(upbeat music) >> The market for enterprise servers is large and it generates well north of $100 billion in annual revenue, and it's growing consistently in the mid to high single digit range. Right now, like many segments, the market for servers is, it's like slingshotting, right? Organizations, they've been replenishing their install bases and upgrading, especially at HQs coming out of the isolation economy. But the macro headwinds, as we've reported, are impacting all segments of the market. CIOs, you know, they're tapping the brakes a little bit, sometimes quite a bit and being cautious with both capital expenditures and discretionary opex, particularly in the cloud. They're dialing it down and just being a little bit more, you know, cautious. The market for enterprise servers, it's dominated as you know, by x86 based systems with an increasingly large contribution coming from alternatives like ARM and NVIDIA. Intel, of course, is the largest supplier, but AMD has been incredibly successful competing with Intel because of its focus, it's got an outsourced manufacturing model and its innovation and very solid execution. Intel's frequent delays with its next generation Sapphire Rapid CPUs, now slated for January 2023 have created an opportunity for AMD, specifically AMD's next generation EPYC CPUs codenamed Genoa will offer as many as 96 Zen 4 cores per CPU when it launches later on this month. Observers can expect really three classes of Genoa. There's a standard Zen 4 compute platform for general purpose workloads, there's a compute density optimized Zen 4 package and then a cache optimized version for data intensive workloads. Indeed, the makers of enterprise servers are responding to customer requirements for more diversity and server platforms to handle different workloads, especially those high performance data-oriented workloads that are being driven by AI and machine learning and high performance computing, HPC needs. OEMs like Dell, they're going to be tapping these innovations and try to get to the market early. Dell, in particular, will be using these systems as the basis for its next generation Gen 16 servers, which are going to bring new capabilities to the market. Now, of course, Dell is not alone, there's got other OEM, you've got HPE, Lenovo, you've got ODMs, you've got the cloud players, they're all going to be looking to keep pace with the market. Now, the other big trend that we've seen in the market is the way customers are thinking about or should be thinking about performance. No longer is the clock speed of the CPU the soul and most indicative performance metric. There's much more emphasis in innovation around all those supporting components in a system, specifically the parts of the system that take advantage, for example, of faster bus speeds. We're talking about things like network interface cards and RAID controllers and memories and other peripheral devices that in combination with microprocessors, determine how well systems can perform and those kind of things around compute operations, IO and other critical tasks. Now, the combinatorial factors ultimately determine the overall performance of the system and how well suited a particular server is to handling different workloads. So we're seeing OEMs like Dell, they're building flexibility into their offerings and putting out products in their portfolios that can meet the changing needs of their customers. Welcome to our ongoing series where we investigate the critical question, does hardware matter? My name is Dave Vellante, and with me today to discuss these trends and the things that you should know about for the next generation of server architectures is former CTO from Oracle and EMC and adjunct faculty and Wharton CTO Academy, David Nicholson. Dave, always great to have you on "theCUBE." Thanks for making some time with me. >> Yeah, of course, Dave, great to be here. >> All right, so you heard my little spiel in the intro, that summary, >> Yeah. >> Was it accurate? What would you add? What do people need to know? >> Yeah, no, no, no, 100% accurate, but you know, I'm a resident nerd, so just, you know, some kind of clarification. If we think of things like microprocessor release cycles, it's always going to be characterized as rolling thunder. I think 2023 in particular is going to be this constant release cycle that we're going to see. You mentioned the, (clears throat) excuse me, general processors with 96 cores, shortly after the 96 core release, we'll see that 128 core release that you referenced in terms of compute density. And then, we can talk about what it means in terms of, you know, nanometers and performance per core and everything else. But yeah, no, that's the main thing I would say, is just people shouldn't look at this like a new car's being released on Saturday. This is going to happen over the next 18 months, really. >> All right, so to that point, you think about Dell's next generation systems, they're going to be featuring these new AMD processes, but to your point, when you think about performance claims, in this industry, it's a moving target. It's that, you call it a rolling thunder. So what does that game of hopscotch, if you will, look like? How do you see it unfolding over the next 12 to 18 months? >> So out of the gate, you know, slated as of right now for a November 10th release, AMD's going to be first to market with, you know, everyone will argue, but first to market with five nanometer technology in production systems, 96 cores. What's important though is, those microprocessors are going to be resident on motherboards from Dell that feature things like PCIe 5.0 technology. So everything surrounding the microprocessor complex is faster. Again, going back to this idea of rolling thunder, we expect the Gen 16 PowerEdge servers from Dell to similarly be rolled out in stages with initial releases that will address certain specific kinds of workloads and follow on releases with a variety of systems configured in a variety of ways. >> So I appreciate you painting a picture. Let's kind of stay inside under the hood, if we can, >> Sure. >> And share with us what we should know about these kind of next generation CPUs. How are companies like Dell going to be configuring them? How important are clock speeds and core counts in these new systems? And what about, you mentioned motherboards, what about next gen motherboards? You mentioned PCIe Gen 5, where does that fit in? So take us inside deeper into the system, please. >> Yeah, so if you will, you know, if you will join me for a moment, let's crack open the box and look inside. It's not just microprocessors. Like I said, they're plugged into a bus architecture that interconnect. How quickly that interconnect performs is critical. Now, I'm going to give you a statistic that doesn't require a PhD to understand. When we go from PCIe Gen 4 to Gen 5, which is going to be featured in all of these systems, we double the performance. So just, you can write that down, two, 2X. The performance is doubled, but the numbers are pretty staggering in terms of giga transactions per second, 128 gigabytes per second of aggregate bandwidth on the motherboard. Again, doubling when going from 4th Gen to 5th Gen. But the reality is, most users of these systems are still on PCIe Gen 3 based systems. So for them, just from a bus architecture perspective, you're doing a 4X or 8X leap in performance, and then all of the peripherals that plug into that faster bus are faster, whether it's RAID control cards from RAID controllers or storage controllers or network interface cards. Companies like Broadcom come to mind. All of their components are leapfrogging their prior generation to fit into this ecosystem. >> So I wonder if we could stay with PCIe for a moment and, you know, just understand what Gen 5 brings. You said, you know, 2X, I think we're talking bandwidth here. Is there a latency impact? You know, why does this matter? And just, you know, this premise that these other components increasingly matter more, Which components of the system are we talking about that can actually take advantage of PCIe Gen 5? >> Pretty much all of them, Dave. So whether it's memory plugged in or network interface cards, so communication to the outside world, which computer servers tend to want to do in 2022, controllers that are attached to internal and external storage devices. All of them benefit from this enhancement and performance. And it's, you know, PCI express performance is measured in essentially bandwidth and throughput in the sense of the numbers of transactions per second that you can do. It's mind numbing, I want to say it's 32 giga transfers per second. And then in terms of bandwidth, again, across the lanes that are available, 128 gigabytes per second. I'm going to have to check if it's gigabits or gigabytes. It's a massive number. And again, it's double what PCIe 4 is before. So what does that mean? Just like the advances in microprocessor technology, you can consolidate massive amounts of work into a much smaller footprint. That's critical because everything in that server is consuming power. So when you look at next generation hardware that's driven by things like AMD Genoa or you know, the EPYC processors, the Zen with the Z4 microprocessors, for every dollar that you're spending on power and equipment and everything else, you're getting far greater return on your investment. Now, I need to say that we anticipate that these individual servers, if you're out shopping for a server, and that's a very nebulous term because they come in all sorts of shapes and sizes, I think there's going to be a little bit of sticker shock at first until you run the numbers. People will look at an individual server and they'll say, wow, this is expensive and the peripherals, the things that are going into those slots are more expensive, but you're getting more bang for your buck. You're getting much more consolidation, lower power usage and for every dollar, you're getting a greater amount of performance and transactions, which translates up the stack through the application layer and, you know, out to the end user's desire to get work done. >> So I want to come back to that, but let me stay on performance for a minute. You know, we all used to be, when you'd go buy a new PC, you'd be like, what's the clock speed of that? And so, when you think about performance of a system today and how measurements are changing, how should customers think about performance in these next gen systems? And where does that, again, where does that supporting ecosystem play? >> So if you are really into the speeds and feeds and what's under the covers, from an academic perspective, you can go in and you can look at the die size that was used to create the microprocessors, the clock speeds, how many cores there are, but really, the answer is look at the benchmarks that are created through testing, especially from third party organizations that test these things for workloads that you intend to use these servers for. So if you are looking to support something like a high performance environment for artificial intelligence or machine learning, look at the benchmarks as they're recorded, as they're delivered by the entire system. So it's not just about the core. So yeah, it's interesting to look at clock speeds to kind of compare where we are with regards to Moore's Law. Have we been able to continue to track along that path? We know there are physical limitations to Moore's Law from an individual microprocessor perspective, but none of that really matters. What really matters is what can this system that I'm buying deliver in terms of application performance and user requirement performance? So that's what I'd say you want to look for. >> So I presume we're going to see these benchmarks at some point, I'm hoping we can, I'm hoping we can have you back on to talk about them. Is that something that we can expect in the future? >> Yeah, 100%, 100%. Dell, and I'm sure other companies, are furiously working away to demonstrate the advantages of this next gen architecture. If I had to guess, I would say that we are going to see quite a few world records set because of the combination of things, like faster network interface cards, faster storage cards, faster memory, more memory, faster cache, more cache, along with the enhanced microprocessors that are going to be delivered. And you mentioned this is, you know, AMD is sort of starting off this season of rolling thunder and in a few months, we'll start getting the initial entries from Intel also, and we'll be able to compare where they fit in with what AMD is offering. I'd expect OEMs like Dell to have, you know, a portfolio of products that highlight the advantages of each processor's set. >> Yeah, I talked in my open Dave about the diversity of workloads. What are some of those emerging workloads and how will companies like Dell address them in your view? >> So a lot of the applications that are going to be supported are what we think of as legacy application environments. A lot of Oracle databases, workloads associated with ERP, all of those things are just going to get better bang for their buck from a compute perspective. But what we're going to be hearing a lot about and what the future really holds for us that's exciting is this arena of artificial intelligence and machine learning. These next gen platforms offer performance that allows us to do things in areas like natural language processing that we just couldn't do before cost effectively. So I think the next few years are going to see a lot of advances in AI and ML that will be debated in the larger culture and that will excite a lot of computer scientists. So that's it, AI/ML are going to be the big buzzwords moving forward. >> So Dave, you talked earlier about this, some people might have sticker shocks. So some of the infrastructure pros that are watching this might be, oh, okay, I'm going to have to pitch this, especially in this, you know, tough macro environment. I'm going to have to sell this to my CIO, my CFO. So what does this all mean? You know, if they're going to have to pay more, how is it going to affect TCO? How would you pitch that to your management? >> As long as you stay away from per unit cost, you're fine. And again, we don't have necessarily, or I don't have necessarily insider access to street pricing on next gen servers yet, but what I do know from examining what the component suppliers tell us is that, these systems are going to be significantly more expensive on a per unit basis. But what does that mean? If the server that you're used to buying for five bucks is now 10 bucks, but it's doing five times as much work, it's a great deal, and anyone who looks at it and says, 10 bucks? It used to only be five bucks, well, the ROI and the TCO, that's where all of this really needs to be measured and a huge part of that is going to be power consumption. And along with the performance tests that we expect to see coming out imminently, we should also be expecting to see some of those ROI metrics, especially around power consumption. So I don't think it's going to be a problem moving forward, but there will be some sticker shock. I imagine you're going to be able to go in and configure a very, very expensive, fully loaded system on some of these configurators online over the next year. >> So it's consolidation, which means you could do more with less. It's going to be, or more with the same, it's going to be lower power, less cooling, less floor space and lower management overhead, which is kind of now you get into staff, so you're going to have to sort of identify how the staff can be productive in other areas. You're probably not going to fire people hopefully. But yeah, it sounds like it's going to be a really consolidation play. I talked at the open about Intel and AMD and Intel coming out with Sapphire Rapids, you know, of course it's been well documented, it's late but they're now scheduled for January. Pat Gelsinger's talked about this, and of course they're going to try to leapfrog AMD and then AMD is going to respond, you talked about this earlier, so that game is going to continue. How long do you think this cycle will last? >> Forever. (laughs) It's just that, there will be periods of excitement like we're going to experience over at least the next year and then there will be a lull and then there will be a period of excitement. But along the way, we've got lurkers who are trying to disrupt this market completely. You know, specifically you think about ARM where the original design point was, okay, you're powered by a battery, you have to fit in someone's pocket. You can't catch on fire and burn their leg. That's sort of the requirement, as opposed to the, you know, the x86 model, which is okay, you have a data center with a raised floor and you have a nuclear power plant down the street. So don't worry about it. As long as an 18-wheeler can get it to where it needs to be, we'll be okay. And so, you would think that over time, ARM is going to creep up as all destructive technologies do, and we've seen that, we've definitely seen that. But I would argue that we haven't seen it happen as quickly as maybe some of us expected. And then you've got NVIDIA kind of off to the side starting out, you know, heavy in the GPU space saying, hey, you know what, you can use the stuff we build for a whole lot of really cool new stuff. So they're running in a different direction, sort of gnawing at the traditional x86 vendors certainly. >> Yes, so I'm glad- >> That's going to be forever. >> I'm glad you brought up ARM and NVIDIA, I think, but you know, maybe it hasn't happened as quickly as many thought, although there's clearly pockets and examples where it is taking shape. But this to me, Dave, talks to the supporting cast. It's not just about the microprocessor unit anymore, specifically, you know, generally, but specifically the x86. It's the supporting, it's the CPU, the NPU, the XPU, if you will, but also all those surrounding components that, to your earlier point, are taking advantage of the faster bus speeds. >> Yeah, no, 100%. You know, look at it this way. A server used to be measured, well, they still are, you know, how many U of rack space does it take up? You had pizza box servers with a physical enclosure. Increasingly, you have the concept of a server in quotes being the aggregation of components that are all plugged together that share maybe a bus architecture. But those things are all connected internally and externally, especially externally, whether it's external storage, certainly networks. You talk about HPC, it's just not one server. It's hundreds or thousands of servers. So you could argue that we are in the era of connectivity and the real critical changes that we're going to see with these next generation server platforms are really centered on the bus architecture, PCIe 5, and the things that get plugged into those slots. So if you're looking at 25 gig or 100 gig NICs and what that means from a performance and/or consolidation perspective, or things like RDMA over Converged Ethernet, what that means for connecting systems, those factors will be at least as important as the microprocessor complexes. I imagine IT professionals going out and making the decision, okay, we're going to buy these systems with these microprocessors, with this number of cores in memory. Okay, great. But the real work starts when you start talking about connecting all of them together. What does that look like? So yeah, the definition of what constitutes a server and what's critically important I think has definitely changed. >> Dave, let's wrap. What can our audience expect in the future? You talked earlier about you're going to be able to get benchmarks, so that we can quantify these innovations that we've been talking about, bring us home. >> Yeah, I'm looking forward to taking a solid look at some of the performance benchmarking that's going to come out, these legitimate attempts to set world records and those questions about ROI and TCO. I want solid information about what my dollar is getting me. I think it helps the server vendors to be able to express that in a concrete way because our understanding is these things on a per unit basis are going to be more expensive and you're going to have to justify them. So that's really what, it's the details that are going to come the day of the launch and in subsequent weeks. So I think we're going to be busy for the next year focusing on a lot of hardware that, yes, does matter. So, you know, hang on, it's going to be a fun ride. >> All right, Dave, we're going to leave it there. Thanks you so much, my friend. Appreciate you coming on. >> Thanks, Dave. >> Okay, and don't forget to check out the special website that we've set up for this ongoing series. Go to doeshardwarematter.com and you'll see commentary from industry leaders, we got analysts on there, technical experts from all over the world. Thanks for watching, and we'll see you next time. (upbeat music)

foreign welcome back everyone to our special presentation here at thecube with Horizon 3.a I'm John Furrier host thecube here in Palo Alto back it's niho and Tony CEO and co-founder of horizon 3 for deep dive on going under the hood around the big news and also the platform autonomous pen testing changing the game and security great to see you welcome back thank you John I love what you guys have been doing with the cube huge fan been here a bunch of times and yeah looking forward to the conversation let's get into it all right so what what's the market look like and how do you see it evolving we're in a down Market relative to startups some say our data we're reporting on siliconangle in the cube that yeah there might be a bit of downturn in the economy with inflation but the tech Market is booming because the hyperscalers are still pumping out massive scale and still innovating so so you know for the first time in history this is a recession or downturn where there's now Cloud scale players that are an economic engine what's your view on this where's the market heading relative to the downturn and how are you guys navigating that so um I think about it one the there's a lot of belief out there that we're going to hit a downturn and we started to see that we started to see deals get longer and longer to close back in May across the board in the industry we continue to see deals get at least backloaded in the quarter as people understand their procurement how much money they really have to spend what their earnings are going to be so we're seeing this across the board one is quarters becoming lumpier for tech companies and we think that that's going to become kind of the norm over the next over the next year but what's interesting in our space of security testing is a very basic supply and demand problem the demand for security testing has skyrocketed when I was a CIO eight years ago I only had to worry about my on-prem attack surface my perimeter and Insider threat those are my primary threat vectors now if I was a CIO I have to include multiple clouds all of the data in my SAS offerings my Salesforce account and so on as well as work from home threat vectors and other pieces and I've got Regulatory Compliance in Europe in Asia in in the U.S tons of demand for testing and there's just not enough Supply there's only 5 000 certified pen testers in the United States so I think for starters you have a fundamental supply and demand problem that plays to our strength because we're able to bring a tremendous amount of pen testing supply to the table but now let's flip to if you are the CEO of a large security company or whether it's a Consulting shop or so on you've got a whole bunch of deferred revenue in your business model around security testing services and what we've done in our past in previous companies I worked at is if we didn't think we were going to make the money the quarter with product Revenue we would start to unlock some of that deferred Services Revenue to make the number to hit what we expected Wall Street to hit what Wall Street expected of us in testing that's not possible because there's not enough Supply except us so if I'm the CEO of an mssp or a large security company and I need I see a huge backlog of security testing revenue on the table the easy button to convert that to recognized revenue is Horizon 3. and when I think about the next six months and the amount of Revenue misses we're going to see in security shops especially those that can't fulfill their orders I think there's a ripe opportunity for us to win yeah one of the few opportunities where on any Market you win because the forces will drive your flywheel that's exactly right very basic supply and demand forces that are only increasing with pressure and there's no way it takes 10 years just to build a master hacker just it's a very hard complex space we become the easy button to address that supply problem yeah and this and the autonomous aspect makes appsec reviews as new things get pushed with Cloud native developers they're shifting left but still the security policies need to stay Pace as these new vectors threat vectors appear yeah I mean because that's what's happening a new new thing makes a vector possible that's exactly right I think there's two aspects one is the as you in increase change in your environment you need to increase testing they are absolutely correlated the second thing though is you know for 20 years we focused on remote code execution or rces as an industry what was the latest rce that gave an attacker access to my environment but if you look over the past few years that entire mindset has shifted credentials are the new code execution what I mean by that is if I have a large organization with a hundred a thousand ten thousand employees all it takes is one of them to have a password I can crack in credential spray and gain access to as an attacker and once I've gained access to a single user I'm going to systematically snowball that into something of consequence and so I think that the attackers have shifted away from looking for code execution and looked more towards harvesting credentials and cascading credentials from a regular domain user into an admin this brings up the conversation I would like to do it more Deep dive now shift into more of like the real kind of landscape of the market and your positioning and value proposition in that and that is managed services are becoming really popular as we move into this next next wave of super cloud and multi-cloud and hybrid Cloud because I mean multi-cloud and hybrid hybrid than multi-cloud sounds good on paper but the security Ops become big and one of the things we're reporting with here on the cube and siliconangle the past six months is devops has made the developer the IT team because they've essentially run it now in CI CD pipeline as they say that means it's replaced by data Ops or AI Ops or security Ops and data and security kind of go hand in hand so I can see that playing out do you believe that to be true that that's kind of the new operational kind of beach head that's critical and if so secure if data is part of security that makes security the new it yeah I I think that if you think about organizations hell even for Horizon 3 right now I don't need to hire a CIO I'll have a CSO and that CSO will own it and governance risk and compliance and security operations because at the end of the day the most pressing question for me to answer as a CEO is my security posture IIT is a supporting function of that security posture and we see that at say or a growth stage company like Horizon 3 but when I thought about my time at GE Capital we really shifted to this mindset of security by Design architecture as code and it was very much security driven conversation and I think that is the norm going forward and how do you view the idea that you have to enable a managed service provider with security also managing comp and which then manages the company to enable them to have agile security um security is code because what you're getting at is this autonomous layer that's going to be automated away to make the next talented layer whether it's coder or architect scale so the question is what is abstracted away at at automation seems to be the conversation that's coming out of this big cloud native or super cloud next wave of cloud scale I think there's uh there's two Dimensions to that and honestly I think the more interesting Dimension is not the technical side of it but rather think of the Equifax hack a bunch of years ago had Equifax used a managed security services provider would the CEO have been fired after the breach and the answer is probably not I think the CEO would have transferred enough reputational risk in operational risk to the third party mssp to save his job from being you know from him being fired you can look at that across the board I think that if if I were a CIO again I would be hard-pressed to build my own internal security function because I'm accepting that risk as an executive and we saw what just happened at Uber there's a ton of risk coming with that with the with accepting that as a security person so I think in the future the role of the mssp becomes more significant as a mechanism for transferring enough reputational and operational and legal risk to a third party so that you as the Core Company are able to protect yourself and your people now then what you think is a super cloud printables and Concepts being applied at mssp scale and I think that becomes really interesting talk about the talent opportunity because I think the managed service providers point to markets that are growing and changing also having managed service means that the customers can't always hire Talent hence they go to a Channel or a partner this seems to be a key part of the growth in your area talk about the talent aspect of it yeah um think back to what we saw in Cloud so as as Cloud picked up we saw IBM HP other Hardware companies sell more servers but to fewer customers Amazon Google and others right and so I think something similar is going to happen in the security space where I think you're going to see security tools providers selling more volume but to fewer customers that are just really big mssps so that is the the path forward and I think that the underlying Talent issue gives us economies at scale and that's what we saw this with Cloud we're going to see the same thing in the mssp space I've got a density of Talent Plus a density of automation plus a density of of relationships and ecosystem that give mssps a huge economies of scale advantage over everybody else I mean I want to get into the mssp business sounds like I make a lot of money yeah definitely it's profitable no doubt about it like that I got to ask more on the more of the burden side of it because if you're a partner I don't need another training class I don't need another tool I don't need someone saying this is the highest margin product I need to actually downsize my tools so right now there's hundreds of tools that mssps have all the time dealing with and does the customer so tools platforms we've kind of teased this out in previous conversations together but more more relevant to the mssp is what they do to the customers so talk about this uh burden of tools and the socks out there in the in in the landscape how do you how do you view that and what's the conversation like on average an organization has 130 different cyber security tools installed none of those tools were designed to work together none of those tools are from the same vendor and in fact oftentimes they're from vendors that have competing products and so what we don't have and they're still getting breached in the industry we don't have a tools problem we have an Effectiveness problem we have to reduce the number of tools we have get more out of out of the the effectiveness out of the existing infrastructure build muscle memory you know how to detect and respond to a breach and continuously verify that posture I think that's what the the most successful security organizations have mastered the fundamentals and they mastered that by making sure they were effective in detection and response not mastering it by buying the next shiny AI tool on the defensive side okay so you mentioned supply and demand early since you're brought up economics we'll get into the economic equations here when you have great profits that's going to attract more entrance into the marketplace so as more mssps enter the market you're going to start to see a little bit of competition maybe some fud maybe some price competitive price penetration all kinds of different Tactics get out go on there um how does that impact you because now does that impact your price or are you now part of them just competing on their own value what's that mean for the channel as more entrants come in hey you know I can compete against that other one does that create conflict is that an opportunity does are you neutral on that what's the position it's a great question actually I think the way it plays out is one we are neutral two the mssp has to stand on their own with their own unique value proposition otherwise they're going to become commoditized we saw this in the early cloud provider days the cloud providers that were just basically wrapping existing Hardware with with a race to the bottom pricing model didn't survive those that use the the cloud infrastructure as a starting point to build higher value capabilities they're the ones that have succeeded to this day the same Mo I think will occur in mssps which is there's a base level of capability that they've got to be able to deliver and it is the burden of the mssp to innovate effectively to elevate their value problem it's interesting Dynamic and I brought it up mainly because if you believe that this is going to be a growing New Market price erosion is more in mature markets so it's interesting to see that Dynamic come up and we'll see how that handles on the on the economics and just the macro side of it getting more into kind of like the next gen autonomous pen testing is a leading indicator that a new kind of security assessment is here um if I said that to you how do you respond to that what is this new security assessment mean what does that mean for the customer and to the partner and that that relationship down that whole chain yeah um back to I'm wearing a CIO hat right now don't tell me we're secure in PowerPoint show me we're secure Today Show me where we're secure tomorrow and then show me we're secure again next week because that's what matters to me if you can show me we're secure I can understand the risk I'm accepting and articulate it up to my board to my Regulators up until now we've had a PowerPoint tell me where secure culture and security and I just don't think that's going to last all that much longer so I think the future of security testing and assessment is this shift from a PowerPoint report to truly showing me that my I'm secure enough you guys auto-generate those statements now you mentioned that earlier that's exactly right because the other part is you know the classic way to do security reports was garbage in garbage out you had a human kind of theoretically fill out a spreadsheet that magically came up with the risk score or security posture that doesn't work that's a check the box mentality what you want to have is an accurate High Fidelity understanding of your blind spots your threat vectors what data is at risk what credentials are at risk you want to look at those results over time how quickly did I find problems how quickly did I fix them how often did they reoccur and that is how you get to a show me where secure culture whether I'm a company or I'm a channel partner working with Horizon 3.ai I have to put my name on the line and say Here's a service level agreement I'm going to stand behind there's levels of compliance you mentioned that earlier how do you guys help that area because that becomes I call the you know below the line I got to do it anyway usually it's you know they grind out the work but it has to be fundamental because if the threats vectors are increasing and you're handling it like you say you are the way it is real time today tomorrow the next day you got to have that other stuff flow into it can you describe how that works under the hood yeah there's there's two parts to it the first part is that attackers don't have to hack in with zero days they log in with credentials that they found but often what attackers are doing is chaining together different types of problems so if you have 10 different tactics you can chain those together a number of different ways it's not just 10 to the 10th it's it's actually because you don't you don't have to use all the tactics at once this is a very large number of combinations that an attacker can apply upon you is what it comes down to and so at the base level what you want to have is what are the the primary tactics that are being used and those tactics are always being added to and evolving what are the primary outcomes that an attacker is trying to achieve steal your data disrupt your systems become a domain admin and borrow and now what you have is it actually looks more like a chess game algorithm than it does any sort of hard-coded automation or anything else which is based on the pieces on the board the the it infrastructure I've discovered what is the next best action to become a domain admin or steal your data and that's the underlying innovation in IP we've created which is next best action Knowledge Graph analytics and adaptiveness to figure out how to combine different problems together to achieve an objective that an attacker cares about so the 3D chess players out there I'd say that's more like 3D chess are the practitioners implementing it but when I think about compliance managers I don't see 3D chess players I see back office accountants in my mind like okay are they actually even understand what comes out of that so how do you handle the compliance side do you guys just check the boxes there is it not part of it is it yeah I I know I don't Envision the compliance guys on the front lines identifying vectors do you know what it doesn't even know what it means yeah it's a great question when you think about uh the market segmentation I think there are we've seen are three basic types of users you've got the the really mature high frequency security testing purple team type folks and for them we are the the force multiplier for them to secure the environment you then have the middle group where the IT person and the security person are the same individual they are barely Treading Water they don't know what their attack surface is and they don't know what to focus on we end up that's actually where we started with the barely Treading Water Persona and that's why we had a product that helped those Network Engineers become superheroes the third segment are those that view security and compliance as synonymous and they don't really care about continuous they care about running and checking the box for PCI and forever else and those customers while they use us they are better served by our partner ecosystem and that's really so the the first two categories tend to use us directly self-service pen tests as often as they want that compliance-minded folks end up going through our partners because they're better served there steel great to have you on thanks for this deep dive on um under the hood section of the interview appreciate it and I think autonomous is is an indicator Beyond pen testing pen testing has become like okay penetration security but this is not going away where do you see this evolving what's next what's next for Horizon take a minute to give a plug for what's going on with copy how do you see it I know you got good margins you're raising Capital always raising money you're not yet public um looking good right now as they say yeah yeah well I think the first thing is our company strategy is in three chapters chapter one is become the best security testing platform in the industry period that's it and be very good at helping you find and fix your security blind spots that's chapter one we've been crushing it there with great customer attraction great partner traction chapter two which we've started to enter is look at our results over time to help that that GRC officer or auditor accurately assess the security posture of an organization and we're going to enter that chapter about this time next year longer term though the big Vision I have is how do I use offense to inform defense so for me chapter three is how do I get away from just security testing towards autonomous security overall where you can use our security testing platform to identify ways to attack that informs defensive tools exactly where to focus how to adjust and so on and now you've got offset and integrated learning Loop between attack and defense that's the future never been done before Master the art of attack to become a better Defender is the bigger vision of the company love the new paradigm security congratulations been following you guys we will continue to follow you thanks for coming on the Special Report congratulations on the new Market expansion International going indirect that a big way congratulations thank you John appreciate it okay this is a special presentation with the cube and Horizon 3.ai I'm John Furrier your host thanks for watching thank you

>>Hello, everyone. Welcome to the cubes presentation of the 80, but startup showcases MarTech is the focus. And this is all about the emerging cloud scale customer experience. This is season two, episode three of the ongoing series covering the exciting, fast growing startups from the cloud AWS ecosystem to talk about the future and what's available now, where are the actions? I'm your host John fur. Today. We joined by Cub alumni, Jerry Chen partner at Greylock ventures. Jerry. Great to see you. Thanks for coming on, >>John. Thanks for having me back. I appreciate you welcome there for season two. Uh, as a, as a guest star, >><laugh>, you know, Hey, you know, season two, it's not a one and done it's continued coverage. We, we got the episodic, uh, cube flicks model going >>Here. Well, you know, congratulations, the, the coverage on this ecosystem around AWS has been impressive, right? I think you and I have talked a long time about AWS and the ecosystem building. It just continues to grow. And so the coverage you did last season, all the events of this season is, is pretty amazing from the data security to now marketing. So it's, it's great to >>Watch. And 12 years now, the cube been running. I remember 2013, when we first met you in the cube, we just left VMware just getting into the venture business. And we were just riffing the next 80. No one really kind of knew how big it would be. Um, but we were kinda riffing on. We kind of had a sense now it's happening. So now you start to see every vertical kind of explode with the right digital transformation and disruption where you see new incumbents. I mean, new Newton brands get replaced the incumbent old guard. And now in MarTech, it's ripe for, for disruption because web two has gone on to web 2.5, 3, 4, 5, um, cookies are going away. You've got more governance and privacy challenges. There's a slew of kind of ad tech baggage, but yet lots of new data opportunities. Jerry, this is a huge, uh, thing. What's your take on this whole MarTech cloud scale, uh, >>Market? I, I think, I think to your point, John, that first the trends are correct and the bad and the good or good old days, the battle days MarTech is really about your webpage. And then email right there. There's, there's the emails, the only channel and the webpage was only real estate and technology to care about fast forward, you know, 10 years you have webpages, mobile apps, VR experiences, car experiences, your, your, your Alexa home experiences. Let's not even get to web three web 18, whatever it is. Plus you got text messages, WhatsApp, messenger, email, still great, et cetera. So I think what we've seen is both, um, explosion and data, uh, explosion of channel. So sources of data have increases and the fruits of the data where you can reach your customers from text, email, phone calls, etcetera have exploded too. So the previous generation created big company responses, Equa, you know, that exact target that got acquired by Oracle or, or, um, Salesforce, and then companies like, um, you know, MailChimp that got acquired as well, but into it, you're seeing a new generation companies for this new stack. So I, I think it's exciting. >>Yeah. And you mentioned all those things about the different channels and stuff, but the key point is now the generation shifts going on, not just technical generation, uh, and platform and tools, it's the people they're younger. They don't do email. They have, you know, proton mail accounts, zillion Gmail accounts, just to get the freebie. Um, they're like, they're, they'll do subscriptions, but not a lot. So the generational piece on the human side is huge. Okay. And then you got the standards, bodies thrown away, things like cookies. Sure. So all this is makes it for a complicated, messy situation. Um, so out of this has to come a billion dollar startup in my mind, >>I, I think multiple billion dollars, but I think you're right in the sense that how we want engage with the company branch, either consumer brands or business brands, no one wants to pick a phone anymore. Right? Everybody wants to either chat or DM people on Twitter. So number one, the, the way we engage is different, both, um, where both, how like chat or phone, but where like mobile device, but also when it's the moment when we need to talk to a company or brand be it at the store, um, when I'm shopping in real life or in my car or at the airport, like we want to reach the brands, the brands wanna reach us at the point of decision, the point of support, the point of contact. And then you, you layer upon that the, the playing field, John of privacy security, right? All these data silos in the cloud, the, the, the, the game has changed and become even more complicated with the startup. So the startups are gonna win. Will do, you know, the collect, all the data, make us secure in private, but then reach your customers when and where they want and how they want it. >>So I gotta ask you, because you had a great podcast just this week, published and snowflake had their event going on the data cloud, there's a new kind of SAS platform vibe going on. You're starting to see it play out. Uh, and one of the things I, I noticed on your podcast with the president of Hashi Corp, who was on people should listen to that podcast. It's on gray matter, which is the Greylocks podcast, uh, plug for you guys. He mentioned he mentions the open source dynamic, right? Sure. And, and I like what he, things, he said, he said, software business has changed forever. It's my words. Now he said infrastructure, but I'm saying software in general, more broader infrastructure and software as a category is all open source. One game over no debate. Right. You agree? >>I, I think you said infrastructure specifically starts at open source, but I would say all open source is one more or less because open source is in every bit of software. Right? And so from your operating system to your car, to your mobile phone, open source, not necessarily as a business model or, or, or whatever, we can talk about that. But open source as a way to build software distribute, software consume software has one, right? It is everywhere. So regardless how you make money on it, how you build software, an open source community ha has >>One. Okay. So let's just agree. That's cool. I agree with that. Let's take it to the next level. I'm a company starting a company to sell to big companies who pay. I gotta have a proprietary advantage. There's gotta be a way. And there is, I know you've talked about it, but I have my opinion. There is needs to be a way to be proprietary in a way that allows for that growth, whether it's integration, it's not gonna be on software license or maybe support or new open source model. But how does startups in the MarTech this area in general, when they disrupt or change the category, they gotta get value creation going. What's your take on, on building. >>You can still build proprietary software on top of open source, right? So there's many companies out there, um, you know, in a company called rock set, they've heavily open source technology like Rock's DB under the hood, but they're running a cloud database. That's proprietary snowflake. You talk about them today. You know, it's not open source technology company, but they use open source software. I'm sure in the hoods, but then there's open source companies, data break. So let's not confus the two, you can still build proprietary software. There's just components of open source, wherever we go. So number one is you can still build proprietary IP. Number two, you can get proprietary data sources, right? So I think increasingly you're seeing companies fight. I call this systems intelligence, right, by getting proprietary data, to train your algorithms, to train your recommendations, to train your applications, you can still collect data, um, that other competitors don't have. >>And then it can use the data differently, right? The system of intelligence. And then when you apply the system intelligence to the end user, you can create value, right? And ultimately, especially marketing tech, the highest level, what we call the system of engagement, right? If, if the chat bot the mobile UI, the phone, the voice app, etcetera, if you own the system of engagement, be a slack, or be it, the operating system for a phone, you can also win. So still multiple levels to play John in multiple ways to build proprietary advantage. Um, just gotta own system record. Yeah. System intelligence, system engagement. Easy, right? Yeah. >>Oh, so easy. Well, the good news is the cloud scale and the CapEx funded there. I mean, look at Amazon, they've got a ton of open storage. You mentioned snowflake, but they're getting a proprietary value. P so I need to ask you MarTech in particular, that means it's a data business, which you, you pointed out and we agree. MarTech will be about the data of the workflows. How do you get those workflows what's changing and how these companies are gonna be building? What's your take on it? Because it's gonna be one of those things where it might be the innovation on a source of data, or how you handle two parties, ex handling encrypted data sets. I don't know. Maybe it's a special encryption tool, so we don't know what it is. What's your what's, what's your outlook on this area? >>I, I, I think that last point just said is super interesting, super genius. It's integration or multiple data sources. So I think either one, if it's a data business, do you have proprietary data? Um, one number two with the data you do have proprietary, not how do you enrich the data and do you enrich the data with, uh, a public data set or a party data set? So this could be cookies. It could be done in Brad street or zoom info information. How do you enrich the data? Number three, do you have machine learning models or some other IP that once you collected the data, enriched the data, you know, what do you do with the data? And then number four is once you have, um, you know, that model of the data, the customer or the business, what do you deal with it? Do you email, do you do a tax? >>Do you do a campaign? Do you upsell? Do you change the price dynamically in our customers? Do you serve a new content on your website? So I think that workflow to your point is you can start from the same place, what to do with the data in between and all the, on the out the side of this, this pipeline is where a MarTech company can have then. So like I said before, it was a website to an email go to website. You know, we have a cookie fill out a form. Yeah. I send you an email later. I think now you, you can't just do a website to email, it's a website plus mobile apps, plus, you know, in real world interaction to text message, chat, phone, call Twitter, a whatever, you know, it's >>Like, it's like, they're playing checkers in web two and you're talking 3d chess. <laugh>, I mean, there's a level, there's a huge gap between what's coming. And this is kind of interesting because now you mentioned, you know, uh, machine learning and data, and AI is gonna factor into all this. You mentioned, uh, you know, rock set. One of your portfolios has under the hood, you know, open source and then use proprietary data and cloud. Okay. That's a configuration, that's an architecture, right? So architecture will be important in terms of how companies posture in this market, cuz MarTech is ripe for innovation because it's based on these old technologies, but there's tons of workflows, but you gotta have the data. Right. And so if I have the best journey map from a client that goes to a website, but then they go and they do something in the organic or somewhere else. If I don't have that, what good is it? It's like a blind spot. >>Correct. So I think you're seeing folks with the data BS, snowflake or data bricks, or an Amazon that S three say, Hey, come to my data cloud. Right. Which, you know, Snowflake's advertising, Amazon will say the data cloud is S3 because all your data exists there anyway. So you just, you know, live on S3 data. Bricks will say, S3 is great, but only use Amazon tools use data bricks. Right. And then, but on top of that, but then you had our SaaS companies like Oracle, Salesforce, whoever, and say, you know, use our qua Marketo, exact target, you know, application as a system record. And so I think you're gonna have a battle between, do I just work my data in S3 or where my data exists or gonna work my data, some other application, like a Marketo Ella cloud Z target, um, or, you know, it could be a Twilio segment, right. Was combination. So you'll have this battle between these, these, these giants in the cloud, easy, the castles, right. Versus, uh, the, the, the, the contenders or the, or the challengers as we call >>'em. Well, great. Always chat with the other. We always talk about castles in the cloud, which is your work that you guys put out, just an update on. So check out greylock.com. They have castles on the cloud, which is a great thesis on and a map by the way ecosystem. So you guys do a really good job props to Jerry and the team over at Greylock. Um, okay. Now I gotta ask kind of like the VC private equity sure. Market question, you know, evaluations. Uh, first of all, I think it's a great time to do a startup. So it's a good time to be in the VC business. I think the next two years, you're gonna find some nice gems, but also you gotta have that cleansing period. You got a lot of overvaluation. So what happened with the markets? So there's gonna be a lot of M and a. So the question is what are some of the things that you see as challenges for product teams in particular that might have that killer answer in MarTech, or might not have the runway if there's no cash, um, how do people partner in this modern era, cuz scale's a big deal, right? Mm-hmm <affirmative> you can measure everything. So you get the combination of a, a new kind of M and a market coming, a potential growth market for the right solution. Again, value's gotta be be there. What's your take on this market? >>I, I, I think you're right. Either you need runway, so cash to make it through, through this next, you know, two, three years, whatever you think the market Turmo is or two, you need scale, right? So if you're at a company of scale and you have enough data, you can probably succeed on your own. If not, if you're kind of in between or early to your point, either one focus, a narrower wedge, John, just like we say, just reduce the surface area. And next two years focus on solving one problem. Very, very well, or number two in this MarTech space, especially there's a lot of partnership and integration opportunities to create a complete solution together, to compete against kind of the incumbents. Right? So I think they're folks with the data, they're folks doing data, privacy, security, they're post focusing their workflow or marketing workflows. You're gonna see either one, um, some M and a, but I definitely can see a lot of Coopers in partnership. And so in the past, maybe you would say, I'm just raise another a hundred million dollars and do what you're doing today. You might say, look, instead of raising more money let's partner together or, or merge or find a solution. So I think people are gonna get creative. Yeah. Like said scarcity often is good. Yeah. I think forces a lot more focus and a lot more creativity. >>Yeah. That's a great point. I'm glad you brought that up up. Cause I didn't think you were gonna go there. I was gonna ask that biz dev activity is going to be really fundamental because runway combined with the fact that, Hey, you know, if you know, get real or you're gonna go under is a real issue. So now people become friends. They're like, okay, if we partner, um, it's clearly a good way to go if you can get there. So what advice would you give companies? Um, even most experienced, uh, founders and operators. This is a different market, right? It's a different kind of velocity, obviously architectural data. You mentioned some of those key things. What's the posture to partner. What's your advice? What's the combat man manual to kind of compete in this new biz dev world where some it's a make or break time, either get the funding, get the customers, which is how you get funding or you get a biz dev deal where you combine forces, uh, go to market together or not. What's your advice? >>I, I think that the combat manual is either you're partnering for one or two things, either one technology or two customers or sometimes both. So it would say which partnerships, youre doing for technology EG solution completers. Like you have, you know, this puzzle piece, I have this puzzle piece data and data privacy and let's work together. Um, or number two is like, who can help you with customers? And that's either a, I, they can be channel for you or, or vice versa or can share customers and you can actually go to market together and find customers jointly. So ideally you're partner for one, if not the other, sometimes both. And just figure out where in your life cycle do you need? Um, friends. >>Yeah. Great. My final question, Jerry, first of all, thanks for coming on and sharing your in insight as usual. Always. Awesome final question for the folks watching that are gonna be partnering and buying product and services from these startups. Um, there's a select few great ones here and obviously every other episode as well, and you've got a bunch you're investing in this, it's actually a good market for the ones that are lean companies that are lean and mean have value. And the cloud scale does provide that. So a lot of companies are getting it right, they're gonna break through. So they're clearly gonna be getting customers the buyer side, how should they be looking through the lens right now and looking at companies, what should they look for? Um, and they like to take chances with seeing that. So it's not so much, they gotta be vetted, but you know, how do they know the winners from the pretenders? >>You know, I, I think the customers are always smart. I think in the, in the, in the past in market market tech, especially they often had a budget to experiment with. I think you're looking now the customers, the buyer technologies are looking for a hard ROI, like a return on investment. And before think they might experiment more, but now they're saying, Hey, are you gonna help me save money or increase revenue or some hardcore metric that they care about? So I think, um, the startups that actually have a strong ROI, like save money or increased revenue and can like point empirically how they do that will, will, you know, rise to the top of, of the MarTech landscape. And customers will see that they're they're, the customers are smart, right? They're savvy buyers. They, they, they, they, they can smell good from bad and they're gonna see the strong >>ROI. Yeah. And the other thing too, I like to point out, I'd love to get your reaction real quick is a lot of the companies have DNA, any open source or they have some community track record where communities now, part of the vetting. I mean, are they real good people? >>Yeah. I, I think open stores, like you said, in the community in general, like especially all these communities that move on slack or discord or something else. Right. I think for sure, just going through all those forums, slack communities or discord communities, you can see what's a good product versus next versus bad. Don't go to like the other sites. These communities would tell you who's working. >>Well, we got a discord channel on the cube now had 14,000 members. Now it's down to six, losing people left and right. We need a moderator, um, to get on. If you know anyone on discord, anyone watching wants to volunteer to be the cube discord, moderator. Uh, we could use some help there. Love discord. Uh, Jerry. Great to see you. Thanks for coming on. What's new at Greylock. What's some of the things happening. Give a quick plug for the firm. When you guys working on, I know there's been some cool things happening, new investments, people moving. >>Yeah. Look we're we're Greylock partners, seed series a firm. I focus at enterprise software. I have a team with me that also does consumer investing as well as crypto investing like all firms. So, but we're we're seed series a occasionally later stage growth. So if you're interested, uh, FA me@jkontwitterorjgreylock.com. Thank you, John. >>Great stuff, Jerry. Thanks for coming on. This is the Cube's presentation of the, a startup showcase. MarTech is the series this time, emerging cloud scale customer experience where the integration and the data matters. This is season two, episode three of the ongoing series covering the hottest cloud startups from the ADWS ecosystem. Um, John farrier, thanks for watching.

>> Welcome back to The Cube's coverage of HPE's GreenLake announcements. My name is Dave Vellante and you're watching the Cube. I'm here with Holger Mueller, who is an analyst at Constellation Research. And Matt Maccaux is the global field CTO of Ezmeral software at HPE. We're going to talk data. Gents, great to see you. >> Holger: Great to be here. >> So, Holger, what do you see happening in the data market? Obviously data's hot, you know, digital, I call it the force marks to digital. Everybody realizes wow, digital business, that's a data business. We've got to get our data act together. What do you see in the market is the big trends, the big waves? >> We are all young enough or old enough to remember when people were saying data is the new oil, right? Nothing has changed, right? Data is the key ingredient, which matters to enterprise, which they have to store, which they have to enrich, which they have to use for their decision-making. It's the foundation of everything. If you want to go into machine learning or (indistinct) It's growing very fast, right? We have the capability now to look at all the data in enterprise, which weren't able 10 years ago to do that. So data is main center to everything. >> Yeah, it's even more valuable than oil, I think, right? 'Cause with oil, you can only use once. Data, you can, it's kind of polyglot. I can go in different directions and it's amazing, right? >> It's the beauty of digital products, right? They don't get consumed, right? They don't get fired up, right? And no carbon footprint, right? "Oh wait, wait, we have to think about carbon footprint." Different story, right? So to get to the data, you have to spend some energy. >> So it's that simple, right? I mean, it really is. Data is fundamental. It's got to be at the core. And so Matt, what are you guys announcing today, and how does that play into what Holger just said? >> What we're announcing today is that organizations no longer need to make a difficult choice. Prior to today, organizations were thinking if I'm going to do advanced machine learning and really exploit my data, I have to go to the cloud. But all my data's still on premises because of privacy rules, industry rules. And so what we're announcing today, through GreenLake Services, is a cloud services way to deliver that same cloud-based analytical capability. Machine learning, data engineering, through hybrid analytics. It's a unified platform to tie together everything from data engineering to advance data science. And we're also announcing the world's first Kubernetes native object store, that is hybrid cloud enabled. Which means you can keep your data connected across clouds in a data fabric, or Dave, as you say, mesh. >> Okay, can we dig into that a little bit? So, you're essentially saying that, so you're going to have data in both places, right? Public cloud, edge, on-prem, and you're saying, HPE is announcing a capability to connect them, I think you used the term fabric. I'm cool, by the way, with the term fabric, we can, we'll parse that out another time. >> I love for you to discuss textiles. Fabrics vs. mesh. For me, every fabric breaks down to mesh if you put it on a microscope. It's the same thing. >> Oh wow, now that's really, that's too detailed for my brain, right this moment. But, you're saying you can connect all those different estates because data by its very nature is everywhere. You're going to unify that, and what, that can manage that through sort of a single view? >> That's right. So, the management is centralized. We need to be able to know where our data is being provisioned. But again, we don't want organizations to feel like they have to make the trade off. If they want to use cloud surface A in Azure, and cloud surface B in GCP, why not connect them together? Why not allow the data to remain in sync or not, through a distributed fabric? Because we use that term fabric over and over again. But the idea is let the data be where it most naturally makes sense, and exploit it. Monetization is an old tool, but exploit it in a way that works best for your users and applications. >> In sync or not, that's interesting. So it's my choice? >> That's right. Because the back of an automobile could be a teeny tiny, small edge location. It's not always going to be in sync until it connects back up with a training facility. But we still need to be able to manage that. And maybe that data gets persisted to a core data center. Maybe it gets pushed to the cloud, but we still need to know where that data is, where it came from, its lineage, what quality it has, what security we're going to wrap around that, that all should be part of this fabric. >> Okay. So, you've got essentially a governance model, at least maybe you're working toward that, and maybe it's not all baked today, but that's the north star. Is this fabric connect, single management view, governed in a federated fashion? >> Right. And it's available through the most common API's that these applications are already written in. So, everybody today's talking S3. I've got to get all of my data, I need to put it into an object store, it needs to be S3 compatible. So, we are extending this capability to be S3 native. But it's optimized for performance. Today, when you put data in an object store, it's kind of one size fits all. Well, we know for those streaming analytical capabilities, those high performance workloads, it needs to be tuned for that. So, how about I give you a very small object on the very fastest disk in your data center and maybe that cheaper location somewhere else. And so we're giving you that balance as part of the overall management estate. >> Holger, what's your take on this? I mean, Frank Slootman says we'll never, we're not going halfway house. We're never going to do on-prem, we're only in the cloud. So that basically says, okay, he's ignoring a pretty large market by choice. You're not, Matt, you must love those words. But what do you see as the public cloud players, kind of the moves on-prem, particularly in this realm? >> Well, we've seen lots of cloud players who were only cloud coming back towards on-premise, right? We call it the next generation compute platform where I can move data and workloads between on-premise and ideally, multiple clouds, right? Because I don't want to be logged into public cloud vendors. And we see two trends, right? One trend is the traditional hardware supplier of on-premise has not scaled to cloud technology in terms of big data analytics. They just missed the boat for that in the past, this is changing. You guys are a traditional player and changing this, so congratulations. The other thing, is there's been no innovation for the on-premise tech stack, right? The only technology stack to run modern application has been invested for a long time in the cloud. So what we see since two, three years, right? With the first one being Google with Kubernetes, that are good at GKE on-premise, then onto us, right? Bringing their tech stack with compromises to on-premises, right? Acknowledging exactly what we're talking about, the data is everywhere, data is important. Data gravity is there, right? It's just the network's fault, where the networks are too slow, right? If you could just move everything anywhere we want like juggling two balls, then we'd be in different place. But that's the not enough investment for the traditional IT players for that stack, and the modern stack being there. And now every public cloud player has an on-premise offering with different flavors, different capabilities. >> I want to give you guys Dave's story of kind of history and you can kind of course correct, and tell me how this, Matt, maybe fits into what's happened with customers. So, you know, before Hadoop, obviously you had to buy a big Oracle database and you know, you running Unix, and you buy some big storage subsystem if you had any money left over, you know, you maybe, you know, do some actual analytics. But then Hadoop comes in, lowers the cost, and then S3 kneecaps the entire Hadoop market, right? >> I wouldn't say that, I wouldn't agree. Sorry to jump on your history. Because the fascinating thing, what Hadoop brought to the enterprise for the first time, you're absolutely right, affordable, right, to do that. But it's not only about affordability because S3 as the affordability. The big thing is you can store information without knowing how to analyze it, right? So, you mentioned Snowflake, right? Before, it was like an Oracle database. It was Starschema for data warehouse, and so on. You had to make decisions how to store that data because compute capabilities, storage capabilities, were too limited, right? That's what Hadoop blew away. >> I agree, no schema on, right. But then that created data lakes, which create a data swamps, and that whole mess, and then Spark comes in and help clean it out, okay, fine. So, we're cool with that. But the early days of Hadoop, you had, companies would have a Hadoop monolith, they probably had their data catalog in Excel or Google sheets, right? And so now, my question to you, Matt, is there's a lot of customers that are still in that world. What do they do? They got an option to go to the cloud. I'm hearing that you're giving them another option? >> That's right. So we know that data is going to move to the cloud, as I mentioned. So let's keep that data in sync, and governed, and secured, like you expect. But for the data that can't move, let's bring those cloud native services to your data center. And so that's a big part of this announcement is this unified analytics. So that you can continue to run the tools that you want to today while bringing those next generation tools based on Apache Spark, using libraries like Delta Lake so you can go anything from Tableaux through Presto sequel, to advance machine learning in your Jupiter notebooks on-premises where you know your data is secured. And if it happens to sit in existing Hadoop data lake, that's fine too. We don't want our customers to have to make that trade off as they go from one to the other. Let's give you the best of both worlds, or as they say, you can eat your cake and have it too. >> Okay, so. Now let's talk about sort of developers on-prem, right? They've been kind of... If they really wanted to go cloud native, they had to go to the cloud. Do you feel like this changes the game? Do on-prem developers, do they want that capability? Will they lean into that capability? Or will they say no, no, the cloud is cool. What's your take? >> I love developers, right? But it's about who makes the decision, who pays the developers, right? So the CXOs in the enterprises, they need exactly, this is why we call the next-gen computing platform, that you can move your code assets. It's very hard to build software, so it's very valuable to an enterprise. I don't want to have limited to one single location or certain computing infrastructure, right? Luckily, we have Kubernetes to be able to move that, but I want to be able to deploy it on-premise if I have to. I want to deploy it, would be able to deploy in the multiple clouds which are available. And that's the key part. And that makes developers happy too, because the code you write has got to run multiple places. So you can build more code, better code, instead of building the same thing multiple places, because a little compiler change here, a little compiler change there. Nobody wants to do portability testing and rewriting, recertified for certain platforms. >> The head of application development or application architecture and the business are ultimately going to dictate that, number one. Number two, you're saying that developers shouldn't care because it can write once, run anywhere. >> That is the promise, and that's the interesting thing which is available now, 'cause people know, thanks to Kubernetes as a container platform and the abstraction which containers provide, and that makes everybody's life easier. But it goes much more higher than the Head of Apps, right? This is the digital transformation strategy, the next generation application the company has to build as a response to a pandemic, as a pivot, as digital transformation, as digital disruption capability. >> I mean, I see a lot of organizations basically modernizing by building some kind of abstraction to their backend systems, modernizing it through cloud native, and then saying, hey, as you were saying Holger, run it anywhere you want, or connect to those cloud apps, or connect across clouds, connect to other on-prem apps, and eventually out to the edge. Is that what you see? >> It's so much easier said than done though. Organizations have struggled so much with this, especially as we start talking about those data intensive app and workloads. Kubernetes and Hadoop? Up until now, organizations haven't been able to deploy those services. So, what we're offering as part of these GreenLake unified analytics services, a Kubernetes runtime. It's not ours. It's top of branch open source. And open source operators like Apache Spark, bringing in Delta Lake libraries, so that if your developer does want to use cloud native tools to build those next generation advanced analytics applications, but prod is still on-premises, they should just be able to pick that code up, and because we are deploying 100% open-source frameworks, the code should run as is. >> So, it seems like the strategy is to basically build, now that's what GreenLake is, right? It's a cloud. It's like, hey, here's your options, use whatever you want. >> Well, and it's your cloud. That's, what's so important about GreenLake, is it's your cloud, in your data center or co-lo, with your data, your tools, and your code. And again, we know that organizations are going to go to a multi or hybrid cloud location and through our management capabilities, we can reach out if you don't want us to control those, not necessarily, that's okay, but we should at least be able to monitor and audit the data that sits in those other locations, the applications that are running, maybe I register your GKE cluster. I don't manage it, but at least through a central pane of glass, I can tell the Head of Applications, what that person's utilization is across these environments. >> You know, and you said something, Matt, that struck, resonated with me, which is this is not trivial. I mean, not as simple to do. I mean what you see, you see a lot of customers or companies, what they're doing, vendors, they'll wrap their stack in Kubernetes, shove it in the cloud, it's essentially hosted stack, right? And, you're kind of taking a different approach. You're saying, hey, we're essentially building a cloud that's going to connect all these estates. And the key is you're going to have to keep, and you are, I think that's probably part of the reason why we're here, announcing stuff very quickly. A lot of innovation has to come out to satisfy that demand that you're essentially talking about. >> Because we've oversimplified things with containers, right? Because containers don't have what matters for data, and what matters for enterprise, which is persistence, right? I have to be able to turn my systems down, or I don't know when I'm going to use that data, but it has to stay there. And that's not solved in the container world by itself. And that's what's coming now, the heavy lifting is done by people like HPE, to provide that persistence of the data across the different deployment platforms. And then, there's just a need to modernize my on-premise platforms. Right? I can't run on a server which is two, three years old, right? It's no longer safe, it doesn't have trusted identity, all the good stuff that you need these days, right? It cannot be operated remotely, or whatever happens there, where there's two, three years, is long enough for a server to have run their course, right? >> Well you're a software guy, you hate hardware anyway, so just abstract that hardware complexity away from you. >> Hardware is the necessary evil, right? It's like TSA. I want to go somewhere, but I have to go through TSA. >> But that's a key point, let me buy a service, if I need compute, give it to me. And if I don't, I don't want to hear about it, right? And that's kind of the direction that you're headed. >> That's right. >> Holger: That's what you're offering. >> That's right, and specifically the services. So GreenLake's been offering infrastructure, virtual machines, IaaS, as a service. And we want to stop talking about that underlying capability because it's a dial tone now. What organizations and these developers want is the service. Give me a service or a function, like I get in the cloud, but I need to get going today. I need it within my security parameters, access to my data, my tools, so I can get going as quickly as possible. And then beyond that, we're going to give you that cloud billing practices. Because, just because you're deploying a cloud native service, if you're still still being deployed via CapEx, you're not solving a lot of problems. So we also need to have that cloud billing model. >> Great. Well Holger, we'll give you the last word, bring us home. >> It's very interesting to have the cloud qualities of subscription-based pricing maintained by HPE as the cloud vendor from somewhere else. And that gives you that flexibility. And that's very important because data is essential to enterprise processes. And there's three reasons why data doesn't go to the cloud, right? We know that. It's privacy residency requirement, there is no cloud infrastructure in the country. It's performance, because network latency plays a role, right? Especially for critical appraisal. And then there's not invented here, right? Remember Charles Phillips saying how old the CIO is? I know if they're going to go to the cloud or not, right? So, it was not invented here. These are the things which keep data on-premise. You know that load, and HP is coming on with a very interesting offering. >> It's physics, it's laws, it's politics, and sometimes it's cost, right? Sometimes it's too expensive to move and migrate. Guys, thanks so much. Great to see you both. >> Matt: Dave, it's always a pleasure. All right, and thank you for watching the Cubes continuous coverage of HPE's big GreenLake announcements. Keep it right there for more great content. (calm music begins)

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 recent earnings reports from key enterprise software and infrastructure players underscore that tech spending remains robust in the post isolation economy especially for those companies that have figured out a cloud strategy now despite covert variant uncertainties and component shortages and hardware most leading tech names outperformed expectations this past week that said investors were not in the mood to reward all names and any variability in product mix or earnings outlook or other nuances were met with a tepid response from the street hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll provide you with commentary and data points on key tech companies that announced this past week including snowflake salesforce workday splunk elastic palo alto networks vmware dell pure storage hp inc and netapp let's start by rolling back a week or so and look at how stocks that are priced to perfection get impacted by any negative news back on august 20th we saw this headline hit snowflake stock falls as analyst says signings growth has slowed the analyst report was put out by a boutique firm cleveland research the stock took a double-digit hit as you can see here i immediately got several texts from investors who know i follow the company asking me what i thought now as a disclaimer i don't give stock picking advice please do your own research but between the cube wikibon and etr we do see a lot of data and i'm happy to share that which i did with this tweet it said lots of talk ahead of snowflake's earnings some analysts have said their data suggests a slowdown etr data looks pretty encouraging and i tagged merv adrian he's a sharp analyst over at gartner who follows data and database he responded i don't speculate about revenues but there's no discernible shift in our client conversations though interest still seems high okay cool but let's let's dig into the etr data a bit and see why we remained positive this is a larger and more detailed version of the chart in the tweet it's a candlestick that shows a time series of the spending data on snowflake using etr's net score methodology the stacked bars represent the percent of customers in the survey that are newly adding the snowflake platform the forest green indicates the number of customers reporting that their spending is increasing by six percent or more the gray is flat spend that's plus or minus five percent the pinkish stack that's decreasing spend by six percent or more and the bright red is where chucking the platform we're leaving now you subtract the reds from the greens and that yields a net score which for snowflake last survey was a very elevated 81.3 percent we've highlighted the spending velocity line that's net score at the top put a picture of that blue line for snowflake in your mind because we're going to come back to it the yellow line down below is market share which is a measure of the pervasiveness in the survey i.e mention share if you will so looking at this chart one might conclude that the lime green i.e new account acquisition is compressing however in further analyzing the data back in january 2019 snowflake's presence in the survey was much lower only 35 accounts in subsequent quarters that number has jumped to over between 120 and 140 snowflake accounts so big much bigger n so while the percentage of respondents may be shrinking the absolute number of new accounts is growing on the snowflake earnings call snowflake said that new customers increased this past quarter to 458 up from 397 in the same period last year what's also telling is the forest green on its very first earnings call as a public company snowflake cfo mike scarpelli said very clearly the company's revenue growth in the near term will come from existing customers and the forest green i.e existing customers spending more is expanding in the etr survey so very strong confirmation of that trend and note the red is virtually non-existent for snowflake so it's no surprise that snowflake handily beat its earnings on the 25th of august which prompted a flurry of texts to me saying you were right thanks don't thank me do your own research we're just one data source okay so here's a snapshot of some of the major players that announced earnings this past week this chart is our popular xy view with net score or spending momentum on the vertical axis and market share or pervasiveness in the survey in the horizontal plane we talked about snowflake already but i'll emphasize they've held that roughly eighty percent net score for ten plus quarterly surveys now and they've continued to move steadily to the right on the horizontal axis let's make some comments on these other names and then dig in a bit more salesforce of course they're the big player amongst these names that we're showing and as we've said in previous breaking analysis segments they have become the next great software company showing 20 plus growth for five consecutive quarters which is quite impressive splunk as we've reported has struggled in the survey but you can see splunk has a great presence in the data set they have an awesome customer base and the acquisition of signal fx plotted on the left with an elevated next net score represents a really good opportunity to enter new markets like observability and pull signalfx to the right to the rest of splunk's customers and that can help accelerate splunk's move toward a subscription model then there's workday we're plotting the company's core hcm business as well as its emerging financial software suite the latter represents workday's tam expansion opportunity and the company appears to be back on track to show sustained growth now let's dig a little deeper into these names and we'll start with salesforce here's the etr spending profile for salesforce salesforce as we showed earlier has a huge and growing presence in the market and a consistently elevated net score in the etr data and while the chart shows much more green than red and a strong uptick in spending momentum from last october survey this doesn't really tell the whole story salesforce's stock price rocketed out of the march 2020 crash and ran up to a peak last august and is on its way back salesforce has made a number of strategic acquisitions including tableau slack mulesoft and several other billion dollar plus buys as well as a number of smaller acquisitions this past quarter saw 23 revenue growth relative to last year with 20 percent plus operating margins that's huge salesforce's acquisition strategy is beginning to demonstrate the company's promised operating leverage and slack in our view will only add to that benefit including continuous improvement and free cash flow sales force revenue will blow through 25 billion dollars this fiscal year it's a company with a 250 billion dollar market cap and appears to be one a name that has meaningful upside opportunity okay let's take a quick look at splunk we're finally seeing an uptick in splunk's spending momentum with within the etr data set eric bradley and i have discussed this in previous breaking analysis segments the key point as we've reported is we see splunk as a company that has been in transition from a traditional license to an arr subscription model and finally the company is showing clarity that there's light at the end of that tunnel investors don't like companies in transition and like salesforce splunk's stock price ran up to an all-time high last august but then came down hard and never fully recovered but it has come off its may lows and there were some real positives this past quarter cloud annual recurring revenue for splunk this past quarter grew 72 percent and its bookings grew 20 29 year on year the company was conservative in its guidance and there still seems to be some uncertainty around cash flow but more clear guidance by splunk on the top line is a welcome sign and now another name that we've been following that announced earnings this week is elastic and as you can see by the etr data that company has an elevated net score with very little red in the bars now note that blue line while it's slowly decelerating it remains very strong and elevated remember the comment earlier i made about freezing that snowflake blue line in your head the reason we said that is because for snowflake to hold its roughly 80 net score position firmly over the past 10 plus quarters is quite astounding and for the most part it's unprecedented in the etr data set in recent memory back to elastic the company grew its top line by 45 which is a healthy beat and that helped operating margins come in above expectations elastic has become the open source poster child for observability but customers often cite challenges related to complexity and scaling with the need often to seek professional services help which sometimes impacts adoption and cost obviously but overall very strong report especially in its cloud business which grew 89 relative to last year all right let's pivot to infrastructure we're going to do that with palo alto networks and then look at a broader more traditional hardware and software players in february of 2020 we reported the valuation of divergence between palo alto networks and fortinet and we cited the challenges that palo alto was having around its shift to cloud that was a clear headwind at the time especially with regard to some of its go to market challenges at the same time we said that we were confident that palo alto would work through these issues and the csos from the etr panels along with other anecdotal information from the cube community suggested that the company would power through these problems well it has palo alto has a huge presence in the market and consistently elevated net scores as you can see here palo alto stock is trading near all-time highs and it reacted very well to its uh to the earnings report this past week where revenue grew nicely at 20 28 year on year the company has consistently impressed despite some hiccups of the past and appears to be well positioned for the emerging hybrid work economy okay now let's take a look at some of the key infrastructure players that announced this past week this chart shows our popular xy view with netscore spending momentum on the vertical axis and market share and or pervasiveness on the horizontal axis we'll start with vmware it has the biggest presence in the market amongst these names vmware's revenue grew nine percent in the quarter which was in line with estimates the company had a solid quarter but only marginally beat expectations and the stock got hit hard it was down 8 percent midday on friday vmware cited stronger than expected perpetual license sales and somewhat softer sas subscription revenue now it's not surprising that we're going to see some lumpiness in those two lines as the company transitions to a subscription model but investors clearly want to see more growth in sas and subscriptions than they do in the traditional perpetual license model vmware cloud on aws grew 80 and that's confirmed in the data here compute was also strong one concern in the etr data is the vmware cloud which is the the core the vm vmr cloud foundation vcf which you can see here is well off its january net score highs now it's possible the etr is picking up some of the conservative clients that don't want to move to an ar or subscription model it's unclear but we'll continue to watch that trend overall vmware's business model is solid in our view and very very strong now let's talk about dell next dell in our view had a great quarter it grew top-line revenues by 15 year-on-year its client business grew 27 percent and you can see the elevated dell laptop net net scores in this chart the isg business was up three percent that comprises service and networking which was up six percent and storage which was off one percent the storage business contin continues to struggle but management reported that its mid-range storage revenue was up 17 now the challenge here is that high-end storage it's cyclical it's exposed sometimes you know somewhat to mainframe cycles but but but but the other thing is that a lot of the mid-range capability is eating away at the high end not the least which by the way is is pure storage competing at the higher end but also dell's own mid-range business so that continues to be a drag on revenue the the size of the traditional high-end business that that v-max power max business still is is is quite large and the the new is not growing fast enough to offset the decline in in the old but i mean i saw these numbers from dell i was surprised to see the stock down nearly five percent at midday on friday and i think what's happening is a couple things one is that hpq hp inc which we show here at a lower net score than dell's laptop business cited supply chain issues and component shortages now dell cited the same but maybe it's off on sympathy it's clear to us that dell is doing a much better job than hp with regard to managing component shortages the frustrating thing for these companies is it might be a 50 part holding up a server or in dell's case or a laptop in dell and hpq's case but demand is good which is a positive but the biggest factor in dell stock price we think is it's getting dragged down with vmware in a way if you think about it with vmware's value comprising so much of dell's market cap being down only four percent while vmware is down eight percent implies that the core dell business is viewed positively by the street but i thought with the vmware spin coming later this year investors might gravitate more aggressively toward dell but that didn't happen maybe over time now you see netapp on the chart netapp beat on top line revenue and earnings this past quarter however the company has not performed well in the etr surveys for several quarters and has a negative net score this is due when you tear apart the the math this is due to a low number of new adoptions and a fat middle very big fat middle of flat spending and a pretty high churn in the data set now the company claims they've picked up 1500 new customers in its cloud business so maybe maybe the etr survey is not picking that up or perhaps it's existing customers that are moving to netapp's cloud service that they're counting as new that's unclear but netapp claims that its public cloud business grew 155 in the quarter regardless the street likes netapp's story the stock has been acting very well this year out passing outpacing the s p 500. now you also see pure on the chart with a nicely elevated net score the company beat top and bottom lines this quarter and its ceo charlie giancarlo promised roughly 20 percent revenue growth going forward the street sure liked that that story and the stock shot up nearly 20 percent on that news and you can see here a little drill down the etr spending data trends in the right direction for pure to support this momentum pure's messaging is all around a modern data platform and it's clear from customer conversations that its storage products are easier to use than traditional storage offerings and it has a leg up on the as a service trend which we've been reporting on which pure has been pursuing for a number of years but it's still a much smaller player a couple billion dollars than the dells and the netapps of the storage world but if it can continue on a strong growth trajectory it will of course become a larger custom company the question will be how to continue to expand its total available market now the obvious path has been share gains which over the years it has accomplished and has served them well but that won't be as easy as it was last decade when pure caught emc and netapp flat-footed without strong flash array strategies pure's port works acquisition is something to watch as well as it tries to transition the market to a true cloud-like program programmable infrastructure model infrastructure as code and we'll leave you with this thought about the infrastructure space generally in storage specifically while cloud storage has exploded over the past several years on-prem storage has been extremely soft this in our view has been due to the double whammy that we've reported the combination of cloud stealing share from on-prem and the big flash injection in other words the latter suppressed the need to buy more spinning spindles and controllers for better performance and it hurt demand you don't need to do that when you have all this flash headroom but as we predicted last year we believe that there's pent up demand as people go back to work and headquarters need refresh there's only so much blood that it managers can squeeze from the stone moving storage around optimizing servers and and improving things like utilization while at the same time maintaining adequate performance and doing so within some kind of reasonable window of a day storage is no longer monolithic there are emerging use cases especially ones that are data intensive different storage types are emerging as satya nadella said recently we've reached peak centralization and as such that will create tailwinds for storage offerings that can accommodate cloud and on-prem because it pros understand that moving data is expensive and risky it's best to keep data where it belongs for reasons of performance and of course compliance so it looks like there's a decent chance that the long storage winter is over and the market could return to solid growth even the face of a continued cloud explosion now to circle back quickly to the enterprise software business there seems to be no end in sight to the shift to cloud-based offerings both sas and snowflake-like consumption models of which we're big believers digital transformation initiatives are real they're meaningful and software spending we believe is going to be robust and power these transformations for quite some time okay that's it for today remember these episodes are all available as podcasts all you got to do is search breaking analysis podcast we publish each week on wikibon.com and siliconangle.com you can reach me at divalante on twitter or my linkedin posts or email me at david.vellante siliconangle.com please do check check out the etr website at etr.plus and see their new data packages and offerings for all the survey data this is dave vellante for the cube insights powered by etr thanks for watching everybody be well and we'll see you next time [Music] you

>> From theCUBE studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> The steepest drop in the stock market since June 11th flipped the narrative and sent investors scrambling. Tech got hammered after a two-month run, and people are asking questions. Is this a bubble popping, or is it a healthy correction? Are we now going to see a rotation into traditional stocks, like banks and maybe certain cyclicals that have lagged behind the technology winners? Hello, everyone, and welcome to this week's episode of Wikibon's CUBE Insights powered by ETR. In this Breaking Analysis, we want to give you our perspective on what's happening in the technology space and unpack what this sentiment flip means for the balance of 2020 and beyond. Let's look at what happened on September 3rd, 2020. The tech markets recoiled this week as the NASDAQ Composite dropped almost 5% in a single day. Apple's market cap alone lost $178 billion. The Big Four: Apple, Microsoft, Amazon, and Google lost a combined value that approached half a trillion dollars. For context, this number is larger than the gross domestic product for countries as large as Thailand, Iran, Austria, Norway, and even the UAE, and many more. The tech stocks that have been running due to COVID, well, they got crushed. These are the ones that we've highlighted as best positioned to thrive during the pandemic, you know, the work-from-home, SaaS, cloud, security stocks. We really have been talking about names like Zoom, ServiceNow, Salesforce, DocuSign, Splunk, and the security names like CrowdStrike, Okta, Zscaler. By the way, DocuSign and CrowdStrike and Okta all had nice earnings beats, but they still got killed underscoring the sentiment shift. Now the broader tech market was off as well on sympathy, and this trend appears to be continuing into the Labor Day holiday. Now why is this happening, and why now? Well, there are a lot of opinions on this. And first, many, like myself, are relatively happy because this market needed to take a little breather. As we've said before, the stock market, it's really not reflecting the realities of the broader economy. Now as we head into September in an election year, uncertainty kicks in, but it really looks like this pullback was fueled by a combination of an overheated market and technical factors. Specifically, take a look at volatility indices. They were high and rising, yet markets kept rising along with them. Robinhood millennial investors who couldn't bet on sports realized that investing in stocks was as much of a rush and potentially more lucrative. The other big wave, which was first reported by the Financial Times, is that SoftBank made a huge bet on tech and bought options tied to around $50 billion worth of high-flying tech stocks. So the option call volumes skyrocketed. The call versus put ratio was getting way too hot, and we saw an imbalance in the market. Now market makers will often buy an underlying stock to hedge call options to ensure liquidity in these cases. So to be more specific, delta in options is a measure of the change in the price of an option relative to the underlying stock, and gamma is a measure of the volatility of the delta. Now usually, volatility is relatively consistent on both sides of the trade, the calls and the puts, because investors often hedge their bets. But in the case of many of these hot stocks, like Tesla, for example, you've seen the call skew be much greater than the skew in the downside. So let's take an example. If people are buying cheap out of the money calls, a market maker might buy the underlying stock to hedge for liquidity. And then if Elon puts out some good news, which he always does, the stock goes up. Market makers have to then buy more of the underlying stock. And then algos kick in to buy even more. And then the price of the call goes up. And as it approaches it at the money price, this forces market makers to keep buying more of that underlying stock. And then the melt up until it stops. And then the market flips like it did this week. When stock prices begin to drop, then market makers were going to rebalance their portfolios and their risk and sell their underlying stocks, and then the rug gets pulled out from the markets. And that's really why some of the stocks that have run dropped so precipitously. Okay, why did I spend so much time on this, and why am I not freaking out? Because I think these market moves are largely technical versus fundamental. It's not like 1999. We had a double whammy of technical rug pulls combined with poor underlying fundamentals for high-flying companies like CMGI and Internet Capital Group, whose businesses, they were all about placing bets on dot-coms that had no business models other than non-monetizable eyeballs. All right, let's take a look at the NASDAQ and dig into the data a little bit. And I think you'll see what I mean and why I'm not too concerned. This is a year-to-date chart of the NASDAQ, and you can see it bottomed on March 23rd at 6,860. And then ran up until June 11th and had that big drop, but was still elevated at 9,492. And then it ran up to over 12,000 and hit an all-time high. And then you see the big drop. And that trend continued on Friday morning. The NASDAQ Composite traded below 11,000. It actually corrected to 10% of its high, 9.8% to be precise, and then it snapped back. But even at its low, that's still up over 20% for the year. In the year of COVID, would that have surprised you in March? It certainly would have surprised me. So to me, this pullback is sort of a relief. It's good and actually very normal and quite predictable. Now the exact timing of these pullbacks, of course, on the other hand is not entirely predictable. Not at all, frankly, at least for this observer. So the big question is where do we go from here? So let's talk about that a little bit. Now the economy continues to get better. Take a look at the August job report; it was good. 1.4 million new jobs, 340,000 came from the government. That was positive numbers. And the other good news is it translates into a drop in unemployment under 10%. It's now at 8.4%. And this is really good relative to expectations. Now the sell-off continued, which suggested that the market wanted to keep correcting, so that's good. Maybe some buying opportunities would emerge in over the next several months, the market snapped back, but for those who have been waiting, I think that's going to happen. And so that snapback, maybe that's an indicator that the market wants to keep going up, we'll see. But I think there are more opportunities ahead because there's really so much uncertainty. What's going to happen with the next round of the stimulus? The jobs report, maybe that's a catalyst for compromise between the Democrats and the Republicans, maybe. The US debt is projected to exceed 100% of GDP this calendar year. That's the highest it's been since World War II. Does that give you a good feeling? That doesn't give me a good feeling. And when we talk about the election, that brings additional uncertainty. So there's a lot to think about for the markets. Now let's talk about what this means for tech. Well, as we've been projecting for months with our colleagues at ETR, despite what's going on in the stock market and its rise, there's those real tech winners, we still see a contraction in 2020 for IT spend of minus 5 to 8%. And we talk a lot about the bifurcation in the market due to COVID accelerating some of these trends that were already in place, like digital transformation and SaaS and cloud. And then the work-from-home kicks in with other trends like video conferencing and the shift to security spend. And we think this is going to continue for years. However, because these stocks have run up so much, they're going to have very tough compares in 2021. So maybe time for a pause. Now let's take a look at the IT spending macroeconomics. This data is from a series of surveys that ETR conducted to try to better understand spending patterns due to COVID. Those yellow slices of the pies show the percent of customers that indicate that their budgets will be impacted by coronavirus. And you can see there's a steady increase from mid-March, which blend into April, and then you can see the June data. It goes from 63% saying yes, which is very high, to 78%, which is very, very high. And the bottom part of the chart shows the degree of that change. So 22% say no change in the latest survey, but you can see much more of a skew to the red declines on the left versus the green upticks on the right-hand side of the chart. Now take a look at how IT buyers are seeing the response to the pandemic. This chart shows what companies are doing as a result of COVID in another recent ETR survey. Now of course, it's no surprise, everybody's working from home. Nobody's traveling for business, not nobody, but most people aren't, we know that. But look at the increase in hiring freezes and freezing new IT deployments, and the sharp rise in layoffs. So IT is yet again being asked to do more with less. They're used to it. Well, we see this driving an acceleration to automation, and that's going to benefit, for instance, the RPA players, cloud providers, and modern software vendors. And it will also precipitate a tailwind for more aggressive AI implementations. And many other selected names are going to continue to do well, which we'll talk about in a second, but they're in the work-from-home, the cloud, the SaaS, and the modern data sectors. But the problem is those sectors are not large enough to offset the declines in the core businesses of the legacy players who have a much higher market share, so the overall IT spend declines. Now where it gets kind of interesting is the legacy companies, look, they all have growth businesses. They're making acquisitions, they're making other bets. IBM, for example, has its hybrid cloud business in Red Hat, Dell has VMware and it's got work-from-home solutions, Oracle has SaaS and cloud, Cisco has its security business, HPE, it's as a service initiative, and so forth. And again, these businesses are growing faster, but they are not large enough to offset the decline in core on-prem legacy and drive anything more than flat growth, overall, for these companies at best. And by the time they're large enough, we'll be into the next big thing, so the cycle continues. But these legacy companies are going to compete with the upstarts, and that's where it gets interesting. So let's get into some of the specific names that we've been talking about for over a year now and make some comments around their prospects. So what we want to do is let's start with one of our favorites: Snowflake. Now Snowflake, along with Asana, JFrog, Sumo Logic, and Unity, has a highly anticipated upcoming IPO. And this chart shows new adoptions in the database sector. And you can see that Snowflake, while down from the October 19th survey, is far outpacing its competitors, with the exception of Google, where BigQuery is doing very well. But you see Mongo and AWS remain strong, and I'm actually quite encouraged that it looks like Cloudera has righted the ship and you kind of saw that in their earnings recently. But my point is that Snowflake is a share gainer, and we think will likely continue to be one for a number of quarters and years if they can execute and compete with the big cloud players, and that's a topic that we've covered extensively in previous Breaking Analysis segments, and, as you know, we think Snowflake can compete. Now let's look at automation. This is another space that we've been talking about quite a bit, and we've largely focused on two leaders: UiPath and Automation Anywhere. But I have to say, I still like Blue Prism. I think they're well-positioned. And I especially like Pegasystems, which has, for years, been embarking on a broader automation agenda. What this chart shows is net score or spending velocity data for those customers who said they were decreasing spend in 2020. Those red bars that we showed earlier are the ones who are decreasing. And you can see both Automation Anywhere and UiPath show elevated levels within that base where spending is declining, so that's a real positive. Now Microsoft, as we've reported, is elbowing its way into the market with what is currently an inferior point product, but, you know, it's Microsoft, so we can't ignore that. And finally, let's have a look at the all-important security sector, which we've covered extensively and put out a report recently. So what this next chart does is cherry-picks of a few of our favorite names, and it shows the net score or spending momentum and the granularity for some of the leaders and emerging players. All of these players are in the green, as you can see in the upper right, and they all have decent presence in the dataset as indicated by the shared NS. Okta is at the top of the list with 58% net score. Palo Alto, they're a more mature player, but still, they have an elevated net score. CrowdStrike's net score dropped this quarter, which was a bit of a concern, but it's still high. And it followed by SailPoint and Zscaler, who are right there. The big three trends in this space right now are cloud security, identity access management, and endpoint security. Those are the tailwinds, and we think these trends have legs. Remember, net score in this survey is a forward-looking metric, so we'll come back and look at the next survey, which is running this month in the field from ETR. Now everyone on this chart has reported earnings, except Zscaler, which reports on September 9th, and all of these companies are doing well and exceeding expectations, but as I said earlier, next year's compares won't be so easy. Oh, and by the way, their stock prices, they all got killed this week as a result of the rug pull that we explained earlier. So we really feel this isn't a fundamental problem for these firms that we're talking about. It's more of a technical in the market. Now Automation Anywhere and UiPath, you really don't know because they're not public and I think they need to get their house in order so they can IPO, so we'll see when they make it to public markets. I don't think that's an if, that I think they will IPO, but the fact that they haven't filed yet says they're not ready. Now why wouldn't you IPO if you are ready in this market despite the recent pullbacks? Okay, let's summarize. So listen, all you new investors out there that think stock picking is easy, look, any fool can make money in a market that goes up every day, but trees don't grow to the moon and there are bulls and bears and pigs, and pigs get slaughtered. And I can throw a dozen other cliches at you, but I am excited that you're learning. You maybe have made a few bucks playing the options game. It's not as easy as you might think. And I'm hoping that you're not trading on margin. But look, I think there are going to be some buying opportunities ahead, there always are, be patient. It's very hard, actually impossible, to time markets, and I'm a big fan of dollar-cost averaging. And young people, if you make less than $137,000 a year, load up on your Roth, it's a government gift that I wish I could have tapped when I was a newbie. And as always, please do your homework. Okay, that's it for today. Remember, these episodes, they're all available as podcasts, wherever you listen, so please subscribe. I publish weekly on wikibon.com and siliconangle.com, so check that out, and please do comment on my LinkedIn posts. Don't forget, check out etr.plus for all the survey action. Get in touch on Twitter, I'm @dvellante, or email me at david.vellante@siliconangle.com. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, everyone. Be well, and we'll see you next time. (gentle upbeat music)

from the silicon angle media office in Boston Massachusetts it's the queue now here's your host David on tape hi everybody welcome to the special edition of the cube insights powered by ETR over the past several weeks we've been running breaking analysis on various market segments and today we're gonna talk about the robotic process automation market the spending data from ETR really shows that that market is poised for for continued growth it's been rocketing these segments are independent editorial they are not sponsored in any way although two of the companies that I'll be talking about today are sponsors of the cube automation anywhere and uipath both sponsor the cube we we attend their shows but they have absolutely no input over these editorial segments it's 100% data-driven based on ETR data and cube insight opinions in my opinions so thank you for watching let's get into it so Alex if you bring up the first slide I want to share with people what the robotic process automation market is and what you need to know about it it's a small but very fast-growing market according to a combination of Forrester and and Gartner data it's around one and a half to 1.7 billion dollars this year and it's growing at over 60 percent per year Gartner calls it the fastest growing software sub segment that they tracked garden just put out a Magic Quadrant on this space which was you know is always interesting reading despite what you think about magic quadrants it's essentially software robots that are automating repetitive mundane tasks and I underline tasks in this chart because it's largely tasks simple tasks that are being automated in a big way as opposed to really big complex processes they tend to be targeted at line of business users and it very popular in environments like finance and service roles and and back office areas where they're a repetitive common tasks that people frankly hate and we're going to give you some feedback from from customers there are a number of upstarts in the space uipath automation anywhere blue prism these these companies have attracted a massive influx of venture capital particularly uipath an automation anywhere over a billion and a half dollars in the last couple of years there monster valuations take those three companies their valuations are up over ten billion dollars and growing uipath for example several months ago announced that it had more than 200 million dollars in annual recurring revenue they were just at eight million dollars two years ago so you're seeing just this this massive growth a lot of influx of capital and a lot of jockeying for position now users that we've talked to will express a great deal of business impact related to the introduction and application of RPA in their business so I want you to take a look at this video of one practitioner that we interviewed at a cube event let's listen to to see what Jeanne younger has to say and then we'll come back and talk about it it's interesting because I also teach the Six Sigma courses there and one of them my slides I've had for years teaching that classes most business processes are like between 3.2 and 3.6 3.8 Sigma which is like 95 to 98% accurate and I said that's all the better we can usually do because of the expense that it would normally be to get us to a Six Sigma you look at the places that have Six Sigma it's life-threatening airline you know airplane engines you hope they're at least 7 Sigma you know those type of things but business processes 3 5 3 2 but now I get to change that because with our PA I can make them Six Sigma very cheap very cheaply because I can pull them in I got my bought it comes over pulls the information and there's no double king there's no miss keys its accuracy 100% accuracy this is a perfect example of how companies are applying robotic process automation to to improve existing business processes you would never try to get a standard business process up to Six Sigma it's just not worth it and as Jean younger explained now she can get there very inexpensively with our PA there many many other use cases but I wanted to share that one with you now the next slide I'm going to show you comes from ETR ETR is an organization that runs a panel is about a 4,500 user panel and they focus on spending intentions they do periodic surveys throughout the year they capture a fairly large number of users and what they're spending on that built this great taxonomy and we've been partnering with ETR to share with you some of that insights and what this slide shows is really spending intentions from the july 2019 survey asking about the second half spending intentions on the sector of robotic process automation you can see here the N is 1068 respondents in that July survey on the left-hand side you can see four vendors that we've chose to profile uipath automation anywhere blue prism and pega systems a company that's been around for a long time and is not exclusively focused on RPA they've got more of a business process focus and I'll come back to that but what this slide shows is really the spending intentions around four areas the bright red is we're going to leave the platform stop spending we're out of here the lighter red is we're gonna spend less in the second half the gray is we're flat the dark green is we're gonna increase spending in the lime green is where a new customer coming on so if you subtract the red from the green you get what ETR calls the net score and that is an indication of spending intentions and momentum so the higher the net score the better you can see here uipath leads the pack with an 81% next score ironically that's the identical next school net score as was snowflake in this survey we profiled the enterprise data warehouse market and snowflake was one of the leaders there so uipath and snowflake even though there are sort of different markets and different levels of maturity sort of around in the same net score so two very hot companies and you can see going down the list automation anywhere 69% blue prism 53% and pega systems 44% actually these are all very strong compared to some of the other market segments we track like for instance if you look at the disk array market and some of the legacy disk array companies some of the enterprise data warehouse companies you'll see sometimes negative scores now on the right-hand side and the black you see shared accounts what this says this is the number of accounts that were mentioned as intending to spend on or in the case of the dark red leave or in the case of the bright green add but the number of counts out of that 1068 corpus of data that mentioned these respective companies so you can see relatively small you know 68 for uipath 42 for automation anywhere 45 for blue prism and only 27% repair systems but these I remind you were still significantly statistically significant enough to at least get indications so you can see again your UI path leads but all of the companies are actually quite strong on a relative basis so the next slide that I want to show you Alex if you bring this up is a time series for some of these leading competitors over over time so we'll go back to January of 18 and the number of shared accounts back then was relatively small it was in the low double digits and in some cases the single digits but as we go to the right you can start to see it it increases in terms of the shared accounts out of that a thousand 1068 from this past survey so you can see uipath at that 81% next score of net score very high but but also automation anywhere very very strong blue prism you can see the decline in that yellow line but again very very strong with a 53% Nets so this space is is new and it's in it's very hot I say it's new and then it's been around for a while but it's really starting to take off and then you can see see Pegasus Thames you're lower than these other companies but still very very strong at 44% now we'll tell you the folks at Wycombe on the the analyst side of our house have gone out they've done some research they maybe it was about 18 months ago they they downloaded the UI paths Community Edition they tried to do the same for automation anywhere in blue prison they tried to get access to the software so they could apply it and you know run some robots against some mundane tasks they were only able to get the automation of the sorry the uipath software which was very simple to install and apply and you know some simple tasks they couldn't get the automation anywhere in blue president you had to go to resellers and it was sort of this complicated you know setup so that was sort of a red flag that we put up but but the UI paths you know claims that their stuff was easy to use some of their users that we've talked to you know talked about it in the context of low code and so we've we've clarified some of that we don't have as much data on automation anywhere in blue prism although we've covered automation anywheres events customers you know seemed quite happy and and reporting strong business impacts don't have as much information at this time on blue prisms on blue prism we have attended some of the peg assistance events just as observers I was saying before I come back to them they take more of a holistic approach to business process it's really not they're not positioning themselves as a standalone RPA vendor which you know frankly I wouldn't do if I were up against uipath and automation anywhere because they've got so much influx of capital they've got modern platforms that are ostensibly easy to use so packet system seems to be look going after our PA in a much sort of broader context around process business process engineering so in summer you just want to say so the very fast-growing market there's a book there's a lot of competition you got uipath automation anywhere blue prism there's about 15 or 20 players in this space that are sort of sizable it's a combination of as they say standalone robotic process automation players with integrated BPM players like Pegasus Thames it's important remember you're largely here automating existing procedures and tasks you know you're not doing a lot of necessarily re-engineering it so that's you know some people are concerned about that saying okay we're kind of paving the cart path at the same time practitioners are reporting that it's having a major business impact and and although they've also said that's not likely to reduce headcount rather we're redirecting resources you're not firing people because you're bringing in robots so people aren't necessarily losing their jobs over this they're just shifting away from that sort of undifferentiated heavy lifting that they hate doing mundane tasks automating that and moving on to more strategic items so a lot of discussion in the industry about artificial intelligence in in machine learning and some folks have said well AI and RP a they have nothing to do with each other I will say this that that machine learning has been injected into the RP a space via computer vision and a good example is it recognized a button like a send button if you know you're sending out you know emails or pushing a certain button every day at the you can automate that process so computer vision is a key part of this and again it's something that certain RPF Enders are touting I know uipath again talks about that a lot but the business impact is tangible and this is based on customer feedback a lot of customer feedback you know generally speaking you're seeing CFOs are hopping on to this they're seeing this is a really good way to take out some of the inefficiencies in their business refocus people on higher value activities and so we're going to continue to watch this RPA space I think it's going to be big we see big s eyes coming into this we're talking about companies like Accenture IBM Deloitte PwC Ernie Young those guys are starting to you know go after the space and I've always said this about the the big sis they love to eat at the trough so with there's money there they find it and they go hard after it so thanks for watching everybody we're gonna continue to report on this space this is Dave Volante with cube insights powered by ETR we'll see you next time

from the silicon angle media office in Boston Massachusetts it's the queue now here's your host David on tape hi everybody welcome to this special cube in size powered by ET our enterprise Technology Research our partner who's got this database to solve the spending data and what we're gonna do is a braking analysis on the analytic database market we're seeing that cloud and cloud players are disrupting that marketplace and that marketplace really traditionally has been known as the enterprise data warehouse market so Alex if you wouldn't mind bringing up the first slide I want to talk about some of the trends in the traditional EDW market I almost don't like to use that term anymore because it's sort of a pejorative but let's look at it's a very large market it's about twenty billion dollars today growing it you know high single digits low double digits it's expected to be in the 30 to 35 billion dollar size by mid next decade now historically this is dominated by teradata who started this market really back in the 1980s with the first appliance the first converged appliance or coal with Exadata you know IBM I'll talk about IBM a little bit they bought a company called mateesah back in the day and they've basically this month just basically killed the t's and killed the brand Microsoft has entered the fray and so it's it's been a fairly large market but I say it's failed to really live up to the promises that we heard about in the late 90s early parts of the 2000 namely that you were going to be able to get a 360 degree view of your data and you're gonna have this flexible easy access to the data you know the reality is data warehouses were really expensive they were slow you had to go through a few experts to to get data it took a long time I'll tell you I've done a lot of research on this space and when you talked to the the data warehouse practitioners they would tell you we always had to chase the chips anytime Intel would come out with a new chip we forced it in there because we just didn't have the performance to really run the analytics as we need to it's took so long one practitioner described it as a snake swallowing a basketball so you've got all those data which is the sort of metaphor for the basketball just really practitioners had a hard time standing up infrastructure and what happened as a spate of new players came into the marketplace these these MPP players trying to disrupt the market you had Vertica who was eventually purchased by HP and then they sold them to Micro Focus greenplum was buy bought by EMC and really you know company is de-emphasized greenplum Netezza 1.7 billion dollar acquisition by IBM IBM just this month month killed the brand they're kind of you know refactoring everything par Excel was interesting was it was a company based on an open-source platform that Amazon AWS did a one-time license with and created a redshift it ever actually put a lot of innovation redshift this is really doing well well show you some data on that we've also at the time saw a major shift toward unstructured data and read much much greater emphasis on analytics it coincided with Hadoop which also disrupted the market economics I often joked it the ROI of a dupe was reduction on investment and so you saw all these data lakes being built and of course they turned into the data swamps and you had dozens of companies come into the database space which used to be rather boring but Mike Amazon with dynamodb s AP with HANA data stacks Redis Mongo you know snowflake is another one that I'm going to talk about in detail today so you're starting to see the blurring of lines between relational and non relational and what was was what once thought of is no sequel became not only sequel sequel became the killer app for Hadoop and so at any rate you saw this new class of data stores emerging and snowflake was one of the more interesting and and I want to share some of that data with you some of the spending intentions so over the last several weeks and months we've shared spending intentions from ETR enterprise technology research they're a company that that the manages of the spending data and has a panel of about 4,500 end-users they go out and do spending in tension surveys periodically so Alex if you bring up this survey data I want to show you this so this is spending intentions and and what it shows is that the public cloud vendors in snowflake who really is a database as a service offering so cloud like are really leading the pack here so the sector that I'm showing is the enterprise data warehouse and I've added in the the analytics business intelligence and Big Data section so what this chart shows is the vendor on the left-hand side and then this bar chart has colors the the red is we're leaving the platform the gray is our spending will be flat so this is from the July survey expect to expectations for the second half of 2019 so gray is flat the the dark green is increase and the lime green is we are a new customer coming on to the platform so if you take the the greens and subtract out the red and there's two Reds the dark red is leaving the lighter red is spending less so if you subtract the Reds from the greens you get what's called a net score so the higher the net score the better so you can see here the net score of snowflake is 81% so that very very high you can also see AWS in Microsoft a very high and Google so the cloud vendors of which I would consider a snowflake at cloud vendor like at the cloud model all kicking butt now look at Oracle look at the the incumbents Oracle IBM and Tara data Oracle and IBM are in the single digits for a net score and the Terra data is in a negative 10% so that's obviously not a good sign for those guys so you're seeing share gains from the cloud company snowflake AWS Microsoft and Google at the expense of certainly of teradata but likely IBM and Oracle Oracle's little for animal they got Exadata and they're putting a lot of investments in there maybe talk about that a little bit more now you see on the right hand side this black says shared accounts so the N in this survey this July survey that ETR did is a thousand sixty eight so of a thousand sixty eight customers each er is asking them okay what's your spending going to be on enterprise data warehouse and analytics big data platforms and you can see the number of accounts out of that thousand sixty eight that are being cited so snowflake only had 52 and I'll show you some other data from from past surveys AWS 319 Microsoft the big you know whale here trillion dollar valuation 851 going down the line you see Oracle a number you know very large number and in Tara data and IBM pretty large as well certainly enough to get statistically valid results so takeaway here is snowflake you know very very strong and the other cloud vendors the hyper scale is AWS Microsoft and Google and their data stores doing very well in the marketplace and challenging the incumbents now the next slide that I want to show you is a time series for selected suppliers that can only show five on this chart but it's the spending intentions again in that EDW and analytics bi big data segment and it shows the spending intentions from January 17 survey all the way through July 19 so you can see the the period the periods that ETR takes this the snapshots and again the latest July survey is over a thousand n the other ones are very very large too so you can see here at the very top snowflake is that yellow line and they just showed up in the January 19 a survey and so you're seeing now actually you go back one yeah January 19 survey and then you see them in July you see the net score is the July next net score that I'm showing that's 35 that's the number of accounts out of the corpus of data that snowflake had in the survey back in January and now it's up to 52 you can see they lead the packet just in terms of the spending intention in terms of mentions AWS and Microsoft also up there very strong you see big gap down to Oracle and Terra data I didn't show I BM didn't show Google Google actually would be quite high to just around where Microsoft is but you can see the pressure that the cloud is placing on the incumbents so what are the incumbents going to do about it well certainly you're gonna see you know in the case of Oracle spending a lot of money trying to maybe rethink the the architecture refactor the architecture Oracle open worlds coming up shortly I'm sure you're gonna see a lot of new announcements around Exadata they're putting a lot of wood behind the the exadata arrow so you know we'll keep in touch with that and stay tuned but you can see again the big takeaways here is that cloud guys are really disrupting the traditional edw marketplace alright let's talk a little bit about snowflakes so I'm gonna highlight those guys and maybe give a little bit of inside baseball here but what you need to know about snowflakes so I've put some some points here just some quick points on the slide Alex if you want to bring that up very fast-growing cloud and SAS based data warehousing player growing that couple hundred percent annually their annual recurring revenue very high these guys are getting ready to do an IPO talk about that a little bit they were founded in 2012 and it kind of came out of stealth and hiding in 2014 after bringing Bob Moog Leon from Microsoft as the CEO it was really the background on these guys is they're three engineers from Oracle will probably bored out of their mind like you know what we got this great idea why should we give it to Oracle let's go pop out and start a company and that NIN's and as such they started a snowflake they really are disrupting the incumbents they've raised over 900 million dollars in venture and they've got almost a four billion dollar valuation last May they brought on Frank salute Minh and this is really a pivot point I think for the company and they're getting ready to do an IPO so and so let's talk a little bit about that in a moment but before we do that I want to bring up just this really simple picture of Alex if you if you'd bring this this slide up this block diagram it's like a kindergarten so that you know people like you know I can even understand it but basically the innovation around the snowflake architecture was that they they separated their claim is that they separated the storage from the compute and they've got this other layer called cloud services so let me talk about that for a minute snowflake fundamentally rethought the architecture of the data warehouse to really try to take advantage of the cloud so traditionally enterprise data warehouses are static you've got infrastructure that kind of dictates what you can do with the data warehouse and you got to predict you know your peak needs and you bring in a bunch of storage and compute and you say okay here's the infrastructure and this is what I got it's static if your workload grows or some new compliance regulation comes out or some new data set has to be analyzed well this is what you got you you got your infrastructure and yeah you can add to it in chunks of compute and storage together or you can forklift out and put in new infrastructure or you can chase more chips as I said it's that snake swallowing a basketball was not pretty so very static situation and you have to over provision whereas the cloud is all about you know pay buy the drink and it's about elasticity and on demand resources you got cheap storage and cheap compute and you can just pay for it as you use it so the innovation from snowflake was to separate the compute from storage so that you could independently scale those and decoupling those in a way that allowed you to sort of tune the knobs oh I need more compute dial it up I need more storage dial it up or dial it down and pay for only what you need now another nuance here is traditionally the computing and data warehousing happens on one cluster so you got contention for the resources of that cluster what snowflake does is you can spin up a warehouse on the fly you can size it up you can size it down based on the needs of the workload so that workload is what dictates the infrastructure also in snowflakes architecture you can access the same data from many many different houses so you got again that three layers that I'm showing you the storage the compute and the cloud services so let me go through some examples so you can really better understand this so you've got storage data you got customer data you got you know order data you got log files you might have parts data you know what's an inventory kind of thing and you want to build warehouses based on that data you might have marketing a warehouse you might have a sales warehouse you might have a finance warehouse maybe there's a supply chain warehouse so again by separating the compute from that sort of virtualized compute from the from the storage layer you can access any data leave the data where it is and I'll talk about this in more and bring the compute to the data so this is what in part the cloud layer does they've got security and governance they got data warehouse management in that cloud layer and and resource optimization but the key in in my opinion is this metadata management I think that's part of snowflakes secret sauce is the ability to leave data where it is and have the smarts and the algorithms to really efficiently bring the compute to the data so that you're not moving data around if you think about how traditional data warehouses work you put all the data into a central location so you can you know operate on it well that data movement takes a long long time it's very very complicated so that's part of the secret sauce is knowing what data lives where and efficiently bringing that compute to the data this dramatically improves performance it's a game changer and it's much much less expensive now when I come back to Frank's Luqman this is somebody that I've is a career that I've followed I've known had him on the cube of a number of times I first met Frank Sloot when he was at data domain he took that company took it public and then sold it originally NetApp made a bid for the company EMC Joe Tucci in the defensive play said no we're not gonna let Ned afgan it there was a little auction he ended up selling the company for I think two and a half billion dollars sloop and came in he helped clean up the the data protection business of EMC and then left did a stint as a VC and then took over service now when snoop and took over ServiceNow and a lot of people know this the ServiceNow is the the shiny toy on Wall Street today service that was a mess when saluteth took it over it's about 100 120 million dollar company he and his team took it to 1.2 billion dramatically increased the the valuation and one of the ways they did that was by thinking about the Tam and expanding that Tim that's part of a CEOs job as Tam expansion Steuben is also a great operational guy and he brought in an amazing team to do that I'll talk a little bit about that team effect uh well he just brought in Mike Scarpelli he was the CFO was the CFO of ServiceNow brought him in to run finance for snowflake so you've seen that playbook emerge you know be interesting Beth white was the CMO at data domain she was the CMO at ServiceNow helped take that company she's an amazing resource she kind of you know and in retirement she's young but she's kind of in retirement doing some advisory roles wonder if slooping will bring her back I wonder if Dan Magee who was ServiceNow is operational you know guru wonder if he'll come out of retirement how about Dave Schneider who runs the sales team at at ServiceNow well he you know be be lord over we'll see the kinds of things that Sluman looks for just in my view of observing his playbook over the years he looks for great product he looks for a big market he looks for disruption and he looks for off-the-chart ROI so his sales teams can go in and really make a strong business case to disrupt the existing legacy players so I one of the things I said that snoopin looks for is a large market so let's look at this market and this is the thing that people missed around ServiceNow and to credit Pat myself and David for in the back you know we saw the Tam potential of ServiceNow is to be many many tens of billions you know Gartner when they when ServiceNow first came out said hey helpdesk it's a small market couple billion dollars we saw the potential to transform not only IT operations but go beyond helpdesk change management at cetera IT Service Management into lines of business and we wrote a piece on wiki Vaughn back then it's showing the potential Tam and we think something similar could happen here so the market today let's call 20 billion growing to 30 Billy big first of all but a lot of players in here what if so one of the things that we see snowflake potentially being able to do with its architecture and its vision is able to bring enterprise search you know to the marketplace 80% of the data that's out there today sits behind firewalls it's not searchable by Google what if you could unlock that data and access it in query at anytime anywhere put the power in the hands of the line of business users to do that maybe think Google search for enterprises but with provenance and security and governance and compliance and the ability to run analytics for a line of business users it's think of it as citizens data analytics we think that tam could be 70 plus billion dollars so just think about that in terms of how this company might this company snowflake might go to market you by the time they do their IPO you know it could be they could be you know three four five hundred billion dollar company so we'll see we'll keep an eye on that now because the markets so big this is not like the ITSM the the market that ServiceNow was going after they crushed BMC HP was there but really not paying attention to it IBM had a product it had all these products that were old legacy products they weren't designed for the cloud and so you know ServiceNow was able to really crush that market and caught everybody by surprise and just really blew it out there's a similar dynamic here in that these guys are disrupting the legacy players with a cloud like model but at the same time so the Amazon with redshift so is Microsoft with its analytics platform you know teradata is trying to figure it out they you know they've got an inertia of a large install base but it's a big on-prem install base I think they struggle a little bit but their their advantages they've got customers locked in or go with exudate is very interesting Oracle has burned the boats and in gone to cloud first in Oracle mark my words is is reacting everything for the cloud now you can say Oh Oracle they're old school they're old guard that's fine but one of the things about Oracle and Larry Ellison they spend money on R&D they're very very heavy investor in Rd and and I think that you know you can see the exadata as it's actually been a very successful product they will react attacked exadata believe you me to to bring compute to the data they understand you can't just move all this the InfiniBand is not gonna solve their problem in terms of moving data around their architecture so you know watch Oracle you've got other competitors like Google who shows up well in the ETR survey so they got bigquery and BigTable and you got a you know a lot of other players here you know guys like data stacks are in there and you've got you've got Amazon with dynamo DB you've got couch base you've got all kinds of database players that are sort of blurring the lines as I said between sequel no sequel but the real takeaway here from the ETR data is you've got cloud again is winning it's driving the discussion and the spending discussion with an IT watch this company snowflake they're gonna do an IPO I guarantee it hopefully they will see if they'll get in before the booth before the market turns down but we've seen this play by Frank Sluman before and his team and and and the spending data shows that this company is hot you see them all over Silicon Valley you're seeing them show up in the in the spending data so we'll keep an eye on this it's an exciting market database market used to be kind of boring now it's red-hot so there you have it folks thanks for listening is a Dave Volante cube insights we'll see you next time

>> Hi everyone, I'm Sam Kahane and you're watching theCUBE, on the ground, extremely excited for our segment here. Wasabi just launched last week on Wednesday. We have their co-founder and CEO with us here today on theCUBE. David, thank you for coming on today. >> Hey, nice to be here Sam. Thank you. >> So, unbelievably exciting. Can you tell the world about Wasabi? >> So if you know what Amazon S3 cloud storage is, you pretty much know what Wasabi is, except we're one-fifth the price and six-times as fast. (laughing) >> Incredible. So, you know, co-founder and CEO of Carbonite decided to start Wasabi. Tell us, why Wasabi? >> Why the name Wasabi? >> Well, the name as well. >> Cause it's hot. (laughing) My co-founder Jeff Flowers, who's one of the great technical geniuses I've ever met in my life, came to me about three years ago, with this paper design for a new storage architecture, and said, "I think we could do something that's going to be far faster and far more efficient in storage than what the cloud providers Google, Amazon and Microsoft are doing," and I said okay, "Well you should go check it out." So he left Carbonite, and we spent about a year doing design work, and eventually we ended up with this design that was so compelling to me that I decided it was time to jump on board, and join Jeff again, and this is this is the sixth company that we founded together since 1980. So we kind of know how to complete each other's sentences. It's been a winning combination, there's been quite a lot of successes there. >> So, I'd love to hear about the vision of Wasabi. >> My vision of Wasabi and cloud storage in general is that cloud storage ought to be like electricity or bandwidth, it should just be a commodity. Right now you have all these silly tiers, you have Coldline and Nearline and Standard and Glacier, and these artificial tiers that Amazon, Google and Microsoft have made to try to protect their high price spread. Wasabi is faster than the fastest of them and it's cheaper than the cheapest of them, so why do you need all these silly things in the middle? It's just like electricity, you go to plug your computer or your blender into the wall, you don't have three different plugs, one for great electricity, one for so-so electricity and one for crumby but cheap electricity, you know, you just have one. So one size fits almost all needs, and I think that's the way cloud storage is going to be as well. When we get to that, it'll be best man wins, right? The guy with the best performance and the lowest cost is going to win, and we feel we can compete in that environment. >> So a buzzword I've been hearing is 'immutable buckets', can you tell me about that? >> Yeah, so that's the one functional difference between Amazon S3 and Wasabi, otherwise Wasabi is completely 100% plug compatible with Amazon. You can unplug Amazon, plug in Wasabi and all your applications should work, and the other way around too. That's part of being a commodity, right? Your suppliers should be interchangeable. But, immutable buckets is something which really came from our Carbonite heritage. We know from Carbonite that most data loss is not due to failing disk drives and things like that today, it's stupid mistakes, you know people accidentally overwrite or delete a file? It's bugs in application software cause data to get overwritten or deleted. Then you get things like Wannacry, which come in, grabs all the data on your computer and encrypts it. So immutability means if you store data in an immutable bucket, it cannot be altered, and it cannot be deleted. It can't be deleted by you, it can't be deleted by us, and it certainly can't be deleted by a hacker or somebody breaking in from the outside. So, about 10 or 20 years ago, people invented something called the WORM tape, write-once-read-many, that was really one of the first forms of immutable digital storage. Once you put your data on there, that was it, when the tape is full, you take it off, put it in the drawer, and it's safe. That's not a very good system by today's standards, but we've built immutability into Wasabi, so that when you create a bucket in Wasabi, and for those people who don't know about object storage technology, a bucket is like a folder, and an object is like a file, when you create a bucket in Wasabi, you can flip a switch and you can say, "I want to make this bucket immutable for 10 years," let's say, and any time you go in and try to erase or alter any of the data that's been written, you just get an error message, which is what the wannabe virus would have gotten had it tried to encrypt that data. So the only downside of immutability is once you put something in there, you can't go in and clean it up. You're going to be stuck paying to store that data for a long time, but at our price of 0.39 cents per gigabyte per month, I don't think anybody would bother ever trying to clean it up anyway. You know, it's like when's a good time to go empty that U-Haul storage locker? Eh, I'll write another cheque for $40 and think about it next time. (laughing) >> So your tag-on is a hot storage? >> Hot storage, yeah. >> So you launched one week ago, on Wednesday. Tell us about that first week, how crazy was it? >> Well the only thing we did was some PR, so there were a number of articles that appeared about us, and we were expecting maybe 15, 20 companies would come sign up in the first week, do a free trial. But by 48 hours in, we were over 150, and by one more day we were at over 200. And we kind of had to shut down new sign-ups because it was just more than we could handle. We were just worried that we would get overwhelmed. Now we're trying to catch up, we just put more storage online in the last 24 hours, and now we're working through the stack of people. I don't know how many more have come in since then, but it's been a lot, so we're working through that now to give people their passcodes so that they can get on the system, hopefully by this time next week we'll be caught up. >> Well congratulations. >> Thanks, thanks! >> Any last words that you want to leave the people with about Wasabi? >> Well anytime you drop the price of anything by 80%, unexpected things are going to happen. When bandwidth suddenly got cheap, you got Netflix and movies over the internet and that kind of stuff, which people hadn't even dreamed about. I'll be really interested to see what people do with really cheap, fast storage. When you think about all these storage intensive apps like Pinterest, Instagram and things that involve videos and so forth, storage has got to be your biggest cost. And most of these apps are free, so the only revenue you're going to get is going to be advertising. I'll bet there are a lot of business models that just won't work at Amazon's prices, but drop those prices by 80%, and now suddenly you say, "Wow, this could be profitable." I'm not going to invent those apps, but I'm sure that some of the people who are signing up for Wasabi today are thinking about things that didn't work in the old regime, but with commodity cloud storage at these low prices, it starts to make sense. So we'll see, I think it's going to change the world. >> I hope so, and it's going to be exciting to watch. >> Yeah, it'll be fun. >> We'll need to catch up again soon and check back in on the growth. But David, thank you for coming on theCUBE tonight! >> You're welcome Sam, thank you. >> And CUBENation, thank you for watching. (Outro music)

(lively music) >> [Announcer] Live from Boston, Massachusetts, this is the Cube, covering Sparks Summit East 2017. Brought to you by Databricks. Now, here are your hosts, Dave Vellante and George Gilbert. >> Welcome back to Sparks Summit in Boston, everybody. This is the Cube, the worldwide leader in live tech coverage. We've been here two days, wall-to-wall coverage of Sparks Summit. George Gilbert, my cohost this week, and I are going to review part two of the Wikibon Big Data Forecast. Now, it's very preliminary. We're only going to show you a small subset of what we're doing here. And so, well, let me just set it up. So, these are preliminary estimates, and we're going to look at different ways to triangulate the market. So, at Wikibon, what we try to do is focus on disruptive markets, and try to forecast those over the long term. What we try to do is identify where the traditional market research estimates really, we feel, might be missing some of the big trends. So, we're trying to figure out, what's the impact, for example, of real time. And, what's the impact of this new workload that we've been talking about around continuous streaming. So, we're beginning to put together ways to triangulate that, and we're going to show you, give you a glimpse today of what we're doing. So, if you bring up the first slide, we showed this yesterday in part one. This is our last year's big data forecast. And, what we're going to do today, is we're going to focus in on that line, that S-curve. That really represents the real time component of the market. The Spark would be in there. The Streaming analytics would be in there. Add some color to that, George, if you would. >> [George] Okay, for 60 years, since the dawn of computing, we have two ways of interacting with computers. You put your punch cards in, or whatever else and you come back and you get your answer later. That's batch. Then, starting in the early 60's, we had interactive, where you're at a terminal. And then, the big revolution in the 80's was you had a PC, but you still were either interactive either with terminal or batch, typically for reporting and things like that. What's happening is the rise of a new interaction mode. Which is continuous processing. Streaming is one way of looking at it but it might be more effective to call it continuous processing because you're not going to get rid of batch or interactive but your apps are going to have a little of each. So, what we're trying to do, since this is early, early in its life cycle, we're going to try and look at that streaming component from a couple of different angles. >> Okay, as I say, that's represented by this Ogive curve, or the S-curve. On the next slide, we're at the beginning when you think about these continuous workloads. We're at the early part of that S-curve, and of course, most of you or many of you know how the S-curve works. It's slow, slow, slow. For a lot of effort, you don't get much in return. Then you hit the steep part of that S-curve. And that's really when things start to take off. So, the challenge is, things are complex right now. That's really what this slide shows. And Spark is designed, really, to reduce some of that complexity. We've heard a lot about that, but take us through this. Look at this data flow from ingest, to explore, to process, to serve. We talked a lot about that yesterday, but this underscores the complexity in the marketplace. >> [George] Right, and while we're just looking mostly at numbers today, the point of the forecast is to estimate when the barriers, representing complexities, start to fall. And then, when we can put all these pieces together, in just explore, process, serve. When that becomes an end-to-end pipeline. When you can start taking the data in on one end, get a scientist to turn it into a model, inject it into an application, and that process becomes automated. That's when it's mature enough for the knee in the curve to start. >> And that's when we think the market's going to explode. But now so, how do you bound this. Okay, when we do forecasts, we always try to bound things. Because if they're not bounded, then you get no foundation. So, if you look at the next slide, we're trying to get a sense of real-time analytics. How big can it actually get? That's what this slide is really trying to-- >> [George] So this one was one firm's take on real-time analytics, where by 2027, they see it peaking just under-- >> [Dave] When you say one firm, you mean somebody from the technology district? >> [George] Publicly available data. And we take it as as a, since they didn't have a lot of assumptions published, we took it as, okay one data point. And then, we're going to come at it with some bottoms-up end top-down data points, and compare. >> [Dave] Okay, so the next slide we want to drill into the DBMS market and when you think about DBMS, you think about the traditional RDBMS and what we know, or the Oracle, SQL Server, IBMDB2's, etc. And then, you have this emergent NewSQL, and noSQL entrance, which are, obviously, we talked today to a number of folks. The number of suppliers is exploding. The revenue's still relatively small. Certainly small relative to the RDBMS marketplace. But, take us through what your expectations is here, and what some of the assumptions are behind this. >> [George] Okay, so the first thing to understand is the DBMS market, overall, is about $40 billion of which 30 billion goes to online transaction processing supporting real operational apps. 10 billion goes to Orlap or business intelligence type stuff. The Orlap one is shrinking materially. The online transaction processing one, new sales is shrinking materially but there's a huge maintenance stream. >> [Dave] Yeah which companies like Oracle and IBM and Microsoft are living off of that trying to fund new development. >> We modeled that declining gently and beginning to accelerate more going out into the latter years of the tenure period. >> What's driving that decline? Obviously, you've got the big sucking sound of a dup in part, is driving that. But really, increasingly it's people shifting their resources to some of these new emergent applications and workloads and new types of databases to support them right? But these are still, those new databases, you can see here, the NewSQL and noSQL still, relatively, small. A lot of it's open source. But then it starts to take off. What's your assumption there? >> So here, what's going on is, if you look at dollars today, it's, actually, interesting. If you take the noSQL databases, you take DynamoDB, you take Cassandra, Hadoop, HBase, Couchbase, Mongo, Kudu and you add all those up, it's about, with DynamoDB, it's, probably, about 1.55 billion out of a $40 billion market today. >> [Dave] Okay but it's starting to get meaningful. We were approaching two billion. >> But where it's meaningful is the unit share. If that were translated into Oracle pricing. The market would be much, much bigger. So the point it. >> Ten X? >> At least, at least. >> Okay, so in terms of work being done. If there's a measure of work being done. >> [George] We're looking at dollars here. >> Operations per second or etcetera, it would be enormous. >> Yes, but that's reflective of the fact that the data volumes are exploding but the prices are dropping precipitously. >> So do you have a metric to demonstrate that. We're, obviously, not going to show it today but. >> [George] Yes. >> Okay great, so-- >> On the business intelligence side, without naming names, the data warehouse appliance vendors are charging anywhere from 25,000 per terabyte up to, when you include running costs, as high as 100,000 a terabyte. That their customers are estimating. That's not the selling cost but that's the cost of ownership per terabyte. Whereas, if you look at, let's say Hadoop, which is comparable for the off loading some of the data warehouse work loads. That's down to the 5K per terabyte range. >> Okay great, so you expect that these platforms will have a bigger and bigger impact? What's your pricing assumption? Is prices going to go up or is it just volume's going to go through the roof? >> I'm, actually, expecting pricing. It's difficult because we're going to add more and more functionality. Volumes go up and if you add sufficient functionality, you can maintain pricing. But as volumes go up, typically, prices go down. So it's a matter of how much do these noSQL and NewSQL databases add in terms of functionality and I distinguish between them because NewSQL databases are scaled out version of Oracle or Teradata but they are based on the more open source pricing model. >> Okay and NoSQL, don't forget, stands for not only SQL, not not SQL. >> If you look at the slides, big existing markets never fall off a cliff when they're in the climb. They just slowly fade. And, eventually, that accelerates. But what's interesting here is, the data volumes could explode but the revenue associated with the NoSQL which is the dark gray and the NewSQL which is the blue. Those don't explode. You could take, what's the DBMS cost of supporting YouTube? It would be in the many, many, many billions of dollars. It would support 1/2 of an Oracle itself probably. But it's all open source there so. >> Right, so that's minimizing the opportunity is what you're saying? >> Right. >> You can see the database market is flat, certainly flattish and even declining but you do expect some growth in the out years as part of that evasion, that volume, presumably-- >> And that's the next slide which is where we've seen that growth come from. >> Okay so let's talk about that. So the next slide, again, I should have set this up better. The X-axis year is worldwide dollars and the horizontal axis is time. And we're talking here about these continuous application work loads. This new work load that you talked about earlier. So take us through the three. >> [George] There's three types of workloads that, in large part, are going to be driving most of this revenue. Now, these aren't completely, they are completely comparable to the DBMS market because some of these don't use traditional databases. Or if they do, they're Torry databases and I'll explain that. >> [Dave] Sure but if I look at the IoT Edge, the Cloud and the micro services and streaming, that's a tail wind to the database forecast in the previous slide, is that right? >> [George] It's, actually, interesting but the application and infrastructure telemetry, this is what Splunk pioneered. Which is all the torrents of data coming out of your data center and your applications and you're trying to manage what's going on. That is a database application. And we know Splunk, for 2016, was 400 million. In software revenue Hadoop was 750 million. And the various other management vendors, New Relic, AppDynamics, start ups and 5% of Azure and AWS revenue. If you add all that up, it comes out to $1.7 billion for 2016. And so, we can put a growth rate on that. And we talked to several vendors to say, okay, how much will that work load be compared to IoT Edge Cloud. And the IoT Edge Cloud is the smart devices at the Edge and the analytics are in the fog but not counting the database revenue up in the Cloud. So it's everything surrounding the Cloud. And that, actually, if you look out five years, that's, maybe, 20% larger than the app and infrastructure telemetry but growing much, much faster. Then the third one where you were talking about was this a tail wind to the database. Micro server systems streaming are very different ways of building applications from what we do now. Now, people build their logic for the application and everyone then, stores their data in this centralized external database. In micro services, you build a little piece of the app and whatever data you need, you store within that little piece of the app. And so the database requirements are, rather, primitive. And so that piece will not drive a lot of database revenue. >> So if you could go back to the previous slide, Patrick. What's driving database growth in the out years? Why wouldn't database continue to get eaten away and decline? >> [George] In broad terms, the overall database market, it staying flat. Because as prices collapse but the data volumes go up. >> [Dave] But there's an assumption in here that the NoSQL space, actually, grows in the out years. What's driving that growth? >> [George] Both the NoSQL and the NewSQL. The NoSQL, probably, is best serving capturing the IoT data because you don't need lots of fancy query capabilities for concurrency. >> [Dave] So it is a tail wind in a sense in that-- >> [George] IoT but that's different. >> [Dave] Yeah sure but you've got the overall market growing. And that's because the new stuff, NewSQL and NoSQL is growing faster than the decline of the old stuff. And it's not in the 2020 to 2022 time frame. It's not enough to offset that decline. And then they have it start growing again. You're saying that's going to be driven by IoT and other Edge use cases? >> Yes, IoT Edge and the NewSQL, actually, is where when they mature, you start to substitute them for the traditional operational apps. For people who want to write database apps not who want to write micro service based apps. >> Okay, alright good. Thank you, George, for setting it up for us. Now, we're going to be at Big Data SV in mid March? Is that right? Middle of March. And George is going to be releasing the actual final forecast there. We do it every year. We use Spark Summit to look at our preliminary numbers, some of the Spark related forecasts like continuous work loads. And then we harden those forecasts going into Big Data SV. We publish our big data report like we've done for the past, five, six, seven years. So check us out at Big Data SV. We do that in conjunction with the Strada events. So we'll be there again this year at the Fairmont Hotel. We got a bunch of stuff going on all week there. Some really good programs going on. So check out siliconangle.tv for all that action. Check out Wikibon.com. Look for new research coming out. You're going to be publishing this quarter, correct? And of course, check out siliconangle.com for all the news. And, really, we appreciate everybody watching. George, been a pleasure co-hosting with you. As always, really enjoyable. >> Alright, thanks Dave. >> Alright, to that's a rap from Sparks. We're going to try to get out of here, hit the snow storm and work our way home. Thanks everybody for watching. A great job everyone here. Seth, Ava, Patrick and Alex. And thanks to our audience. This is the Cube. We're out, see you next time. (lively music)

>> [Announcer] Live from Boston, Massachusetts, this is theCUBE, covering Spark Summit East 2017, brought to you by Databricks. Now, here are your hosts, Dave Vellante and George Gilbert. >> We're back, welcome to Boston, everybody, this is a special presentation that George Gilbert and I are going to provide to you now. SiliconANGLE Media is the umbrella brand of our company, and we've got three sub-brands. One of them is Wikibon, it's the research organization that Gorge works in, and then of course, we have theCUBE and then SiliconANGLE, which is the tech publication, and then we extensively, as you may know, use CrowdChat and other social data, but we want to drill down now on the Wikibon, Wikibon research side of things. Wikibon was the first research company ever to do a big data forecast. Many, many years ago, our friend Jeff Kelly produced that for several years, we opensourced it, and it really, I think helped the industry a lot, sort of framing the big data opportunity, and then George last year did the first Spark forecast, really Spark adoption, so what we want to do now is talk about some of the trends in the marketplace, this is going to be done in two parts, today's part one, and we're really going to talk about the overall market trends and the market conditions, and then we're going to go to part two tomorrow, where you're going to release some of the numbers, right? And we'll share some of the numbers today. So, we're going to start on the first slide here, we're going to share with you some slides. The Wikibon forecast review, and George is going to, I'm going to ask you to talk about where we are at with big data apps, everybody's saying it's peaked, big data's now going mainstream, where are we at with big data apps? >> [George] Okay, so, I want to quote, just to provide context, the former CTO on VMware, Steve Herrod. He said, "In the end, it wasn't big data, "it was big analytics." And what's interesting is that when we start thinking about it, there have been three classes of, there have been traditionally two classes of workloads, one batch, and in the context of analytics, that means running reports in the background, doing offline business intelligence, but then there was also the interactive-type work. What's emerging is something that's continuously happening, and it doesn't mean that all apps are going to be always on, it just means that there are, all apps will have a batch component, an interactive component, like with the user, and then a streaming, or continuous component. >> [Dave] So it's a new type of workload. >> Yes. >> Okay. Anything else you want to point out here? >> Yeah, what's worth mentioning, this is, it's not like it's going to burst fully-formed out of the clouds, and become sort of a new standard, there's two things that has to happen, the technology has to mature, so right now you have some pretty tough trade-offs between integration, which provides simplicity, and choice and optimization, which gives you fragmentation, and then skillset, and both of those need to develop. >> [Dave] Alright, we're going to talk about both of those a little bit later in this segment. Let's go to the next slide, which really talks to some of the high-level forecast that we released last year, so these are last year's numbers, correct? >> Yes, yes. >> [Dave] Okay, so, what's changed? You've got the ogive curve, which is sort of the streaming penetration, Spark/streaming, that's what, was last year, this is now reflective of continuous, you'll be updating that, how is this changing, what do you want us to know here? >> [George] Okay, so the key takeaways here are, first, we took three application patterns, the first being the data lake, which is sort of the original canonical repository of all your data. That never goes away, but on top of it, you layer what we were calling last year systems of engagement, which is where you've got the interactive machine learning component helping to anticipate and influence a user's decision, and then on top of that, which was the aqua color, was the self-tuning systems, which is probably more IIoT stuff, where you've got a whole ecosystem of devices and intelligence in the cloud and at the edge, and you don't necessarily need a human in the loop. But, these now, when you look at them, you can break them down as having three types of workloads, the batch, the interactive, and the continuous. >> Okay, and that is sort of a new workload here, and this is a real big theme of your research now is, we all remember, no, we don't all remember, I remember punch cards, that's the ultimate batch, and then of course, the terminals were interactive, and you think of that as closer to real time, but now, this notion of continuous, if you go to the next slide, Patrick, we can take a look at how workloads are changing, so George, take us through that dynamic. >> [George] Okay so, to understand where we're going, sometimes it helps to look at where we've come from, and the traditional workloads, if we talk about applications, they were divided into, now, we talked about sort of batch versus interactive, but now, they were also divided into online transaction processing, operational application, systems of record, and then there was the analytic side, which was reporting on it, but this was sort of backward-looking reporting, and we begin to see some convergence between the two with web and mobile apps, where a user was interacting both with the analytics that informed an interaction that they might have. That's looking backwards, and we're going to take a quick look at some of the new technologies that augmented those older application patterns. Then we're going to go look at the emergent workloads and what they look like. >> Okay so, let's have a quick conversation about this before we go on to the next segment. Hadoop obviously was batch. It really was a way, as we've talked about today and many other dates in theCUBE, a way to reduce the expense of doing data warehousing and business intelligence, I remember we were interviewing Jeff Hammerbacher, and he said, "When I was at Facebook, "my mission was to break the dependency "and the container, the storage container." So he really wanted to, needed to reduce costs, he saw that infrastructure needed to change, so if you look at the next slide, which is really sort of talking to Hadoop doing batch in traditional BI, take us through that, and then we'll sort of evolve to the future. >> Okay, so this is an example of traditional workloads, batch business intelligence, because Hadoop has not really gotten to the maturity point of view where you can really do interactive business intelligence. It's going to take a little more work. But here, you've basically put in a repository more data than you could possibly ever fit in a data warehouse, and the key is, this environment was very fragmented, there were many different engines involved, and so there was a high developer complexity, and a high operational complexity, and we're getting to the point where we can do somewhat better on the integration, and we're getting to the point where we might be able to do interactive business intelligence and start doing a little bit of advanced analytics like machine learning. >> Okay. Let's talk a little bit about why we're here, we're here 'cause it's Spark Summit, Spark was designed to simplify big data, simplify a lot of the complexity in Hadoop, so on the next slide, you've got this red line of Spark, so what is Spark's role, what does that red line represent? >> Okay, so the key takeaway from this slide is, couple things. One, it's interesting, but when you listen to Matei Zaharia, who is the creator of Spark, he said, "I built this to be a better MapReduce than MapReduce," which was the old crufty heart of Hadoop. And of course, they've stretched it far beyond their original intentions, but it's not the panacea yet, and if you put it in the context of a data lake, it can help you with what a data engineer does with exploring and munging the data, and what a data scientist might do in terms of processing the data and getting it ready for more advanced analytics, but it doesn't give you an end-to-end solution, not even within the data lake. The point of explaining this is important, because we want to explain how, even in the newer workloads, Spark isn't yet mature to handle the end-to-end integration, and by making that point, we'll show where it needs still more work, and where you have to substitute other products. >> Okay, so let's have a quick discussion about those workloads. Workloads really kind of drive everything, a lot of decisions for organizations, where to put things, and how to protect data, where the value is, so in this next slide you've got, you're juxtaposing traditional workloads with emerging workloads, so let's talk about these new continuous apps. >> Okay, so, this tees it up well, 'cause we focused on the traditional workloads. The emerging ones are where data is always coming in. You could take a big flow of data and sort of end it and bucket it, and turn it into a batch process, but now that we have the capability to keep processing it, and you want answers from it very near real time, you don't want to stop it from flowing, so the first one that took off like this was collecting telemetry about the operation and performance of your apps and your infrastructure, and Splunk sort of conquered that workload first. And then the second one, the one that everyone's talking about now is sort of Internet of Things, but more accurately, the Industrial Internet of Things, and that stream of data is, again, something you'll want to analyze and act on with as little delay as possible. The third one is interesting, asynchronous microservices. This is difficult, because this doesn't necessarily require a lot of new technology, so much as a new skillset for developers, and that's going to mean it takes off fairly slowly. Maybe new developers coming out of school will adopt it whole cloth, but this is where you don't rely on a big central database, this is where you break things into little pieces, and each piece manages itself. >> So you say the components of these arrows that you're showing in just explore processor, these are all sort of discrete elements of the data flow that you have to then integrate as a customer? >> [George] Yes, frankly, these are all steps that could be an end-to-end integrative process, but it's not yet mature enough really to do it end-to-end. For example, we don't even have a data store that can go all the way from ingest to serve, and by ingest, I mean taking the millions, potentially millions or more, events per second coming in from your Internet of Things devices, the explorer would be in that same data store, letting you visualize what's there, and process doing the analysis, and serving then is, from that same data store, letting your industrial devices, or your business intelligence workloads get real-time updates. For this to work as one whole, we need a data store, for example, that can go from end-to-end, in addition to the compute and analytic capabilities that go end-to-end. The point of this is, for continuous workloads, we do want to get to this integrated point somehow, sometime, but we're not there yet. >> Okay, let's go deeper, and take a look at the next slide, you've got this data feedback loop, and you've got this prediction on top of this, what does all that mean, let's double-click on that. >> Okay, so now we're unpacking the slide we just looked at, in that we're unpacking it into two different elements, one is what you're doing when you're running the system, and the next one will be what you're doing when you're designing it. And so for this one, what you're doing when you're running the system, I've grayed out the where's the data coming from and where's it going to, just to focus on how we're operating on the data, and again, to repeat the green part, which is storage, we don't have an end-to-end integrated store that could cost-effectively, scalably handle this whole chain of steps, but what we do have is that in the runtime, you're going to ingest the data, you're going to process it and make it ready for prediction, then there's a step that's called devops for data science, we know devops for developers, but devops for data science, as we're going to see, actually unpacks a whole 'nother level of complexity, but this devops for data science, this is where you get the prediction, of, okay, so, if this turbine is vibrating and has a heat spike, it means shut it down because something's going to fail. That's the prediction component, and the serve part then takes that prediction, and makes sure that that device gets it fast. >> So you're putting that capability in the hands of the data science component so they can effect that outcome virtually instantaneously? >> Yes, but in this case, the data scientist will have done that at design time. We're still at run time, so this is, once the data scientist has built that model, here, it's the engineer who's keeping it running. >> Yeah, but it's designed into the process, that's the devops analogy. Okay great, well let's go to that sort of next piece, which is design, so how does this all affect design, what are the implications there? >> So now, before we had ingest process, then prediction with devops for data science, and then serving, now when you're at design time, you ingest the data, and there's a whole unpacking of steps, which requires a handful, or two fistfuls of tools right now to make operate. This is to acquire the data, explore it, prepare it, model it, assess it, distribute it, all those things are today handled by a collection of tools that you have to stitch together, and then you have process at which could be typically done in Spark, where you do the analysis, and then serving it, Spark isn't ready to serve, that's typically a high-speed database, one that either has tons of data for history, or gets very, very fast updates, like a Redis that's almost like a cache. So the point of this is, we can't yet take Spark as gospel from end to end. >> Okay so, there's a lot of complexity here. >> [George] Right, that's the trade-off. >> So let's take a look at the next slide, which talks to where that complexity comes from, let's look at it first from the developer side, and then we'll look at the admin, so, so on the next slide, we're looking at the complexity from the dev perspective, explain the axes here. >> Okay, okay. So, there's two axes. If you look at the x-axis at the bottom, there's ingest, explore, process, serve. Those were the steps at a high level that we said a developer has to master, and it's going to be in separate products, because we don't have the maturity today. Then on the y-axis, we have some, but not all, this is not an exhaustive list of all the different things a developer has to deal with, with each product, so the complexity is multiplying all the steps on the y-axis, data model, addressing, programming model, persistence, all the stuff's on the y-axis, by all the products he needs on the x-axis, it's a mess, which is why it's very, very hard to build these types of systems today. >> Well, and why everybody's pushing on this whole unified integration, that was a major thing that we heard throughout the day today. What about from the admin's side, let's take a look at the next slide, which is our last slide, in terms of the operational complexity, take us through that. >> [George] Okay, so, the admin is when the system's running, and reading out the complexity, or inferring the complexity, follows the same process. On the y-axis, there's a separate set of tasks. These are admin-related. Governance, scheduling and orchestration, a high availability, all the different types of security, resource isolation, each of these is done differently for each product, and the products are on the x-axis, ingest, explore, process, serve, so that when you multiply those out, and again, this isn't exhaustive, you get, again, essentially a mess of complexity. >> Okay, so we got the message, if you're a practitioner of these so-called big data technologies, you're going to be dealing with more complexity, despite the industry's pace of trying to address that, but you're seeing new projects pop up, but nonetheless, it feels like the complexity curve is growing faster than customer's ability to absorb that complexity. Okay, well, is there hope? >> Yes. But here's where we've had this conundrum. The Apache opensource community has been the most amazing source of innovation I think we've ever seen in the industry, but the problem is, going back to the amazing book, The Cathedral and the Bazaar, about opensource innovation versus top-down, the cathedral has this central architecture that makes everything fit together harmoniously, and beautifully, with simplicity. But the bazaar is so much faster, 'cause it's sort of this free market of innovation. The Apache ecosystem is the bazaar, and the burden is on the developer and the administrator to make it work together, and it was most appropriate for the big internet companies that had the skills to do that. Now, the companies that are distributing these Apache opensource components are doing a Herculean job of putting them together, but they weren't designed to fit together. On the other hand, you've got the cloud service providers, who are building, to some extent, services that have standard APIs that might've been supported by some of the Apache products, but they have proprietary implementations, so you have lock-in, but they have more of the cathedral-type architecture that-- >> And they're delivering 'em their services, even though actually, many of those data services are discrete APIs, as you point out, are proprietary. Okay, so, very useful, George, thank you, if you have questions on this presentation, you can hit Wikibon.com and fire off a question to us, we'll make sure it gets to George and gets answered. This is part one, part two tomorrow is we're going to dig into some of the numbers, right? So if you care about where the trends are, what the numbers look like, what the market size looks like, we'll be sharing that with you tomorrow, all this stuff, of course, will be available on-demand, we'll be doing CrowdChats on this, George, excellent job, thank you very much for taking us through this. Thanks for watching today, it is a wrap of day one, Spark Summit East, we'll be back live tomorrow from Boston, this is theCUBE, so check out siliconangle.com for a review of all the action today, all the news, check out Wikibon.com for all the research, siliconangle.tv is where we house all these videos, check that out, we start again tomorrow at 11 o'clock east coast time, right after the keynotes, this is theCUBE, we're at Spark Summit, #SparkSummit, we're out, see you tomorrow. (electronic music jingle)

(soft music) >> Announcer: TheCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (upbeat music) >> Welcome back to Spain, everybody. We're here at the Fira in MWC23. Is just an amazing day. This place is packed. They said 80,000 people. I think it might even be a few more walk-ins. I'm Dave Vellante, Lisa Martin is here, David Nicholson. But right now we have the Analyst Hot Takes with three friends of theCUBE. Chris Lewis is back again with me in the co-host seat. Zeus Kerravala, analyst extraordinaire. Great to see you, Z. and Sarbjeet SJ Johal. Good to see you again, theCUBE contributor. And that's my new name for him. He says that is his nickname. Guys, thanks for coming back on. We got the all male panel, sorry, but it is what it is. So Z, is this the first time you've been on it at MWC. Take aways from the show, Hot Takes. What are you seeing? Same wine, new bottle? >> In a lot of ways, yeah. I mean, I was talking to somebody this earlier that if you had come from like MWC five years ago to this year, a lot of the themes are the same. Telco transformation, cloud. I mean, 5G is a little new. Sustainability is certainly a newer theme here. But I think it highlights just the difficulty I think the telcos have in making this transformation. And I think, in some ways, I've been unfair to them in some degree 'cause I've picked on them in the past for not moving fast enough. These are, you know, I think these kind of big transformations almost take like a perfect storm of things that come together to happen, right? And so, in the past, we had technologies that maybe might have lowered opex, but they're hard to deploy. They're vertically integrated. We didn't have the software stacks. But it appears today that between the cloudification of, you know, going to cloud native, the software stacks, the APIs, the ecosystems, I think we're actually in a position to see this industry finally move forward. >> Yeah, and Chris, I mean, you have served this industry for a long time. And you know, when you, when you do that, you get briefed as an analyst, you actually realize, wow, there's a lot of really smart people here, and they're actually, they have challenges, they're working through it. So Zeus was saying he's been tough on the industry. You know, what do you think about how the telcos have evolved in the last five years? >> I think they've changed enormously. I think the problem we have is we're always looking for the great change, the big step change, and there is no big step change in a way. What telcos deliver to us as individuals, businesses, society, the connectivity piece, that's changed. We get better and better and more reliable connectivity. We're shunting a load more capacity through. What I think has really changed is their attitude to their suppliers, their attitude to their partners, and their attitude to the ecosystem in which they play. Understanding that connectivity is not the end game. Connectivity is part of the emerging end game where it will include storage, compute, connect, and analytics and everything else. So I think the realization that they are not playing their own game anymore, it's a much more open game. And some things they will continue to do, some things they'll stop doing. We've seen them withdraw from moving into adjacent markets as much as we used to see. So a lot of them in the past went off to try and do movies, media, and a lot went way way into business IT stuff. They've mainly pulled back from that, and they're focusing on, and let's face it, it's not just a 5G show. The fixed environment is unbelievably important. We saw that during the pandemic. Having that fixed broadband connection using wifi, combining with cellular. We love it. But the problem as an industry is that the users often don't even know the connectivity's there. They only know when it doesn't work, right? >> If it's not media and it's not business services, what is it? >> Well, in my view, it will be enabling third parties to deliver the services that will include media, that will include business services. So embedding the connectivity all the way into the application that gets delivered or embedding it so the quality mechanism deliver the gaming much more accurately or, I'm not a gamer, so I can't comment on that. But no, the video quality if you want to have a high quality video will come through better. >> And those cohorts will pay for that value? >> Somebody will pay somewhere along the line. >> Seems fuzzy to me. >> Me too. >> I do think it's use case dependent. Like you look at all the work Verizon did at the Super Bowl this year, that's a perfect case where they could have upsold. >> Explain that. I'm not familiar with it. >> So Verizon provided all the 5G in the Super Bowl. They provided a lot of, they provided private connectivity for the coaches to talk to the sidelines. And that's a mission critical application, right? In the NFL, if one side can't talk, the other side gets shut down. You can't communicate with the quarterback or the coaches. There's a lot of risk at that. So, but you know, there's a case there, though, I think where they could have even made that fan facing. Right? And if you're paying 2000 bucks to go to a game, would you pay 50 bucks more to have a higher tier of bandwidth so you can post things on social? People that go there, they want people to know they were there. >> Every football game you go to, you can't use your cell. >> Analyst: Yeah, I know, right? >> All right, let's talk about developers because we saw the eight APIs come out. I think ISVs are going to be a big part of this. But it's like Dee Arthur said. Hey, eight's better than zero, I guess. Okay, so, but so the innovation is going to come from ISVs and developers, but what are your hot takes from this show and now day two, we're a day and a half in, almost two days in. >> Yeah, yeah. There's a thing that we have talked, I mentioned many times is skills gravity, right? Skills have gravity, and also, to outcompete, you have to also educate. That's another theme actually of my talks is, or my research is that to puts your technology out there to the practitioners, you have to educate them. And that's the only way to democratize your technology. What telcos have been doing is they have been stuck to the proprietary software and proprietary hardware for too long, from Nokia's of the world and other vendors like that. So now with the open sourcing of some of the components and a few others, right? And they're open source space and antenna, you know? Antennas are becoming software now. So with the invent of these things, which is open source, it helps us democratize that to the other sort of skirts of the practitioners, if you will. And that will bring in more applications first into the IOT space, and then maybe into the core sort of California, if you will. >> So what does a telco developer look like? I mean, all the blockchain developers and crypto developers are moving into generative AI, right? So maybe those worlds come together. >> You'd like to think though that the developers would understand everything's network centric today. So you'd like to think they'd understand that how the network responds, you know, you'd take a simple app like Zoom or something. If it notices the bandwidth changes, it should knock down the resolution. If it goes up it, then you can add different features and things and you can make apps a lot smarter that way. >> Well, G2 was saying today that they did a deal with Mercedes, you know this probably better than I do, where they're going to embed WebEx in the car. And if you're driving, it'll shut off the camera. >> Of course. >> I'm like, okay. >> I'll give you a better example though. >> But that's my point. Like, isn't there more that we can do? >> You noticed down on the SKT stand the little helicopter. That's a vertical lift helicopter. So it's an electric vertical lift helicopter. Just think of that for a second. And then think of the connectivity to control that, to securely control that. And then I was recently at an event with Zeus actually where we saw an air traffic control system where there was no people manning the tower. It was managed by someone remotely with all the cameras around them. So managing all of those different elements, we call it IOT, but actually it's way more than what we thought of as IOT. All those components connecting, communicating securely and safely. 'Cause I don't want that helicopter to come down on my head, do you? (men laugh) >> Especially if you're in there. (men laugh) >> Okay, so you mentioned sustainability. Everybody's talking about power. I don't know if you guys have a lot of experience around TCO, but I'm trying to get to, well, is this just because energy costs are so high, and then when the energy becomes cheap again, nobody's going to pay any attention to it? Or is this the real deal? >> So one of the issues around the, if we want to experience all that connectivity locally or that helicopter wants to have that connectivity, we have to ultimately build denser, more reliable networks. So there's a CapEx, we're going to put more base stations in place. We need more fiber in the ground to support them. Therefore, the energy consumption will go up. So we need to be more efficient in the use of energy. Simple as that. >> How much of the operating expense is energy? Like what percent of it? Is it 10%? Is it 20%? Is it, does anybody know? >> It depends who you ask and it depends on the- >> I can't get an answer to that. I mean, in the enterprise- >> Analyst: The data centers? >> Yeah, the data centers. >> We have the numbers. I think 10 to 15%. >> It's 10 to 12%, something like that. Is it much higher? >> I've got feeling it's 30%. >> Okay, so if it's 30%, that's pretty good. >> I do think we have to get better at understanding how to measure too. You know, like I was talking with John Davidson at Sysco about this that every rev of silicon they come out with uses more power, but it's a lot more dense. So at the surface, you go, well, that's using a lot more power. But you can consolidate 10 switches down to two switches. >> Well, Intel was on early and talking about how they can intelligently control the cores. >> But it's based off workload, right? That's the thing. So what are you running over it? You know, and so, I don't think our industry measures that very well. I think we look at things kind of boxed by box versus look at total consumption. >> Well, somebody else in theCUBE was saying they go full throttle. That the networks just say just full throttle everything. And that obviously has to change from the power consumption standpoint. >> Obviously sustainability and sensory or sensors from IOT side, they go hand in hand. Just simple examples like, you know, lights in the restrooms, like in public areas. Somebody goes in there and just only then turns. The same concept is being applied to servers and compute and storage and every aspects and to networks as well. >> Cell tower. >> Yeah. >> Cut 'em off, right? >> Like the serverless telco? (crosstalk) >> Cell towers. >> Well, no, I'm saying, right, but like serverless, you're not paying for the compute when you're not using it, you know? >> It is serverless from the economics point of view. Yes, it's like that, you know? It goes to the lowest level almost like sleep on our laptops, sleep level when you need more power, more compute. >> I mean, some of that stuff's been in networking equipment for a long time, it just never really got turned on. >> I want to ask you about private networks. You wrote a piece, Athenet was acquired by HPE right after Dell announced a relationship with Athenet, which was kind of, that was kind of funny. And so a good move, good judo move by by HP. I asked Dell about it, and they said, look, we're open. They said the right things. We'll see, but I think it's up to HP. >> Well, and the network inside Dell is. >> Yeah, okay, so. Okay, cool. So, but you said something in that article you wrote on Silicon Angle that a lot of people feel like P5G is going to basically replace wireless or cannibalize wireless. You said you didn't agree with that. Explain why? >> Analyst: Wifi. >> Wifi, sorry, I said wireless. >> No, that's, I mean that's ridiculous. Pat Gelsinger said that in his last VMware, which I thought was completely irresponsible. >> That it was going to cannibalize? >> Cannibalize wifi globally is what he said, right? Now he had Verizon on stage with him, so. >> Analyst: Wifi's too inexpensive and flexible. >> Wifi's cheap- >> Analyst: It's going to embed really well. Embedded in that. >> It's reached near ubiquity. It's unlicensed. So a lot of businesses don't want to manage their own spectrum, right? And it's great for this, right? >> Analyst: It does the job. >> For casual connectivity. >> Not today. >> Well, it does for the most part. Right now- >> For the most part. But never at these events. >> If it's engineered correctly, it will. Right? Where you need private 5G is when reliability is an absolute must. So, Chris, you and I visited the Port of Rotterdam, right? So they're putting 5G, private 5G there, but there's metal containers everywhere, right? And that's going to disrupt it. And so there are certain use cases where it makes sense. >> I've been in your basement, and you got some pretty intense equipment in there. You have private 5G in there. >> But for carpeted offices, it does not make sense to bring private. The economics don't make any sense. And you know, it runs hot. >> So where's it going to be used? Give us some examples of where we should be looking for. >> The early ones are obviously in mining, and you say in ports, in airports. It broadens cities because you've got so many moving parts in there, and always think about it, very expensive moving parts. The cranes in the port are normally expensive piece of kits. You're moving that, all that logistics around. So managing that over a distance where the wifi won't work over the distance. And in mining, we're going to see enormous expensive trucks moving around trying to- >> I think a great new use case though, so the Cleveland Browns actually the first NFL team to use it for facial recognition to enter the stadium. So instead of having to even pull your phone out, it says, hey Dave Vellante. You've got four tickets, can we check you all in? And you just walk through. You could apply that to airports. You could do put that in a hotel. You could walk up and check in. >> Analyst: Retail. >> Yeah, retail. And so I think video, realtime video analytics, I think it's a perfect use case for that. >> But you don't need 5G to do that. You could do that through another mechanism, couldn't you? >> You could do wire depending on how mobile you want to do it. Like in a stadium, you're pulling those things in and out all the time. You're moving 'em around and things, so. >> Yeah, but you're coming in at a static point. >> I'll take the contrary view here. >> See, we can't even agree on that. (men laugh) >> Yeah, I love it. Let's go. >> I believe the reliability of connection is very important, right? And the moving parts. What are the moving parts in wifi? We have the NIC card, you know, the wifi card in these suckers, right? In a machine, you know? They're bigger in size, and the radios for 5G are smaller in size. So neutralization is important part of the whole sort of progress to future, right? >> I think 5G costs as well. Yes, cost as well. But cost, we know that it goes down with time, right? We're already talking about 60, and the 5G stuff will be good. >> Actually, sorry, so one of the big boom areas at the moment is 4G LTE because the component price has come down so much, so it is affordable, you can afford to bring it all together. People don't, because we're still on 5G, if 5G standalone everywhere, you're not going to get a consistent service. So those components are unbelievably important. The skillsets of the people doing integration to bring them all together, unbelievably important. And the business case within the business. So I was talking to one of the heads of one of the big retail outlets in the UK, and I said, when are you going to do 5G in the stores? He said, well, why would I tear out all the wifi? I've got perfectly functioning wifi. >> Yeah, that's true. It's already there. But I think the technology which disappears in front of you, that's the best technology. Like you don't worry about it. You don't think it's there. Wifi, we think we think about that like it's there. >> And I do think wifi 5G switching's got to get easier too. Like for most users, you don't know which is better. You don't even know how to test it. And to your point, it does need to be invisible where the user doesn't need to think about it, right? >> Invisible. See, we came back to invisible. We talked about that yesterday. Telecom should be invisible. >> And it should be, you know? You don't want to be thinking about telecom, but at the same time, telecoms want to be more visible. They want to be visible like Netflix, don't they? I still don't see the path. It's fuzzy to me the path of how they're not going to repeat what happened with the over the top providers if they're invisible. >> Well, if you think about what telcos delivers to consumers, to businesses, then extending that connectivity into your home to help you support secure and extend your connection into Zeus's basement, whatever it is. Obviously that's- >> His awesome setup down there. >> And then in the business environment, there's a big change going on from the old NPLS networks, the old rigid structures of networks to SD1 where the control point is moved outside, which can be under control of the telco, could be under the control of a third party integrator. So there's a lot changing. I think we obsess about the relative role of the telco. The demand is phenomenal for connectivity. So address that, fulfill that. And if they do that, then they'll start to build trust in other areas. >> But don't you think they're going to address that and fulfill that? I mean, they're good at it. That's their wheelhouse. >> And it's a 1.6 trillion market, right? So it's not to be sniffed at. That's fixed on mobile together, obviously. But no, it's a big market. And do we keep changing? As long as the service is good, we don't move away from it. >> So back to the APIs, the eight APIs, right? >> I mean- >> Eight APIs is a joke actually almost. I think they released it too early. The release release on the main stage, you know? Like, what? What is this, right? But of course they will grow into hundreds and thousands of APIs. But they have to spend a lot of time and effort in that sort of context. >> I'd actually like to see the GSMA work with like AWS and Microsoft and VMware and software companies and create some standardization across their APIs. >> Yeah. >> I spoke to them yes- >> We're trying to reinvent them. >> Is that not what they're doing? >> No, they said we are not in the business of a defining standards. And they used a different term, not standard. I mean, seriously. I was like, are you kidding me? >> Let's face it, there aren't just eight APIs out there. There's so many of them. The TM forum's been defining when it's open data architecture. You know, the telcos themselves are defining them. The standards we talked about too earlier with Danielle. There's a lot of APIs out there, but the consistency of APIs, so we can bring them together, to bring all the different services together that will support us in our different lives is really important. I think telcos will do it, it's in their interest to do it. >> All right, guys, we got to wrap. Let's go around the horn here, starting with Chris, Zeus, and then Sarbjeet, just bring us home. Number one hot take from Mobile World Congress MWC23 day two. >> My favorite hot take is the willingness of all the participants who have been traditional telco players who looked inwardly at the industry looking outside for help for partnerships, and to build an ecosystem, a more open ecosystem, which will address our requirements. >> Zeus? >> Yeah, I was going to talk about ecosystem. I think for the first time ever, when I've met with the telcos here, I think they're actually, I don't think they know how to get there yet, but they're at least aware of the fact that they need to understand how to build a big ecosystem around them. So if you think back like 50 years ago, IBM and compute was the center of everything in your company, and then the ecosystem surrounded it. I think today with digital transformation being network centric, the telcos actually have the opportunity to be that center of excellence, and then build an ecosystem around them. I think the SIs are actually in a really interesting place to help them do that 'cause they understand everything top to bottom that I, you know, pre pandemic, I'm not sure the telcos were really understand. I think they understand it today, I'm just not sure they know how to get there. . >> Sarbjeet? >> I've seen the lot of RN demos and testing companies and I'm amazed by it. Everything is turning into software, almost everything. The parts which are not turned into software. I mean every, they will soon. But everybody says that we need the hardware to run something, right? But that hardware, in my view, is getting miniaturized, and it's becoming smaller and smaller. The antennas are becoming smaller. The equipment is getting smaller. That means the cost on the physicality of the assets is going down. But the cost on the software side will go up for telcos in future. And telco is a messy business. Not everybody can do it. So only few will survive, I believe. So that's what- >> Software defined telco. So I'm on a mission. I'm looking for the monetization path. And what I haven't seen yet is, you know, you want to follow the money, follow the data, I say. So next two days, I'm going to be looking for that data play, that potential, the way in which this industry is going to break down the data silos I think there's potential goldmine there, but I haven't figured out yet. >> That's a subject for another day. >> Guys, thanks so much for coming on. You guys are extraordinary partners of theCUBE friends, and great analysts and congratulations and thank you for all you do. Really appreciate it. >> Analyst: Thank you. >> Thanks a lot. >> All right, this is a wrap on day two MWC 23. Go to siliconangle.com for all the news. Where Rob Hope and team are just covering all the news. John Furrier is in the Palo Alto studio. We're rocking all that news, taking all that news and putting it on video. Go to theCUBE.net, you'll see everything on demand. Thanks for watching. This is a wrap on day two. We'll see you tomorrow. (soft music)

(bright, upbeat music) >> Hello, brilliant humans and welcome to this special programming on theCUBE featuring Women of the Cloud, brought to you by AWS. My name is Savannah Peterson, and I am very excited to be joined by a brilliant woman both in supply chain as well as digital transformation. Please welcome Jeanette Barlow, VP of Product at Instacart. Jeanette, thank you so much for joining us from Boston today. How you doing? >> Thank you. I'm doing well, thank you. And thank you to the Amazon team for letting me join you. I'm excited to participate in this. I think it's such an important topic to learn all about how as women we're helping shape the future of business, supply chain, consumer experiences. So thank you very much. >> That's fantastic to have you and to be really celebrating women of the cloud properly. To start us off, how long, let's just, let's run with this. How long have you been a woman of the cloud? (Jeanette and Savannah laugh) >> Oh, probably since there, before there was a cloud, actually I have spent my entire career in enterprise technology and I spent nearly 25 years actually with IBM. And, you know, I remember when the internet really took off as far as a highly accessible thing and then the very beginnings of e-commerce where it was really the wild west and it was such a different experience than you get now. And I've been very fortunate throughout that journey to have a variety of roles from sales, marketing, communications. I eventually landed in product management and that's pretty much where I stayed. >> Savannah: At least for now. >> At least for now. >> Sounds like you're very curious. I can tell that you are a very curious person. Since you've been around for what I would consider a, an impressive period of time in an industry, especially when there were not a ton of women to reference or receive mentorship from, what was the initial catalyst or spark or inspiration for you to pursue a career in technology? >> I'll be really honest, getting out of college with college debt, money. (Savannah laughs) The best salary, I'm not going to sugarcoat that but once I landed there, it just was so amazing how technological advance advances were fundamentally changing the way businesses would work or how humans could get things done. And that whole, my whole career trajectory has been very much working at the forefront of new areas whether that be collaboration, software or supply chain which is, obviously we're all well aware, such a deep and important area and even low-code workflow automation before I came to Instacart. >> I love the transparency there. It's a indicator of a great leader and that level of authenticity. Were there any hurdles that you felt you had to overcome in the beginning or was the curiosity enough to power through the initial first few years that are always tough for anyone, no matter their gender or career? >> I think I was a very fortunate person. I do want to say that, sure, there are a lot of long hours and I often felt that I had to be more prepared, maybe than some of my colleagues that were men back, way back in the day. But I had the very good fortune of working for companies throughout my history that really believed in an equitable and respectful workplace. And I had wonderful mentors, both women and men, along the way who really were there to help develop talent. So I never felt that I had sort of a glass ceiling. I definitely felt that I had to to sit there and assert a point of view, at times. >> Savannah: Mm-Hm. >> But, I've seen this whole industry and space change and it's not just gender, but also racial backgrounds educational backgrounds, that neurodiversity I'm now seeing much greater respect for listening to that chorus of voices because we do get better, much better outcomes that way. >> Absolutely. I couldn't agree more and I'm happy to hear that you've been supported along your journey. I think the industry can definitely get a bad rap and there are a lot of people paving the way for us. I want to talk a little bit about supply chain because I don't know about you, but for me I don't think there were as many people talking about the industry and probably what you do, say four years ago, as are now. How did you find your way into supply chain and what is it about helping that be more efficient that excites you? >> Yes. There's nothing like a shortage of toilet paper to get people to. (Savannah laughs) Or to understand what supply chain means. And I, as tough as those times were, especially at the beginning of the pandemic and the uncertainty, it was so exciting for those of us in supply chain because suddenly people got what we did like- >> Savannah: Mm-Hm. >> And they were interested in hearing about it. So I really, I really have, we did enjoy that. I got exposed to that because ultimately I served as the Vice President of Product Management and Strategy for IBM, Sterling Supply Chain which was a very large brand within the IBM portfolio, serving over 10,000 clients worldwide, really focused on their omnichannel order management and their other supply chain processes around order to cash, procure to pay, logistics and things like that. And when you start to learn about the intricacies and that choreography needed across so many players in the value chain, it's an absolutely fascinating puzzle. And- >> Savannah: Yeah. >> Often the further away from the consumer experience you got, the more analog it became. And so the opportunity to start to digitize and transform that was really something that was very, very intriguing. And now here at Instacart, the opportunity to sort of parlay that into one of probably the most complex supply chains that there are, grocery, food just adds another level- >> Yeah. >> Of excitement intrigue to the work. >> I can only imagine there are, I'm just thinking about it right now. I'm not sure there are many supply chains, if any that touch as many lives as food does, as, I mean so is that what brought you, you joined Instacart relatively recently if I'm not mistaken, within the last year. Is that what brought you to them? Was the complexity of that global challenge? >> Absolutely. That was definitely the start of it, was so intriguing to me to see, to, the more I learned about Instacart when they approached me was also they're really changing an industry that's been very static for many, many years, right? And they're fundamentally reshaping that industry. One that's, as you said, is crucial to the everyday lives of pretty much everyone. And I was intrigued by that. But I was also intrigued by the breadth at which they're approaching this, not just the marketplace, but how we are helping retailers through our Instacart platform actually reach their consumers in ways that they like to shop whether it's online or in the store. We are also very, very committed to not just serving from a convenience standpoint, but actually improving access to healthy and nutritious food for as many people as might need that. So it just, core to the complexity of the problem the criticality of it, but also just frankly speaking to the core of who Instacart is as a company, I, it just felt like it was like a culmination of a lot of things to have this opportunity to work here. >> Sounds like a fantastic opportunity. I want to dive a little bit deeper into the technology side there. How is Instacart's technology helping grocers with varying levels of scale and geographical challenges and I'm sure a variety of other things and even a digital skillset. How are you helping them navigate their digital transformation? >> You know, this is probably one of the sectors that lags behind other retail sectors as far as digital transformation. And when the progress that's been made over the last four years is tremendous. And the road ahead is still before us is still a long way to go. I mean Instacart built the world's largest grocery marketplace, if you want to think about that. And so we have more than 10 years of experience in understanding the complexity of that. With, again a supply chain that is very, very complex. So last spring we announced the Instacart platform as a way of really putting a name to a lot of work we were already doing. And it's all about opening up the capability and the technology that we have to help grocers reach their customers directly as well as through our marketplace. So we help grocers like Publix, Wegmans, The Fresh Market just hundreds of grocers build out their own storefronts, their own mobile apps and that we are actually powering for them. We help them create some very unique fulfillment models that might serve customers or be new market opportunities. Certainly we have the traditional full service shop, but we also have virtual convenience that can enable delivery in minutes. And in certain geographies and demographics, that's, you know, really important. We are even going in the store with our connected stores technologies that we announced earlier this year, and that is everything from smart cards to scan and pay to wayfinding that it just, it's a lot of very interesting work we're doing and we're very, very fortunate to be able to partner with some of the best and brightest grocery retailers out there as well as retailers and other verticals as well. But grocery store is sort of our core. >> Yeah, I can only imagine some of the conversations that you have and the user behaviors that you get to learn about as people are on their food journey. You teased a little bit there about what's coming next. What else do you think is in our food future? >> Well, I think, you know, the pandemic pushed the grocery industry to get online to start to digitally transform itself, but we believe it's not an either or. There are virtually no one that's exclusively online and we know more and more there's no one that's exclusively you know, only in the store. We really expect to have that blend and I think as long as we're very, very savvy about understanding the, our retailers' needs as well as their customers' needs on how they can really traverse seamlessly between whether they're online or in store, how they can have an engaging experience that's consistent to the brand of the retailer. >> Savannah: Mm-Hm. >> How they can be rewarded for their loyalty. How they can be encouraged to try new things and just have a much more engaging experience with that grocer because food is a very emotional sort of buy, right? I mean, it's a very sensory rich. And so how- >> Sort of? I think you can go ahead and just make that claim. Just for a lot of people, yeah, yeah. We'll endorse that. >> You're right, yeah, it is. Right, we're passionate about our brand of this or that or we want to touch or smell or do things like that. So there's a tremendous amount of innovation you get online, like personalization and other things that you don't get when you get, you walk into the store, everybody's got the same end cap like I see the same end cap as you see and we might be very different. And then vice versa. I get a very much a sensory experience when I'm in the store, right? That I don't have, how do we blend that? And so there's some really interesting things that we're working on with our retail partners to embrace that omnichannel approach. So we create that flywheel of experience and innovation between the two. So I think you're going to see a lot more focus on an omnichannel experience that traverses between the on and the in, online and the in-store. >> Yeah, I, so I love this because you know, we, there's a continued debate around remote and in-person, working remote and in-person events, but it sounds like hybrid is here to stay when it comes to food and and how we eat, which is very exciting. Last question for you, Jeanette. What would you say to someone, a woman of any age who is looking at this video or maybe dreaming about a career in cloud technology? What's your moment of inspiration? >> You know, I think my best advice is all, you know, stay curious. Just be in love with not even just the technology for technology's sake, but what the technology can unlock as far as an experience and focus on building those experiences. Not only for your direct customer in my case, retailers, grocers, but for their customer. Trying to understand that. And I think if you can connect those dots, you know the cloud is the limit, let's put it that way. (Jeanette and Savannah laugh) >> I'll take it upon that. I love that. Jeanette Barlow, thank you so much for joining us. The team at Instacart is lucky to have you. And thank you to our audience for joining us for this special program on theCUBE featuring Women of the Cloud. My name is Savannah Peterson and I look forward to celebrating more brilliant women like Jeanette with you all soon. (upbeat, happy music)

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