>> Announcer: From Boston, Massachusetts, it's theCUBE! Covering Cloud Foundry Summit 2018. Brought to you by the Cloud Foundry Foundation. >> I'm Stu Minamin and this is theCUBE's coverage of Cloud Foundry Summit 2018. Here in beautiful Boston, Massachusetts. Happy to welcome back to the program Chip Childers, who is the CTO of the Cloud Foundry Foundation. Chip, you started off this morning saying the runners this morning got a taste of the Boston Marathon. >> They did, they did! >> It's raining, it's cold, it's miserable. >> Yesterday was beautiful. >> At least there was less wind. >> Yesterday was absolutely beautiful. So we kicked off the summit, beautiful sun, but then we had our Fun Run this morning. >> As a local, I do apologize for the weather. Normally April's a great time. We want more tech coverage here in the area. More tech shows. We're in the center of a great tech hub, here in the Boston Seaport. We've talked to a couple of Boston startups, you know, here at the show. And, you know, great ecosystem if you go there. Thank you for bringing your show here. >> Absolutely, happy to be here. >> All right, so, last time we caught up was year ago at the show. And I think it was, what, 213 working days or something? I think Molly said >> Something like that Something like that yeah. >> The good thing is in our industry, nothings changing, we can talk about the same stuff as last year. >> Leisurely pace >> No concern, let's just sit back and you know, talk about our favorite pop culture references. Chip what's hot on your plate? And what are you hearing from the users in the community? >> Sure. So this year the theme Our events team came up with a very fun pun, which is Running at Scale. It means two things. One, the Boston Marathon was on Monday, but two it really does represent the stories that we're getting from our users, the customers, and the distributions, those that use the open source directly. So not only are we seeing a broadening of adoption across new organizations, but they're getting really deep into using it. We filled a survey, user survey, just did our second run of it. In fact we didn't have this data back in Santa Clara last year. So it's been less than a year since the 2017 one. And what we found was that there was a 21 point swing in those companies that were using Cloud Foundry with more than 50 developers, alright. So 50 developers and higher When you really talk to the interesting, large scale Fortune 500 companies, they're talking thousands of developers, that are working on the platform, being productive, and that truly is kind of what this event is about for us. >> I grew up around the infrastructure stuff, and scale means a lot of things to a lot of people, but had a great discussion with Dr. Nick, just before talking about how if you were to build your kind of utopian environment You look at some of the hyper-scale companies, the Facebooks and Googles of the world, and thing is they're such a scale that if they don't have good automation, and don't have you know really the distributive architectures that we're all talking about and things like that, there's no way that they could run their businesses. >> Yeah and the reality is a lot of the businesses that aren't Google, aren't Facebook, they have to be able to think about that level of scale. To me it really boils down to three basic principles, and to me this is the best definition of what Cloud native means. Whether you're talking about a platform, whether you're talking about how you design your applications, it's simple patterns, highly automated, which can be scaled with ease, right? And through that you can do really amazing things with software, but it has to be easily scaled, it has be easily managed, and you do that through the simplicity of the patterns that you apply. >> Yeah, and being simple is difficult. >> Yes >> How much we have arguments in the industry it's like well, let's throw an abstraction layer in there, do an overlay or underlay, but you know really building kind of distributed systems, is a little bit different. >> It is a little bit different. So one of the things that the Cloud Foundry ecosystem has, is a rich history of iterating towards a better and better developer experience. At its heart, the Cloud Foundry ecosystem of distribution, and tools, and the different products we have, they're all about helping the developer be a better developer in the context of their organization. So we've been iterating on that experience and just doing incremental constant improvement and change and we're very proud of that productivity, right? And that's really what drive these organizations to say look, this is a platform that is operated very easily with small teams. I think you've spoken with a couple companies, and if you ever ask them hot many operators do you have to handle thousands of engineers, tens of thousands of applications, they say, well, maybe ten. >> The T-Mobile example is >> Great example >> Ten to fifteen operators with 17000 developers so >> Chip: Yep, yep >> It's funny cause I remember we used to talk about you know in the enterprise how many servers can a single admin handle and then if you go to the hyper-scale ones it was three orders magnitude different. But in the hyper-scale ones they didn't really have server people, they had people that brought in servers, and people threw them in the wood chipper when they were done >> Chip: Absolutely >> And they didn't manage them. It was the old cattle versus pets analogy that we talked about in the other room, It's just totally different mindsets is how we think about this. I love, For me, it was in the enterprise you know, we harden the hardware, we think about this, and in the software world it's you know, No no, I built it in the application layer, because One of my favorite lines I use is you know, Hardware will eventually fail, and software will eventually work right? >> Absolutely. I think that's the difference between, So application centric thinking leads you to Necessarily, you have to have infrastructure to run it right? My favorite thing is this whole server-less term is absolutely ridiculous if anybody understands it, but there's a little bit behind it, which is, in fact I'd argue Cloud Foundry's fundamentally server-less because when you push code into it, you don't care what operating system's underneath it, right? All you care about is the fact that you've written some code in Java or in Nojass or in Ruby, you're handing it to a platform it deals with all of the details of building a container image, scaling it, managing it, pulling independencies, you don't care what underlying operating systems there, and then that ten person platform operations team, in the Cloud Foundry world, they have the benefit of upstream projects actually producing the operating system image that they can consume, within hours of major vulnerabilities being announced. >> I love actually, at this show you've got a containers and server-less track >> We do >> And I'm an infrastructure guy by background and when we went to virtualization we went little bit up the stack, I don't think about servers I'm trying to get closer to that application. Love you to comment on is Cloud Foundry helps gives some stability and control at that infrastructure level, but it still involved with infrastructure, from in my own data center, >> Chip: Yep >> or hosted data center or I know what could I'm on. When I start going up to like server-less, I'm a little bit higher up the stack, and that's why they can live together, >> Yeah, yeah >> And its closer tied to the application than it is to the infrastructure, so maybe you can tease that out for us a little. >> Yeah, so I think one of the main things that we've heard from the user community and this is actually coming from users of a number of the different distributions. They're saying, look there are roughly, today, roughly two different modes that we care about, cloud native application workloads. And this might expand to include functions and service but predominantly there's two. There's the custom software that we write, which the past experience is great for, and then there's the ISV delivered software, which today increasingly the medium of software delivery is becoming the container image, whether it's an OCI container, whether it's a Docker image, ISV ships software as container images, and you need a great place to land that, so those two abstractions, that paths, just hand the system your code, or the container service just hand it a container image, both of them work really well together, and part of what we're trying to do as a community, a technical community, is we're evolving those integrations so that we can work really well with the Kubernetes ecosystem. There are different options for how these things might be stacked, depending on the vendor that you're talking to, I think mostly that's immaterial to the customers, I think mostly the customers care about having those two experiences be unified from their developer or app owner prospective. >> When you come to this show, there's more than just Cloud Foundry. There's a lot of other projects >> Chip: For sure >> That are coming on to the space Gives us a little viewpoint as to how the foundation looks at this. What's the charter which it fits under Linux foundation There's so many different pieces, Some kind of bleed into what the CNCF is doing, and just try to help map out >> Chip: Yeah how some of these pieces and it's this great toolbox that we've talked about in open source. I love like the zip car guy got up and he's like, I use all the peripheral stuff, and none of the core stuff >> Right >> And that's okay >> Absolutely, that's the fun of open source. So there's a couple ways to look at this. So first, the open source communities collectively. There's a lot of innovations going on in this space, obviously What the Cloud Foundry ecosystem generally does, historically has done, and will continue to do, is that we are focused on the user needs, first and foremost. And what our technical project teams do is they look at what's available in the broader open source ecosystem. They adopt and integrate what makes sense, where we have to build something ourselves, simply because there isn't an equivalent, or it's necessary for technical reasons. We'll build that software. But our architecture has changed many times. In fact, since 2015, right. It hasn't been that many years, as you said, we move slow in this industry (Stu laughs) We've changed this architecture constantly. The upstream projects releasing at minimum of twice a month. That's a pretty high velocity. And it's a big coordinated release. So we're going to continue to evolve the architecture, to bring in new components, this is where CNCF relates. We've integrated Envoy, which is a CNCF project. We're now bringing in Kubernetes, in a couple of different ways. We're working closely with Istio, which is not a CNCF project, yet. But it looks like it might head that way. Service mesh capabilities, We were an early adopter of the container networking interface. Another Linux foundation effort was the open container initiative, right. Seeded from some code from Docker, again one of the earliest platforms to adopt that, outside of Docker. So we really look at the entire spectrum of open source software as a rich market of componentry that can be brought together. And we bring it together so that all these great users that you're talking to, can go along this journey, and think of it almost as a rationalization of the innovative chaos that's occurring. So we rationalize that. Our job is to rationalize our distributions, use that rationalization, and then all of the users get to take advantage of new things that come up. But also we take what gets integrated very seriously, because it has to reach a point of maturity. T-Mobile again, running their whole business on Cloud Foundry. Comcast, running their whole business on Cloud Foundry. US Air Force, fundamentally running their air traffic control, right, how do they get fuel to the jets, on Cloud Foundry. So we take that seriously. And so it's this combination of, harvesting innovation from where we can harvest it, bring it all together, be very thoughtful about how we bring it together, and then the distributions get the advantage of saying, here's a stable core that's going to evolve and take us into the future. >> Chip I've loved the discussion with real customers, doing digital transformation. What that means for them. How they're moving their business forward. Want to give you the final word, for those that couldn't come to the show, I know a lot of the stuffs online, there's a lot of information out there, anything particular do you want to call out, or say hey this is cool, interesting, or exciting you that you'd want to point to. >> Yeah, I actually. There are a lot of things but the one thing that I'll point to is as a US citizen, I'm particularly proud of some of the work that's happening in the US Government. Through 18F, with cloud.gov as an example, but if I step back even further, Cloud Foundry is serving as a vehicle for collaboration across multiple nations right now. We're seeing Australia, we're seeing the United Kingdom, Netherlands, Canada, South Korea, all of these national governments, are trying to figure out how to change citizen engagement to follow the lead of the startups, which are the internet companies, at the same time that these large Fortune 500 companies, are also trying to digitally transform. Governments are trying to do the same thing. So we had a, we're almost done for the day here, but there was almost a full day track of governments talking about their use of the tech, talking about that same digital transformation journey. So to me that's actually really inspiring to see that happen >> Alright well Chip Childers. He was a dancing stick figure >> Chip: I was in the keynote this morning, but here with us on theCUBE. Thank you so much for joining once again, and thank you to the foundation for helping us bring this program to our audience. >> Chip: We're happy to have you here. >> I'm Stu Miniman, and this is theCUBE. Thanks for watching (bright popping music)

>> Announcer: Live from the Javits Center in New York City, it's theCUBE. Covering Inforum 2017, brought to you by Infor. >> Welcome back to theCUBE's coverage of Inforum 2017, I am your host, Rebecca Knight, along with my co-host, Dave Vellante. We are joined by Chip Coyle. He is Infor's CMO. Thanks so much for sitting down with theCUBE today. >> Thank you for having me. >> So we just kicked off the show, the general session, Charles Philips, a lot of other Infor executives up there on the main stage talking. Lay it out for us. How many people are here. What are sort of the big themes that you're trying to get across here. >> Yeah, well, first of all it's great for Infor to be having our conference here at the Javits Center. It's about 10 blocks from our home-- >> Rebecca: Your own back yard. >> In New York City, and so this year, we've got nearly 7,000 attendees over the course of the week. Many component programs as we do every year with our partner summit, with our various conferences for the different individual customer constituencies, and executive forum, and of course, a big customer appreciation event happening tomorrow night. >> You've also made some big announcements. I'm talking mostly about Coleman AI, and Burst. I want you, if you can unpack those for our viewers a little bit. >> Yeah, I would say the theme of the conference this year is the age of networked intelligence. And what does that mean? Well, we've had, for the last several years, a layered strategy in our business, starting at the foundation with very deep industry functional applications. Purpose built for the different industries. We've taken all of that technology and moved it to the cloud, so that you get the benefits of the efficiencies and the network capability of taking your applications to the cloud. We recently, a year ago, acquired GT Nexus, which expands our capability, in a broader sense, to a commerce network, and we're able to incorporate that into our traditional applications in different industries. And then, just a couple of months ago, we acquired a business intelligence software company, Burst, which brings some really great technology for business intelligence that we can layer on top of all of our applications in this network environment. And then finally, today, the big announcement was Coleman, as you said, and that was to take our new artificial intelligence platform and really create just profound new ways that the workers in the different industries and their different companies across the networked enterprise, can interact in a business setting, much like people do in a commercial setting today. >> Can you, Chip, talk about the evolution of the brand promise. So when we first met Infor, at AWS Reinvent, it was like who was Infor? Trying to educate people on who Infor is. And so I felt like last year was your sort of stamp of this is how Infor and why Infor is relevant, and now, there seems to be sort of an undertone of innovation. So can you talk about the evolution of the brand and what you see as the brand promise. >> Well, we are very consistent in our branding and positioning of Infor as really the first industry cloud company. We're the ones who have been, at an accelerated pace, bringing the most deep, industry-rich, functional applications to the cloud. And that has created a great layer now, for all of these future innovations that we have talked about today with the benefits of business intelligence enabled applications built right in, so that you can truly have all the information you need at the right time, for the right purpose to make immediate business decisions. And then the potential and capability of artificial intelligence on top of that. >> As the chief marketing officer, can you talk a little bit about how these innovations change how you do your job, and how they make your life easier, in terms of making the right decision at the right time, making the decision better, having the right data? >> Yeah, well some of the other announcements that we're making this week, actually are in my particular line of business, which is marketing, and one of those, for example, is we're broadening our Infor CRM suite, with a link to LinkedIn's Sales Navigator. So that brings a whole set of important data to, about customers, to enable better customer interactions, for our customers. So that's something that we look to be using in our business, along with Marketo, which is a new business partner, as the engine, or the marketing automation platform to fuel our marketing business. So that's how it's impacting me directly in what I do. >> So I wonder if you could help us sort of debunk some of the myths. So Oracle would say enterprise apps aren't moving to the cloud, and we are the company to move them to the cloud, and we're the only company that can move them to the cloud. You know, SAP, it's got it sort of some cloud going on, but most of the stuff remains on prem. We heard today 55% of your revenue comes from cloud. And we know you made a decision years ago to run on AWS. Help us understand, I mean these are core, hard core enterprise apps that are running in the cloud. So help us debunk some of those myths and add some color to that. >> The traditional processes of rolling out major applications and enterprise applications in an enterprise is completely changing. And it's also changing because of the capabilities of the cloud. And the approach that Infor takes, which is very easy to assemble and configure with our Ion technology and collaboration technology, such as Mingle, to put these applications in place in a much faster way for our customers than some of the traditional players in the ERP market have been accustomed to do. And they just don't have the current technology approach or foundation to be able to move quickly to the cloud, as we do at Infor. >> In talking about Infor, you talked a little bit about the brand evolution, how are you getting the word out? Infor is really a sleeping giant in the technology industry. How are you getting your name out there? >> Well one thing that we want to do with our brand is show, well first of all, introduce Infor to the world at large, that hasn't heard of us. And the way that we want to do that is by showing what kind of benefits we can give to customers in different industries. So we just recently launched our first-ever TV commercials. They have run on shows like Meet the Press, and some of the CNBC and MSNBC shows. That has, incidental, all of that was developed entirely, 100% in house, with Hook and Loop, our creative in-house creative agency. So we're very proud of that. We're looking to do more of that with TV. We also have a relationship with the Brooklyn Nets here in New York, where on the business side, we're enabling them with performance and team analytics with a whole slew of applications of that with biometric readings and imagery, when they're moving around on the court. That can then be used to help fine tune and make decisions on which personnel to use, which, what are the best players to be able to, say, shoot a free throw after one day of rest versus two days of rest. That level of analytics. So we are, in that partnership with the Nets, are also in a branding way, going to be on the Nets jersey starting this September with an Infor patch on the jersey. And we're announcing that also, this week. >> Awesome. This is definitely a New York theme here. We're here at the Javits Center, Brooklyn Nets, Hudson Yards, another huge project that you guys are intimately involved in. Not a lot of vendors are explicitly mentioned in that. Maybe talk about that a little bit. >> Well, Hudson Yards as a development is unique in that it is really a completely self-contained city in all respects. Where the concept is to be able to network the data and information of anybody within that city, with respect to where they live in the high-rises, where they shop in the retail stores or grocery stores, where they eat in the restaurants, and where they work with all of the businesses that are locating there, too. So that gives you so much potential to rethink how information can enable, just the way that you move about, even in the city. From keyless entry into facilities, to voice-activated tasks, like, can you please restock in my groceries in my refrigerator in my condo. So there's so many ways that that can be a broad showcase for the true smart city of the future. >> These are high-end clientele. This is very New York. I want to shift gears and talk about the eco system a little bit. There's a few names that I, maybe they were here before, but I hadn't seen them, at least prominently, certainly IBM, you mentioned Marketo, a great interesting partner, hot company, and some of the SIs are sort of coming out of the woodwork. >> Chip: Yes. >> Now when you think about your strategy for sort of micro verticals, the SIs, I always say, they love to eat at the trough. And if there's not a lot of customizations, they're not interested. However, you've attracted them, because you've now got a substantial enough estate. So talk about that evolution of the eco system. >> We're proud to have as our diamond sponsors this year, AVAAP, as well as Marketo. And AVAAP has been a longstanding partner for, implementation partner for us, in expanding areas. Their heritage is with Lawson in health care and they're doing a lot of implementations across our business in all geographies, in all industries. But what's new this year is we also have attracted some new, some of the big SIs, such as Deloitte and Accenture, Capgemini, Grant Thornton. So they have all come in as sponsors and we're really on the cusp of some big and bigger and better things with them in the different businesses. >> The other thing I wanted to ask you about is Infor has a unique way of attracting interesting speakers. I've done probably five or six thousand interviews in the last five or six years, and some of the most interesting have been at Inforum. Deborah Norville came on in New Orleans, last year Lara Logan, Naomi Tutu, Karina Hollekim, amazing three women interviews. >> Rebecca: This year Susan Rice. >> This year Susan Rice was here, so what's that all about? They're not techies, they're just interesting people. What are you trying to do there? >> Well, we have a program, the Women's Infor Network, WIN, that was created by Pam Murphy, our chief operating officer, and starting a few Inforums ago, we wanted to use Inforum as a platform to showcase innovative women in the world. And it's a little bit of a departure from our product and technology messages. And this year, we've got, as you mentioned, some great inspiring women, like Jill Biden, the former first, vice president-- >> Rebecca: Second lady. >> And also, Susan Rice, as you mentioned. So, it's going to be, it's always a very popular session. >> Yes, and we're looking forward to having those women on theCUBE, too, tomorrow. >> Chip: Absolutely. >> Chip, thanks so much for joining us, it's been a pleasure. >> Thank you for having me. >> I'm Rebecca Knight, for Dave Vellante. We'll have more from Inforum 2017 after this. (techno music)

>> Narrator: Live, from Santa Clara in the heart of Silicon Valley, it's theCUBE. Covering Cloud Foundry Summit 2017. Brought to you by the Cloud Foundry Foundation and Pivotal. >> Hi this is Stu Miniman, joined with my cohost, John Troyer. Happy to welcome to the program a first-time guest, Chip Childers, who's the CTO of the Cloud Foundry Foundation. Chip, fresh off the keynote stage, >> Yep. >> how's everything going? >> It's going great. We're really happy with the turnout of the conference. We are really happy with the number of large enterprises that are here to share their story. The really active vendor ecosystem around the project. It's great. It's a wonderful event so far. >> Yeah, I was looking back, I think the last time I came to the Cloud Foundry Show, it was before the Foundation existed, We were in the Hilton in San Francisco, it was obviously a way smaller group. Tell us kind of the goals of the Foundation, doing the event, bringing the community in. >> Yeah, you can think about our goals as being of course, we're the stewards of the intellectual property, the actual software that the vendors distribute. We see our role in the ecosystem as being really two key things. One: we're focused on supporting the users, the customers, and the direct uses of the Open Source software. That's first and foremost. Second though, we want to make sure there is a really robust market ecosystem that is wrapped around this project, right. Both in terms of the distribution, the regional providers that offer Cloud Foundry based services, but also large system integrators that are helping those customers go through digital transformation. Re-platform applications, you know really figure out their way through this process. So, it's all about supporting the users and then supporting the market around it. >> Yeah, as we go to a lot of these events, you know, there are certain themes that emerge. There were two big ones that both of them showed up in what you did in the Keynote. Number one is Multicloud, number two is you got all of these various open sourced pieces, >> Chip: Yep. you know, what fits together, what interlocks together, you know which ones sit side by side. Why don't we start with kind of the open source piece first? Because you're heavily involved in a lot of those. Cloud Foundry, you know, what are the new pieces that are bolting on, or sitting on top, or digging into it, and what's going on there? >> You know, I think first I want to start with a basic philosophy of our upstream community. There are billions of dollars that rely on this platform today. And that continues to grow. Right, because we're showing up in Fortune 500, Global 2000, as well as lots of small start-ups, that are using Cloud Foundry to get code shipped faster. So our community that builds the UpStream software, spends a lot of time being very thoughtful about their technical decisions. So what we release and that what gets productized by the down streams is a complete system. From operating system all the way up to including the various programming languages and frameworks and everything in between. And because we release a complete platform, at a really high velocity, so many people rely on it's quality, we're very thoughtful about when is the right time to build our own, when should we adopt and embrace and continue to support another OpenSource project, so we spend a lot of time really thinking about that. And the areas today that I highlight around specific collaborations include the Open Service Broker API which we actually spun out of being just a Club Foundry implementation. And we embrace other communities, and found a way to share the governance of that. So we move forward as a big industry together. >> Stu: Yeah and speaking on that a little bit more. Very interesting to see. I saw Red Hat for instance speaking with Open Shift, Kubernetes is there. So, how should customers think about this? Are the path wars over? Now you can choose all the pieces that you want? Or, it's probably oversimplifying it. >> I think it's over simplifying it, it depends. You can go try to build your own platform if you want, through a number of serious components, or you can just use something like Cloud Foundry, that has solve for that. But the important thing is that we have specifically designed Cloud Foundry to allow for the backing services to come from anywhere. And so, it's both a differentiator for the various distributions of Cloud Foundry, but also an opportunity for Cloud providers, and even more importantly, it's an opportunity for the enterprise users that live in complex worlds, right? They're going to have multiple platforms, they're going be multiple levels of abstraction from Bms to containers, you know, to the path abstraction even event driven frameworks. We want that all to work really well together. Regardless of the choices you make, because that's what's most valuable to the customers. >> Okay, the other piece, networking you talked about. Why don't you share. >> Yeah, yeah so, besides the Service Broker API, we've added support for what's called Container to Container Networking. I don't necessarily need to dig into the details there, but let's just say that when you're building microservices that the application that the user is experiencing is actually a combination of a lot of different applications. That all talk to each other and rely on each other. So we want to make sure there's a policy-based framework for describing how the webs here is going to talk to the authentication service or is going to talk to the booking service, or the inventory service. They all need to have rules about how they communicate with each other. And we want to do that in the most efficient way possible. So we've adopted the Containing Networking interface as the standard plugin that is now at CNCF, the Cloud Native Computing Foundation. We think it's the right abstraction, we think it's great. It gives us access to all the fascinating work that is going on around software networking, overlay networking, industry standard API plugin to our policy-driven framework. >> Along the same theme, Kubo, a big new news project also kind of integration of some Cloud Foundry concepts with a broader ecosystem, in this case another CNCF project, Kubernetes. Could you speak a little bit to that? >> The Kubernetes community is doing a great job creating great container driven experience. You know that abstraction is all about the container. It's not about, you know, the code. So it's different than Cloud Foundry. There are workloads that make sense to run in one or the other. And we want to make sure that they run really well. Right, so the problem that we're solving with the Kuber project is what deploys Kubernetes? What supports Kubernetes if there is an infrastructure adage and a node goes offline? Right, because it does a great job of restarting containers, but if you have ten nodes in a cluster, and then now you're down to nine, that's a problem. So what Bosh does, is it takes care of solving the node outage level problem. You can also do rolling upgrades that are seamless, no downtime for the Kubernetes cluster. It brings a level of operational maturity to the Kubernetes users that they may not have had otherwise. >> Chip, can you bring us inside a little bit the creation of Kubo, is that something that the market and customers drove towards you? I talked to a couple other Cloud Foundry ecosystem members that were doing some other ways of integrating in Kubernetes. So what lead to this way of deploying it with Bosh? >> Yeah, absolutely so, it came out of a direct collaboration between Pivotal and Google. And it was driven based on Pivotal customer demand. It also, if you speak with people from Google that are involved in the project, they also see it as a need, for the Kubernetes ecosystem. So it's driven based on real-world large financial services companies that wanted to have the multiple abstractions available, they wanted to do it with a common operational platform that is proven mature that they've already adopted. And then as that collaboration board, the fruit of the project, and it was announced by Pivotal and Google several months back, they realized that they needed to move it to the vendor neutral locations so that we can continue to expand the community that can work on it, that can build up the story. >> The other topic I raised at the beginning of the interview, was the Multicloud. So in a panel, Microsoft, Google, MTC for Amazon was there. All of the Cloud guys are going to tell you we have the best platform and can do the best things for you. >> Of course they do. >> How do you balance the "We want to live in a multicultural Cloud world" and be able to go there versus "Oh I'm going to take standard plus and get in a little bit deeper to make sure that we're stickier with the customers there." What role does Cloud Foundry play? What have you seen in the marketplace for that? >> Well the public lab providers are, if you look at the services that they offer, you can roughly categorize them with two things. One, are the infrastructure building blocks. Two, are the higher level services, like their database capabilities, their analytics capabilities, log aggregation, you know, and they all have a portfolio that varies, some have specific things that are very similar. So when we talk about MultiCloud we talk about Cloud Foundry as a way to make use of those common capabilities, now they're going to differentiate based on speeds and feeds, availability, whatever they choose to, but you can then as a user have choice. And then secondarily, that Open Service Broker initiative is what's really about saying "great, there's also all these really valuable additional capabilities, that, as a user, I may choose to integrate with a Google machine learning-service, or I may choose to integrate with a wonderful Microsoft capability, or an Amazon capability." And we just want to make that easy for a developer to make that choice. >> Chip, Cloud Founder was very early in terms of a concept of a platform of services, let's not call it platform as a service right now. But you know, this platform that going to make developers lives easier, multi-target, MultiCloud we call it now, on from your laptop to anywhere. And it's been a really interesting discussion over the last couple years as this parallel container thread can come up with Kubernetes and Mesosphere and all the orchestration tools, and the focus has been on orchestration tools. And I've always thought Cloud Foundry was kind of way ahead of the game in saying "wait a minute, there's a set of services that you're going to have for full life-cycles, day two operation, at scale that you all are going to have to pull together from components." As we're doing this interview here, and this year at Cloud Foundry Summit are there anything that you think people don't kind of realize that over and over again people who are using Cloud Foundry go, "Wow I'm really glad "I had logging or identity management," or what are some of the frameworks that people sometimes don't realize is in there that actually is a huge time-savor. >> Yeah, there are a lot of operational capabilities in the Cloud Foundry platform. When you include both our Bosh layer, as well as the elastic runtime which is in the developer centers experience-- >> John: Anything that people don't often realize is in there? >> Well, I think that the right way to think of it is, it's all the things you need in one application, right? So we've been doing this for years as developers. In the applications operators team, we've been doing it. We've just been doing it via bunch of tickets, we've been doing it via bunch of scripts. What Cloud Foundry does is it takes all of those capabilities you need to really trust a platform to operate something on your behalf, and give you the right view into it, right? The appropriate telemetry, log aggregation, and know that there's going to be help monitoring there. It makes it really easy. Right, so we were talking earlier about the haiku, that Onsi Fakhouri from Pivotal had authored, it's appropriate. It's a promise that a platform makes. And platforms designed to let a user trust that the declarative nature of asking a platform to do X, Y, or Z, will be delivered. >> Chip, we've been hearing Pivotal talks a lot about Spring, when Cloud Foundry's involved. Is it so much so that the Foundation needs to be behind that, or support that? How does that interact and work? >> Well, we're super supportive of all the languages in the framework communities that are out there. You know, even if you pick a particular vendor, Pivotal in this case has a very strong investment in the Spring, Spring Cloud, Spring Boot, they're doing really amazing things. But that's also, it's our software, you know, they steward that community, so all the other vendors actually get the advantage of that. Let's take Dot Net and Microsoft. Microsoft open sourced Dot Net. So now you can run Dot Net applications on Linux. They're embrace of the container details and the APIs and their operating system is making it so that now it can also run on Windows. So the whole Microsoft technology stack, languages and frameworks, they matter quite a bit to the enterprise as well. So we see ourselves as supportive of all of these communities, right? Even ones like the Ruby community. When there's an enterprise developer that chooses to use something like Ruby, with the Ruby on Rails framework, if they use Cloud Foundry, they're getting the latest and greatest version of that language, framework, they know that it's secure, they know that it's going to be patched for them. So it's actually a great experience for that developer, that's working with the language. So, we like to support all of them, we're big fans of any that work really well with the platform and maybe integrate deeper. But it's a polyglot platform. >> We want to give you the final word. People take away from Cloud Foundry Summit 2017, what would you want them to take away? >> Yeah the simple takeaway that I can give you is that this is an absolutely enterprise grade open source ecosystem. And you don't hear that often, right? Because normally we talk about products, being enterprise great. >> Did somebody say in the keynote enterprise great mean that there's a huge salesforce that's going to try sell you stuff? (Chip laughs) Well that's coming from the buying side of the market for years. And you know, it was a bit of a joke. What is "enterprise great?" Well, it means that there's a piece of paper that says, this product will cost x dollars and the salesperson is offering it to you. So of course it's going to be enterprise great. But really, we see it as four key things, right? It's about security, it's about being well-integrated, it's about being able to scale to the needs of even the largest enterprises, and it's also about that great developer experience. So, Cloud Foundry is an ecosystem and all of our downstream distributions get the advantage of this really robust and mature technical community that is producing this software. >> Chip, really appreciate you sharing all the updates with us, and appreciate the foundation's support to bring theCUBE here. We'll be back with lots more coverage here from The Cloud Foundry Summit 2017, you're watching theCUBE. (techno music)

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

>> From "theCube" Studios in Palo Alto in Boston bringing you data-driven insights from "theCube" and ETR. This is "Breaking Analysis" with Dave Vellante. >> As an independent pure play company, Palo Alto Networks has earned its status as the leader in security. You can measure this in a variety of ways. Revenue, market cap, execution, ethos, and most importantly, conversations with customers generally. In CISO specifically, who consistently affirm this position. The company's on track to double its revenues in fiscal year 23 relative to fiscal year 2020. Despite macro headwinds, which are likely to carry through next year, Palo Alto owes its position to a clarity of vision and strong execution on a TAM expansion strategy through acquisitions and integration into its cloud and SaaS offerings. Hello and welcome to this week's "Wikibon Cube Insights" powered by ETR and this breaking analysis and ahead of Palo Alto Ignite the company's user conference, we bring you the next chapter on top of the last week's cybersecurity update. We're going to dig into the ETR data on Palo Alto Networks as we promised and provide a glimpse of what we're going to look for at "Ignite" and posit what Palo Alto needs to do to stay on top of the hill. Now, the challenges for cybersecurity professionals. Dead simple to understand. Solving it, not so much. This is a taxonomic eye test, if you will, from Optiv. It's one of our favorite artifacts to make the point the cybersecurity landscape is a mosaic of stovepipes. Security professionals have to work with dozens of tools many legacy combined with shiny new toys to try and keep up with the relentless pace of innovation catalyzed by the incredibly capable well-funded and motivated adversaries. Cybersecurity is an anomalous market in that the leaders have low single digit market shares. Think about that. Cisco at one point held 60% market share in the networking business and it's still deep into the 40s. Oracle captures around 30% of database market revenue. EMC and storage at its peak had more than 30% of that market. Even Dell's PC market shares, you know, in the mid 20s or even over that from a revenue standpoint. So cybersecurity from a market share standpoint is even more fragmented perhaps than the software industry. Okay, you get the point. So despite its position as the number one player Palo Alto might have maybe three maybe 4% of the total market, depending on what you use as your denominator, but just a tiny slice. So how is it that we can sit here and declare Palo Alto as the undisputed leader? Well, we probably wouldn't go that far. They probably have quite a bit of competition. But this CISO from a recent ETR round table discussion with our friend Eric Bradley, summed up Palo Alto's allure. We thought pretty well. The question was why Palo Alto Networks? Here's the answer. Because of its completeness as a platform, its ability to integrate with its own products or they acquire, integrate then rebrand them as their own. We've looked at other vendors we just didn't think they were as mature and we already had implemented some of the Palo Alto tools like the firewalls and stuff and we thought why not go holistically with the vendor a single throat to choke, if you will, if stuff goes wrong. And I think that was probably the primary driver and familiarity with the tools and the resources that they provided. Now here's another stat from ETR's Eric Bradley. He gave us a glimpse of the January survey that's in the field now. The percent of IT buyers stating that they plan to consolidate redundant vendors, it went from 34% in the October survey and now stands at 44%. So we fo we feel this bodes well for consolidators like Palo Alto networks. And the same is true from Microsoft's kind of good enough approach. It should also be true for CrowdStrike although last quarter we saw softness reported on in their SMB market, whereas interestingly MongoDB actually saw consistent strength from its SMB and its self-serve. So that's something that we're watching very closely. Now, Palo Alto Networks has held up better than most of its peers in the stock market. So let's take a look at that real quick. This chart gives you a sense of how well. It's a one year comparison of Palo Alto with the bug ETF. That's the cyber basket that we like to compare often CrowdStrike, Zscaler, and Okta. Now remember Palo Alto, they didn't run up as much as CrowdStrike, ZS and Okta during the pandemic but you can see it's now down unquote only 9% for the year. Whereas the cyber basket ETF is off 27% roughly in line with the NASDAQ. We're not showing that CrowdStrike down 44%, Zscaler down 61% and Okta off a whopping 72% in the past 12 months. Now as we've indicated, Palo Alto is making a strong case for consolidating point tools and we think it will have a much harder time getting customers to switch off of big platforms like Cisco who's another leader in network security. But based on the fragmentation in the market there's plenty of room to grow in our view. We asked breaking analysis contributor Chip Simington for his take on the technicals of the stock and he said that despite Palo Alto's leadership position it doesn't seem to make much difference these days. It's all about interest rates. And even though this name has performed better than its peers, it looks like the stock wants to keep testing its 52 week lows, but he thinks Palo Alto got oversold during the last big selloff. And the fact that the company's free cash flow is so strong probably keeps it at the one 50 level or above maybe bouncing around there for a while. If it breaks through that under to the downside it's ne next test is at that low of around one 40 level. So thanks for that, Chip. Now having get that out of the way as we said on the previous chart Palo Alto has strong opinions, it's founder and CTO, Nir Zuk, is extremely clear on that point of view. So let's take a look at how Palo Alto got to where it is today and how we think you should think about his future. The company was founded around 18 years ago as a network security company focused on what they called NextGen firewalls. Now, what Palo Alto did was different. They didn't try to stuff a bunch of functionality inside of a hardware box. Rather they layered network security functions on top of its firewalls and delivered value as a service through software running at the time in its own cloud. So pretty obvious today, but forward thinking for the time and now they've moved to a more true cloud native platform and much more activity in the public cloud. In February, 2020, right before the pandemic we reported on the divergence in market values between Palo Alto and Fort Net and we cited some challenges that Palo Alto was happening having transitioning to a cloud native model. And at the time we said we were confident that Palo Alto would make it through the knot hole. And you could see from the previous chart that it has. So the company's architectural approach was to do the heavy lifting in the cloud. And this eliminates the need for customers to deploy sensors on prem or proxies on prem or sandboxes on prem sandboxes, you know for instance are vulnerable to overwhelming attacks. Think about it, if you're a sandbox is on prem you're not going to be updating that every day. No way. You're probably not going to updated even every week or every month. And if the capacity of your sandbox is let's say 20,000 files an hour you know a hacker's just going to turn up the volume, it'll overwhelm you. They'll send a hundred thousand emails attachments into your sandbox and they'll choke you out and then they'll have the run of the house while you're trying to recover. Now the cloud doesn't completely prevent that but what it does, it definitely increases the hacker's cost. So they're going to probably hit some easier targets and that's kind of the objective of security firms. You know, increase the denominator on the ROI. All right, the next thing that Palo Alto did is start acquiring aggressively, I think we counted 17 or 18 acquisitions to expand the TAM beyond network security into endpoint CASB, PaaS security, IaaS security, container security, serverless security, incident response, SD WAN, CICD pipeline security, attack service management, supply chain security. Just recently with the acquisition of Cider Security and Palo Alto by all accounts takes the time to integrate into its cloud and SaaS platform called Prisma. Unlike many acquisitive companies in the past EMC was a really good example where you ended up with a kind of a Franken portfolio. Now all this leads us to believe that Palo Alto wants to be the consolidator and is in a good position to do so. But beyond that, as multi-cloud becomes more prevalent and more of a strategy customers tell us they want a consistent experience across clouds. And is going to be the same by the way with IoT. So of the next wave here. Customers don't want another stove pipe. So we think Palo Alto is in a good position to build what we call the security super cloud that layer above the clouds that brings a common experience for devs and operational teams. So of course the obvious question is this, can Palo Alto networks continue on this path of acquire and integrate and still maintain best of breed status? Can it? Will it? Does it even have to? As Holger Mueller of Constellation Research and I talk about all the time integrated suites seem to always beat best of breed in the long run. We'll come back to that. Now, this next graphic that we're going to show you underscores this question about portfolio. Here's a picture and I don't expect you to digest it all but it's a screen grab of Palo Alto's product and solutions portfolios, network cloud, network security rather, cloud security, Sassy, CNAP, endpoint unit 42 which is their threat intelligence platform and every imaginable security service and solution for customers. Well, maybe not every, I'm sure there's more to come like supply chain with the recent Cider acquisition and maybe more IoT beyond ZingBox and earlier acquisition but we're sure there will be more in the future both organic and inorganic. Okay, let's bring in more of the ETR survey data. For those of you who don't know ETR, they are the number one enterprise data platform surveying thousands of end customers every quarter with additional drill down surveys and customer round tables just an awesome SaaS enabled platform. And here's a view that shows net score or spending momentum on the vertical axis in provision or presence within the ETR data set on the horizontal axis. You see that red dotted line at 40%. Anything at or over that indicates a highly elevated net score. And as you can see Palo Alto is right on that line just under. And I'll give you another glimpse it looks like Palo Alto despite the macro may even just edge up a bit in the next survey based on the glimpse that Eric gave us. Now those colored bars in the bottom right corner they show the breakdown of Palo Alto's net score and underscore the methodology that ETR uses. The lime green is new customer adoptions, that's 7%. The forest green at 38% represents the percent of customers that are spending 6% or more on Palo Alto solutions. The gray is at that 40 or 8% that's flat spending plus or minus 5%. The pinkish at 5% is spending is down on Palo Alto network products by 6% or worse. And the bright red at only 2% is churn or defections. Very low single digit numbers for Palo Alto, that's a real positive. What you do is you subtract the red from the green and you get a net score of 38% which is very good for a company of Palo Alto size. And we'll note this is based on just under 400 responses in the ETR survey that are Palo Alto customers out of around 1300 in the total survey. It's a really good representation of Palo Alto. And you can see the other leading companies like CrowdStrike, Okta, Zscaler, Forte, Cisco they loom large with similar aspirations. Well maybe not so much Okta. They don't necessarily rule want to rule the world. They want to rule identity and of course the ever ubiquitous Microsoft in the upper right. Now drilling deeper into the ETR data, let's look at how Palo Alto has progressed over the last three surveys in terms of market presence in the survey. This view of the data shows provision in the data going back to October, 2021, that's the gray bars. The blue is July 22 and the yellow is the latest survey from October, 2022. Remember, the January survey is currently in the field. Now the leftmost set of data there show size a company. The middle set of data shows the industry for a select number of industries in the right most shows, geographic region. Notice anything, yes, Palo Alto up across the board relative to both this past summer and last fall. So that's pretty impressive. Palo Alto network CEO, Nikesh Aurora, stressed on the last earnings call that the company is seeing somewhat elongated deal approvals and sometimes splitting up size of deals. He's stressed that certain industries like energy, government and financial services continue to spend. But we would expect even a pullback there as companies get more conservative. But the point is that Nikesh talked about how they're hiring more sales pros to work the pipeline because they understand that they have to work harder to pull deals forward 'cause they got to get more approvals and they got to increase the volume that's coming through the pipeline to account for the possibility that certain companies are going to split up the deals, you know, large deals they want to split into to smaller bite size chunks. So they're really going hard after they go to market expansion to account for that. All right, so we're going to wrap by sharing what we expect and what we're going to probe for at Palo Alto Ignite next week, Lisa Martin and I will be hosting "theCube" and here's what we'll be looking for. First, it's a four day event at the MGM with the meat of the program on days two and three. That's day two was the big keynote. That's when we'll start our broadcasting, we're going for two days. Now our understanding is we've never done Palo Alto Ignite before but our understanding it's a pretty technically oriented crowd that's going to be eager to hear what CTO and founder Nir Zuk has to say. And as well CEO Nikesh Aurora and as in addition to longtime friend of "theCube" and current president, BJ Jenkins, he's going to be speaking. Wendy Whitmore runs Unit 42 and is going to be several other high profile Palo Alto execs, as well, Thomas Kurian from Google is a featured speaker. Lee Claridge, who is Palo Alto's, chief product officer we think is going to be giving the audience heavy doses of Prisma Cloud and Cortex enhancements. Now, Cortex, you might remember, came from an acquisition and does threat detection and attack surface management. And we're going to hear a lot about we think about security automation. So we'll be listening for how Cortex has been integrated and what kind of uptake that it's getting. We've done some, you know, modeling in from the ETR. Guys have done some modeling of cortex, you know looks like it's got a lot of upside and through the Palo Alto go to market machine, you know could really pick up momentum. That's something that we'll be probing for. Now, one of the other things that we'll be watching is pricing. We want to talk to customers about their spend optimization, their spending patterns, their vendor consolidation strategies. Look, Palo Alto is a premium offering. It charges for value. It's expensive. So we also want to understand what kind of switching costs are customers willing to absorb and how onerous they are and what's the business case look like? How are they thinking about that business case. We also want to understand and really probe on how will Palo Alto maintain best of breed as it continues to acquire and integrate to expand its TAM and appeal as that one-stop shop. You know, can it do that as we talked about before. And will it do that? There's also an interesting tension going on sort of changing subjects here in security. There's a guy named Edward Hellekey who's been in "theCube" before. He hasn't been in "theCube" in a while but he's a security pro who has educated us on the nuances of protecting data privacy, public policy, how it varies by region and how complicated it is relative to security. Because securities you technically you have to show a chain of custody that proves unequivocally, for example that data has been deleted or scrubbed or that metadata does. It doesn't include any residual private data that violates the laws, the local laws. And the tension is this, you need good data and lots of it to have good security, really the more the better. But government policy is often at odds in a major blocker to sharing data and it's getting more so. So we want to understand this tension and how companies like Palo Alto are dealing with it. Our customers testing public policy in courts we think not quite yet, our government's making exceptions and policies like GDPR that favor security over data privacy. What are the trade-offs there? And finally, one theme of this breaking analysis is what does Palo Alto have to do to stay on top? And we would sum it up with three words. Ecosystem, ecosystem, ecosystem. And we said this at CrowdStrike Falcon in September that the one concern we had was the pace of ecosystem development for CrowdStrike. Is collaboration possible with competitors? Is being adopted aggressively? Is Palo Alto being adopted aggressively by global system integrators? What's the uptake there? What about developers? Look, the hallmark of a cloud company which Palo Alto is a cloud security company is a thriving ecosystem that has entries into and exits from its platform. So we'll be looking at what that ecosystem looks like how vibrant and inclusive it is where the public clouds fit and whether Palo Alto Networks can really become the security super cloud. Okay, that's a wrap stop by next week. If you're in Vegas, say hello to "theCube" team. We have an unbelievable lineup on the program. Now if you're not there, check out our coverage on theCube.net. I want to thank Eric Bradley for sharing a glimpse on short notice of the upcoming survey from ETR and his thoughts. And as always, thanks to Chip Symington for his sharp comments. Want to thank Alex Morrison, who's on production and manages the podcast Ken Schiffman as well in our Boston studio, Kristen Martin and Cheryl Knight they help get the word out on social and of course in our newsletters, Rob Hoof, is our editor in chief over at Silicon Angle who does some awesome editing, thank you to all. Remember all these episodes they're available as podcasts. Wherever you listen, all you got to do is search "Breaking Analysis" podcasts. I publish each week on wikibon.com and silicon angle.com where you can email me at david.valante@siliconangle.com or dm me at D Valante or comment on our LinkedIn post. And please do check out etr.ai. They've got the best survey data in the enterprise tech business. This is Dave Valante for "theCube" Insights powered by ETR. Thanks for watching. We'll see you next week on "Ignite" or next time on "Breaking Analysis". (upbeat music)

(upbeat music) (logo swooshing) >> Good morning and welcome back to Dallas, ladies and gentlemen, we are here with theCUBE Live from Supercomputing 2022. David, my cohost, how are you doing? Exciting, day two, feeling good? >> Very exciting. Ready to start off the day. >> Very excited. We have two fascinating guests joining us to kick us off. Please welcome Pete and Armando. Gentlemen, thank you for being here with us. >> Thank you for having us. >> Thank you for having us. >> I'm excited that you're starting off the day because we've been hearing a lot of rumors about Ethernet as the fabric for HPC, but we really haven't done a deep dive yet during the show. You all seem all in on Ethernet. Tell us about that. Armando, why don't you start? >> Yeah, I mean, when you look at Ethernet, customers are asking for flexibility and choice. So when you look at HPC, InfiniBand's always been around, right? But when you look at where Ethernet's coming in, it's really our commercial in their enterprise customers. And not everybody wants to be in the top 500, what they want to do is improve their job time and improve their latency over the network. And when you look at Ethernet, you kind of look at the sweet spot between 8, 12, 16, 32 nodes, that's a perfect fit for Ethernet in that space and those types of jobs. >> I love that. Pete, you want to elaborate? >> Yeah, sure. I mean, I think one of the biggest things you find with Ethernet for HPC is that, if you look at where the different technologies have gone over time, you've had old technologies like, ATM, Sonic, Fifty, and pretty much everything is now kind of converged toward Ethernet. I mean, there's still some technologies such as InfiniBand, Omni-Path, that are out there. But basically, they're single source at this point. So what you see is that there is a huge ecosystem behind Ethernet. And you see that also the fact that Ethernet is used in the rest of the enterprise, is used in the cloud data centers, that is very easy to integrate HPC based systems into those systems. So as you move HPC out of academia into enterprise, into cloud service providers, it's much easier to integrate it with the same technology you're already using in those data centers, in those networks. >> So what's the state of the art for Ethernet right now? What's the leading edge? what's shipping now and what's in the near future? You're with Broadcom, you guys designed this stuff. >> Pete: Yeah. >> Savannah: Right. >> Yeah, so leading edge right now, got a couple things-- >> Savannah: We love good stage prop here on the theCUBE. >> Yeah, so this is Tomahawk 4. So this is what is in production, it's shipping in large data centers worldwide. We started sampling this in 2019, started going into data centers in 2020. And this is 25.6 terabytes per second. >> David: Okay. >> Which matches any other technology out there. Like if you look at say, InfinBand, highest they have right now that's just starting to get into production is 25.6 T. So state of the art right now is what we introduced, We announced this in August, This is Tomahawk 5, so this is 51.2 terabytes per second. So double the bandwidth, out of any other technology that's out there. And the important thing about networking technology is when you double the bandwidth, you don't just double the efficiency, actually, winds up being a factor of six efficiency. >> Savannah: Wow. >> 'Cause if you want, I can go into that, but... >> Why not? >> Well, what I want to know, please tell me that in your labs, you have a poster on the wall that says T five, with some like Terminator kind of character. (all laughs) 'Cause that would be cool. If it's not true, just don't say anything. I'll just... >> Pete: This can actually shift into a terminator. >> Well, so this is from a switching perspective. >> Yeah. >> When we talk about the end nodes, when we talk about creating a fabric, what's the latest in terms of, well, the nicks that are going in there, what speed are we talking about today? >> So as far as 30 speeds, it tends to be 50 gigabits per second. >> David: Okay. >> Moving to a hundred gig PAM-4. >> David: Okay. >> And we do see a lot of nicks in the 200 gig Ethernet port speed. So that would be four lanes, 50 gig. But we do see that advancing to 400 gig fairly soon, 800 gig in the future. But say state of the art right now, we're seeing for the end node tends to be 200 gig E based on 50 gig PAM-4. >> Wow. >> Yeah, that's crazy. >> Yeah, that is great. My mind is act actively blown. I want to circle back to something that you brought up a second ago, which I think is really astute. When you talked about HPC moving from academia into enterprise, you're both seeing this happen, where do you think we are on the adoption curve and sort of in that cycle? Armando, do you want to go? >> Yeah, well, if you look at the market research, they're actually telling you it's 50/50 now. So Ethernet is at the level of 50%, InfinBand's at 50%, right? >> Savannah: Interesting. >> Yeah, and so what's interesting to us, customers are coming to us and say, hey, we want to see flexibility and choice and, hey, let's look at Ethernet and let's look at InfiniBand. But what is interesting about this is that we're working with Broadcom, we have their chips in our lab, we their have switches in our lab. And really what we're trying to do is make it easy to simple and configure the network for essentially MPI. And so the goal here with our validated designs is really to simplify this. So if you have a customer that, hey, I've been InfiniBand but now I want to go Ethernet, there's going to be some learning curves there. And so what we want to do is really simplify that so that we can make it easy to install, get the cluster up and running and they can actually get some value out the cluster. >> Yeah, Pete, talk about that partnership. what does that look like? I mean, are you working with Dell before the T six comes out? Or you just say what would be cool is we'll put this in the T six? >> No, we've had a very long partnership both on the hardware and the software side. Dell's been an early adopter of our silicon. We've worked very closely on SI and Sonic on the operating system, and they provide very valuable feedback for us on our roadmap. So before we put out a new chip, and we have actually three different product lines within the switching group, within Broadcom, we've then gotten very valuable feedback on the hardware and on the APIs, on the operating system that goes on top of those chips. So that way when it comes to market, Dell can take it and deliver the exact features that they have in the current generation to their customers to have that continuity. And also they give us feedback on the next gen features they'd like to see again, in both the hardware and the software. >> So I'm fascinated by... I always like to know like what, yeah, exactly. Look, you start talking about the largest supercomputers, most powerful supercomputers that exist today, and you start looking at the specs and there might be two million CPUs, 2 million CPU cores. Exoflap of performance. What are the outward limits of T five in switches, building out a fabric, what does that look like? What are the increments in terms of how many... And I know it's a depends answer, but how many nodes can you support in a scale out cluster before you need another switch? Or what does that increment of scale look like today? >> Yeah, so this is 51.2 terabytes per second. Where we see the most common implementation based on this would be with 400 gig Ethernet ports. >> David: Okay. >> So that would be 128, 400 gig E ports connected to one chip. Now, if you went to 200 gig, which is kind of the state of the art for the nicks, you can have double that. So in a single hop, you can have 256 end nodes connected through one switch. >> Okay, so this T five, that thing right there, (all laughing) inside a sheet metal box, obviously you've got a bunch of ports coming out of that. So what's the form factor look like for where that T five sits? Is there just one in a chassis or you have.. What does that look like? >> It tends to be pizza boxes these days. What you've seen overall is that the industry's moved away from chassis for these high end systems more towardS pizza boxes. And you can have composable systems where, in the past you would have line cards, either the fabric cards that the line cards are plug into or interfaced to. These days what tends to happen is you'd have a pizza box and if you wanted to build up like a virtual chassis, what you would do is use one of those pizza boxes as the fabric card, one of them as the line card. >> David: Okay. >> So what we see, the most common form factor for this is they tend to be two, I'd say for North America, most common would be a 2RU, with 64 OSFP ports. And often each of those OSFP, which is an 800 gig E or 800 gig port, we've broken out into two 400 gig ports. >> So yeah, in 2RU, and this is all air cooled, in 2RU, you've got 51.2 T. We do see some cases where customers would like to have different optics and they'll actually deploy 4RU, just so that way they have the phase-space density. So they can plug in 128, say QSFP 112. But yeah, it really depends on which optics, if you want to have DAK connectivity combined with optics. But those are the two most common form factors. >> And Armando, Ethernet isn't necessarily Ethernet in the sense that many protocols can be run over it. >> Right. >> I think I have a projector at home that's actually using Ethernet physical connections. But, so what are we talking about here in terms of the actual protocol that's running over this? Is this exactly the same as what you think of as data center Ethernet, or is this RDMA over converged Ethernet? What Are we talking about? >> Yeah, so RDMA, right? So when you look at running, essentially HPC workloads, you have the NPI protocol, so message passing interface, right? And so what you need to do is you may need to make sure that that NPI message passing interface runs efficiently on Ethernet. And so this is why we want to test and validate all these different things to make sure that that protocol runs really, really fast on Ethernet. If you look at NPIs officially, built to, hey, it was designed to run on InfiniBand but now what you see with Broadcom, with the great work they're doing, now we can make that work on Ethernet and get same performance, so that's huge for customers. >> Both of you get to see a lot of different types of customers. I kind of feel like you're a little bit of a looking into the crystal ball type because you essentially get to see the future knowing what people are trying to achieve moving forward. Talk to us about the future of Ethernet in HPC in terms of AI and ML, where do you think we're going to be next year or 10 years from now? >> You want to go first or you want me to go first? >> I can start, yeah. >> Savannah: Pete feels ready. >> So I mean, what I see, I mean, Ethernet, what we've seen is that as far as on, starting off of the switch side, is that we've consistently doubled the bandwidth every 18 to 24 months. >> That's impressive. >> Pete: Yeah. >> Nicely done, casual, humble brag there. That was great, I love that. I'm here for you. >> I mean, I think that's one of the benefits of Ethernet, is the ecosystem, is the trajectory the roadmap we've had, I mean, you don't see that in any of the networking technology. >> David: More who? (all laughing) >> So I see that, that trajectory is going to continue as far as the switches doubling in bandwidth, I think that they're evolving protocols, especially again, as you're moving away from academia into the enterprise, into cloud data centers, you need to have a combination of protocols. So you'll probably focus still on RDMA, for the supercomputing, the AI/ML workloads. But we do see that as you have a mix of the applications running on these end nodes, maybe they're interfacing to the CPUs for some processing, you might use a different mix of protocols. So I'd say it's going to be doubling a bandwidth over time, evolution of the protocols. I mean, I expect that Rocky is probably going to evolve over time depending on the AI/ML and the HPC workloads. I think also there's a big change coming as far as the physical connectivity within the data center. Like one thing we've been focusing on is co-packed optics. So right now, this chip is, all the balls in the back here, there's electrical connections. >> How many are there, by the way? 9,000 plus on the back of that-- >> 9,352. >> I love how specific it is. It's brilliant. >> Yeah, so right now, all the SERDES, all the signals are coming out electrically based, but we've actually shown, we actually we have a version of Tomahawk 4 at 25.6 T that has co-packed optics. So instead of having electrical output, you actually have optics directly out of the package. And if you look at, we'll have a version of Tomahawk 5. >> Nice. >> Where it's actually even a smaller form factor than this, where instead of having the electrical output from the bottom, you actually have fibers that plug directly into the sides. >> Wow. Cool. >> So I see there's the bandwidth, there's radix's increasing, protocols, different physical connectivity. So I think there's a lot of things throughout, and the protocol stack's also evolving. So a lot of excitement, a lot of new technology coming to bear. >> Okay, You just threw a carrot down the rabbit hole. I'm only going to chase this one, okay? >> Peter: All right. >> So I think of individual discreet physical connections to the back of those balls. >> Yeah. >> So if there's 9,000, fill in the blank, that's how many connections there are. How do you do that many optical connections? What's the mapping there? What does that look like? >> So what we've announced for Tomahawk 5 is it would have FR4 optics coming out. So you'd actually have 512 fiber pairs coming out. So basically on all four sides, you'd have these fiber ribbons that come in and connect. There's actually fibers coming out of the sides there. We wind up having, actually, I think in this case, we would actually have 512 channels and it would wind up being on 128 actual fiber pairs because-- >> It's miraculous, essentially. >> Savannah: I know. >> Yeah. So a lot of people are going to be looking at this and thinking in terms of InfiniBand versus Ethernet, I think you've highlighted some of the benefits of specifically running Ethernet moving forward as HPC which sort of just trails slightly behind super computing as we define it, becomes more pervasive AI/ML. What are some of the other things that maybe people might not immediately think about when they think about the advantages of running Ethernet in that environment? Is it about connecting the HPC part of their business into the rest of it? What are the advantages? >> Yeah, I mean, that's a big thing. I think, and one of the biggest things that Ethernet has again, is that the data centers, the networks within enterprises, within clouds right now are run on Ethernet. So now, if you want to add services for your customers, the easiest thing for you to do is the drop in clusters that are connected with the same networking technology. So I think one of the biggest things there is that if you look at what's happening with some of the other proprietary technologies, I mean, in some cases they'll have two different types of networking technologies before they interface to Ethernet. So now you've got to train your technicians, you train your assist admins on two different network technologies. You need to have all the debug technology, all the interconnect for that. So here, the easiest thing is you can use Ethernet, it's going to give you the same performance and actually, in some cases, we've seen better performance than we've seen with Omni-Path, better than in InfiniBand. >> That's awesome. Armando, we didn't get to you, so I want to make sure we get your future hot take. Where do you see the future of Ethernet here in HPC? >> Well, Pete hit on a big thing is bandwidth, right? So when you look at, train a model, okay? So when you go and train a model in AI, you need to have a lot of data in order to train that model, right? So what you do is essentially, you build a model, you choose whatever neural network you want to utilize. But if you don't have a good data set that's trained over that model, you can't essentially train the model. So if you have bandwidth, you want big pipes because you have to move that data set from the storage to the CPU. And essentially, if you're going to do it maybe on CPU only, but if you do it on accelerators, well, guess what? You need a big pipe in order to get all that data through. And here's the deal, the bigger the pipe you have, the more data, the faster you can train that model. So the faster you can train that model, guess what? The faster you get to some new insight, maybe it's a new competitive advantage, maybe it's some new way you design a product, but that's a benefit of speed, you want faster, faster, faster. >> It's all about making it faster and easier-- for the users. >> Armando: It is. >> I love that. Last question for you, Pete, just because you've said Tomahawk seven times, and I'm thinking we're in Texas, stakes, there's a lot going on with that. >> Making me hungry. >> I know, exactly. I'm sitting out here thinking, man, I did not have big enough breakfast. How did you come up with the name Tomahawk? >> So Tomahawk, I think it just came from a list. So we have a tried end product line. >> Savannah: Ah, yes. >> Which is a missile product line. And Tomahawk is being kind of like the bigger and batter missile, so. >> Savannah: Love this. Yeah, I mean-- >> So do you like your engineers? You get to name it. >> Had to ask. >> It's collaborative. >> Okay. >> We want to make sure everyone's in sync with it. >> So just it's not the Aquaman tried. >> Right. >> It's the steak Tomahawk. I think we're good now. >> Now that we've cleared that-- >> Now we've cleared that up. >> Armando, Pete, it was really nice to have both you. Thank you for teaching us about the future of Ethernet and HCP. David Nicholson, always a pleasure to share the stage with you. And thank you all for tuning in to theCUBE live from Dallas. We're here talking all things HPC and supercomputing all day long. We hope you'll continue to tune in. My name's Savannah Peterson, thanks for joining us. (soft music)

>>You can put this in a conference. >>Good morning and welcome back to Dallas. Ladies and gentlemen, we are here with the cube Live from, from Supercomputing 2022. David, my cohost, how you doing? Exciting. Day two. Feeling good. >>Very exciting. Ready to start off the >>Day. Very excited. We have two fascinating guests joining us to kick us off. Please welcome Pete and Armando. Gentlemen, thank you for being here with us. >>Having us, >>For having us. I'm excited that you're starting off the day because we've been hearing a lot of rumors about ethernet as the fabric for hpc, but we really haven't done a deep dive yet during the show. Y'all seem all in on ethernet. Tell us about that. Armando, why don't you start? >>Yeah. I mean, when you look at ethernet, customers are asking for flexibility and choice. So when you look at HPC and you know, infinite band's always been around, right? But when you look at where Ethernet's coming in, it's really our commercial and their enterprise customers. And not everybody wants to be in the top 500. What they want to do is improve their job time and improve their latency over the network. And when you look at ethernet, you kinda look at the sweet spot between 8, 12, 16, 32 nodes. That's a perfect fit for ethernet and that space and, and those types of jobs. >>I love that. Pete, you wanna elaborate? Yeah, yeah, >>Yeah, sure. I mean, I think, you know, one of the biggest things you find with internet for HPC is that, you know, if you look at where the different technologies have gone over time, you know, you've had old technologies like, you know, atm, Sonic, fitty, you know, and pretty much everything is now kind of converged toward ethernet. I mean, there's still some technologies such as, you know, InfiniBand, omnipath that are out there. Yeah. But basically there's single source at this point. So, you know, what you see is that there is a huge ecosystem behind ethernet. And you see that also, the fact that ethernet is used in the rest of the enterprise is using the cloud data centers that is very easy to integrate HPC based systems into those systems. So as you move HPC out of academia, you know, into, you know, into enterprise, into cloud service providers is much easier to integrate it with the same technology you're already using in those data centers, in those networks. >>So, so what's this, what is, what's the state of the art for ethernet right now? What, you know, what's, what's the leading edge, what's shipping now and what and what's in the near future? You, you were with Broadcom, you guys design this stuff. >>Yeah, yeah. Right. Yeah. So leading edge right now, I got a couple, you know, Wes stage >>Trough here on the cube. Yeah. >>So this is Tomahawk four. So this is what is in production is shipping in large data centers worldwide. We started sampling this in 2019, started going into data centers in 2020. And this is 25.6 tets per second. Okay. Which matches any other technology out there. Like if you look at say, infin band, highest they have right now that's just starting to get into production is 25 point sixt. So state of the art right now is what we introduced. We announced this in August. This is Tomahawk five. So this is 51.2 terabytes per second. So double the bandwidth have, you know, any other technology that's out there. And the important thing about networking technology is when you double the bandwidth, you don't just double the efficiency, it's actually winds up being a factor of six efficiency. Wow. Cause if you want, I can go into that, but why >>Not? Well, I, what I wanna know, please tell me that in your labs you have a poster on the wall that says T five with, with some like Terminator kind of character. Cause that would be cool if it's not true. Don't just don't say anything. I just want, I can actually shift visual >>It into a terminator. So. >>Well, but so what, what are the, what are the, so this is, this is from a switching perspective. Yeah. When we talk about the end nodes, when we talk about creating a fabric, what, what's, what's the latest in terms of, well, the kns that are, that are going in there, what's, what speed are we talking about today? >>So as far as 30 speeds, it tends to be 50 gigabits per second. Okay. Moving to a hundred gig pan four. Okay. And we do see a lot of Knicks in the 200 gig ethernet port speed. So that would be, you know, four lanes, 50 gig. But we do see that advancing to 400 gig fairly soon. 800 gig in the future. But say state of the art right now, we're seeing for the end nodes tends to be 200 gig E based on 50 gig pan four. Wow. >>Yeah. That's crazy. Yeah, >>That is, that is great. My mind is act actively blown. I wanna circle back to something that you brought up a second ago, which I think is really astute. When you talked about HPC moving from academia into enterprise, you're both seeing this happen. Where do you think we are on the adoption curve and sort of in that cycle? Armand, do you wanna go? >>Yeah, yeah. Well, if you look at the market research, they're actually telling it's 50 50 now. So ethernet is at the level of 50%. InfiniBand is at 50%. Right. Interesting. Yeah. And so what's interesting to us, customers are coming to us and say, Hey, we want to see, you know, flexibility and choice and hey, let's look at ethernet and let's look at InfiniBand. But what is interesting about this is that we're working with Broadcom, we have their chips in our lab, we have our switches in our lab. And really what we're trying to do is make it easy to simple and configure the network for essentially mpi. And so the goal here with our validated designs is really to simplify this. So if you have a customer that, Hey, I've been in fbe, but now I want to go ethernet, you know, there's gonna be some learning curves there. And so what we wanna do is really simplify that so that we can make it easy to install, get the cluster up and running, and they can actually get some value out of the cluster. >>Yeah. Peter, what, talk about that partnership. What, what, what does that look like? Is it, is it, I mean, are you, you working with Dell before the, you know, before the T six comes out? Or you just say, you know, what would be cool, what would be cool is we'll put this in the T six? >>No, we've had a very long partnership both on the hardware and the software side. You know, Dell has been an early adopter of our silicon. We've worked very closely on SI and Sonic on the operating system, you know, and they provide very valuable feedback for us on our roadmap. So before we put out a new chip, and we have actually three different product lines within the switching group within Broadcom, we've then gotten, you know, very valuable feedback on the hardware and on the APIs, on the operating system that goes on top of those chips. So that way when it comes to market, you know, Dell can take it and, you know, deliver the exact features that they have in the current generation to their customers to have that continuity. And also they give us feedback on the next gen features they'd like to see again in both the hardware and the software. >>So, so I, I'm, I'm just, I'm fascinated by, I I, I always like to know kind like what Yeah, exactly. Exactly right. Look, you, you start talking about the largest super supercomputers, most powerful supercomputers that exist today, and you start looking at the specs and there might be 2 million CPUs, 2 million CPU cores, yeah. Ex alop of, of, of, of performance. What are the, what are the outward limits of T five in switches, building out a fabric, what does that look like? What are the, what are the increments in terms of how many, and I know it, I know it's a depends answer, but, but, but how many nodes can you support in a, in a, in a scale out cluster before you need another switch? What does that increment of scale look like today? >>Yeah, so I think, so this is 51.2 terras per second. What we see the most common implementation based on this would be with 400 gig ethernet ports. Okay. So that would be 128, you know, 400 giggi ports connected to, to one chip. Okay. Now, if you went to 200 gig, which is kind of the state of the art for the Nicks, you can have double that. Okay. So, you know, in a single hop you can have 256 end nodes connected through one switch. >>So, okay, so this T five, that thing right there inside a sheet metal box, obviously you've got a bunch of ports coming out of that. So what is, what does that, what's the form factor look like for that, for where that T five sits? Is there just one in a chassis or you have, what does that look >>Like? It tends to be pizza boxes these days. Okay. What you've seen overall is that the industry's moved away from chassis for these high end systems more towards pizza, pizza boxes. And you can have composable systems where, you know, in the past you would have line cards, either the fabric cards that the line cards are plugged into or interface to these days, what tends to happen is you'd have a pizza box, and if you wanted to build up like a virtual chassis, what you would do is use one of those pizza boxes as the fabric card, one of them as the, the line card. >>Okay. >>So what we see, the most common form factor for this is they tend to be two, I'd say for North America, most common would be a two R U with 64 OSF P ports. And often each of those OSF p, which is an 800 gig e or 800 gig port, we've broken out into two 400 gig quarts. Okay. So yeah, in two r u you've got, and this is all air cooled, you know, in two re you've got 51.2 T. We do see some cases where customers would like to have different optics, and they'll actually deploy a four U just so that way they have the face place density, so they can plug in 128, say qsf P one 12. But yeah, it really depends on which optics, if you wanna have DAK connectivity combined with, with optics. But those are the two most common form factors. >>And, and Armando ethernet isn't, ethernet isn't necessarily ethernet in the sense that many protocols can be run over it. Right. I think I have a projector at home that's actually using ethernet physical connections. But what, so what are we talking about here in terms of the actual protocol that's running over this? Is this exactly the same as what you think of as data center ethernet, or, or is this, you know, RDMA over converged ethernet? What, what are >>We talking about? Yeah, so our rdma, right? So when you look at, you know, running, you know, essentially HPC workloads, you have the NPI protocol, so message passing interface, right? And so what you need to do is you may need to make sure that that NPI message passing interface runs efficiently on ethernet. And so this is why we want to test and validate all these different things to make sure that that protocol runs really, really fast on ethernet, if you look at NPI is officially, you know, built to, Hey, it was designed to run on InfiniBand, but now what you see with Broadcom and the great work they're doing now, we can make that work on ethernet and get, you know, it's same performance. So that's huge for customers. >>Both of you get to see a lot of different types of customers. I kind of feel like you're a little bit of a, a looking into the crystal ball type because you essentially get to see the future knowing what people are trying to achieve moving forward. Talk to us about the future of ethernet in hpc in terms of AI and ml. Where, where do you think we're gonna be next year or 10 years from now? >>You wanna go first or you want me to go first? I can start. >>Yeah. Pete feels ready. >>So I mean, what I see, I mean, ethernet, I mean, is what we've seen is that as far as on the starting off of the switch side, is that we've consistently doubled the bandwidth every 18 to 24 months. That's >>Impressive. >>Yeah. So nicely >>Done, casual, humble brag there. That was great. That was great. I love that. >>I'm here for you. I mean, I think that's one of the benefits of, of Ethan is like, is the ecosystem, is the trajectory, the roadmap we've had, I mean, you don't see that in any other networking technology >>More who, >>So, you know, I see that, you know, that trajectory is gonna continue as far as the switches, you know, doubling in bandwidth. I think that, you know, they're evolving protocols. You know, especially again, as you're moving away from academia into the enterprise, into cloud data centers, you need to have a combination of protocols. So you'll probably focus still on rdma, you know, for the supercomputing, the a AIML workloads. But we do see that, you know, as you have, you know, a mix of the applications running on these end nodes, maybe they're interfacing to the, the CPUs for some processing, you might use a different mix of protocols. So I'd say it's gonna be doubling a bandwidth over time evolution of the protocols. I mean, I expect that Rocky is probably gonna evolve over time depending on the a AIML and the HPC workloads. I think also there's a big change coming as far as the physical connectivity within the data center. Like one thing we've been focusing on is co-pack optics. So, you know, right now this chip is all, all the balls in the back here, there's electrical connections. How >>Many are there, by the way? 9,000 plus on the back of that >>352. >>I love how specific it is. It's brilliant. >>Yeah. So we get, so right now, you know, all the thirties, all the signals are coming out electrically based, but we've actually shown, we have this, actually, we have a version of Hawk four at 25 point sixt that has co-pack optics. So instead of having electrical output, you actually have optics directly out of the package. And if you look at, we'll have a version of Tomahawk five Nice. Where it's actually even a smaller form factor than this, where instead of having the electrical output from the bottom, you actually have fibers that plug directly into the sides. Wow. Cool. So I see, you know, there's, you know, the bandwidth, there's radis increasing protocols, different physical connectivity. So I think there's, you know, a lot of things throughout, and the protocol stack's also evolving. So, you know, a lot of excitement, a lot of new technology coming to bear. >>Okay. You just threw a carrot down the rabbit hole. I'm only gonna chase this one. Okay. >>All right. >>So I think of, I think of individual discreet physical connections to the back of those balls. Yeah. So if there's 9,000, fill in the blank, that's how many connections there are. How do you do that in many optical connections? What's, what's, what's the mapping there? What does that, what does that look like? >>So what we've announced for TAMA five is it would have fr four optics coming out. So you'd actually have, you know, 512 fiber pairs coming out. So you'd have, you know, basically on all four sides, you'd have these fiber ribbons that come in and connect. There's actually fibers coming out of the, the sides there. We wind up having, actually, I think in this case, we would actually have 512 channels and it would wind up being on 128 actual fiber pairs because >>It's, it's miraculous, essentially. It's, I know. Yeah, yeah, yeah, yeah. Yeah. So, so, you know, a lot of people are gonna be looking at this and thinking in terms of InfiniBand versus versus ethernet. I think you've highlighted some of the benefits of specifically running ethernet moving forward as, as hpc, you know, which is sort of just trails slightly behind supercomputing as we define it, becomes more pervasive AI ml. What, what are some of the other things that maybe people might not immediately think about when they think about the advantages of running ethernet in that environment? Is it, is it connecting, is it about connecting the HPC part of their business into the rest of it? What, or what, what are the advantages? >>Yeah, I mean, that's a big thing. I think, and one of the biggest things that ethernet has again, is that, you know, the data centers, you know, the networks within enterprises within, you know, clouds right now are run on ethernet. So now if you want to add services for your customers, the easiest thing for you to do is, you know, the drop in clusters that are connected with the same networking technology, you know, so I think what, you know, one of the biggest things there is that if you look at what's happening with some of the other proprietary technologies, I mean, in some cases they'll have two different types of networking technologies before they interface to ethernet. So now you've got to train your technicians, you train your, your assist admins on two different network technologies. You need to have all the, the debug technology, all the interconnect for that. So here, the easiest thing is you can use ethernet, it's gonna give you the same performance. And actually in some cases we seen better performance than we've seen with omnipath than, you know, better than in InfiniBand. >>That's awesome. Armando, we didn't get to you, so I wanna make sure we get your future hot take. Where do you see the future of ethernet here in hpc? >>Well, Pete hit on a big thing is bandwidth, right? So when you look at train a model, okay, so when you go and train a model in ai, you need to have a lot of data in order to train that model, right? So what you do is essentially you build a model, you choose whatever neural network you wanna utilize, but if you don't have a good data set that's trained over that model, you can't essentially train the model. So if you have bandwidth, you want big pipes because you have to move that data set from the storage to the cpu. And essentially, if you're gonna do it maybe on CPU only, but if you do it on accelerators, well guess what? You need a big pipe in order to get all that data through. And here's the deal. The bigger the pipe you have, the more data, the faster you can train that model. So the faster you can train that model, guess what? The faster you get to some new insight, maybe it's a new competitive advantage. Maybe it's some new way you design a product, but that's a benefit of speed you want faster, faster, faster. >>It's all about making it faster and easier. It is for, for the users. I love that. Last question for you, Pete, just because you've said Tomahawk seven times, and I'm thinking we're in Texas Stakes, there's a lot going on with with that making >>Me hungry. >>I know exactly. I'm sitting up here thinking, man, I did not have a big enough breakfast. How do you come up with the name Tomahawk? >>So Tomahawk, I think you just came, came from a list. So we had, we have a tri end product line. Ah, a missile product line. And Tomahawk is being kinda like, you know, the bigger and batter missile, so, oh, okay. >>Love this. Yeah, I, well, I >>Mean, so you let your engineers, you get to name it >>Had to ask. It's >>Collaborative. Oh good. I wanna make sure everyone's in sync with it. >>So just so we, it's not the Aquaman tried. Right, >>Right. >>The steak Tomahawk. I >>Think we're, we're good now. Now that we've cleared that up. Now we've cleared >>That up. >>Armando P, it was really nice to have both you. Thank you for teaching us about the future of ethernet N hpc. David Nicholson, always a pleasure to share the stage with you. And thank you all for tuning in to the Cube Live from Dallas. We're here talking all things HPC and Supercomputing all day long. We hope you'll continue to tune in. My name's Savannah Peterson, thanks for joining us.

>> From the CUBE Studios in Palo Alto in Boston, bringing you data driven insights from the Cube and ETR. This is Breaking Analysis with Dave Vellante. >> A better than expected earnings report in late August got people excited about Snowflake again, but the negative sentiment in the market is weighed heavily on virtually all growth tech stocks and Snowflake is no exception. As we've stressed many times the company's management is on a long term mission to dramatically simplify the way organizations use data. Snowflake is tapping into a multi hundred billion dollar total available market and continues to grow at a rapid pace. In our view, Snowflake is embarking on its third major wave of innovation data apps, while its first and second waves are still bearing significant fruit. Now for short term traders focused on the next 90 or 180 days, that probably doesn't matter. But those taking a longer view are asking, "Should we still be optimistic about the future of this high flyer or is it just another over hyped tech play?" Hello and welcome to this week's Wiki Bond Cube Insights powered by ETR. Snowflake's Quarter just ended. And in this breaking analysis we take a look at the most recent survey data from ETR to see what clues and nuggets we can extract to predict the near term future in the long term outlook for Snowflake which is going to announce its earnings at the end of this month. Okay, so you know the story. If you've been investor in Snowflake this year, it's been painful. We said at IPO, "If you really want to own this stock on day one, just hold your nose and buy it." But like most IPOs we said there will be likely a better entry point in the future, and not surprisingly that's been the case. Snowflake IPOed a price of 120, which you couldn't touch on day one unless you got into a friends and family Delio. And if you did, you're still up 5% or so. So congratulations. But at one point last year you were up well over 200%. That's been the nature of this volatile stock, and I certainly can't help you with the timing of the market. But longer term Snowflake is targeting 10 billion in revenue for fiscal year 2028. A big number. Is it achievable? Is it big enough? Tell you what, let's come back to that. Now shorter term, our expert trader and breaking analysis contributor Chip Simonton said he got out of the stock a while ago after having taken a shot at what turned out to be a bear market rally. He pointed out that the stock had been bouncing around the 150 level for the last few months and broke that to the downside last Friday. So he'd expect 150 is where the stock is going to find resistance on the way back up, but there's no sign of support right now. He said maybe at 120, which was the July low and of course the IPO price that we just talked about. Now, perhaps earnings will be a catalyst, when Snowflake announces on November 30th, but until the mentality toward growth tech changes, nothing's likely to change dramatically according to Simonton. So now that we have that out of the way, let's take a look at the spending data for Snowflake in the ETR survey. Here's a chart that shows the time series breakdown of snowflake's net score going back to the October, 2021 survey. Now at that time, Snowflake's net score stood at a robust 77%. And remember, net score is a measure of spending velocity. It's a proprietary network, and ETR derives it from a quarterly survey of IT buyers and asks the respondents, "Are you adopting the platform new? Are you spending 6% or more? Is you're spending flat? Is you're spending down 6% or worse? Or are you leaving the platform decommissioning?" You subtract the percent of customers that are spending less or churning from those that are spending more and adopting or adopting and you get a net score. And that's expressed as a percentage of customers responding. In this chart we show Snowflake's in out of the total survey which ranges... The total survey ranges between 1,200 and 1,400 each quarter. And the very last column... Oh sorry, very last row, we show the number of Snowflake respondents that are coming in the survey from the Fortune 500 and the Global 2000. Those are two very important Snowflake constituencies. Now what this data tells us is that Snowflake exited 2021 with very strong momentum in a net score of 82%, which is off the charts and it was actually accelerating from the previous survey. Now by April that sentiment had flipped and Snowflake came down to earth with a 68% net score. Still highly elevated relative to its peers, but meaningfully down. Why was that? Because we saw a drop in new ads and an increase in flat spend. Then into the July and most recent October surveys, you saw a significant drop in the percentage of customers that were spending more. Now, notably, the percentage of customers who are contemplating adding the platform is actually staying pretty strong, but it is off a bit this past survey. And combined with a slight uptick in planned churn, net score is now down to 60%. That uptick from 0% and 1% and then 3%, it's still small, but that net score at 60% is still 20 percentage points higher than our highly elevated benchmark of 40% as you recall from listening to earlier breaking analysis. That 40% range is we consider a milestone. Anything above that is actually quite strong. But again, Snowflake is down and coming back to churn, while 3% churn is very low, in previous quarters we've seen Snowflake 0% or 1% decommissions. Now the last thing to note in this chart is the meaningful uptick in survey respondents that are citing, they're using the Snowflake platform. That's up to 212 in the survey. So look, it's hard to imagine that Snowflake doesn't feel the softening in the market like everyone else. Snowflake is guiding for around 60% growth in product revenue against the tough compare from a year ago with a 2% operating margin. So like every company, the reaction of the street is going to come down to how accurate or conservative the guide is from their CFO. Now, earlier this year, Snowflake acquired a company called Streamlit for around $800 million. Streamlit is an open source Python library and it makes it easier to build data apps with machine learning, obviously a huge trend. And like Snowflake, generally its focus is on simplifying the complex, in this case making data science easier to integrate into data apps that business people can use. So we were excited this summer in the July ETR survey to see that they added some nice data and pick on Streamlit, which we're showing here in comparison to Snowflake's core business on the left hand side. That's the data warehousing, the Streamlit pieces on the right hand side. And we show again net score over time from the previous survey for Snowflake's core database and data warehouse offering again on the left as compared to a Streamlit on the right. Snowflake's core product had 194 responses in the October, 22 survey, Streamlit had an end of 73, which is up from 52 in the July survey. So significant uptick of people responding that they're doing business in adopting Streamlit. That was pretty impressive to us. And it's hard to see, but the net scores stayed pretty constant for Streamlit at 51%. It was 52% I think in the previous quarter, well over that magic 40% mark. But when you blend it with Snowflake, it does sort of bring things down a little bit. Now there are two key points here. One is that the acquisition seems to have gained exposure right out of the gate as evidenced by the large number of responses. And two, the spending momentum. Again while it's lower than Snowflake overall, and when you blend it with Snowflake it does pull it down, it's very healthy and steady. Now let's do a little pure comparison with some of our favorite names in this space. This chart shows net score or spending velocity in the Y-axis, an overlap or presence, pervasiveness if you will, in the data set on the X-axis. That red dotted line again is that 40% highly elevated net score that we like to talk about. And that table inserted informs us as to how the companies are plotted, where the dots set up, the net score, the ins. And we're comparing a number of database players, although just a caution, Oracle includes all of Oracle including its apps. But we just put it in there for reference because it is the leader in database. Right off the bat, Snowflake jumps out with a net score of 64%. The 60% from the earlier chart, again included Streamlit. So you can see its core database, data warehouse business actually is higher than the total company average that we showed you before 'cause the Streamlit is blended in. So when you separate it out, Streamlit is right on top of data bricks. Isn't that ironic? Only Snowflake and Databricks in this selection of names are above the 40% level. You see Mongo and Couchbase, they know they're solid and Teradata cloud actually showing pretty well compared to some of the earlier survey results. Now let's isolate on the database data platform sector and see how that shapes up. And for this analysis, same XY dimensions, we've added the big giants, AWS and Microsoft and Google. And notice that those three plus Snowflake are just at or above the 40% line. Snowflake continues to lead by a significant margin in spending momentum and it keeps creeping to the right. That's that end that we talked about earlier. Now here's an interesting tidbit. Snowflake is often asked, and I've asked them myself many times, "How are you faring relative to AWS, Microsoft and Google, these big whales with Redshift and Synapse and Big Query?" And Snowflake has been telling folks that 80% of its business comes from AWS. And when Microsoft heard that, they said, "Whoa, wait a minute, Snowflake, let's partner up." 'Cause Microsoft is smart, and they understand that the market is enormous. And if they could do better with Snowflake, one, they may steal some business from AWS. And two, even if Snowflake is winning against some of the Microsoft database products, if it wins on Azure, Microsoft is going to sell more compute and more storage, more AI tools, more other stuff to these customers. Now AWS is really aggressive from a partnering standpoint with Snowflake. They're openly negotiating, not openly, but they're negotiating better prices. They're realizing that when it comes to data, the cheaper that you make the offering, the more people are going to consume. At scale economies and operating leverage are really powerful things at volume that kick in. Now Microsoft, they're coming along, they obviously get it, but Google is seemingly resistant to that type of go to market partnership. Rather than lean into Snowflake as a great partner Google's field force is kind of fighting fashion. Google itself at Cloud next heavily messaged what they call the open data cloud, which is a direct rip off of Snowflake. So what can we say about Google? They continue to be kind of behind the curve when it comes to go to market. Now just a brief aside on the competitive posture. I've seen Slootman, Frank Slootman, CEO of Snowflake in action with his prior companies and how he depositioned the competition. At Data Domain, he eviscerated a company called Avamar with their, what he called their expensive and slow post process architecture. I think he actually called it garbage, if I recall at one conference I heard him speak at. And that sort of destroyed BMC when he was at ServiceNow, kind of positioning them as the equivalent of the department of motor vehicles. And so it's interesting to hear how Snowflake openly talks about the data platforms of AWS, Microsoft, Google, and data bricks. I'll give you this sort of short bumper sticker. Redshift is just an on-prem database that AWS morphed to the cloud, which by the way is kind of true. They actually did a brilliant job of it, but it's basically a fact. Microsoft Excel, a collection of legacy databases, which also kind of morphed to run in the cloud. And even Big Query, which is considered cloud native by many if not most, is being positioned by Snowflake as originally an on-prem database to support Google's ad business, maybe. And data bricks is for those people smart enough to get it to Berkeley that love complexity. And now Snowflake doesn't, they don't mention Berkeley as far as I know. That's my addition. But you get the point. And the interesting thing about Databricks and Snowflake is a while ago in the cube I said that there was a new workload type emerging around data where you have AWS cloud, Snowflake obviously for the cloud database and Databricks data for the data science and EML, you bring those things together and there's this new workload emerging that's going to be very powerful in the future. And it's interesting to see now the aspirations of all three of these platforms are colliding. That's quite a dynamic, especially when you see both Snowflake and Databricks putting venture money and getting their hooks into the loyalties of the same companies like DBT labs and Calibra. Anyway, Snowflake's posture is that we are the pioneer in cloud native data warehouse, data sharing and now data apps. And our platform is designed for business people that want simplicity. The other guys, yes, they're formidable, but we Snowflake have an architectural lead and of course we run in multiple clouds. So it's pretty strong positioning or depositioning, you have to admit. Now I'm not sure I agree with the big query knockoffs completely. I think that's a bit of a stretch, but snowflake, as we see in the ETR survey data is winning. So in thinking about the longer term future, let's talk about what's different with Snowflake, where it's headed and what the opportunities are for the company. Snowflake put itself on the map by focusing on simplifying data analytics. What's interesting about that is the company's founders are as you probably know from Oracle. And rather than focusing on transactional data, which is Oracle's sweet spot, the stuff they worked on when they were at Oracle, the founder said, "We're going to go somewhere else. We're going to attack the data warehousing problem and the data analytics problem." And they completely re-imagined the database and how it could be applied to solve those challenges and reimagine what was possible if you had virtually unlimited compute and storage capacity. And of course Snowflake became famous for separating the compute from storage and being able to completely shut down compute so you didn't have to pay for it when you're not using it. And the ability to have multiple clusters hit the same data without making endless copies and a consumption/cloud pricing model. And then of course everyone on the planet realized, "Wow, that's a pretty good idea." Every venture capitalist in Silicon Valley has been funding companies to copy that move. And that today has pretty much become mainstream in table stakes. But I would argue that Snowflake not only had the lead, but when you look at how others are approaching this problem, it's not necessarily as clean and as elegant. Some of the startups, the early startups I think get it and maybe had an advantage of starting later, which can be a disadvantage too. But AWS is a good example of what I'm saying here. Is its version of separating compute from storage was an afterthought and it's good, it's... Given what they had it was actually quite clever and customers like it, but it's more of a, "Okay, we're going to tier to storage to lower cost, we're going to sort of dial down the compute not completely, we're not going to shut it off, we're going to minimize the compute required." It's really not true as separation is like for instance Snowflake has. But having said that, we're talking about competitors with lots of resources and cohort offerings. And so I don't want to make this necessarily all about the product, but all things being equal architecture matters, okay? So that's the cloud S-curve, the first one we're showing. Snowflake's still on that S-curve, and in and of itself it's got legs, but it's not what's going to power the company to 10 billion. The next S-curve we denote is the multi-cloud in the middle. And now while 80% of Snowflake's revenue is AWS, Microsoft is ramping up and Google, well, we'll see. But the interesting part of that curve is data sharing, and this idea of data clean rooms. I mean it really should be called the data sharing curve, but I have my reasons for calling it multi-cloud. And this is all about network effects and data gravity, and you're seeing this play out today, especially in industries like financial services and healthcare and government that are highly regulated verticals where folks are super paranoid about compliance. There not going to share data if they're going to get sued for it, if they're going to be in the front page of the Wall Street Journal for some kind of privacy breach. And what Snowflake has done is said, "Put all the data in our cloud." Now, of course now that triggers a lot of people because it's a walled garden, okay? It is. That's the trade off. It's not the Wild West, it's not Windows, it's Mac, it's more controlled. But the idea is that as different parts of the organization or even partners begin to share data that they need, it's got to be governed, it's got to be secure, it's got to be compliant, it's got to be trusted. So Snowflake introduced the idea of, they call these things stable edges. I think that's the term that they use. And they track a metric around stable edges. And so a stable edge, or think of it as a persistent edge is an ongoing relationship between two parties that last for some period of time, more than a month. It's not just a one shot deal, one a done type of, "Oh guys shared it for a day, done." It sent you an FTP, it's done. No, it's got to have trajectory over time. Four weeks or six weeks or some period of time that's meaningful. And that metric is growing. Now I think sort of a different metric that they track. I think around 20% of Snowflake customers are actively sharing data today and then they track the number of those edge relationships that exist. So that's something that's unique. Because again, most data sharing is all about making copies of data. That's great for storage companies, it's bad for auditors, and it's bad for compliance officers. And that trend is just starting out, that middle S-curve, it's going to kind of hit the base of that steep part of the S-curve and it's going to have legs through this decade we think. And then finally the third wave that we show here is what we call super cloud. That's why I called it multi-cloud before, so it could invoke super cloud. The idea that you've built a PAS layer that is purpose built for a specific objective, and in this case it's building data apps that are cloud native, shareable and governed. And is a long-term trend that's going to take some time to develop. I mean, application development platforms can take five to 10 years to mature and gain significant adoption, but this one's unique. This is a critical play for Snowflake. If it's going to compete with the big cloud players, it has to have an app development framework like Snowpark. It has to accommodate new data types like transactional data. That's why it announced this thing called UniStore last June, Snowflake a summit. And the pattern that's forming here is Snowflake is building layer upon layer with its architecture at the core. It's not currently anyway, it's not going out and saying, "All right, we're going to buy a company that's got to another billion dollars in revenue and that's how we're going to get to 10 billion." So it's not buying its way into new markets through revenue. It's actually buying smaller companies that can complement Snowflake and that it can turn into revenue for growth that fit in to the data cloud. Now as to the 10 billion by fiscal year 28, is that achievable? That's the question. Yeah, I think so. Would the momentum resources go to market product and management prowess that Snowflake has? Yes, it's definitely achievable. And one could argue to $10 billion is too conservative. Indeed, Snowflake CFO, Mike Scarpelli will fully admit his forecaster built on existing offerings. He's not including revenue as I understand it from all the new stuff that's in the pipeline because he doesn't know what it's going to look like. He doesn't know what the adoption is going to look like. He doesn't have data on that adoption, not just yet anyway. And now of course things can change quite dramatically. It's possible that is forecast for existing businesses don't materialize or competition picks them off or a company like Databricks actually is able in the longer term replicate the functionality of Snowflake with open source technologies, which would be a very competitive source of innovation. But in our view, there's plenty of room for growth, the market is enormous and the real key is, can and will Snowflake deliver on the promises of simplifying data? Of course we've heard this before from data warehouse, the data mars and data legs and master data management and ETLs and data movers and data copiers and Hadoop and a raft of technologies that have not lived up to expectations. And we've also, by the way, seen some tremendous successes in the software business with the likes of ServiceNow and Salesforce. So will Snowflake be the next great software name and hit that 10 billion magic mark? I think so. Let's reconnect in 2028 and see. Okay, we'll leave it there today. I want to thank Chip Simonton for his input to today's episode. Thanks to Alex Myerson who's on production and manages the podcast. Ken Schiffman as well. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hove is our Editor in Chief over at Silicon Angle. He does some great editing for us. Check it out for all the news. Remember all these episodes are available as podcasts. Wherever you listen, 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.vallante@siliconangle.com. DM me @dvellante or comment on our LinkedIn post. And please do check out etr.ai, they've got the best survey data in the enterprise tech business. This is Dave Vellante for the CUBE Insights, powered by ETR. Thanks for watching, thanks for listening and we'll see you next time on breaking analysis. (upbeat music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Like a marathon runner pumped up on adrenaline, UiPath sprinted to the lead in what is surely going to be a long journey toward enabling the modern automated enterprise. Now, in doing so the company has established itself as a leader in enterprise automation while at the same time, it got out over its skis on critical execution items and it disappointed investors along the way. In our view, the company has plenty of upside potential, but will have to slog through its current challenges, including restructuring its go-to market, prioritizing investments, balancing growth with profitability and dealing with a very difficult macro environment. Hello and welcome to this week's Wikibon Cube insights powered by ETR. In this Breaking Analysis and ahead of Forward 5, UiPath's big customer event, we once again dig into RPA and automation leader, UiPath, to share our most current data and view of the company's prospects relative to the competition and the market overall. Now, since the pandemic, four sectors have consistently outperformed in the overall spending landscape in the ETR dataset, cloud, containers, machine learning/AI, and robotic process automation. For the first time in a long time ML and AI and RPA have dropped below the elevated 40% line shown in this ETR graph with the red dotted line. The data here plots the net score or spending momentum for each sector with we put in video conferencing, we added it in simply to provide height to the vertical access. Now, you see those squiggly lines, they show the pattern for ML/AI and RPA, and they demonstrate the downward trajectory over time with only the most current period dropping below the 40% net score mark. While this is not surprising, it underscores one component of the macro headwinds facing all companies generally and UiPath specifically, that is the discretionary nature of certain technology investments. This has been a topic of conversation on theCUBE since the spring spanning data players like Mongo and Snowflake, the cloud, security, and other sectors. The point is ML/AI and RPA appear to be more discretionary than certain sectors, including cloud. Containers most likely benefit from the fact that much of the activity is spending on internal resources, staff like developers as much of the action in containers is free and open source. Now, security is not shown on this graphic, but as we've reported extensively in the last week at CrowdStrike's Falcon conference, security is somewhat less discretionary than other sectors. Now, as it relates to the big four that we've been highlighting since the pandemic hit, we're starting to see priorities shift from strategic investments like AI and automation to more tactical areas to keep the lights on. UiPath has not been immune to this downward pressure, but the company is still able to show some impressive metrics. Here's a snapshot chart from its investor deck. For the first time UiPath's ARR has surpassed $1 billion. The company now has more than 10,000 customers with a large number generating more than $100,000 in ARR. While not shown in this data, UiPath reported this month in its second quarter close that it had $191 million plus ARR customers, which is up 13% sequentially from its Q1. As well, the company's NRR is over 130%, which is very solid and underscores the low churn that we've previously reported for the company. But with that increased ARR comes slower growth. Here's some data we compiled that shows the dramatic growth in ARR, the blue bars, compared with the rapid deceleration and growth. That's the orange line on the right hand access there. For the first time UiPath's ARR growth dipped below 50% last quarter. Now, we've projected 34% and 25% respectively for the company's Q3 in Q4, which is slightly higher than the upper range of UiPath's CFO, Ashim Gupta's guidance from the last earnings call. That still puts UiPath exiting its fiscal year at a 25% ARR growth rate. While it's not unexpected that a company reaching $1 billion in ARR, that milestone, will begin to show lower, slower growth, net new ARR is well off its fiscal year '22 levels. The other perhaps more concerning factor is the company, despite strong 80% gross margins, remains unprofitable and free cash flow negative. New CEO, Rob Enslin, has emphasized the focus on profitability, and we'd like to see a consistent and more disciplined Rule of 40 or Rule of 45 to 50 type of performance going forward. As a result of this decelerating growth and lowered guidance stemming from significant macro challenges including currency fluctuations and weaker demand, especially in Europe and EP and inconsistent performance, the stock, as shown here, has been on a steady decline. What all growth stocks are facing, you know, challenges relative to inflation, rising interest rates, and looming recession, but as seen here, UiPath has significantly underperformed relative to the tech-heavy NASDAQ. UiPath has admitted to execution challenges, and it has brought in an expanded management team to facilitate its sales transition and desire to become a more strategic platform play versus a tactical point product. Now, adding to this challenge of foreign exchange issues, as we've previously reported unlike most high flying tech companies from Silicon Valley, UiPath has a much larger proportion of its business coming from locations outside of the United States, around 50% of its revenue, in fact. Because it prices in local currencies, when you convert back to appreciated dollars, there are less of them, and that weighs down on revenue. Now, we asked Breaking Analysis contributor, Chip Simonton, for his take on this stock, and he told us, "From a technical standpoint, there's really not much you can say, it just looks like a falling knife. It's trading at an all time low but that doesn't mean it can't go lower. New management with a good product is always a positive with a stock like this, but this is just a bad environment for UiPath and all growth stocks really, and," he added, "95% of money managers have never operated in this type of environment before. So that creates more uncertainty. There will be a bottom, but picking it in this high-inflation, high-interest rate world hasn't worked too well lately. There's really no floor to these stocks that don't have earnings, until you start to trade to cash levels." Well, okay, let's see, UiPath has $1.6 billion in cash in the balance sheet and no debt, so we're a long ways off from that target, the cash value with its current $7 billion valuation. You have to go back to April 2019 to UiPaths Series D to find a $7 billion valuation. So Simonton says, "The stock still could go lower." The valuation range for this stock has been quite remarkable from around $50 billion last May to $7 billion today. That's quite a swing. And the spending data from ETR sort of supports this story. This graphic here shows the net score or spending momentum granularity for UiPath. The lime green is new additions to the platform. The forest green is spending 6% or more. The gray is flat spending. The pink is spending down 6% or worse. And the bright red is churn. Subtract the red from the green and you get net score, which is that blue line. The yellow line is pervasiveness within the data set. Now, that yellow line is skewed somewhat because of Microsoft citations. There's a belief from some that competition from Microsoft is the reason for UiPath's troubles, but Microsoft is really delivering RPA for individuals and isn't an enterprise automation platform at least not today, but it's Microsoft, so you can't discount their presence in the market. And it probably is having some impact, but we think there are many other factors weighing on UiPath. Now, this is data through the July survey but taking a glimpse at the early October returns they're trending with the arrows, meaning less green more gray and red, which is going to lower UiPath's overall net score, which is consistent with the macro headwinds and the business performance that it's been seeing. Now, nonetheless, UiPath continues to get high marks from its customers, and relative to it's peers it maintains a leadership position. So this chart from ETR, shows net score or spending velocity in the vertical access, an overlap or presence in the dataset on the horizontal access. Microsoft continues to have a big presence, and as we mentioned, somewhat skews the data. UiPath has maintained its lead relative to automation anywhere on the horizontal access, and remains ahead of the legacy pack of business process and other RPA vendors. Solonis has popped up in the ETR data set recently as a process mining player and has a pretty high net score. It's a critical space UiPath has entered, via its acquisition of ProcessGold back in October 2019. Now, you can also see what we did is we added in the Gartner Magic Quadrant for robotic process automation. We didn't blow it up here but we circled the position of UiPath. You can see it's leading in both the vertical and the horizontal access, ahead of automation anywhere as well as Microsoft and others. Now, we're still not seeing the likes of SAP, Service Now, and Salesforce showing up in the ETR data, but these enterprise software vendors are in a reasonable position to capitalize on automation opportunities within their installed basis. This is why it's so important that UiPath transitions to an enterprise-wide horizontal play that can cut across multiple ERP, CRM, HCM, and service management platforms. While the big software companies can add automation to their respective stovepipes, and they're doing that, UiPath's opportunity is to bring automation to enable enterprises to build on top of and across these SaaS platforms that most companies are running. Now, on the chart, you see the red arrows slanting down. That signifies the expected trend from the upcoming October ETR survey, which is currently in the field and will run through early next month. Suffice it to say that there is downward spending pressure across the board, and we would expect most of these names, including UiPath, to dip below the 40% dotted line. Now, as it relates to the conversation about platform versus product, let's dig into that a bit more. Here's a graphic from UiPath's investor deck that underscores the move from product to platform. UiPath has expanded its platform from its initial on-prem point product to focus on automating tasks for individuals and back offices to a cloud-first platform approach. The company has added in technology from a number of acquisitions and added organically to those. These include, the previously mentioned, ProcessGold for process discovery, process documentation from the acquisition of StepShot, API automation via the acquisition of Cloud Elements, to its more recent acquisition of Re:infer, a natural language processing specialist. Now, we expect the platform to be a big focus of discussion at Forward 5 next week in Las Vegas. So let's close in on our expectations for the three-day event next week at the Venetian. UiPath's user conference has grown over the years and the Venetian should be by far be the biggest and most heavily attended in the company's history. We expect UiPath to really emphasize the role of automation, specifically in the context of digital transformation, and how UiPath has evolved, again, from point product to platform to support digital transformation. Expect to focus on platform maturity. When UiPath announced its platform intentions back in 2019, which was the last physical face-to-face customer event prior to COVID, it essentially was laying out a statement of direction. And over the past three years, it has matured the platform and taken it from vision to reality. You know, I said the last event, actually, the last event was 2021. Of course, theCUBE was there at the Bellagio in Las Vegas. But prior to that, 2019 is when they laid out that platform vision. Now, in a conjunction with this evolution, the company has evolved its partnerships, pairing up with the likes of Snowflake and the data cloud, CrowdStrike, to provide better security, and, of course, the big Global System Integrators, to help implement enterprise automation. And this is where we expect to hear a lot from customers. I've heard, there'll be over 100 speaking at the show about the outcomes and how they're digitally transforming. Now, I mentioned earlier that we haven't seen the big ERP and enterprise software companies show up yet in the ETR data, but believe me they're out there and they're selling automation and RPA and they're competing. So expect UiPath to position themselves and deposition those companies. Position UiPath as a layer above these bespoke platforms shown here on number four. With process discovery and task discovery, building automation across enterprise apps, and operationalizing process workflows as a horizontal play. And I'm sure there'll be some new graphics on this platform that we can share after the event that will emphasize this positioning. And finally, as we showed earlier in the platform discussion, we expect to hear a lot about the new platform capabilities and use cases, and not just RPA, but process mining, testing, testing automation, which is a new vector of growth for UiPath, document processing. And also, we expect UiPath to address its low code development capabilities to expand the number of people in the organization that can create automation capabilities and automations. Those domain experts is what we're talking about here that deeply understand the business but aren't software engineers. Enabling them is going to be really important, and we expect to hear more about that. And we expect this conference to set the tone for a new chapter in UiPath's history. The company's second in-person gathering, but the first one was last October. So really this is going to be sort of a build upon that, and many in-person events. For the first time this year, UiPath was one of the first to bring back its physical event, but we expect it to be bigger than what was at the Bellagio, and a lot of people were concerned about traveling. Although UiPath got a lot of customers there, but I think they're going to really up the game in terms of attendance this year. And really, that comparison is unfair because UiPath, again, it was sort of the middle of COVID last year. But anyway, we expect this new operations and go-to-market oriented focus from co-CEO, Rob Enslin, and new sales management, we're going to be, you know, hearing from them. And the so-called adult supervision has really been lacking at UiPath, historically. Daniel Dines will no doubt continue to have a big presence at the event and at the company. He's not a figurehead by any means. He's got a deep understanding of the product and the market and we'll be interviewing both Daniel and Rob Enslin on theCUBE to find out how they see the future. So tune in next week, or if you're in Las Vegas, definitely stop by theCUBE. If you're not go to thecube.net, you'll be able to watch all of our coverage. Okay, we're going to leave it there today. I want to thank Chip Simonton again for his input to today's episode. Thanks to Alex Morrison who's on production and manages our podcasts. Ken Schiffman, as well, from our Boston office, our Boston studio. Kristen Martin, and Cheryl Knight, they helped get the word out on social media and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE that does some great editing. Thanks all. Remember, these episodes are all available as podcasts wherever you listen. All you got to do is search Breaking Analysis Podcasts. I publish each week on wikibon.com and siliconangle.com, and you could email me at david.vellante@siliconangle.com or DM me @dvellante. If you got anything interesting, I'll respond. If not, please keep trying, or 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, and we'll see you next time on Breaking Analysis. (gentle techno music)

>> From theCUBE studios in Palo Alto in Boston bringing you data driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> In just over 10 years, CrowdStrike has become a leading independent security firm with more than 2 billion in annual recurring revenue, nearly 60% ARR growth, and approximate $40 billion market capitalization, very high retention rates, low churn, and a path to 5 billion in revenue by mid decade. The company has joined Palo Alto Networks as a gold standard pure play cyber security firm. It has achieved this lofty status with an architecture that goes beyond a point product. With outstanding go to market and financial execution, some sharp acquisitions and an ever increasing total available market. Hello, and welcome to this week's Wikibon Cube Insights powered by ETR. In this "Breaking Analysis" and ahead of Falcon, Fal.Con, CrowdStrike's user conference, we take a deeper look into CrowdStrike, its performance, its platform, and survey data from our partner ETR. Now, the general consensus is that spending on Cyber is non-discretionary and is held up better than other technology sectors. While this is generally true, as this data shows, it's nuanced. Let's explore this a bit. First, this is a year-to-date chart of the stock performance of CrowdStrike relative to Palo Alto, the BUG ETF, which is a Cyber index, the NASDAQ and SentinelOne, a relatively new entrant to the IPO public markets. Now, as you can see the security sector as evidenced by the orange line, that Cyber ETF, is holding up better than the overall NASDAQ which is off 28% year-to-date. Palo Alto has held up incredibly well, the best, being off only around 4% year-to-date. Whereas CrowdStrike is off in the double digits this year. But up as we talked about in one of our last "Breaking Analysis" on Cyber, up from its lows this past May. Now, CrowdStrike had a very nice beat and raise on August 30th. But the stop didn't respond well initially. We asked "Breaking Analysis" contributor, Chip Simonton for his technical take and he stated that CrowdStrike has bounced around for the last three months in its current range. He said that Cyber stocks have held up better than the rest of the market, as we're showing. And now might be a good time to take a shot but he is cautious. FedEx had a warning today of a global recession and that's obvious case for a concern. You know, maybe some of these quality Cyber stocks like Palo Alto and CrowdStrike and Zscaler will outperform in a recession, but that play is not for the faint of heart. In fact, it's feeling like a longer, more drawn out tech lash than many had hoped. Perhaps as much as 12 to 18 months of bouncing around with sellers still in control, is generally the sentiment from Simonton. So in terms of Cyber spending being non-discretionary, we'd say it's less discretionary than other it sectors but the CISO still does not have an open wallet, as we've reported before. We've seen that spending momentum has decelerated in all sectors throughout the year. This is an across the board trend. Now, independent of the stock price, George Kurtz, CEO of CrowdStrike, he's running a marathon, not a sprint. And this company is running at a nice pace despite tough macro headwinds. The company is free cash flow positive and is in the black, or a non-GAAP operating profit basis and yet it's growing ARR at nearly 60%. Frank Slootman uses the term inherent profitability, meaning that the company could drive more profits if it wanted to dial down expenses especially in go to market costs. But that would be a mistake for a company like CrowdStrike, in our opinion. While it has an impressive nearly 20,000 customers, there are hundreds of thousands of customers that CrowdStrike could penetrate. So like Snowflake and Slootman, Kurtz is not taking its foot off the gas. Now, the fundamental strength of CrowdStrike and its secret sauce is its architecture and platform, in our view, so let's take a deeper look. CrowdStrike believes that the unstoppable breach is a myth. Now, CISOs don't agree with that because they assume they're going to get breached, but that's CrowdStrike's point of view, so lofty vision. CrowdStrike's mission is to consolidate the patchwork of solutions by introducing modules that go beyond point products. CrowdStrike has more than 20 modules, I think 22, that span a range of capabilities as shown in this table. Now, there are a few critical aspects of the CrowdStrike architecture that bear mentioning. First is the lightweight agent, that is fundamental. You know, we're used to thinking that agentless is good and agent is bad, but in this case, a powerful but small, slim and easy to install but unobtrusive agent has its advantages because it supports multiple CrowdStrike modules. The second point is CrowdStrike from the beginning has been dogmatic about getting all the telemetry data into the cloud. It sort of shunned doing bespoke on prem so that all the data could be analyzed. So the more agents that CrowdStrike installs around the world, the more data it has access to and the better its intelligence. Few companies have access to more data, perhaps Microsoft given it scale and size is an exception in that endpoint space. CrowdStrike has developed a purpose-built threat graph and analytics platform that allows it to quickly ingest in near real time key telemetry data and detect not only known malware, that's pretty straightforward, pretty much anybody could do that. But using machine intelligence, it can also detect unknown malware and other potentially malicious behavior using indicators of attack, IOC, or IOAs. Humio is shown here as a company that CrowdStrike bought for around 400 million in early 2020, early 2021. It's the company's Splunk killer and will serve as an observability platform. It's really starting to take off, that's a great market for them to go after. CrowdStrike, to try to put it into sort of a summary, uses a three pronged approach. First is it's next generation anti-virus, meaning it's SaaS base. SAS based solution that can do fast lookups to telemetry data and that data lives in the cloud. And this leverages cloud strikes proprietary threat graph. Now, the second is endpoint detection and response. CrowdStrike sends all endpoint activity to the cloud and can process the data in real time. CrowdStrike EDR allows you to search data history and its partners with threat intelligent platforms who push the data into CrowdStrike, the CrowdStrike cloud. This increases CloudStrike's observation space. It also has containment capabilities in EDR to fence off compromised system. Now, the third leg of the stool is CrowdStrike's world class manage hunting approach. Like many firms, CrowdStrike has a crack team of experts that is looking at the data, but CrowdStrike's advantage is the amount of data, that observation space that we just talked about, and near real time capabilities of the architecture thanks to that proprietary database that they've developed. And all this is built in the cloud and so it enables global scale. And of course, agility. Now, let's dig into some of the survey data and take a look at what ETR respondents are saying about the spending momentum for CrowdStrike in context with its peers. Here's a very recent dataset, the October preliminary data from the October dataset in ETR's survey. Eric Bradley shared with us, ETR's head of strategy, and he runs the round tables, he's a frequent "Breaking Analysis" contributor. This is an XY graph with Netcore or spending momentum on the vertical axis and the overlap or pervasiveness in the survey on the horizontal axis. That dotted red line at 40% indicates an elevated level of spending velocity. Anything above that, we consider really impressive. Note the CrowdStrike progression since the pandemic started. The two notable points are one, that CrowdStrike has remained consistently above that 40% mark and two, it has made notable progress to the right. You can see that sort of squiggly line consistently increasing its share with one little anomaly there in the early days of over a two-year period. The other call out here is Microsoft in the upper-right. We circled Microsoft as usual. Microsoft messes up the data because it's such a dominant player and has referenced earlier as a massive scale and very quality telemetry from its endpoints. Unlike AWS, Microsoft is a direct competitor of CrowdStrike's. Nonetheless, the sector remains very strong with lots of players. Cyber is a large and expanding TAM with too many point tools that CrowdStrike is well positioned to consolidate, in our view. Now, here's a more narrow view of that same XY graph. What it does is it takes out Microsoft to kind of normalize the data a bit and it compares a number of firms that specialize in endpoint, along with CrowdStrike such as Tanium which also has a lightweight agent, by the way, and appears to be doing pretty well. SentinelOne did a relatively recent IPO, took off, stock hasn't done as well since, as you saw earlier. Carbon Black which VMware bought for around $2 billion and Cylance which is the Blackberry pivot. Now, we've also for context included Palo Alto and Cisco because they are major players with the big presence in security and they've got solutions that compete with CrowdStrike. But you can see how CrowdStrike looms large with a higher net score than these others. Although Palo Alto is very impressive, as is Cisco, steady. But Palo Alto also, sorry, CrowdStrike also has a very steady posture instead of just looming on that X axis. Let's now take a look at XDR, extended detection and response. XDR is kind of this bit of a buzzword but CrowdStrike seems to be taking the mantle and trying to sort of own the category and define it, in our view. It's a natural evolution of endpoint detection and response, EDR. In a recent ETR Roundtable hosted by our colleague, Eric Bradley, the sentiment among several CIOs is that existing SIEM, security information and event management platforms are inadequate and some see XDR as a replacement for, or at least a strong compliment to SIEM. CISOs want a single view of their data. Hmm, you haven't heard that before. They want help prioritizing potentially high impact breaches and they want to automate the low level stuff because the problem is sometimes too much information becomes information overload and you can't prioritize. So they want to consolidate platforms. They want better co consistency. They have too many dashboards, too many stove pipes. They have difficulty scaling and they have inconsistent telemetry data. As one CISO said, it's a call out here. "If the regulatory requirement isn't there, I absolutely would get rid of my SIEM." So CrowdStrike, we feel, is in a good position to continue to gain, share and disrupt this space. And that's what Dave Nicholson and I will be looking for next week when theCUBE is at Fal.Con, CrowdStrike's user conference. We'll be there for two days at the area in Vegas. In addition to CrowdStrike CEO, we'll hear from government cyber experts. We always hear that at security conferences and the CEO of Mandiant. Google just the other day closed its $5 billion plus acquisition of Mandiant, which is a threat intelligence expert and MSSP. I'm going to hear a lot about MSSPs by the way. CrowdStrike is a growing MSSP base. We think that's a really interesting sector because many companies don't have a SOC. As many as 50% of companies in the United States don't have a security operations center. So they need help, that's where MSPs come in. At the conference, there'll be a real focus on the Falcon platform. And we expect CrowdStrike to educate the audience on its multiple modules and how to take advantage of the capabilities beyond endpoint. And we'll also be watching for the ecosystem conversations. We saw this at reinforced, for example, where CrowdStrike and Okta were presenting together to show how these companies products compliment each other in the marketplace. Sometimes it gets confusing when you hear that CrowdStrike has an identity product. Okta, of course, is the identity specialist. So we'll be helping extract that signal from the noise. Because a generational company must have a strong ecosystem. CrowdStrike is evolving and our belief is that it has some work to do to create a stronger partner flywheel, and we're eager to dig into that next week. So if you're at the event, please do stop by theCUBE, say hello to Dave Nicholson and myself. Okay, we're going to leave it there today. Many thanks to Chip Simonton and Eric Bradley for their input and contributions to today's episode. Thanks to Alex Myerson, who does production, he also manages our podcast, Ken Schiffman as well, in our Boston studios, Kristen Martin and Cheryl Knight help get the word out on social media and our newsletters, and Rob Hof is our editor in chief over at siliconangle.com. He does some wonderful editing and I really appreciate that. Remember, all these episodes are available as podcasts wherever you listen, just search "Breaking Analysis" Podcast. I publish each week on wikibon.com and siliconangle.com and you can email me at david.vellante@siliconangle.com or DM me @DVellante or comment on our 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, and we'll see you next time on "Breaking Analysis". (upbeat 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)

>> From theCUBE Studios in Palo Alto and Boston bringing you data driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> After a two year hiatus, AWS re:Inforce is back on as an in-person event in Boston next week. Like the All-Star break in baseball, re:Inforce gives us an opportunity to evaluate the cyber security market overall, the state of cloud security and cross cloud security and more specifically what AWS is up to in the sector. Welcome to this week's Wikibon cube insights powered by ETR. In this Breaking Analysis we'll share our view of what's changed since our last cyber update in May. We'll look at the macro environment, how it's impacting cyber security plays in the market, what the ETR data tells us and what to expect at next week's AWS re:Inforce. We start this week with a checkpoint from Breaking Analysis contributor and stock trader Chip Simonton. We asked for his assessment of the market generally in cyber stocks specifically. So we'll summarize right here. We've kind of moved on from a narrative of the sky is falling to one where the glass is half empty you know, and before today's big selloff it was looking more and more like glass half full. The SNAP miss has dragged down many of the big names that comprise the major indices. You know, earning season as always brings heightened interest and this time we're seeing many cross currents. It starts as usual with the banks and the money centers. With the exception of JP Morgan the numbers were pretty good according to Simonton. Investment banks were not so great with Morgan and Goldman missing estimates but in general, pretty positive outlooks. But the market also shrugged off IBM's growth. And of course, social media because of SNAP is getting hammered today. The question is no longer recession or not but rather how deep the recession will be. And today's PMI data was the weakest since the start of the pandemic. Bond yields continue to weaken and there's a growing consensus that Fed tightening may be over after September as commodity prices weaken. Now gas prices of course are still high but they've come down. Tesla, Nokia and AT&T all indicated that supply issues were getting better which is also going to help with inflation. So it's no shock that the NASDAQ has done pretty well as beaten down as tech stocks started to look oversold you know, despite today's sell off. But AT&T and Verizon, they blamed their misses in part on people not paying their bills on time. SNAP's huge miss even after guiding lower and then refusing to offer future guidance took that stock down nearly 40% today and other social media stocks are off on sympathy. Meta and Google were off, you know, over 7% at midday. I think at one point hit 14% down and Google, Meta and Twitter have all said they're freezing new hires. So we're starting to see according to Simonton for the first time in a long time, the lower income, younger generation really feeling the pinch of inflation. Along of course with struggling families that have to choose food and shelter over discretionary spend. Now back to the NASDAQ for a moment. As we've been reporting back in mid-June and NASDAQ was off nearly 33% year to date and has since rallied. It's now down about 25% year to date as of midday today. But as I say, it had been, you know much deeper back in early June. But it's broken that downward trend that we talked about where the highs are actually lower and the lows are lower. That's started to change for now anyway. We'll see if it holds. But chip stocks, software stocks, and of course the cyber names have broken those down trends and have been trading above their 50 day moving averages for the first time in around four months. And again, according to Simonton, we'll see if that holds. If it does, that's a positive sign. Now remember on June 24th, we recorded a Breaking Analysis and talked about Qualcomm trading at a 12 X multiple with an implied 15% growth rate. On that day the stock was 124 and it surpassed 155 earlier this month. That was a really good call by Simonton. So looking at some of the cyber players here SailPoint is of course the anomaly with the Thoma Bravo 7 billion acquisition of the company holding that stock up. But the Bug ETF of basket of cyber stocks has definitely improved. When we last reported on cyber in May, CrowdStrike was off 23% year to date. It's now off 4%. Palo Alto has held steadily. Okta is still underperforming its peers as it works through the fallout from the breach and the ingestion of its Auth0 acquisition. Meanwhile, Zscaler and SentinelOne, those high flyers are still well off year to date, with Ping Identity and CyberArk not getting hit as hard as their valuations hadn't run up as much. But virtually all these tech stocks generally in cyber issues specifically, they've been breaking their down trend. So it will now come down to earnings guidance in the coming months. But the SNAP reaction is quite stunning. I mean, the environment is slowing, we know that. Ad spending gets cut in that type of market, we know that too. So it shouldn't be a huge surprise to anyone but as Chip Simonton says, this shows that sellers are still in control here. So it's going to take a little while to work through that despite the positive signs that we're seeing. Okay. We also turned to our friend Eric Bradley from ETR who follows these markets quite closely. He frequently interviews CISOs on his program, on his round tables. So we asked to get his take and here's what ETR is saying. Again, as we've reported while CIOs and IT buyers have tempered spending expectations since December and early January when they called for an 8% plus spending growth, they're still expecting a six to seven percent uptick in spend this year. So that's pretty good. Security remains the number one priority and also is the highest ranked sector in the ETR data set when you measure in terms of pervasiveness in the study. Within security endpoint detection and extended detection and response along with identity and privileged account management are the sub-sectors with the most spending velocity. And when you exclude Microsoft which is just dominant across the board in so many sectors, CrowdStrike has taken over the number one spot in terms of spending momentum in ETR surveys with CyberArk and Tanium showing very strong as well. Okta has seen a big dropoff in net score from 54% last survey to 45% in July as customers maybe put a pause on new Okta adoptions. That clearly shows in the survey. We'll talk about that in a moment. Look Okta still elevated in terms of spending momentum, but it doesn't have the dominant leadership position it once held in spend velocity. Year on year, according to ETR, Tenable and Elastic are seeing the biggest jumps in spending momentum, with SailPoint, Tanium, Veronis, CrowdStrike and Zscaler seeing the biggest jump in new adoptions since the last survey. Now on the downside, SonicWall, Symantec, Trellic which is McAfee, Barracuda and TrendMicro are seeing the highest percentage of defections and replacements. Let's take a deeper look at what the ETR data tells us about the cybersecurity space. This is a popular view that we like to share with net score or spending momentum on the Y axis and overlap or pervasiveness in the data on the X axis. It's a measure of presence in the data set we used to call it market share. With the data, the dot positions, you see that little inserted table, that's how the dots are plotted. And it's important to note that this data is filtered for firms with at least 100 Ns in the survey. That's why some of the other ones that we mentioned might have dropped off. The red dotted line at 40% that indicates highly elevated spending momentum and there are several firms above that mark including of course, Microsoft, which is literally off the charts in both dimensions in the upper right. It's quite incredible actually. But for the rest of the pack, CrowdStrike has now taken back its number one net score position in the ETR survey. And CyberArk and Okta and Zscaler, CloudFlare and Auth0 now Okta through the acquisition, are all above the 40% mark. You can stare at the data at your leisure but I'll just point out, make three quick points. First Palo Alto continues to impress and as steady as she goes. Two, it's a very crowded market still and it's complicated space. And three there's lots of spending in different pockets. This market has too many tools and will continue to consolidate. Now I'd like to drill into a couple of firms net scores and pick out some of the pure plays that are leading the way. This series of charts shows the net score or spending velocity or granularity for Okta, CrowdStrike, Zscaler and CyberArk. Four of the top pure plays in the ETR survey that also have over a hundred responses. Now the colors represent the following. Bright red is defections. We're leaving the platform. The pink is we're spending less, meaning we're spending 6% or worse. The gray is flat spend plus or minus 5%. The forest green is spending more, i.e, 6% or more and the lime green is we're adding the platform new. That red dotted line at the 40% net score mark is the same elevated level that we like to talk about. All four are above that target. Now that blue line you see there is net score. The yellow line is pervasiveness in the data. The data shown in each bar goes back 10 surveys all the way back to January 2020. First I want to call out that all four again are seeing down trends in spending momentum with the whole market. That's that blue line. They're seeing that this quarter, again, the market is off overall. Everybody is kind of seeing that down trend for the most part. Very few exceptions. Okta is being hurt by fewer new additions which is why we highlighted in red, that red dotted area, that square that we put there in the upper right of that Okta bar. That lime green, new ads are off as well. And the gray for Okta, flat spending is noticeably up. So it feels like people are pausing a bit and taking a breather for Okta. And as we said earlier, perhaps with the breach earlier this year and the ingestion of Auth0 acquisition the company is seeing some friction in its business. Now, having said that, you can see Okta's yellow line or presence in the data set, continues to grow. So it's a good proxy from market presence. So Okta remains a leader in identity. So again, I'll let you stare at the data if you want at your leisure, but despite some concerns on declining momentum, notice this very little red at these companies when it comes to the ETR survey data. Now one more data slide which brings us to our four star cyber firms. We started a tradition a few years ago where we sorted the ETR data by net score. That's the left hand side of this graphic. And we sorted by shared end or presence in the data set. That's the right hand side. And again, we filtered by companies with at least 100 N and oh, by the way we've excluded Microsoft just to level the playing field. The red dotted line signifies the top 10. If a company cracks the top 10 in both spending momentum and presence, we give them four stars. So Palo Alto, CrowdStrike, Okta, Fortinet and Zscaler all made the cut this time. Now, as we pointed out in May if you combined Auth0 with Okta, they jumped to the number two on the right hand chart in terms of presence. And they would lead the pure plays there although it would bring down Okta's net score somewhat, as you can see, Auth0's net score is lower than Okta's. So when you combine them it would drag that down a little bit but it would give them bigger presence in the data set. Now, the other point we'll make is that Proofpoint and Splunk both dropped off the four star list this time as they both saw marked declines in net score or spending velocity. They both got four stars last quarter. Okay. We're going to close on what to expect at re:Inforce this coming week. Re:Inforce, if you don't know, is AWS's security event. They first held it in Boston back in 2019. It's dedicated to cloud security. The past two years has been virtual and they announced that reinvent that it would take place in Houston in June, which everybody said, that's crazy. Who wants to go to Houston in June and turns out nobody did so they postponed the event, thankfully. And so now they're back in Boston, starting on Monday. Not that it's going to be much cooler in Boston. Anyway, Steven Schmidt had been the face of AWS security at all these previous events as the Chief Information Security Officer. Now he's dropped the I from his title and is now the Chief Security Officer at Amazon. So he went with Jesse to the mothership. Presumably he dropped the I because he deals with physical security now too, like at the warehouses. Not that he didn't have to worry about physical security at the AWS data centers. I don't know. Anyway, he and CJ Moses who is now the new CISO at AWS will be keynoting along with some others including MongoDB's Chief Information Security Officer. So that should be interesting. Now, if you've been following AWS you'll know they like to break things down into, you know, a couple of security categories. Identity, detection and response, data protection slash privacy slash GRC which is governance, risk and compliance, and we would expect a lot more talk this year on container security. So you're going to hear also product updates and they like to talk about how they're adding value to services and try to help, they try to help customers understand how to apply services. Things like GuardDuty, which is their threat detection that has machine learning in it. They'll talk about Security Hub, which centralizes views and alerts and automates security checks. They have a service called Detective which does root cause analysis, and they have tools to mitigate denial of service attacks. And they'll talk about security in Nitro which isolates a lot of the hardware resources. This whole idea of, you know, confidential computing which is, you know, AWS will point out it's kind of become a buzzword. They take it really seriously. I think others do as well, like Arm. We've talked about that on previous Breaking Analysis. And again, you're going to hear something on container security because it's the hottest thing going right now and because AWS really still serves developers and really that's what they're trying to do. They're trying to enable developers to design security in but you're also going to hear a lot of best practice advice from AWS i.e, they'll share the AWS dogfooding playbooks with you for their own security practices. AWS like all good security practitioners, understand that the keys to a successful security strategy and implementation don't start with the technology, rather they're about the methods and practices that you apply to solve security threats and a top to bottom cultural approach to security awareness, designing security into systems, that's really where the developers come in, and training for continuous improvements. So you're going to get heavy doses of really strong best practices and guidance and you know, some good preaching. You're also going to hear and see a lot of partners. They'll be very visible at re:Inforce. AWS is all about ecosystem enablement and AWS is going to host close to a hundred security partners at the event. This is key because AWS doesn't do it all. Interestingly, they don't even show up in the ETR security taxonomy, right? They just sort of imply that it's built in there even though they have a lot of security tooling. So they have to apply the shared responsibility model not only with customers but partners as well. They need an ecosystem to fill gaps and provide deeper problem solving with more mature and deeper security tooling. And you're going to hear a lot of positivity around how great cloud security is and how it can be done well. But the truth is this stuff is still incredibly complicated and challenging for CISOs and practitioners who are understaffed when it comes to top talent. Now, finally, theCUBE will be at re:Inforce in force. John Furry and I will be hosting two days of broadcast so please do stop by if you're in Boston and say hello. We'll have a little chat, we'll share some data and we'll share our overall impressions of the event, the market, what we're seeing, what we're learning, what we're worried about in this dynamic space. Okay. That's it for today. Thanks for watching. Thanks to Alex Myerson, who is on production and manages the podcast. Kristin Martin and Cheryl Knight, they helped get the word out on social and in our newsletters and Rob Hoff is our Editor in Chief over at siliconangle.com. You did some great editing. Thank you all. Remember all these episodes they're available, this podcast. Wherever you listen, all you do is search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. You can get in touch with me by emailing avid.vellante@siliconangle.com or DM me @dvellante, or 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 and we'll see you in Boston next week if you're there or next time on Breaking Analysis (soft music)

>> From theCUBE studios in Palo Alto in Boston bringing you data driven insights from theCUBE and ETR. This is breaking analysis with Dave Vellante. >> Despite fears of inflation, supply chain issues skyrocketing energy and home prices and global instability caused by the Ukraine crisis CIOs and IT buyers continue to expect overall spending to increase more than 6% in 2022. Now, while this is lower than our 8% prediction that we made earlier this year in January, it remains in line with last year's roughly six to 7% growth and is holding firm with the expectations reported by tech executives on the ETR surveys last quarter. Hello and welcome to this week's wiki bond cube insights powered by ETR in this breaking analysis, we'll update you on our latest look at tech spending with a preliminary take from ETR's latest macro drill down survey. We'll share some insights to which vendors have shown the biggest change in spending trajectory. And we'll tap our technical analysts to get a read on what they think it means for technology stocks going forward. The IT spending sentiment among IT buyers remains pretty solid. >> In the past two months, we've had conversations with dozens of CIOs, chief digital officers data executives, IT managers, and application developers, and across the board, they've indicated that for now at least their spending levels remain largely unchanged. The latest ETR drill down data which will share shortly, confirms these anecdotal checks. However, the interpretation of this data it's somewhat nuanced. Part of the reason for the spending levels being you know reasonably strong and holding up is inflation. Stuff costs more so spending levels are higher forcing IT managers to prioritize. Now security remains the number one priority and is less susceptible to cuts, cloud migration, productivity initiatives and other data projects remain top priorities. >> So where are CIO's robbing from Peter to pay Paul to focus on these priorities? Well, we've seen a slight uptick in certain speculative. IT projects being put on hold or frozen for a period of time. And according to ETR survey data we've seen some hiring freezes reported and this is especially notable in the healthcare sector. ETR also surveyed its buyer base to find out where they were adjusting their budgets and the strategies and tactics they were using to do so. Consolidating IT vendors was by far the most cited tactic. Now this makes sense as companies in an effort to negotiate better deals will often forego investments in newer so-called best of breed products and services, and negotiate bundles from larger suppliers. You know, even though they might not be as functional, the buyers >> can get a better deal if they bundle together from one of their larger suppliers. Think Microsoft or a Dell or other, you know, large companies. ETR survey respondents also cited cutting the cloud bill where discretionary spending was in play was another strategy or tactic that they were using. We certainly saw this with some of the largest snowflake customers this past quarter. Where even though they were still growing consumption rapidly certain snowflake customers dialed down their consumption and pushed spending off to future quarters. Now remember in the case of snowflake, anyway, customers negotiate consumption rates and their pricing based on a total commitment over a period of time. So while they may consume less in one quarter, over the lifetime of the contract, snowflake, as do many other cloud companies, have good visibility on the lifetime value of a deal. Now this next chart shows the latest ETR spending expectations among more than 900 respondents. The bars represent spending growth expectations from the periods of December, 2021 that's the gray bars, March of 2022 survey in the blue, and the most recent June data, That's the yellow bar. So you can see spending expectations for the quarter is down slightly in the mid 5% range. But overall for the year expectations remain in the mid 6% range. Now it's down from 8%, 8.3% in December where it looked like 2022 was going to really be a breakout year and have more momentum than even last year. Now, remember this was before Russia invaded Ukraine which occurred in mid-February of this year. So expectations were a little higher. So look, generally speaking CIOs have told us that their CFOs and CEOs have lowered their earnings outlooks and communicated that to Wall Street. They've told us that unless and until these revised forecasts appear at risk, they continue to expect their budget levels to remain pretty constant. Now there's still plenty of momentum and spending velocity on specific vendor platforms. Let's take a look at that. >> This chart shows the companies with the greatest spending momentum as measured by ETRs proprietary net score methodology. Net score essentially measures the net percent of customers spending more on a particular platform. That measurement is shown on the Y axis. The red line there that's inserted that red dotted line at 40%, we consider to be a highly elevated mark. And the green dots are companies in the ETR survey that are near or above that line. The X axis measures the presence in the data set, how much, you know sort of pervasiveness, if you will, is in the data. It's kind of a proxy for market presence. Now, of course we all know Kubernetes is not a company, but it remains an area where organizations are spending lots of resources and time particularly to modernize and mobilize applications. Snowflake remains the company which leads all firms in spending velocity, but as you'll see momentarily, despite its highest position relative to everybody else in the survey, it's still down from its previous levels in the high seventies and low 80% range. AWS is incredibly impressive because it has an elevated level but also a big presence in the data set in the survey. Same with Microsoft, same with ServiceNow which also stands out. And you can see the other smaller vendors like HashiCorp which is increasingly being seen as a strategic cross cloud enabler. They're showing, spending momentum. The RPA vendors you see in there automation anywhere and UI path are in the mix with numerous security companies, CrowdStrike, CyberArk, Netskope, Cloudflare, Tenable Okta, Zscaler Palo Alto networks, Sale Point Fortunate. A big number of cybersecurity firms hovering at or above that 40% mark you can see pure storage remains elevated as do PagerDuty and Coupa. So plenty of good news here, despite the recent tech crash. So that was the good, here's the not so good. So >> there is no 40% line on this chart because all these companies are well below that line. Now this doesn't mean these companies are bad companies. They just don't have the spending velocity of the ones we showed earlier. A good example here is Oracle. Look how they stand out on the X axis with a huge market presence. And Oracle remains an incredibly successful company selling to high end customers and really owning that mission critical data and application space. And remember ETR measures spending activity, but not actual spending dollars. So Oracle is skewed as a result because Oracle customers spend big bucks. But the fact is that Oracle has a large legacy install base that pulls down their growth rates. And that does show up in the ETR survey data. Broadcom is another example. They're one of the most successful companies in the industry, and they're not going after growth at all costs at all. They're going after EBITDA and of course ETR doesn't measure EBIT. So just keep that in mind, as you look at this data. Now another way to look at the data and the survey, is exploring the net score movement over the last period amongst companies. So how are they moving? What's happening to the net score over time. And this chart shows the year over year >> net score change for vendors that participate in at least three sectors within the ETR taxonomy. Remember ETR taxonomy has 12, 15 different segments. So the names above or below the gray dotted line are those companies where the net score has increased or decreased meaningfully. So to the earlier chart, it's all relative, right? Look at Oracle. While having lower net scores has also shown a more meaningful improvement in net score than some of the others, as have SAP and Teradata. Now what's impressive to me here is how AWS, Microsoft, and Google are actually holding that dotted line that gray line pretty well despite their size and the other ironically interesting two data points here are Broadcom and Nutanix. Now Broadcom, of course, as we've reported and dug into, is buying VMware and, and of, of course most customers are concerned about getting hit with higher prices. Once Broadcom takes over. Well Nutanix despite its change in net scores, in a good position potentially to capture some of that VMware business. Just yesterday, I talked to a customer who told me he migrated his entire portfolio off VMware using Nutanix AHV, the Acropolis hypervisor. And that was in an effort to avoid the VTEX specifically. Now this was a smaller customer granted and it's not representative of what I feel is Broadcom's ICP the ideal customer profile, but look, Nutanix should benefit from the Broadcom acquisition. If it can position itself to pick up the business that Broadcom really doesn't want. That kind of bottom of the pyramid. One person's trash is another's treasure as they say, okay. And here's that same chart for companies >> that participate in less than three segments. So, two or one of the segments in the ETR taxonomy. Only three names are seeing positive movement year over year in net score. SUSE under the leadership of amazing CEO, Melissa Di Donato. She's making moves. The company went public last year and acquired rancher labs in 2020. Look, we know that red hat is the big dog in Kubernetes but since the IBM acquisition people have looked to SUSE as a possible alternative and it's showing up in the numbers. It's a nice business. It's going to do more than 600 million this year in revenue, SUSE that is. It's got solid double digit growth in kind of the low teens. It's profitability is under pressure but they're definitely a player that is found a niche and is worth watching. The SolarWinds, What can I say there? I mean, maybe it's a dead cat bounce coming off the major breach that we saw a couple years ago. Some of its customers maybe just can't move off the platform. Constant contact we really don't follow and don't really, you know, focus on them. So, not much to say there. Now look at all the high priced earning stocks or infinite PE stocks that have no E and divide by zero or a negative number and boom, you have infinite PE and look at how their net scores have dropped. We've reported extensively on snowflake. They're still number one as we showed you earlier, net score, but big moves off their highs. Okta, Datadog, Zscaler, SentinelOne Dynatrace, big downward moves, and you can see the rest. So this chart really speaks to the change in expectations from the COVID bubble. Despite the fact that many of these companies CFOs would tell you that the pandemic wasn't necessarily a tailwind for them, but it certainly seemed to be the case when you look back in some of the ETR data. But a big question in the community is what's going to happen to these tech stocks, these tech companies in the market? We reached out to both Eric Bradley of ETR who used to be a technical analyst on Wall Street, and the long time trader and breaking analysis contributor, Chip Symington to get a read on what they thought. First, you know the market >> first point of the market has been off 11 out of the past 12 weeks. And bare market rallies like what we're seeing today and yesterday, they happen from time to time and it was kind of expected. Chair Powell's testimony was broadly viewed as a positive by the street because higher interest rates appear to be pushing commodity prices down. And a weaker consumer sentiment may point to a less onerous inflation outlook. That's good for the market. Chip Symington pointed out to breaking analysis a while ago that the NASDAQ has been on a trend line for the past six months where its highs are lower and the lows are lower and that's a bad sign. And we're bumping up against that trend line here. Meaning if it breaks through that trend it could be a buying signal. As he feels that tech stocks are oversold. He pointed to a recent bounce in semiconductors and cited the Qualcomm example. Here's a company trading at 12 times forward earnings with a sustained 14% growth rate over the next couple of years. And their cash flow is able to support their 2.4, 2% annual dividend. So overall Symington feels this rally was absolutely expected. He's cautious because we're still in a bear market but he's beginning to, to turn bullish. And Eric Bradley added that He feels the market is building a base here and he doesn't expect a 1970s or early 1980s year long sideways move because of all the money that's still in the system. You know, but it could bounce around for several months And remember with higher interest rates there are going to be more options other than equities which for many years has not been the case. Obviously inflation and recession. They are like two looming towers that we're all watching closely and will ultimately determine if, when, and how this market turns around. Okay, that's it for today. Thanks to my colleagues, Stephanie Chan, who helps research breaking analysis topics sometimes, and Alex Myerson who is on production in the podcast. Kristin Martin and Cheryl Knight they help get the word out and do all of our newsletters. And Rob Hof is our Editor in Chief over at siliconangle.com and does some wonderful editing for breaking analysis. Thank you. Remember, all these episodes are available as podcasts wherever you listen. All you got to do is search breaking analysis podcasts. I publish each week on wikibon.com and Siliconangle.com. And of course you can reach me by email at david.vellante@siliconangle.com or DM me at 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 the CUBE insights powered by ETR. Stay safe, be well. And we'll see you next time. (soft music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Cybersecurity stocks have been sending mixed signals as of late, mostly negative like much of tech, but some such as Palo Alto Networks, despite a tough go of it recently have held up better than most tech names. Others like CrowdStrike, had been out performing Broader Tech in March, but then flipped in May. Okta's performance was pretty much tracking along with CrowdStrike for most of the past several months, a little bit below, but then the Okta hack changed the trajectory of that name. Zscaler has crossed the critical billion dollar ARR revenue milestone, and now sees a path to five billion dollars in revenue, but the company stock fell sharply after its last earnings report and has been on a down trend since last November. Meanwhile, CyberArk's recent beat and raise, was encouraging and the stock acted well after its last report. Security remains the number one initiative priority amongst IT organizations and the spending momentum for many high flying cyber names remain strong. So what gives in cyber security? Hello, and welcome to this week's Wikibon CUBE insights powered by ETR. In this breaking analysis, we focus on security and will update you on the latest data from ETR to try to make sense out of the market and read into what this all means in both the near and long term, for some of our favorite names in cyber. First, the news. There's always something happening in security news cycles. The big recent news is new President Rodrigo Chavez declared a national emergency in Costa Rica due to the preponderance of Russian cyber attacks on the country's critical infrastructure. Such measures are normally reserved for natural disasters like earthquakes, but this move speaks to the nature of today's cyber threats. Of no surprise is modern superpower warfare even for a depleted power like Russia almost certainly involves cyber warfare as we continue to see in Ukraine. Privately held Arctic Wolf Networks hired Dustin Williams as its new CFO. Williams has taken three companies to IPO, including Nutanix in 2016, a very successful IPO for that company. Whether AWN chooses to pull the trigger this year or will wait until markets are less choppy or obviously remains to be seen. But it's a pretty clear sign the company is headed to IPO at some point. Now, big point of discussion this week at Red Hat Summit in Boston and the prior week at Dell technologies world was security. In the case of Red Hat, securing the digital supply chain was the main theme. And from Dell building, many security features into its storage arrays and cyber resilience services into its as a service offering called Apex. And we're seeing a trend where buyers want to reduce the number of bespoke tools they use if they, in fact can. Here's IDC's Jim Mercer, sharing data from a recent survey they conducted on the topic. Play the clip. >> Interestingly, we did a survey, I think around last August or something. And one of the questions was around where do you want your security, right? Where do you want to get your DevSecOps security from? Do you want to get it from individual vendors, right? Or do you want to get it from like your platforms that you're using and deploying changes in Kubernetes? >> Great question. What did they say? >> The majority of them, they're hoping they can get it built into the platform. That's really what they want-- >> Now, whether that's actually achievable is debatable because you have so much innovation and investment going on from the likes of startups and for instance, lace work or sneak and security companies that you see even trying to build platforms, you've got CrowdStrike, Okta, Zscaler and many others, trying to build security platforms and put it all under their umbrella. Now the last point will hit here is there was a lot of buzz in the news about Okta. The reaction to what was a relatively benign hack was pretty severe and probably overblown, but Okta's stock is paying the price of what is generally considered a blown communications plan versus a technical failure. Remember, identity is not an easy thing to rip and replace and Okta remains a best-of-breed player and leader in the space. So we're going to look at some ETR data later in this segment to try and make sense of the recent action in the market and certain names. Speaking of which let's take a look at how some of the names in cybersecurity have fared relative to some of the indices and relative indicators that we like to look at. Here's a Google finance comparison for a number of stocks and names in the bottom there you can see we plot the hack ETF which tracks security stocks. This is a year to date view. And so we don't show it here but the tech heavy NASDAQ is off around 26% year to date whereas the cyber ETF that we're showing is down 18%, okay. So cyber holding up a little bit better than broader tech as we've reported earlier, was actually much better and still seems to be a gap there, but the data are mixed. You can see Okta is way off relative to its peers. That's a combination of the breach that we talked about but also the run up in the stock since COVID. CrowdStrike was actually faring better but broke this month, we'll see how it's upcoming earnings announcements are received when it announces on June 2nd after the close. Palo Alto in the light blue has done better than most and until recently was holding up quite well. And of course, Sailpoint is another identity specialist, it is kind of off the charts here because it's going private with the acquisition by Thoma Bravo at nearly seven billion dollars. So you see some mixed signals in cyber these past several months and weeks. And so we're trying to understand what that all means. So let's take a look at the survey data and see how spending momentum is holding up. As we've reported IT spending forecast, at the macro level, they've come off their 8% highs from the end of the year, the ETRS December survey, but robust tech spending is still there. It's expected at nearly seven percent and this is amongst 1200 ETR respondents. Here's a picture from the ETR survey of the cybersecurity landscape. That y-axis that's net score or a measure of spending momentum and that horizontal access is overlap. We used to talk about it as a market share which is a measure of pervasiveness in the data set. That dotted red line at 40% indicates an elevated spending momentum level on the vertical axis and we filter the names and limited to only those with a hundred or more responses in the ETR survey. Then the pictures still pretty crowded as you can see. You got lots of companies above the red dotted line, including Microsoft which is up into the right, they're so far off the chart, it's just amazing. But also Palo Alto and Okta, Auth0, which of course is now owned by Okta, Zscaler, CyberArk is making moves. Sailpoint and Cloudflare, they're all above that magic 40% line. Now, you look at Cisco, it shows a very large presence in the horizontal axis in the data set. And it's got pretty respectable momentum and you see Splunk doing okay, no before and tenable just below that 40% line and a lot of names in the very respectable 20% zone. And we've included some legacy names just for context that fall below the zero percent line with a negative net score. And that means a larger proportion, that negative net score means a larger proportion of their customers in the survey are spending less than those that are spending more. Now, typically for these legacy names you're going to have a huge proportion of customers who have flat spending that kind of fat middle and that's why they sort of don't have that highly elevated score, but they're still viable as they get the recurring revenue each year. But the bottom line is that spending remains robust for some of the top names that we've talked about earlier despite their rocky stock performance. Now, let's filter this data a bit more to make it a little bit easier to read. So to do that, we take out Microsoft because they're just so dominant and we cherry pick some names to make the data more consumable and scannable. The other data point we've added is Okta's net score breakdown, the multicolored rows there, that row in the bottom right. Net score, it measures the percent of customers that are adding the platform new, that's the lime green, at 18% for Okta. The forest green is at 42%. That's the percent of customers in the survey that are spending six percent or more. The gray is flat spending. That's 32% for Okta, this past survey. The pink is customers that are spending less, that's three percent. They're spending six percent or worse in the survey, so only three percent for Okta. And the bright red at three percent is decommissioning the platform. You subtract the reds from the greens and you get a net score, well, into the 50s for Okta and you can see. We highlight Okta here because it's a name that we've been following for quite some time and customers have given us really solid feedback on the technology and up until the hack, they're affinity to Okta, but that seems to be continuing. We'll talk more about that. This recent breach to Okta has caused us to take a closer look. And you may recall, we reported with our ETR colleague, Eric Bradley. The breach was announced right in the middle of ETR collecting data in the last survey. And while we did see a noticeable downtick right after the announcement, the exposure of the hack and Okta's net score just after the breach was disclosed, you can see the combination of Okta and Auth0 remains very strong. I asked Eric Bradley this morning what he thought about Okta, and he pointed out that you can't evaluate this company on its price to earnings ratio. But it's forward sales multiple is now below 7X. And while attractive, these high flyers at some point, Eric says, they got to start making a profit. So you going to hold that thought, we'll come back to that. Now, another cut of the ETR data to look at our four star security names here. A while back we developed a methodology to try and cut through the noise of the crowded security sector using the ETR data to evaluate two key metrics; net score and shared N. Net score again is, spending momentum, the latter is an indicator of presence in the data set which is a proxy for market presence. Okay, we assigned those companies that cracked the top 10 in both net score and shared N, we give them four stars, okay, if they make the top 10. This chart here shows the April survey data for those companies with an N that's greater than, equal to a hundred responses. So again, we're filtering on those with a hundred or more responses. The table on the left that you see there, that's sorted by net score, okay. So we're sorting by spending momentum. And then the one on the right is sorted by shared N, so their presence in the data set. Seven companies hit the top 10 for both categories; Palo Alto Network, Splunk, CrowdStrike Okta, Proofpoint, Fortinet and Zscaler. Now, remember, take a look, Okta excludes Auth0, in this little methodology that we came up with. Auth0 didn't make the cuts but it hits the top 10 for net score. So if you add in Auth0's 112 N there that you see on the right. You add that into Okta, we put Okta in the number two spot in the survey on the right most table with the shared N of 354. Only Cisco has a higher presence in the data set. And you can see Cisco in the left lands just below that red dotted line. That's the top 10 in security. So if we were to combine Okta and Auth0 as one, Cisco would make the cut and earn four stars. Now, some other notables are CyberArk, which is just below the red line on the right most chart with an impressive 177 shared N. Again, if you combine Auth0 and Okta, CyberArk makes the four star grade because it's in the top 10 for net score on the left. And Sailpoint is another notable with a net score above 50% and it's got a shared N of 122, which is respectable. So despite the market's choppy waters, we're seeing some positive signs in the survey data for some of the more prominent names that we've been following for the last couple of years. So what does this mean for the markets going forward? As always, when we see these confusing signs we like to reach out to the network and one of the sharpest traders out there is Chip Simonton. We've quoted him before and we like to share some of his insights. And so we're going to highlight some of that here. So technically, almost every good tech stock is oversold. And as such, he suggested we might see a bounce here. We certainly are seeing that on this Friday, the 13th. But the right call tactically has been to sell into the rally these past several months, so we'll see what happens on Monday. The key issue with the name like Okta and some other momentum names like CrowdStrike and Zscaler is that when money comes back into tech, it's likely going to go to the FAANG stocks, the Facebook, Apple, Amazon, Netflix, Google, and of course, you put Microsoft in there as well. And we'll see about Amazon, by the way, it's kind of out of favor right now, as everyone's focused on the retail side of the business meanwhile it's cloud business is booming and that's where all the profit is. We think that should be the real focus for Amazon. But the point is, for these momentum names in cybersecurity that don't make money, they face real headwinds, as growth is slowing overall and interest rates rise, that makes the net present value of these investments much less attractive. We've talked about that before. But longer term, we agree with Chip Simonton that these are excellent companies and they will weather the storm and we think they're going to lead their respective markets. And in cyber, we would expect continued M&A activity, which could act as a booster shot in the arms of these names. Now in 2019, we saw the ETR data, it pointed to CrowdStrike, Zscaler, Okta and others in the security space. Some of those names that really looked to us like they were moving forward and the pandemic just created a surge in these names and admittedly they got out over their skis. But the data suggests that these leading companies have continued momentum and the potential for stay in power. Unlike the SolarWinds hack, it seems at this point anyway that Okta will recover in the market. For the reasons that we cited, investors, they might stay away for some time but longer term, there's a shift in CSO security strategies that appear to be permanent. They're really valuing cloud-based modern platforms, these platforms will likely continue to gain share and carry their momentum forward. Okay, that's it for now, thanks to Stephanie Chan, who helps with the background research and with social, Kristen Martin and Cheryl Knight help get the word out and do some great work as well. Alex Morrison is on production and handles all of our podcast. Alex, thank you. And Rob Hof is our Editor in Chief at SiliconANGLE. Remember, all these episodes, they're available as podcast, you can pop in the headphones and listen, just search "Breaking Analysis Podcast." I publish each week on wikibon.com and SiliconANGLE.com. Don't forget to check out etr.ai, best in the business for real customer data. It's an awesome platform. You can reach me at dave.vellante@siliconangle.com or @dvellante. You can comment on our LinkedIn posts. This is Dave Vellante for the CUBEinsights powered by ETR. Thanks for watching. And we'll see you next time. (bright upbeat music)