodes. That's one aspect on the query processing side. Now to answer the question as to why so much of an emphasis it's because this is something which is extremely difficult to do in query processing that as you scale the size of the cluster, the kind of algorithms, the kind of techniques you have to use so that you achieve a very high efficiency with a very large cluster. These are things which are easy to do, because what we want to make sure is that as customers have the need for like, like a processing larger amount of data, one of the big benefits customers get by using a cloud as opposed to on-premise is that they don't need to worry about provisioning gear ahead of time. So if they have more data with the cloud, they should be able to like process pool data easily. But when they process more data, they should expect the same kind of performance. So same kind of efficiency on a larger data size, similar to a smaller data size. And this is something traditionally other database vendors have struggled to provide. So this is a important problem. This is a tough engineering problem. And that's why a lot of emphasis on this to make sure that we provide our customers with very high efficiency of processing as they increase the size of the data. >> You're saying, traditionally, you'll get diminishing returns as you scale. So sort of as, as the volume grows, you're not able to take as much advantage or you're less efficient. And you're saying you've, you've largely solved that problem you're able to use. I mean, people always talk about scaling linearly and I'm always skeptical, but, but you're saying, especially in database, that's been a challenge, but you're, you're saying you've solved that problem largely. >> Right. What I would say is that we have a system which is very efficient, more efficient than like, you know, any of the database we are aware of. So as you said, perfect scaling is hard with you, right? I mean, that's a critical limit of scale factor one. That's very hard to achieve. We are now close to 90% efficiency for n2n queries. This is not for primitives. This is for n2n queries, both on industry benchmarks, as well as real world customer workloads. So this 90% efficiency we believe is very good and higher than what many of the vendors provide. >> Yeah. Right. So you're not, not just primitives the whole end to end cycle. I think 0.89, I think was the number that I, that I saw just to be technically correct there, but that's pretty, pretty good. Now let's talk about the benchmarks. It wouldn't be an Oracle announcement with some, some benchmarks. So you laid out today in your announcement, some, some pretty outstanding performance and price performance numbers, particularly you called out it's, it's. I feel like it's a badge of honor. If, if Oracle calls me out, I feel like I'm doing well. You called out Snowflake and Amazons. So maybe you could go over those benchmark results that we could peel the onion on that a little bit. >> Right. So the first thing to realize is that we want to have benchmarks, which are credible, right? So it's not the case that we have taken some specific unique workloads where HeatWave shines. That's not the case. What we did was we took a industry standard benchmark, which is like, you know, TPC-H. And furthermore, we had a third party, independent firm do this comparison. So let's first compare with Snowflake. On a 10 terabyte TPC-H benchmark HeatWave is seven times faster and one fifth the cost. So with this, it is 35 times better price performance compared to Snowflake, right? So seven times faster than Snowflake and one fifth of the cost. So HeatWave is 35 times better price performance compared to Snowflake. Not just that, Snowflake only does analytics, whereas MySQL HeatWave does both transactional processing and analytics. It's not a specialized database, MySQL HeatWave is a general purpose database, which can do both OLTP analytics whereas Snowflake can only do analytics. So to be 35 times more efficient than a database service, which is specialized only for one case, which is analytics, we think it's pretty good. So that's a comparison with Snowflake. >> So that's, that's you're using, I presume you got to be using list prices for that, obviously. >> That is correct. >> So there's discounts, let's put that into context of maybe 35 X better. You're not going to get that kind of discount. I wouldn't think. >> That is correct. >> Okay. What about Redshift? Aqua for Redshift has gained a lot of momentum in the marketplace. How do you compare against that? >> Right. So we did a comparison with Redshift, Aqua, same benchmark, 10 terabytes, TPC-H. And again, this was done by a third party. Here, HeatWave is six and a half times faster at half the cost. So HeatWave is 13 times better price performance compared to Redshift Aqua. And the same thing for Redshift. It's a specialized database only for analytics. So customers need to have two databases, one for transaction processing, one for analytics, with Redshift. Whereas with MySQL HeatWave, it's a single database for both. And it is so much faster than Redshift. That again, we feel is a pretty remarkable. >> Now, you mentioned earlier, but you're not, you're obviously I presume not, you're not cheating here. You're not including the cost of the transaction processing data store. Right? We're, we're, we're ignoring that for a minute. Ignoring that you got to, you know, move data, ETL, we're just talking about like the like, is that correct? >> Right. This is extremely fair and extremely generous comparison. Not only are we not including the cost of the source OLTP database, the cost in the case of the Redshift I'm talking about is the cost for one year paid full upfront. So this is a best pricing. A customer can get for one year subscription with Redshift. Whereas when I'm talking about HeatWave, this is the pay as you go price. And the third aspect is, this is Redshift when it is completely fully optimized. I don't think anyone else can get much better numbers on Redshift than we have. Right? So fully optimized configuration of Redshift looking at the one year pre-pay cost of Redshift and not including the source database. >> Okay. And then speaking of transaction processing database, what about Aurora? You mentioned earlier that that you're seeing a lot of migration from Aurora. Can you add some color to that? >> Right. And this is a very interesting question in a, it was a very interesting observation for us when we did the launch back in December, we had numbers on four terabytes, TPC-H with Aurora. So if you look at the same benchmark, four terabytes TPC-H HeatWave is 1,400 times faster than Aurora at half the cost, which makes it 2,800 times better price performance compared to Aurora. So very good number. What we have found is that many customers who are running on Aurora started migrating to HeatWave, and these customers had a mix of transaction processing and analytics, and the data sizes are much smaller. Even those customers found that there was a significant improvement in performance and reduction in costs when they migrated to HeatWave. In the announcement today, many of the references are those class of customers. So for that, we decided to choose another benchmark, which is called CH-benchmark on a much smaller data size. And even for that, even for mixed workloads, we find that HeatWave is 18 times faster, provides over a hundred times higher throughput than Aurora at 42% of the cost. So in terms of price performance gain, it is much, much better than Aurora, even for mixed workloads. And then if you consider a pure OLTP assume you have an application, which has only OLTP, which by the way is like, you know, a very uncommon scenario, but even if that were be the case, in that case for pure OLTP only, MySQL HeatWave is at par with Aurora, with respect to performance, but MySQL HeatWave costs 42% of Aurora. So the point is that in the whole spectrum, pure OLTP, mixed workloads or analytics, MySQL HeatWave is going to be fraction of the cost of a Aurora. And depending upon your query workload, your acceleration can be anywhere from 14,000 times to 18 times faster. >> That's interesting. I mean, you've been at this for the better part of a decade, because my sense is that HeatWave is all about OLAP. And that's really where you've put the majority, if not all of the innovation. But you're saying just coming into December's announcement, you were at par with a, in a, in a, in a, in a rare, but, but hypothetical OLTP workload. >> That is correct. >> Yeah. >> Well, you know, I got to push you still on this because a lot of times these benchmarks are a function of the skills of the individuals performing these tests, right? So can I, if I want to run them myself, you know, if you publish these benchmarks, what if a customer wants to replicate these tests and try to see if they can tune up, you know, Redshift better than you guys did? >> Sure. So I'll say a couple of things. One is all the numbers which I'm talking about both for Redshift and Snowflake were done by a third party firm, but all the numbers we is talking about, TPC-H, as well has CH-benchmark. All the scripts are published on GitHub. So anyone is very welcome. In fact, we encourage customers to go and try it for themselves, and they will find that the numbers are absolutely as advertised. In fact, we had couple of companies like in the last several months who went to GitHub, they downloaded our TPCH scripts and they reported that the performance numbers they were seeing with HeatWave were actually better than we had published back in December. And the reason was that since December we had new code, which was running. So our numbers were actually better than advertised. So all the benchmarks are published. They are all available on GitHub. You can go to the HeatWave website on oracle.com and get the link for it. And we welcome anyone to come and try these numbers for themselves. >> All right. Good. Great. Thank you for that. Now you mentioned earlier that you were somewhat surprised, not surprised that you got customers migrating from on-prem databases, but you also saw migration from other clouds. How do you expect the trend with regard to this new announcement? Do you have any sense as to how that's going to go? >> Right. So one of the big changes from December to now is that we have now focused quite a bit on mixed workloads. So in the past, in December, when we first went out, HeatWave was designed primarily for analytics. Now, what we have found is that there's a very large class of customers who have mixed workloads and who also have smaller data sizes. We now have introduced a lot of technology, including things like auto scheduling, definitely improvement in performance, where MySQL HeatWave is a very superior solution compared to Aurora or other databases out there, both in terms of performance as well as price for these mixed workloads and better latency, better throughput, lower costs. So we expect this trend of migration to MySQL HeatWave, to accelerate. So we are seeing customers migrate from Azure. We are seeing customers migrate from GCP and by far the number one migrations we are seeing are from AWS. So I think based on the new features and technologies, we have announced today, this migration is going to accelerate. >> All right, last question. So I said earlier, it's, it's, it seems like you're applying what are generally well understood and proven technologies, like in-memory, you like clustering to solve these problems. And I think about, you know, the, the things that you're doing, and I wonder, you know, I mean, these things have been around for awhile and why has this type of approach not been introduced by others previously? >> Right. Well, so the main thing is it takes time, right? That we designed HeatWave from the ground up for the cloud. And as a part of that, we had to invent new algorithms for distributed query processing for the cloud. We put in the hooks for machine learning processes. We're sealing processing right from the ground up. So this has taken us close to a decade. It's been hundreds of person-years of investment, dozens of patents which have gone in. Another aspect is it takes talent from different areas. So we have like, you know, people working in distributed query processing, we have people who have a lot of like background in machine learning. And then given that we are like the custodians of the MySQL database, we have a very rich set of customers we can reach out to, to get feedback from them as to what are the pinpoints. So culmination of these trends, which we have this talent, the customer base and the time, so we spent almost close to a decade to make this thing work. So that's what it takes. It takes time, patience, patience, and talent. >> A lot of software innovation bringing together, as I said, that hardware and software strategy. Very interesting. Nipun, thanks so much. I appreciate your, your insights and coming on this video exclusive. >> Thank you, Dave. Thank you for the opportunity. >> My pleasure. And thank you for watching everybody. This is Dave Vellante for theCUBE. We'll see you next time. (bright music)

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. Yeah, welcome back to the cubes. Ongoing coverage of AWS reinvent virtual Cuba's Gone Virtual along with most events these days are all events and continues to bring our digital coverage of reinvent With me is Rahul Pathak, who is the vice president of analytics at AWS A Ro. It's great to see you again. Welcome. And thanks for joining the program. >>They have Great co two and always a pleasure. Thanks for having me on. >>You're very welcome. Before we get into your leadership discussion, I want to talk about some of the things that AWS has announced. Uh, in the early parts of reinvent, I want to start with a glue elastic views. Very notable announcement allowing people to, you know, essentially share data across different data stores. Maybe tell us a little bit more about glue. Elastic view is kind of where the name came from and what the implication is, >>Uh, sure. So, yeah, we're really excited about blue elastic views and, you know, as you mentioned, the idea is to make it easy for customers to combine and use data from a variety of different sources and pull them together into one or many targets. And the reason for it is that you know we're really seeing customers adopt what we're calling a lake house architectural, which is, uh, at its core Data Lake for making sense of data and integrating it across different silos, uh, typically integrated with the data warehouse, and not just that, but also a range of other purpose. Both stores like Aurora, Relation of Workloads or dynamodb for non relational ones. And while customers typically get a lot of benefit from using purpose built stores because you get the best possible functionality, performance and scale forgiven use case, you often want to combine data across them to get a holistic view of what's happening in your business or with your customers. And before glue elastic views, customers would have to either use E. T. L or data integration software, or they have to write custom code that could be complex to manage, and I could be are prone and tough to change. And so, with elastic views, you can now use sequel to define a view across multiple data sources pick one or many targets. And then the system will actually monitor the sources for changes and propagate them into the targets in near real time. And it manages the anti pipeline and can notify operators if if anything, changes. And so the you know the components of the name are pretty straightforward. Blues are survivalists E T Elling data integration service on blue elastic views about our about data integration their views because you could define these virtual tables using sequel and then elastic because it's several lists and will scale up and down to deal with the propagation of changes. So we're really excited about it, and customers are as well. >>Okay, great. So my understanding is I'm gonna be able to take what's called what the parlance of materialized views, which in my laypersons terms assumes I'm gonna run a query on the database and take that subset. And then I'm gonna be ableto thio. Copy that and move it to another data store. And then you're gonna automatically keep track of the changes and keep everything up to date. Is that right? >>Yes. That's exactly right. So you can imagine. So you had a product catalog for example, that's being updated in dynamodb, and you can create a view that will move that to Amazon Elasticsearch service. You could search through a current version of your catalog, and we will monitor your dynamodb tables for any changes and make sure those air all propagated in the real time. And all of that is is taken care of for our customers as soon as they defined the view on. But they don't be just kept in sync a za long as the views in effect. >>Let's see, this is being really valuable for a person who's building Looks like I like to think in terms of data services or data products that are gonna help me, you know, monetize my business. Maybe, you know, maybe it's a simple as a dashboard, but maybe it's actually a product. You know, it might be some content that I want to develop, and I've got transaction systems. I've got unstructured data, may be in a no sequel database, and I wanna actually combine those build new products, and I want to do that quickly. So So take me through what I would have to do. You you sort of alluded to it with, you know, a lot of e t l and but take me through in a little bit more detail how I would do that, you know, before this innovation. And maybe you could give us a sense as to what the possibilities are with glue. Elastic views? >>Sure. So, you know, before we announced elastic views, a customer would typically have toe think about using a T l software, so they'd have to write a neat L pipeline that would extract data periodically from a range of sources. They then have to write transformation code that would do things like matchup types. Make sure you didn't have any invalid values, and then you would combine it on periodically, Write that into a target. And so once you've got that pipeline set up, you've got to monitor it. If you see an unusual spike in data volume, you might have to add more. Resource is to the pipeline to make a complete on time. And then, if anything changed in either the source of the destination that prevented that data from flowing in the way you would expect it, you'd have toe manually, figure that out and have data, quality checks and all of that in place to make sure everything kept working but with elastic views just gets much simpler. So instead of having to write custom transformation code, you right view using sequel and um, sequel is, uh, you know, widely popular with data analysts and folks that work with data, as you well know. And so you can define that view and sequel. The view will look across multiple sources, and then you pick your destination and then glue. Elastic views essentially monitors both the source for changes as well as the source and the destination for any any issues like, for example, did the schema changed. The shape of the data change is something briefly unavailable, and it can monitor. All of that can handle any errors, but it can recover from automatically. Or if it can't say someone dropped an important table in the source. That was part of your view. You can actually get alerted and notified to take some action to prevent bad data from getting through your system or to prevent your pipeline from breaking without your knowledge and then the final pieces, the elasticity of it. It will automatically deal with adding more resource is if, for example, say you had a spiky day, Um, in the markets, maybe you're building a financial services application and you needed to add more resource is to process those changes into your targets more quickly. The system would handle that for you. And then, if you're monetizing data services on the back end, you've got a range of options for folks subscribing to those targets. So we've got capabilities like our, uh, Amazon data exchange, where people can exchange and monetize data set. So it allows this and to end flow in a much more straightforward way. It was possible before >>awesome. So a lot of automation, especially if something goes wrong. So something goes wrong. You can automatically recover. And if for whatever reason, you can't what happens? You quite ask the system and and let the operator No. Hey, there's an issue. You gotta go fix it. How does that work? >>Yes, exactly. Right. So if we can recover, say, for example, you can you know that for a short period of time, you can't read the target database. The system will keep trying until it can get through. But say someone dropped a column from your source. That was a key part of your ultimate view and destination. You just can't proceed at that point. So the pipeline stops and then we notify using a PS or an SMS alert eso that programmatic action can be taken. So this effectively provides a really great way to enforce the integrity of data that's going between the sources and the targets. >>All right, make it kindergarten proof of it. So let's talk about another innovation. You guys announced quicksight que, uh, kind of speaking to the machine in my natural language, but but give us some more detail there. What is quicksight Q and and how doe I interact with it. What What kind of questions can I ask it >>so quick? Like you is essentially a deep, learning based semantic model of your data that allows you to ask natural language questions in your dashboard so you'll get a search bar in your quick side dashboard and quick site is our service B I service. That makes it really easy to provide rich dashboards. Whoever needs them in the organization on what Q does is it's automatically developing relationships between the entities in your data, and it's able to actually reason about the questions you ask. So unlike earlier natural language systems, where you have to pre define your models, you have to pre define all the calculations that you might ask the system to do on your behalf. Q can actually figure it out. So you can say Show me the top five categories for sales in California and it'll look in your data and figure out what that is and will prevent. It will present you with how it parse that question, and there will, in line in seconds, pop up a dashboard of what you asked and actually automatically try and take a chart or visualization for that data. That makes sense, and you could then start to refine it further and say, How does this compare to what happened in New York? And we'll be able to figure out that you're tryingto overlay those two data sets and it'll add them. And unlike other systems, it doesn't need to have all of those things pre defined. It's able to reason about it because it's building a model of what your data means on the flight and we pre trained it across a variety of different domains So you can ask a question about sales or HR or any of that on another great part accused that when it presents to you what it's parsed, you're actually able toe correct it if it needs it and provide feedback to the system. So, for example, if it got something slightly off you could actually select from a drop down and then it will remember your selection for the next time on it will get better as you use it. >>I saw a demo on in Swamis Keynote on December 8. That was basically you were able to ask Quick psych you the same question, but in different ways, you know, like compare California in New York or and then the data comes up or give me the top, you know, five. And then the California, New York, the same exact data. So so is that how I kind of can can check and see if the answer that I'm getting back is correct is ask different questions. I don't have to know. The schema is what you're saying. I have to have knowledge of that is the user I can. I can triangulate from different angles and then look and see if that's correct. Is that is that how you verify or there are other ways? >>Eso That's one way to verify. You could definitely ask the same question a couple of different ways and ensure you're seeing the same results. I think the third option would be toe, uh, you know, potentially click and drill and filter down into that data through the dash one on, then the you know, the other step would be at data ingestion Time. Typically, data pipelines will have some quality controls, but when you're interacting with Q, I think the ability to ask the question multiple ways and make sure that you're getting the same result is a perfectly reasonable way to validate. >>You know what I like about that answer that you just gave, and I wonder if I could get your opinion on this because you're you've been in this business for a while? You work with a lot of customers is if you think about our operational systems, you know things like sales or E r. P systems. We've contextualized them. In other words, the business lines have inject context into the system. I mean, they kind of own it, if you will. They own the data when I put in quotes, but they do. They feel like they're responsible for it. There's not this constant argument because it's their data. It seems to me that if you look back in the last 10 years, ah, lot of the the data architecture has been sort of generis ized. In other words, the experts. Whether it's the data engineer, the quality engineer, they don't really have the business context. But the example that you just gave it the drill down to verify that the answer is correct. It seems to me, just in listening again to Swamis Keynote the other day is that you're really trying to put data in the hands of business users who have the context on the domain knowledge. And that seems to me to be a change in mindset that we're gonna see evolve over the next decade. I wonder if you could give me your thoughts on that change in the data architecture data mindset. >>David, I think you're absolutely right. I mean, we see this across all the customers that we speak with there's there's an increasing desire to get data broadly distributed into the hands of the organization in a well governed and controlled way. But customers want to give data to the folks that know what it means and know how they can take action on it to do something for the business, whether that's finding a new opportunity or looking for efficiencies. And I think, you know, we're seeing that increasingly, especially given the unpredictability that we've all gone through in 2020 customers are realizing that they need to get a lot more agile, and they need to get a lot more data about their business, their customers, because you've got to find ways to adapt quickly. And you know, that's not gonna change anytime in the future. >>And I've said many times in the The Cube, you know, there are industry. The technology industry used to be all about the products, and in the last decade it was really platforms, whether it's SAS platforms or AWS cloud platforms, and it seems like innovation in the coming years, in many respects is coming is gonna come from the ecosystem and the ability toe share data we've We've had some examples today and then But you hit on. You know, one of the key challenges, of course, is security and governance. And can you automate that if you will and protect? You know the users from doing things that you know, whether it's data access of corporate edicts for governance and compliance. How are you handling that challenge? >>That's a great question, and it's something that really emphasized in my leadership session. But the you know, the notion of what customers are doing and what we're seeing is that there's, uh, the Lake House architectural concept. So you've got a day late. Purpose build stores and customers are looking for easy data movement across those. And so we have things like blue elastic views or some of the other blue features we announced. But they're also looking for unified governance, and that's why we built it ws late formation. And the idea here is that it can quickly discover and catalog customer data assets and then allows customers to define granular access policies centrally around that data. And once you have defined that, it then sets customers free to give broader access to the data because they put the guardrails in place. They put the protections in place. So you know you can tag columns as being private so nobody can see them on gun were announced. We announced a couple of new capabilities where you can provide row based control. So only a certain set of users can see certain rose in the data, whereas a different set of users might only be able to see, you know, a different step. And so, by creating this fine grained but unified governance model, this actually sets customers free to give broader access to the data because they know that they're policies and compliance requirements are being met on it gets them out of the way of the analyst. For someone who can actually use the data to drive some value for the business, >>right? They could really focus on driving value. And I always talk about monetization. However monetization could be, you know, a generic term, for it could be saving lives, admission of the business or the or the organization I meant to ask you about acute customers in bed. Uh, looks like you into their own APs. >>Yes, absolutely so one of quick sites key strengths is its embed ability. And on then it's also serverless, so you could embed it at a really massive scale. And so we see customers, for example, like blackboard that's embedding quick side dashboards into information. It's providing the thousands of educators to provide data on the effectiveness of online learning. For example, on you could embed Q into that capability. So it's a really cool way to give a broad set of people the ability to ask questions of data without requiring them to be fluent in things like Sequel. >>If I ask you a question, we've talked a little bit about data movement. I think last year reinvent you guys announced our A three. I think it made general availability this year. And remember Andy speaking about it, talking about you know, the importance of having big enough pipes when you're moving, you know, data around. Of course you do. Doing tearing. You also announced Aqua Advanced Query accelerator, which kind of reduces bringing the computer. The data, I guess, is how I would think about that reducing that movement. But then we're talking about, you know, glue, elastic views you're copying and moving data. How are you ensuring you know, maintaining that that maximum performance for your customers. I mean, I know it's an architectural question, but as an analytics professional, you have toe be comfortable that that infrastructure is there. So how does what's A. W s general philosophy in that regard? >>So there's a few ways that we think about this, and you're absolutely right. I think there's data volumes were going up, and we're seeing customers going from terabytes, two petabytes and even people heading into the exabyte range. Uh, there's really a need to deliver performance at scale. And you know, the reality of customer architectures is that customers will use purpose built systems for different best in class use cases. And, you know, if you're trying to do a one size fits all thing, you're inevitably going to end up compromising somewhere. And so the reality is, is that customers will have more data. We're gonna want to get it to more people on. They're gonna want their analytics to be fast and cost effective. And so we look at strategies to enable all of this. So, for example, glue elastic views. It's about moving data, but it's about moving data efficiently. So What we do is we allow customers to define a view that represents the subset of their data they care about, and then we only look to move changes as efficiently as possible. So you're reducing the amount of data that needs to get moved and making sure it's focused on the essential. Similarly, with Aqua, what we've done, as you mentioned, is we've taken the compute down to the storage layer, and we're using our nitro chips to help with things like compression and encryption. And then we have F. P. J s in line to allow filtering an aggregation operation. So again, you're tryingto quickly and effectively get through as much data as you can so that you're only sending back what's relevant to the query that's being processed. And that again leads to more performance. If you can avoid reading a bite, you're going to speed up your queries. And that Awkward is trying to do. It's trying to push those operations down so that you're really reducing data as close to its origin as possible on focusing on what's essential. And that's what we're applying across our analytics portfolio. I would say one other piece we're focused on with performance is really about innovating across the stack. So you mentioned network performance. You know, we've got 100 gigabits per second throughout now, with the next 10 instances and then with things like Grab it on to your able to drive better price performance for customers, for general purpose workloads. So it's really innovating at all layers. >>It's amazing to watch it. I mean, you guys, it's a It's an incredible engineering challenge as you built this hyper distributed system. That's now, of course, going to the edge. I wanna come back to something you mentioned on do wanna hit on your leadership session as well. But you mentioned the one size fits all, uh, system. And I've asked Andy Jassy about this. I've had a discussion with many folks that because you're full and and of course, you mentioned the challenges you're gonna have to make tradeoffs if it's one size fits all. The flip side of that is okay. It's simple is you know, 11 of the Swiss Army knife of database, for example. But your philosophy is Amazon is you wanna have fine grained access and to the primitives in case the market changes you, you wanna be able to move quickly. So that puts more pressure on you to then simplify. You're not gonna build this big hairball abstraction layer. That's not what he gonna dio. Uh, you know, I think about, you know, layers and layers of paint. I live in a very old house. Eso your That's not your approach. So it puts greater pressure on on you to constantly listen to your customers, and and they're always saying, Hey, I want to simplify, simplify, simplify. We certainly again heard that in swamis presentation the other day, all about, you know, minimizing complexity. So that really is your trade office. It puts pressure on Amazon Engineering to continue to raise the bar on simplification. Isn't Is that a fair statement? >>Yeah, I think so. I mean, you know, I think any time we can do work, so our customers don't have to. I think that's a win for both of us. Um, you know, because I think we're delivering more value, and it makes it easier for our customers to get value from their data way. Absolutely believe in using the right tool for the right job. And you know you talked about an old house. You're not gonna build or renovate a house of the Swiss Army knife. It's just the wrong tool. It might work for small projects, but you're going to need something more specialized. The handle things that matter. It's and that is, uh, that's really what we see with that, you know, with that set of capabilities. So we want to provide customers with the best of both worlds. We want to give them purpose built tools so they don't have to compromise on performance or scale of functionality. And then we want to make it easy to use these together. Whether it's about data movement or things like Federated Queries, you can reach into each of them and through a single query and through a unified governance model. So it's all about stitching those together. >>Yeah, so far you've been on the right side of history. I think it serves you well on your customers. Well, I wanna come back to your leadership discussion, your your leadership session. What else could you tell us about? You know, what you covered there? >>So we we've actually had a bunch of innovations on the analytics tax. So some of the highlights are in m r, which is our managed spark. And to do service, we've been able to achieve 1.7 x better performance and open source with our spark runtime. So we've invested heavily in performance on now. EMR is also available for customers who are running and containerized environment. So we announced you Marnie chaos on then eh an integrated development environment and studio for you Marco D M R studio. So making it easier both for people at the infrastructure layer to run em are on their eks environments and make it available within their organizations but also simplifying life for data analysts and folks working with data so they can operate in that studio and not have toe mess with the details of the clusters underneath and then a bunch of innovation in red shift. We talked about Aqua already, but then we also announced data sharing for red Shift. So this makes it easy for red shift clusters to share data with other clusters without putting any load on the central producer cluster. And this also speaks to the theme of simplifying getting data from point A to point B so you could have central producer environments publishing data, which represents the source of truth, say into other departments within the organization or departments. And they can query the data, use it. It's always up to date, but it doesn't put any load on the producers that enables these really powerful data sharing on downstream data monetization capabilities like you've mentioned. In addition, like Swami mentioned in his keynote Red Shift ML, so you can now essentially train and run models that were built in sage maker and optimized from within your red shift clusters. And then we've also automated all of the performance tuning that's possible in red ships. So we really invested heavily in price performance, and now we've automated all of the things that make Red Shift the best in class data warehouse service from a price performance perspective up to three X better than others. But customers can just set red shift auto, and it'll handle workload management, data compression and data distribution. Eso making it easier to access all about performance and then the other big one was in Lake Formacion. We announced three new capabilities. One is transactions, so enabling consistent acid transactions on data lakes so you can do things like inserts and updates and deletes. We announced row based filtering for fine grained access control and that unified governance model and then automated storage optimization for Data Lake. So customers are dealing with an optimized small files that air coming off streaming systems, for example, like Formacion can auto compact those under the covers, and you can get a 78 x performance boost. It's been a busy year for prime lyrics. >>I'll say that, z that it no great great job, bro. Thanks so much for coming back in the Cube and, you know, sharing the innovations and, uh, great to see you again. And good luck in the coming here. Well, >>thank you very much. Great to be here. Great to see you. And hope we get Thio see each other in person against >>I hope so. All right. And thank you for watching everybody says Dave Volonte for the Cube will be right back right after this short break

>>Yeah, yeah. Welcome back to the third session in our building, A vibrant data ecosystem track. This session is unleash the power of your cloud data warehouse. So what comes after you've moved your data to the cloud in this session will explore White Enterprise Analytics is finally ready for the cloud, and we'll discuss how you can consume Enterprise Analytics in the very same way he would cloud services. We'll also explore where analytics meets cloud and see firsthand how thought spot is open for everyone. Let's get going. I'm happy to say we'll be hearing from two folks from thought spot today, Michael said Cassie, VP of strategic partnerships, and Vika Valentina, senior product marketing manager. And I'm very excited to welcome from our partner at AWS Gal Bar MIA, product engineering manager with Red Shift. We'll also be sharing a live demo of thought spot for BTC Marketing Analytics directly on Red Shift data. Gal, please kick us off. >>Thank you, Military. And thanks. The talks about team and everyone attending today for joining us. When we talk about data driven organizations, we hear that 85% of businesses want to be data driven. However, on Lee. 37% have been successful in We ask ourselves, Why is that and believe it or not, Ah, lot of customers tell us that they struggled with live in defining what being data driven it even means, and in particular aligning that definition between the business and the technology stakeholders. Let's talk a little bit. Let's look at our own definition. A data driven organization is an organization that harnesses data is an asset. The drive sustained innovation and create actionable insights. The super charge, the experience of their customers so they demand more. Let's focus on a few things here. One is data is an asset. Data is very much like a product needs to evolve sustained innovation. It's not just innovation innovation, it's sustained. We need to continuously innovate when it comes to data actionable insights. It's not just interesting insights these air actionable that the business can take and act upon, and obviously the actual experience we. Whether whether the customers are internal or external, we want them to request Mawr insights and as such, drive mawr innovation, and we call this the for the flywheel. We use the flywheel metaphor here where we created that data set. Okay, Our first product. Any focused on a specific use case? We build an initial NDP around that we provided with that with our customers, internal or external. They provide feedback, the request, more features. They want mawr insights that enables us to learn bringing more data and reach that actual data. And again we create MAWR insights. And as the flywheel spins faster, we improve on operational efficiencies, supporting greater data richness, and we reduce the cost of experimentation and legacy environments were never built for this kind of agility. In many cases, customers have struggled to keep momentum in their fleet, flywheel in particular around operational efficiency and experimentation. This is where Richie fits in and helps customer make the transition to a true data driven organization. Red Shift is the most widely used data warehouse with tens of thousands of customers. It allows you to analyze all your data. It is the only cloud data warehouse that sits, allows you to analyze data that sits in your data lake on Amazon, a street with no loading duplication or CTL required. It is also allows you to scale with the business with its hybrid architectures it also accelerates performance. It's a shared storage that provides the ability to scale toe unlimited concurrency. While the UN instant storage provides low late and say access to data it also provides three. Key asks that customers consistently tell us that matter the most when it comes to cost. One is usage based pricing Instead of license based pricing. Great value as you scale your data warehouse using, for example, reserved instances they can save up to 75% compared to on the mind demand prices. And as your data grows, infrequently accessed data can be stored. Cost effectively in S three encouraged through Amazon spectrum, and the third aspect is predictable. Month to month spend with no hitting charges and surprises. Unlike and unlike other cloud data warehouses, where you need premium versions for additional enterprise capabilities. Wretched spicing include building security compression and data transfer. >>Great Thanks. Scout um, eso. As you can see, everybody wins with the cloud data warehouses. Um, there's this evolution of movement of users and data and organizations to get value with these cloud data warehouses. And the key is the data has to be accessible by the users, and this data and the ability to make business decisions on the data. It ranges from users on the front line all the way up to the boardroom. So while we've seen this evolution to the Cloud Data Warehouse, as you can see from the statistic from Forrester, we're still struggling with how much of that data actually gets used for analytics. And so what is holding us back? One of the main reasons is old technology really trying to work with today's modern cloud data warehouses? They weren't built for it. So you run into issues of trying to do data replication, getting the data out of the cloud data warehouse. You can do analysis and then maintaining these middle layers of data so that you can access it quickly and get the answers you need. Another issue that's holding us back is this idea that you have to have your data in perfect shape with the perfect pipeline based on the exact dashboard unique. Um, this isn't true. Now, with Cloud data warehouse and the speed of important business data getting into those cloud data warehouses, you need a solution that allows you to access it right away without having everything to be perfect from the start, and I think this is a great opportunity for GAL and I have a little further discussion on what we're seeing in the marketplace. Um, one of the primary ones is like, What are the limiting factors, your Siegel of legacy technologies in the market when it comes to this cloud transformation we're talking about >>here? It's a great question, Michael and the variety of aspect when it comes to legacy, the other warehouses that are slowing down innovation for companies and businesses. I'll focus on 21 is performance right? We want faster insights. Companies want the ability to analyze MAWR data faster. And when it comes to on prem or legacy data warehouses, that's hard to achieve because the second aspect comes into display, which is the lack of flexibility, right. If you want to increase your capacity of your warehouse, you need to ensure request someone needs to go and bring an actual machine and install it and expand your data warehouse. When it comes to the cloud, it's literally a click of a button, which allows you to increase the capacity of your data warehouse and enable your internal and external users to perform analytics at scale and much faster. >>It falls right into the explanation you provided there, right as the speed of the data warehouses and the data gets faster and faster as it scales, older solutions aren't built toe leverage that, um, you know, they're either they're having to make technical, you know, technical cuts there, either looking at smaller amounts of data so that they can get to the data quicker. Um, or it's taking longer to get to the data when the data warehouse is ready, when it could just be live career to get the answers you need. And that's definitely an issue that we're seeing in the marketplace. I think the other one that you're looking at is things like governance, lineage, regulatory requirements. How is the cloud you know, making it easier? >>That's That's again an area where I think the cloud shines. Because AWS AWS scale allows significantly more investment in securing security policies and compliance, it allows customers. So, for example, Amazon redshift comes by default with suck 1 to 3 p. C. I. Aiso fared rampant HIPPA compliance, all of them out of the box and at our scale. We have the capacity to implement those by default for all of our customers and allow them to focus. Their very expensive, valuable ICTY resource is on actual applications that differentiate their business and transform the customer experience. >>That's a great point, gal. So we've talked about the, you know, limiting factors. Technology wise, we've mentioned things like governance. But what about the cultural aspect? Right? So what do you see? What do you see in team struggling in meeting? You know, their cloud data warehouse strategy today. >>And and that's true. One of the biggest challenges for large large organizations when they moved to the cloud is not about the technology. It's about people, process and culture, and we see differences between organizations that talk about moving to the cloud and ones that actually do it. And first of all, you wanna have senior leadership, drive and be aligned and committed to making the move to the cloud. But it's not just that you want. We see organizations sometimes Carol get paralyzed. If they can't figure out how to move each and every last work clothes, there's no need to boil the ocean, so we often work with organizations to find that iterative motion that relative process off identifying the use cases are date identifying workloads in migrating them one at a time and and through that allowed organization to grow its knowledge from a cloud perspective as well as adopt its tooling and learn about the new capabilities. >>And from an analytics perspective, we see the same right. You don't need a pixel perfect dashboard every single time to get value from your data. You don't need to wait until the data warehouse is perfect or the pipeline to the data warehouse is perfect. With today's technology, you should be able to look at the data in your cloud data warehouse immediately and get value from it. And that's the you know, that's that change that we're pushing and starting to see today. Thanks. God, that was That was really interesting. Um, you know, as we look through that, you know, this transformation we're seeing in analytics, um, isn't really that old? 20 years ago, data warehouses were primarily on Prem and the applications the B I tools used for analytics around them were on premise well, and so you saw things like applications like Salesforce. That live in the cloud. You start having to pull data from the cloud on Prem in order to do analytics with it. Um, you know, then we saw the shift about 10 years ago in the explosion of Cloud Data Warehouse Because of their scale, cost reduced, reduce shin reduction and speed. You know, we're seeing cloud data. Warehouses like Amazon Red Shift really take place, take hold of the marketplace and are the predominant ways of storing data moving forward. What we haven't seen is the B I tools catch up. And so when you have this new cloud data warehouse technology, you really need tools that were custom built for it to take advantage of it, to be able to query the cloud data warehouse directly and get results very quickly without having to worry about creating, you know, a middle layer of data or pipelines in order to manage it. And, you know, one company captures that really Well, um, chick fil A. I'm sure everybody has heard of is one of the largest food chains in America. And, you know, they made a huge investment in red shift and one of the purposes of that investment is they wanted to get access to the data mawr quickly, and they really wanted to give their business users, um, the ability to do some ad hoc analysis on the data that they were capturing. They found that with their older tools, the problems that they were finding was that all the data when they're trying to do this analysis was staying at the analyst level. So somebody needed to create a dashboard in order to share that data with a user. And if the user's requirements changed, the analysts were starting to become burdened with requests for changes and the time it took to reflect those changes. So they wanted to move to fought spot with embrace to connect to Red Shift so they could start giving business users that capability. Query the database right away. And with this, um, they were able to find, you know, very common things in in the supply chain analysis around the ability to figure out what store should get, what product that was selling better. The other part was they didn't have to wait for the data to get settled into some sort of repository or second level database. They were able to query it quickly. And then with that, they're able to make changes right in the red shift database that were then reflected to customers and the business users right away. So what they found from this is by adopting thought spot, they were actually able to arm business users with the ability to make decisions very quickly. And they cleared up the backlog that they were having and the delay with their analysts. And they're also putting their analysts toe work on different projects where they could get better value from. So when you look at the way we work with a cloud data warehouse, um, you have to think of thoughts about embrace as the tool that access that layer. The perfect analytic partner for the Cloud Data Warehouse. We will do the live query for the business user. You don't need to know how to script and sequel, um Thio access, you know, red shift. You can type the question that you want the answer to and thought spot will take care of that query. We will do the indexing so that the results come back faster for you and we will also do the analysis on. This is one of the things I wanted to cover, which is our spot i. Q. This is new for our ability to use this with embrace and our partners at Red Shift is now. We can give you the ability to do auto analysis to look at things like leading indicators, trends and anomalies. So to put this in perspective amount imagine somebody was doing forecasting for you know Q three in the western region. And they looked at how their stores were doing. And they saw that, you know, one store was performing well, Spot like, you might be able to look at that analysis and see if there's a leading product that is underperforming based on perhaps the last few quarters of data. And bring that up to the business user for analysis right away. They don't need to have to figure that out. And, um, you know, slice and dice to find that issue on their own. And then finally, all the work you do in data management and governance in your cloud data warehouse gets reflected in the results in embrace right away. So I've done a lot of talking about embrace, and I could do more, but I think it would be far better toe. Have Vika actually show you how the product works, Vika. >>Thanks, Michael. We learned a lot today about the power of leveraging your red shift data and thought spot. But now let me show you how it works. The coronavirus pandemic has presented extraordinary challenges for many businesses, and some industries have fared better than others. One industry that seems to weather the storm pretty well actually is streaming media. So companies like Netflix and who Lou. And in this demo, we're going to be looking at data from B to C marketing efforts. First streaming media company in 2020 lately, we've been running campaigns for comedy, drama, kids and family and reality content. Each of our campaigns last four weeks, and they're staggered on a weekly basis. Therefore, we always have four campaigns running, and we can focus on one campaign launch per >>week, >>and today we'll be digging into how our campaigns are performing. We'll be looking at things like impressions, conversions and users demographic data. So let's go ahead and look at that data. We'll see what we can learn from what's happened this year so far, and how we can apply those learnings to future decision making. As you can already see on the thoughts about homepage, I've created a few pin boards that I use for reporting purposes. The homepage also includes what others on my team and I have been looking at most recently. Now, before we dive into a search, will first take a look at how to make a direct connection to the customer database and red shift to save time. I've already pre built the connection Red Shift, but I'll show you how easy it is to make that connection in just three steps. So first we give the connection name and we select our connection type and was on red Shift. Then we enter our red shift credentials, and finally, we select the tables that we want to use Great now ready to start searching. So let's start in this data to get a better idea of how our marketing efforts have been affected either positively or negatively by this really challenging situation. When we think of ad based online marketing campaigns, we think of impressions, clicks and conversions. Let's >>look at those >>on a daily basis for our purposes. So all this data is available to us in Thought spot, and we can easily you search to create a nice line chart like this that shows US trends over the last few months and based on experience. We understand that we're going to have more clicks than impressions and more impressions and conversions. If we started the chart for a minute, we could see that while impressions appear to be pretty steady over the course of the year, clicks and especially conversions both get a nice boost in mid to late March, right around the time that pandemic related policies were being implemented. So right off the bat, we found something interesting, and we can come back to this now. There are few metrics that we're gonna focus on as we analyze our marketing data. Our overall goal is obviously to drive conversions, meaning that we bring new users into our streaming service. And in order to get a visitor to sign up in the first place, we need them to get into our sign up page. A compelling campaign is going to generate clicks, so if someone is interested in our ad, they're more likely to click on it, so we'll search for Click through Rape 5% and we'll look this up by campaign name. Now even compare all the campaigns that we've launched this year to see which have been most effective and bring visitors star site. And I mentioned earlier that we have four different types of campaign content, each one aligned with one of our most popular genres. So by adding campaign content, yeah, >>and I >>just want to see the top 10. I could limit my church. Just these top 10 campaigns automatically sorted by click through rate and assigned a color for each category so we could see right away that comedy and drama each of three of the top 10 campaigns by click through rate reality is, too, including the top spot and kids and family makes one appearance as well. Without spot. We know that any non technical user can ask a question and get an answer. They can explore the answer and ask another question. When you get an answer that you want to share, keep an eye on moving forward, you pin the answer to pin board. So the BBC Marketing Campaign Statistics PIN board gives us a solid overview of our campaign related activities and metrics throughout 2020. The visuals here keep us up to date on click through rate and cost per click, but also another really important metrics that conversions or cost proposition. Now it's important to our business that we evaluate the effectiveness of our spending. Let's do another search. We're going to look at how many new customers were getting so conversions and the price cost per acquisition that we're spending to get each of these by the campaign contact category. So >>this is a >>really telling chart. We can basically see how much each new users costing us, based on the content that they see prior to signing up to the service. Drama and reality users are actually relatively expensive compared to those who joined based on comedy and kids and family content that they saw. And if all the genres kids and family is actually giving us the best bang for our marketing >>buck. >>And that's good news because the genres providing the best value are also providing the most customers. We mentioned earlier that we actually saw a sizable uptick in conversions as stay at home policies were implemented across much of the country. So we're gonna remove cost per acquisition, and we're gonna take a daily look how our campaign content has trended over the years so far. Eso By doing this now, we can see a comparison of the different genres daily. Some campaigns have been more successful than others. Obviously, for example, kids and family contact has always fared pretty well Azaz comedy. But as we moved into the stay at home area of the line chart, we really saw these two genres begin to separate from the rest. And even here in June, as some states started to reopen, we're seeing that they're still trending up, and we're also seeing reality start to catch up around that time. And while the first pin board that we looked at included all sorts of campaign metrics, this is another PIN board that we've created so solely to focus on conversions. So not only can we see which campaigns drug significant conversions, we could also dig into the demographics of new users, like which campaigns and what content brought users from different parts of the country or from different age groups. And all this is just a quick search away without spot search directly on a red shift. Data Mhm. All right, Thank you. And back to you, Michael. >>Great. Thanks, Vika. That was excellent. Um, so as you can see, you can very quickly go from zero to search with thought Spot, um, connected to any cloud data warehouse. And I think it's important to understand that we mentioned it before. Not everything has to be perfect. In your doubt, in your cloud data warehouse, um, you can use thought spot as your initial for your initial tool. It's for investigatory purposes, A Z you can see here with star, Gento, imax and anthem. And a lot of these cases we were looking at billions of rows of data within minutes. And as you as your data warehouse maturity grows, you can start to add more and more thoughts about users to leverage the data and get better analysis from it. So we hope that you've enjoyed what you see today and take the step to either do one of two things. We have a free trial of thoughts about cloud. If you go to the website that you see below and register, we can get you access the thought spots so you can start searching today. Another option, by contacting our team, is to do a zero to search workshop where 90 minutes will work with you to connect your data source and start to build some insights and exactly what you're trying to find for your business. Um thanks, everybody. I would especially like to thank golf from AWS for joining us on this today. We appreciate your participation, and I hope everybody enjoyed what they saw. I think we have a few questions now. >>Thank you, Vika, Gal and Michael. It's always exciting to see a live demo. I know that I'm one of those comedy numbers. We have just a few minutes left, but I would love to ask a couple of last questions Before we go. Michael will give you the first question. Do I need to have all of my data cleaned and ready in my cloud data warehouse before I begin with thought spot? >>That's a great question, Mallory. No, you don't. You can really start using thought spot for search right away and start getting analysis and start understanding the data through the automatic search analysis and the way that we query the data and we've seen customers do that. Chick fil a example that we talked about earlier is where they were able to use thoughts bought to notice an anomaly in the Cloud Data Warehouse linking between product and store. They were able to fix that very quickly. Then that gets reflected across all of the users because our product queries the Cloud Data Warehouse directly so you can get started right away without it having to be perfect. And >>that's awesome. And gal will leave a fun one for you. What can we look forward to from Amazon Red Shift next year? >>That's a great question. And you know, the team has been innovating extremely fast. We released more than 200 features in the last year and a half, and we continue innovating. Um, one thing that stands out is aqua, which is a innovative new technology. Um, in fact, lovely stands for Advanced Square Accelerator, and it allows customers to achieve performance that up to 10 times faster, uh, than what they've seen really outstanding and and the way we've achieved that is through a shift in paradigm in the actual technological implementation section. Uh, aqua is a new distributed and hardware accelerated processing layer, which effectively allows us to push down operations analytics operations like compression, encryption, filtering and aggregations to the storage there layer and allow the aqua nodes that are built with custom. AWS designed analytics processors to perform these operations faster than traditional soup use. And we no longer need to bring, you know, scan the data and bring it all the way to the computational notes were able to apply these these predicates filtering and encourage encryption and compression and aggregations at the storage level. And likewise is going to be available for every are a three, um, customer out of the box with no changes to come. So I apologize for being getting out a little bit, but this is really exciting. >>No, that's why we invited you. Call. Thank you on. Thank you. Also to Michael and Vika. That was excellent. We really appreciate it. For all of you tuning in at home. The final session of this track is coming up shortly. You aren't gonna want to miss it. We're gonna end strong, come back and hear directly from our customer a T mobile on how T Mobile is building a data driven organization with thought spot in which >>pro, It's >>up next, see you then.