(upbeat music) >> Welcome back to theCUBE. We're covering Red Hat Summit 2022. We're going to wrap up now, Dave Vellante, Paul Gillin. We want to introduce you to Stephanie Chan, who's our new correspondent. Stephanie, one of your first events, your very first CUBE event. So welcome. >> Thank you. >> Up from NYC. Smaller event, but intimate. You got a chance to meet some folks last night at some of the after parties. What are your overall impressions? What'd you learn this week? >> So this has been my first in-person event in over two years. And even though, like you said, is on the smaller scale, roughly around 1000 attendees, versus it's usual eight to 10,000 attendees. There's so much energy, and excitement, and openness in these events and sessions. Even before and after the sessions people have been mingling and socializing and hanging out. So, I think a lot of people appreciate these in-person events and are really excited to be here. >> Cool. So, you also sat in some of the keynotes, right? Pretty technical, right? Which is kind of new to sort of your genre, right? I mean, I know you got a financial background but, so what'd you think of the keynotes? What'd you think of the format, the theater in the round? Any impressions of that? >> So, I think there's three things that are really consistent in these Red Hat Summit keynotes. There's always a history lesson. There's always, you know, emphasis in the culture of openness. And, there's also inspirational stories about how people utilize open source. And I found a lot of those examples really compelling and interesting. For instance, people use open source in (indistinct), and even in space. So I really enjoyed, you know, learning about all these different people and stories. What about you guys? What do you think were the big takeaways and the best stories that came out of the keynotes? >> Paul, want to start? >> Clearly the Red Hat Enterprise Linux 9 is a major rollout. They do that only about every three years. So that's a big deal to this audience. I think what they did in the area of security, with rolling out sigstore, which is a major new, I think an important new project that was sort of incubated at Red Hat. And they're trying to put in to create an open source ecosystem around that now. And the alliances. I'm usually not that much on partnerships, but the Accenture and the Microsoft partnerships do seem to be significant to the company. And, finally, the GM partnership which I think was maybe kind of the bombshell that they sort of rushed in at the last minute. But I think has the biggest potential impact on Red Hat and its partner ecosystem that is really going to anchor their edge architecture going forward. So I didn't see it so much on the product front, but the sense of Red Hat spreading its wings, and partnering with more companies, and seeing its itself as really the center of an ecosystem indicates that they are, you know, they're in a very solid position in their business. >> Yeah, and also like the pandemic has really forced us into this new normal, right? So customer demand is changing. There has been the shift to remote. There's always going to be a new normal according to Paul, and open source carries us through that. So how do you guys think Red Hat has helped its portfolio through this new normal and the shift? >> I mean, when you think of Red Hat, you think of Linux. I mean, that's where it all started. You think OpenShift which is the application development platforms. Linux is the OS. OpenShift is the application development platform for Kubernetes. And then of course, Ansible is the automation framework. And I agree with you, ecosystem is really the other piece of this. So, I mean, I think you take those three pieces and extend that into the open source community. There's a lot of innovation that's going around each of those, but ecosystems are the key. We heard from Stefanie Chiras, that fundamental, I mean, you can't do this without those gap fillers and those partnerships. And then another thing that's notable here is, you know, this was, I mean, IBM was just another brand, right? I mean, if anything it was probably a sub-brand, I mean, you didn't hear much about IBM. You certainly had no IBM presence, even though they're right across the street running Think. No Arvind present, no keynote from Arvind, no, you know, Big Blue washing. And so, I think that's a testament to Arvind himself. We heard that from Paul Cormier, he said, hey, this guy's been great, he's left us alone. And he's allowed us to continue innovating. It's good news. IBM has not polluted Red Hat. >> Yes, I think that the Red Hat was, I said at the opening, I think Red Hat is kind of the tail wagging the dog right now. And their position seems very solid in the market. Clearly the market has come to them in terms of their evangelism of open source. They've remained true to their business model. And I think that gives them credibility that, you know, a lot of other open source companies have lacked. They have stuck with the plan for over 20 years now and have really not changed it, and it's paying off. I think they're emerging as a company that you can trust to do business with. >> Now I want to throw in something else here. I thought the conversation with IDC analyst, Jim Mercer, was interesting when he said that they surveyed customers and they wanted to get the security from their platform vendor, versus having to buy these bespoke tools. And it makes a lot of sense to me. I don't think that's going to happen, right? Because you're going to have an identity specialist. You're going to have an endpoint specialist. You're going to have a threat detection specialist. And they're going to be best of breed, you know, Red Hat's never going to be all of those things. What they can do is partner with those companies through APIs, through open source integrations, they can add them in as part of the ecosystem and maybe be the steward of that. Maybe that's the answer. They're never going to be the best at all those different security disciplines. There's no way in the world, Red Hat, that's going to happen. But they could be the integration point. And that would be, that would be a simplifying layer to the equation. >> And I think it's smart. You know, they're not pretending to be an identity in access management or an anti-malware company, or even a zero trust company. They are sticking to their knitting, which is operating system and developers. Evangelizing DevSecOps, which is a good thing. And, that's what they're going to do. You know, you have to admire this company. It has never gotten outside of its swim lane. I think it's understood well really what it wants to be good at. And, you know, in the software business knowing what not to do is more important than knowing what to do. Is companies that fail are usually the ones that get overextended, this company has never overextended itself. >> What else do you want to know? >> And a term that kept popping up was multicloud, or otherwise known as metacloud. We know what the cloud is, but- >> Oh, supercloud, metacloud. >> Supercloud, yeah, here we go. We know what the cloud is but, what does metacloud mean to you guys? And why has it been so popular in these conversations? >> I'm going to boot this to Dave, because he's the expert on this. >> Well, expert or not, but I mean, again, we've coined this term supercloud. And the idea behind the supercloud or what Ashesh called metacloud, I like his name, cause it allows Web 3.0 to come into the equation. But the idea is that instead of building on each individual cloud and have compatibility with that cloud, you build a layer across clouds. So you do the hard work as a platform supplier to hide the underlying primitives and APIs from the end customer, or the end developer, they can then add value on top of that. And that abstraction layer spans on-prem, clouds, across clouds, ultimately out to the edge. And it's new, a new value layer that builds on top of the hyperscale infrastructure, or existing data center infrastructure, or emerging edge infrastructure. And the reason why that is important is because it's so damn complicated, number one. Number two, every company's becoming a software company, a technology company. They're bringing their services through digital transformation to their customers. And you've got to have a cloud to do that. You're not going to build your own data center. That's like Charles Wang says, not Charles Wang. (Paul laughing) Charles Phillips. We were just talking about CA. Charles Phillips. Friends don't let friends build data centers. So that supercloud concept, or what Ashesh calls metacloud, is this new layer that's going to be powered by ecosystems and platform companies. And I think it's real. I think it's- >> And OpenShift, OpenShift is a great, you know, key card for them or leverage for them because it is perhaps the best known Kubernetes platform. And you can see here they're really doubling down on adding features to OpenShift, security features, scalability. And they see it as potentially this metacloud, this supercloud abstraction layer. >> And what we said is, in order to have a supercloud you got to have a superpaz layer and OpenShift is that superpaz layer. >> So you had conversations with a lot of people within the past two days. Some people include companies, from Verizon, Intel, Accenture. Which conversation stood out to you the most? >> Which, I'm sorry. >> Which conversation stood out to you the most? (Paul sighs) >> The conversation with Stu Miniman was pretty interesting because we talked about culture. And really, he has a lot of credibility in that area because he's not a Red Hat. You know, he hasn't been a Red Hat forever, he's fairly new to the company. And got a sense from him that the culture there really is what they say it is. It's a culture of openness and that's, you know, that's as important as technology for a company's success. >> I mean, this was really good content. I mean, there were a lot, I mean Stefanie's awesome. Stefanie Chiras, we're talking about the ecosystem. Chris Wright, you know, digging into some of the CTO stuff. Ashesh, who coined metacloud, I love that. The whole in vehicle operating system conversation was great. The security discussion that we just had. You know, the conversations with Accenture were super thoughtful. Of course, Paul Cormier was a highlight. I think that one's going to be a well viewed interview, for sure. And, you know, I think that the customer conversations are great. Red Hat did a really good job of carrying the keynote conversations, which were abbreviated this year, to theCUBE. >> Right. >> I give 'em a lot of kudos for that. And because, theCUBE, it allows us to double click, go deeper, peel the onion a little bit, you know, all the buzz words, and cliches. But it's true. You get to clarify some of the things you heard, which were, you know, the keynotes were, were scripted, but tight. And so we had some good follow up questions. I thought it was super useful. I know I'm leaving somebody out, but- >> We're also able to interview representatives from Intel and Nvidia, which at a software conference you don't typically do. I mean, there's the assimilation, the combination of hardware and software. It's very clear that, and this came out in the keynote, that Red Hat sees hardware as matter. It matters. It's important again. And it's going to be a source of innovation in the future. That came through clearly. >> Yeah. The hardware matters theme, you know, the old days you would have an operating system and the hardware were intrinsically linked. MVS in the mainframe, VAX, VMS in the digital mini computers. DG had its own operating system. Wang had his own operating system. Prime with Prime OS. You remember these days? >> Oh my God. >> Right? (Paul laughs) And then of course Microsoft. >> And then x86, everything got abstracted. >> Right. >> Everything became x86 and now it's all atomizing again. >> Although WinTel, right? I mean, MS-DOS and Windows were intrinsically linked for many, many years with Intel x86. And it wasn't until, you know, well, and then, you know, Sun Solaris, but it wasn't until Linux kind of blew that apart. And the internet is built on the lamp stack. And of course, Linux is the fundamental foundation for Red Hat. So my point is, that the operating system and the hardware have always been very closely tied together. Whether it's security, or IO, or registries and memory management, everything controlled by the OS are very close to the hardware. And so that's why I think you've got an affinity in Red Hat to hardware. >> But Linux is breaking that bond, don't you think? >> Yes, but it still has to understand the underlying hardware. >> Right. >> You heard today, how taking advantage of Nvidia, and the AI capabilities. You're seeing that with ARM, you're seeing that with Intel. How you can optimize the operating system to take advantage of new generations of CPU, and NPU, and CPU, and PU, XPU, you know, across the board. >> Yep. >> Well, I really enjoyed this conference and it really stressed how important open source is to a lot of different industries. >> Great. Well, thanks for coming on. Paul, thank you. Great co-hosting with you. And thank you. >> Always, Dave. >> For watching theCUBE. We'll be on the road, next week we're at KubeCon in Valencia, Spain. We're at VeeamON. We got a ton of stuff going on. Check out thecube.net. Check out siliconangle.com for all the news. Wikibon.com. We publish there weekly, our breaking analysis series. Thanks for watching everybody. Dave Vellante, for Paul Gillin, and Stephanie Chan. Thanks to the crew. Shout out, Andrew, Alex, Sonya. Amazing job, Sonya. Steven, thanks you guys for coming out here. Mark, good job corresponding. Go to SiliconANGLE, Mark's written some great stuff. And thank you for watching. We'll see you next time. (calm 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. >> Snowflake Summit 2022 underscored that the ecosystem excitement which was once forming around Hadoop is being reborn, escalated and coalescing around Snowflake's data cloud. What was once seen as a simpler cloud data warehouse and good marketing with the data cloud is evolving rapidly with new workloads of vertical industry focus, data applications, monetization, and more. The question is, will the promise of data be fulfilled this time around, or is it same wine, new bottle? Hello, and welcome to this week's Wikibon CUBE Insights powered by ETR. In this "Breaking Analysis," we'll talk about the event, the announcements that Snowflake made that are of greatest interest, the major themes of the show, what was hype and what was real, the competition, and some concerns that remain in many parts of the ecosystem and pockets of customers. First let's look at the overall event. It was held at Caesars Forum. Not my favorite venue, but I'll tell you it was packed. Fire Marshall Full, as we sometimes say. Nearly 10,000 people attended the event. Here's Snowflake's CMO Denise Persson on theCUBE describing how this event has evolved. >> Yeah, two, three years ago, we were about 1800 people at a Hilton in San Francisco. We had about 40 partners attending. This week we're close to 10,000 attendees here. Almost 10,000 people online as well, and over over 200 partners here on the show floor. >> Now, those numbers from 2019 remind me of the early days of Hadoop World, which was put on by Cloudera but then Cloudera handed off the event to O'Reilly as this article that we've inserted, if you bring back that slide would say. The headline it almost got it right. Hadoop World was a failure, but it didn't have to be. Snowflake has filled the void created by O'Reilly when it first killed Hadoop World, and killed the name and then killed Strata. Now, ironically, the momentum and excitement from Hadoop's early days, it probably could have stayed with Cloudera but the beginning of the end was when they gave the conference over to O'Reilly. We can't imagine Frank Slootman handing the keys to the kingdom to a third party. Serious business was done at this event. I'm talking substantive deals. Salespeople from a host sponsor and the ecosystems that support these events, they love physical. They really don't like virtual because physical belly to belly means relationship building, pipeline, and deals. And that was blatantly obvious at this show. And in fairness, all theCUBE events that we've done year but this one was more vibrant because of its attendance and the action in the ecosystem. Ecosystem is a hallmark of a cloud company, and that's what Snowflake is. We asked Frank Slootman on theCUBE, was this ecosystem evolution by design or did Snowflake just kind of stumble into it? Here's what he said. >> Well, when you are a data clouding, you have data, people want to do things with that data. They don't want just run data operations, populate dashboards, run reports. Pretty soon they want to build applications and after they build applications, they want build businesses on it. So it goes on and on and on. So it drives your development to enable more and more functionality on that data cloud. Didn't start out that way, you know, we were very, very much focused on data operations. Then it becomes application development and then it becomes, hey, we're developing whole businesses on this platform. So similar to what happened to Facebook in many ways. >> So it sounds like it was maybe a little bit of both. The Facebook analogy is interesting because Facebook is a walled garden, as is Snowflake, but when you come into that garden, you have assurances that things are going to work in a very specific way because a set of standards and protocols is being enforced by a steward, i.e. Snowflake. This means things run better inside of Snowflake than if you try to do all the integration yourself. Now, maybe over time, an open source version of that will come out but if you wait for that, you're going to be left behind. That said, Snowflake has made moves to make its platform more accommodating to open source tooling in many of its announcements this week. Now, I'm not going to do a deep dive on the announcements. Matt Sulkins from Monte Carlo wrote a decent summary of the keynotes and a number of analysts like Sanjeev Mohan, Tony Bear and others are posting some deeper analysis on these innovations, and so we'll point to those. I'll say a few things though. Unistore extends the type of data that can live in the Snowflake data cloud. It's enabled by a new feature called hybrid tables, a new table type in Snowflake. One of the big knocks against Snowflake was it couldn't handle and transaction data. Several database companies are creating this notion of a hybrid where both analytic and transactional workloads can live in the same data store. Oracle's doing this for example, with MySQL HeatWave and there are many others. We saw Mongo earlier this month add an analytics capability to its transaction system. Mongo also added sequel, which was kind of interesting. Here's what Constellation Research analyst Doug Henschen said about Snowflake's moves into transaction data. Play the clip. >> Well with Unistore, they're reaching out and trying to bring transactional data in. Hey, don't limit this to analytical information and there's other ways to do that like CDC and streaming but they're very closely tying that again to that marketplace, with the idea of bring your data over here and you can monetize it. Don't just leave it in that transactional database. So another reach to a broader play across a big community that they're building. >> And you're also seeing Snowflake expand its workload types in its unique way and through Snowpark and its stream lit acquisition, enabling Python so that native apps can be built in the data cloud and benefit from all that structure and the features that Snowflake is built in. Hence that Facebook analogy, or maybe the App Store, the Apple App Store as I propose as well. Python support also widens the aperture for machine intelligence workloads. We asked Snowflake senior VP of product, Christian Kleinerman which announcements he thought were the most impactful. And despite the who's your favorite child nature of the question, he did answer. Here's what he said. >> I think the native applications is the one that looks like, eh, I don't know about it on the surface but he has the biggest potential to change everything. That's create an entire ecosystem of solutions for within a company or across companies that I don't know that we know what's possible. >> Snowflake also announced support for Apache Iceberg, which is a new open table format standard that's emerging. So you're seeing Snowflake respond to these concerns about its lack of openness, and they're building optionality into their cloud. They also showed some cost op optimization tools both from Snowflake itself and from the ecosystem, notably Capital One which launched a software business on top of Snowflake focused on optimizing cost and eventually the rollout data management capabilities, and all kinds of features that Snowflake announced that the show around governance, cross cloud, what we call super cloud, a new security workload, and they reemphasize their ability to read non-native on-prem data into Snowflake through partnerships with Dell and Pure and a lot more. Let's hear from some of the analysts that came on theCUBE this week at Snowflake Summit to see what they said about the announcements and their takeaways from the event. This is Dave Menninger, Sanjeev Mohan, and Tony Bear, roll the clip. >> Our research shows that the majority of organizations, the majority of people do not have access to analytics. And so a couple of the things they've announced I think address those or help to address those issues very directly. So Snowpark and support for Python and other languages is a way for organizations to embed analytics into different business processes. And so I think that'll be really beneficial to try and get analytics into more people's hands. And I also think that the native applications as part of the marketplace is another way to get applications into people's hands rather than just analytical tools. Because most people in the organization are not analysts. They're doing some line of business function. They're HR managers, they're marketing people, they're sales people, they're finance people, right? They're not sitting there mucking around in the data, they're doing a job and they need analytics in that job. >> Primarily, I think it is to contract this whole notion that once you move data into Snowflake, it's a proprietary format. So I think that's how it started but it's usually beneficial to the customers, to the users because now if you have large amount of data in paket files you can leave it on S3, but then you using the Apache Iceberg table format in Snowflake, you get all the benefits of Snowflake's optimizer. So for example, you get the micro partitioning, you get the metadata. And in a single query, you can join, you can do select from a Snowflake table union and select from an iceberg table and you can do store procedure, user defined function. So I think what they've done is extremely interesting. Iceberg by itself still does not have multi-table transactional capabilities. So if I'm running a workload, I might be touching 10 different tables. So if I use Apache Iceberg in a raw format, they don't have it, but Snowflake does. So the way I see it is Snowflake is adding more and more capabilities right into the database. So for example, they've gone ahead and added security and privacy. So you can now create policies and do even cell level masking, dynamic masking, but most organizations have more than Snowflake. So what we are starting to see all around here is that there's a whole series of data catalog companies, a bunch of companies that are doing dynamic data masking, security and governance, data observability which is not a space Snowflake has gone into. So there's a whole ecosystem of companies that is mushrooming. Although, you know, so they're using the native capabilities of Snowflake but they are at a level higher. So if you have a data lake and a cloud data warehouse and you have other like relational databases, you can run these cross platform capabilities in that layer. So that way, you know, Snowflake's done a great job of enabling that ecosystem. >> I think it's like the last mile, essentially. In other words, it's like, okay, you have folks that are basically that are very comfortable with Tableau but you do have developers who don't want to have to shell out to a separate tool. And so this is where Snowflake is essentially working to address that constituency. To Sanjeev's point, and I think part of it, this kind of plays into it is what makes this different from the Hadoop era is the fact that all these capabilities, you know, a lot of vendors are taking it very seriously to put this native. Now, obviously Snowflake acquired Streamlit. So we can expect that the Streamlit capabilities are going to be native. >> I want to share a little bit about the higher level thinking at Snowflake, here's a chart from Frank Slootman's keynote. It's his version of the modern data stack, if you will. Now, Snowflake of course, was built on the public cloud. If there were no AWS, there would be no Snowflake. Now, they're all about bringing data and live data and expanding the types of data, including structured, we just heard about that, unstructured, geospatial, and the list is going to continue on and on. Eventually I think it's going to bleed into the edge if we can figure out what to do with that edge data. Executing on new workloads is a big deal. They started with data sharing and they recently added security and they've essentially created a PaaS layer. We call it a SuperPaaS layer, if you will, to attract application developers. Snowflake has a developer-focused event coming up in November and they've extended the marketplace with 1300 native apps listings. And at the top, that's the holy grail, monetization. We always talk about building data products and we saw a lot of that at this event, very, very impressive and unique. Now here's the thing. There's a lot of talk in the press, in the Wall Street and the broader community about consumption-based pricing and concerns over Snowflake's visibility and its forecast and how analytics may be discretionary. But if you're a company building apps in Snowflake and monetizing like Capital One intends to do, and you're now selling in the marketplace, that is not discretionary, unless of course your costs are greater than your revenue for that service, in which case is going to fail anyway. But the point is we're entering a new error where data apps and data products are beginning to be built and Snowflake is attempting to make the data cloud the defacto place as to where you're going to build them. In our view they're well ahead in that journey. Okay, let's talk about some of the bigger themes that we heard at the event. Bringing apps to the data instead of moving the data to the apps, this was a constant refrain and one that certainly makes sense from a physics point of view. But having a single source of data that is discoverable, sharable and governed with increasingly robust ecosystem options, it doesn't have to be moved. Sometimes it may have to be moved if you're going across regions, but that's unique and a differentiator for Snowflake in our view. I mean, I'm yet to see a data ecosystem that is as rich and growing as fast as the Snowflake ecosystem. Monetization, we talked about that, industry clouds, financial services, healthcare, retail, and media, all front and center at the event. My understanding is that Frank Slootman was a major force behind this shift, this development and go to market focus on verticals. It's really an attempt, and he talked about this in his keynote to align with the customer mission ultimately align with their objectives which not surprisingly, are increasingly monetizing with data as a differentiating ingredient. We heard a ton about data mesh, there were numerous presentations about the topic. And I'll say this, if you map the seven pillars Snowflake talks about, Benoit Dageville talked about this in his keynote, but if you map those into Zhamak Dehghani's data mesh framework and the four principles, they align better than most of the data mesh washing that I've seen. The seven pillars, all data, all workloads, global architecture, self-managed, programmable, marketplace and governance. Those are the seven pillars that he talked about in his keynote. All data, well, maybe with hybrid tables that becomes more of a reality. Global architecture means the data is globally distributed. It's not necessarily physically in one place. Self-managed is key. Self-service infrastructure is one of Zhamak's four principles. And then inherent governance. Zhamak talks about computational, what I'll call automated governance, built in. And with all the talk about monetization, that aligns with the second principle which is data as product. So while it's not a pure hit and to its credit, by the way, Snowflake doesn't use data mesh in its messaging anymore. But by the way, its customers do, several customers talked about it. Geico, JPMC, and a number of other customers and partners are using the term and using it pretty closely to the concepts put forth by Zhamak Dehghani. But back to the point, they essentially, Snowflake that is, is building a proprietary system that substantially addresses some, if not many of the goals of data mesh. Okay, back to the list, supercloud, that's our term. We saw lots of examples of clouds on top of clouds that are architected to spin multiple clouds, not just run on individual clouds as separate services. And this includes Snowflake's data cloud itself but a number of ecosystem partners that are headed in a very similar direction. Snowflake still talks about data sharing but now it uses the term collaboration in its high level messaging, which is I think smart. Data sharing is kind of a geeky term. And also this is an attempt by Snowflake to differentiate from everyone else that's saying, hey, we do data sharing too. And finally Snowflake doesn't say data marketplace anymore. It's now marketplace, accounting for its application market. Okay, let's take a quick look at the competitive landscape via this ETR X-Y graph. Vertical access remembers net score or spending momentum and the x-axis is penetration, pervasiveness in the data center. That's what ETR calls overlap. Snowflake continues to lead on the vertical axis. They guide it conservatively last quarter, remember, so I wouldn't be surprised if that lofty height, even though it's well down from its earlier levels but I wouldn't be surprised if it ticks down again a bit in the July survey, which will be in the field shortly. Databricks is a key competitor obviously at a strong spending momentum, as you can see. We didn't draw it here but we usually draw that 40% line or red line at 40%, anything above that is considered elevated. So you can see Databricks is quite elevated. But it doesn't have the market presence of Snowflake. It didn't get to IPO during the bubble and it doesn't have nearly as deep and capable go-to market machinery. Now, they're getting better and they're getting some attention in the market, nonetheless. But as a private company, you just naturally, more people are aware of Snowflake. Some analysts, Tony Bear in particular, believe Mongo and Snowflake are on a bit of a collision course long term. I actually can see his point. You know, I mean, they're both platforms, they're both about data. It's long ways off, but you can see them sort of in a similar path. They talk about kind of similar aspirations and visions even though they're quite in different markets today but they're definitely participating in similar tam. The cloud players are probably the biggest or definitely the biggest partners and probably the biggest competitors to Snowflake. And then there's always Oracle. Doesn't have the spending velocity of the others but it's got strong market presence. It owns a cloud and it knows a thing about data and it definitely is a go-to market machine. Okay, we're going to end on some of the things that we heard in the ecosystem. 'Cause look, we've heard before how particular technology, enterprise data warehouse, data hubs, MDM, data lakes, Hadoop, et cetera. We're going to solve all of our data problems and of course they didn't. And in fact, sometimes they create more problems that allow vendors to push more incremental technology to solve the problems that they created. Like tools and platforms to clean up the no schema on right nature of data lakes or data swamps. But here are some of the things that I heard firsthand from some customers and partners. First thing is, they said to me that they're having a hard time keeping up sometimes with the pace of Snowflake. It reminds me of AWS in 2014, 2015 timeframe. You remember that fire hose of announcements which causes increased complexity for customers and partners. I talked to several customers that said, well, yeah this is all well and good but I still need skilled people to understand all these tools that I'm integrated in the ecosystem, the catalogs, the machine learning observability. A number of customers said, I just can't use one governance tool, I need multiple governance tools and a lot of other technologies as well, and they're concerned that that's going to drive up their cost and their complexity. I heard other concerns from the ecosystem that it used to be sort of clear as to where they could add value you know, when Snowflake was just a better data warehouse. But to point number one, they're either concerned that they'll be left behind or they're concerned that they'll be subsumed. Look, I mean, just like we tell AWS customers and partners, you got to move fast, you got to keep innovating. If you don't, you're going to be left. Either if your customer you're going to be left behind your competitor, or if you're a partner, somebody else is going to get there or AWS is going to solve the problem for you. Okay, and there were a number of skeptical practitioners, really thoughtful and experienced data pros that suggested that they've seen this movie before. That's hence the same wine, new bottle. Well, this time around I certainly hope not given all the energy and investment that is going into this ecosystem. And the fact is Snowflake is unquestionably making it easier to put data to work. They built on AWS so you didn't have to worry about provisioning, compute and storage and networking and scaling. Snowflake is optimizing its platform to take advantage of things like Graviton so you don't have to, and they're doing some of their own optimization tools. The ecosystem is building optimization tools so that's all good. And firm belief is the less expensive it is, the more data will get brought into the data cloud. And they're building a data platform on which their ecosystem can build and run data applications, aka data products without having to worry about all the hard work that needs to get done to make data discoverable, shareable, and governed. And unlike the last 10 years, you don't have to be a keeper and integrate all the animals in the Hadoop zoo. Okay, that's it for today, thanks for watching. Thanks to my colleague, Stephanie Chan who helps research "Breaking Analysis" topics. Sometimes Alex Myerson is on production and manages the podcasts. Kristin Martin and Cheryl Knight help get the word out on social and in our newsletters, and Rob Hof is our editor in chief over at Silicon, and Hailey does some wonderful editing, thanks to all. 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 you can email me at David.Vellante@siliconangle.com or DM me @DVellante. If you got something interesting, I'll respond. If you don't, I'm sorry I won't. Or comment on my LinkedIn post. Please 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. (upbeat music)

>>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 ante. >>When Frank sluman took service, now public many people undervalued the company, positioning it as just a better help desk tool. You know, it turns out that the firm actually had a massive Tam expansion opportunity in it. SM customer service, HR, logistics, security marketing, and service management. Generally now stock price followed over the years, the stellar execution under Slootman and CFO, Mike scar Kelly's leadership. Now, when they took the reins at snowflake expectations were already set that they'd repeat the feet, but this time, if anything, the company was overvalued out of the gate, the thing is people didn't really better understand the market opportunity this time around, other than that, it was a bet on Salman's track record of execution and on data, pretty good bets, but folks really didn't appreciate that snowflake. Wasn't just a better data warehouse that it was building what they call a data cloud, and we've turned a data super cloud. >>Hello and welcome to this. Week's Wikibon cube insights powered by ETR in this breaking analysis, we'll do four things. First. We're gonna review the recent narrative and concerns about snowflake and its value. Second, we're gonna share survey data from ETR that will confirm precisely what the company's CFO has been telling anyone who will listen. And third, we're gonna share our view of what snowflake is building IE, trying to become the defacto standard data platform, and four convey our expectations for the upcoming snowflake summit. Next week at Caesar's palace in Las Vegas, Snowflake's most recent quarterly results they've been well covered and well documented. It basically hit its targets, which for snowflake investors was bad news wall street piled on expressing concerns about Snowflake's consumption, pricing model, slowing growth rates, lack of profitability and valuation. Given the, given the current macro market conditions, the stock dropped below its IPO offering price, which you couldn't touch on day one, by the way, as the stock opened well above that and, and certainly closed well above that price of one 20 and folks express concerns about some pretty massive insider selling throughout 2021 and early 2022, all this caused the stock price to drop quite substantially. >>And today it's down around 63% or more year to date, but the only real substantive change in the company's business is that some of its largest consumer facing companies, while still growing dialed back, their consumption this past quarter, the tone of the call was I wouldn't say contentious the earnings call, but Scarelli, I think was getting somewhat annoyed with the implication from some analyst questions that something is fundamentally wrong with Snowflake's business. So let's unpack this a bit first. I wanna talk about the consumption pricing on the earnings call. One of the analysts asked if snowflake would consider more of a subscription based model so that they could better weather such fluctuations and demand before the analyst could even finish the question, CFO Scarelli emphatically interrupted and said, no, <laugh> the analyst might as well have asked, Hey Mike, have you ever considered changing your pricing model and screwing your customers the same way most legacy SaaS companies lock their customers in? >>So you could squeeze more revenue out of them and make my forecasting life a little bit easier. <laugh> consumption pricing is one of the things that makes a company like snowflake so attractive because customers is especially large customers facing fluctuating demand can dial and their end demand can dial down usage for certain workloads that are maybe not yet revenue producing or critical. Now let's jump to insider trading. There were a lot of insider selling going on last year and into 2022 now, I mean a lot sloop and Scarelli Christine Kleinman. Mike SP several board members. They sold stock worth, you know, many, many hundreds of millions of dollars or, or more at prices in the two hundreds and three hundreds and even four hundreds. You remember the company at one point was valued at a hundred billion dollars, surpassing the value of service now, which is this stupid at this point in the company's tenure and the insider's cost basis was very often in the single digit. >>So on the one hand, I can't blame them. You know what a gift the market gave them last year. Now also famed investor, Peter Linsey famously said, insiders sell for many reasons, but they only buy for one. But I have to say there wasn't a lot of insider buying of the stock when it was in the three hundreds and above. And so yeah, this pattern is something to watch our insiders buying. Now, I'm not sure we'll keep watching snowflake. It's pretty generous with stock based compensation and insiders still own plenty of stock. So, you know, maybe not, but we'll see in future disclosures, but the bottom line is Snowflake's business. Hasn't dramatically changed with the exception of these large consumer facing companies. Now, another analyst pointed out that companies like snap, he pointed to company snap, Peloton, Netflix, and face Facebook have been cutting back. >>And Scarelli said, and what was a bit of a surprise to me? Well, I'm not gonna name the customers, but it's not the ones you mentioned. So I, I thought I would've, you know, if I were the analyst I would've follow up with, how about Walmart target visa, Amex, Expedia price line, or Uber? Any of those Mike? I, I doubt he would've answered me anything. Anyway, the one thing that Scarelli did do is update Snowflake's fiscal year 2029 outlook to emphasize the long term opportunity that the company sees. This chart shows a financial snapshot of Snowflake's current business using a combination of quarterly and full year numbers in a model of what the business will look like. According to Scarelli in Dave ante with a little bit of judgment in 2029. So this is essentially based on the company's framework. Snowflake this year will surpass 2 billion in revenues and targeting 10 billion by 2029. >>Its current growth rate is 84% and its target is 30% in the out years, which is pretty impressive. Gross margins are gonna tick up a bit, but remember Snowflake's cost a good sold they're dominated by its cloud cost. So it's got a governor. There has to pay AWS Azure and Google for its infrastructure. But high seventies is a, is a good target. It's not like the historical Microsoft, you know, 80, 90% gross margin. Not that Microsoft is there anymore, but, but snowflake, you know, was gonna be limited by how far it can, how much it can push gross margin because of that factor. It's got a tiny operating margin today and it's targeting 20% in 2029. So that would be 2 billion. And you would certainly expect it's operating leverage in the out years to enable much, much, much lower SGNA than the current 54%. I'm guessing R and D's gonna stay healthy, you know, coming in at 15% or so. >>But the real interesting number to watch is free cash flow, 16% this year for the full fiscal year growing to 25% by 2029. So 2.5 billion in free cash flow in the out years, which I believe is up from previous Scarelli forecast in that 10, you know, out year view 2029 view and expect the net revenue retention, the NRR, it's gonna moderate. It's gonna come down, but it's still gonna be well over a hundred percent. We pegged it at 130% based on some of Mike's guidance. Now today, snowflake and every other stock is well off this morning. The company had a 40 billion value would drop well below that midday, but let's stick with the 40 billion on this, this sad Friday on the stock market, we'll go to 40 billion and who knows what the stock is gonna be valued in 2029? No idea, but let's say between 40 and 200 billion and look, it could get even ugly in the market as interest rates rise. >>And if inflation stays high, you know, until we get a Paul Voker like action, which is gonna be painful from the fed share, you know, let's hope we don't have a repeat of the long drawn out 1970s stagflation, but that is a concern among investors. We're gonna try to keep it positive here and we'll do a little sensitivity analysis of snowflake based on Scarelli and Ante's 2029 projections. What we've done here is we've calculated in this chart. Today's current valuation at about 40 billion and run a CAGR through 2029 with our estimates of valuation at that time. So if it stays at 40 billion valuation, can you imagine snowflake grow into a 10 billion company with no increase in valuation by the end, by by 2029 fiscal 2029, that would be a major bummer and investors would get a, a 0% return at 50 billion, 4% Kager 60 billion, 7%. >>Kegar now 7% market return is historically not bad relative to say the S and P 500, but with that kind of revenue and profitability growth projected by snowflake combined with inflation, that would again be a, a kind of a buzzkill for investors. The picture at 75 billion valuation, isn't much brighter, but it picks up at, at a hundred billion, even with inflation that should outperform the market. And as you get to 200 billion, which would track by the way, revenue growth, you get a 30% plus return, which would be pretty good. Could snowflake beat these projections. Absolutely. Could the market perform at the optimistic end of the spectrum? Sure. It could. It could outperform these levels. Could it not perform at these levels? You bet, but hopefully this gives a little context and framework to what Scarelli was talking about and his framework, not with notwithstanding the market's unpredictability you're you're on your own. >>There. I can't help snowflake looks like it's going to continue either way in amazing run compared to other software companies historically, and whether that's reflected in the stock price. Again, I, I, I can't predict, okay. Let's look at some ETR survey data, which aligns really well with what snowflake is telling the street. This chart shows the breakdown of Snowflake's net score and net score. Remember is ETS proprietary methodology that measures the percent of customers in their survey that are adding the platform new. That's the lime green at 19% existing snowflake customers that are ex spending 6% or more on the platform relative to last year. That's the forest green that's 55%. That's a big number flat spend. That's the gray at 21% decreasing spending. That's the pinkish at 5% and churning that's the red only 1% or, or moving off the platform, tiny, tiny churn, subtract the red from the greens and you get a net score that, that, that nets out to 68%. >>That's an, a very impressive net score by ETR standards. But it's down from the highs of the seventies and mid eighties, where high seventies and mid eighties, where snowflake has been since January of 2019 note that this survey of 1500 or so organizations includes 155 snowflake customers. What was really interesting is when we cut the data by industry sector, two of Snowflake's most important verticals, our finance and healthcare, both of those sectors are holding a net score in the ETR survey at its historic range. 83%. Hasn't really moved off that, you know, 80% plus number really encouraging, but retail consumer showed a dramatic decline. This past survey from 73% in the previous quarter down to 54%, 54% in just three months time. So this data aligns almost perfectly with what CFO Scarelli has been telling the street. So I give a lot of credibility to that narrative. >>Now here's a time series chart for the net score and the provision in the data set, meaning how penetrated snowflake is in the survey. Again, net score measures, spending velocity and a specific platform and provision measures the presence in the data set. You can see the steep downward trend in net score this past quarter. Now for context note, the red dotted line on the vertical axis at 40%, that's a bit of a magic number. Anything above that is best in class in our view, snowflake still a well, well above that line, but the April survey as we reported on May 7th in quite a bit of detail shows a meaningful break in the snowflake trend as shown by ETRS call out on the bottom line. You can see a steady rise in the survey, which is a proxy for Snowflake's overall market penetration. So steadily moving up and up. >>Here's a bit of a different view on that data bringing in some of Snowflake's peers and other data platforms. This XY graph shows net score on the vertical axis and provision on the horizontal with the red dotted line. At 40%, you can see from the ETR callouts again, that snowflake while declining in net score still holds the highest net score in the survey. So of course the highest data platforms while the spending velocity on AWS and Microsoft, uh, data platforms, outperforms that have, uh, sorry, while they're spending velocity on snowflake outperforms, that of AWS and, and Microsoft data platforms, those two are still well above the 40% line with a stronger market presence in the category. That's impressive because of their size. And you can see Google cloud and Mongo DB right around the 40% line. Now we reported on Mongo last week and discussed the commentary on consumption models. >>And we referenced Ray Lenchos what we thought was, was quite thoughtful research, uh, that rewarded Mongo DB for its forecasting transparency and, and accuracy and, and less likelihood of facing consumption headwinds. And, and I'll reiterate what I said last week, that snowflake, while seeing demand fluctuations this past quarter from those large customers is, is not like a data lake where you're just gonna shove data in and figure it out later, no schema on, right. Just throw it into the pond. That's gonna be more discretionary and you can turn that stuff off. More likely. Now you, you bring data into the snowflake data cloud with the intent of driving insights, which leads to actions, which leads to value creation. And as snowflake adds capabilities and expands its platform features and innovations and its ecosystem more and more data products are gonna be developed in the snowflake data cloud and by data products. >>We mean products and services that are conceived by business users. And that can be directly monetized, not just via analytics, but through governed data sharing and direct monetization. Here's a picture of that opportunity as we see it, this is our spin on our snowflake total available market chart that we've published many, many times. The key point here goes back to our opening statements. The snowflake data cloud is evolving well beyond just being a simpler and easier to use and more elastic cloud database snowflake is building what we often refer to as a super cloud. That is an abstraction layer that companies that, that comprises rich features and leverages the underlying primitives and APIs of the cloud providers, but hides all that complexity and adds new value beyond that infrastructure that value is seen in the left example in terms of compressed cycle time, snowflake often uses the example of pharmaceutical companies compressing time to discover a drug by years. >>Great example, there are many others this, and, and then through organic development and ecosystem expansion, snowflake will accelerate feature delivery. Snowflake's data cloud vision is not about vertically integrating all the functionality into its platform. Rather it's about creating a platform and delivering secure governed and facile and powerful analytics and data sharing capabilities to its customers, partners in a broad ecosystem so they can create additional value. On top of that ecosystem is how snowflake fills the gaps in its platform by building the best cloud data platform in the world, in terms of collaboration, security, governance, developer, friendliness, machine intelligence, etcetera, snowflake believes and plans to create a defacto standard. In our view in data platforms, get your data into the data cloud and all these native capabilities will be available to you. Now, is that a walled garden? Some might say it is. It's an interesting question and <laugh>, it's a moving target. >>It's definitely proprietary in the sense that snowflake is building something that is highly differentiatable and is building a moat around it. But the more open snowflake can make its platform. The more open source it uses, the more developer friendly and the great greater likelihood people will gravitate toward snowflake. Now, my new friend Tani, she's the creator of the data mesh concept. She might bristle at this narrative in favor, a more open source version of what snowflake is trying to build, but practically speaking, I think she'd recognize that we're a long ways off from that. And I also think that the benefits of a platform that despite requiring data to be inside of the data cloud can distribute data globally, enable facile governed, and computational data sharing, and to a large degree be a self-service platform for data, product builders. So this is how we see snow, the snowflake data cloud vision evolving question is edge part of that vision on the right hand side. >>Well, again, we think that is going to be a future challenge where the ecosystem is gonna have to come to play to fill those gaps. If snowflake can tap the edge, it'll bring even more clarity as to how it can expand into what we believe is a massive 200 billion Tam. Okay, let's close on next. Week's snowflake summit in Las Vegas. The cube is very excited to be there. I'll be hosting with Lisa Martin and we'll have Frank son as well as Christian Kleinman and several other snowflake experts. Analysts are gonna be there, uh, customers. And we're gonna have a number of ecosystem partners on as well. Here's what we'll be looking for. At least some of the things, evidence that our view of Snowflake's data cloud is actually taking shape and evolving in the way that we showed on the previous chart, where we also wanna figure out where snowflake is with it. >>Streamlet acquisition. Remember streamlet is a data science play and an expansion into data, bricks, territory, data, bricks, and snowflake have been going at it for a while. Streamlet brings an open source Python library and machine learning and kind of developer friendly data science environment. We also expect to hear some discussion, hopefully a lot of discussion about developers. Snowflake has a dedicated developer conference in November. So we expect to hear more about that and how it's gonna be leveraging further leveraging snow park, which it has previously announced, including a public preview of programming for unstructured data and data monetization along the lines of what we suggested earlier that is building data products that have the bells and whistles of native snowflake and can be directly monetized by Snowflake's customers. Snowflake's already announced a new workload this past week in security, and we'll be watching for others. >>And finally, what's happening in the all important ecosystem. One of the things we noted when we covered service now, cause we use service now as, as an example because Frank Lupin and Mike Scarelli and others, you know, DNA were there and they're improving on that service. Now in his post IPO, early adult years had a very slow pace. In our view was often one of our criticism of ecosystem development, you know, ServiceNow. They had some niche SI uh, like cloud Sherpa, and eventually the big guys came in and, and, and began to really lean in. And you had some other innovators kind of circling the mothership, some smaller companies, but generally we see sluman emphasizing the ecosystem growth much, much more than with this previous company. And that is a fundamental requirement in our view of any cloud or modern cloud company now to paraphrase the crazy man, Steve bomber developers, developers, developers, cause he screamed it and ranted and ran around the stage and was sweating <laugh> ecosystem ecosystem ecosystem equals optionality for developers and that's what they want. >>And that's how we see the current and future state of snowflake. Thanks today. If you're in Vegas next week, please stop by and say hello with the cube. Thanks to my colleagues, Stephanie Chan, who sometimes helps research breaking analysis topics. Alex, my is, and OS Myerson is on production. And today Andrew Frick, Sarah hiney, Steven Conti Anderson hill Chuck all and the entire team in Palo Alto, including Christian. Sorry, didn't mean to forget you Christian writer, of course, Kristin Martin and Cheryl Knight, they helped get the word out. And Rob ho is our E IIC over at Silicon angle. Remember, all these episodes are available as podcast, wherever you listen to search breaking analysis podcast, I publish each week on wikibon.com and Silicon angle.com. You can email me directly anytime David dot Valante Silicon angle.com. If you got something interesting, I'll respond. If not, I won't or DM me@deteorcommentonmylinkedinpostsandpleasedocheckoutetr.ai for the best survey data in the enterprise tech business. This is Dave Valante for the insights powered by ETR. Thanks for watching. And we'll see you next week. I hope if not, we'll see you next time on breaking analysis.

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 earnings season has shown a conflicting mix of signals for software companies well virtually all firms are expressing caution over so-called macro headwinds we're talking about ukraine inflation interest rates europe fx headwinds supply chain just overall i.t spend mongodb along with a few other names appeared more sanguine thanks to a beat in the recent quarter and a cautious but upbeat outlook for the near term hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis ahead of mongodb world 2022 we drill into mongo's business and what etr survey data tells us in the context of overall demand and the patterns that we're seeing from other software companies and we're seeing some distinctly different results from major firms these days we'll talk more about [ __ ] in this session which beat eps by 30 cents in revenue by more than 18 million dollars salesforce had a great quarter and its diversified portfolio is paying off as seen by the stocks noticeable uptick post earnings uipath which had been really beaten down prior to this quarter it's brought in a new co-ceo and it's business is showing a nice rebound with a small three cent eps beat and a nearly 20 million dollar top line beat crowdstrike is showing strength as well meanwhile managements at microsoft workday and snowflake expressed greater caution about the macroeconomic climate and especially on investors minds his concern about consumption pricing models snowflake in particular which had a small top-line beat cited softness and effects from reduced consumption especially from certain consumer-facing customers which has analysts digging more deeply into the predictability of their models in fact barclays analyst ramo lenchow published an especially thoughtful piece on this topic concluding that [ __ ] was less susceptible to consumption headwinds than for example snowflake essentially for a few reasons one because atlas mongo's cloud managed service which is the consumption model comprises only about 60 percent of mongo's revenue second is the premise that [ __ ] is supporting core operational applications that can't be easily dialed down or turned off and three that snowflake customers it sounds like has a more concentrated customer base and due to that fact there's a preponderance of its revenue is consumption driven and would be more sensitive to swings in these consumption patterns now i'll say this first consumption pricing models are here to stay and the much preferred model for customers is consumption the appeal of consumption is i can actually dial down turn off if i need to and stop spending for a while which happened or at least happened to a certain extent this quarter for certain companies but to the point about [ __ ] supporting core applications i do believe that over time you're going to see the increased emergence of data products that will become core monetization drivers in snowflake along with other data platforms is going to feed those data products and services and become over time maybe less susceptible and less sensitive to these consumption patterns it'll always be there but i think increasingly it's going to be tied to operational revenue last two points here in this slide software evaluations have reverted to their historical mean which is a good thing in our view we've taken some air out of the bubble and returned to more normalized valuations was really predicted and looked forward to look we're still in a lousy market for stocks it's really a bear market for tech the market tends to be at least six months ahead of the economy and often not always but often is a good predictor we've had some tough compares relative to the pandemic days in tech and we'll be watching next quarter very closely because the macro headwinds have now been firmly inserted into the guidance of software companies okay let's have a look at how certain names have performed relative to a software index benchmark so far this year here's a year-to-date chart comparing microsoft salesforce [ __ ] and snowflake to the igv software heavy etf which is shown in the darker blue line which by the way it does not own the ctf does not own snowflake or [ __ ] you can see that these big super caps have fared pretty well whereas [ __ ] and especially snowflake those higher growth companies have been much more negatively impacted year to date from a stock price standpoint now let's move on let's take a financial snapshot of [ __ ] and put it next to snowflake so we can compare these two higher growth names what we've done here in this chart has taken the most recent quarters revenue and multiplied it by 4x to get a revenue run rate and we've parenthetically added a projection for the full year revenue [ __ ] as you see will do north of a billion dollars in revenue while snowflake will begin to approach three billion dollars 2.7 and run right through that that four quarter run rate that they just had last quarter and you can see snowflake is growing faster than [ __ ] at 85 percent this past quarter and we took now these most of these profit of these next profitability ratios off the current quarter with one exception both companies have high gross margins of course you'd expect that but as we've discussed not as high as some traditional software companies in part because of their cloud costs but also you know their maturity or lack thereof both [ __ ] and snowflake because they are in high growth mode have thin operating margins they spend nearly half or more than half of their revenue on growth that's the sg a line mostly the s the sales and marketing is really where they're spending money uh and and they're specialists so they spend a fair amount of their revenue on r d but maybe not as high as you might think but a pretty hefty percentage the free cash flow as a percentage of revenue line we calculated off the full year projections because there was a kind of an anomaly this quarter in the in the snowflake numbers and you can see snowflakes free cash flow uh which again was abnormally high this quarter is going to settle in around 16 this year versus mongo's six percent so strong focus by snowflake on free cash flow and its management snowflake is about four billion dollars in cash and marketable securities on its balance sheet with little or no debt whereas [ __ ] has about two billion dollars on its balance sheet with a little bit of longer term debt and you can see snowflakes market cap is about double that of mongos so you're paying for higher growth with snowflake you're paying for the slootman scarpelli execution engine the expectation there a stronger balance sheet etc but snowflake is well off its roughly 100 billion evaluation which it touched during the peak days of tech during the pandemic and just that as an aside [ __ ] has around 33 000 customers about five times the number of customers snowflake has so a bit of a different customer mix and concentration but both companies in our view have no lack of market in terms of tam okay now let's dig a little deeper into mongo's business and bring in some etr data this colorful chart shows the breakdown of mongo's net score net score is etr's proprietary methodology that measures the percent of customers in the etr survey that are adding the platform new that's the lime green at nine percent existing customers that are spending six percent or more on the platform that's the forest green at 37 spending flat that's the gray at 46 percent decreasing spend that's the pinkish at around 5 and churning that's only 3 that's the bright red for [ __ ] subtract the red from the greens and you net out to a 38 which is a very solid net score figure note this is a survey of 1500 or so organizations and it includes 150 mongodb customers which includes by the way 68 global 2000 customers and they show a spending velocity or a net score of 44 so notably higher among the larger clients and while it's a smaller sample only 27 emea's net score for [ __ ] is 33 now that's down from 60 last quarter note that [ __ ] cited softness in its european business on its earning calls so that aligns to the gtr data okay now let's plot [ __ ] relative to some other data platforms these don't all necessarily compete head to head with [ __ ] but they are in data and database platforms in the etr data set and that's what this chart shows it's an xy graph with net score or as we say spending momentum on the vertical axis and overlap or presence or pervasiveness in the data set on the horizontal axis see that red dotted line there at 40 that indicates an elevated level of spending anything above that is highly elevated we've highlighted [ __ ] in that red box which is very close to that 40 percent line it has a pretty strong presence on the x-axis right there with gcp snowflake as we've reported has come down to earth but still well elevated again that aligns with the earnings releases uh aws and microsoft they have many data platforms especially aws so their plot position reflects their broad portfolio massive size on the x-axis um that's the presence and and very impressive on the vertical axis so despite that size they have strong spending momentum and you can see the pack of others including cockroach small on the verdict on the horizontal but elevated on the vertical couch base is creeping up since its ipo redis maria db which was launched the day that oracle bought sun and and got my sequel and some legacy platforms including the leader in database oracle as well as ibm and teradata's both cloud and on-prem platforms now one interesting side note here is on mongo's earning call it clearly cited the advantages of its increasingly all-in-one approach relative to others that offer a portfolio of bespoke or what we some sometimes call horses for courses databases [ __ ] cited the advantages of its simplicity and lower costs as it adds more and more functionality this is an argument often made by oracle and they often target aws as the company with too many databases and of course [ __ ] makes that argument uh as well but they also make the argument that oracle they don't necessarily call them out but they talk about traditional relational databases of course they're talking about oracle and others they say that's more complex less flexible and less appealing to developers than is [ __ ] now oracle of course would retur we retort saying hey we now support a mongodb api so why go anywhere else we're the most robust and the best for mission critical but this gives credence to the fact that if oracle is trying to capture business by offering a [ __ ] api for example that [ __ ] must be doing something right okay let's look at why they buy [ __ ] here's an etr chart that addresses that question it's it's mongo's feature breadth is the number one reason lower cost or better roi is number two integrations and stack alignment is third and mongo's technology lead is fourth those four kind of stand out with notice on the right hand side security and vision much lower there in the right that doesn't necessarily mean that [ __ ] doesn't have good security and and good vision although it has been cited uh security concerns um and and so we keep an eye on that but look [ __ ] has a document database it's become a viable alternative to traditional relational databases meaning you have much more flexibility over your schema um and in fact you know it's kind of schema-less you can pretty much put anything into a document database uh developers seem to love it generally it's fair to say mongo's architecture would favor consistency over availability because it uses a single master architecture as a primary and you can create secondary nodes in the event of a primary failure but you got to think about that and how to architect availability into the platform and got to consider recovery more carefully now now no schema means it's not a tables and rows structure and you can again shove anything you want into the database but you got to think about how to optimize performance um on queries now [ __ ] has been hard at work evolving the platform from the early days when you go back and look at its roadmap it's been you know started as a document database purely it added graph processing time series it's made search you know much much easier and more fundamental it's added atlas that fully managed cloud database uh service which we said now comprises 60 of its revenue it's you know kubernetes integrations and kind of the modern microservices stack and dozens and dozens and dozens of other features mongo's done a really fine job we think of creating a leading database platform today that is loved by customers loved by developers and is highly functional and next week the cube will be at mongodb world and we'll be looking for some of these items that we're showing here and this this chart this always going to be main focus on developers [ __ ] prides itself on being a developer friendly platform we're going to look for new features especially around security and governance and simplification of configurations and cluster management [ __ ] is likely going to continue to advance its all-in-one appeal and add more capabilities that reduce the need to to spin up bespoke platforms and we would expect enhance enhancements to atlas further enhancements there is atlas really is the future you know maybe adding you know more cloud native features and integrations and perhaps simplified ways to migrate to the cloud to atlas and improve access to data sources generally making the lives of developers and data analysts easier that's going to be we think a big theme at the event so these are the main things that we'll be scoping out at the event so please stop by if you're in new york city new york city at mongodb world or tune in to thecube.net okay that's it for today thanks to my colleagues stephanie chan who helps research breaking analysis from time to time alex meyerson is on production as today is as is andrew frick sarah kenney steve conte conte anderson hill and the entire team in palo alto thank you kristen martin and cheryl knight helped get the word out and rob hof is our editor-in-chief over there at siliconangle remember all these episodes are available as podcasts wherever you listen just search breaking analysis podcast we do publish each week on wikibon.com and siliconangle.com want to reach me email me david.velante siliconangle.com or dm me at divalante or a 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 thanks for watching see you next time [Music] you

>> 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 the words of my colleague CTO David Nicholson, Broadcom buys old cars, not to restore them to their original luster and beauty. Nope. They buy classic cars to extract the platinum that's inside the catalytic converter and monetize that. Broadcom's planned 61 billion acquisition of VMware will mark yet another new era and chapter for the virtualization pioneer, a mere seven months after finally getting spun out as an independent company by Dell. For VMware, this means a dramatically different operating model with financial performance and shareholder value creation as the dominant and perhaps the sole agenda item. For customers, it will mean a more focused portfolio, less aspirational vision pitches, and most certainly higher prices. Hello and welcome to this week's Wikibon CUBE Insights powered by ETR. In this Breaking Analysis, we'll share data, opinions and customer insights about this blockbuster deal and forecast the future of VMware, Broadcom and the broader ecosystem. Let's first look at the key deal points, it's been well covered in the press. But just for the record, $61 billion in a 50/50 cash and stock deal, resulting in a blended price of $138 per share, which is a 44% premium to the unaffected price, i.e. prior to the news breaking. Broadcom will assume 8 billion of VMware debt and promises that the acquisition will be immediately accretive and will generate 8.5 billion in EBITDA by year three. That's more than 4 billion in EBITDA relative to VMware's current performance today. In a classic Broadcom M&A approach, the company promises to dilever debt and maintain investment grade ratings. They will rebrand their software business as VMware, which will now comprise about 50% of revenues. There's a 40 day go shop and importantly, Broadcom promises to continue to return 60% of its free cash flow to shareholders in the form of dividends and buybacks. Okay, with that out of the way, we're going to get to the money slide literally in a moment that Broadcom shared on its investor call. Broadcom has more than 20 business units. It's CEO Hock Tan makes it really easy for his business unit managers to understand. Rule number one, you agreed to an operating plan with targets for revenue, growth, EBITDA, et cetera, hit your numbers consistently and we're good. You'll be very well compensated and life will be wonderful for you and your family. Miss the number, and we're going to have a frank and uncomfortable bottom line discussion. You'll four, perhaps five quarters to turn your business around, if you don't, we'll kill it or sell it if we can. Rule number two, refer to rule number one. Hello, VMware, here's the money slide. I'll interpret the bullet points on the left for clarity. Your fiscal year 2022 EBITDA was 4.7 billion. By year three, it will be 8.5 billion. And we Broadcom have four knobs to turn with you, VMware to help you get there. First knob, if it ain't recurring revenue with rubber stamp renewals, we're going to convert that revenue or kill it. Knob number two, we're going to focus R&D in the most profitable areas of the business. AKA expect the R&D budget to be cut. Number three, we're going to spend less on sales and marketing by focusing on existing customers. We're not going to lose money today and try to make it up many years down the road. And number four, we run Broadcom with 1% GNA. You will too. Any questions? Good. Now, just to give you a little sense of how Broadcom runs its business and how well run a company it is, let's do a little simple comparison with this financial snapshot. All we're doing here is taking the most recent quarterly earnings reports from Broadcom and VMware respectively. We take the quarterly revenue and multiply by four X to get the revenue run rate and then we calculate the ratios off of the most recent quarters revenue. It's worth spending some time on this to get a sense of how profitable the Broadcom business actually is and what the spreadsheet gurus at Broadcom are seeing with respect to the possibilities for VMware. So combined, we're talking about a 40 plus billion dollar company. Broadcom is growing at more than 20% per year. Whereas VMware's latest quarter showed a very disappointing 3% growth. Broadcom is mostly a hardware company, but its gross margin is in the high seventies. As a software company of course VMware has higher gross margins, but FYI, Broadcom's software business, the remains of Symantec and what they purchased as CA has 90% gross margin. But the I popper is operating margin. This is all non gap. So it excludes things like stock based compensation, but Broadcom had 61% operating margin last quarter. This is insanely off the charts compared to VMware's 25%. Oracle's non gap operating margin is 47% and Oracle is an incredibly profitable company. Now the red box is where the cuts are going to take place. Broadcom doesn't spend much on marketing. It doesn't have to. It's SG&A is 3% of revenue versus 18% for VMware and R&D spend is almost certainly going to get cut. The other eye popper is free cash flow as a percentage of revenue at 51% for Broadcom and 29% for VMware. 51%. That's incredible. And that my dear friends is why Broadcom a company with just under 30 billion in revenue has a market cap of 230 billion. Let's dig into the VMware portfolio a bit more and identify the possible areas that will be placed under the microscope by Hock Tan and his managers. The data from ETR's latest survey shows the net score or spending momentum across VMware's portfolio in this chart, net score essentially measures the net percent of customers that are spending more on a specific product or vendor. The yellow bar is the most recent survey and compares the April 22 survey data to April 21 and January of 22. Everything is down in the yellow from January, not surprising given the economic outlook and the change in spending patterns that we've reported. VMware Cloud on AWS remains the product in the ETR survey with the most momentum. It's the only offering in the portfolio with spending momentum above the 40% line, a level that we consider highly elevated. Unified Endpoint Management looks more than respectable, but that business is a rock fight with Microsoft. VMware Cloud is things like VMware Cloud foundation, VCF and VMware's cross cloud offerings. NSX came from the Nicira acquisition. Tanzu is not yet pervasive and one wonders if VMware is making any money there. Server is ESX and vSphere and is the bread and butter. That is where Broadcom is going to focus. It's going to look at VSAN and NSX, which is software probably profitable. And of course the other products and see if the investments are paying off, if they are Broadcom will keep, if they are not, you can bet your socks, they will be sold off or killed. Carbon Black is at the far right. VMware paid $2.1 billion for Carbon Black. And it's the lowest performer on this list in terms of net score or spending momentum. And that doesn't mean it's not profitable. It just doesn't have the momentum you'd like to see, so you can bet that is going to get scrutiny. Remember VMware's growth has been under pressure for the last several years. So it's been buying companies, dozens of them. It bought AirWatch, bought Heptio, Carbon Black, Nicira, SaltStack, Datrium, Versedo, Bitnami, and on and on and on. Many of these were to pick up engineering teams. Some of them were to drive new revenue. Now this is definitely going to be scrutinized by Broadcom. So that helps explain why Michael Dell would sell VMware. And where does VMware go from here? It's got great core product. It's an iconic name. It's got an awesome ecosystem, fantastic distribution channel, but its growth is slowing. It's got limited developer chops in a world that developers and cloud native is all the rage. It's got a far flung R&D agenda going at war with a lot of different places. And it's increasingly fighting this multi front war with cloud companies, companies like Cisco, IBM Red Hat, et cetera. VMware's kind of becoming a heavy lift. It's a perfect acquisition target for Broadcom and why the street loves this deal. And we titled this Breaking Analysis taming the VMware beast because VMware is a beast. It's ubiquitous. It's an epic software platform. EMC couldn't control it. Dell used it as a piggy bank, but really didn't change its operating model. Broadcom 100% will. Now one of the things that we get excited about is the future of systems architectures. We published a breaking analysis about a year ago, talking about AWS's secret weapon with Nitro and it's Annapurna custom Silicon efforts. Remember it acquired Annapurna for a measly $350 million. And we talked about how there's a new architecture and a new price performance curve emerging in the enterprise, driven by AWS and being followed by Microsoft, Google, Alibaba, a trend toward custom Silicon with the arm based Nitro and which is AWS's hypervisor and Nick strategy, enabling processor diversity with things like Graviton and Trainium and other diverse processors, really diversifying away from x86 and how this leads to much faster product cycles, faster tape out, lower costs. And our premise was that everyone in the data center is going to competes, is going to need a Nitro to be competitive long term. And customers are going to gravitate toward the most economically favorable platform. And as we describe the landscape with this chart, we've updated this for this Breaking Analysis and we'll come back to nitro in a moment. This is a two dimensional graphic with net score or spending momentum on the vertical axis and overlap formally known as market share or presence within the survey, pervasiveness that's on the horizontal axis. And we plot various companies and products and we've inserted VMware's net score breakdown. The granularity in those colored bars on the bottom right. Net score is essentially the green minus the red and a couple points on that. VMware in the latest survey has 6% new adoption. That's that lime green. It's interesting. The question Broadcom is going to ask is, how much does it cost you to acquire that 6% new. 32% of VMware customers in the survey are increasing spending, meaning they're increasing spending by 6% or more. That's the forest green. And the question Broadcom will dig into is what percent of that increased spend (chuckles) you're capturing is profitable spend? Whatever isn't profitable is going to be cut. Now that 52% gray area flat spending that is ripe for the Broadcom picking, that is the fat middle, and those customers are locked and loaded for future rent extraction via perpetual renewals and price increases. Only 8% of customers are spending less, that's the pinkish color and only 3% are defecting, that's the bright red. So very, very sticky profile. Perfect for Broadcom. Now the rest of the chart lays out some of the other competitor names and we've plotted many of the VMware products so you can see where they fit. They're all pretty respectable on the vertical axis, that's spending momentum. But what Broadcom wants is that core ESX vSphere base where we've superimposed the Broadcom logo. Broadcom doesn't care so much about spending momentum. It cares about profitability potential and then momentum. AWS and Azure, they're setting the pace in this business, in the upper right corner. Cisco very huge presence in the data center, as does Intel, they're not in the ETR survey, but we've superimposed them. Now, Intel of course, is in a dog fight within Nvidia, the Arm ecosystem, AMD, don't forget China. You see a Google cloud platform is in there. Oracle is also on the chart as well, somewhat lower on the vertical axis, but it doesn't have that spending momentum, but it has a big presence. And it owns a cloud as we've talked about many times and it's highly differentiated. It's got a strategy that allows it to differentiate from the pack. It's very financially driven. It knows how to extract lifetime value. Safra Catz operates in many ways, similar to what we're seeing from Hock Tan and company, different from a portfolio standpoint. Oracle's got the full stack, et cetera. So it's a different strategy. But very, very financially savvy. You could see IBM and IBM Red Hat in the mix and then Dell and HP. I want to come back to that momentarily to talk about where value is flowing. And then we plotted Nutanix, which with Acropolis could suck up some V tax avoidance business. Now notice Symantec and CA, relatively speaking in the ETR survey, they have horrible spending momentum. As we said, Broadcom doesn't care. Hock Tan is not going for growth at the expense of profitability. So we fully expect VMware to come down on the vertical axis over time and go up on the profit scale. Of course, ETR doesn't measure the profitability here. Now back to Nitro, VMware has this thing called Project Monterey. It's essentially their version of Nitro and will serve as their future architecture diversifying off x86 and accommodating alternative processors. And a much more efficient performance, price in energy consumption curve. Now, one of the things that we've advocated for, we said this about Dell and others, including VMware to take a page out of AWS and start developing custom Silicon to better integrate hardware and software and accelerate multi-cloud or what we call supercloud. That layer above the cloud, not just running on individual clouds. So this is all about efficiency and simplicity to own this space. And we've challenged organizations to do that because otherwise we feel like the cloud guys are just going to have consistently better costs, not necessarily price, but better cost structures, but it begs the question. What happens to Project Monterey? Hock Tan and Broadcom, they don't invest in something that is unproven and doesn't throw off free cash flow. If it's not going to pay off for years to come, they're probably not going to invest in it. And yet Project Monterey could help secure VMware's future in not only the data center, but at the edge and compete more effectively with cloud economics. So we think either Project Monterey is toast or the VMware team will knock on the door of one of Broadcom's 20 plus business units and say, guys, what if we work together with you to develop a version of Monterey that we can use and sell to everyone, it'd be the arms dealer to everyone and be competitive with the cloud and other players out there and create the de facto standard for data center performance and supercloud. I mean, it's not outrageously expensive to develop custom Silicon. Tesla is doing it for example. And Broadcom obviously is capable of doing it. It's got good relationships with semiconductor fabs. But I think this is going to be a tough sell to Broadcom, unless VMware can hide this in plain site and make it profitable fast, like AWS most likely has with Nitro and Graviton. Then Project Monterey and our pipe dream of alternatives to Nitro in the data center could happen but if it can't, it's going to be toast. Or maybe Intel or Nvidia will take it over or maybe the Monterey team will spin out a VMware and do a Pensando like deal and demonstrate the viability of this concept and then Broadcom will buy it back in 10 years. Here's a double click on that previous data that we put in tabular form. It's how the data on that previous slide was plotted. I just want to give you the background data here. So net score spending momentum is the sorted on the left. So it's sorted by net score in the left hand chart, that was the y-axis in the previous data set and then shared and or presence in the data set is the right hand chart. In other words, it's sorted on the right hand chart, right hand table. That right most column is shared and you can see it's sorted top to bottom, and that was the x-axis on the previous chart. The point is not many on the left hand side are above the 40% line. VMware Cloud on AWS is, it's expensive, so it's probably profitable and it's probably a keeper. We'll see about the rest of VMware's portfolio. Like what happens to Tanzu for example. On the right, we drew a red line, just arbitrarily at those companies and products with more than a hundred mentions in the survey, everything but Tanzu from VMware makes that cut. Again, this is no indication of profitability here, and that's what's going to matter to Broadcom. Now let's take a moment to address the question of Broadcom as a software company. What the heck do they know about software, right. Well, they're not dumb over there and they know how to run a business, but there is a strategic rationale to this move beyond just doing portfolios and extracting rents and cutting R&D, et cetera, et cetera. Why, for example, isn't Broadcom going after coming back to Dell or HPE, it could pick up for a lot less than VMware, and they got way more revenue than VMware. Well, it's obvious, software's more profitable of course, and Broadcom wants to move up the stack, but there's a trend going on, which Broadcom is very much in touch with. First, it sells to Dell and HPE and Cisco and all the OEM. so it's not going to disrupt that. But this chart shows that the value is flowing away from traditional servers and storage and networking to two places, merchant Silicon, which itself is morphing. Broadcom... We focus on the left hand side of this chart. Broadcom correctly believes that the world is shifting from a CPU centric center of gravity to a connectivity centric world. We've talked about this on theCUBE a lot. You should listen to Broadcom COO Charlie Kawwas speak about this. It's all that supporting infrastructure around the CPU where value is flowing, including of course, alternative GPUs and XPUs, and NPUs et cetera, that are sucking the value out of the traditional x86 architecture, offloading some of the security and networking and storage functions that traditionally have been done in x86 which are part of the waste right now in the data center. This is that shifting dynamic of Moore's law. Moore's law, not keeping pace. It's slowing down. It's slower relative to some of the combinatorial factors. When you add up in all the CPU and GPU and NPU and accelerators, et cetera. So we've talked about this a lot in Breaking Analysis episodes. So the value is shifting left within that middle circle. And it's shifting left within that left circle toward components, other than CPU, many of which Broadcom supplies. And then you go back to the middle, value is shifting from that middle section, that traditional data center up into hyperscale clouds, and then to the right toward infrastructure software to manage all that equipment in the data center and across clouds. And look Broadcom is an arms dealer. They simply sell to everyone, locking up key vectors of the value chain, cutting costs and raising prices. It's a pretty straightforward strategy, but not for the fate of heart. And Broadcom has become pretty good at it. Let's close with the customer feedback. I spoke with ETRs Eric Bradley this morning. He and I both reached out to VMware customers that we know and got their input. And here's a little snapshot of what they said. I'll just read this. Broadcom will be looking to invest in the core and divest of any underperforming assets, right on. It's just what we were saying. This doesn't bode well for future innovation, this is a CTO at a large travel company. Next comment, we're a Carbon Black customer. VMware didn't seem to interfere with Carbon Black, but now that we're concerned about short term disruption to their tech roadmap and long term, are they going to split and be sold off like Symantec was, this is a CISO at a large hospitality organization. Third comment, I got directly from a VMware practitioner, an IT director at a manufacturing firm. This individual said, moving off VMware would be very difficult for us. We have over 500 applications running on VMware, and it's really easy to manage. We're not going to move those into the cloud and we're worried Broadcom will raise prices and just extract rents. Last comment, we'll share as, Broadcom sees the cloud data center and IoT is their next revenue source. The VMware acquisition provides them immediate virtualization capabilities to support a lightweight IoT offering. Big concern for customers is what technology they will invest in and innovate, and which will be stripped off and sold. Interesting. I asked David Floyer to give me a back of napkin estimate for the following question. I said, David, if you're running mission critical applications on VMware, how much would it increase your operating cost moving those applications into the cloud? Or how much would it save? And he said, Dave, VMware's really easy to run. It can run any application pretty much anywhere, and you don't need an army of people to manage it. All your processes are tied to VMware, you're locked and loaded. Move that into the cloud and your operating cost would double by his estimates. Well, there you have it. Broadcom will pinpoint the optimal profit maximization strategy and raise prices to the point where customers say, you know what, we're still better off staying with VMware. And sadly, for many practitioners there aren't a lot of choices. You could move to the cloud and increase your cost for a lot of your applications. You could do it yourself with say Zen or OpenStack. Good luck with that. You could tap Nutanix. That will definitely work for some applications, but are you going to move your entire estate, your application portfolio to Nutanix? It's not likely. So you're going to pay more for VMware and that's the price you're going to pay for two decades of better IT. So our advice is get out ahead of this, do an application portfolio assessment. If you can move apps to the cloud for less, and you haven't yet, do it, start immediately. Definitely give Nutanix a call, but going to have to be selective as to what you actually can move, forget porting to OpenStack, or do it yourself Hypervisor, don't even go there. And start building new cloud native apps where it makes sense and let the VMware stuff go into manage decline. Let certain apps just die through attrition, shift your development resources to innovation in the cloud and build a brick wall around the stable apps with VMware. As Paul Maritz, the former CEO of VMware said, "We are building the software mainframe". Now marketing guys got a hold of that and said, Paul, stop saying that, but it's true. And with Broadcom's help that day we'll soon be here. That's it for today. Thanks to Stephanie Chan who helps research our topics for Breaking Analysis. Alex Myerson does the production and he also manages the Breaking Analysis podcast. Kristen Martin and Cheryl Knight help get the word out on social and thanks to Rob Hof, who was our editor in chief at siliconangle.com. Remember, these episodes are all available as podcast, wherever you listen, just search Breaking Analysis podcast. Check out ETRs website at etr.ai for all the survey action. We publish a full report every week on wikibon.com and siliconangle.com. You can email me directly at david.vellante@siliconangle.com. You can DM me at DVellante or comment on our LinkedIn posts. This is Dave Vellante for theCUBE Insights powered by ETR. Have a great week, stay safe, be well. And we'll see you next time. (upbeat music)

>> 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. >> Last year, we noted in a breaking analysis that the cloud ecosystem is innovating beyond the idea or notion of multi-cloud. We've said for years that multi-cloud is really not a strategy but rather a symptom of multi-vendor. And we coined this term supercloud to describe an abstraction layer that lives above the hyperscale infrastructure that hides the underlying complexities, the APIs, and the primitives of each of the respective clouds. It interconnects whether it's On-Prem, AWS, Azure, Google, stretching out to the edge and creates a value layer on top of that. So our vision is that supercloud is more than running an individual service in cloud native mode within an individual individual cloud rather it's this new layer that builds on top of the hyperscalers. And does things irrespective of location adds value and we'll get into that in more detail. Now it turns out that we weren't the only ones thinking about this, not surprisingly, the majority of the technology ecosystem has been working towards this vision in various forms, including some examples that actually don't try to hide the underlying primitives. And we'll talk about that, but give a consistent experience across the DevSecOps tool chain. Hello, and welcome to this week's Wikibon, Cube insights powered by ETR. In this breaking analysis, we're going to share some recent examples and direct quotes about supercloud from the many Cube guests that we've had on over the last several weeks and months. And we've been trying to test this concept of supercloud. Is it technically feasible? Is it business rational? Is there business case for it? And we'll also share some recent ETR data to put this into context with some of the players that we think are going after this opportunity and where they are in their supercloud build out. And as you can see I'm not in the studio, everybody's got COVID so the studios shut down temporarily but breaking analysis continues. So here we go. Now, first thing is we uncovered an article from earlier this year by Lori MacVittie, is entitled, Supercloud: The 22 Answer to Multi-Cloud Challenges. What a great title. Of course we love it. Now, what really interested us here is not just the title, but the notion that it really doesn't matter what it's called, who cares? Supercloud, distributed cloud, someone even called it Metacloud recently, and we'll get into that. But Lori is a technologist. She's a developer by background. She works at F-Five and she's partial to the supercloud definition that was put forth by Cornell. You can see it here. That's a cloud architecture that enables application migration as a service across different availability zones or cloud providers, et cetera. And that the supercloud provides interfaces to allocate, migrate and terminate resources... And can span all major public cloud providers as well as private clouds. Now, of course, we would take that as well to the edge. So sure. That sounds about right and provides further confirmation that something new is really happening out there. And that was our initial premise when we put this fourth last year. Now we want to dig deeper and hear from the many Cube guests that we've interviewed recently probing about this topic. We're going to start with Chuck Whitten. He's Dell's new Co-COO and most likely part of the Dell succession plan, many years down the road hopefully. He coined the phrase multi-cloud by default versus multi-cloud by design. And he provides a really good business perspective. He's not a deep technologist. We're going to hear from Chuck a couple of times today including one where John Furrier asks him about leveraging hyperscale CapEx. That's an important concept that's fundamental to supercloud. Now, Ashesh Badani heads products at Red Hat and he talks about what he calls Metacloud. Again, it doesn't matter to us what you call it but it's the ecosystem gathering and innovating and we're going to get his perspective. Now we have a couple of clips from Danny Allan. He is the CTO of Veeam. He's a deep technologist and super into the weeds, which we love. And he talks about how Veeam abstracts the cloud layer. Again, a concept that's fundamental to supercloud and he describes what a supercloud is to him. And we also bring with Danny the edge discussion to the conversation. Now the bottom line from Danny is we want to know is supercloud technically feasible? And is it a thing? And then we have Jeff Clarke. Jeff Clark is the Co-COO and Vice Chairman of Dell super experienced individual. He lays out his vision of supercloud and what John Furrier calls a business operating system. You're going to hear from John a couple times. And he, Jeff Clark has a dropped the mic moment, where he says, if we can do this X, we'll describe what X is, it's game over. Okay. So of course we wanted to then go to HPE, one of Dell's biggest competitors and Patrick Osborne is the vice president of the storage business unit at Hewlett Packet Enterprise. And so given Jeff Clarke's game over strategy, we want to understand how HPE sees supercloud. And the bottom line, according to Patrick Osborne is that it's real. So you'll hear from him. And now Raghu Raghuram is the CEO of VMware. He threw a curve ball at this supercloud concept. And he flat out says, no, we don't want to hide the underlying primitives. We want to give developers access to those. We want to create a consistent developer experience in that DevsSecOps tool chain and Kubernetes runtime environments, and connect all the elements in the application development stack. So that's a really interesting perspective that Raghu brings. And then we end on Itzik Reich. Itzik is a technologist and a technical team leader who's worked as a go between customers and product developers for a number of years. And we asked Itzik, is supercloud technically feasible and will it be a reality? So let's hear from these experts and you can decide for yourselves how real supercloud is today and where it is, run the sizzle >> Operative phrase is multi-cloud by default that's kind of the buzz from your keynote. What do you mean by that? >> Well, look, customers have woken up with multiple clouds, multiple public clouds, On-Premise clouds increasingly as the edge becomes much more a reality for customers clouds at the edge. And so that's what we mean by multi-cloud by default. It's not yet been designed strategically. I think our argument yesterday was, it can be and it should be. It is a very logical place for architecture to land because ultimately customers want the innovation across all of the hyperscale public clouds. They will see workloads and use cases where they want to maintain an On-Premise cloud, On-Premise clouds are not going away, I mentioned edge clouds, so it should be strategic. It's just not today. It doesn't work particularly well today. So when we say multi-cloud by default we mean that's the state of the world today. Our goal is to bring multi-cloud by design as you heard. >> Really great question, actually, since you and I talked, Dave, I've been spending some time noodling just over that. And you're right. There's probably some terminology, something that will get developed either by us or in collaboration with the industry. Where we sort of almost have the next almost like a Metacloud that we're working our way towards. >> So we manage both the snapshots and we convert it into the Veeam portable data format. And here's where the supercloud comes into play. Because if I can convert it into the Veeam portable data format, I can move that OS anywhere. I can move it from physical to virtual, to cloud, to another cloud, back to virtual, I can put it back on physical if I want to. It actually abstracts the cloud layer. There are things that we do when we go between cloud some use BIOS, some use UEFI, but we have the data in backup format, not snapshot format, that's theirs, but we have it in backup format that we can move around and abstract workloads across all of the infrastructure. >> And your catalog is control in control of that. Is that right? Am I thinking about that the right way? >> Yeah it is, 100%. And you know what's interesting about our catalog, Dave, the catalog is inside the backup. Yes. So here's, what's interesting about the edge, two things, on the edge you don't want to have any state, if you can help it. And so containers help with that You can have stateless environments, some persistent data storage But we not not only provide the portability in operating systems, we also do this for containers. And that's true. If you go to the cloud and you're using say EKS with relational database services RDS for the persistent data later, we can pick that up and move it to GKE or move it to OpenShift On-Premises. And so that's why I call this the supercloud, we have all of this data. Actually, I think you termed the term supercloud. >> Yeah. But thank you for... I mean, I'm looking for a confirmation from a technologist that it's technically feasible. >> It is technically feasible and you can do it today. >> You said also technology and business models are tied together and enabler. If you believe that then you have to believe that it's a business operating system that they want. They want to leverage whatever they can. And at the end of the day, they have to differentiate what they do. >> Well, that's exactly right. If I take that in what Dave was saying and I summarize it the following way, if we can take these cloud assets and capabilities, combine them in an orchestrated way to deliver a distributed platform, game over. >> We have a number of platforms that are providing whether it's compute or networking or storage, running those workloads that they plum up into the cloud they have an operational experience in the cloud and they now they have data services that are running in the cloud for us in GreenLake. So it's a reality, we have a number of platforms that support that. We're going to have a a set of big announcements coming up at HPE Discover. So we led with Electra and we have a block service. We have VM backup as a service and DR on top of that. So that's something that we're providing today. GreenLake has over, I think it's actually over 60 services right now that we're providing in the GreenLake platform itself. Everything from security, single sign on, customer IDs, everything. So it's real. We have the proofpoint for it. >> Yeah. So I want to clarify something that you said because this tends to be very commonly confused by customers. I use the word abstraction. And usually when people think of abstraction, they think it hides capabilities of the cloud providers. That's not what we are trying to do. In fact, that's the last thing we are trying to do. What we are trying to do is to provide a consistent developer experience regardless of where you want to build your application. So that you can use the cloud provider services if that's what you want to use. But the DevSecOp tool chain, the runtime environment which turns out to be Kubernetes and how you control the Kubernetes environment, how do you manage and secure and connect all of these things. Those are the places where we are adding the value. And so really the VMware value proposition is you can build on the cloud of your choice but providing these consistent elements, number one, you can make better use of us, your scarce developer or operator resources and expertise. And number two, you can move faster. And number three, you can just spend less as a result of this. So that's really what we are trying to do. We are not... So I just wanted to clarify the word abstraction. In terms of where are we? We are still, I would say, in the early stages. So if you look at what customers are trying to do, they're trying to build these greenfield applications. And there is an entire ecosystem emerging around Kubernetes. There is still, Kubernetes is not a developer platform. The developer experience on top of Kubernetes is highly inconsistent. And so those are some of the areas where we are introducing new innovations with our Tanzu Application Platform. And then if you take enterprise applications, what does it take to have enterprise applications running all the time be entirely secure, et cetera. >> Well, look, the multi-cloud by default today are isolated clouds. They don't work together. Your data is siloed. It's locked up and it is expensive to move and make sense of it. So I think the word you and I were batting around before, this is an interconnected tissue. That's what the world needs. They need the clouds to work together as a single platform. That's the problem that we're trying to solve. And you saw it in some of our announcements here that we're starting to make steps on that journey to make multi-cloud work together much simpler. >> It's interesting, you mentioned the hyperscalers and all that CapEx investments. Why wouldn't you want to take advantage of a cloud and build on the CapEx and then ultimately have the solutions machine learning as one area. You see some specialization with the clouds. But you start to see the rise of superclouds, Dave calls them, and that's where you can innovate on a cloud then go to the multiple clouds. Snowflakes is one, we see a lot of examples of supercloud... >> Project Alpine was another one. I mean, it's early, but it's its clearly where you're going. The technology is just starting to come around. I mean it's real. >> Yeah. I mean, why wouldn't you want to take advantage of all of the cloud innovation out there? >> Is that something that's, that supercloud idea is a reality from a technologist perspective. >> I think it is. So for example Katie Gordon, which I believe you've interviewed earlier this week, was demonstrating the Kubernetes data mobility aspect which is another project. That's exactly part of the it's rationale, the rationale of customers being able to move some of their Kubernetes workloads to the cloud and back and between different clouds. Why are we doing? Because customers wants to have the ability to move between different cloud providers, using a common API that will be able to orchestrate all of those things with a self-service that may be offered via the APEX console itself. So it's all around enabling developers and meeting them where they are today and also meeting them into tomorrow's world where they actually may have changed their mind to do those things. So yes we are walking on all of those different aspects. >> Okay. Let's take a quick look at some of the ETR data. This is an X-Y graph. You've seen it a number of times on breaking analysis, it plots the net score or spending momentum on the Y-axis and overlap or pervasiveness in the ETR dataset on the X-axis, used to be called market share. I think that term was off putting to some people, but anyway it's an indicator of presence in the dataset. Now that red dotted line that's rarefied air where anything above that line is considered highly elevated. Now you can see we've plotted Azure and AWS in the upper right. GCP is in there and Kubernetes. We've done that as reference points. They're not necessarily building supercloud platforms. We'll see if they ever want to do so. And Kubernetes of course not a company, but we put 'em in there for context. And we've cherry picked a few players that we believe are building out or are important for supercloud build out. Let's start with Snowflake. We've talked a lot about this company. You can see they're highly elevated on the vertical axis. We see the data cloud as a supercloud in the making. You've got pure storage in there. They made the public, the early part of its supercloud journey at Accelerate 2019 when it unveiled a hybrid block storage service inside of AWS, it connects its On-Prem to AWS and creates that singular experience for pure customers. We see Hashi, HashiCorp as an enabling infrastructure, as code. So they're enabling infrastructure as code across different clouds and different locations. You see Nutanix. They're embarking on their multi-cloud strategy but it's doing so in a way that we think is supercloud, like now. Now Veeam, we were just at VeeamON. And this company has tied Dell for the number one revenue player in data protection. That's according to IDC. And we don't think it won't be long before it holds that position alone at the top as it's growing faster than in Dell in the space. We'll see, Dell is kind of waking up a little bit and putting more resource on that. But Veeam, they're a pure play vendor in data protection. And you heard their CTO, Danny Allan's view on Supercloud, they're doing it today. And we heard extensive comments as well from Dell that's clearly where they're headed, project Alpine was an early example from Dell technologies world of Supercloud in our view. And HPE with GreenLake. Finally beginning to talk about that cross cloud experience. I think it in initially HPE has been more focused on the private cloud, we'll continue to probe. We'll be at HPE discover later on the spring, actually end of June. And we'll continue to probe to see what HPE is doing specifically with GreenLake. Now, finally, Cisco, we put them on the chart. We don't have direct quotes from recent shows and events but this data really shows you the size of Cisco's footprint within the ETR data set that's on the X-axis. Now the cut of this ETR data includes all sectors across the ETR taxonomy which is not something that we commonly show but you can see the magnitude of Cisco's presence. It's impressive. Now, they had better, Cisco that is, had better be building out a supercloud in our view or they're going to be left behind. And I'm quite certain that they're actually going to do so. So we have a lot of evidence that we're putting forth here and seeing in the marketplace what we said last year, the ecosystem is take taking shape, supercloud is forming and becoming a thing. And really in our view, is the future of cloud. But there are always risks to these predictive scenarios and we want to acknowledge those. So first, look, we could end up with a bunch of bespoke superclouds. Now one supercloud is better than three separate cloud native services that do fundamentally the same thing from the same vendor. One for AWS, one for GCP and one for Azure. So maybe that's not all that bad. But to point number two, we hope there evolves a set of open standards for self-service infrastructure, federated governance, and data sharing that will evolve as a horizontal layer versus a set of proprietary vendor specific tools. Now, maybe a company like Veeam will provide that as a data management layer or some of Veeam's competitors or maybe it'll emerge again as open source. As well, and this next point, we see the potential for edge disruptions, changing the economics of the data center. Edge in fact could evolve on its own, independent of the cloud. In fact, David Floria sees the edge somewhat differently from Danny Allan. Floria says he sees a requirement for distributed stateful environments that are ephemeral where recovery is built in. And I said, David, stateful? Ephemeral? Stateful ephemeral? Isn't that an oxymoron? And he responded that, look, if it's not ephemeral the costs are going to be prohibitive. He said the biggest mistake the companies could make is thinking that the edge is simply an extension of their current cloud strategies. We're seeing that a lot. Dell largely talks about the edge as retail. Now, and Telco is a little bit different, but back to Floria's comments, he feels companies have to completely reimagine an integrated file and recovery system which is much more data efficient. And he believes that the technology will evolve with massive volumes and eventually seep into enterprise cloud and distributed data centers with better economics. In other words, as David Michelle recently wrote, we're about 15 years into the most recent cloud cycle and history shows that every 15 years or so, something new comes along that is a blind spot and highly disruptive to existing leaders. So number four here is really important. Remember, in 2007 before AWS introduced the modern cloud, IBM outpost, sorry, IBM outspent Amazon and Google and RND and CapEx and was really comparable to Microsoft. But instead of inventing cloud, IBM spent hundreds of billions of dollars on stock buybacks and dividends. And so our view is that innovation rewards leaders. And while it's not without risks, it's what powers the technology industry it always has and likely always will. So we'll be watching that very closely, how companies choose to spend their free cash flow. Okay. That's it for now. Thanks for watching this episode of The Cube Insights, powered by ETR. Thanks to Stephanie Chan who does some of the background research? Alex Morrison is on production and is going to compile all this stuff. Thank you, Alex. We're all remote this week. Kristen Nicole and Cheryl Knight do Cube distribution and social distribution and get the word out, so thank you. Robert Hof is our editor in chief. Don't forget the checkout etr.ai for all the survey action. Remember I publish each week on wikibon.com and siliconangle.com and you can check out all the breaking analysis podcasts. All you can do is search breaking analysis podcast so you can pop in the headphones and listen while you're on a walk. You can email me at david.vellante@siliconangle.com. If you want to get in touch or DM me at DVellante, you can always hit me up into a comment on our LinkedIn posts. This is Dave Vellante. Thank you for watching this episode of break analysis, stay safe, be well and we'll see you next time. (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. >> 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)

>> 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 the pre-cloud era hardware companies would run benchmarks, showing how database and or application performance ran better on their systems relative to competitors or previous generation boxes. And they would make a big deal out of it. And the independent software vendors, you know they'd do a little golf clap if you will, in the form of a joint press release it became a game of leaprog amongst hardware competitors. That was pretty commonplace over the years. The Dell Snowflake Deal underscores that the value proposition between hardware companies and ISVs is changing and has much more to do with distribution channels, volumes and the amount of data that lives On-Prem in various storage platforms. For cloud native ISVs like Snowflake they're realizing that despite their Cloud only dogma they have to grit their teeth and deal with On-premises data or risk getting shut out of evolving architectures. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis, we unpack what little is known about the Snowflake announcement from Dell Technologies World and discuss the implications of a changing Cloud landscape. We'll also share some new data for Cloud and Database platforms from ETR that shows Snowflake has actually entered the Earth's orbit when it comes to spending momentum on its platform. Now, before we get into the news I want you to listen to Frank's Slootman's answer to my question as to whether or not Snowflake would ever architect the platform to run On-Prem because it's doable technically, here's what he said, play the clip >> Forget it, this will only work in the Public Cloud. Because it's, this is how the utility model works, right. I think everybody is coming through this realization, right? I mean, excuses are running out at this point. You know, we think that it'll, people will come to the Public Cloud a lot sooner than we will ever come to the Private Cloud. It's not that we can't run a private Cloud. It's just diminishes the potential and the value that we bring. >> So you may be asking yourselves how do you square that circle? Because basically the Dell Snowflake announcement is about bringing Snowflake to the private cloud, right? Or is it let's get into the news and we'll find out. Here's what we know at Dell Technologies World. One of the more buzzy announcements was the, by the way this was a very well attended vet event. I should say about I would say 8,000 people by my estimates. But anyway, one of the more buzzy announcements was Snowflake can now run analytics on Non-native Snowflake data that lives On-prem in a Dell object store Dell's ECS to start with. And eventually it's software defined object store. Here's Snowflake's clark, Snowflake's Clark Patterson describing how it works this past week on theCUBE. Play the clip. The way it works is I can now access Non-native Snowflake data using what materialized views, external tables How does that work? >> Some combination of the, all the above. So we've had in Snowflake, a capability called External Tables, which you refer to, it goes hand in hand with this notion of external stages. Basically there's a through the combination of those two capabilities, it's a metadata layer on data, wherever it resides. So customers have actually used this in Snowflake for data lake data outside of Snowflake in the Cloud, up until this point. So it's effectively an extension of that functionality into the Dell On-Premises world, so that we can tap into those things. So we use the external stages to expose all the metadata about what's in the Dell environment. And then we build external tables in Snowflake. So that data looks like it is in Snowflake. And then the experience for the analyst or whomever it is, is exactly as though that data lives in the Snowflake world. >> So as Clark explained, this capability of External tables has been around in the Cloud for a while, mainly to suck data out of Cloud data lakes. Snowflake External Tables use file level metadata, for instance, the name of the file and the versioning so that it can be queried in a stage. A stage is just an external location outside of Snowflake. It could be an S3 bucket or an Azure Blob and it's soon will be a Dell object store. And in using this feature, the Dell looks like it lives inside of Snowflake and Clark essentially, he's correct to say to an analyst that looks exactly like the data is in Snowflake, but uh, not exactly the data's read only which means you can't do what are called DML operations. DML stands for Data Manipulation Language and allows for things like inserting data into tables or deleting and modifying existing data. But the data can be queried. However, the performance of those queries to External Tables will almost certainly be slower. Now users can build things like materialized views which are going to speed things up a bit, but at the end of the day, it's going to run faster than the Cloud. And you can be almost certain that's where Snowflake wants it to run, but some organizations can't or won't move data into the Cloud for a variety of reasons, data sovereignty, compliance security policies, culture, you know, whatever. So data can remain in place On-prem, or it can be moved into the Public Cloud with this new announcement. Now, the compute today presumably is going to be done in the Public Cloud. I don't know where else it's going to be done. They really didn't talk about the compute side of things. Remember, one of Snowflake's early innovations was to separate compute from storage. And what that gave them is you could more efficiently scale with unlimited resources when you needed them. And you could shut off the compute when you don't need us. You didn't have to buy, and if you need more storage you didn't have to buy more compute and vice versa. So everybody in the industry has copied that including AWS with Redshift, although as we've reported not as elegantly as Snowflake did. RedShift's more of a storage tiering solution which minimizes the compute required but you can't really shut it off. And there are companies like Vertica with Eon Mode that have enabled this capability to be done On-prem, you know, but of course in that instance you don't have unlimited elastic compute scale on-Prem but with solutions like Dell Apex and HPE GreenLake, you can certainly, you can start to simulate that Cloud elasticity On-prem. I mean, it's not unlimited but it's sort of gets you there. According to a Dell Snowflake joint statement, the companies the quote, the companies will pursue product integrations and joint go to market efforts in the second half of 2022. So that's a little vague and kind of benign. It's not really clear when this is going to be available based on that statement from the two first, but, you know, we're left wondering will Dell develop an On-Prem compute capability and enable queries to run locally maybe as part of an extended apex offering? I mean, we don't know really not sure there's even a market for that but it's probably a good bet that again, Snowflake wants that data to land in the Snowflake data Cloud kind of makes you wonder how this deal came about. You heard Sloop on earlier Snowflake has always been pretty dogmatic about getting data into its native snowflake format to enable the best performance as we talked about but also data sharing and governance. But you could imagine that data architects they're building out their data mesh we've reported on this quite extensively and their data fabric and those visions around that. And they're probably telling Snowflake, Hey if you want to be a strategic partner of ours you're going to have to be more inclusive of our data. That for whatever reason we're not putting in your Cloud. So Snowflake had to kind of hold its nose and capitulate. Now the good news is it further opens up Snowflakes Tam the total available market. It's obviously good marketing posture. And ultimately it provides an on ramp to the Cloud. And we're going to come back to that shortly but let's look a little deeper into what's happening with data platforms and to do that we'll bring in some ETR data. Now, let me just say as companies like Dell, IBM, Cisco, HPE, Lenovo, Pure and others build out their hybrid Clouds. The cold hard fact is not only do they have to replicate the Cloud Operating Model. You will hear them talk about that a lot, but they got to do that. So it, and that's critical from a user experience but in order to gain that flywheel momentum they need to build a robust ecosystem that goes beyond their proprietary portfolios. And, you know, honestly they're really not even in the first inning most companies and for the likes of Snowflake to sort of flip this, they've had to recognize that not everything is moving into the Cloud. Now, let's bring up the next slide. One of the big areas of discussion at Dell Tech World was Apex. That's essentially Dell's nascent as a service offering. Apex is infrastructure as a Service Cloud On-prem and obviously has the vision of connecting to the Cloud and across Clouds and out to the Edge. And it's no secret that database is one of the most important ingredients of infrastructure as a service generally in Cloud Infrastructure specifically. So this chart here shows the ETR data for data platforms inside of Dell accounts. So the beauty of ETR platform is you can cut data a million different ways. So we cut it. We said, okay, give us the Cloud platforms inside Dell accounts, how are they performing? Now, this is a two dimensional graphic. You got net score or spending momentum on the vertical axis and what ETR now calls Overlap formally called Market Share which is a measure of pervasiveness in the survey. That's on the horizontal axis that red dotted line at 40% represents highly elevated spending on the Y. The table insert shows the raw data for how the dots are positioned. Now, the first call out here is Snowflake. According to ETR quote, after 13 straight surveys of astounding net scores, Snowflake has finally broken the trend with its net score dropping below the 70% mark among all respondents. Now, as you know, net score is measured by asking customers are you adding the platform new? That's the lime green in the bar that's pointing from Snowflake in the graph and or are you increasing spend by 6% or more? That's the forest green is spending flat that's the gray is you're spend decreasing by 6% or worse. That's the pinkish or are you decommissioning the platform bright red which is essentially zero for Snowflake subtract the reds from the greens and you get a net score. Now, what's somewhat interesting is that snowflakes net score overall in the survey is 68 which is still huge, just under 70%, but it's net score inside the Dell account base drops to the low sixties. Nonetheless, this chart tells you why Snowflake it's highly elevated spending momentum combined with an increasing presence in the market over the past two years makes it a perfect initial data platform partner for Dell. Now and in the Ford versus Ferrari dynamic. That's going on between the likes of Dell's apex and HPE GreenLake database deals are going to become increasingly important beyond what we're seeing with this recent Snowflake deal. Now noticed by the way HPE is positioned on this graph with its acquisition of map R which is now part of HPE Ezmeral. But if these companies want to be taken seriously as Cloud players, they need to further expand their database affinity to compete ideally spinning up databases as part of their super Clouds. We'll come back to that that span multiple Clouds and include Edge data platforms. We're a long ways off from that. But look, there's Mongo, there's Couchbase, MariaDB, Cloudera or Redis. All of those should be on the short list in my view and why not Microsoft? And what about Oracle? Look, that's to be continued on maybe as a future topic in a, in a Breaking Analysis but I'll leave you with this. There are a lot of people like John Furrier who believe that Dell is playing with fire in the Snowflake deal because he sees it as a one way ticket to the Cloud. He calls it a one way door sometimes listen to what he said this past week. >> I would say that that's a dangerous game because we've seen that movie before, VMware and AWS. >> Yeah, but that we've talked about this don't you think that was the right move for VMware? >> At the time, but if you don't nurture the relationship AWS will take all those customers ultimately from VMware. >> Okay, so what does the data say about what John just said? How is VMware actually doing in Cloud after its early missteps and then its subsequent embracing of AWS and other Clouds. Here's that same XY graphic spending momentum on the Y and pervasiveness on the X and the same table insert that plots the dots and the, in the breakdown of Dell's net score granularity. You see that at the bottom of the chart in those colors. So as usual, you see Azure and AWS up and to the right with Google well behind in a distant third, but still in the mix. So very impressive for Microsoft and AWS to have both that market presence in such elevated spending momentum. But the story here in context is that the VMware Cloud on AWS and VMware's On-Prem Cloud like VMware Cloud Foundation VCF they're doing pretty well in the market. Look, at HPE, gaining some traction in Cloud. And remember, you may not think HPE and Dell and VCF are true Cloud but these are customers answering the survey. So their perspective matters more than the purest view. And the bad news is the Dell Cloud is not setting the world on fire from a momentum standpoint on the vertical axis but it's above the line of zero and compared to Dell's overall net score of 20 you could see it's got some work to do. Okay, so overall Dell's got a pretty solid net score to you know, positive 20, as I say their Cloud perception needs to improve. Look, Apex has to be the Dell Cloud brand not Dell reselling VMware. And that requires more maturity of Apex it's feature sets, its selling partners, its compensation models and it's ecosystem. And I think Dell clearly understands that. I think they're pretty open about that. Now this includes partners that go beyond being just sellers has to include more tech offerings in the marketplace. And actually they got to build out a marketplace like Cloud Platform. So they got a lot of work to do there. And look, you've got Oracle coming up. I mean they're actually kind of just below the magic 40% in the line which is pro it's pretty impressive. And we've been telling you for years, you can hate Oracle all you want. You can hate its price, it's closed system all of that it's red stack shore. You can say it's legacy. You can say it's old and outdated, blah, blah, blah. You can say Oracle is irrelevant in trouble. You are dead wrong. When it comes to mission critical workloads. Oracle is the king of the hill. They're a founder led company that knows exactly what it's doing and they're showing Cloud momentum. Okay, the last point is that while Microsoft AWS and Google have major presence as shown on the X axis. VMware and Oracle now have more than a hundred citations in the survey. You can see that on the insert in the right hand, right most column. And IBM had better keep the momentum from last quarter going, or it won't be long before they get passed by Dell and HP in Cloud. So look, John might be right. And I would think Snowflake quietly agrees that this Dell deal is all about access to Dell's customers and their data. So they can Hoover it into the Snowflake Data Cloud but the data right now, anyway doesn't suggest that's happening with VMware. Oh, by the way, we're keeping an eye close eye on NetApp who last September ink, a similar deal to VMware Cloud on AWS to see how that fares. Okay, let's wrap with some closing thoughts on what this deal means. We learned a lot from the Cloud generally in AWS, specifically in two pizza teams, working backwards, customer obsession. We talk about flywheel all the time and we've been talking today about marketplaces. These have all become common parlance and often fundamental narratives within strategic plans investor decks and customer presentations. Cloud ecosystems are different. They take both competition and partnerships to new heights. You know, when I look at Azure service offerings like Apex, GreenLake and similar services and I see the vendor noise or hear the vendor noise that's being made around them. I kind of shake my head and ask, you know which movie were these companies watching last decade? I really wish we would've seen these initiatives start to roll out in 2015, three years before AWS announced Outposts not three years after but Hey, the good news is that not only was Outposts a wake up call for the On-Prem crowd but it's showing how difficult it is to build a platform like Outposts and bring it to On-Premises. I mean, Outpost isn't currently even a rounding era in the marketplace. It really doesn't do much in terms of database support and support of other services. And, you know, it's unclear where that that is going. And I don't think it has much momentum. And so the Hybrid Cloud Vendors they've had time to figure it out. But now it's game on, companies like Dell they're promising a consistent experience between On-Prem into the Cloud, across Clouds and out to the Edge. They call it MultCloud which by the way my view has really been multi-vendor Chuck, Chuck Whitten. Who's the new co-COO of Dell called it Multi-Cloud by default. (laughing) That's really, I think an accurate description of that. I call this new world Super Cloud. To me, it's different than MultiCloud. It's a layer that runs on top of hyperscale infrastructure kind of hides the underlying complexity of the Cloud. It's APIs, it's primitives. And it stretches not only across Clouds but out to the Edge. That's a big vision and that's going to require some seriously intense engineering to build out. It's also going to require partnerships that go beyond the portfolios of companies like Dell like their own proprietary stacks if you will. It's going to have to replicate the Cloud Operating Model and to do that, you're going to need more and more deals like Snowflake and even deeper than Snowflake, not just in database. Sure, you'll need to have a catalog of databases that run in your On-Prem and Hybrid and Super Cloud but also other services that customers can tap. I mean, can you imagine a day when Dell offers and embraces a directly competitive service inside of apex. I have trouble envisioning that, you know not with their historical posture, you think about companies like, you know, Nutanix, you know, or Cisco where they really, you know those relationships cooled quite quickly but you know, look, think about it. That's what AWS does. It offers for instance, Redshift and Snowflake side by side happily and the Redshift guys they probably hate Snowflake. I wouldn't blame them, but the EC Two Folks, they love them. And Adam SloopesKy understands that ISVs like Snowflake are a key part of the Cloud ecosystem. Again, I have a hard time envisioning that occurring with Dell or even HPE, you know maybe less so with HPE, but what does this imply that the Edge will allow companies like Dell to a reach around on the Cloud and somehow create a new type of model that begrudgingly accommodates the Public Cloud but drafts of the new momentum of the Edge, which right now to these companies is kind of mostly telco and retail. It's hard to see that happening. I think it's got to evolve in a more comprehensive and inclusive fashion. What's much more likely is companies like Dell are going to substantially replicate that Cloud Operating Model for the pieces that they own pieces that they control which admittedly are big pieces of the market. But unless they're able to really tap that ecosystem magic they're not going to be able to grow much beyond their existing install bases. You take that lime green we showed you earlier that new adoption metric from ETR as an example, by my estimates, AWS and Azure are capturing new accounts at a rate between three to five times faster than Dell and HPE. And in the more mature US and mere markets it's probably more like 10 X and a major reason is because of the Cloud's robust ecosystem and the optionality and simplicity of transaction that that is bringing to customers. Now, Dell for its part is a hundred billion dollar revenue company. And it has the capability to drive that kind of dynamic. If it can pivot its partner ecosystem mindset from kind of resellers to Cloud services and technology optionality. Okay, that's it for now? Thanks to my colleagues, Stephanie Chan who helped research topics for Breaking Analysis. Alex Myerson is on the production team. Kristen Martin and Cheryl Knight and Rob Hof, on editorial they helped get the word out and thanks to Jordan Anderson for the new Breaking Analysis branding and graphics package. Remember these episodes are all available as podcasts wherever you listen. All you do is search Breaking Analysis podcasts. You could check out ETR website @etr.ai. We publish a full report every week on wikibon.com and siliconangle.com. You want to get in touch. @dave.vellente @siliconangle.com. You can DM me @dvellante. You can make a comment on our LinkedIn posts. This is Dave Vellante for the Cube Insights powered by ETR. Have a great week, stay safe, be well. And we'll see you next time. (upbeat 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. >> Despite a mixed bag of earnings reports from tech companies, negative GDP growth this past quarter, and rising inflation, the cloud continues its relentless expandtion on the IT landscape. AWS, Microsoft, and Alphabet of all reported earnings, and when you include Alibaba Cloud in the mix, the Big 4 hyperscalers are on track to generate 167 billion in revenue this year based on our projections. But as we said many times on theCUBE, the definition of cloud is expanding and hybrid environments are becoming the norm at major organizations. We're seeing the largest enterprise tech companies focus on solving for hybrid, and every public cloud company now has a strategy to bring their environments closer to where customers workloads live, at data centers, and at the edge. Hello, and welcome to this week's Wikibon CUBE Insights powered by ETR. In this Breaking Analysis will update you on our latest cloud projections and outlook. We'll share some fresh ETR data and commentary on what's happening in the hybrid zone of cloud. Let's start with the market data for the Big 4 hyperscalers. In this chart, we share our Big 4 cloud share for IaaS and PaaS for 2020, 2021, and the first quarter of 2022, and our estimate for 2022 full year and growth. Remember, only AWS and Alibaba report relatively clean IaaS and PaaS figures, whereas Microsoft and Google, they bundled their cloud infrastructure in with their SaaS numbers. We both firms, however, they do give guidance and we use survey data and other tidbits to create an apples to apples comparison, and that's what we show here. For the quarter, the Big 4 approach to 37 billion in revenue as a group. Azure's growth rate is reported by Microsoft but the absolute revenue is not. Azure growth accelerated sequentially by 49% to just over 13 billion in the quarter by our estimates while AWS's growth moderated, sequentially, but revenue still hit 18.4 billion. Azure, by our estimates, now is more than 2/3 the size of AWS's cloud business. Google and Alibaba are fighting for the bronze medal, but well behind the two leaders. Microsoft's Azure acceleration is quite remarkable for such a large revenue base, but it's not unprecedented as we've seen this pattern before with AWS. Nonetheless, the fact that Azure is growing at the same rate as GCP is quite impressive. Now, a couple of other tidbits of information. Amazon, its stock is getting hammered today because of inflation and slowing growth rates at the top line. But AWS continues to beat Wall Street's expectations. A look at Amazon's operating income this quarter tells the story. Amazon overall had operating income of -3.66 billion and AWS's operating income with 6.5 billion. AWS's operating margin grew sequentially from nearly 30% last quarter to 35.3%. That's an astoundingly profitable figure. This is comparable to insanely profitable companies like Oracle and Microsoft. These are software companies with software marginal economics. Is that level of sustainable? Probably not for AWS, but it's eye opening, nonetheless. ETR survey data shows why these companies are doing so well with customers. This chart shows the net score granularity for the Big 4 cloud players. Net score, remember, measures spending momentum by asking customers, are you adopting new? That's the lime green. Increasing spend by 6% or more, that's the forest green. Flats spend is the gray. Spending dropping by 6% or worse is the light pink. And the red is decommissioning the platform. Subtract the reds from the greens and you get a net score which is shown on the right. Anything, by the way, over 40% we consider highly elevated. Now some key points here. Microsoft includes its entire business in this chart, we are including, ETR is including Microsoft's entire business, not just its cloud. Its Azure-only net score is 67%, higher than even AWS's, and that's huge. Google Cloud, on the other hand, while still elevated is well behind the two leaders. Alibaba's data sample in the ETR survey is small and China has had its foot on the neck of Big Tech for a while so we can't read too much into a net score of 26. But notice the replacements in red across the boards single digits for all and low single digits for the two giants, 1% for Amazon and Azure. Very impressive. Now the other really telling reality check is CapEx spending on cloud. CapEx spend tends to be a pretty good indicator of scale. And Charles Fitzgerald who runs the Platformonomics blog spends a fair amount of his time on this topic and we borrowed this chart from a recent post he did, and then we put in some estimates of our own. It shows CapEx spend over time for five cloud companies, the Big 3 US firms that we just talked about, plus IBM and Oracle. And it's always astounding to me to go back to the pre-cloud era and look at IBM. They were in a great position prior to 2006 to really dominate this notion of as a service and the transition to what is now known as cloud. But they really couldn't get their head out of professional services and their outsourcing business. There was some conflicts there as well. And so, you know, IBM you see is that dark blue or black line and spent significantly more than the others way back when, not anymore. Charles is kind of a snark. He loves to make fun of our super cloud concept even though I'm confident it's evolving and is real. But his point above in this chart is right on, the Big 3 US players spend far more on CapEx than IBM and Oracle. He states that Oracle's uptick in CapEx spend puts them past IBM, but the two of them are battling to distance themselves and differentiate from the X-axis. Funny guy, Charles. In its recent earnings report, Amazon stated that around 40% of its CapEx goes to infrastructure and most of that goes to AWS. It expects CapEx to grow this year and around 50% will go toward infrastructure. So we've superimposed our rough estimate of where AWS lands when you subtract out all of Amazon's warehouses for retail. And once again, Microsoft is notable because unlike Amazon, it doesn't have a zillion warehouses to ship products to consumers. And while Google spending is massive, it's mostly on servers to power its ad network. But there's no question that GCP can leverage that infrastructure and the tech behind it, and it does. And by the way, so can everyone else, by the way, leverage all this CapEx spend. We're going to come back to that and talk about super cloud in a moment. Okay, let's close by looking at the ever-expanding cloud landscape. This chart shows a two-dimensional view of the ETR data for cloud computing. On the vertical-axis is net score or spending momentum, and in the horizontal-axis is pervasiveness in the data set. It's like market share within the survey, if you will. The chart insert shows the data for how the dots are plotted on each axis. The red dotted line at 40%, remember, indicates a highly elevated position with net score and significant spending momentum. And the green arrows show the movement for some companies relative to three months ago. Okay, so Microsoft and AWS, they're kind of circled way up in the right-hand corner, very impressive. Just to reduce the clutter, we're not showing AWS Lambda here and some other highly elevated services which would push up, ticked up AWS's net score but it's still really, really good. As is azure, they're both moving solidly to the right relative to last quarters survey. So gaining presence in the data set and presumably in the market as well. Google is, as we've said, well behind and has much work to do. It was announced this past week that the head of sales at Google Cloud, Rob Enslin, is leaving to join UiPath, so some interesting news there. We've highlighted the hybrid zone. Now to the theme of this Breaking Analysis, the ever-expanding cloud, AWS announced that it's completed the launch of 16 local zones in the US and there are 32 more coming across 26 countries. Local zones basically bring cloud infrastructure to regions where there's a lot of IT that isn't going to move. And for proximity and latency reasons, they have to move closer, move the cloud closer, the cloud operating model if you will, closer to the customers. And there's that CapEx build out showing its head again. Now the reason this hybrid zone becomes interesting is you're seeing the large enterprise players finally go after the hybrid cloud in Earnest. It's almost like the AWS outposts announcement in 2018 was a wake up call to infrastructure players like Dell, HPE, and IBM. It took a while, but Oracle is kind of skipping to its own tune, but they're in that hybrid zone as well. IBM had a really good quarter and the Red Hat acquisition seems to be working to support its hybrid cloud strategy. Now VMware several years ago clean up its fuzzy cloud strategy and partnered up with AWS and everyone else. And you see VMware Cloud on AWS doing well as is VMware Cloud, its on-prem offering. Even though it's somewhat lower on the X-axis, based on that green arrow was showing relative to last quarter. It's moving to the right with a greater presence in the data set so that we see that as a positive sign. Now, Dell and HP are interesting. Both companies are going hard after as a service with APEX and GreenLake, respectively. HPE, based on the survey data from ETR, seems to have a lead in spending momentum while Dell has a larger presence in the market, naturally, as a much bigger company. HPE is climbing up on the X-axis, as is Dell, although, not as quickly. And the point we come back to often is the definition of cloud is in the eye of the customer. AWS can say, "No, no that's not cloud." And the on-prem crowd can say, "Ooh, we have cloud too." It really doesn't matter. What matters is what the customer thinks and which platforms they choose to invest. And I'll close by circling back to the idea of super cloud. You are seeing it evolve and you're going to hear more and more about it. Yeah, maybe not the term, many don't like it. We're going to continue to use it as a metaphor for a layer that leverages the CapEx build, the gift that the hyperscalers are providing the industry. This is a real opportunity for the likes of Dell, HPE, IBM, Cisco, and dozens of other companies providing compute and storage infrastructure, networking, security, database, and other parts of the stack. By hiding the underlying complexity of the cloud, dealing with all the API and primitive muck, creating singular experience across on-prem, across clouds, and out to the edge is a definite need from customers. This is a new battle that's shaping up and it's going to be expensive to build and it require an ecosystem cooperating across this API economy, as some like to call it. It's going to have to do that to make it a reality. Now there's a definite, as I say, customer need for this common experience, and in our view, we're seeing it manifest in pockets today and in strategies and in R&D projects, both within startups and established players. Okay, that's it for today. Thanks to Stephanie Chan who helps research Breaking Analysis topics. Alex Myerson is on production and he also manages the Breaking Analysis podcast. Kristen and Martin and Cheryl Knight get the word out on social. Thanks to all, including Rob Hof, our editor in chief at SiliconANGLE. Remember these episodes are all available as podcast wherever you listen. All you got to do is search Breaking Analysis podcast. Check out ETR website at etr.ai. We publish a full report every week on wikibon.com and siliconangle.com. You can email me directly at david.vellante@siliconangle.com, or DM me @dvellante or comment on our LinkedIn posts. This is Dave Vellante for theCUBE Insights powered by ETR. Have a great week, stay safe, be well and we'll see you next time. (upbeat music)

>> From theCUBE Studios in Palo Alto and Boston, bringing you data driven insights from theCUBE in ETR, this is Breaking Analysis with Dave Vellante. >> The introduction in socialization of data mesh has caused practitioners, business technology executives, and technologists to pause, and ask some probing questions about the organization of their data teams, their data strategies, future investments, and their current architectural approaches. Some in the technology community have embraced the concept, others have twisted the definition, while still others remain oblivious to the momentum building around data mesh. Here we are in the early days of data mesh adoption. Organizations that have taken the plunge will tell you that aligning stakeholders is a non-trivial effort, but necessary to break through the limitations that monolithic data architectures and highly specialized teams have imposed over frustrated business and domain leaders. However, practical data mesh examples often lie in the eyes of the implementer, and may not strictly adhere to the principles of data mesh. Now, part of the problem is lack of open technologies and standards that can accelerate adoption and reduce friction, and that's what we're going to talk about today. Some of the key technology and architecture questions around data mesh. Hello, and welcome to this week's Wikibon CUBE Insights powered by ETR, and in this Breaking Analysis, we welcome back the founder of data mesh and director of Emerging Technologies at Thoughtworks, Zhamak Dehghani. Hello, Zhamak. Thanks for being here today. >> Hi Dave, thank you for having me back. It's always a delight to connect and have a conversation. Thank you. >> Great, looking forward to it. Okay, so before we get into it in the technology details, I just want to quickly share some data from our friends at ETR. You know, despite the importance of data initiative since the pandemic, CIOs and IT organizations have had to juggle of course, a few other priorities, this is why in the survey data, cyber and cloud computing are rated as two most important priorities. Analytics and machine learning, and AI, which are kind of data topics, still make the top of the list, well ahead of many other categories. And look, a sound data architecture and strategy is fundamental to digital transformations, and much of the past two years, as we've often said, has been like a forced march into digital. So while organizations are moving forward, they really have to think hard about the data architecture decisions that they make, because it's going to impact them, Zhamak, for years to come, isn't it? >> Yes, absolutely. I mean, we are moving really from, slowly moving from reason based logical algorithmic to model based computation and decision making, where we exploit the patterns and signals within the data. So data becomes a very important ingredient, of not only decision making, and analytics and discovering trends, but also the features and applications that we build for the future. So we can't really ignore it, and as we see, some of the existing challenges around getting value from data is not necessarily that no longer is access to computation, is actually access to trustworthy, reliable data at scale. >> Yeah, and you see these domains coming together with the cloud and obviously it has to be secure and trusted, and that's why we're here today talking about data mesh. So let's get into it. Zhamak, first, your new book is out, 'Data Mesh: Delivering Data-Driven Value at Scale' just recently published, so congratulations on getting that done, awesome. Now in a recent presentation, you pulled excerpts from the book and we're going to talk through some of the technology and architectural considerations. Just quickly for the audience, four principles of data mesh. Domain driven ownership, data as product, self-served data platform and federated computational governance. So I want to start with self-serve platform and some of the data that you shared recently. You say that, "Data mesh serves autonomous domain oriented teams versus existing platforms, which serve a centralized team." Can you elaborate? >> Sure. I mean the role of the platform is to lower the cognitive load for domain teams, for people who are focusing on the business outcomes, the technologies that are building the applications, to really lower the cognitive load for them, to be able to work with data. Whether they are building analytics, automated decision making, intelligent modeling. They need to be able to get access to data and use it. So the role of the platform, I guess, just stepping back for a moment is to empower and enable these teams. Data mesh by definition is a scale out model. It's a decentralized model that wants to give autonomy to cross-functional teams. So it is core requires a set of tools that work really well in that decentralized model. When we look at the existing platforms, they try to achieve this similar outcome, right? Lower the cognitive load, give the tools to data practitioners, to manage data at scale because today centralized teams, really their job, the centralized data teams, their job isn't really directly aligned with a one or two or different, you know, business units and business outcomes in terms of getting value from data. Their job is manage the data and make the data available for then those cross-functional teams or business units to use the data. So the platforms they've been given are really centralized around or tuned to work with this structure as a team, structure of centralized team. Although on the surface, it seems that why not? Why can't I use my, you know, cloud storage or computation or data warehouse in a decentralized way? You should be able to, but some changes need to happen to those online platforms. As an example, some cloud providers simply have hard limits on the number of like account storage, storage accounts that you can have. Because they never envisaged you have hundreds of lakes. They envisage one or two, maybe 10 lakes, right. They envisage really centralizing data, not decentralizing data. So I think we see a shift in thinking about enabling autonomous independent teams versus a centralized team. >> So just a follow up if I may, we could be here for a while. But so this assumes that you've sorted out the organizational considerations? That you've defined all the, what a data product is and a sub product. And people will say, of course we use the term monolithic as a pejorative, let's face it. But the data warehouse crowd will say, "Well, that's what data march did. So we got that covered." But Europe... The primest of data mesh, if I understand it is whether it's a data march or a data mart or a data warehouse, or a data lake or whatever, a snowflake warehouse, it's a node on the mesh. Okay. So don't build your organization around the technology, let the technology serve the organization is that-- >> That's a perfect way of putting it, exactly. I mean, for a very long time, when we look at decomposition of complexity, we've looked at decomposition of complexity around technology, right? So we have technology and that's maybe a good segue to actually the next item on that list that we looked at. Oh, I need to decompose based on whether I want to have access to raw data and put it on the lake. Whether I want to have access to model data and put it on the warehouse. You know I need to have a team in the middle to move the data around. And then try to figure organization into that model. So data mesh really inverses that, and as you said, is look at the organizational structure first. Then scale boundaries around which your organization and operation can scale. And then the second layer look at the technology and how you decompose it. >> Okay. So let's go to that next point and talk about how you serve and manage autonomous interoperable data products. Where code, data policy you say is treated as one unit. Whereas your contention is existing platforms of course have independent management and dashboards for catalogs or storage, et cetera. Maybe we double click on that a bit. >> Yeah. So if you think about that functional, or technical decomposition, right? Of concerns, that's one way, that's a very valid way of decomposing, complexity and concerns. And then build solutions, independent solutions to address them. That's what we see in the technology landscape today. We will see technologies that are taking care of your management of data, bring your data under some sort of a control and modeling. You'll see technology that moves that data around, will perform various transformations and computations on it. And then you see technology that tries to overlay some level of meaning. Metadata, understandability, discovery was the end policy, right? So that's where your data processing kind of pipeline technologies versus data warehouse, storage, lake technologies, and then the governance come to play. And over time, we decomposed and we compose, right? Deconstruct and reconstruct back this together. But, right now that's where we stand. I think for data mesh really to become a reality, as in independent sources of data and teams can responsibly share data in a way that can be understood right then and there can impose policies, right then when the data gets accessed in that source and in a resilient manner, like in a way that data changes structure of the data or changes to the scheme of the data, doesn't have those downstream down times. We've got to think about this new nucleus or new units of data sharing. And we need to really bring back transformation and governing data and the data itself together around these decentralized nodes on the mesh. So that's another, I guess, deconstruction and reconstruction that needs to happen around the technology to formulate ourselves around the domains. And again the data and the logic of the data itself, the meaning of the data itself. >> Great. Got it. And we're going to talk more about the importance of data sharing and the implications. But the third point deals with how operational, analytical technologies are constructed. You've got an app DevStack, you've got a data stack. You've made the point many times actually that we've contextualized our operational systems, but not our data systems, they remain separate. Maybe you could elaborate on this point. >> Yes. I think this is, again, has a historical background and beginning. For a really long time, applications have dealt with features and the logic of running the business and encapsulating the data and the state that they need to run that feature or run that business function. And then we had for anything analytical driven, which required access data across these applications and across the longer dimension of time around different subjects within the organization. This analytical data, we had made a decision that, "Okay, let's leave those applications aside. Let's leave those databases aside. We'll extract the data out and we'll load it, or we'll transform it and put it under the analytical kind of a data stack and then downstream from it, we will have analytical data users, the data analysts, the data sciences and the, you know, the portfolio of users that are growing use that data stack. And that led to this really separation of dual stack with point to point integration. So applications went down the path of transactional databases or urban document store, but using APIs for communicating and then we've gone to, you know, lake storage or data warehouse on the other side. If we are moving and that again, enforces the silo of data versus app, right? So if we are moving to the world that our missions that are ambitions around making applications, more intelligent. Making them data driven. These two worlds need to come closer. As in ML Analytics gets embedded into those app applications themselves. And the data sharing, as a very essential ingredient of that, gets embedded and gets closer, becomes closer to those applications. So, if you are looking at this now cross-functional, app data, based team, right? Business team, then the technology stacks can't be so segregated, right? There has to be a continuum of experience from app delivery, to sharing of the data, to using that data, to embed models back into those applications. And that continuum of experience requires well integrated technologies. I'll give you an example, which actually in some sense, we are somewhat moving to that direction. But if we are talking about data sharing or data modeling and applications use one set of APIs, you know, HTTP compliant, GraQL or RAC APIs. And on the other hand, you have proprietary SQL, like connect to my database and run SQL. Like those are very two different models of representing and accessing data. So we kind of have to harmonize or integrate those two worlds a bit more closely to achieve that domain oriented cross-functional teams. >> Yeah. We are going to talk about some of the gaps later and actually you look at them as opportunities, more than barriers. But they are barriers, but they're opportunities for more innovation. Let's go on to the fourth one. The next point, it deals with the roles that the platform serves. Data mesh proposes that domain experts own the data and take responsibility for it end to end and are served by the technology. Kind of, we referenced that before. Whereas your contention is that today, data systems are really designed for specialists. I think you use the term hyper specialists a lot. I love that term. And the generalist are kind of passive bystanders waiting in line for the technical teams to serve them. >> Yes. I mean, if you think about the, again, the intention behind data mesh was creating a responsible data sharing model that scales out. And I challenge any organization that has a scaled ambitions around data or usage of data that relies on small pockets of very expensive specialists resources, right? So we have no choice, but upscaling cross-scaling. The majority population of our technologists, we often call them generalists, right? That's a short hand for people that can really move from one technology to another technology. Sometimes we call them pandric people sometimes we call them T-shaped people. But regardless, like we need to have ability to really mobilize our generalists. And we had to do that at Thoughtworks. We serve a lot of our clients and like many other organizations, we are also challenged with hiring specialists. So we have tested the model of having a few specialists, really conveying and translating the knowledge to generalists and bring them forward. And of course, platform is a big enabler of that. Like what is the language of using the technology? What are the APIs that delight that generalist experience? This doesn't mean no code, low code. We have to throw away in to good engineering practices. And I think good software engineering practices remain to exist. Of course, they get adopted to the world of data to build resilient you know, sustainable solutions, but specialty, especially around kind of proprietary technology is going to be a hard one to scale. >> Okay. I'm definitely going to come back and pick your brain on that one. And, you know, your point about scale out in the examples, the practical examples of companies that have implemented data mesh that I've talked to. I think in all cases, you know, there's only a handful that I've really gone deep with, but it was their hadoop instances, their clusters wouldn't scale, they couldn't scale the business and around it. So that's really a key point of a common pattern that we've seen now. I think in all cases, they went to like the data lake model and AWS. And so that maybe has some violation of the principles, but we'll come back to that. But so let me go on to the next one. Of course, data mesh leans heavily, toward this concept of decentralization, to support domain ownership over the centralized approaches. And we certainly see this, the public cloud players, database companies as key actors here with very large install bases, pushing a centralized approach. So I guess my question is, how realistic is this next point where you have decentralized technologies ruling the roost? >> I think if you look at the history of places, in our industry where decentralization has succeeded, they heavily relied on standardization of connectivity with, you know, across different components of technology. And I think right now you are right. The way we get value from data relies on collection. At the end of the day, collection of data. Whether you have a deep learning machinery model that you're training, or you have, you know, reports to generate. Regardless, the model is bring your data to a place that you can collect it, so that we can use it. And that leads to a naturally set of technologies that try to operate as a full stack integrated proprietary with no intention of, you know, opening, data for sharing. Now, conversely, if you think about internet itself, web itself, microservices, even at the enterprise level, not at the planetary level, they succeeded as decentralized technologies to a large degree because of their emphasis on open net and openness and sharing, right. API sharing. We don't talk about, in the API worlds, like we don't say, you know, "I will build a platform to manage your logical applications." Maybe to a degree but we actually moved away from that. We say, "I'll build a platform that opens around applications to manage your APIs, manage your interfaces." Right? Give you access to API. So I think the shift needs to... That definition of decentralized there means really composable, open pieces of the technology that can play nicely with each other, rather than a full stack, all have control of your data yet being somewhat decentralized within the boundary of my platform. That's just simply not going to scale if data needs to come from different platforms, different locations, different geographical locations, it needs to rethink. >> Okay, thank you. And then the final point is, is data mesh favors technologies that are domain agnostic versus those that are domain aware. And I wonder if you could help me square the circle cause it's nuanced and I'm kind of a 100 level student of your work. But you have said for example, that the data teams lack context of the domain and so help us understand what you mean here in this case. >> Sure. Absolutely. So as you said, we want to take... Data mesh tries to give autonomy and decision making power and responsibility to people that have the context of those domains, right? The people that are really familiar with different business domains and naturally the data that that domain needs, or that naturally the data that domains shares. So if the intention of the platform is really to give the power to people with most relevant and timely context, the platform itself naturally becomes as a shared component, becomes domain agnostic to a large degree. Of course those domains can still... The platform is a (chuckles) fairly overloaded world. As in, if you think about it as a set of technology that abstracts complexity and allows building the next level solutions on top, those domains may have their own set of platforms that are very much doing agnostic. But as a generalized shareable set of technologies or tools that allows us share data. So that piece of technology needs to relinquish the knowledge of the context to the domain teams and actually becomes domain agnostic. >> Got it. Okay. Makes sense. All right. Let's shift gears here. Talk about some of the gaps and some of the standards that are needed. You and I have talked about this a little bit before, but this digs deeper. What types of standards are needed? Maybe you could walk us through this graphic, please. >> Sure. So what I'm trying to depict here is that if we imagine a world that data can be shared from many different locations, for a variety of analytical use cases, naturally the boundary of what we call a node on the mesh will encapsulates internally a fair few pieces. It's not just the boundary of that, not on the mesh, is the data itself that it's controlling and updating and maintaining. It's of course a computation and the code that's responsible for that data. And then the policies that continue to govern that data as long as that data exists. So if that's the boundary, then if we shift that focus from implementation details, that we can leave that for later, what becomes really important is the scene or the APIs and interfaces that this node exposes. And I think that's where the work that needs to be done and the standards that are missing. And we want the scene and those interfaces be open because that allows, you know, different organizations with different boundaries of trust to share data. Not only to share data to kind of move that data to yes, another location, to share the data in a way that distributed workloads, distributed analytics, distributed machine learning model can happen on the data where it is. So if you follow that line of thinking around the centralization and connection of data versus collection of data, I think the very, very important piece of it that needs really deep thinking, and I don't claim that I have done that, is how do we share data responsibly and sustainably, right? That is not brittle. If you think about it today, the ways we share data, one of the very common ways is around, I'll give you a JDC endpoint, or I give you an endpoint to your, you know, database of choice. And now as technology, whereas a user actually, you can now have access to the schema of the underlying data and then run various queries or SQL queries on it. That's very simple and easy to get started with. That's why SQL is an evergreen, you know, standard or semi standard, pseudo standard that we all use. But it's also very brittle, because we are dependent on a underlying schema and formatting of the data that's been designed to tell the computer how to store and manage the data. So I think that the data sharing APIs of the future really need to think about removing this brittle dependencies, think about sharing, not only the data, but what we call metadata, I suppose. Additional set of characteristics that is always shared along with data to make the data usage, I suppose ethical and also friendly for the users and also, I think we have to... That data sharing API, the other element of it, is to allow kind of computation to run where the data exists. So if you think about SQL again, as a simple primitive example of computation, when we select and when we filter and when we join, the computation is happening on that data. So maybe there is a next level of articulating, distributed computational data that simply trains models, right? Your language primitives change in a way to allow sophisticated analytical workloads run on the data more responsibly with policies and access control and force. So I think that output port that I mentioned simply is about next generation data sharing, responsible data sharing APIs. Suitable for decentralized analytical workloads. >> So I'm not trying to bait you here, but I have a follow up as well. So you schema, for all its good creates constraints. No schema on right, that didn't work, cause it was just a free for all and it created the data swamps. But now you have technology companies trying to solve that problem. Take Snowflake for example, you know, enabling, data sharing. But it is within its proprietary environment. Certainly Databricks doing something, you know, trying to come at it from its angle, bringing some of the best to data warehouse, with the data science. Is your contention that those remain sort of proprietary and defacto standards? And then what we need is more open standards? Maybe you could comment. >> Sure. I think the two points one is, as you mentioned. Open standards that allow... Actually make the underlying platform invisible. I mean my litmus test for a technology provider to say, "I'm a data mesh," (laughs) kind of compliant is, "Is your platform invisible?" As in, can I replace it with another and yet get the similar data sharing experience that I need? So part of it is that. Part of it is open standards, they're not really proprietary. The other angle for kind of sharing data across different platforms so that you know, we don't get stuck with one technology or another is around APIs. It is around code that is protecting that internal schema. So where we are on the curve of evolution of technology, right now we are exposing the internal structure of the data. That is designed to optimize certain modes of access. We're exposing that to the end client and application APIs, right? So the APIs that use the data today are very much aware that this database was optimized for machine learning workloads. Hence you will deal with a columnar storage of the file versus this other API is optimized for a very different, report type access, relational access and is optimized around roles. I think that should become irrelevant in the API sharing of the future. Because as a user, I shouldn't care how this data is internally optimized, right? The language primitive that I'm using should be really agnostic to the machine optimization underneath that. And if we did that, perhaps this war between warehouse or lake or the other will become actually irrelevant. So we're optimizing for that human best human experience, as opposed to the best machine experience. We still have to do that but we have to make that invisible. Make that an implementation concern. So that's another angle of what should... If we daydream together, the best experience and resilient experience in terms of data usage than these APIs with diagnostics to the internal storage structure. >> Great, thank you for that. We've wrapped our ankles now on the controversy, so we might as well wade all the way in, I can't let you go without addressing some of this. Which you've catalyzed, which I, by the way, I see as a sign of progress. So this gentleman, Paul Andrew is an architect and he gave a presentation I think last night. And he teased it as quote, "The theory from Zhamak Dehghani versus the practical experience of a technical architect, AKA me," meaning him. And Zhamak, you were quick to shoot back that data mesh is not theory, it's based on practice. And some practices are experimental. Some are more baked and data mesh really avoids by design, the specificity of vendor or technology. Perhaps you intend to frame your post as a technology or vendor specific, specific implementation. So touche, that was excellent. (Zhamak laughs) Now you don't need me to defend you, but I will anyway. You spent 14 plus years as a software engineer and the better part of a decade consulting with some of the most technically advanced companies in the world. But I'm going to push you a little bit here and say, some of this tension is of your own making because you purposefully don't talk about technologies and vendors. Sometimes doing so it's instructive for us neophytes. So, why don't you ever like use specific examples of technology for frames of reference? >> Yes. My role is pushes to the next level. So, you know everybody picks their fights, pick their battles. My role in this battle is to push us to think beyond what's available today. Of course, that's my public persona. On a day to day basis, actually I work with clients and existing technology and I think at Thoughtworks we have given the talk we gave a case study talk with a colleague of mine and I intentionally got him to talk about (indistinct) I want to talk about the technology that we use to implement data mesh. And the reason I haven't really embraced, in my conversations, the specific technology. One is, I feel the technology solutions we're using today are still not ready for the vision. I mean, we have to be in this transitional step, no matter what we have to be pragmatic, of course, and practical, I suppose. And use the existing vendors that exist and I wholeheartedly embrace that, but that's just not my role, to show that. I've gone through this transformation once before in my life. When microservices happened, we were building microservices like architectures with technology that wasn't ready for it. Big application, web application servers that were designed to run these giant monolithic applications. And now we're trying to run little microservices onto them. And the tail was riding the dock, the environmental complexity of running these services was consuming so much of our effort that we couldn't really pay attention to that business logic, the business value. And that's where we are today. The complexity of integrating existing technologies is really overwhelmingly, capturing a lot of our attention and cost and effort, money and effort as opposed to really focusing on the data product themselves. So it's just that's the role I have, but it doesn't mean that, you know, we have to rebuild the world. We've got to do with what we have in this transitional phase until the new generation, I guess, technologies come around and reshape our landscape of tools. >> Well, impressive public discipline. Your point about microservice is interesting because a lot of those early microservices, weren't so micro and for the naysayers look past this, not prologue, but Thoughtworks was really early on in the whole concept of microservices. So be very excited to see how this plays out. But now there was some other good comments. There was one from a gentleman who said the most interesting aspects of data mesh are organizational. And that's how my colleague Sanji Mohan frames data mesh versus data fabric. You know, I'm not sure, I think we've sort of scratched the surface today that data today, data mesh is more. And I still think data fabric is what NetApp defined as software defined storage infrastructure that can serve on-prem and public cloud workloads back whatever, 2016. But the point you make in the thread that we're showing you here is that you're warning, and you referenced this earlier, that the segregating different modes of access will lead to fragmentation. And we don't want to repeat the mistakes of the past. >> Yes, there are comments around. Again going back to that original conversation that we have got this at a macro level. We've got this tendency to decompose complexity based on technical solutions. And, you know, the conversation could be, "Oh, I do batch or you do a stream and we are different."' They create these bifurcations in our decisions based on the technology where I do events and you do tables, right? So that sort of segregation of modes of access causes accidental complexity that we keep dealing with. Because every time in this tree, you create a new branch, you create new kind of new set of tools and then somehow need to be point to point integrated. You create new specialization around that. So the least number of branches that we have, and think about really about the continuum of experiences that we need to create and technologies that simplify, that continuum experience. So one of the things, for example, give you a past experience. I was really excited around the papers and the work that came around on Apache Beam, and generally flow based programming and stream processing. Because basically they were saying whether you are doing batch or whether you're doing streaming, it's all one stream. And sometimes the window of time, narrows and sometimes the window of time over which you're computing, widens and at the end of today, is you are just getting... Doing the stream processing. So it is those sort of notions that simplify and create continuum of experience. I think resonate with me personally, more than creating these tribal fights of this type versus that mode of access. So that's why data mesh naturally selects kind of this multimodal access to support end users, right? The persona of end users. >> Okay. So the last topic I want to hit, this whole discussion, the topic of data mesh it's highly nuanced, it's new, and people are going to shoehorn data mesh into their respective views of the world. And we talked about lake houses and there's three buckets. And of course, the gentleman from LinkedIn with Azure, Microsoft has a data mesh community. See you're going to have to enlist some serious army of enforcers to adjudicate. And I wrote some of the stuff down. I mean, it's interesting. Monte Carlo has a data mesh calculator. Starburst is leaning in, chaos. Search sees themselves as an enabler. Oracle and Snowflake both use the term data mesh. And then of course you've got big practitioners J-P-M-C, we've talked to Intuit, Orlando, HelloFresh has been on, Netflix has this event based sort of streaming implementation. So my question is, how realistic is it that the clarity of your vision can be implemented and not polluted by really rich technology companies and others? (Zhamak laughs) >> Is it even possible, right? Is it even possible? That's a yes. That's why I practice then. This is why I should practice things. Cause I think, it's going to be hard. What I'm hopeful, is that the socio-technical, Leveling Data mentioned that this is a socio-technical concern or solution, not just a technology solution. Hopefully always brings us back to, you know, the reality that vendors try to sell you safe oil that solves all of your problems. (chuckles) All of your data mesh problems. It's just going to cause more problem down the track. So we'll see, time will tell Dave and I count on you as one of those members of, (laughs) you know, folks that will continue to share their platform. To go back to the roots, as why in the first place? I mean, I dedicated a whole part of the book to 'Why?' Because we get, as you said, we get carried away with vendors and technology solution try to ride a wave. And in that story, we forget the reason for which we even making this change and we are going to spend all of this resources. So hopefully we can always come back to that. >> Yeah. And I think we can. I think you have really given this some deep thought and as we pointed out, this was based on practical knowledge and experience. And look, we've been trying to solve this data problem for a long, long time. You've not only articulated it well, but you've come up with solutions. So Zhamak, thank you so much. We're going to leave it there and I'd love to have you back. >> Thank you for the conversation. I really enjoyed it. And thank you for sharing your platform to talk about data mesh. >> Yeah, you bet. All right. And I want to thank my colleague, Stephanie Chan, who helps research topics for us. Alex Myerson is on production and Kristen Martin, Cheryl Knight and Rob Hoff on editorial. Remember all these episodes are available as podcasts, wherever you listen. And all you got to do is search Breaking Analysis Podcast. Check out ETR's website at etr.ai for all the data. And we publish a full report every week on wikibon.com, siliconangle.com. You can reach me by email david.vellante@siliconangle.com or DM me @dvellante. Hit us up on our LinkedIn post. This is Dave Vellante for theCUBE Insights powered by ETR. Have a great week, stay safe, be well. And we'll see you next time. (bright music)

>> From the theCUBE studios in Palo Alto, in Boston, bringing you data-driven insights from theCUBE and ETR. This is "Breaking Analysis", with Dave Vellante. >> The recent security breach of an Okta third party supplier has been widely reported. The criticisms of Okta's response have been harsh, and the impact on Okta's value has been obvious, investors shaved about $6 billion off the company's market cap during the week the hack was made public. We believe Okta's claim that the customer technical impact was, "Near zero," may be semantically correct. However, based on customer data, we feel Okta has a blind spot. There are customer ripple effects that require clear action which are missed in Okta's public statements, in our view. Okta's product portfolio remains solid, it's a clear leader in the identity space. But in our view, one part of the long journey back to credibility requires Okta to fully understand and recognize the true scope of this breach on its customers. Hello, and welcome to this week's Wikibon "CUBE Insights", powered by ETR. In this "Breaking Analysis", we welcome our ETR colleague, Erik Bradley, to share new data from the community. Erik, welcome. >> Thank you, Dave, always enjoy being on the show, particularly when we get to talk about a topic that's not being well covered in the mainstream media in my opinion. >> Yeah, I agree, you've got some new data, and we're going to share some of that today. Let's first review the timeline of this hack. On January 20th this year, Okta got an alert that something was amiss at one of its partners, a company called Sitel, that provides low-level contact center support for Okta. The next day, Sitel retained a forensic firm to investigate, which was completed, that investigation was completed on February 28th. A report dated March 10th was created, and Okta received a summary of that from Sitel on March 17th. Five days later, Lapsus$ posted the infamous screenshots on Twitter. And later that day, sheesh, Okta got the full report from Sitel, and then responded publicly. Then the media frenzy in the back and forth ensued. So Erik, you know, there's so much wrong with this timeline, it's been picked apart by the media. But I will say this, what appeared to be a benign incident and generally has turned into a PR disaster for Okta, and I imagine Sitel as well. Who I reached out to by the way, but they did not provide a comment, whereas Okta did. We'll share that later. I mean, where do we start on this, Erik? >> It's a great question, "Where do we start?" As you know, our motto here is opinions only exist due to a lack of data, so I'm going to start with the data. What we were able to do is because we had a survey that was in the field when the news broke, is that we were able to observe the data in realtime. So we sequestered the data up until that moment when it was announced, so before March 23rd and then after March 23rd. And although most of the responses came in prior, so it wasn't as much of an end as we would've liked. It really was telling to see the difference of how the survey responses changed from before the breach was announced to after, and we can get into a little bit more- >> So let's... Sorry, sorry to interrupt, let's bring that up, let's look at some of that data. And as followers of this program know... Let me just set it up, Erik. Every quarter, ETR, they have a proprietary net score methodology to determine customer spending momentum, and that's what we're talking about here. Essentially measuring the net number of customers spending more on a particular product or platform. So apologize for interrupting, but you're on this data right here. >> Not at all. >> So take us through this. >> Yeah, so again, let's caveat. Okta is still a premier company in our work. Top five in overall security, not just in their niche, and they still remained extremely strong at the end of the survey. However, when you kind of look at that at a more of a micro analysis, what you noticed was a true difference between before March 23rd and after. Overall, their cumulative net score or proprietary spending intention score that we use, was 56% prior. That dropped to 44% during the time period after, that is a significant drop. Even a little bit more telling, and again, small sample size, I want to be very fair about that. Before March 23rd, only three of our community members indicated any indication of replacing Okta. That number went to eight afterwards. So again, small number, but a big difference when you're talking about a percentage change. >> Yeah, so that's that sort of green line that was shown there. You know, not too damaging, but definitely a noticeable downturn with the caveat that it's a small end. But here's the thing that I love working with you, we didn't stop there. You went out, you talked to customers, I talked to a number of customers. You actually organized a panel. This week, Erik hosted a deep dive on the topic with CISOs. And we have, if we could bring up that next slide, Alex. These are some of the top CISOs in the community, and I'm going to just summarize the comments and then turn it over to you, Erik. The first one was really concerning, "We heard about this in the media," ooh, ooh, ouch. Next one, "Not a huge hit, but loss of trust." "We can't just shut Okta off like SolarWinds." So there's definitely a lock in effect there. "We may need to hire new people," i.e, "There's a business impact to us beyond the technical impact." "We're rethinking contract negotiations with Okta." And bottom line, "It's still a strong solution." "We're not really worried about our Okta environment, but this is a trust and communications issue." Erik, these are painful to read, and in the end of the day, Okta has to own this. Todd McKinnon did acknowledge this. As I said at the top, there are domino business impacts that Okta may not be seeing. What are your thoughts? >> There's a lot we're going to need to get into in a little bit, and I think you were spot on earlier, when McKinnon said there was no impact. And that's not actually true, there's a lot of peripheral, derivative impact that was brought up in our panel. Before we even did the panel though, I do want to say we went out quickly to about 20 customers and asked them if they were willing to give an opinion. And it was sort of split down the middle where about, you know, half of them were saying, "You know, this is okay. We're going to stand by 'em, Okta's the best in the industry." A few were cautious, "Opinion's unchanged, but we're going to take a look deeper." And then another 40% were just flat out negative. And again, small sample size, but you don't want to see that. It's indicative of reputational damage right away. That was what led us to say, "You know what, let's go do this panel." And as you know, from reading it and looking at the panel, well, a lot of topics were brought up about the derivative impact of it. And whether that's your own, you know, having to hire people to go look into your backend to deal with and manage Okta. Whether it's cyber insurance ramifications down the road, there's a lot of aspects that need to be discussed about this. >> Yeah now, so before I go on... And by the way, I've spent a fair amount of time just parsing, listening very carefully to Todd McKinnon's commentary. He did an interview with Emily Chang, it was quite useful. But before I go on, I reached out to Okta, and they were super responsive and I appreciate that. And I do believe they're taking this seriously, here's a statement they provided to theCUBE. Quote, "As a global leader in identity, we recognize the critical role Okta plays for our customers and our customers' end users. Okta has a culture of learning and improving, and we are taking the steps to prevent this from happening again. We know trust is earned, and building back our customers' trust in Okta through our actions and our ongoing support as their secure identity partner is our top priority." Okay, so look, you know, what are you going to say, right? I mean, I think they do own it. Again, the concern is the blind spots. So we put together this visual to try to explain how Okta is describing the impact, and maybe another way to look at it. So let me walk you through this. Here's a simple way in which organizations think about the impact of a breach. What's the probability of a breach, that's the vertical axis, and what's the impact on the horizontal. Now I feel as though business impact really is the financial, you know, condition. But we've narrowed this to map to Todd McKinnon's statements of the technical impact. And they've said the technical impact in terms of things customers need to do or change, is near zero, and that's the red dot that you see there. Look, the fact is, that Okta has more than 15,000 customers, and at most, 366 were directly impacted by this. That's less than 3% of the base, and it's probably less than that, they're just being conservative. And the technical impact which Todd McKinnon described in an interview, again, with Emily Chang, was near zero in terms of actions the customers had to take on things like reporting and changes and remediation. Basically negligible. But based on the customer feedback outside of that 366, that's what we're calling that blind spot and that bracket. And then we list the items that we are hearing from customers on things that they have to do now, despite that minimal exposure. Erik, this is new information that we've uncovered through the ETR process, and there's a long list of collateral impacts that you just referred to before, actions that customers have to take, right? >> Yeah, there's a lot, and the panel really brought that to life even more than I expected to be quite honest. First of all, you're right, most of them believe that this was a minimal impact. The true damage here was reputational, and the derivatives that come from it. We had one panelist say that they now have to go hire people, because, and I hate to say this, but Okta isn't known for their best professional support. So they have to go get people now in to kind of do that themselves and manage that. That's obviously not the easiest thing to do in this environment. We had other ones express concern about, "Hey I'm an Okta customer. When I have to do my cyber insurance renewal, is my policy going to go up? Is my premium going to go up?" And it's not something that they even want to have to handle, but they do. There were a lot of concerns. One particular person didn't think the impact was minimal, and I just think it's worth bringing up. There was no demand for ransom here. So there were only two and a half percent of Okta customers that were hit, but we don't know what the second play is, right, this could just be stage one. And I think that there was one particular person on the panel who truly believes that, that could be the case, that this was just the first step. And in his opinion, there wasn't anything specific about those 366 customers that made him feel like the bad actor was targeting them. So he does believe that this might be a step one of a step two situation. Now that's a, you know, bit of an alarmist opinion and the rest of the panel didn't really echo it, but it is something that's kind of worth bringing up out there. >> Well, you know, it just pays to be paranoid. I mean, you know, it was reported that supposedly, this hack was done by a 16-year-old in England, out of his, you know, mother's house, but who knows? You know, other actors might have paid that individual to see what they could do. It could have been a little bit of reconnaissance, throw the pawn in there and see how, you know, what the response is like. So I want to parse some of Todd McKinnon's statements from that Bloomberg interview. Look, we've always, you and I both have been impressed with Okta, and Todd McKinnon's management. His decisions, execution, leadership, super impressive individual. You know, big fans of the company. And in the interview, it looked like (chuckles) the guy hadn't slept in three weeks, so really you have to feel for him. But I think there are some statements that have to be unpacked. The first one, McKinnon took responsibility and talked about how they'll be transparent about steps they're taking in the future to avoid you know, similar problems. We talked about the near-zero technical impact, we don't need to go there anymore. But Erik, the two things that struck me as communication misfires were the last two. Especially the penultimate statement there, quote, "The competitor product was at fault for this breach." You know, by the way, I believe this to be true. Evidently, Sitel was not using Okta as its identity access platform. You know, we're all trying to figure out who that is. I can tell you it definitely was not CyberArk, we're still digging to find out who. But you know, you can't say in my view, "We are taking responsibility," and then later say it was the competitor's fault. And I know that's not what he meant, but that's kind of how it came across. And even if it's true, you just don't say that later in a conversation after saying that, "We own it." Now on the last point, love your thoughts on this, Erik? My first reaction was Okta's throwing Sitel under the bus. You know, Okta's asking for forgiveness from its customers, but it just shot its partner, and I kind of get it. This shows that they're taking action but I would've preferred something like, "Look, we've suspended our use of Sitel for the time being pending a more detailed review. We've shut down that relationship to block any exposures. Our focus right now is on customers, and we'll take a look at that down the road." But I have to say in looking at the timeline, it looks like Sitel did hide the ball a little bit, and so you can't blame 'em. And you know, what are your thoughts on that? >> Well, I'll go back to my panelists again, who unanimously agreed this was a masterclass on how not to handle crisis management. And I do feel for 'em, they're a fantastic management team. The acquisition of Auth0 alone, was just such a brilliant move that you have to kind of wonder what went wrong here, they clearly were blindsided. I agree with you that Sitel was not forthcoming quickly enough, and I have a feeling that, that's what got them in this position, in a bad PR. However, you can't go ahead and fire your partner and then turn around and ask other people not to fire you. Particularly until a very thorough investigation and a root cause analysis has been released to everyone. And the customers that I have spoken to don't believe that, that is done yet. Now, when I ask them directly, "Would you consider leaving Okta?" Their answers were, "No, it is not easy to rip and replace, and we're not done doing our due diligence." So it's interesting that Okta's customers are giving them that benefit of the doubt, but we haven't seen it, you know, flow the other way with Okta's partner. >> Yeah, and that's why I would've preferred a different public posture, because who knows? I mean, is Sitel the only partner that's not using Okta as its identity management, who knows? I'd like to learn more about that. And to your point, you know, maybe Okta's got to vertically integrate here and start, you know, supporting the lower level stuff directly itself, you know, and/or tightening up those partnerships. Now of course, the impact on Okta obviously has been really serious, big hit on the stock. You know, they're piling on inflation and quantitative tightening and rate hikes. But the real damage, as we've said, is trust and reputation, which Okta has earned, and now it has to work hard to earn back. And it's unfortunate. Look, Okta was founded in 2009 and in over a decade, you know, by my count, there have been no major incidents that are obvious. And we've seen the damage that hackers can do by going after the digital supply chain and third and fourth party providers. You know, rules on disclosure is still not tight and that maybe is part of the problem here. Perhaps the new law The House just sent over to President Biden, is going to help. But the point, Erik, is Okta is not alone here. It feels like they got what looked like a benign alert. Sitel wasn't fully transparent, and Okta is kind of fumbling on the comms, which creates this spiraling effect. Look, we're going to have to wait for the real near-term and midterm impacts, but longterm, I personally believe Okta is going to be fine. But they're going to have to sacrifice some margin possibly in the near to midterm, and go through more pain to regain the loyalty of its customers. And I really would like to hear from Okta that they understand that customers, the impact of this breach to customers, actually does go beyond the 366 that were possibly compromised. Erik, I'll give you the final word. >> Yeah, there's a couple of things there if I can have a moment, and yes, Okta... Well, there was a great quote, one of the guys said, "Okta's built like a tank, but they just gave the keys to a 16 year old valet." So he said, "There is some concern here." But yes, they are best of breed, they are the leader, but there is some concern. And every one of the guys I spoke to, all CISOs, said, "This is going to come up at renewal time. At a minimum, this is leverage. I have to ask them to audit their third parties and their partners. I have to bring this up when it comes time." And then the other one that's a little bit of a concern is data-wise. We saw Ping Identity jump big, from 9% net score to 24% net score. Don't know if it's causative or correlated, but it did happen. Another thing to be concerned about out there, is Microsoft is making absolutely massive strides in security. And all four of the panelists said, "Hey, I've got an E5 license, why don't I get the most out of it? I'm at least going to look." So for Okta to say, you know, "Hey, there's no impact here," it's just not true, there is an impact, they're saying what they need to say. But there's more to this, you know, their market cap definitely got hit. But you know, I think over time if the market stabilized, we could see that recover. It's a great management team, but they did just open the door for a big, big player like Microsoft. And you and I also both know that there's a lot of emerging names out there too, that would like to, you know, take a little bit of that share. >> And you know, but here's the thing, I want to keep going here for a minute. Microsoft got hit by lapses, Nvidia got hit by lapses. But I think, Erik, I feel like people, "Oh yeah, Microsoft, they get hit all the time." They're kind of used to it with Microsoft, right? So that's why I'm saying, it's really interesting here. Customers want to consolidate their security portfolio and the number of tools that they have, you know. But then you look at something like this and you say, "Okay, we're narrowing the blast radius. You know, maybe we have to rethink that and that creates more complexity," and so it's a very complicated situation. But you know, your point about Microsoft is ironic, right. Because you know, when you see Microsoft, Amazon, you know, customers get hit all the time and it's oftentimes the fault of the customer, or the partner. And so it seems like, again, coming back to the comms of this, is that really is the one thing that they just didn't get right. >> Yeah, the biggest takeaway from this without a doubt is it's not the impact of the breach, it was the impact of their delay and how they handled it and how they managed it. That's through the course of 25 CISOs I've spoken to now, that's unanimous. It's not about that this was a huge damaging hit, but the damage really came from their reaction or lack thereof. >> Yeah, and it's unfortunate, 'cause it feels like a lot of it was sort of, I want to say out of their control because obviously they could have audited the partners. But still, I feel like they got thrown a curve ball that they really had a, you know, difficult time, you know, parsing through that. All right, hey, we got to leave it there for now. Thank you, Erik Bradley, appreciate you coming on, It's always a pleasure to have you >> Always good talking to you too, Dave, thanks a lot. >> ETR team, you guys are amazing, do some great work. I want to thank Stephanie Chan, who helps me with background research for "Breaking Analysis". Kristen Martin and Cheryl Knight, help get the word out, as do some others. Alex Myerson on production, Alex, thank you. And Rob Hof, is our EIC at SiliconANGLE. Remember, all these episodes, they are available as podcasts. Wherever you listen, just search, "Breaking Analysis podcast." I publish each week on wikibon.com and siliconangle.com. Check out etr.ai, it's the best in the business for real customer data real-time, near real-time, awesome platform. You can reach out to me at david.vellante@siliconangle.com, or @DVellante, or comment on my LinkedIn post. This is Dave Vellante, for Erik Bradley, and "theCUBE Insights", powered by ETR. Thanks for watching, be well, and we'll see you next time. (bright 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. >> New data from ETR's soon to be released April survey, shows a clear deceleration in spending and a more cautious posture from technology buyers. Just this week, we saw sell side downgrades in hardware companies like Dell and HP and revised guidance from high flyer UiPath, citing exposures to Russia, Europe and certain sales execution challenges, but these headlines, we think are a canary in the coal mine. According to ETR analysis and channel checks in theCUBE, the real story is these issues are not isolated. Rather we're seeing signs of caution from buyers across the board in enterprise tech. Hello and welcome to this week's Wikibon CUBE insights powered by ETR. In this Breaking Analysis, we are the bearers of bad news. Don't shoot the messenger. We'll share a first look at fresh data that suggests a tightening in tech spending calling for 6% growth this year which is below our January prediction of 8% for 2022. Now, unfortunately the party may be coming to an end at least for a while. You know, it's really not surprising, right? We've had a two year record run in tech spending and meteoric rises in high flying technology stocks. Hybrid work, equipping and securing remote workers. The forced march to digital that we talk about sometimes. These were all significant tailwinds for tech companies. The NASDAQ peaked late last year and then as you can see in this chart, bottomed in mid-March of 2022, and it made a nice run up through the 29th of last month, but the mini rally appears to be in jeopardy with FED rate hikes, Russia, supply chain challenges. There's a lot of uncertainty so we should expect the C-suite to be saying, hey, wait slow down. Now we don't think the concerns are confined to companies with exposure to Russia and Europe. We think it's more broad based than that and we're seeing caution from technology companies and tech buyers that we think is prudent, given the conditions. You know, looks like the two year party has ended and as my ETR colleague Erik Bradley said, a little hangover shouldn't be a surprise to anybody. So let's get right to the new spending data. I'm limited to what I can share with you today because ETR is in its quiet period and hasn't released full results yet outside of its client base. But, they did put out an alert today and I can share this slide. It shows the expectation on spending growth from more than a thousand CIOs and IT buyers who responded in the most recent survey. It measures their expectations for spending. The key focus areas that I want you to pay attention to in this data are the yellow bars. The most recent survey is the yellow compared to the blue and the gray bars, which are the December and September '21 surveys respectively. And you can see a steep drop from last year in Q1, lowered expectations for Q2 in the far right, a drop from nearly 9% last September to around 6% today. Now you may think a 200 basis point downgrade from our prediction in January of 8% seems somewhat benign, but in a $4 trillion IT market, that's 80 billion coming off the income statements of some tech companies. Now the good news is that 6% growth is still very healthy and higher than pre pandemic spending levels. And the buyers we've talked to this week are saying, look, we're still spending money. We just have to be more circumspect about where and how fast. Now, there were a few other callouts in the ETR data and in my discussions today with Erik Bradley on this. First, it looks like in response to expected supply chain constraints that buyers pulled forward their orders late last year and earlier this year. You remember when we couldn't buy toilet paper, people started the stockpile and it created this rubber banding effect. So we see clear signs of receding momentum in the PC and laptop market. But as we said, this is not isolated to PCs, UiPath's earning guidance confirm this but the story doesn't end there. This isn't isolated to UiPath in our view, rather it's a more based slowdown. The other big sign is spending in outsourced IT which is showing a meaningful deceleration in the last survey, showing a net score drop from 13% in January to 6% today. Net score remember is a measure of the net percentage of customers in the survey that on balance are spending more than last survey. It's derived by subtracting the percent of customers spending less from those spending more. And there's a, that's a 700 basis point drop in three months. This isn't a market where you can't hire enough people. The percent of companies hiring has gone from 10% during the pandemic to 50% today according to recent data from ETR. And we know there's still an acute skills shortage. So you would expect more IT outsourcing, but you don't see that in the data, it's down. And as this quote from Erik Bradley explains, historically, when outsourced IT drops like this, especially in a tight labor market, it's not good news for IT spending. All right, now, the other interesting callout from ETR were some specific company names that appear to be seeing the biggest change in spending momentum. Here's the list of those companies that all have meaningful exposure to Europe. That's really where the focus was. SAP has big exposure to on-premises installations and of course, Europe as well. ServiceNow has European exposure and also broad based exposure in IT in across the globe, especially in the US. Zoom didn't go to the moon, no surprise there given the quasi return to work and Zoom fatigue. McAfee is a bit of a concern because security seemed to be one of those areas, when you look at some of the other data, that is per actually insulated from all the spending caution. Of course we saw the Okta hack and we're going to cover that next week with hopefully some new data from ETR, but generally security's been holding up pretty well. You look at CrowdStrike, you look at Zscaler in particular. Adobe's another company that's had a nice bounce in the last couple of weeks. Accenture, again, speaks to that outsourcing headwinds that we mentioned earlier. And now the Google Cloud platform is a bit of a concern. It's still elevated overall, you know but down and well down in Europe. Under that magic, you know we often show that magic 40% dotted line, that red dotted line of net score anything above that we cite as elevated. Well, some important callouts to hear that you see companies that have Euro exposure. And again, we see this as just not confined to Europe and this is something we're going to pay close attention to and continue to report on in the next several weeks and months. All right, so what should we expect from here? The Ark investment stocks of Cathie Wood fame have been tracking in a downward trend since last November, meaning, you know, these high PE stocks are making lower lows and higher, sorry, lower highs and lower lows since then, right? The trend is not their friend. Investors I talk to are being much more cautious about buying the dip. They're raising cash and being a little bit more patient. You know, traders can trade in this environment but unless you can pay attention to in a minute by minute you're going to get whipsawed. Investors tell me that they're still eyeing big tech even though Apple has been on a recent tear and has some exposure with supply change challenges, they're looking for maybe entry points in, within that chop for Apple, Amazon, Microsoft, and Alphabet. And look, as I've been stressing, 6% spending growth is still very solid. It's a case of resetting the outlook relative to previous expectations. So when you zoom out and look at the growth in data, getting digital right, security investments, automation, cloud, AI containers, all the fundamentals are really strong and they have not changed. They're all powering this new digital economy and we believe it's just prudence versus a shift in the importance of IT. Now, one point of caution is there's a lot of discussion around a shift in global economies. Supply chain uncertainty, persistent semiconductor shortages especially in areas like, you know driver ICs and boring things like parts for displays and analog and micro controllers and power regulators. Stuff that's, you know, just not playing nice these days and wreaking havoc. And this creates uncertainty, which sometimes can pick up momentum in a snowballing effect. And that's something that we're watching closely and we're going to be vigilant reporting to you when we see changes in the data and in our forecast even when we think our forecast are wrong. Okay, that's it for today. Thanks to Alex Merson who does the production and podcasts for Breaking Analysis and Stephanie Chan who provides background research. Kristen Martin and Cheryl Knight, and all theCUBE writers they help get the word out, and thanks to Rob Hof, our EIC over at SiliconANGLE. Remember I publish weekly on wikibon.com and siliconangle.com. These episodes are all available as podcasts wherever you listen. All you got to do is search Breaking Analysis podcasts. etr.ai that's where you can get access to all this survey data and make your own cuts. It's awesome, check that out. Keep in touch with me. You can email me at dave.vellante@siliconangle.com. You can hit me up on LinkedIn. This is Dave Vellante for theCUBE insights powered by ETR. Be safe, stay well, and we'll see you next time. (gentle music)

>>Welcome everyone. This is Stephanie Chan with the cube, but this conversation is part of Ws 2022 coverage. Today, we'll be speaking with Lila cor managing director at Google. Welcome to show Lila. >>Thank you so much. It's great to be here. >>So how did you come to be a data science leader? >>Yeah. Thank you. Um, you know, let me tell you how I came to be a data science leader, and also just thank you again to, uh, WIDS for having me here, this mission to support those in university or aspiring to be data scientists and those who are in the fee. It's just so important and inspiring to me. So it's been great to see this interest in WIDS and data science from young people all across the globe. So just thanks for having me here, uh, let me tell you how I came to be a data science leader. It really starts with identifying what you're passionate about and what you enjoy and what you're good at really passionate about using data to solve problems. I enjoy problem solving with data and analytics and following these passions led me to take classes in math and economics and econometrics. >>And I also took classes in political science and public policy have a diverse background. Um, and I think that having diverse backgrounds around the table are critical and an asset, um, but that those, uh, courses and that, uh, getting a, that undergrad and a master's started, I started my career as an economist at the us treasury department. And then I moved into technology over 20 years ago. I joined a startup in early 2000 and I've been a big tech companies like Google as well as at startups. And I really realized early on, uh, in my career, how much I enjoy data driven decision making. And I understood how powerful of a role data plays in making informed business decisions. There's just so much uncertainty in the problem that we're trying to address. There's a lot of ambiguity. Um, and data science is just absolutely critical to helping think through making those decisions and uncertainty. >>Another passion of mine is asking questions. My teams will tell you, I like asking a lot of questions and that is key. Uh, when you're a data scientist and we, you lead data science team, who's asking a lot of really good questions and impact. That's also super important to me. The best feeling is the impact you can make on a company with data. Finally, I'm also passionate about managing people and team leadership and applying data to solve cross-functional problems across so many different functions. I've been able to work product and marketing and strategy and operations. And by following these passions, I've gravitated towards managing more quantitative and analytical teams that are also passionate about using data and analytics to grow businesses. And when you work at Google, you're surrounded by this culture of innovation and a culture that's focused on translating data into value. So that's how I ended up becoming a data science leader. Um, you know, my advice to everyone is just like, uh, you know, to is stay curious, think about your passions, what you enjoy doing and the type of problems that you enjoy solving and that, that, and think about the kind of impact that you are looking to drive in this world. For me, that's led to leading data science teams and other broader teams, um, and I had to be open and flexible to where that might take take you, uh, it, it might be different from what you envisioned front >>And speaking of your teams, can you tell us about your teams and the work they do? >>Absolutely. Um, so I'm currently the managing director of Google's technical professional services and marketing data science teams for a group called the global clients and agency solutions. And we help the world's largest brands. In the digital age. We work directly with some of the world's most sophisticated, uh, chief marketing officers and marketing organizations in the world. So I'm honored to lead an organization that develops advanced engineering and data science solutions for Google's largest customers and largest advertisers. My team include customer solution engineers. They include engagement managers, they include technical specialists, and they also include, uh, data scientists. There's, you know, PhD, statisticians, economists, former consultants with deep experience in data science, machine learning and marketing analytics, uh, in my teams and my data teams, they find insights that change the business, uh, in the future. They're, they're amazing. And they do work. That's really groundbreaking. Actually. Can I tell you about some of the superpowers of the data scientists on my teams? >>Of course, I would love to hear it. >>Yeah, well, um, our marketing data science teams, they help measure optimize marketing, uh, a return on investment for Google's largest global clients. So one superpower is their customer obsessed. Um, they, we sit down at the C level table and with our customers, we ask a lot of questions so that we can understand the customer's business objective and how data can help them think through the various options they have. Another superpower is my teams are really good at asking really important questions. You know, you need to really have that back and forth to understand what your customer, what your exec, what they're trying to achieve. Um, and then they build cutting edge complex models that address our customers, key business questions that translates into things like marketing analytics, marketing, mix modeling, statistical modeling, machine learning, uh, you know, digital attribution, predictive analytics, my teams, they create rigorous experiments to help deliver the best possible solutions to our customers. >>Um, another superpower is helping to make better decisions in uncertainty. This is key data. Scientists are so good at this and my teams help these big cutting edge, you know, C level execs and CMOs and marketing organizations all around the world. Um, they help them find better ways to achieve peak marketing ROI. I've been a VP of marketing, um, you know, in startups and throughout my career, I know how hard it is and how important it, it is to grow your business with marketing, um, and really impact, uh, a business and all of their customers. So I'm really proud of the groundbreaking work that my teams are doing to help the world's biggest brands grow, uh, in the digital age, this just one of the types of careers and data science. Uh, my teams were in the, a business organization that works with marketers and advertisers, but I've also been able to lead teams that work with Google's product and engineering leadership to improve our products as well with data science. And there are data science teams in so many different parts of Google that are working on really complex, important challenges, whether it's in our global business organization, whether it's in Google cloud or Google product, YouTube, Google health, I mean, Google health, you know, has done some amazing things using artificial intelligence to prevent blindness. So that's a little bit about my teams and the work that they do >>And what career skills and experiences are most important to you as a data science leader at Google. >>Yeah. Um, thank you. Like I mentioned, we have such a great culture here at Google, a culture of innovation, um, a culture of really trying to solve complex and hard problems, important problems. And these problems have a lot of ambiguity. A lot of uncertainty, there's not always a, a clear right answer. This is where data science can just have such a huge impact. So of course, there's the strong foundation that we look for in the core data, science skills, stats, econometrics, and math, but some of the other skills that are so important, I would say being clear on the problem that you're trying to solve, uh, and focusing on what matters most, this is so important when you're faced with complex ambiguous, multifaceted problems to not get lost in the details or lose focus, asking those really important and, uh, questions and really trying to understand the problem that your customer or your exec is trying to solve. >>So ask, uh, being clear on the problem that you're trying to solve and asking really good questions. That's a, a, a key skill, um, that I think is very important at Google. Another one is the importance of storytelling. I mean, without a good narrative, it can be hard to move from data to insight. And when you're faced with lots of data, you know, being able to distill that complex data into a meaningful and coherent and impactful story. So those strong narrative and communication skills, they're critical, they're critical to ensure that your customer or your exec or your audience, here's the insight that these types of skills, data science skills can help uncover. And I've just add one more, which is there's a skill around thriving and uncertainty and thriving and ambiguity. You know, there's, you'll, it's just inevitable. Um, you've got to, you're gonna hit roadblocks. There's gonna be setbacks. There's a lot of complexity. So being able to be flexible, being able to pivot, being a leader, a role model about how to bounce back, helping others to do so. That's a really critical skill because a lot of the work that we're doing, uh, at Google and specifically data science, they're here to help people think through uncertainty. So those are some of the, um, the skills and experiences that I think are most important to me as a data science leader at Google >>And throughout your career, what is the best piece of advice you have received? >>Uh, thank you for that question. Um, I've received a lot of really great advice, but if I were to pick one for this group, it would be never underestimate the power of showing the world. What's possible. Ruth Perra said that, um, I heard her say that once, and she's the CFO at Google, and it really resonates with me. It's a great reminder of how powerful role models are. They provide us with inspiration and a vision for who we can aspire to be. They help us dream bigger dreams for ourselves. I know I've benefited so much from role models all throughout my life and career who show, show me what's possible. And that idea that you are showing someone else what's possible that they may not have envisioned for themselves. Well, that's super inspiring, motivating to me. So don't underestimate that power that you are providing visual proof, uh, for others about being leaders in data science or to technology. >>And I'd, you know, when I reflect on that advice, I also realize you don't need to have a big title to do this, to show the world what's possible. I have two daughters, my 10 year old daughter. She's inspiring people all the time, including me. Uh, my eight year old daughter is a role model for others in the community, including me. Um, I see courage and inspiration all around me every single day from my team members. Like I, uh, mentioned from friends, from colleagues, from community members. There, there there's so many important firsts that they're role modeling, whether they're first in their family to go to college or the first to pursue data science or just so many other important firsts. So I would say never underestimate the power of showing the world. What's possible. That's a great piece of advice I've received. >>And this, my last question for you, what, what is one thing that you want all of the aspiring data scientists or women in the field who are listening to this interview to take away? >>Yeah. I would want them to take away that your voice matters. You belong at this table for everyone who is listening in the audience. You know, those of you in universe are aspiring to be data scientists. Those in the field, the world needs you. We need you to be data scientists. We need your voice and your insights at the table to address the biggest challenges in business and technology in the environment, in health, in society, you belong, you belong in data science, you belong at that sea sweet table. You belong here, you belong in your voice matters. >>Well, thank you so much for teaching us more about science and all your advice. >>It's a pleasure. Thank you again for having me. I really appreciate it. >>I'm Stephanie Chan with de cube. We'll see you next time.

(upbeat music) >> Welcome everyone, this is Stephanie Chan with theCUBE. Recently analyst Dave Ross RADIO entitled, Pat Gelsinger has a vision. It just needs the time, the cash and a miracle where he highlights why he thinks Intel is years away from reversing position in the semiconductor industry. Welcome Dave. >> Hey thanks, Stephanie. Good to see you. >> So, Dave you been following the company closely over the years. If you look at Wall Street Journal most analysts are saying to hold onto Intel. can you tell us why you're so negative on it? >> Well, you know, I'm not a stock picker Stephanie, but I've seen the data there are a lot of... some buys some sells, but most of the analysts are on a hold. I think they're, who knows maybe they're just hedging their bets they don't want to a strong controversial call that kind of sitting in the fence. But look, Intel still an amazing company they got tremendous resources. They're an ICON and they pay a dividend. So, there's definitely an investment case to be made to hold onto the stock. But I would generally say that investors they better be ready to hold on to Intel for a long, long time. I mean, Intel's they're just not the dominant player that it used to be. And the challenges have been mounting for a decade and look competitively Intel's fighting a five front war. They got AMD in both PCs and the data center the entire Arm Ecosystem` and video coming after with the whole move toward AI and GPU they're dominating there. Taiwan Semiconductor is by far the leading fab in the world with terms of output. And I would say even China is kind of the fifth leg of that stool, long term. So, lot of hurdles to jump competitively. >> So what are other sources of Intel's trouble sincere besides what you just mentioned? >> Well, I think they started when PC volumes peaked which was, or David Floyer, Wikibon wrote back in 2011, 2012 that he tells if it doesn't make some moves, it's going to face some trouble. So, even though PC volumes have bumped up with the pandemic recently, they pair in comparison to the wafer volume that are coming out of the Arm Ecosystem, and TSM and Samsung factories. The volumes of the Arm Ecosystem, Stephanie they dwarf the output of Intel by probably 10 X in semiconductors. I mean, the volume in semiconductors is everything. And because that's what costs down and Intel they just knocked a little cost manufacture any anymore. And in my view, they may never be again, not without a major change in the volume strategy, which of course Gelsinger is doing everything he can to affect that change, but they're years away and they're going to have to spend, north of a 100 billion dollars trying to get there, but it's all about volume in the semiconductor game. And Intel just doesn't have it right now. >> So you mentioned Pat Gelsinger he was a new CEO last January. He's a highly respected CEO and in truth employed more than four decades, I think he has knowledge and experience. including 30 years at Intel where he began his career. What's your opinion on his performance thus far besides the volume and semiconductor industry position of Intel? >> Well, I think Gelsinger is an amazing executive. He's a technical visionary, he's an execution machine, he's doing all the right things. I mean, he's working, he was at the state of the union address and looking good in a suit, he's saying all the right things. He's spending time with EU leaders. And he's just a very clear thinker and a super strong strategist, but you can't change Physics. The thing about Pat is he's known all along what's going on with Intel. I'm sure he's watched it from not so far because I think it's always been his dream to run the company. So, the fact that he's made a lot of moves. He's bringing in new management, he's repairing some of the dead wood at Intel. He's launched, kind of relaunched if you will, the Foundry Business. But I think they're serious about that. You know, this time around, they're spinning out mobile eye to throw off some cash mobile eye was an acquisition they made years ago to throw off some more cash to pay for the fabs. They have announced things like; a fabs in Ohio, in the Heartland, Ze in Heartland which is strikes all the right chords with the various politicians. And so again, he's doing all the right things. He's trying to inject. He's calling out his best Andrew Grove. I like to say who's of course, The Iconic CEO of Intel for many, many years, but again you can't change Physics. He can't compress the cycle any faster than the cycle wants to go. And so he's doing all the right things. It's just going to take a long, long time. >> And you said that competition is better positioned. Could you elaborate on why you think that, and who are the main competitors at this moment? >> Well, it's this Five Front War that I talked about. I mean, you see what's happened in Arm changed everything, Intel remember they passed on the iPhone didn't think it could make enough money on smartphones. And that opened the door for Arm. It was eager to take Apple's business. And because of the consumer volumes the semiconductor industry changed permanently just like the PC volume changed the whole mini computer business. Well, the smartphone changed the economics of semiconductors as well. Very few companies can afford the capital expense of building semiconductor fabrication facilities. And even fewer can make cutting edge chips like; five nanometer, three nanometer and beyond. So companies like AMD and Invidia, they don't make chips they design them and then they ship them to foundries like TSM and Samsung to manufacture them. And because TSM has such huge volumes, thanks to large part to Apple it's further down or up I guess the experience curve and experience means everything in terms of cost. And they're leaving Intel behind. I mean, the best example I can give you is Apple would look at the, a series chip, and now the M one and the M one ultra, I think about the traditional Moore's law curve that we all talk about two X to transistor density every two years doubling. Intel's lucky today if can keep that pace up, let's assume it can. But meanwhile, look at Apple's Arm based M one to M one Ultra transition. It occurred in less than two years. It was more like, 15 or 18 months. And it went from 16 billion transistors on a package to over a 100 billion. And so we're talking about the competition Apple in this case using Arm standards improving it six to seven X inside of a two year period while Intel's running it two X. And that says it all. So Intel is on a curve that's more expensive and slower than the competition. >> Well recently, until what Lujan Harrison did with 5.4 billion So it can make more check order companies last February I think the middle of February what do you think of that strategic move? >> Well, it was designed to help with Foundry. And again, I said left that out of my things that in Intel's doing, as Pat's doing there's a long list actually and many more. Again I think, it's an Israeli based company they're a global company, which is important. One of the things that Pat stresses is having a a presence in Western countries, I think that's super important, he'd like to get the percentage of semiconductors coming out of Western countries back up to at least maybe not to where it was previously but by the end of the decade, much more competitive. And so that's what that acquisition was designed to do. And it's a good move, but it's, again it doesn't change Physics. >> So Dave, you've been putting a lot of content out there and been following Intel for years. What can Intel do to go back on track? >> Well, I think first it needs great leadership and Pat Gelsinger is providing that. Since we talked about it, he's doing all the right things. He's manifesting his best. Andrew Grove, as I said earlier, splitting out the Foundry business is critical because we all know Moore's law. This is Right Law talks about volume in any business not just semiconductors, but it's crucial in semiconductors. So, splitting out a separate Foundry business to make chips is important. He's going to do that. Of course, he's going to ask Intel's competitors to allow Intel to manufacture their chips which they very well may well want to do because there's such a shortage right now of supply and they need those types of manufacturers. So, the hope is that that's going to drive the volume necessary for Intel to compete cost effectively. And there's the chips act. And it's EU cousin where governments are going to possibly put in some money into the semiconductor manufacturing to make the west more competitive. It's a key initiative that Pat has put forth and a challenge. And it's a good one. And he's making a lot of moves on the design side and committing tons of CapEx in these new fabs as we talked about but maybe his best chance is again the fact that, well first of all, the market's enormous. It's a trillion dollar market, but secondly there's a very long term shortage in play here in semiconductors. I don't think it's going to be cleared up in 2022 or 2023. It's just going to be keep being an explotion whether it's automobiles and factory devices and cameras. I mean, virtually every consumer device and edge device is going to use huge numbers of semiconductor chip. So, I think that's in Pat's favor, but honestly Intel is so far behind in my opinion, that I hope by the end of this decade, it's going to be in a position maybe a stronger number two position, and volume behind TSM maybe number three behind Samsung maybe Apple is going to throw Intel some Foundry business over time, maybe under pressure from the us government. And they can maybe win that account back but that's still years away from a design cycle standpoint. And so again, maybe in the 2030's, Intel can compete for top dog status, but that in my view is the best we can hope for this national treasure called Intel. >> Got it. So we got to leave it right there. Thank you so much for your time, Dave. >> You're welcome Stephanie. Good to talk to you >> So you can check out Dave's breaking analysis on theCUBE.net each Friday. This is Stephanie Chan for theCUBE. We'll see you next time. (upbeat music)