>>Hey everyone. Welcome back to the program. Lisa Martin here, I've got two guests joining me, please. Welcome back to the cube. Paula Hansen, the chief revenue officer and president at Al alters and Jackie Vander lake grayling joins us as well. The global head of tax technology at eBay. They're gonna share with you how an alter Ricks is helping eBay innovate with analytics. Ladies. Welcome. It's great to have you both on the program. >>Thank you, Lisa. It's great to be here. >>Yeah, Paula, we're gonna start with you in this program. We've heard from Jason Klein, we've heard from Alan Jacobson, they talked about the need to democratize analytics across any organization to really drive innovation with analytics. As they talked about at the forefront of software investments, how's alters helping its customers to develop roadmaps for success with analytics. >>Well, thank you, Lisa. It absolutely is about our customer's success. And we partner really closely with our customers to develop a holistic approach to their analytics success. And it starts of course, with our innovative technology and platform, but ultimately we help our customers to create a culture of data literacy and analytics from the top of the organization, starting with the C-suite. And we partner with our customers to build their roadmaps for scaling that culture of analytics through things like enablement programs, skills, assessments, hackathons, setting up centers of excellence to help their organizations scale and drive governance of this analytics capability across the enterprise. So at the end of the day, it's really about helping our customers to move up their analytics, maturity curve with proven technologies and best practices so they can make better business decisions and compete in their respective industries. >>Excellent. Sounds like a very strategic program. We're gonna unpack that Jackie, let's bring you into the conversation. Speaking of analytics maturity, one of the things that we talked about in this event is the IDC report that showed that 93% of organizations are not utilizing the analytics skills of their employees, but then there's eBay. How Jackie did eBay become one of the 7% of organizations who's really maturing and how are you using analytics across the organization at eBay? >>So I think the main thing for us is just when we started out was is that, you know, our, especially in finance, they became spreadsheet professionals instead of the things that we really want our employees to add value to. And we realized we had to address that. And we also knew we couldn't wait for all our data to be centralized until we actually start using the data or start automating and be more effective. So ultimately we really started very, very actively embedding analytics in our people and our data and our processes, >>Starting with people is really critical. Jackie, continuing with you, what were some of the roadblocks to analytics adoption that you faced and how did you overcome them? >>So I think, you know, eBay is a very data driven company. We have a lot of data. I think we are 27 years around this year, so we have the data, but it is everywhere. And how do you use that data? How do you use it efficiently? How do you get to the data? And I believe that that is definitely one of our biggest roadblocks when we started out and, and just finding those data sources and finding ways to connect to them to move forward. The other thing is, is that, you know, people were experiencing a lot of frustration. I mentioned before about the spreadsheet professionals, right? And we, there was no, we're not independent. You couldn't move forward. You would've opinion on somebody else's roadmap to get to data and to get the information you wanted. So really finding something that everybody could access analytics or access data. >>And finally we have to realize is that this is uncharted territory. This is not exactly something that everybody is used to working with every day. So how do you find something that is easy? And that is not so daunting on somebody who's brand new to the field. And I would, I would call those out as your, as your major roadblocks, because you always have not always, but most of the times you have support from the top in our case, we have, but in the end of the day, it's, it's our people that need to actually really embrace it and, and making that accessible for them, I would say is definitely not per se, a roadblock, but basically some, a block you wanna be able to move. >>It's really all about putting people. First question for both of you and Paula will start with you. And then Jackie will go to you. I think the message in this program that the audience is watching with us is very clear. Analytics is for everyone should be for everyone. Let's talk now about how both of your organizations are empowering people, those in the organization that may not have technical expertise to be able to leverage data so that they can actually be data driven Paula. >>Yes. Well, we leverage our platform across all of our business functions here at Altrix and just like Jackie explained it, eBay finances is probably one of the best examples of how we leverage our own platform to improve our business performance. So just like Jackie mentioned, we have this huge amount of data flowing through our enterprise and the opportunity to leverage that into insights and analytics is really endless. So our CFO, Kevin Rubin has been a, a key sponsor for using our own technology. We use Altrix for forecasting, all of our key performance metrics for business planning across our audit function, to help with compliance and regulatory requirements tax, and even to close our books at the end of each quarter. So it's really remain across our business. And at the end of the day, it comes to how do you train users? How do you engage users to lean into this analytic opportunity to discover use cases? >>And so one of the other things that we've seen many companies do is to gamify that process, to build a game that brings users into the experience for training and to work with each other, to problem solve and along the way, maybe earn badges depending on the capabilities and trainings that they take. And just have a little healthy competition as an employee base around who can become more sophisticated in their analytic capability. So I think there's a lot of different ways to do it. And as Jackie mentioned, it's really about ensuring that people feel comfortable, that they feel supported, that they have access to the training that they need. And ultimately that they are given both the skills and the confidence to be able to be a part of this great opportunity of analytics. >>That confidence is key. Jackie, talk about some of the ways that you're empowering folks without that technical expertise to really be data driven. >>Yeah, I think it means to what Paula has said in terms of, you know, you know, getting people excited about it, but it's also understanding that this is a journey and everybody's the different place in their journey. You have folks that's already really advanced who has done this every day. And then you have really some folks that this is brand new and, or maybe somewhere in between. And it's about how you put, get everybody in their different phases to get to the, the initial destination. I say initially, because I believe the journey is never really complete. What we have done is, is that we decided to invest in an Ebola group of concept. And we got our CFO to sponsor a hackathon. We opened it up to everybody in finance, in the middle of the pandemic. So everybody was on zoom and we had, and we told people, listen, we're gonna teach you this tool super easy. >>And let's just see what you can do. We ended up having 70 entries. We had only three weeks. So, and these are people that has N that do not have a background. They are not engineers, they're not data scientists. And we ended up with a 25,000 hour savings at the end of that hackathon from the 70 inches with people that have never, ever done anything like this before and there you had the result. And then it just went from there. It was, people had a proof of concept. They, they knew that it worked and they overcame the initial barrier of change. And that's where we are seeing things really, really picking up. Now >>That's fantastic. And the, the business outcome that you mentioned there, the business impact is massive helping folks get that confidence to be able to overcome. Sometimes the, the cultural barriers is key. I think another thing that this program has really highlighted is there is a clear demand for data literacy in the job market, regardless of organization. Can each of you share more about how you are empowering the next generation of data workers, Paula will start with you? >>Absolutely. And, and Jackie says it so well, which is that it really is a journey that organizations are on. And, and we, as people in society are on in terms of upskilling our capabilities. So one of the things that we're doing here at Altrix to help address this skillset gap on a global level is through a program that we call sparked, which is essentially a, no-cost a no cost analytics education program that we take to universities and colleges globally to help build the next generation of data workers. When we talk to our customers like eBay and many others, they say that it's difficult to find the skills that they want when they're hiring people into the job market. And so this program's really developed just to, to do just that, to close that gap and to work hand in hand with students and educators to improve data literacy for the next generation. So we're just getting started with sparked. We started last may, but we currently have over 850 educational institutions globally engaged across 47 countries. And we're gonna continue to invest here because there's so much opportunity for people, for society and for enterprises, when we close gap and empower more people within necessary analytics skills to solve all the problems that data can help solve. >>So spark has made a really big impact in such a short time period. And it's gonna be fun to watch the progress of that. Jackie, let's go over to you now talk about some of the things that eBay is doing to empower the next generation of data workers. >>So we basically wanted to make sure that we keep that momentum from the hackathon that we don't lose that excitement, right? So we just launched a program called Ebo masterminds. And what it basically is, it's an inclusive innovation initiative where we firmly believe that innovation is all up scaling for all analytics for. So it doesn't matter. Your background doesn't matter which function you are in, come and participate in, in this where we really focus on innovation, introducing new technologies and upskilling our people. We are apart from that, we also say, well, we should just keep it to inside eBay. We, we have to share this innovation with the community. So we are actually working on developing an analytics high school program, which we hope to pilot by the end of this year, where we will actually have high schoolers come in and teach them data essentials, the soft skills around analytics, but also how to use alter alter. And we're working with actually, we're working with spark and they're helping us develop that program. And we really hope that as a say, by the end of the year, have a pilot and then also make you, so we roll it out in multiple locations in multiple countries and really, really focus on, on that whole concept of analytics, role >>Analytics for all sounds like ultra and eBay have a great synergistic relationship there that is jointly aimed at, especially kind of going down the staff and getting people when they're younger, interested, and understanding how they can be empowered with data across any industry. Paula, let's go back to you. You were recently on the Cube's super cloud event just a couple of weeks ago. And you talked about the challenges the companies are facing as they're navigating. What is by default a multi-cloud world? How does the alters analytics cloud platform enable CIOs to democratize analytics across their organization? >>Yes, business leaders and CIOs across all industries are realizing that there just aren't enough data scientists in the world to be able to make sense of the massive amounts of data that are flowing through organizations. Last I check there was 2 million data scientists in the world. So that's woefully underrepresented in terms of the opportunity for people to be a part of the analytics solution. So what we're seeing now with CIOs with business leaders is that they're integrating data analysis and the skill of data analysis into virtually every job function. And that is what we think of when we think of analytics for all. And so our mission with Altrics analytics cloud is to empower all of those people in every job function, regardless of their skillset. As Jackie pointed out from people that would, you know, are just getting started all the way to the most sophisticated of technical users. Every worker across that spectrum can have a meaningful role in the opportunity to unlock the potential of the data for their company and their organizations. So that's our goal with Altrics analytics cloud, and it operates in a multi cloud world and really helps across all sizes of data sets to blend, cleanse, shape, analyze, and report out so that we can break down data silos across the enterprise and drive real business outcomes. As a result of unlocking the potential of data, >>As well as really re lessening that skill gap. As you were saying, there's only 2 million data scientists. You don't need to be a data scientist. That's the, the beauty of what Altrics is enabling. And, and eBay is a great example of that. Jackie, let's go ahead and wrap things with you. You talked a great deal about the analytics maturity that you have fostered at eBay. It obviously has the right culture to adapt to that. Can you talk a little bit and take us out here in terms of where alters fits in on as that analytics maturity journey continues and what are some of the things that you are most excited about as analytics truly gets democratized across eBay? >>When we start about getting excited about things, when it comes to analytics, I can go on all day, but I I'll keep it short and sweet for you. I do think we are on the topic full of, of, of data scientists. And I really feel that that is your next step for us anyways, is that, how do we get folks to not see data scientists as this big thing, like a rocket scientist, it's, it's something completely different. And it's something that, that is in everybody to a certain extent. So again, partner with three X would just released the AI ML solution, allowing, you know, folks to not have a data scientist program, but actually build models and be able to solve problems that way. So we have engaged with alters and we, we purchased a license, this quite a few. And right now through our mastermind program, we're actually running a four months program for all skill levels, teaching, teaching them AI ML and machine learning and how they can build their own models. >>We are really excited about that. We have over 50 participants without the background from all over the organization. We have members from our customer services. We have even some of our engineers are actually participating in the program. We just kicked it off. And I really believe that that is our next step. I wanna give you a quick example of, of the beauty of this is where we actually just allow people to go out and think about ideas and come up with things. And one of the people in our team who doesn't have a data scientist background at all, was able to develop a solution where, you know, there is a checkout feedback checkout functionality on the eBay site where sellers or buyers can verbatim add information. And she build a model to be able to determine what relates to tax specific, what is the type of problem, and even predict how that problem can be solved before we, as a human even step in, and now instead of us or somebody going to verbatim and try to figure out what's going on there, we can focus on fixing the error versus actually just reading through things and not adding any value. >>And it's a beautiful tool and very impressed. You saw the demo and they developing that further. >>That sounds fantastic. And I think just the one word that keeps coming to mind, and we've said this a number of times in the program today is empowerment. What you're actually really doing to truly empower people across the organization with, with varying degrees of skill level, going down to the high school level, really exciting, we'll have to stay tuned to see what some of the great things are that come from this continued partnership. Ladies, I wanna thank you so much for joining me on the program today and talking about how alters and eBay are really partnering together to democratize analytics and to facilitate its maturity. It's been great talking to you. >>Thank you. >>As you heard over the course of our program organizations, where more people are using analytics who have the deeper capabilities in each of the four E's, that's, everyone, everything everywhere and easy analytics, those organizations achieve more ROI from their respective investments in analytics and automation than those who don't. We also heard a great story from eBay, great example of an enterprise that is truly democratizing analytics across its organization. It's enabling an empowering line of business users to use analytics, not only focused on key aspects of their job, but develop new skills rather than doing the same repetitive tasks. We wanna thank you so much for watching the program today. Remember you can find all of the content on the cue.net. You can find all of the news from today on Silicon angle.com and of course, alter.com. We also wanna thank alt alters for making this program possible and for sponsored in the queue for all of my guests. I'm Lisa Martin. We wanna thank you for watching and bye for now.

[Music] we're here with steve melanie the president and ceo of aviatrix steve john and i started this whole super cloud narrative as a way to describe that something different is happening specifically within the aws ecosystem but more broadly across the cloud landscape at re invent last year you and i spoke on the cube and you said one of your investors guy named nick sterile said to you at the show it's happening steve welcome to the cube what's happening what did nick mean by that yeah we were we were just getting ready to go on and i leaned over and he looked at me and he whispered in my ear and said it's happening he said it just like that and and you're right it was it was kind of funny and we talked about that and what he means is enterprises you know this is why i went to aviatrix three and a half years ago is the the the flip switch for enterprises and they said now we mean it we've been talking about cloud for 12 years or 15 years now we mean it we are digitally transforming we are the movement to cloud is going to make that happen and oh by the way of course it's multi-cloud because enterprises put workloads where they run best where they have the best security the best performance the best cost and the business is driving this transformation and they decide that i'm going to use that azure and another business unit decides i'm using google and another one says i'm using aws and so of course it's going to be multi-cloud and i think we're going to start seeing actual multi-cloud applications once that infrastructure and you know you call it the super cloud once that starts getting built developers are going to go wait a minute so i can pick this feature from google and and that service from azure and that service from aws easily without any hesitation once that happens they're going to start really developing today there aren't multi-cloud applications but but but the what's happening is the enterprise embracing public cloud they're using multiple clouds many of them call it four plus one right they're four different public clouds plus what they have on prem that to me is what's happening i am now re-architecting my enterprise infrastructure from applications all the way down to the network and i am embracing uh uh public clouds in that in that process so i mean you nailed us so many things in there i mean digitally transforming to me this is the digital transformation it's leveraging embracing the capex from the hyperscalers now you know people in the industry we're not trying to do what gartner does and create a new category per se but we do use super cloud as a metaphor so i don't expect necessarily vendors to use it or not but but i and i get that but when you talk about multi-cloud what specifically is new in other words what you touched on some of this stuff what constitutes a modern multi-cloud or what we would call a super cloud you know network architecture what are the salient attributes yeah i would say today so two years ago there was no such thing even as multiple clouds it was aws let's be clear everything was aws and for people to even back then two three years ago to even envision that there would be anything else other than aws people couldn't even envision now people kind of go yeah that was done we now see that we're going to use multiple clouds we're going to use azure we're going to use gcp and we're going to use this and we'll guess we're going to use oracle and even ollie cloud we're going to use five or four or five different public clouds what's but that would be i think of as multiple clouds but from an i.t perspective they need to be able to support all those clouds in these shared services and what they're going to do i actually think we're starting and you may have hit on something in the super cloud or i know you've talked about metacloud that that's got bad connotations for facebook i know everybody's like no please not another meta thing but there is that concept of this abstracted layer above you know writing we call it you know altitude you know aviatrix everything's you know riding above the clouds right that that that common abstracted layer this application infrastructure that runs the application that rides above all the different public clouds and i think once we do that you know dave what's going to happen is i think really what's going to happen is you're going to start seeing these these multi-cloud applications which to my knowledge really doesn't exist today i i think that might be the next phase and in order for that to happen you have to have all of the infrastructure be multi-cloud meaning not just networking and network security from from from aviation but you need snowflake you need hashtag you need datadog you need all the new horsemen of the new multi-cloud which isn't the old guys right this is all new people aviatrix dashie snowflake datadonk you name it that are going to be able to deliver all this multi-cloud cross-cloud wherever you want to talk about it such that application development and deployment can happen seamlessly and frictionlessly across multi-cloud once that happens the entire stack then you're going to start seeing and that to me starts enabling this what you guys call you know the super cloud the meta cloud the whatever cloud but that then rides above all the individual clouds that that's going to start getting a whole new realm of application development in my mind so we've got some work to do to basic do some basic blocking and tackling then the application developers can really build on top of that so so some of the skeptics on on this topic would ask how do you envision this changing networking versus it just being a bolt-on to existing fossilized network infrastructure in other words yeah how do we get from point a where we are today to point b you know so-called networking so we can actually build those uh super cloud applications yeah so you know what it is it's interesting because it goes back to my background at nasira and what we used to talk about then it isn't about managing complexity it's about creating simplicity it's very different and when you put the intelligence into the software right this is what computer science is all about we're turning networking into computer science when you create an abstraction layer we are not just an overlay day we dave we actually integrate in with the native services of the cloud we are not managing the complexity of these multi-clouds we are using it you know controlling the native constructs adding our own intelligence to this and then creating what is basically simplification for the people above it so we're simplifying things not just managing the complexity that's how you get the agility for cloud that's how you get to be able to do this because if all you are is a veneer on top of complexity you're just hiding complexity you're not creating simplicity and what happens is it actually probably gets more complex because if all you're doing is hiding the bad stuff you're not getting rid of it i love that i love that we're doing that at the networking and network security layer you're going to see snowflake and datadog and other people do it at their layers you know i reminds of a conversation i had with cause the one of the founders of pure storage who they're all about simplicity this idea of of creating simplicity versus like you said just creating you know a way to handle the complexity compare you know pure storage with the sort of old legacy emc storage devices and that's what you had you had you you had emc managing the complexity at pure storage disrupting by creating simplicity so what are the challenges of creating that simplicity and delivering that seamless experience that continuous experience across cloud is it engineering is it mindset is it culture is it technology what is it well i mean look at look you see the recession that we're we're hitting you see there is a significant problem that we have in the general it industry right now and it's called skills gap skills shortage it's two problems we don't have enough people and we don't have enough people that know cloud and the reason is everybody on the same tuesday three and a half years ago all said now i mean i'm moving the cloud we're a technology company we don't make sneakers anymore we don't make beer we're a technology company and we're going to digitally transform and we're going to move the cloud guess what three years ago there were probably seven people that understood cloud now everyone on the same tuesday morning all decides to try to hire those same seven people there's just not enough people around so you're going to need software and you're going to have to put the intelligence into the software because you're not going to be able to a hire those people and b even if you hire them you can't keep them as soon as they learn cloud guess what happens dave they're off they're on to the next job at the next highest bidder so how are you going to handle that you have to have software that intelligent software that is going to simplify things for you we have people managing massive multi-cloud network and network security people with two people on-prem they got hundreds right you it's not about taking that complex model that it had on-prem and jam it into the cloud you don't have the people to do it and you're not going to get the people to do it you know i want to ask you yeah so i want to ask you about the go to market challenges because we our industry gets a bad rap for for selling we're really good at selling and then but but actually delivering what we sell sometimes we fall down there so so i love tom sweet as cfo of of dell he talks about the the say do ratio uh how that's actually got to be low but you know but you know what i mean uh the math the fraction guy right so but do do what you say you're going to do are there specific go to market challenges related to this type of cross cloud selling where you can set you have to set the customer's expectations because what you're describing is not going to happen overnight it's a journey but how do you handle that go to market challenge in terms of setting those customer expectations and actually delivering what you say you can sell and selling enough to actually have a successful business um so i think everything's outside in so so i think the the what really is exciting to me about this cloud computing model that with the transformation that we're going through is it is business-led and it is led by the ceo and it is led by the business units they run the business it is all about agility is about enabling my developers and it's all about driving the business market share revenue all these kind of things you know the last transformation of mainframe to on to pc client server was led by technologists it wasn't led by the business and it was it was really hard to tie that to the business so then so this is great because we can look at the initiatives you can look at the the the initiatives of the ceo in your company and now as an i.t person you can tie to that and they're going to have two or three or four initiatives and you can actually map it to that so that's where we start is let's look at what the c your ceo cares about he cares about this he cares about that he cares about driving revenue he cares about agility of getting new applications out to the market sooner to get more revenue there's this and oh by the way transfer made transforming your infrastructure to the cloud is the number one thing so it's all about agility so guess what you need to be able to respond to that immediately because tomorrow the business is going to go to you and say great news dave we're moving to gcp wait what no one told me about that well we're telling you now and uh you need to be ready tomorrow and if you're sitting there and you're tied to the low-level constructs and all you know is aws well i don't have those people and even if i have even if i could hire them i'm not allowed to because i can't hire anybody how am i going to respond to the business and the needs of the business now all of a sudden i'm in the way as the infrastructure team of the ceo's goals because we decided we need to we need to get the ai capabilities of gcp and we're moving to gcp or i just did a big deal with gcp and uh miraculously they said i need to run on gcp right i did a big deal with google right guess what comes along with that oh you're moving to gcp great the business says we're moving to gcp and the i.t guys are sitting there going well no one told me well sorry so it's all about agility it's all about that and the and and complexity is the killer to agility this is all about business they're going to come to you and say we just acquired a company we need to integrate them oh but they got they use the same ip address range as we do there's overlapping ips and oh by the way they're in a different cloud how do i do that no one cares the business doesn't care they're like me they're very impatient get it done or we'll find someone who will yeah so you've got to get ahead of that and so when we in terms of when we talk to customers that's what we do this isn't just about defenses this is about making you get promoted making you do good for your company such that you can respond to that and maybe even enable the company to go do that like we're going to enable people to do true multi-cloud applications because the infrastructure has to come first right you you put the foundation in your big skyscraper like the crew behind me and the plumbing before you start building the floors right so infrastructure comes first then comes then comes the applications yeah so you know again some people call it super cloud like us multi-cloud 2.0 but the the real mega trend that i see steve and i'd love you to bottom line this and bring us home is you know andreessen's all companies are software companies it's like version 2.0 of that and the applications that are going to be built on that top this tie into the digital transformations it was goldman it's jpmc it's walmart it's capital one b of a oracle's acquisition of cerner is going to be really interesting to see these super clouds form within industries bringing their data their tooling and their specific software expertise built on top of that hyperscale infrastructure and infrastructure for companies like yours so bottom line is stephen steve what's the future of cloud how do you see it the future is n plus one so two years ago people had one plus one i had what i had on prem and then what i had in aws they today if you talk to an enterprise they'll have what they call four plus one right which is four public clouds plus what i have on prem it's going to n plus one right and what's going to happen is exactly what you said you're going to have industry clouds you're going to the the multi-cloud aspect of it is going to end it's not going to go from four to one some people think oh it's not going to be four it's going down to one or two bs it's going to end it's going to a lot as they start extending to the edge and they start integrating out to the to the branch offices it's not going to be about that branch offer so that edge iot or edge computing or data centers or campus connecting into the cloud it's going to be the other way around the cloud is going to extend to those areas and you're going to have ai clouds you know whether it's you know ultra beauty who's a customer of ours who's starting to roll out ar and vr out to their retail stores to show you know makeup and this and the other thing these are new applications transformations are always driven by new applications that don't exist this isn't about lift and shift of the existing applications the 10x tam in this market is going to becomes all the new things that's where the explosion is going to happen and you're going to see end level those those branch offices are going to look like clouds and they're going to need to be stitched together and treated like one infrastructure so it's going to go from four plus one to n plus one and that's what you're gonna want as an enterprise i'm gonna want n clouds so we're gonna see an explosion it's not going to be four it's going to be end now at the end underneath all of that will be leveraging and effectively commoditizing the existing csps yeah and but you're going to have an explosion of people commoditizing them and just like the goldmans and the industry clubs are going to do they're going to build their own eye as well right no way no way it's that's what's going to happen it's going to be a 10x on what we saw last decade with sas it's all going to happen around clouds and supercloud steve malini thanks so much for coming back in the cube and helping us sort of formulate this thinking i mean it really started with with with you and myself and john and nick and really trying to think this through and watching this unfold before our eyes so great to have you back thank you yeah it's fun thanks for having me are you welcome but keep it right there for more action from super cloud 22 be right back [Music] you

(upbeat music) >> Hey everyone, welcome back to theCUBE's three day coverage of Snowflake Summit 22. Lisa Martin here with Dave Vellante. We have been here as I said for three days. Dave, we have had an amazing three days. The energy, the momentum, the number of people still here speaks volumes for- >> Yeah, I was just saying, you look back, theCUBE, when it started, early days was a big part of the Hadoop ecosystem. You know Cloudera kind of got it started, the whole big data movement, it was awesome energy, and that whole ecosystem has been, I think, just hoovered into the Snowflake ecosystem. They've taken over as the data company, the data cloud, I mean, that was Cloudera, it could have been Cloudera, and now they didn't, they missed it, it was a variety of factors, but Snowflake has nailed it. And now it's theirs to lose. Benoit talked about that on our previous segment, how he knew that technically Hadoop was too complex, and was going to fail, and they didn't know it was going to do this. They were going to turn their company into what we see here. But the event itself, Lisa, is almost 10,000 people, the right people, people are doing business, we've had a number of people tell us that they're booking deals. That's why people come to face-to-face shows, right? That's the criticism of virtual. It takes too long to close business. Salespeople want to be belly-to-belly. And this is a belly-to belly-show. >> It absolutely is. When you and I were trying to get into the keynote on Tuesday, we finally got in standing room only, multiple overflow rooms, and we're even hearing that, so this is day four of the summit for them, there are still queues to get into breakout sessions. The momentum, but the appetite for this flywheel, and what they're creating, but also they're involving this massively growing ecosystem in its evolution. It's that synergy was really very much heard, and echoed throughout pretty much all of our segments the last couple days. >> Yeah, it was amazing actually. So we like to go, we want to be in the front row in the keynotes, we're taking notes, we always do that. Sometimes we listen remotely, but when you listen remotely, you miss some things. When you're there, you can see the executives, you can feel their energy, you can chit chat to them on the side, be seen, whatever. And it was crazy, we couldn't get in. So we had to do our thing, and sneak our way in, and "Hey, we're media." "Oh yeah, come on in." And then no, they were taking us to a breakout room. We had to sneak in a side door, got like the last two seats, and wow, I'm glad we were in there because it gave us a better sense. When you're in the remote watching rooms you just can't get a sense of the energy. That's why I like to be there, I know you do too. And then to your point about ecosystem. So we've said many times that what Snowflake is developing is what we call supercloud. It's not just a SaaS, it's not just a cloud database, it's a new layer that they're creating. And so what are the attributes of that layer? Well, it hides the underlying complexity of the underlying primitives of the cloud. We've said that ad nauseam, and it adds new value on top. Well, what's that value that they're adding? Well, they're adding value of being able to share data, collaborate, have data that's governed, and secure, globally. And now the other hallmark of a cloud company is ecosystem. And so they're building that ecosystem much more rapidly than we saw at ServiceNow, which is Slootman's previous company. And the key to me is they've launched an application development platform, essentially a super PaaS, so that you can develop applications on top of the data cloud. And we're hearing tons about monetization. Duh, you could actually make money with data. You can package data into data products, and data services, or feed data products and services, and actually sell that in a cloud, in a supercloud. That's exactly what's happening here. So that's critical. I think my one question mark if I had to lay one out, is the other hallmark of a cloud is startup, startups come into that cloud. And I think we're seeing that, maybe not at the pace that AWS did, it's a little different. Snowflake are, they're whale hunters. They're after big companies. But it looks to me like they're relying on the ecosystem to be the startup innovators. That's the important thing about cloud, cloud brings scale. It definitely brings lower cost 'cause you're eliminating all this undifferentiated labor, but it also brings innovation through startups. So unlike AWS, who sold the startups directly, and startups built businesses on AWS, and by paying AWS, it's a little bit indirect, but it's actually happening where startups in the ecosystem are building products on the data cloud, and that ultimately is going to drive value for customers, and money for Snowflake, and ultimately AWS, and Google, and Azure. The other thing I would say is the criticism or concern that the cost of goods sold for cloud are going to be so high that it's going to force people to come back on-prem. I think it's a step in the wrong direction. I think cloud, and the cloud operating model is here to stay. I think it's going to be very difficult to replicate that on-prem. I don't think you can do cloud without cloud, and we'll see what the edge brings. >> Curious what your thoughts are. We were just at Dell technologies world a month or so ago when the big announcement, the Snowflake partnership there, cloud native companies recognizing, ah, there's still a lot of data that lives on-prem. Given that, and everything that we've heard the last couple of days, what are your thoughts around that and their partnerships there? >> So Dell is, I think finally, now maybe they weren't publicly talking like this, but certainly their marketing was defensive. But in the last year or so, Dell has really embraced cloud, not just the cloud operating model, Dell has said, "Look, we can build value on top of all these hyperscalers." And we saw some examples at Dell Tech World of them stepping their toe into supercloud. Project Alpine is an example, and there are others. And then of course the Snowflake deal, where Snowflake and Dell got together, I asked Frank Slootman how that deal came about. And 'cause I said, "Did the customer get you into a headlock?" 'Cause I presume that was the case. Customer said, "You got to do this or we're not going to do business with you." He said, "Well, no, not really. Michael and I had a chat, and that's how it started." Which was my other scenario, and that's exactly what happened I guess. The point being that those worlds are coming together. And so what it means for Dell is as they embrace cloud, as they develop supercloud capabilities, they're going to do a lot of business. Dell for sure knows how to sell, they know how to execute. What I would be doing if I were Dell, is I would be trying to substantially replicate what's happening in the cloud on-prem with on-prem data. So what happens with that Snowflake deal is, it's read-only data, you read the data into the cloud, the compute is in the cloud. And I should've asked Terry this, I mean Benoit. Can there be an architecture on-prem? We've seen at Vertica has one, it's called Vertica Eon where you separate compute from storage. It doesn't have unlimited elasticity, but you can grow, compute, and storage independently, and have a lot more. With Dell doing APEX on demand, it's cloudlike, they could begin to develop a little mini data cloud, or a big data cloud within on-prem that connects to the public cloud. So what Snowflake is missing, a big part of their TAM that they're missing is the on-prem. The Dell and Pure deals are forays into that, but this on-prem is massive, and Dell is the on-prem poster child. So I think again what it means for them is they've got to continue to embrace it, they got to do more in software, more in data management, they got to push on APEX. And I'd say the same thing for HPE. I think they're both well behind this in terms of ecosystems. I mean they're not even close. But they have to start, and they got to start somewhere, and they've got resources to make it happen. >> You said in your breaking analysis that you published just a few days ago before the event that Snowflake plans to create a de facto standard in data platforms. What we heard from our guests on this program, your mainstage session with Frank Slootman. Still think that? >> I do. I think it more than I believed it coming in. And the reason I called it that is because I am a super fan of Zhamak Dehghani and her data mesh. And what her vision is, it's kind of the Immaculate Conception, where she wants everything to be open, open standards, and those don't exist today. And I think she perfectly realizes the practicality of de facto standards are going to get to market, and add value sooner than open standards. Now open standards over time, and I'll come back to that, may occur, but that's clear to me what Snowflake is creating, is the de facto standard for data platforms, the data cloud, the supercloud. And what's most impressive, or I think really important, is they're layering applications now on top of that. The metric to me, and I don't know if we can even count this, but VMware used to use it. For every dollar spent on VMware license, $15 was spent in the ecosystem. It started at 1 to 1.5, 1 to 2, 1 to 10, 1 to 15, I think it went up to 1 to 30 at the max. I don't know how they counted that, but it's countable. Reasonable people can make estimates like that. And I think as the ecosystem grows, what Snowflake's doing is it's in many respects modeling the cloud, what the cloud has. Cloud has ecosystems, we talked about startups, and the cloud also has optionality. And optionality means open source. So what you saw with Apache Iceberg is we're going to extend to open technologies. What you saw with Hybrid tables is we're going to extend a new workloads like transactions. The other thing about Snowflake that's really impressive is you're seeing the vertical focus. Financial services, healthcare, retail, media and entertainment. It's very rare for a company in this tenure, they're only 10 years old, to really start going vertical with their go-to-market, and building expertise around that. I think what's going to happen is the GSIs are going to come in, they love to eat at the trough, the trough here is maybe not big enough for them yet, but it will be. And they're going to start to align with the GSIs, and they're going to do really well within those industries, connecting people, collaborating with data. But I think it's a killer strategy, but they're executing on it. >> Right, and we heard a lot of great customer stories from all of those four verticals that you talked about, and then some, that that direction and that pivot from a customer perspective, from a sales and marketing perspective is all aligned. And that was kind of one of the themes as well that Frank talked about in his keynote is mission alignment, mission alignment with customers, but also with the ecosystem. And I feel that I heard that with every customer conversation, with every partner conversation, and Snowflake conversation that we had over the last I think 36 segments, Dave. >> Yeah, I mean, yeah, it's the power of many versus the resources of one. And even though Snowflake tell you they have $5 billion in cash, and assets on the balance sheet, and that's fine, that's nothing compared to what an ecosystem has. And Amazon's part of that ecosystem. Azure is part of that ecosystem. Google is part of that ecosystem. Those companies have huge resources, and Snowflake it seems has figured out how to tap those resources, and build value on top of it. To me they're doing a better job than a lot of the cloud databases out there. They don't necessarily have a better database, in fact, I could argue that their database is less functional. And I would argue that actually in many cases. Their database is less functional if you just want a database. But if you want a data cloud, and an ecosystem, and develop applications on top of that, and to be able to monetize, that's unique, and that is a moat that they're building that is highly differentiable, and being able to do that relatively easily. I mean, I think they overstate the simplicity with which that is being done. We talked to some customers who said, he didn't say same wine, new bottle. I did ask him that, about Hadoop complexity. And he said, "No, it's not that bad." But you still got to put this stuff together. And I think in the early parts of a market that are immature, people get really excited because it's so much easier than what was previous. So my other question is, okay, what's somebody working on now, that's looking at what Snowflake's doing and saying, I can improve on that. And what's going to be really interesting to see is, can they improve on it in a way, and can they raise enough capital such that they can disrupt, or is Snowflake going to keep staying paranoid, 'cause they got good leaders, and keep executing? And then I think the other wild card is edge. Snowflake doesn't really have an edge strategy right now. I think they will develop one. >> Through the ecosystem? >> And I don't think they're missing the boat, and they'll do it through the ecosystem, exactly. I don't think they're missing the boat, I think they're just like, "Well, we don't know what to do today." It's all distributed data, and it's ephemeral, and nobody's storing the data. You know anything that comes back to the cloud, we get. But new architectures are emerging on the edge that are going to bring new economics. There's new silicon, you see what's happening with Apple, and the M1, the M1 Ultra, and the new systems that they've just developed. What Tesla is doing with custom silicon, and amazing things, and programmability of the arm model. So it's early days, but semiconductors are the mainspring of innovation in this industry. Without chips, you got nothing. And when you get innovations in silicon, it drives innovations in software, because developers go, "Wow, I can do that now?" I can do things in parallel, I can do things faster, I can do things more simply, and programmable at scale. So that's happening. And that's going to bring a new set of economics that the premise is that will eventually bleed into the data center. It will, it always does. And I guess the other thing is every 15 years or so, the world gets disrupted, the tech world. We're about 15, 16 years in now to the cloud. So at this point, everybody's like, "Wow this is insurmountable, this is all we'll ever see. Everything that's ever been invented, this is the model of the future." We know that's not the case. I don't know how it's going to get disrupted, but I think edge is going to be part of that. It could be public policy. Governments could come in and take big tech on, seems like Sharekhan wants to do that. So that's what makes this industry so fun. >> Never a dull moment, Dave. This has been a great three days hosting this show with you. We've uncovered a lot. Your breaking analysis was great to get me prepared for the show. If you haven't seen it, check it out on siliconangle.com. Thanks, Dave, I appreciate all of your insights. >> Thank you, Lisa, It's been a pleasure working with you. >> Always good to work with you. >> Awesome, great job. >> Likewise. Great job to the team. >> Yes, thank you to our awesome production team. They've kept us going for three days. >> Yes, and the team back, Kristin, and Cheryl, and everybody back at the office. >> Exactly, it takes a village. For Dave Vellante, I am Lisa Martin. We are wrappin' up three days of wall-to-wall coverage at Snowflake Summit 22 from Vegas. Thanks for watching guys, we'll see you soon. (upbeat music)

>> We're back at the Big Apple, theCUBE's coverage of MongoDB World 2022. Sahir Azam is here, he's the Chief Product Officer of MongoDB, and Guillermo Rauch who's the CEO of Vercel. Hot off the keynotes from this morning guys, good job. >> Thank you. >> Thank you. >> Thank you for joining us here. Thanks for having us. Guillermo when it comes to modern web development, you know the back-end, the cloud guys got to it kind of sewn up, >> you know- >> Guillermo: Forget about it. >> But all the action's in the front end, and that's where you are. Explain Vercel. >> Yeah so Vercel is the company that pioneers front-end development as serverless infrastructure. So we built Next.js which is the most popular React framework in the world. This is what front-end engineers choose to build innovative UI's, beautiful websites. Companies like Dior and GitHub and TikTok and Twitch, which we mentioned in the keynote, are powering their entire dot-coms or all of their new parts of their dot-coms with Next.js. And Vercel is the serverless platform where you can deploy frameworks like in Next.js and others like Svelte and Vue to create really fast experiences on the web. >> So I hear, so serverless, I hear that's the hot trend. You guys made some announcements today. I mean when you look at the, we have spending data with our friends at ETR right down the street. I mean it's just off the charts, whether it's Amazon, Google, Azure Functions, I mean it's just exploding. >> Sahir: Yeah, it's I think in many ways, it's a natural trend. You know, we talk a lot about, whether it be today's keynote or another industry talks you see around our industry that developers are constantly looking for ways to focus on innovation and the business logic that defines their application and as opposed to managing the plumbing, and management of infrastructure. And we've seen this happen over and over again across every layer of the stack. And so for us, you know MongoDB, we have a bit of, you know sort of a lens of a broad spectrum of the market. We certainly have you know, large enterprises that are modernizing existing kind of core systems, then we have developers all over the world who are building the next big best thing. And that's what led us to partner with Vercel is just the bleeding edge of developers building in a new way, in a much more efficient way. And we wanted to make sure we provide a data platform that fits naturally in the way they want to work. >> So explain to our audience the trade-offs of serverless, and I want to get into sort of how you've resolved that. And then I want to hear from Guillermo, what that means for developers. >> Sahir: Yeah in our case, we don't view it as an either or, there are certain workloads and definitely certain companies that will gravitate towards a more traditional database infrastructure where they're choosing the configuration of their cluster. They want full control over it. And that provides, you know, certain benefits around cost predictability or isolation or perceived benefits at least of those things. And customers will gravitate towards that. Now on the flip side, if you're building a new application or you want the ability to scale seamlessly and not have to worry about any of the plumbing, serverless is clearly the easier model. So over the long term, we certainly expect to see as a mix of things, more and more serverless workloads being built on our platform and just generally in the industry, which is why we leaned in so heavily on investing in Atlas serverless. But the flexibility to not be forced into a particular model, but to get the same database experience across your application and even switch between them is an important characteristic for us as we build going forward. >> And you stressed the cost efficiency, and not having to worry about, you know, starting cold. You've architected around that, and what does that mean for a developer? >> Guillermo: For a developer it means that you kind of get the best of both worlds, right? Like you get the best possible performance. Front-end developers are extremely sensitive to this. That's why us pioneering this concept, serverless front-end, has put us in a very privileged position because we have to deliver that really quick time to first buy, that really quick paint. So any of the old trade-offs of serverless are not accepted by the market. You have to be extremely fast. You have to be instant to deliver that front-end content. So what we talked about today for example, with the Vercel Edge network, we're removing all of the cost of that like first hit. That cold start doesn't really exist. And now we're seeing it all across the board, going into the back-end where Mongo has also gotten rid of it. >> Dave: How do you guys collaborate? What's the focus of integration specifically from, you know, an engineering resource standpoint? >> Yeah the main idea is, idea to global app in seconds, right? You have your idea. We give you the framework. We don't give you infrastructure primitives. We give you all the necessary tools to start your application. In practice this means you host it in a Git repo. You import it onto Vercel. You install the Mongo integration. Now your front-end and your data back-end are connected. And then your application just goes global in seconds. >> So, okay. So you've abstracted away the complexity of those primitives, is that correct? >> Guillermo: Absolutely. >> Do do developers ever say, "That's awesome but I'd like to get to them every now and then." Or do you not allow that? >> Definitely. We expose all the underlying APIs, and the key thing we hear is that, especially with the push for usage-based billing models, observability is of the essence. So at any time you have to be able to query, in real time, every data point that the platform is observing. We give you performance analytics in real time to see how your front-end is performing. We give you statistics about how often you're querying your back-end and so on, and your cache hit ratios. So what I talked about today in the keynote is, it's not just about throwing more compute at the problem, but the ability to use the edge to your advantage to memoize computation and reuse it across different visits. >> When we think of mission critical historically, you know, you think about going to the ATM, right? I mean a financial transaction. But Mongo is positioning for mission critical applications across a variety of industries. Do we need to rethink what mission critical means? >> I think it's all in the eye of the beholder so to speak. If you're a new business starting up, your software and your application is your entire business. So if you have a cold start latency or God forbid something actually goes down, you don't have a business. So it's just as mission critical to that founder of a new business and new technology as it is, you know, an established enterprise that's running sort of a more, you know, day-to-day application that we may all interact with. So we treat all of those scenarios with equal fervor and importance right? And many times, it's a lot of those new experiences that the become the day-to-day experiences for us globally, and are super important. And we power all of those, whether it be an established enterprise all the way to the next big startup. >> I often talk about COVID as the forced march to digital. >> Sahir: Mm-Hmm. >> Which was obviously a little bit rushed, but if you weren't in digital business, you were out of business. And so now you're seeing people step back and say, "All right, let's be more thoughtful about our digital transformation. We've got some time, we've obviously learned some things made some mistakes." It's all about the customer experience though. And that becomes mission critical right? What are you seeing Guillermo, in terms of the patterns in digital transformation now that we're sort of exiting the isolation economy? >> One thing that comes to mind is, we're seeing that it's not always predictable how fast you're going to grow in this digital economy. So we have customers in the ecommerce space, they do a drop and they're piggybacking on serverless to give them that ability to instantly scale. And they couldn't even prepare for some of these events. We see that a lot with the Web3 space and NFT drops, where they're building in such a way that they're not sensitive to this massive fluctuations in traffic. They're taking it for granted. We've put in so much work together behind the scenes to support it. But the digital native creator just, "Oh things are scaling from one second to the next like I'm hitting like 20,000 requests per second, no problem Vercel is handling it." But the amount of infrastructural work that's gone behind the scenes in support has been incredible. >> We see that in gaming all the time, you know it's really hard for a gaming company to necessarily predict where in the globe a game's going to be particularly hot. Games get super popular super fast if they're successful, it's really hard to predict. It's another vertical that's got a similar dynamic. >> So gaming, crypto, so you're saying that you're able to assist your customers in architecting so that the website doesn't crash. >> Guillermo: Absolutely. >> But at the same time, if the the business dynamic changes, they can dial down. >> Yeah. >> Right and in many ways, slow is the new down, right? And if somebody has a slow experience they're going to leave your site just as much as if it's- >> I'm out of here- >> You were down. So you know, it's really maintaining that really fast performance, that amazing customer experience. Because this is all measured, it's scientific. Like anytime there's friction in the process, you're going to lose customers. >> So obviously people are excited about your keynote, but what have they been saying? Any specific comments you can share, or questions that you got that were really interesting or? >> I'm already getting links to the apps that people are deploying. So the whole idea- >> Come on! >> All over the world. Yeah so it's already working I'm excited. >> So they were show they were showing off, "Look what I did" Really? >> Yeah on Twitter. >> That's amazing. >> I think from my standpoint, I got a question earlier, we were with a bunch of financial analysts and investors, and they said they've been talking to a lot of the customers in the halls. And just to see, you know, from the last time we were all in person, the number of our customers that are using multiple capabilities across this idea of a developer data platform, you know, certainly MongoDB's been a popular core database open source for a long time. But the new capabilities around search, analytics, mobile being adopted much more broadly to power these experiences is the most exciting thing from our side. >> So from 2019 to now, you're saying substantial uptick in adoption for these features? >> Yeah. And many of them are new. >> Time series as well, that's pretty new, so yeah. >> Yeah and you know, our philosophy of development at MongoDB is to get capabilities in the hands of customers early. Get that feedback to enrich and drive that product-market fit. And over the last three years especially, we've been transitioning from a single product kind of core, you know, non relational modern database to a data platform, a developer data platform that adds more and more capabilities to power these modern applications. And a lot of those were released during the pandemic. Certainly we talked about them in our virtual conferences and all the zoom meetings we had over the years. But to actually go talk to all these customers, this is the largest conference we've ever put on, and to get a sense of, wow all the amazing things they're doing with them, it's definitely a different feeling when we're all together. >> So that's interesting, when you have such a hot product, product-led growth which is what Mongo has been in, and you add these new features. They're coming from the developers who are saying, "Hey, we need this." >> Yip. >> Okay so you have a pretty high degree of confidence, but how do you know when you have product-market fit? I mean, is it adoption, usage, renewals? What's your metric? >> Yeah I think it's a mix of quantitative measures that you know, around conversion rates, the size of your funnel, the retention rate, NPS which obviously can be measured, but also just qualitative. You know when you're talking to a developer or a technology executive around what their needs are, and then you see how they actually apply it to solve a problem, it's that balance between the qualitative and the quantitative measurement of things. And you can just sort of, frankly you can feel it. You can see it in the numbers sure, but you can kind of feel that excitement, you can see that adoption and what it empowers people to do. And so to me, as a product leader, it's always a blend of those things. If you get too obsessed with purely the metrics, you can always over optimize something for the wrong reason. So you have to bring in that qualitative feedback to balance yourself out. >> Right. >> Guillermo, what's next? What do you not have that you want from Sahir and Mongo? >> So the natural next step for serverless computing is, is the Edge. So we have to auto-scale, we have to tolerate fares. We have to be avail. We have to be easy, but we have to be global. And right now we've been doing this by using a lot of techniques like caching and replication and things like this. But the future's about personalizing even more to each visitor depending on where they are. So if I'm in New York, I want to get the latest offers for New York on demand, just for me, and using AI to continue to personalize that experience. So giving the developer these tools in a way where it feels natural to build an application like this. It doesn't feel like, "Oh I'm going to do this year 10 if I make it, I'm going to do it since the very beginning." >> Dave: Okay interesting. So that says to me that I'm not going to make a round trip to the cloud necessarily for that experience. So I'm going to have some kind, Apple today, at the Worldwide Developer Conference announced the M2, right. I've been looking at the M1 Ultra, and I'm going wow look at that! And so- >> Sahir: You were talking about that new one backstage. >> I mean it's this amazing pace of Silicon development and they're focusing on the NPU and you look at what Tesla's doing. I mean it's just incredible. So you're going to have some new hardware architecture that emerges. Most of the AI that's done today is modeling in the cloud. You're going to have a real time inferencing at the Edge. So that's not going to do the round trip. There's going to be a data store there, I think it has to be. You're going to persist some of the data, maybe not all of it. So it's a whole new architecture- >> Sahir: Absolutely. >> That's developing. That sounds very disruptive. >> Sahir: Yeah. >> How do you think about that, and how does Mongo play there? Guillermo first. >> What I spent a lot of time thinking about is obviously the developer experience, giving the programmer a programming model that is natural, intuitive, and produces its great results. So if they have to think about data that's local because of regulatory reasons for example, how can we let the framework guide them to success? I'm just writing an application I deployed to the cloud and then everything else is figured out. >> Yeah or speed of light is another challenge. (Sahir and Guillermo laugh) >> How can we overcome the speed of light is our next task for sure. >> Well you're working on that aren't you? You've got the best engineers on that one. (Sahir and Guillermo laugh) >> We can solve a lot of problems, I'm not sure of that one. >> So Mongo plays in that scenario or? >> Yeah so I think, absolutely you know, we've been focused heavily on becoming the globally distributed cloud data layer. The back-end data layer that allows you to persist data to align with performance and move data where it needs to be globally or deal with data sovereignty, data nationalism that's starting to rise, but absolutely there is more data being pushed out to the Edge, to your point around processing or inference happening at the Edge. And there's going to be a globally distributed front-end layer as well, whether data and processing takes apart. And so we're focused on one, making sure the data connectivity and the layer is all connected into one unified architecture. We do that in combination with technologies that we have that do with mobility or edge distribution and synchronization of data with realm. And we do it with partnerships. We have edge partnerships with AWS and Verizon. We have partnerships with a lot of CVM players who are building out that Edge platform and making sure that MongoDB is either connected to it or just driving that synchronization back and forth. >> I call that unified experience super cloud, Robbie Belson from Verizon the cloud continuum, but that consistent experience for developers whether you're on Prim, whether you're in you know, Azure, Google, AWS, and ultimately the Edge. That's the big- >> That's where it's going. >> White space right now I'm hearing, Guillermo, right? >> I think it'll define the next generation of how software is built. And we're seeing this almost like a coalition course between some of the ideas that the Web3 developers are excited about, which is like decentralization almost to the extreme. But the Web2 also needs more decentralization, because we're seeing it with like, the data needs to be local to me, I need more privacy. I was looking at the latest encryption features in Mongo, like I think both Web2 need to incorporate more of the ideas of Web3 and vice versa to create the best possible consumer experience. Privacy matters more than ever before. Latency for conversion matters more than ever before. And regulations are changing. >> Sahir: Yeah. >> And you talked about Web3 earlier, talked about new protocols, a new distributed you know, decentralized system emerging, new hardware architectures. I really believe we really think that new economics are going to bleed back into the data center, and yeah every 15 years or so this industry gets disrupted. >> Sahir: Yeah. >> Guillermo: Absolutely. >> You know you ain't see nothing yet guys. >> We all talked about hardware becoming commoditized 10, 15 years ago- >> Yeah of course. >> We get the virtualization, and it's like nope not at all. It's actually a lot of invention happening. >> The lower the price the more the consumption. So guys thanks so much. Great conversation. >> Thank you. >> Really appreciate your time. >> Really appreciate it I enjoyed the conversation. >> All right and thanks for watching. Keep it right there. We'll be back with our next segment right after this short break. Dave Vellante for theCUBE's coverage of MongoDB World 2022. >> Man Offscreen: Clear. (clapping) >> All right wow. Don't get up. >> Sahir: Okay. >> Is that a Moonwatch? >> Sahir: It is a Speedmaster but it's that the-

>> Man Offscreen: Standby. Dave is coming you in 5, 4, 3, 2. >> We're back at the Big Apple, theCUBE's coverage of MongoDB World 2022. Sahir Azam is here, he's the Chief Product Officer of MongoDB, and Guillermo Rauch who's the CEO of Vercel. Hot off the keynotes from this morning guys, good job. >> Thank you. >> Thank you. >> Thank you for joining us here. Thanks for having us. Guillermo when it comes to modern web development, you know the back-end, the cloud guys got to it kind of sewn up, >> you know- >> Guillermo: Forget about it. >> But all the action's in the front end, and that's where you are. Explain Vercel. >> Yeah so Vercel is the company that pioneers front-end development as serverless infrastructure. So we built Next.js which is the most popular React framework in the world. This is what front-end engineers choose to build innovative UI's, beautiful websites. Companies like Dior and GitHub and TikTok and Twitch, which we mentioned in the keynote, are powering their entire dot-coms or all of their new parts of their dot-coms with Next.js. And Vercel is the serverless platform where you can deploy frameworks like in Next.js and others like Svelte and Vue to create really fast experiences on the web. >> So I hear, so serverless, I hear that's the hot trend. You guys made some announcements today. I mean when you look at the, we have spending data with our friends at ETR right down the street. I mean it's just off the charts, whether it's Amazon, Google, Azure Functions, I mean it's just exploding. >> Sahir: Yeah, it's I think in many ways, it's a natural trend. You know, we talk a lot about, whether it be today's keynote or another industry talks you see around our industry that developers are constantly looking for ways to focus on innovation and the business logic that defines their application and as opposed to managing the plumbing, and management of infrastructure. And we've seen this happen over and over again across every layer of the stack. And so for us, you know MongoDB, we have a bit of, you know sort of a lens of a broad spectrum of the market. We certainly have you know, large enterprises that are modernizing existing kind of core systems, then we have developers all over the world who are building the next big best thing. And that's what led us to partner with Vercel is just the bleeding edge of developers building in a new way, in a much more efficient way. And we wanted to make sure we provide a data platform that fits naturally in the way they want to work. >> So explain to our audience the trade-offs of serverless, and I want to get into sort of how you've resolved that. And then I want to hear from Guillermo, what that means for developers. >> Sahir: Yeah in our case, we don't view it as an either or, there are certain workloads and definitely certain companies that will gravitate towards a more traditional database infrastructure where they're choosing the configuration of their cluster. They want full control over it. And that provides, you know, certain benefits around cost predictability or isolation or perceived benefits at least of those things. And customers will gravitate towards that. Now on the flip side, if you're building a new application or you want the ability to scale seamlessly and not have to worry about any of the plumbing, serverless is clearly the easier model. So over the long term, we certainly expect to see as a mix of things, more and more serverless workloads being built on our platform and just generally in the industry, which is why we leaned in so heavily on investing in Atlas serverless. But the flexibility to not be forced into a particular model, but to get the same database experience across your application and even switch between them is an important characteristic for us as we build going forward. >> And you stressed the cost efficiency, and not having to worry about, you know, starting cold. You've architected around that, and what does that mean for a developer? >> Guillermo: For a developer it means that you kind of get the best of both worlds, right? Like you get the best possible performance. Front-end developers are extremely sensitive to this. That's why us pioneering this concept, serverless front-end, has put us in a very privileged position because we have to deliver that really quick time to first buy, that really quick paint. So any of the old trade-offs of serverless are not accepted by the market. You have to be extremely fast. You have to be instant to deliver that front-end content. So what we talked about today for example, with the Vercel Edge network, we're removing all of the cost of that like first hit. That cold start doesn't really exist. And now we're seeing it all across the board, going into the back-end where Mongo has also gotten rid of it. >> Dave: How do you guys collaborate? What's the focus of integration specifically from, you know, an engineering resource standpoint? >> Yeah the main idea is, idea to global app in seconds, right? You have your idea. We give you the framework. We don't give you infrastructure primitives. We give you all the necessary tools to start your application. In practice this means you host it in a Git repo. You import it onto Vercel. You install the Mongo integration. Now your front-end and your data back-end are connected. And then your application just goes global in seconds. >> So, okay. So you've abstracted away the complexity of those primitives, is that correct? >> Guillermo: Absolutely. >> Do do developers ever say, "That's awesome but I'd like to get to them every now and then." Or do you not allow that? >> Definitely. We expose all the underlying APIs, and the key thing we hear is that, especially with the push for usage-based billing models, observability is of the essence. So at any time you have to be able to query, in real time, every data point that the platform is observing. We give you performance analytics in real time to see how your front-end is performing. We give you statistics about how often you're querying your back-end and so on, and your cache hit ratios. So what I talked about today in the keynote is, it's not just about throwing more compute at the problem, but the ability to use the edge to your advantage to memoize computation and reuse it across different visits. >> When we think of mission critical historically, you know, you think about going to the ATM, right? I mean a financial transaction. But Mongo is positioning for mission critical applications across a variety of industries. Do we need to rethink what mission critical means? >> I think it's all in the eye of the beholder so to speak. If you're a new business starting up, your software and your application is your entire business. So if you have a cold start latency or God forbid something actually goes down, you don't have a business. So it's just as mission critical to that founder of a new business and new technology as it is, you know, an established enterprise that's running sort of a more, you know, day-to-day application that we may all interact with. So we treat all of those scenarios with equal fervor and importance right? And many times, it's a lot of those new experiences that the become the day-to-day experiences for us globally, and are super important. And we power all of those, whether it be an established enterprise all the way to the next big startup. >> I often talk about COVID as the forced march to digital. >> Sahir: Mm-Hmm. >> Which was obviously a little bit rushed, but if you weren't in digital business, you were out of business. And so now you're seeing people step back and say, "All right, let's be more thoughtful about our digital transformation. We've got some time, we've obviously learned some things made some mistakes." It's all about the customer experience though. And that becomes mission critical right? What are you seeing Guillermo, in terms of the patterns in digital transformation now that we're sort of exiting the isolation economy? >> One thing that comes to mind is, we're seeing that it's not always predictable how fast you're going to grow in this digital economy. So we have customers in the ecommerce space, they do a drop and they're piggybacking on serverless to give them that ability to instantly scale. And they couldn't even prepare for some of these events. We see that a lot with the Web3 space and NFT drops, where they're building in such a way that they're not sensitive to this massive fluctuations in traffic. They're taking it for granted. We've put in so much work together behind the scenes to support it. But the digital native creator just, "Oh things are scaling from one second to the next like I'm hitting like 20,000 requests per second, no problem Vercel is handling it." But the amount of infrastructural work that's gone behind the scenes in support has been incredible. >> We see that in gaming all the time, you know it's really hard for a gaming company to necessarily predict where in the globe a game's going to be particularly hot. Games get super popular super fast if they're successful, it's really hard to predict. It's another vertical that's got a similar dynamic. >> So gaming, crypto, so you're saying that you're able to assist your customers in architecting so that the website doesn't crash. >> Guillermo: Absolutely. >> But at the same time, if the the business dynamic changes, they can dial down. >> Yeah. >> Right and in many ways, slow is the new down, right? And if somebody has a slow experience they're going to leave your site just as much as if it's- >> I'm out of here- >> You were down. So you know, it's really maintaining that really fast performance, that amazing customer experience. Because this is all measured, it's scientific. Like anytime there's friction in the process, you're going to lose customers. >> So obviously people are excited about your keynote, but what have they been saying? Any specific comments you can share, or questions that you got that were really interesting or? >> I'm already getting links to the apps that people are deploying. So the whole idea- >> Come on! >> All over the world. Yeah so it's already working I'm excited. >> So they were show they were showing off, "Look what I did" Really? >> Yeah on Twitter. >> That's amazing. >> I think from my standpoint, I got a question earlier, we were with a bunch of financial analysts and investors, and they said they've been talking to a lot of the customers in the halls. And just to see, you know, from the last time we were all in person, the number of our customers that are using multiple capabilities across this idea of a developer data platform, you know, certainly MongoDB's been a popular core database open source for a long time. But the new capabilities around search, analytics, mobile being adopted much more broadly to power these experiences is the most exciting thing from our side. >> So from 2019 to now, you're saying substantial uptick in adoption for these features? >> Yeah. And many of them are new. >> Time series as well, that's pretty new, so yeah. >> Yeah and you know, our philosophy of development at MongoDB is to get capabilities in the hands of customers early. Get that feedback to enrich and drive that product-market fit. And over the last three years especially, we've been transitioning from a single product kind of core, you know, non relational modern database to a data platform, a developer data platform that adds more and more capabilities to power these modern applications. And a lot of those were released during the pandemic. Certainly we talked about them in our virtual conferences and all the zoom meetings we had over the years. But to actually go talk to all these customers, this is the largest conference we've ever put on, and to get a sense of, wow all the amazing things they're doing with them, it's definitely a different feeling when we're all together. >> So that's interesting, when you have such a hot product, product-led growth which is what Mongo has been in, and you add these new features. They're coming from the developers who are saying, "Hey, we need this." >> Yip. >> Okay so you have a pretty high degree of confidence, but how do you know when you have product-market fit? I mean, is it adoption, usage, renewals? What's your metric? >> Yeah I think it's a mix of quantitative measures that you know, around conversion rates, the size of your funnel, the retention rate, NPS which obviously can be measured, but also just qualitative. You know when you're talking to a developer or a technology executive around what their needs are, and then you see how they actually apply it to solve a problem, it's that balance between the qualitative and the quantitative measurement of things. And you can just sort of, frankly you can feel it. You can see it in the numbers sure, but you can kind of feel that excitement, you can see that adoption and what it empowers people to do. And so to me, as a product leader, it's always a blend of those things. If you get too obsessed with purely the metrics, you can always over optimize something for the wrong reason. So you have to bring in that qualitative feedback to balance yourself out. >> Right. >> Guillermo, what's next? What do you not have that you want from Sahir and Mongo? >> So the natural next step for serverless computing is, is the Edge. So we have to auto-scale, we have to tolerate fares. We have to be avail. We have to be easy, but we have to be global. And right now we've been doing this by using a lot of techniques like caching and replication and things like this. But the future's about personalizing even more to each visitor depending on where they are. So if I'm in New York, I want to get the latest offers for New York on demand, just for me, and using AI to continue to personalize that experience. So giving the developer these tools in a way where it feels natural to build an application like this. It doesn't feel like, "Oh I'm going to do this year 10 if I make it, I'm going to do it since the very beginning." >> Dave: Okay interesting. So that says to me that I'm not going to make a round trip to the cloud necessarily for that experience. So I'm going to have some kind, Apple today, at the Worldwide Developer Conference announced the M2, right. I've been looking at the M1 Ultra, and I'm going wow look at that! And so- >> Sahir: You were talking about that new one backstage. >> I mean it's this amazing pace of Silicon development and they're focusing on the NPU and you look at what Tesla's doing. I mean it's just incredible. So you're going to have some new hardware architecture that emerges. Most of the AI that's done today is modeling in the cloud. You're going to have a real time inferencing at the Edge. So that's not going to do the round trip. There's going to be a data store there, I think it has to be. You're going to persist some of the data, maybe not all of it. So it's a whole new architecture- >> Sahir: Absolutely. >> That's developing. That sounds very disruptive. >> Sahir: Yeah. >> How do you think about that, and how does Mongo play there? Guillermo first. >> What I spent a lot of time thinking about is obviously the developer experience, giving the programmer a programming model that is natural, intuitive, and produces its great results. So if they have to think about data that's local because of regulatory reasons for example, how can we let the framework guide them to success? I'm just writing an application I deployed to the cloud and then everything else is figured out. >> Yeah or speed of light is another challenge. (Sahir and Guillermo laugh) >> How can we overcome the speed of light is our next task for sure. >> Well you're working on that aren't you? You've got the best engineers on that one. (Sahir and Guillermo laugh) >> We can solve a lot of problems, I'm not sure of that one. >> So Mongo plays in that scenario or? >> Yeah so I think, absolutely you know, we've been focused heavily on becoming the globally distributed cloud data layer. The back-end data layer that allows you to persist data to align with performance and move data where it needs to be globally or deal with data sovereignty, data nationalism that's starting to rise, but absolutely there is more data being pushed out to the Edge, to your point around processing or inference happening at the Edge. And there's going to be a globally distributed front-end layer as well, whether data and processing takes apart. And so we're focused on one, making sure the data connectivity and the layer is all connected into one unified architecture. We do that in combination with technologies that we have that do with mobility or edge distribution and synchronization of data with realm. And we do it with partnerships. We have edge partnerships with AWS and Verizon. We have partnerships with a lot of CVM players who are building out that Edge platform and making sure that MongoDB is either connected to it or just driving that synchronization back and forth. >> I call that unified experience super cloud, Robbie Belson from Verizon the cloud continuum, but that consistent experience for developers whether you're on Prim, whether you're in you know, Azure, Google, AWS, and ultimately the Edge. That's the big- >> That's where it's going. >> White space right now I'm hearing, Guillermo, right? >> I think it'll define the next generation of how software is built. And we're seeing this almost like a coalition course between some of the ideas that the Web3 developers are excited about, which is like decentralization almost to the extreme. But the Web2 also needs more decentralization, because we're seeing it with like, the data needs to be local to me, I need more privacy. I was looking at the latest encryption features in Mongo, like I think both Web2 need to incorporate more of the ideas of Web3 and vice versa to create the best possible consumer experience. Privacy matters more than ever before. Latency for conversion matters more than ever before. And regulations are changing. >> Sahir: Yeah. >> And you talked about Web3 earlier, talked about new protocols, a new distributed you know, decentralized system emerging, new hardware architectures. I really believe we really think that new economics are going to bleed back into the data center, and yeah every 15 years or so this industry gets disrupted. >> Sahir: Yeah. >> Guillermo: Absolutely. >> You know you ain't see nothing yet guys. >> We all talked about hardware becoming commoditized 10, 15 years ago- >> Yeah of course. >> We get the virtualization, and it's like nope not at all. It's actually a lot of invention happening. >> The lower the price the more the consumption. So guys thanks so much. Great conversation. >> Thank you. >> Really appreciate your time. >> Really appreciate it I enjoyed the conversation. >> All right and thanks for watching. Keep it right there. We'll be back with our next segment right after this short break. Dave Vellante for theCUBE's coverage of MongoDB World 2022. >> Man Offscreen: Clear. (clapping) >> All right wow. Don't get up. >> Sahir: Okay. >> Is that a Moonwatch? >> Sahir: It is a Speedmaster but it's that the-

[Music] thecube's coverage of red hat summit 2022 thecube has been covering red hat summit for a number of years of course the last two years were virtual coverage now the red hat summit is one of the industry's most premier events and and typically red hat summits are many thousands of people i think the last one i went to was eight or nine thousand people very heavy developer conference this year red hat has taken a different approach it's a hybrid event it's kind of a vip event at the westin in boston with a lot more executives here than we would normally expect versus developers but a huge virtual audience my name is dave vellante i'm here with my co-host paul gillin paul this is a location that you and i have broadcast from many times and um of course 2019 the summer of 2019 ibm acquired red hat and um we of course we did red hat summit that year but now we're seeing a completely new red hat and a new ibm and you wouldn't know ibm owned red hat for what they've been talking about at this conference we just came out of the keynote where uh in the in the hour-long keynote ibm was not mentioned once and only appeared the logo only appeared once on the screen in fact so this is uh very much red hat being red hat not being a subsidiary at ibm and perhaps that's justified given that ibm's track record with acquisitions is that they gradually envelop the acquired company and and it becomes part of the ibm board yeah they blue wash the whole thing right it's ironic because ibm think is going on right across the street arvin krishna is here but no presence here and i think that's by design i mean it reminds me of when you know emc owned vmware you know the vmware team didn't want to publicize that they had an ecosystem of partners that they wanted to cater to and they wanted to treat everybody equally even though perhaps behind the scenes they were forced to do certain things that they might not have necessarily wanted to because they were owned by another company and i think that you know certainly ibm's done a good job of leaving the brand separate but when they talk about the con the conference calls ibm's earnings calls you certainly get a heavy dose of red hat when red hat was acquired by ibm it was just north of three billion dollars in revenue obviously ibm paid 34 billion dollars for the company actually by today's valuations probably a bargain you know despite the market sell-off in the last several months uh but now we've heard public statements from arvind kushner that that red hat is a 5 billion plus revenue company it's a little unclear what's in there of course when you listen to ibm earnings you know consulting is their big business red hat's growing at 21 but when i remember paul when red hat was acquired stu miniman and i did a session and i said this is not about cloud this is about consulting and modernizing applications and sure there's some cloud in there with openshift but from a financial standpoint ibm was able to take red hat and jam it right into its application modernization initiatives so it's hard to tell how much of that 5 billion is actually you know legacy red hat but i guess it doesn't matter anymore it's working ibm mathematics is notoriously opaque they if the business isn't going well it'll tend to be absorbed into another number in the in the earnings report that that does show some growth so we've heard uh certainly ibm talks a lot about red hat on its earnings calls it's very clear that red hat is the growth engine within ibm i'd say it's a bit of the tail wagging the dog right now where red hat really is dictating where ibm goes with its hypercloud strategy which is the foundation not only of its technology portfolio but of its consulting business and so red hat is really in the driver's seat of of hybrid cloud and that's the future for ibm and you see that very much at this conference where uh red hat is putting out its uh series of announcements today about improvements to his hybrid cloud the new release of route 9 red hat enterprise linux 9 improvements to its hybrid cloud portfolio it very much is going its own way with that and i sense that ibm is going to go along with wherever red hat chooses to go yeah i think you're absolutely right if by the way if you go to siliconangle.com paul just published a piece on red hat reds hats their roll out of their parade which of course is as you pointed out led by enterprise linux but to your point about hybrid cloud it is the linchpin of of certainly ibm strategy but many companies hybrid cloud strategies if you think about it openshift in particular it's it's the modern application development environment for kubernetes you can get kubernetes you can buy eks you can get that for free in a lot of places but you have to do dozens and dozens of things and acquire dozens of services to do what openshift does to get the reliability the recoverability the security and that's really red hat's play and they're the the thing about red hat combining with linux their linux heritage they're doing that everywhere it's going to open shift everywhere red hat everywhere whether it's on-prem in aws azure google out to the edge you heard paul cormier today saying he expects that in the next several years hardware is going to become one of the most important you know factors i agree i think we're going to enter a hardware renaissance you've seen the work that we've done on arm i think 2017 was when red hat and arm announced kind of their initial collaboration could have even been before that today we're hearing a lot about intel and nvidia and so affinity with all of these alternative processes i think they did throw in today in the keynote power and so i think i heard that that was the other ibm branding they sort of tucked that in there but the point is red hat runs everywhere so it's fundamental to building out hybrid cloud and that is fundamental to a lot of company strategies and red hat has been all over kubernetes with openshift it's i mean it's a drum beat here uh the openshift strategy is what really makes hybrid cloud possible because kubernetes is what makes it possible to shift workloads seamlessly from platform to platform you make an interesting point about hardware we have seen kind of a renaissance in hardware these last couple of years as these specific chipsets and uh and even full-scale processors have come to market we're seeing several in the ai area right now where startups are developing full-blown chipsets and and systems uh just for ai processing and nvidia of course that's that's really kind of their stock and trade these days so uh a a company that can run across all of those different platforms a platform like like rel which can run all across those different platforms is going to have a leg up on on anybody else and the implications for application development are considerable when you when you think about we talk about a lot about these alternative processes when flash replaced the spinning disk that had a huge impact on how applications are developed developers now didn't have to wait for that that disc to spin even though it's spinning very fast it's mechanical compared to electrons forget it and and the second big piece here is how memory is actually utilized the x86 you know traditional x86 you know memory everything goes through that core processor intel for years grabbed more and more function and you're seeing now that function become dispersed in fact a lot of people think we're moving from a processor-centric world to a connect centric world meaning connecting all these piece parts alternative processors memory controllers you know storage controllers io network interface cards smartnics and things like that where the communication across those resources is now where a lot of the innovation is going you see you're seeing a lot of that and now of course applications can take advantage of that especially now at the edge which is just a whole new frontier the edge certainly is part of that equation when you look at machine learning at training machine learning models the cpu actually does relatively little work most of it is happening in gpus in these parallel processes that are going on and the cpu is kind of acting as a traffic cop and you see that in the edge as well it's the same model at the edge where more of the intelligence is going to be out in discrete devices spread across the network and the cpu is going to be less of a uh you know less of a engine of intelligence at the same time though we've got cpus with we've got 100 core cpus are on the horizon and there are even 200 and 300 core cpus that we may see in the next uh in the next couple of years so cpus aren't standing still they are evolving to become really kind of super traffic cops for all of these other processors out in the network and on the edge so it's a very exciting time to be in hardware because so much innovation is happening really at the microprocessor level well we saw this you and i lived through the pc era and we saw a whole raft of applications come about as a result of the microprocessor the shift of the microprocessor-based economy we're going to see so we are seeing something similar with mobile and the edge you know just think about some of the numbers if you think about the traditional moore's law doubling a number of transistors every let's call it two years 18 to 24 months pat gelsinger at intel promises that intel is on that pace still but if you look at the apple m1 ultra they increased the transistor density 6x in the last 15 months okay so where is this another data point is the historical moore's law curve is 40 that's moderating to somewhere down you know down in the low 30s if you look at the apple a series i mean that thing is on average increasing performance at 110 a year when you add up into the combinatorial factors of the cpu the neural processing unit the gpu all the accelerators so we are seeing a new era the thing i i i wanted to bring up paul is you mentioned ai much of the ai work that's done today is modeling that's done in the cloud and when we talk about edge we think that the future of ai is ai inferencing in real time at the edge so you may not even be persisting that data but you're going to create a lot of data you're going to be operating on that data in streams and it's going to require a whole new new architectural thinking of hardware very low cost very low power very high performance to drive all that intelligence at the edge and a lot of that data is going to stay at the edge and and that's we're going to talk about some of that today with some of the ev innovations and the vehicle innovations and the intelligence in these vehicles yeah and in talking in its edge strategy which it outlined today and the announcements that are made today red hat very much uh playing to the importance of being able to run red hat enterprise linux at the edge the idea is you do these big machine learning models centrally and then you you take the you take what results from that and you move it out to smaller processors it's the only way we can cope with it with the explosion of data that will be uh that these sensors and other devices will be generating so some of the themes we're hearing in the uh announcements today that you wrote about paul obviously rel9 is huge uh red hat enterprise linux version nine uh new capabilities a lot of edge a lot of security uh new cross portfolio capabilities for the edge security in the software supply chain that's a big conversation especially post solar winds managed ansible when you think about red hat you really i think anyway about three things rel which is such as linux it powers the internet powers everything uh you think of openshift which is application development you think about ansible which is automation so itops so that's one of the announcements ansible on azure and then a lot of hybrid cloud talk and you're gonna hear a lot of talk this week about red hat's cloud services portfolio packaging red hat as services as managed services that's you know a much more popular delivery mechanism with clients because they're trying to make it easy and this is complicated stuff and it gets more complicated the more features they add and the more the more components of the red hat portfolio are are available it's it's gonna be complex to build these hybrid clouds so like many of these so thecube started doing physical events last summer by the way and so this is this is new to a lot of people uh they're here for the first time people are really excited we've definitely noticed a trend people are excited to be back together paul cormier talked about that he talked about the new normal you can define the new normal any way you want so paul cormier gave the uh the the intro keynote bidani interviewed amex stephanie cheris interviewed accenture both those firms are coming out stephanie's coming on with the in accenture as well matt hicks talked about product innovation i loved his reference to ada lovelace that was very cool he talked about uh serena uh ramyanajan a famous mathematician who nobody knew about when he was just a kid these were ignored individuals in the 1800s for years and years and years in the case of ada lovelace for a century even he asked the question what if we had discovered them earlier and acted on them and been able to iterate on them earlier and his point tied that to open source very brilliantly i thought and um keynotes which i appreciate are much shorter much shorter intimate they did a keynote in the round this time uh which i haven't seen before there's maybe a thousand people in there so a much smaller group much more intimate setting not a lot of back and forth but uh but there is there is a feeling of a more personal feel to this event than i've seen it past red hat summits yeah and i think that's a trend that we're going to see more of where the live audience is kind of the on the ground it's going to the vip audience but still catering to the virtual audience you don't want to lose them so that's why the keynotes are a lot tighter okay paul thank you for setting up red hat summit 2022 you're watching the cube's coverage we'll be right back wall-to-wall coverage for two days right after this short break [Music] you

(upbeat music) >> The ascendancy of cloud and SAS has shown new light on how organizations think about, pay for, and value hardware. Once sought after skills for practitioners with expertise in hardware troubleshooting, configuring ports, tuning storage arrays, and maximizing server utilization has been superseded by demand for cloud architects, DevOps pros, developers with expertise in microservices, container, application development, and like. Even a company like Dell, the largest hardware company in enterprise tech touts that it has more software engineers than those working in hardware. Begs the question, is hardware going the way of Coball? Well, not likely. Software has to run on something, but the labor needed to deploy, and troubleshoot, and manage hardware infrastructure is shifting. At the same time, we've seen the value flow also shifting in hardware. Once a world dominated by X86 processors value is flowing to alternatives like Nvidia and arm based designs. Moreover, other componentry like NICs, accelerators, and storage controllers are becoming more advanced, integrated, and increasingly important. The question is, does it matter? And if so, why does it matter and to whom? What does it mean to customers, workloads, OEMs, and the broader society? Hello and welcome to this week's Wikibon theCUBE Insights powered by ETR. In this breaking analysis, we've organized a special power panel of industry analysts and experts to address the question, does hardware still matter? Allow me to introduce the panel. Bob O'Donnell is president and chief analyst at TECHnalysis Research. Zeus Kerravala is the founder and principal analyst at ZK Research. David Nicholson is a CTO and tech expert. Keith Townson is CEO and founder of CTO Advisor. And Marc Staimer is the chief dragon slayer at Dragon Slayer Consulting and oftentimes a Wikibon contributor. Guys, welcome to theCUBE. Thanks so much for spending some time here. >> Good to be here. >> Thanks. >> Thanks for having us. >> Okay before we get into it, I just want to bring up some data from ETR. This is a survey that ETR does every quarter. It's a survey of about 1200 to 1500 CIOs and IT buyers and I'm showing a subset of the taxonomy here. This XY axis and the vertical axis is something called net score. That's a measure of spending momentum. It's essentially the percentage of customers that are spending more on a particular area than those spending less. You subtract the lesses from the mores and you get a net score. Anything the horizontal axis is pervasion in the data set. Sometimes they call it market share. It's not like IDC market share. It's just the percentage of activity in the data set as a percentage of the total. That red 40% line, anything over that is considered highly elevated. And for the past, I don't know, eight to 12 quarters, the big four have been AI and machine learning, containers, RPA and cloud and cloud of course is very impressive because not only is it elevated in the vertical access, but you know it's very highly pervasive on the horizontal. So what I've done is highlighted in red that historical hardware sector. The server, the storage, the networking, and even PCs despite the work from home are depressed in relative terms. And of course, data center collocation services. Okay so you're seeing obviously hardware is not... People don't have the spending momentum today that they used to. They've got other priorities, et cetera, but I want to start and go kind of around the horn with each of you, what is the number one trend that each of you sees in hardware and why does it matter? Bob O'Donnell, can you please start us off? >> Sure Dave, so look, I mean, hardware is incredibly important and one comment first I'll make on that slide is let's not forget that hardware, even though it may not be growing, the amount of money spent on hardware continues to be very, very high. It's just a little bit more stable. It's not as subject to big jumps as we see certainly in other software areas. But look, the important thing that's happening in hardware is the diversification of the types of chip architectures we're seeing and how and where they're being deployed, right? You refer to this in your opening. We've moved from a world of x86 CPUs from Intel and AMD to things like obviously GPUs, DPUs. We've got VPU for, you know, computer vision processing. We've got AI-dedicated accelerators, we've got all kinds of other network acceleration tools and AI-powered tools. There's an incredible diversification of these chip architectures and that's been happening for a while but now we're seeing them more widely deployed and it's being done that way because workloads are evolving. The kinds of workloads that we're seeing in some of these software areas require different types of compute engines than traditionally we've had. The other thing is (coughs), excuse me, the power requirements based on where geographically that compute happens is also evolving. This whole notion of the edge, which I'm sure we'll get into a little bit more detail later is driven by the fact that where the compute actually sits closer to in theory the edge and where edge devices are, depending on your definition, changes the power requirements. It changes the kind of connectivity that connects the applications to those edge devices and those applications. So all of those things are being impacted by this growing diversity in chip architectures. And that's a very long-term trend that I think we're going to continue to see play out through this decade and well into the 2030s as well. >> Excellent, great, great points. Thank you, Bob. Zeus up next, please. >> Yeah, and I think the other thing when you look at this chart to remember too is, you know, through the pandemic and the work from home period a lot of companies did put their office modernization projects on hold and you heard that echoed, you know, from really all the network manufacturers anyways. They always had projects underway to upgrade networks. They put 'em on hold. Now that people are starting to come back to the office, they're looking at that now. So we might see some change there, but Bob's right. The size of those market are quite a bit different. I think the other big trend here is the hardware companies, at least in the areas that I look at networking are understanding now that it's a combination of hardware and software and silicon that works together that creates that optimum type of performance and experience, right? So some things are best done in silicon. Some like data forwarding and things like that. Historically when you look at the way network devices were built, you did everything in hardware. You configured in hardware, they did all the data for you, and did all the management. And that's been decoupled now. So more and more of the control element has been placed in software. A lot of the high-performance things, encryption, and as I mentioned, data forwarding, packet analysis, stuff like that is still done in hardware, but not everything is done in hardware. And so it's a combination of the two. I think, for the people that work with the equipment as well, there's been more shift to understanding how to work with software. And this is a mistake I think the industry made for a while is we had everybody convinced they had to become a programmer. It's really more a software power user. Can you pull things out of software? Can you through API calls and things like that. But I think the big frame here is, David, it's a combination of hardware, software working together that really make a difference. And you know how much you invest in hardware versus software kind of depends on the performance requirements you have. And I'll talk about that later but that's really the big shift that's happened here. It's the vendors that figured out how to optimize performance by leveraging the best of all of those. >> Excellent. You guys both brought up some really good themes that we can tap into Dave Nicholson, please. >> Yeah, so just kind of picking up where Bob started off. Not only are we seeing the rise of a variety of CPU designs, but I think increasingly the connectivity that's involved from a hardware perspective, from a kind of a server or service design perspective has become increasingly important. I think we'll get a chance to look at this in more depth a little bit later but when you look at what happens on the motherboard, you know we're not in so much a CPU-centric world anymore. Various application environments have various demands and you can meet them by using a variety of components. And it's extremely significant when you start looking down at the component level. It's really important that you optimize around those components. So I guess my summary would be, I think we are moving out of the CPU-centric hardware model into more of a connectivity-centric model. We can talk more about that later. >> Yeah, great. And thank you, David, and Keith Townsend I really interested in your perspectives on this. I mean, for years you worked in a data center surrounded by hardware. Now that we have the software defined data center, please chime in here. >> Well, you know, I'm going to dig deeper into that software-defined data center nature of what's happening with hardware. Hardware is meeting software infrastructure as code is a thing. What does that code look like? We're still trying to figure out but servicing up these capabilities that the previous analysts have brought up, how do I ensure that I can get the level of services needed for the applications that I need? Whether they're legacy, traditional data center, workloads, AI ML, workloads, workloads at the edge. How do I codify that and consume that as a service? And hardware vendors are figuring this out. HPE, the big push into GreenLake as a service. Dale now with Apex taking what we need, these bare bone components, moving it forward with DDR five, six CXL, et cetera, and surfacing that as cold or as services. This is a very tough problem. As we transition from consuming a hardware-based configuration to this infrastructure as cold paradigm shift. >> Yeah, programmable infrastructure, really attacking that sort of labor discussion that we were having earlier, okay. Last but not least Marc Staimer, please. >> Thanks, Dave. My peers raised really good points. I agree with most of them, but I'm going to disagree with the title of this session, which is, does hardware matter? It absolutely matters. You can't run software on the air. You can't run it in an ephemeral cloud, although there's the technical cloud and that's a different issue. The cloud is kind of changed everything. And from a market perspective in the 40 plus years I've been in this business, I've seen this perception that hardware has to go down in price every year. And part of that was driven by Moore's law. And we're coming to, let's say a lag or an end, depending on who you talk to Moore's law. So we're not doubling our transistors every 18 to 24 months in a chip and as a result of that, there's been a higher emphasis on software. From a market perception, there's no penalty. They don't put the same pressure on software from the market to reduce the cost every year that they do on hardware, which kind of bass ackwards when you think about it. Hardware costs are fixed. Software costs tend to be very low. It's kind of a weird thing that we do in the market. And what's changing is we're now starting to treat hardware like software from an OPEX versus CapEx perspective. So yes, hardware matters. And we'll talk about that more in length. >> You know, I want to follow up on that. And I wonder if you guys have a thought on this, Bob O'Donnell, you and I have talked about this a little bit. Marc, you just pointed out that Moore's laws could have waning. Pat Gelsinger recently at their investor meeting said that he promised that Moore's law is alive and well. And the point I made in breaking analysis was okay, great. You know, Pat said, doubling transistors every 18 to 24 months, let's say that Intel can do that. Even though we know it's waning somewhat. Look at the M1 Ultra from Apple (chuckles). In about 15 months increased transistor density on their package by 6X. So to your earlier point, Bob, we have this sort of these alternative processors that are really changing things. And to Dave Nicholson's point, there's a whole lot of supporting components as well. Do you have a comment on that, Bob? >> Yeah, I mean, it's a great point, Dave. And one thing to bear in mind as well, not only are we seeing a diversity of these different chip architectures and different types of components as a number of us have raised the other big point and I think it was Keith that mentioned it. CXL and interconnect on the chip itself is dramatically changing it. And a lot of the more interesting advances that are going to continue to drive Moore's law forward in terms of the way we think about performance, if perhaps not number of transistors per se, is the interconnects that become available. You're seeing the development of chiplets or tiles, people use different names, but the idea is you can have different components being put together eventually in sort of a Lego block style. And what that's also going to allow, not only is that going to give interesting performance possibilities 'cause of the faster interconnect. So you can share, have shared memory between things which for big workloads like AI, huge data sets can make a huge difference in terms of how you talk to memory over a network connection, for example, but not only that you're going to see more diversity in the types of solutions that can be built. So we're going to see even more choices in hardware from a silicon perspective because you'll be able to piece together different elements. And oh, by the way, the other benefit of that is we've reached a point in chip architectures where not everything benefits from being smaller. We've been so focused and so obsessed when it comes to Moore's law, to the size of each individual transistor and yes, for certain architecture types, CPUs and GPUs in particular, that's absolutely true, but we've already hit the point where things like RF for 5g and wifi and other wireless technologies and a whole bunch of other things actually don't get any better with a smaller transistor size. They actually get worse. So the beauty of these chiplet architectures is you could actually combine different chip manufacturing sizes. You know you hear about four nanometer and five nanometer along with 14 nanometer on a single chip, each one optimized for its specific application yet together, they can give you the best of all worlds. And so we're just at the very beginning of that era, which I think is going to drive a ton of innovation. Again, gets back to my comment about different types of devices located geographically different places at the edge, in the data center, you know, in a private cloud versus a public cloud. All of those things are going to be impacted and there'll be a lot more options because of this silicon diversity and this interconnect diversity that we're just starting to see. >> Yeah, David. David Nicholson's got a graphic on that. They're going to show later. Before we do that, I want to introduce some data. I actually want to ask Keith to comment on this before we, you know, go on. This next slide is some data from ETR that shows the percent of customers that cited difficulty procuring hardware. And you can see the red is they had significant issues and it's most pronounced in laptops and networking hardware on the far right-hand side, but virtually all categories, firewalls, peripheral servers, storage are having moderately difficult procurement issues. That's the sort of pinkish or significant challenges. So Keith, I mean, what are you seeing with your customers in the hardware supply chains and bottlenecks? And you know we're seeing it with automobiles and appliances but so it goes beyond IT. The semiconductor, you know, challenges. What's been the impact on the buyer community and society and do you have any sense as to when it will subside? >> You know, I was just asked this question yesterday and I'm feeling the pain. People question, kind of a side project within the CTO advisor, we built a hybrid infrastructure, traditional IT data center that we're walking with the traditional customer and modernizing that data center. So it was, you know, kind of a snapshot of time in 2016, 2017, 10 gigabit, ARISTA switches, some older Dell's 730 XD switches, you know, speeds and feeds. And we said we would modern that with the latest Intel stack and connected to the public cloud and then the pandemic hit and we are experiencing a lot of the same challenges. I thought we'd easily migrate from 10 gig networking to 25 gig networking path that customers are going on. The 10 gig network switches that I bought used are now double the price because you can't get legacy 10 gig network switches because all of the manufacturers are focusing on the more profitable 25 gig for capacity, even the 25 gig switches. And we're focused on networking right now. It's hard to procure. We're talking about nine to 12 months or more lead time. So we're seeing customers adjust by adopting cloud. But if you remember early on in the pandemic, Microsoft Azure kind of gated customers that didn't have a capacity agreement. So customers are keeping an eye on that. There's a desire to abstract away from the underlying vendor to be able to control or provision your IT services in a way that we do with VMware VP or some other virtualization technology where it doesn't matter who can get me the hardware, they can just get me the hardware because it's critically impacting projects and timelines. >> So that's a great setup Zeus for you with Keith mentioned the earlier the software-defined data center with software-defined networking and cloud. Do you see a day where networking hardware is monetized and it's all about the software, or are we there already? >> No, we're not there already. And I don't see that really happening any time in the near future. I do think it's changed though. And just to be clear, I mean, when you look at that data, this is saying customers have had problems procuring the equipment, right? And there's not a network vendor out there. I've talked to Norman Rice at Extreme, and I've talked to the folks at Cisco and ARISTA about this. They all said they could have had blowout quarters had they had the inventory to ship. So it's not like customers aren't buying this anymore. Right? I do think though, when it comes to networking network has certainly changed some because there's a lot more controls as I mentioned before that you can do in software. And I think the customers need to start thinking about the types of hardware they buy and you know, where they're going to use it and, you know, what its purpose is. Because I've talked to customers that have tried to run software and commodity hardware and where the performance requirements are very high and it's bogged down, right? It just doesn't have the horsepower to run it. And, you know, even when you do that, you have to start thinking of the components you use. The NICs you buy. And I've talked to customers that have simply just gone through the process replacing a NIC card and a commodity box and had some performance problems and, you know, things like that. So if agility is more important than performance, then by all means try running software on commodity hardware. I think that works in some cases. If performance though is more important, that's when you need that kind of turnkey hardware system. And I've actually seen more and more customers reverting back to that model. In fact, when you talk to even some startups I think today about when they come to market, they're delivering things more on appliances because that's what customers want. And so there's this kind of app pivot this pendulum of agility and performance. And if performance absolutely matters, that's when you do need to buy these kind of turnkey, prebuilt hardware systems. If agility matters more, that's when you can go more to software, but the underlying hardware still does matter. So I think, you know, will we ever have a day where you can just run it on whatever hardware? Maybe but I'll long be retired by that point. So I don't care. >> Well, you bring up a good point Zeus. And I remember the early days of cloud, the narrative was, oh, the cloud vendors. They don't use EMC storage, they just run on commodity storage. And then of course, low and behold, you know, they've trot out James Hamilton to talk about all the custom hardware that they were building. And you saw Google and Microsoft follow suit. >> Well, (indistinct) been falling for this forever. Right? And I mean, all the way back to the turn of the century, we were calling for the commodity of hardware. And it's never really happened because you can still drive. As long as you can drive innovation into it, customers will always lean towards the innovation cycles 'cause they get more features faster and things. And so the vendors have done a good job of keeping that cycle up but it'll be a long time before. >> Yeah, and that's why you see companies like Pure Storage. A storage company has 69% gross margins. All right. I want to go jump ahead. We're going to bring up the slide four. I want to go back to something that Bob O'Donnell was talking about, the sort of supporting act. The diversity of silicon and we've marched to the cadence of Moore's law for decades. You know, we asked, you know, is Moore's law dead? We say it's moderating. Dave Nicholson. You want to talk about those supporting components. And you shared with us a slide that shift. You call it a shift from a processor-centric world to a connect-centric world. What do you mean by that? And let's bring up slide four and you can talk to that. >> Yeah, yeah. So first, I want to echo this sentiment that the question does hardware matter is sort of the answer is of course it matters. Maybe the real question should be, should you care about it? And the answer to that is it depends who you are. If you're an end user using an application on your mobile device, maybe you don't care how the architecture is put together. You just care that the service is delivered but as you back away from that and you get closer and closer to the source, someone needs to care about the hardware and it should matter. Why? Because essentially what hardware is doing is it's consuming electricity and dollars and the more efficiently you can configure hardware, the more bang you're going to get for your buck. So it's not only a quantitative question in terms of how much can you deliver? But it also ends up being a qualitative change as capabilities allow for things we couldn't do before, because we just didn't have the aggregate horsepower to do it. So this chart actually comes out of some performance tests that were done. So it happens to be Dell servers with Broadcom components. And the point here was to peel back, you know, peel off the top of the server and look at what's in that server, starting with, you know, the PCI interconnect. So PCIE gen three, gen four, moving forward. What are the effects on from an interconnect versus on performance application performance, translating into new orders per minute, processed per dollar, et cetera, et cetera? If you look at the advances in CPU architecture mapped against the advances in interconnect and storage subsystem performance, you can see that CPU architecture is sort of lagging behind in a way. And Bob mentioned this idea of tiling and all of the different ways to get around that. When we do performance testing, we can actually peg CPUs, just running the performance tests without any actual database environments working. So right now we're at this sort of imbalance point where you have to make sure you design things properly to get the most bang per kilowatt hour of power per dollar input. So the key thing here what this is highlighting is just as a very specific example, you take a card that's designed as a gen three PCIE device, and you plug it into a gen four slot. Now the card is the bottleneck. You plug a gen four card into a gen four slot. Now the gen four slot is the bottleneck. So we're constantly chasing these bottlenecks. Someone has to be focused on that from an architectural perspective, it's critically important. So there's no question that it matters. But of course, various people in this food chain won't care where it comes from. I guess a good analogy might be, where does our food come from? If I get a steak, it's a pink thing wrapped in plastic, right? Well, there are a lot of inputs that a lot of people have to care about to get that to me. Do I care about all of those things? No. Are they important? They're critically important. >> So, okay. So all I want to get to the, okay. So what does this all mean to customers? And so what I'm hearing from you is to balance a system it's becoming, you know, more complicated. And I kind of been waiting for this day for a long time, because as we all know the bottleneck was always the spinning disc, the last mechanical. So people who wrote software knew that when they were doing it right, the disc had to go and do stuff. And so they were doing other things in the software. And now with all these new interconnects and flash and things like you could do atomic rights. And so that opens up new software possibilities and combine that with alternative processes. But what's the so what on this to the customer and the application impact? Can anybody address that? >> Yeah, let me address that for a moment. I want to leverage some of the things that Bob said, Keith said, Zeus said, and David said, yeah. So I'm a bit of a contrarian in some of this. For example, on the chip side. As the chips get smaller, 14 nanometer, 10 nanometer, five nanometer, soon three nanometer, we talk about more cores, but the biggest problem on the chip is the interconnect from the chip 'cause the wires get smaller. People don't realize in 2004 the latency on those wires in the chips was 80 picoseconds. Today it's 1300 picoseconds. That's on the chip. This is why they're not getting faster. So we maybe getting a little bit slowing down in Moore's law. But even as we kind of conquer that you still have the interconnect problem and the interconnect problem goes beyond the chip. It goes within the system, composable architectures. It goes to the point where Keith made, ultimately you need a hybrid because what we're seeing, what I'm seeing and I'm talking to customers, the biggest issue they have is moving data. Whether it be in a chip, in a system, in a data center, between data centers, moving data is now the biggest gating item in performance. So if you want to move it from, let's say your transactional database to your machine learning, it's the bottleneck, it's moving the data. And so when you look at it from a distributed environment, now you've got to move the compute to the data. The only way to get around these bottlenecks today is to spend less time in trying to move the data and more time in taking the compute, the software, running on hardware closer to the data. Go ahead. >> So is this what you mean when Nicholson was talking about a shift from a processor centric world to a connectivity centric world? You're talking about moving the bits across all the different components, not having the processor you're saying is essentially becoming the bottleneck or the memory, I guess. >> Well, that's one of them and there's a lot of different bottlenecks, but it's the data movement itself. It's moving away from, wait, why do we need to move the data? Can we move the compute, the processing closer to the data? Because if we keep them separate and this has been a trend now where people are moving processing away from it. It's like the edge. I think it was Zeus or David. You were talking about the edge earlier. As you look at the edge, who defines the edge, right? Is the edge a closet or is it a sensor? If it's a sensor, how do you do AI at the edge? When you don't have enough power, you don't have enough computable. People were inventing chips to do that. To do all that at the edge, to do AI within the sensor, instead of moving the data to a data center or a cloud to do the processing. Because the lag in latency is always limited by speed of light. How fast can you move the electrons? And all this interconnecting, all the processing, and all the improvement we're seeing in the PCIE bus from three, to four, to five, to CXL, to a higher bandwidth on the network. And that's all great but none of that deals with the speed of light latency. And that's an-- Go ahead. >> You know Marc, no, I just want to just because what you're referring to could be looked at at a macro level, which I think is what you're describing. You can also look at it at a more micro level from a systems design perspective, right? I'm going to be the resident knuckle dragging hardware guy on the panel today. But it's exactly right. You moving compute closer to data includes concepts like peripheral cards that have built in intelligence, right? So again, in some of this testing that I'm referring to, we saw dramatic improvements when you basically took the horsepower instead of using the CPU horsepower for the like IO. Now you have essentially offload engines in the form of storage controllers, rate controllers, of course, for ethernet NICs, smart NICs. And so when you can have these sort of offload engines and we've gone through these waves over time. People think, well, wait a minute, raid controller and NVMe? You know, flash storage devices. Does that make sense? It turns out it does. Why? Because you're actually at a micro level doing exactly what you're referring to. You're bringing compute closer to the data. Now, closer to the data meaning closer to the data storage subsystem. It doesn't solve the macro issue that you're referring to but it is important. Again, going back to this idea of system design optimization, always chasing the bottleneck, plugging the holes. Someone needs to do that in this value chain in order to get the best value for every kilowatt hour of power and every dollar. >> Yeah. >> Well this whole drive performance has created some really interesting architectural designs, right? Like Nickelson, the rise of the DPU right? Brings more processing power into systems that already had a lot of processing power. There's also been some really interesting, you know, kind of innovation in the area of systems architecture too. If you look at the way Nvidia goes to market, their drive kit is a prebuilt piece of hardware, you know, optimized for self-driving cars, right? They partnered with Pure Storage and ARISTA to build that AI-ready infrastructure. I remember when I talked to Charlie Giancarlo, the CEO of Pure about when the three companies rolled that out. He said, "Look, if you're going to do AI, "you need good store. "You need fast storage, fast processor and fast network." And so for customers to be able to put that together themselves was very, very difficult. There's a lot of software that needs tuning as well. So the three companies partner together to create a fully integrated turnkey hardware system with a bunch of optimized software that runs on it. And so in that case, in some ways the hardware was leading the software innovation. And so, the variety of different architectures we have today around hardware has really exploded. And I think it, part of the what Bob brought up at the beginning about the different chip design. >> Yeah, Bob talked about that earlier. Bob, I mean, most AI today is modeling, you know, and a lot of that's done in the cloud and it looks from my standpoint anyway that the future is going to be a lot of AI inferencing at the edge. And that's a radically different architecture, Bob, isn't it? >> It is, it's a completely different architecture. And just to follow up on a couple points, excellent conversation guys. Dave talked about system architecture and really this that's what this boils down to, right? But it's looking at architecture at every level. I was talking about the individual different components the new interconnect methods. There's this new thing called UCIE universal connection. I forget what it stands answer for, but it's a mechanism for doing chiplet architectures, but then again, you have to take it up to the system level, 'cause it's all fine and good. If you have this SOC that's tuned and optimized, but it has to talk to the rest of the system. And that's where you see other issues. And you've seen things like CXL and other interconnect standards, you know, and nobody likes to talk about interconnect 'cause it's really wonky and really technical and not that sexy, but at the end of the day it's incredibly important exactly. To the other points that were being raised like mark raised, for example, about getting that compute closer to where the data is and that's where again, a diversity of chip architectures help and exactly to your last comment there Dave, putting that ability in an edge device is really at the cutting edge of what we're seeing on a semiconductor design and the ability to, for example, maybe it's an FPGA, maybe it's a dedicated AI chip. It's another kind of chip architecture that's being created to do that inferencing on the edge. Because again, it's that the cost and the challenges of moving lots of data, whether it be from say a smartphone to a cloud-based application or whether it be from a private network to a cloud or any other kinds of permutations we can think of really matters. And the other thing is we're tackling bigger problems. So architecturally, not even just architecturally within a system, but when we think about DPUs and the sort of the east west data center movement conversation that we hear Nvidia and others talk about, it's about combining multiple sets of these systems to function together more efficiently again with even bigger sets of data. So really is about tackling where the processing is needed, having the interconnect and the ability to get where the data you need to the right place at the right time. And because those needs are diversifying, we're just going to continue to see an explosion of different choices and options, which is going to make hardware even more essential I would argue than it is today. And so I think what we're going to see not only does hardware matter, it's going to matter even more in the future than it does now. >> Great, yeah. Great discussion, guys. I want to bring Keith back into the conversation here. Keith, if your main expertise in tech is provisioning LUNs, you probably you want to look for another job. So maybe clearly hardware matters, but with software defined everything, do people with hardware expertise matter outside of for instance, component manufacturers or cloud companies? I mean, VMware certainly changed the dynamic in servers. Dell just spun off its most profitable asset and VMware. So it obviously thinks hardware can stand alone. How does an enterprise architect view the shift to software defined hyperscale cloud and how do you see the shifting demand for skills in enterprise IT? >> So I love the question and I'll take a different view of it. If you're a data analyst and your primary value add is that you do ETL transformation, talk to a CDO, a chief data officer over midsize bank a little bit ago. He said 80% of his data scientists' time is done on ETL. Super not value ad. He wants his data scientists to do data science work. Chances are if your only value is that you do LUN provisioning, then you probably don't have a job now. The technologies have gotten much more intelligent. As infrastructure pros, we want to give infrastructure pros the opportunities to shine and I think the software defined nature and the automation that we're seeing vendors undertake, whether it's Dell, HP, Lenovo take your pick that Pure Storage, NetApp that are doing the automation and the ML needed so that these practitioners don't spend 80% of their time doing LUN provisioning and focusing on their true expertise, which is ensuring that data is stored. Data is retrievable, data's protected, et cetera. I think the shift is to focus on that part of the job that you're ensuring no matter where the data's at, because as my data is spread across the enterprise hybrid different types, you know, Dave, you talk about the super cloud a lot. If my data is in the super cloud, protecting that data and securing that data becomes much more complicated when than when it was me just procuring or provisioning LUNs. So when you say, where should the shift be, or look be, you know, focusing on the real value, which is making sure that customers can access data, can recover data, can get data at performance levels that they need within the price point. They need to get at those datasets and where they need it. We talked a lot about where they need out. One last point about this interconnecting. I have this vision and I think we all do of composable infrastructure. This idea that scaled out does not solve every problem. The cloud can give me infinite scale out. Sometimes I just need a single OS with 64 terabytes of RAM and 204 GPUs or GPU instances that single OS does not exist today. And the opportunity is to create composable infrastructure so that we solve a lot of these problems that just simply don't scale out. >> You know, wow. So many interesting points there. I had just interviewed Zhamak Dehghani, who's the founder of Data Mesh last week. And she made a really interesting point. She said, "Think about, we have separate stacks. "We have an application stack and we have "a data pipeline stack and the transaction systems, "the transaction database, we extract data from that," to your point, "We ETL it in, you know, it takes forever. "And then we have this separate sort of data stack." If we're going to inject more intelligence and data and AI into applications, those two stacks, her contention is they have to come together. And when you think about, you know, super cloud bringing compute to data, that was what Haduck was supposed to be. It ended up all sort of going into a central location, but it's almost a rhetorical question. I mean, it seems that that necessitates new thinking around hardware architectures as it kind of everything's the edge. And the other point is to your point, Keith, it's really hard to secure that. So when you can think about offloads, right, you've heard the stats, you know, Nvidia talks about it. Broadcom talks about it that, you know, that 30%, 25 to 30% of the CPU cycles are wasted on doing things like storage offloads, or networking or security. It seems like maybe Zeus you have a comment on this. It seems like new architectures need to come other to support, you know, all of that stuff that Keith and I just dispute. >> Yeah, and by the way, I do want to Keith, the question you just asked. Keith, it's the point I made at the beginning too about engineers do need to be more software-centric, right? They do need to have better software skills. In fact, I remember talking to Cisco about this last year when they surveyed their engineer base, only about a third of 'em had ever made an API call, which you know that that kind of shows this big skillset change, you know, that has to come. But on the point of architectures, I think the big change here is edge because it brings in distributed compute models. Historically, when you think about compute, even with multi-cloud, we never really had multi-cloud. We'd use multiple centralized clouds, but compute was always centralized, right? It was in a branch office, in a data center, in a cloud. With edge what we creates is the rise of distributed computing where we'll have an application that actually accesses different resources and at different edge locations. And I think Marc, you were talking about this, like the edge could be in your IoT device. It could be your campus edge. It could be cellular edge, it could be your car, right? And so we need to start thinkin' about how our applications interact with all those different parts of that edge ecosystem, you know, to create a single experience. The consumer apps, a lot of consumer apps largely works that way. If you think of like app like Uber, right? It pulls in information from all kinds of different edge application, edge services. And, you know, it creates pretty cool experience. We're just starting to get to that point in the business world now. There's a lot of security implications and things like that, but I do think it drives more architectural decisions to be made about how I deploy what data where and where I do my processing, where I do my AI and things like that. It actually makes the world more complicated. In some ways we can do so much more with it, but I think it does drive us more towards turnkey systems, at least initially in order to, you know, ensure performance and security. >> Right. Marc, I wanted to go to you. You had indicated to me that you wanted to chat about this a little bit. You've written quite a bit about the integration of hardware and software. You know, we've watched Oracle's move from, you know, buying Sun and then basically using that in a highly differentiated approach. Engineered systems. What's your take on all that? I know you also have some thoughts on the shift from CapEx to OPEX chime in on that. >> Sure. When you look at it, there are advantages to having one vendor who has the software and hardware. They can synergistically make them work together that you can't do in a commodity basis. If you own the software and somebody else has the hardware, I'll give you an example would be Oracle. As you talked about with their exit data platform, they literally are leveraging microcode in the Intel chips. And now in AMD chips and all the way down to Optane, they make basically AMD database servers work with Optane memory PMM in their storage systems, not MVME, SSD PMM. I'm talking about the cards itself. So there are advantages you can take advantage of if you own the stack, as you were putting out earlier, Dave, of both the software and the hardware. Okay, that's great. But on the other side of that, that tends to give you better performance, but it tends to cost a little more. On the commodity side it costs less but you get less performance. What Zeus had said earlier, it depends where you're running your application. How much performance do you need? What kind of performance do you need? One of the things about moving to the edge and I'll get to the OPEX CapEx in a second. One of the issues about moving to the edge is what kind of processing do you need? If you're running in a CCTV camera on top of a traffic light, how much power do you have? How much cooling do you have that you can run this? And more importantly, do you have to take the data you're getting and move it somewhere else and get processed and the information is sent back? I mean, there are companies out there like Brain Chip that have developed AI chips that can run on the sensor without a CPU. Without any additional memory. So, I mean, there's innovation going on to deal with this question of data movement. There's companies out there like Tachyon that are combining GPUs, CPUs, and DPUs in a single chip. Think of it as super composable architecture. They're looking at being able to do more in less. On the OPEX and CapEx issue. >> Hold that thought, hold that thought on the OPEX CapEx, 'cause we're running out of time and maybe you can wrap on that. I just wanted to pick up on something you said about the integrated hardware software. I mean, other than the fact that, you know, Michael Dell unlocked whatever $40 billion for himself and Silverlake, I was always a fan of a spin in with VMware basically become the Oracle of hardware. Now I know it would've been a nightmare for the ecosystem and culturally, they probably would've had a VMware brain drain, but what does anybody have any thoughts on that as a sort of a thought exercise? I was always a fan of that on paper. >> I got to eat a little crow. I did not like the Dale VMware acquisition for the industry in general. And I think it hurt the industry in general, HPE, Cisco walked away a little bit from that VMware relationship. But when I talked to customers, they loved it. You know, I got to be honest. They absolutely loved the integration. The VxRail, VxRack solution exploded. Nutanix became kind of a afterthought when it came to competing. So that spin in, when we talk about the ability to innovate and the ability to create solutions that you just simply can't create because you don't have the full stack. Dell was well positioned to do that with a potential span in of VMware. >> Yeah, we're going to be-- Go ahead please. >> Yeah, in fact, I think you're right, Keith, it was terrible for the industry. Great for Dell. And I remember talking to Chad Sakac when he was running, you know, VCE, which became Rack and Rail, their ability to stay in lockstep with what VMware was doing. What was the number one workload running on hyperconverged forever? It was VMware. So their ability to remain in lockstep with VMware gave them a huge competitive advantage. And Dell came out of nowhere in, you know, the hyper-converged market and just started taking share because of that relationship. So, you know, this sort I guess it's, you know, from a Dell perspective I thought it gave them a pretty big advantage that they didn't really exploit across their other properties, right? Networking and service and things like they could have given the dominance that VMware had. From an industry perspective though, I do think it's better to have them be coupled. So. >> I agree. I mean, they could. I think they could have dominated in super cloud and maybe they would become the next Oracle where everybody hates 'em, but they kick ass. But guys. We got to wrap up here. And so what I'm going to ask you is I'm going to go and reverse the order this time, you know, big takeaways from this conversation today, which guys by the way, I can't thank you enough phenomenal insights, but big takeaways, any final thoughts, any research that you're working on that you want highlight or you know, what you look for in the future? Try to keep it brief. We'll go in reverse order. Maybe Marc, you could start us off please. >> Sure, on the research front, I'm working on a total cost of ownership of an integrated database analytics machine learning versus separate services. On the other aspect that I would wanted to chat about real quickly, OPEX versus CapEx, the cloud changed the market perception of hardware in the sense that you can use hardware or buy hardware like you do software. As you use it, pay for what you use in arrears. The good thing about that is you're only paying for what you use, period. You're not for what you don't use. I mean, it's compute time, everything else. The bad side about that is you have no predictability in your bill. It's elastic, but every user I've talked to says every month it's different. And from a budgeting perspective, it's very hard to set up your budget year to year and it's causing a lot of nightmares. So it's just something to be aware of. From a CapEx perspective, you have no more CapEx if you're using that kind of base system but you lose a certain amount of control as well. So ultimately that's some of the issues. But my biggest point, my biggest takeaway from this is the biggest issue right now that everybody I talk to in some shape or form it comes down to data movement whether it be ETLs that you talked about Keith or other aspects moving it between hybrid locations, moving it within a system, moving it within a chip. All those are key issues. >> Great, thank you. Okay, CTO advisor, give us your final thoughts. >> All right. Really, really great commentary. Again, I'm going to point back to us taking the walk that our customers are taking, which is trying to do this conversion of all primary data center to a hybrid of which I have this hard earned philosophy that enterprise IT is additive. When we add a service, we rarely subtract a service. So the landscape and service area what we support has to grow. So our research focuses on taking that walk. We are taking a monolithic application, decomposing that to containers, and putting that in a public cloud, and connecting that back private data center and telling that story and walking that walk with our customers. This has been a super enlightening panel. >> Yeah, thank you. Real, real different world coming. David Nicholson, please. >> You know, it really hearkens back to the beginning of the conversation. You talked about momentum in the direction of cloud. I'm sort of spending my time under the hood, getting grease under my fingernails, focusing on where still the lions share of spend will be in coming years, which is OnPrem. And then of course, obviously data center infrastructure for cloud but really diving under the covers and helping folks understand the ramifications of movement between generations of CPU architecture. I know we all know Sapphire Rapids pushed into the future. When's the next Intel release coming? Who knows? We think, you know, in 2023. There have been a lot of people standing by from a practitioner's standpoint asking, well, what do I do between now and then? Does it make sense to upgrade bits and pieces of hardware or go from a last generation to a current generation when we know the next generation is coming? And so I've been very, very focused on looking at how these connectivity components like rate controllers and NICs. I know it's not as sexy as talking about cloud but just how these opponents completely change the game and actually can justify movement from say a 14th-generation architecture to a 15th-generation architecture today, even though gen 16 is coming, let's say 12 months from now. So that's where I am. Keep my phone number in the Rolodex. I literally reference Rolodex intentionally because like I said, I'm in there under the hood and it's not as sexy. But yeah, so that's what I'm focused on Dave. >> Well, you know, to paraphrase it, maybe derivative paraphrase of, you know, Larry Ellison's rant on what is cloud? It's operating systems and databases, et cetera. Rate controllers and NICs live inside of clouds. All right. You know, one of the reasons I love working with you guys is 'cause have such a wide observation space and Zeus Kerravala you, of all people, you know you have your fingers in a lot of pies. So give us your final thoughts. >> Yeah, I'm not a propeller heady as my chip counterparts here. (all laugh) So, you know, I look at the world a little differently and a lot of my research I'm doing now is the impact that distributed computing has on customer employee experiences, right? You talk to every business and how the experiences they deliver to their customers is really differentiating how they go to market. And so they're looking at these different ways of feeding up data and analytics and things like that in different places. And I think this is going to have a really profound impact on enterprise IT architecture. We're putting more data, more compute in more places all the way down to like little micro edges and retailers and things like that. And so we need the variety. Historically, if you think back to when I was in IT you know, pre-Y2K, we didn't have a lot of choice in things, right? We had a server that was rack mount or standup, right? And there wasn't a whole lot of, you know, differences in choice. But today we can deploy, you know, these really high-performance compute systems on little blades inside servers or inside, you know, autonomous vehicles and things. I think the world from here gets... You know, just the choice of what we have and the way hardware and software works together is really going to, I think, change the world the way we do things. We're already seeing that, like I said, in the consumer world, right? There's so many things you can do from, you know, smart home perspective, you know, natural language processing, stuff like that. And it's starting to hit businesses now. So just wait and watch the next five years. >> Yeah, totally. The computing power at the edge is just going to be mind blowing. >> It's unbelievable what you can do at the edge. >> Yeah, yeah. Hey Z, I just want to say that we know you're not a propeller head and I for one would like to thank you for having your master's thesis hanging on the wall behind you 'cause we know that you studied basket weaving. >> I was actually a physics math major, so. >> Good man. Another math major. All right, Bob O'Donnell, you're going to bring us home. I mean, we've seen the importance of semiconductors and silicon in our everyday lives, but your last thoughts please. >> Sure and just to clarify, by the way I was a great books major and this was actually for my final paper. And so I was like philosophy and all that kind of stuff and literature but I still somehow got into tech. Look, it's been a great conversation and I want to pick up a little bit on a comment Zeus made, which is this it's the combination of the hardware and the software and coming together and the manner with which that needs to happen, I think is critically important. And the other thing is because of the diversity of the chip architectures and all those different pieces and elements, it's going to be how software tools evolve to adapt to that new world. So I look at things like what Intel's trying to do with oneAPI. You know, what Nvidia has done with CUDA. What other platform companies are trying to create tools that allow them to leverage the hardware, but also embrace the variety of hardware that is there. And so as those software development environments and software development tools evolve to take advantage of these new capabilities, that's going to open up a lot of interesting opportunities that can leverage all these new chip architectures. That can leverage all these new interconnects. That can leverage all these new system architectures and figure out ways to make that all happen, I think is going to be critically important. And then finally, I'll mention the research I'm actually currently working on is on private 5g and how companies are thinking about deploying private 5g and the potential for edge applications for that. So I'm doing a survey of several hundred us companies as we speak and really looking forward to getting that done in the next couple of weeks. >> Yeah, look forward to that. Guys, again, thank you so much. Outstanding conversation. Anybody going to be at Dell tech world in a couple of weeks? Bob's going to be there. Dave Nicholson. Well drinks on me and guys I really can't thank you enough for the insights and your participation today. Really appreciate it. Okay, and thank you for watching this special power panel episode of theCube Insights powered by ETR. Remember we publish each week on Siliconangle.com and wikibon.com. All these episodes they're available as podcasts. DM me or any of these guys. I'm at DVellante. You can email me at David.Vellante@siliconangle.com. Check out etr.ai for all the data. This is Dave Vellante. We'll see you next time. (upbeat music)

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

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR, this is "Breaking Analysis" with Dave Vellante. >> If it weren't for Pat Gelsinger, Intel's future would be a disaster. Even with his clear vision, fantastic leadership, deep technical and business acumen, and amazing positivity, the company's future is in serious jeopardy. It's the same story we've been telling for years. Volume is king in the semiconductor industry, and Intel no longer is the volume leader. Despite Intel's efforts to change that dynamic With several recent moves, including making another go at its Foundry business, the company is years away from reversing its lagging position relative to today's leading foundries and design shops. Intel's best chance to survive as a leader in our view, will come from a combination of a massive market, continued supply constraints, government money, and luck, perhaps in the form of a deal with apple in the midterm. Hello, and welcome to this week's "Wikibon CUBE Insights, Powered by ETR." In this "Breaking Analysis," we'll update you on our latest assessment of Intel's competitive position and unpack nuggets from the company's February investor conference. Let's go back in history a bit and review what we said in the early 2010s. If you've followed this program, you know that our David Floyer sounded the alarm for Intel as far back as 2012, the year after PC volumes peaked. Yes, they've ticked up a bit in the past couple of years but they pale in comparison to the volumes that the ARM ecosystem is producing. The world has changed from people entering data into machines, and now it's machines that are driving all the data. Data volumes in Web 1.0 were largely driven by keystrokes and clicks. Web 3.0 is going to be driven by machines entering data into sensors, cameras. Other edge devices are going to drive enormous data volumes and processing power to boot. Every windmill, every factory device, every consumer device, every car, will require processing at the edge to run AI, facial recognition, inference, and data intensive workloads. And the volume of this space compared to PCs and even the iPhone itself is about to be dwarfed with an explosion of devices. Intel is not well positioned for this new world in our view. Intel has to catch up on the process, Intel has to catch up on architecture, Intel has to play catch up on security, Intel has to play catch up on volume. The ARM ecosystem has cumulatively shipped 200 billion chips to date, and is shipping 10x Intel's wafer volume. Intel has to have an architecture that accommodates much more diversity. And while it's working on that, it's years behind. All that said, Pat Gelsinger is doing everything he can and more to close the gap. Here's a partial list of the moves that Pat is making. A year ago, he announced IDM 2.0, a new integrated device manufacturing strategy that opened up its world to partners for manufacturing and other innovation. Intel has restructured, reorganized, and many executives have boomeranged back in, many previous Intel execs. They understand the business and have a deep passion to help the company regain its prominence. As part of the IDM 2.0 announcement, Intel created, recreated if you will, a Foundry division and recently acquired Tower Semiconductor an Israeli firm, that is going to help it in that mission. It's opening up partnerships with alternative processor manufacturers and designers. And the company has announced major investments in CAPEX to build out Foundry capacity. Intel is going to spin out Mobileye, a company it had acquired for 15 billion in 2017. Or does it try and get a $50 billion valuation? Mobileye is about $1.4 billion in revenue, and is likely going to be worth more around 25 to 30 billion, we'll see. But Intel is going to maybe get $10 billion in cash from that, that spin out that IPO and it can use that to fund more FABS and more equipment. Intel is leveraging its 19,000 software engineers to move up the stack and sell more subscriptions and high margin software. He got to sell what he got. And finally Pat is playing politics beautifully. Announcing for example, FAB investments in Ohio, which he dubbed Silicon Heartland. Brilliant! Again, there's no doubt that Pat is moving fast and doing the right things. Here's Pat at his investor event in a T-shirt that says, "torrid, bringing back the torrid pace and discipline that Intel is used to." And on the right is Pat at the State of the Union address, looking sharp in shirt and tie and suit. And he has said, "a bet on Intel is a hedge against geopolitical instability in the world." That's just so good. To that statement, he showed this chart at his investor meeting. Basically it shows that whereas semiconductor manufacturing capacity has gone from 80% of the world's volume to 20%, he wants to get it back to 50% by 2030, and reset supply chains in a market that has become important as oil. Again, just brilliant positioning and pushing all the right hot buttons. And here's a slide underscoring that commitment, showing manufacturing facilities around the world with new capacity coming online in the next few years in Ohio and the EU. Mentioning the CHIPS Act in his presentation in The US and Europe as part of a public private partnership, no doubt, he's going to need all the help he can get. Now, we couldn't resist the chart on the left here shows wafer starts and transistor capacity growth. For Intel, overtime speaks to its volume aspirations. But we couldn't help notice that the shape of the curve is somewhat misleading because it shows a two-year (mumbles) and then widens the aperture to three years to make the curve look steeper. Fun with numbers. Okay, maybe a little nitpick, but these are some of the telling nuggets we pulled from the investor day, and they're important. Another nitpick is in our view, wafers would be a better measure of volume than transistors. It's like a company saying we shipped 20% more exabytes or MIPS this year than last year. Of course you did, and your revenue shrank. Anyway, Pat went through a detailed analysis of the various Intel businesses and promised mid to high double digit growth by 2026, half of which will come from Intel's traditional PC they center in network edge businesses and the rest from advanced graphics HPC, Mobileye and Foundry. Okay, that sounds pretty good. But it has to be taken into context that the balance of the semiconductor industry, yeah, this would be a pretty competitive growth rate, in our view, especially for a 70 plus billion dollar company. So kudos to Pat for sticking his neck out on this one. But again, the promise is several years away, at least four years away. Now we want to focus on Foundry because that's the only way Intel is going to get back into the volume game and the volume necessary for the company to compete. Pat built this slide showing the baby blue for today's Foundry business just under a billion dollars and adding in another $1.5 billion for Tower Semiconductor, the Israeli firm that it just acquired. So a few billion dollars in the near term future for the Foundry business. And then by 2026, this really fuzzy blue bar. Now remember, TSM is the new volume leader, and is a $50 billion company growing. So there's definitely a market there that it can go after. And adding in ARM processors to the mix, and, you know, opening up and partnering with the ecosystems out there can only help volume if Intel can win that business, which you know, it should be able to, given the likelihood of long term supply constraints. But we remain skeptical. This is another chart Pat showed, which makes the case that Foundry and IDM 2.0 will allow expensive assets to have a longer useful life. Okay, that's cool. It will also solve the cumulative output problem highlighted in the bottom right. We've talked at length about Wright's Law. That is, for every cumulative doubling of units manufactured, cost will fall by a constant percentage. You know, let's say around 15% in semiconductor world, which is vitally important to accommodate next generation chips, which are always more expensive at the start of the cycle. So you need that 15% cost buffer to jump curves and make any money. So let's unpack this a bit. You know, does this chart at the bottom right address our Wright's Law concerns, i.e. that Intel can't take advantage of Wright's Law because it can't double cumulative output fast enough? Now note the decline in wafer starts and then the slight uptick, and then the flattening. It's hard to tell what years we're talking about here. Intel is not going to share the sausage making because it's probably not pretty, But you can see on the bottom left, the flattening of the cumulative output curve in IDM 1.0 otherwise known as the death spiral. Okay, back to the power of Wright's Law. Now, assume for a second that wafer density doesn't grow. It does, but just work with us for a second. Let's say you produce 50 million units per year, just making a number up. That gets you cumulative output to $100 million in, sorry, 100 million units in the second year to take you two years to get to that 100 million. So in other words, it takes two years to lower your manufacturing cost by, let's say, roughly 15%. Now, assuming you can get wafer volumes to be flat, which that chart showed, with good yields, you're at 150 now in year three, 200 in year four, 250 in year five, 300 in year six, now, that's four years before you can take advantage of Wright's Law. You keep going at that flat wafer start, and that simplifying assumption we made at the start and 50 million units a year, and well, you get to the point. You get the point, it's now eight years before you can get the Wright's Law to kick in, and you know, by then you're cooked. But now you can grow the density of transistors on a chip, right? Yes, of course. So let's come back to Moore's Law. The graphic on the left says that all the growth is in the new stuff. Totally agree with that. Huge term that Pat presented. Now he also said that until we exhaust the periodic table of elements, Moore's Law is alive and well, and Intel is the steward of Moore's Law. Okay, that's cool. The chart on the right shows Intel going from 100 billion transistors today to a trillion by 2030. Hold that thought. So Intel is assuming that we'll keep up with Moore's Law, meaning a doubling of transistors every let's say two years, and I believe it. So bring that back to Wright's Law, in the previous chart, it means with IDM 2.0, Intel can get back to enjoying the benefits of Wright's Law every two years, let's say, versus IDM 1.0 where they were failing to keep up. Okay, so Intel is saved, yeah? Well, let's bring into this discussion one of our favorite examples, Apple's M1 ARM-based chip. The M1 Ultra is a new architecture. And you can see the stats here, 114 billion transistors on a five nanometer process and all the other stats. The M1 Ultra has two chips. They're bonded together. And Apple put an interposer between the two chips. An interposer is a pathway that allows electrical signals to pass through it onto another chip. It's a super fast connection. You can see 2.5 terabytes per second. But the brilliance is the two chips act as a single chip. So you don't have to change the software at all. The way Intel's architecture works is it takes two different chips on a substrate, and then each has its own memory. The memory is not shared. Apple shares the memory for the CPU, the NPU, the GPU. All of it is shared, meaning it needs no change in software unlike Intel. Now Intel is working on a new architecture, but Apple and others are way ahead. Now let's make this really straightforward. The original Apple M1 had 16 billion transistors per chip. And you could see in that diagram, the recently launched M1 Ultra has $114 billion per chip. Now if you take into account the size of the chips, which are increasing, and the increase in the number of transistors per chip, that transistor density, that's a factor of around 6x growth in transistor density per chip in 18 months. Remember Intel, assuming the results in the two previous charts that we showed, assuming they were achievable, is running at 2x every two years, versus 6x for the competition. And AMD and Nvidia are close to that as well because they can take advantage of TSM's learning curve. So in the previous chart with Moore's Law, alive and well, Intel gets to a trillion transistors by 2030. The Apple ARM and Nvidia ecosystems will arrive at that point years ahead of Intel. That means lower costs and significantly better competitive advantage. Okay, so where does that leave Intel? The story is really not resonating with investors and hasn't for a while. On February 18th, the day after its investor meeting, the stock was off. It's rebound a little bit but investors are, you know, they're probably prudent to wait unless they have really a long term view. And you can see Intel's performance relative to some of the major competitors. You know, Pat talked about five nodes in for years. He made a big deal out of that, and he shared proof points with Alder Lake and Meteor Lake and other nodes, but Intel just delayed granite rapids last month that pushed it out from 2023 to 2024. And it told investors that we're going to have to boost spending to turn this ship around, which is absolutely the case. And that delay in chips I feel like the first disappointment won't be the last. But as we've said many times, it's very difficult, actually, it's impossible to quickly catch up in semiconductors, and Intel will never catch up without volume. So we'll leave you by iterating our scenario that could save Intel, and that's if its Foundry business can eventually win back Apple to supercharge its volume story. It's going to be tough to wrestle that business away from TSM especially as TSM is setting up shop in Arizona, with US manufacturing that's going to placate The US government. But look, maybe the government cuts a deal with Apple, says, hey, maybe we'll back off with the DOJ and FTC and as part of the CHIPS Act, you'll have to throw some business at Intel. Would that be enough when combined with other Foundry opportunities Intel could theoretically produce? Maybe. But from this vantage point, it's very unlikely Intel will gain back its true number one leadership position. If it were really paranoid back when David Floyer sounded the alarm 10 years ago, yeah, that might have made a pretty big difference. But honestly, the best we can hope for is Intel's strategy and execution allows it to get competitive volumes by the end of the decade, and this national treasure survives to fight for its leadership position in the 2030s. Because it would take a miracle for that to happen in the 2020s. Okay, that's it for today. Thanks to David Floyer for his contributions to this research. Always a pleasure working with David. Stephanie Chan helps me do much of the background research for "Breaking Analysis," and works with our CUBE editorial team. Kristen Martin and Cheryl Knight to get the word out. And thanks to SiliconANGLE's editor in chief Rob Hof, who comes up with a lot of the great titles that we have for "Breaking Analysis" and gets the word out to the SiliconANGLE audience. Thanks, guys. Great teamwork. Remember, these episodes are all available as podcast wherever you listen. Just search "Breaking Analysis Podcast." You'll want to check out ETR's website @etr.ai. We also publish a full report every week on wikibon.com and siliconangle.com. You could always get in touch with me on email, david.vellante@siliconangle.com or DM me @dvellante, and comment on my LinkedIn posts. This is Dave Vellante for "theCUBE Insights, Powered by ETR." Have a great week. Stay safe, be well, and we'll see you next time. (upbeat music)

(upbeat music) >> Hello everyone, and welcome to this CUBE conversation where we're going to go deep into system performance. We're here with an expert. Kim Leyenaar is the Principal Performance Architect at Broadcom. Kim. Great to see you. Thanks so much for coming on. >> Thanks so much too. >> So you have a deep background in performance, performance assessment, benchmarking, modeling. Tell us a little bit about your background, your role. >> Thanks. So I've been a storage performance engineer and architect for about 22 years. And I'm specifically been for abroad with Broadcom for I think next month is going to be my 14 year mark. So what I do there is initially I built and I manage their international performance team, but about six years ago I moved back into architecture, and what my roles right now are is I generate performance projections for all of our next generation products. And then I also work on marketing material and I interface with a lot of the customers and debugging customer issues, and looking at how our customers are actually using our storage. >> Great. Now we have a graphic that we want to share. It talks to how storage has evolved over the past decade. So my question is what changes have you seen in storage and how has that impacted the way you approach benchmarking. In this graphic we got sort of big four items that impact performance, memory processor, IO pathways, and the storage media itself, but walk us through this data if you would. >> Sure. So what I put together is a little bit of what we've seen over the past 15 to 20 years. So I've been doing this for about 22 years and kind of going back and focusing a little bit on the storage, we looked back at hard disk, they ruled for. And nearly they had almost 50 years of ruling. And our first hard drive that came out back in the 1950s was only capable of five megabytes in capacity. and one and a half iOS per second. It had almost a full second in terms of seat time. So we've come a long way since then. But when I first came on, we were looking at Ultra 320 SCSI. And one of the biggest memories that I have of that was my office is located close to our tech support. And I could hear the first question was always, what's your termination like? And so we had some challenges with SCSI, and then we moved on into SAS and data protocols. And we continued to move on. But right now, back in the early 2000s when I came on board, the best drives really could do maybe 400 iOS per second. Maybe two 250 megabytes per second, with millisecond response times. And so when I was benchmarking way back when it was always like, well, IOPS are IOPS. We were always faster than what the drives to do. And that was just how it was. The drives were always the bottleneck in the system. And so things started changing though by the early 2000s, mid 2000s. We started seeing different technologies come out. We started seeing that virtualization and multi-tenant infrastructures becoming really popular. And then we had cloud computing that was well on the horizon. And so at this point, we're like, well, wait a minute, we really can't make processors that much faster. And so everybody got excited to include (indistinct) and the home came out but, they had two cores per processor and four cores per processor. And so we saw a little time period where actually the processing capability kind of pulled ahead of everybody else. And memory was falling behind. We had good old DVR, 2, 6, 67. It was new with the time, but we only had maybe one or two memory channels per processor. And then in 2007 we saw disk capacity hit one terabyte. And we started seeing a little bit of an imbalance because we were seeing these drives are getting massive, but their performance per drive was not really kind of keeping up. So now we see a revolution around 2010. And my co-worker and I at the time, we have these little USB discs, if you recall, we would put them in. They were so fast. We were joking at the time. "Hey, you know what, wonder if we could make a raid array out of these little USB disks?" They were just so fast. The idea was actually kind of crazy until we started seeing it actually happen. So in 2010 SSD started revolutionizing storage. And the first SSDs that we really worked with these plaint LS-300 and they were amazing because they were so over-provisioned that they had almost the same reader, right performance. But to go from a drive that could do maybe 400 IOS per second to a drive like 40,000 plus iOS per second, really changed our thought process about how our storage controller could actually try and keep up with the rest of the system. So we started falling behind. That was a big challenge for us. And then in 2014, NVMe came around as well. So now we've got these drives, they're 30 terabytes. They can do one and a half million iOS per second, and over 6,000 megabytes per second. But they were expensive. So people start relegating SSDs more towards tiered storage or cash. And as the prices of these drives kind of came down, they became a lot more mainstream. And then the memory channels started picking up. And they started doubling every few years. And we're looking now at DVR 5 4800. And now we're looking at cores that used to go from two to four cores per processor up to 48 with some of the latest different processes that are out there. So our ability to consume the computing and the storage resources, it's astounding, you know, it's like that whole saying, 'build it and they will come.' Because I'm always amazed, I'm like, how are we going to possibly utilize all this memory bandwidth? How are we going to utilize all these cores? But we do. And the trick to this is having just a balanced infrastructure. It's really critical. Because if you have a performance mismatch between your server and your storage, you really lose a lot of productivity and it does impact your revenue. >> So that's such a key point. Pardon, begin that slide up again with the four points. And that last point that you made Kim about balance. And so here you have these, electronic speeds with memory and IO, and then you've got the spinning disc, this mechanical disc. You mentioned that SSD kind of changed the game, but it used to be, when I looked at benchmarks, it was always the D stage bandwidth of the cash out to the spinning disc was always the bottleneck. And, you go back to the days of you it's symmetrics, right? The huge backend disk bandwidth was how they dealt with that. But, and then you had things the oxymoron of the day was high spin speed disks of a high performance disk. Compared to memories. And, so the next chart that we have is show some really amazing performance increases over the years. And so you see these bars on the left-hand side, it looks at historical performance for 4k random IOPS. And on the right-hand side, it's the storage controller performance for sequential bandwidth from 2008 to 2022. That's 22 is that yellow line. It's astounding the increases. I wonder if you could tell us what we're looking at here, when did SSD come in and how did that affect your thinking? (laughs) >> So I remember back in 2007, we were kind of on the precipice of SSDs. We saw it, the writing was on the wall. We had our first three gig SAS and SATA capable HPAs that had come out. And it was a shock because we were like, wow, we're going to really quickly become the bottleneck once this becomes more mainstream. And you're so right though about people work in, building these massive hard drive based back ends in order to handle kind of that tiered architecture that we were seeing that back in the early 2010s kind of when the pricing was just so sky high. And I remember looking at our SAS controllers, our very first one, and that was when I first came in at 2007. We had just launched our first SAS controller. We're so proud of ourselves. And I started going how many IOPS can this thing, even handled? We couldn't even attach enough drives to figure it out. So what we would do is we'd do these little tricks where we would do a five 12 byte read, and we would do it on a 4k boundary, so that it was actually reading sequentially from the disc, but we were handling these discrete IOPS. So we were like, oh, we can do around 35,000. Well, that's just not going to hit it anymore. Bandwidth wise we were doing great. Really our limitation and our bottleneck on bandwidth was always either the host or the backend. So, our controllers are there basically, there were three bottlenecks for our storage controllers. The first one is the bottleneck from the host to the controller. So that is typically a PCIe connection. And then there's another bottleneck on the controller to the disc. And that's really the number of ports that we have. And then the third one is the discs themselves. So in typical storage, that's what we look at. And we say, well, how do we improve this? So some of these are just kind of evolutionary, such as PCIE generations. And we're going to talk a little bit about that, but some of them are really revolutionary, and those are some of the things that we've been doing over the last five or six years to try and make sure that we are no longer the bottleneck. And we can enable these really, really fast drives. >> So can I ask a question? I'm sorry to interrupted but on these blue bars here. So these all spinning disks, I presume, out years they're not. Like when did flash come in to these blue bars? is that..you said 27 you started looking at it, but on these benchmarks, is it all spinning disc? Is it all flash? How should we interpret that? >> No, no. Initially they were actually all hard drives. And the way that we would identify, the max iOS would be by doing very small sequential reads to these hard drives. We just didn't have SSDs at that point. And then somewhere around 2010 is where we.. it was very early in that chart, we were able to start incorporating SSD technology into our benchmarking. And so what you're looking at here is really the max that our controller is capable of. So we would throw as many drives as we could and do what we needed to do in order to just make sure our controller was the bottleneck and what can we expose. >> So the drive then when SSD came in was no longer the bottleneck. So you guys had to sort of invent and rethink sort of how, what your innovation and your technology, because, I mean, these are astounding increases in performance. I mean, I think in the left-hand side, we've built this out pad, you got 170 X increase for the 4k random IOPS, and you've got a 20 X increase for the sequential bandwidth. How were you able to achieve that level of performance over time? >> Well, in terms of the sequential bandwidth, really those come naturally by increases in the PCIe or the SAS generation. So we just make sure we stay out of the way, and we enable that bandwidth. But the IOPS that's where it got really, really tricky. So we had to start thinking about different things. So, first of all, we started optimizing all of our pathways, all of our IO management, we increased the processing capabilities on our IO controllers. We added more on-chip memory. We started putting in IO accelerators, these hardware accelerators. We put in SAS poor kind of enhancements. We even went and improved our driver to make sure that our driver was as thin as possible. So we can make sure that we can enable all the IOPS on systems. But a big thing happening a few couple of generations ago was we started introducing something called tri capable controllers, which means that you could attach NVMe. You could attach SAS or you could attach SATA. So you could have this really amazing deployment of storage infrastructure based around your customized needs and your cost requirements by using one controller. >> Yeah. So anybody who's ever been to a trade show where they were displaying a glass case with a Winchester disc drive, for example, you see it's spinning and its actuators is moving, wow, that's so fast. Well, no. That's like a tourist slower. It's like a snail compared to the system's speed. So it's, in a way life was easy back in those days, because when you did a right to a disk, you had plenty of time to do stuff, right. And now it's changed. And so I want to talk about Gen3 versus Gen4, and how all this relates to what's new in Gen4 and the impacts of PCIe here, you have a chart here that you've shared with us that talks to that. And I wonder if you could elaborate on that, Kim. >> Sure. But first, you said something that kind of hit my funny bone there. And I remember I made a visit once about 15 or 20 years ago to IBM. And this gentleman actually had one of those old ones in his office and he referred to them as disk files. And he never until the day he retired, he'd never stopped calling them disc files. And it's kind of funny to be a part of that history. >> Yeah. DASD. They used to call it. (both laughing) >> SD, DASD. I used to get all kinds of, you know, you don't know what it was like back then, but yeah. But now nowadays we've got it quite easily enabled because back then, we had, SD DASD and all that. And then, ATA and then SCSI, well now we've got PCIe. And what's fabulous about PCIe is that it just has the generations are already planned out. It's incredible. You know, we're looking at right now, Gen3 moving to Gen4, and that's a lot about what we're going to be talking about. And that's what we're trying to test out. What is Gen4 PCIe when to bias? And it really is. It's fantastic. And PCIe came around about 18 years ago and Broadcom is, and we do participate and contribute to the PCIe SIG, which is, who develops the standards for PCIe, but the host in both our host interface in our NVMe desk and utilize the standards. So this is really, really a big deal, really critical for us. But if you take a look here, you can see that in terms of the capabilities of it, it's really is buying us a lot. So most of our drives right now NVMe drives tend to be by four. And a lot of people will connect them. And what that means is four lanes of NVMe and a lot of people that will connect them either at by one or by two kind of depending on what their storage infrastructure will allow. But the majority of them you could buy, or there are so, as you can see right now, we've gone from eight gig transfers per second to 16 gig of transfers per second. What that means is for a by four, we're going from one drive being able to do 4,000 to do an almost 8,000 megabytes per second. And in terms of those 4k IOPS that really evade us, they were really really tough sometimes to squeeze out of these drives, but now we're got 1 million, all we have to 2 million, it's just, it's insane. You know, just the increase in performance. And there's a lot of other standards that are going to be sitting on top of PCIe. So it's not going away anytime soon. We've got to open standards like CXL and things like that, but we also have graphics cards. You've got all of your hosts connections, they're also sitting on PCIe. So it's fantastic. It's backwards, it's orbits compatible, and it really is going to be our future. >> So this is all well and good. And I think I really believe that a lot of times in our industry, the challenges in the plumbing are underappreciated. But let's make it real for the audience because we have all these new workloads coming out, AI, heavily data oriented. So I want to get your thoughts on what types of workloads are going to benefit from Gen4 performance increases. In other words, what does it mean for application performance? You shared a chart that lists some of the key workloads, and I wonder if we could go through those. >> Yeah, yeah. I could have a large list of different workloads that are able to consume large amounts of data, whether or not it's in small or large kind of bytes of data. But as you know right now, and I said earlier, our ability to consume these compute and storage resources is amazing. So you build it and we'll use it. And the world's data we're expected to grow 61% to 175 zettabytes by the year 2025, according to IDC. So that's just a lot of data to manage. It's a lot of data to have, and it's something that's sitting around, but to be useful, you have to actually be able to access it. And that's kind of where we come in. So who is accessing it? What kind of applications? I spend a lot of time trying to understand that. And recently I attended a virtual conference SDC and what I like to do when I attend these conferences is to try to figure out what the buzz words are. What's everybody talking about? Because every year it's a little bit different, but this year was edge, edge everything. And so I kind of put edge on there first in, even you can ask anybody what's edge computing and it's going to mean a lot of different things, but basically it's all the computing outside of the cloud. That's happening typically at the edge of the network. So it tends to encompass a lot of real time processing on those instant data. So in the data is usually coming from either users or different sensors. It's that last mile. It's where we kind of put a lot of our content caching. And, I uncovered some interesting stuff when I was attending this virtual conference and they say only about 25% of all the usable data actually even reach the data center. The rest is ephemeral and it's localized, locally and in real time. So what it does is in the goal of edge computing is to try and reduce the bandwidth costs for these kinds of IOT devices that go over a long distance. But the reality is the growth of real-time applications that require these kinds of local processing are going to drive this technology forward over the coming years. So Dave, your toaster and your dishwasher they're, IOT edge devices probably in the next year, if they're not already. So edge is a really big one and consumes a lot of the data. >> The buzzword does your now is met the metaverse, it's almost like the movie, the matrix is going to come in real time. But the fact is it's all this data, a lot of videos, some of the ones that I would call out here, you mentioned facial recognition, real-time analytics. A lot of the edge is going to be real-time inferencing, applying AI. And these are just a massive, massive data sets that you again, you and of course your customers are enabling. >> When we first came out with our very first Gen3 product, our marketing team actually asked me, "Hey, how can we show users how they can consume this?" So I actually set up a head to environment. I decided I'm going to learn how to do this. I set up this massive environment with Hadoop, and at the time they called big data, the 3V's, I don't know if you remember these big 3Vs, the volume, velocity and variety. Well Dave, did you know, there are now 10 Vs? So besides those three, we got velocity, we got valued, we got variability, validity, vulnerability, volatility, visualization. So I'm thinking we need just to add another beat of that. >> Yeah. (both laughing) Well, that's interesting. You mentioned that, and that sort of came out of the big data world, a dupe world, which was very centralized. You're seeing the cloud is expanding, the world's getting, you know, data is by its very nature decentralized. And so you've got to have the ability to do an analysis in place. A lot of the edge analytics are going to be done in real time. Yes, sure. Some of it's going to go back in the cloud for detailed modeling, but we are the next decade Kim, ain't going to be like the last I often say. (laughing) I'll give you the last word. I mean, how do you see this sort of evolving, who's going to be adopting this stuff. Give us a sort of a timeframe for this kind of rollout in your world. >> In terms of the timeframe. I mean really nobody knows, but we feel like Gen5, that it's coming out next year. It may not be a full rollout, but we're going to start seeing Gen5 devices and Gen5 infrastructure is being built out over the next year. And then follow very, very, very quickly by Gen6. And so what we're seeing though is, we're starting to see these graphics processors, These GPU's, and I'm coming out as well, that are going to be connecting, using PCIe interfaces as well. So being able to access lots and lots and lots of data locally is going to be a really, really big deal and order because worldwide, all of our companies they're using business analytics. Data is money. And the person that actually can improve their operational efficiency, bolster those sales and increase your customer satisfaction. Those are the companies that are going on to win. And those are the companies that are going to be able to effectively store, retrieve and analyze all the data that they're collecting over the years. And that requires an abundance of data. >> Data is money and it's interesting. It kind of all goes back to when Steve jobs decided to put flash inside of an iPhone and the industry exploded, consumer economics kicked in 5G now edge AI, a lot of the things you talked about, GPU's the neural processing unit. It's all going to be coming together in this decade. Very exciting. Kim, thanks so much for sharing this data and your perspectives. I'd love to have you back when you got some new perspectives, new benchmark data. Let's do that. Okay. >> I look forward to it. Thanks so much. >> You're very welcome. And thank you for watching this CUBE conversation. This is Dave Vellante and we'll see you next time. (upbeat music)

(happy techno music) >> Welcome everybody to this cube conversation. My name is Dave Vellante and we're joined today by Richard Goodwin. Who's the group director of IT at Ultra Leap and Avishek Kumar who manages Dell's power store product line and directs that product line along with several other lines for the company, gentlemen, welcome to the cube. >> Hi Dave >> Hi >> So Richard ultra leap, very cool company tracks, hand movements, and so forth. Tell us about the company and the technology, I'm really interested in how it's used. >> Yeah, we've had many product lines, obviously. We're at very innovative, um and the organization was spun up from PhD, a number of PhD students who were the co-founders for ultra leap. um And initially with mid-air haptics, um as you, as many people may have seen, but also hand tracking, mid-air had such a sense and feel. So, yeah, it's, it's, it's quite impressive what we have produced and the number of sectors and markets that we're in. Um And obviously to push us to, to where we are, we have relied upon lots of the Dell technology, both software and hardware. >> And what's your role at the company. >> Uh, I'm the group IT director, uh, I'm responsible for the it and business platforms or infrastructure network hardware software, and also the transparency of those platforms to ensure that we're scalable. And we are able to develop our software and hardware as rapidly as possible. >> Awesome. Yeah, a lot of data behind that too. I bet. Um okay Avishek, you direct a number of products at Dell across the portfolio, unity, extreme IO, the SC series, and of course, power vault. It's, it's quite the portfolio that you look after. So let's get into the case study. If we can, a bit, Richard, maybe you could paint a picture of, of your environment, some of the key applications that you're supporting and maybe what your infrastructure looks like. Give us a high level view. >> Sure. So pretty power store. We had quite a disparate architecture, so a fairly significant split and siding on the side of cloud, not as hybrid as we would like, and not, not as much as our on-prem as we would have liked and Hey, that, that has changed quite significantly. So we now have a number of servers and storage and storage arrays that we have on, on premise. And then we host ourselves. So we are moving quite rapidly, you know, as a, as a startup and then moving to a scale-up we needed that, that scalability and that versatility, and also the whole OPEX versus CapEx and, um, and also not being driven by lots of SaaS products and architecture and infrastructure where we needed to be in control because of our development cycles and our products, product development. >> So wait, oh okay. So, so too much cloud, you wanted to run a little bit of a dose of on-prem explain that a little bit more the cloud wasn't doing it for you in terms of your development cycle, your control, can you double click on that? >> Yeah. Some of the, some of the control and, you know, there's always a balance because there are certain elements of our development cycles and our engineering software engineering, where we need a very high parallelism for some of the work that we're doing, which then, you know, the CapEx investment makes things very, very challenging, not commercially that the right thing to do. However, there are some of our information, some of IP, some of the secure things that we do, we also do not want upgrades as an example, or any outages or certain types of server and spec that we need to be quite bespoke and unique, and that needs to be within our control. >> Got it, okay. Thank you for that. Avishek, we're going to talk about power store today. So set it up, please tell us about power store, what it is, you know, why it's important to this conversation. >> Sure, so power store store is a product that we launched may of 2020, roughly a little bit more than a year now. And it's a brand new architecture that Dell technologies released. And at the end of the day, I'll talk about a few unique aspects of the product, but at the end of the day, the, where we start with it's a storage platform, right? So where we see similar to what Richard is saying here, in terms of being able to consolidate the customer's environment, whether it is blog file, weevils, physical virtual environments, and, and it's, as I said, it's a brand new architecture where we leveraged pieces of existing products, where it makes sense and it's, we are using all the latest and greatest technologies delivering the best performance based data reduction and, and where we see a lot of traction is the options that it brings to the table for our customers in terms of flexibility, whether they want to add capacity compute, whether in fact, we have apps on the current model where customers can consolidate their compute as well on the static storage platform if needed. So a lot of innovation from a platform perspective itself, and it's not just about the platform itself, but what comes along with it, right? So we refer to it as an ecosystem, part of it, where we work with Ansible playbooks, CSI plugin, you name it, right. And it's, the storage platform by itself. Doesn't that, doesn't stand by itself in a customer's environment there are other aspects of the infrastructure that it needs to integrate with as well. Right? So if they're using Ansible playbooks, we want to make sure the integration is there. >> Got it. >> And last but not the least is the intelligence built into the platform, right? So as we are building these capabilities into the product, there is intelligence built into the product, as well as outside the product where things like cloud IQ, things like uh, um, technologies built into power suit itself makes it that much easier for the customers to manage the infrastructure and go from there. >> Thank you for that, so, Richard, what was the workload? So it actually, you started with sort of a Greenfield on prem. If I understand it correctly, what was the workload that you were sort of building around or workloads? >> Sorry, we had a, a number of different applications. Some of which we cannot really talk about too much, but we had, we had a VxRail, we had a, a smaller Dell array, and we have lots of what we classes, runners, cubeanetics cluster that we that we run and quite a few different VMs that run on our, on-prem server infrastructure and storage rates and the issues that we began to hit because of the high IO, from some of our, um, workloads that we were hitting very high latency, which rapidly stopped, began to cause us issues, especially with some of our software engineering teams. And that is when we embarked upon a competitive RFP for uh, Dell power store. Dell were already engaged from an end-user compute where they'd been selected as the end-user compute provider from a previous competitive RFP. And then we engaged them regarding the storage issue that we had, and we engaged the, our account leading count exec, and a number of solution architects were working with us to ensure that we have the optimal solution. Dell were selected over the competitors because of many reasons, you know, the, the, the new technology, the DG plication, the compression, that data overall data reduction, and the guarantee that also came, uh, came with that, with the four to one data reduction guarantee, which was significant to us because of the amount of data that we hold. Um, And we have, you know, as I mentioned, we're pulling further, further data of ours back into our hosted environments, which will end up on the power store, especially with the duplication that we're now getting. We've actually hit nine to one, which is significant. We were expecting four to one, maybe five to one with some of the data types. And what was excellent Dale were that confident that they did not even review our data types prior. And they were willing to stand by that guarantee of four to one and we've excelled that we've got different data types on, on that array, and we've hit nine to one and that's gradually grown over the last nine months. You know, we were kind of six them we moved to seven and now we're hitting nine to one ratios. >> That's great. So you get a little free storage. That's interesting what you're saying, Richard, cause I just assumed that a company that's guaranteed four to one is going to say, okay, let us, let us inspect your workload first and then we'll do the deal. So Avishek, what's the tech behind that data reduction that you're able to with such confidence, not have to pre inspect the workload in this case anyway. >> Yeah. So, so it goes back to the technologies that goes behind the product, right? So, so we, we stand behind the technology and we want to make it simpler for our customers as well. Where again, we don't want to spend weeks looking at all the data, scanning all the data before giving the guarantee. So we stand behind the technology where we understand that as the data is coming in, we are always going to be duplicated. We are always going to compress it. There is technology within the product where we are offloading some of that to the outside the CPU. So it is not impacting the performance that the applications are going to see. So a data reduction by itself is not going to get enough performance by itself is not good enough. Both of them have to be together. Right. So, and that's what powers to brings to the table. >> Yeah. Thank you. So Richard, I'm interested. I mean, I remember the power store announcement, sort of saw it leading up to it. And one of the big thrusts from Dell was the way I phrase it is essentially trying to create a cloud-like experience on-prem. So really focused on simplicity. So my question to you is, let's start with just the deployment. You know, how complicated was it to install? What was that process like? How many clicks, I mean, not that you have to tell me how many clicks, but you know, what I'm asking is, is how difficult was it to get from zero to, you know, up and running? >> Well, we actually sat down with a very difficult challenge. We were in quite a difficult situation where we'd pretty much got off of a cliff in terms of IOPS performance. So the RFP was quite rapid. And then we needed to get which, whoever, which vendor was successful, we need to get that deployed rather rapidly and on the floor in our data center and server rooms, which we did. And it was very, very simplistic within three weeks of placing the order. We had that array in our server rack and we'd begun the migration that it was very simple to set up. And the management of that array has been, we we've seen say 40% reduction in terms of effort it took to be able to manage our storage because it is very self-contained, you know, even from a reporting perspective, the deployment, the migration was all very, very, very simplistic. And, you know, we we've done some work recently where we had to also do some work on the array and some other migrations that we were doing and the resilience came, came to, came to the forefront of where the whole architecture and no single point of failure enabled us to do some things that we needed to do quite rapidly because of the, the jole notes and the resilience within, within the unit and within the power store itself was considerable where we, we kept performance up. People also prioritize any discreet rebuilds, keeps the incoming ingest rates high and prioritizes that, you know, the workloads, which is really impressive, especially when we are moving so quickly with our technology. We don't really have much time to, you know, micromanage the estate. >> Can you, can you just repeat what you said on the percent reduction? I think I heard you cut out there a little bit, a percent reduction on, on, on management, on, on, on the labor side. >> So our lead storage engineer is estimated around 40% less management. >> Wow. Okay. So that's, that's good. So actually, I, I love this conversation because, you know, in the early the days of automation, people are like, ah, that's my job provisioning, LUNs. I'm really good at it. But I think people are realizing that it's actually not something that you want to be really good at. It's something that you want to eliminate. So it now maybe it's a, that, storage engineer got his or her nights and weekends back, uh, but, but what do they do now when they get that extra time, what do you, what do you put them on? You know, no more strategic initiatives or, you know, other, other tech things in the to-do list, what's that like? >> You know, any of my team, whether it's the storage leads or some of the infrastructure team that are also involved in engaged, cause you know, the organization, we have to be quite versatile as a team in our skillsets. We don't want to be doing those BAU mundane tasks. Even the storage engineer does not want to be, you know, allocating Luns and allocating storage to physical servers, VMs, et cetera. We want all of that to be automated. And the, you know, those engineers, are they working on some of the cutting edge things that we're trying to do with machine learning as a, as an example, which is much more interesting, it's what they want to be doing. Um, you know, that aides, the obvious things like retention interest and personal development, we don't want to be, you know, that base IT infrastructure management is, is, is not, not where any of the engineers wants to be. >> In terms of the decision to go with Dell power store. I, I, I'm definitely hearing there was a relationship. There was an existing relationship with Dell. I'm sure that played into it. And you, you mentioned a couple of times that RFP, so, so you kind of lined up various various vendors. What can you tell us about that in, in addition to the relationship, what was it that led you to power store? >> Uh, there were many things saying, you know, the relationship wasn't really part of this, even though I've mentioned the end user compute in any sets or anything that we're procuring, we want best of breed and best of set, but, and there were four vendors that were engaged in the RFP and it was down selected to, two, and that was done on the cost is definitely a driver. The technology, you know, is a big trust to us. We're a tech company. New technology to us is also fascinating, not only our own but also the, the storage guarantee, the simplicity, the resilience within, within the unit. Also the, the ability which was key to us because of what we're trying to do with our hybrid model and bring, bring back and repatreize some of the data as it were um, from the client, we needed that ability to, with ease, to be able to scale up and scale out, and the power store gave us that. >> When you say cost of, I want to dig into that price or, you know, the, the, the, the price tag or the, the cost. I mean, when you do the business case, and I wonder if we could add a little color to that. >> Yeah, the, the, there there's two elements to this, so there's not even the cost of the price tag, but then also cost of ownership and the comparisons that we were running against the other vendors, but also the comparisons that we were running from a CapEx investment against OPEX and what we have in the cloud, and also the performance and performance that we get from the cloud and our cloud storage and a resilience within that, and then also the initial price tag, and then comparing the CapEx investments to the OPEX were all elements that were, were key to us making our decision. And you know that there has to be some credit taken by the Dell account team and their relationship towards the final phrase of that RFP, you know, were key, initially, not at all, we were just looking for the best possible storage solution for ultra-leap. >> And to, to determine that on your end, was that like a feature, because it's sometimes fuzzy what the business impact is going to be like that 40% you mentioned, or the data reduction at nine to one, when there's a promise of four to one, did you, what did you do? Did you kind of do a feature function analysis and sort of line that up and, and say, okay, I'm going to map that to our business, our processes, our IT processes, and try to predict what the impact would be. Is that how you did it, or did you take a different approach? >> We did. So we did that, obviously between vendors as you'd expected in RFP, but then also mapping to how that would impact the business. And that that is not an easy process to go through. We've seen more gains, even comparing one vendor to another, some of that because of the technology, the terminology is very, very different and sometimes you have to bring that up a level and also gain a much more detailed understanding, which at times can be challenging, but we did a very like-for-like comparison and, and also lots of research, but you're quite right. The, the, the business analysis to what we needed. We had quite a good forecast and from my supplier stock and information data, and also our engineering and business and strategic roadmap, we were able to map those two together, not the easiest of experiences, not one that I want to repeat, but we got through it. >> Yeah, a little bit of art and science involved. Avishek, maybe you could talk about power store, what, you know, give us the commercial. What makes it different from other products in the market? Things like cloud IQ, maybe you could talk about that a little bit. >> Sure, so, so again, from a, a it's music to my ears, when Richard talks about the ease of deployment and the management, because there is a lot of focus on that. But even as I said earlier, from a manned technology perspective, a lot of goodness built in, in terms of being able to consolidate a customer's environment into, onto the platform. So that's more from a storage point of view that will give the best performance, give the best data reduction, storage efficiencies. Um, the second part, of course, the flexibility, the options that power store it gives to the customers in terms of sort of desegregating the storage and the compute aspects of it. So if, as a customer, I want to start with different points in terms of what our customer requirements are today, but going forward as requirements changed from a compute capacity perspective, you can use a scale up and scale out capabilities, and then the intelligence built in, right? So as you scale out your cluster, being able to move storage around right, as needed being able to do that non-disruptively. So instead of saying that Mr. Customer you're, you're storage is going to, you're at 90% capacity, being able to say that based on your historical trending, we expect you run out of capacity in six months, some small things like that. Right. And of course, if the, the dial home, the support assist capabilities that are enabled, cloud IQ brings a lot of intelligence to the table as well. In addition to that, as they mentioned earlier, there is apps on capability that gives another level of flexibility to the customers to integrate your storage infrastructure into a virtual environment. If the customer chooses to do that. And last but not the least, it's not just about the product right? So it's about the programs that we have put around it. Any anytime I'll create is a big differentiator for us, where it's an investment protection program for customers, where if they want to have the peace of mind, in terms of three months, nine months, three years down the line, if we come out with new technologies, being able to be upgrade to that non-disruptively is a big part of it as well. It's a peace of mind for the customers that, yes, I'm getting into the power store architecture today, but going forward, I am I'm protected from that point. So anytime I upgrade, it's a new business program that we put around leveraging the architectural benefits of power stool, whether your compute requirements, your storage requirements change you're, you're, you're covered from that point of view. So again, very quick a overview of, of what power store is, why it is different, and again, that's where that comes from. >> Thank you for that. Richard, are you, are you actively using cloud IQ? Do you get, what kind of value do you get from it? >> Not currently. However, we have, we have had plans to do that. The uptake and BCR, our internal workload has not allowed us to do that, but one of the other key reasons for selecting power source was the, the non-disruptive element, you know, with other SaaS products, other providers, and other issues that we have experienced. That was one, that was a, a key decision for us from a, a power store perspective. One of the other, you know, I would like to go back to the conversation slightly, in terms of performance, we are getting, getting now, you know, there's a 400% speed of improvement of publishing. We've got an 80% faster code coverage. So our firmware builds a 1300% quicker than they were previously and, and the time savings of the storage engineer and, you know, as a, as director of IT, I often asked for certain reports from, from the storage array, when we're working out for, um, storage forecast, performance forecast. And, you know, when we're coming close to product releases, code drops that we're trying to manage, the reporting or the power stories is impressive. Whereas previously my storage engineer would not be the, the most happiest of people when I would be trying to pull, you know, monthly and quarterly reports, et cetera. Whereas now it's, it's easy and we have live dashboards running, and we can easily extract that information. >> I love that, because, you know, so often we talk about the 40% reduction in IT, labor, uh, which, which, okay, that's cool. But then your CFO's going to say, yeah, but it's not like we're getting rid of people. We, you know, we're still spending that money and you, okay. They're getting you're now into soft dollars, but when you talk about 400%, 18%, 1300% of what you're talking about, business impact and that's telephone numbers to a CFO. So I love those metrics. Thank you for sharing. >> Yeah. But what would, they, obviously, in some of our dashboards when they visualize that they are very hard hitting, you know, the impact that you're quite right that the CFO does chase down, you know, the availability and the resource profile, however, we're on a huge upward trajectory. So having the right resilience and infrastructure in places is exactly what we need. And as I mentioned before, those engineers are all reallocated to much more interesting work. And, you know, the, the areas that will actually drive our business forward. >> Speaking of resilience, are you doing any replication? >> Not currently. However, there, uh, we've actually got a meeting regarding this today with some of that was a surprise that some of their storage specialists in a couple of hours time, actually, because that is a very high on the agenda for us to be able to replicate and have a high availability cluster and another potentially power store name. >> So I was going to ask you kind of where you want to take this thing. I'm hearing you, you're looking at cloud IQ, really try to exploit that. So you've got some headroom here in terms of the value that you can get out of this platform to, to do replication, faster recovery, et cetera, maybe protect against, you know, events. Any other things that you would identify as things you would either want from Dell or things that you'd like to see this platform direction you'd like to see it take in the future? >> Uh, yeah. We, we actually had some discussions recently and we are actively involved in some of the power store roadmap, which is, which is really good for us because we get visibility. And we also get to feed back to Dell on some of the features that we would like to see. So one of the things that we're discussing is a virtual kind of power store is what we would like to see. So some of that resilience would be really useful for us to be able to fail over quite rapidly and have live access to you are sick of data rather than potentially having hole sites. And we're looking at some of the Dell service offerings, which are quite impressive and is currently ticking. You know, we're very early in the, in the stages of the discovery, but there's quite a few boxes being ticked. Currently. >> Guys, we got to leave it there. I love this example of where you've got infrastructure, really connecting directly to a fast growth company, helping it scale, guys, thanks so much for your time. Really appreciate your insights. >> Thank you >> And thank you And thank you for watching this cube conversation. This is Dave Volante, and we'll see you next time. (upbeat music)

(bright upbeat music) >> Welcome to theCUBE, our coverage of Dell Technologies World, the Digital Experience continues. I have a long-time guest coming back, joining me in the next segment here. Jon Siegal is back, the Vice President of Product Marketing at Dell Technologies. Jon, it's good to see you, welcome back to the program. >> Thanks Lisa, always great to be on. >> We last spoke about six months ago and here we are still at home. >> I know. >> But there has been no slowdown whatsoever in the last year. We were talking to you a lot about Edge last time but we're going to talk about PowerStore today. It's just coming up on its one year anniversary. You launched it right when the pandemic happened. >> That's right. >> Talk to me about what's happened in the last year with respect to PowerStore. Adoption, momentum, what's going on? >> Yeah, great, listen, what a year it's been, right? But certainly for PowerStore especially, I mean, customers and partners around the world have really embraced PowerStore, specifically really it's modern architecture. What many people may not know is this is actually the fastest ramping new architecture we've had in all of Dell's history, which is quite a history of course. And we saw 4 X quarter over quarter growth in the most recent quarter. And you know, in terms of shipments, we've shipped well over 400 petabytes of PowerStore, you know, so special thanks to lots of our customers around the world and industries like education, gaming, transportation, retail. More than 60 countries, I think 62 countries now. They include customers like Columbia Southern University, Habib Bank, Real Page, the University of Pisa and Ultra Leap, just to name a few. And to give you a sense of how truly game changing it's been in the market is that approximately 20% of the customers with PowerStore are new to Dell, new to Dell Technologies. And we've tripled the number of wins against some of our key competitors in just the last quarter as well. So look, it's been quite a year, like you said and we're not stopping there. >> Yeah, you must have to wear a neck brace from that whiplash of moving so quickly. (both laughing) But that's actually a good problem to have. >> It is. >> And curious about, is it 20% of the PowerStore customers are net new to Dell? >> Yeah. >> Interesting that you've captured that much in a very turbulent year. Any industries in particular that you see as really being transformed by the technology? >> Yeah, it's a great question. I think just like we're bringing a disruptive technology to market, there's a lot of industries out there that are disrupting themselves as well, right, and how they transform, particularly with, you know, in this new era during the pandemic. I think, I can give you a great example. One of the new capabilities of PowerStore is AppsON just for those that aren't familiar. AppsON is the ability for PowerStore to run apps directly on the appliance, good name, right? And it's thanks to a built-in VMware ESXi hypervisor. And where we've seen really good traction with AppsON, is in storage intensive applications at the edge. And that brings me to my example. And this one's in retail. And you know, of course just like every industry I think it's been up-ended in the past year. There's a large supermarket chain in northern China that is new to Dell. During the pandemic they needed to fast-track the development of a smart autonomous retail system in all their stores, so that their customers could make their purchases via smartphone app. And again, just limiting the essentially the person to person interaction during the pandemic and this required a significant increase in transaction processing to get to the store locations that they didn't have equipment for before, as well as support for big data analytics applications to understand the customer behavior that's going on in real-time. So the net result is they chose PowerStore. They were new to Dell and they deployed it in their stores and delivered a seamless shopping experience via smartphone apps. The whole shopping experience was completely revolutionized. And I think this is really a great example of again, how the innovations that are in PowerStore are enabling our customers to really rethink how they're transacting business. >> Well, enabling the supermarkets to be the edge but also in China where everything started, so much, the market dynamics are still going on, but how quickly were they able to get PowerStore up and running and facilitate that seamless smartphone shopping experience? >> It was only weeks, only weeks, weeks from beginning to getting them up to speed. I mean, we've had great coverage, great support. And again, they embraced, I mean, they happened to leverage the AppsON capabilities, so they were able to run some of their applications directly on the appliance and they were able to get that up and running very quickly. And they were already a VMware customer as well. So they were already familiar with some of the tools and the integration of the VMware. And again, that's also been a sweetspot for this particular offer. >> Okay, got it. So a lot in it's first year. You said 4 X growth, over 60 countries, 400 petabytes plus shipped, a lot of new net new customers. What is new? What are you announcing that's new and that's going to take that up even a higher level? >> That's right. We're always going to up the ante, right? We're always going to, we can't rest on our laurels for too long. Look, we're very excited to share what's new for PowerStore. And that is one of the reasons we're here of course. I can break it down into two key highlights. First is a major software update that brings more enterprise innovation, more speed, more automation in particular to both new and existing customers. And we're also excited to announce a new lower cost entry model for the PowerStore family called the PowerStore 500. And this offers an incredible amount of enterprise class storage capabilities, much of which I have talked about and will talk more about today, for the price. And the price itself is what's going to surprise some folks. It starts as low as 28,000 US street price which is pretty significant, you know, in terms of a game changer, we think, in this industry. >> So let's talk about the software update first. You've got PowerStore 2.0, happy birthday to your customers who are going to take advantage of this. >> That's right. >> Kind of talk me through what some of the technological advancements are that your customers are going to be able to leverage? >> That's a great point. Yeah, so from a software perspective I like how you said that, happy birthday, yeah so all of our, just to be clear from a software update perspective, all of our existing customers are going to get this as a simple free non-disruptive update. And this is a commitment we've had to our customers for some time. And really it's the mantra if you will, of PowerStore, which is all about ensuring that our customers can encounter our very flexible platform that will keep giving them the latest and greatest. So really a couple of things I want to highlight from PowerStore that are brand new. One is we're giving a speed boost to the entire PowerStore lineup. Customers now, existing customers, you get up to 25% faster, mixed workload performance which is incredible, right off the bat. Secondly, we're enabling our customers to take full advantage of NVME now across the data center with the option of running NVME over fiber channel. And this again requires just a simple software update and no additional hardware if they already have 32 gig capable switches and HBAs on-prem. We've also made our unique AppsON feature, which I just talked about in the China example, we've made that more powerful and with scale out. This means more aggregate power, more aggregate capacity and it makes it even more ideal now for storage intensive apps to run at the edge with PowerStore. Another capability that's been very popular with our customers is our data reduction specifically our intelligent Dido which is always on and automated. And now what it does is it enables customers to boost performance while still guaranteeing the four to one data reduction that we have, at the same time. So just to give a quick example, when the system is under extreme IO, duress if you will, it automatically prioritize that IO versus the DDUP itself and provides a 20% turbo boost if you will, of performance boost for the applications running. All this is done automatically, zero management effort, zero impact to the data reduction guarantee of four to one that we already have in place. And then the last highlight I'd like to bring up is, last but not least, is one we're really proud of is the ability for our customers to now take more cost advantage, if you will, cost effective advantage of SCM or storage class memory. PowerStore now differentiates between SCM drives and NVME drives within the same chassis. So they can use SCM as a high-performance layer, if you will with as few as one drive, right? So they don't have to populate the whole chassis, they can use just one SCM drive for cost-effectiveness, for embedded data access. And this actually helps reduce the workload latency by up to 15%. So, another great example on top of NVME that I already mentioned, of how PowerStore is leading the practical adoption of next generation technologies. >> Are you seeing with the lower cost PowerStore 500, is that an opportunity for Dell to expand into the midsize market and an opportunity for those smaller customers to be able to take advantage of this technology? >> Absolutely, yeah. So the PowerStore finder, which we're really excited about introducing does exactly what you just said, Lisa. It is going to allow us to bring PowerStore and the experience of PowerStore to a broad range of businesses, a much broader range of edge use cases as well. And we're really excited about that. It's an incredible amount of enterprise storage class performance, as I mentioned, and functionality for the price that is again, 28,000 starting. And this includes all of the enterprise software capabilities I've been talking about. The ability to cluster, four to one data reduction guarantee, anytime upgrades. And to put this in context, a single 2U appliance, the PowerStore 500 supports up to 2.4 million SQL transactions per minute. I mean, this thing packs a punch, like no other, right? And it's a great fit for stand-alone or edge deployments in virtually every industry, we've mentioned retail already also healthcare, manufacturing, education and more. It's an offering that's really ideal for any solution that requires an optimization of price/performance, small footprint and effortless automation. And I can tell you, it's not just customers that are excited about this, as you can imagine our channel partners, they can't wait to get their hands on this either. >> Was just going to ask you about the channel. >> It is going to help them reach new sets of customers that they never had before. You mentioned midsize, but also in addition to that, it's just going to open it up to all new sets of use cases as well. So I'm really excited to see the creativity from our channel partners and customers and how they adopt and use the PowerStore 500 going forward. >> Tell me about some of those new use cases that it's going to open up. We've seen so many new things in the last year and such acceleration. What are some of the new use cases that this is going to help unlock value for? >> Yeah, again, I think it's going to come down a lot to the edge in particular, as well as mid-size, it can run, again, this can run storage, intensive applications. So it's really about coming down to a price point that I think the biggest example will be mid-sized businesses that now, it's now affordable to. That they weren't able to get this enterprise class capabilities in the past more than anything else. Cause it's all the same capabilities that I've mentioned but it allows them to run all types of things. It could be, they could run, new next-generation intensive data, intensive databases. They can run VDI, they can run SQL, it does, essentially more than anything else makes existing use cases more accessible to mid-sized businesses. >> Got it, okay. So, so much momentum going on in the first year. A lot of that you're souping it up with this your new software, we talked about the new mid-size enterprise version PowerStore 500. What else can we expect from PowerStore, the rest of calendar 2021? >> Yeah, I think lots of things. So first of all we're so pleased at the amount of commitment to innovation that we've had over the past year. We're going to continue to work very closely with VMware to drive more and more innovation and enhancements with capabilities like AppsON that I talked about, and VM-ware or (indistinct) which is a key enabler for that. We're also committed to continuing to lead the industry in the adoption of modern technologies. I gave some good examples today of NVME and AppsON and SCM, storage class memory, and customers can expect that continued commitment. Look, we've designed PowerStore from the ground up to be very flexible so that it can be enhanced and improved non-disruptively. And I think we did that with this release. We proved that and no one can predict the future, clearly, it's been a crazy year. And so businesses need storage that's going to be flexible with them and grow with them and evolve with them. And customers can expect that from PowerStore. And we plan on doing just that. >> So customers can, that are interested can go direct to Dell. They can also go through your huge channel, you said, in terms of those customers that are thinking about it maybe adding to the percentage of new customers. What's your advice on them in terms of next steps? >> Yeah, next steps is, you know, I got to say this, we've done, it's crazy, we've done over 20,000 demos of PowerStore in one year, no joke. And you know, it's a new world. And so the next step is to reach out to Dell. We'd love to showcase this through a demo, give them whether it's a remote experience that way or remote proof of concept but yeah, reach out to Dell, your local rep or local channel partner and we'd love to show you what's possible more than anything else and look, we're really proud of what we've accomplished here. Just as impressive as these updates, I must say, is that in many instances, the team that brought this to market, the engineering team, they did this just like we're doing today, right? Over Zoom, remotely, while balancing life and work. So I just also want to thank the team for their commitment to delivering innovation to our customers. It hasn't wavered at all and I want to thank our top notch team. >> Right, an amazing amount of work done. You've had a very busy year and glad that you're well and healthy and been as successful with PowerStore. We can't wait to see in the next year those numbers that you shared even go up even more. Jon, thank you for joining us >> Looking forward to it. and sharing what's new with PowerStore. We appreciate your time. >> Always a pleasure, Lisa. >> Likewise >> Look forward to talking to you soon. >> Yeah >> Take care. >> For Jon Siegal, I'm Lisa Martin, you're watching theCUBE's coverage of Dell Technologies World, a Digital Experience. (slow upbeat music)

>>live from San Francisco. It's the Q covering Micron Insight 2019 >>Not to You, by Micron. >>Welcome back to San Francisco, everybody. This is a Micron Insight 2019 and you're watching the Cube, the leader in live coverage on Day Volonte with my co host, David Floyd. Di Mooney is here. She's the executive director of Micron gives. That's right. Give us the story. What's happening with Micron gives Tech for good. We love the tech for good stories. Tech companies are really taking this seriously. This is not just lip service. Give us the update. >>That's right. That's right. We're so proud of our company that they established a foundation 20 years ago to give back to our global communities. And since then we have given $115 million away and over 10,000 grands. So we have seen a lot of different opportunities in our global communities, and it's just been fabulous that our company supports >>you talk today about water dot or what's going on there. Why is that important in what your role there. >>So what we did is we started taking a look at an organization that we have. We have started recently binning beam or engaged with basic human needs and the grants that those support And when we were taking a look at, Really, what is the primary basic human need? Way discovered? It really is the need for water, and there are millions of people that cannot access this precious resource, and it's just was really surprising to us to think way, take it for granted so much. But yet it is very difficult to get. So as we took a look at this, there was a lot of information that this organization collects. And so we thought, Well, this will be a great opportunity for us to utilize information to enrich and bring in some of our advanced computing expertise along with our philanthropy, help them reach their mission even greater. >>This is huge. I was an event earlier this week, and the keynote speaker was an ultra marathoner, and he literally at one point he ran 4500 miles across the continent of Africa. He and two other ultra runners and people were asking what was The biggest challenge was that the heat was the painting. You know, the biggest challenge was see the challenges of of the community's getting part of the water. That was the number one thing that you know. He left the impression So I mean, this is a huge global problem. >>It really is. And our manufacturing operations were global, and we are located in water scarce areas of the world. And so what really became you know, it's a Micron issue to one of our biggest environmental issues that we talked about, and water dot org's has just been a >>leader in this space, and it has been just fabulous to work with on >>really, they have so much passion and dedication towards this. They've been ableto help. 22 million people already. >>All right, so they're lining up for the main stage. Just give us real quick some of the grants that you guys have. >>Last year at this event, we announced our advancing curiosity, and we announced three recipients last year, and since then we have four more. That's U C L. A. All right T, University of Texas at Austin and University of Washington. >>Awesome. That's great. Listen, congratulations. D on all your great work. We really appreciate your ticket sometime in the queue. All right, and thank you for watching her body. We're back with our next guest from Micron inside. 2019 on the Cube, right back.

>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>Welcome everybody to the port of Miami. My name is Dave Vellante and you're watching the cube, the leader in live tech coverage. We go out to the events, we extract the signal from the noise and we're here at the IBM data and AI form. The hashtag is data AI forum. This is IBM's. It's formerly known as the, uh, IBM analytics university. It's a combination of learning peer network and really the focus is on AI and data. And there are about 1700 people here up from, Oh, about half of that last year, uh, when it was the IBM, uh, analytics university, about 600 customers, a few hundred partners. There's press here, there's, there's analysts, and of course the cube is covering this event. We'll be here for one day, 128 hands-on sessions or ER or sessions, 35 hands on labs. As I say, a lot of learning, a lot of technical discussions, a lot of best practices. >>What's happening here. For decades, our industry has marched to the cadence of Moore's law. The idea that you could double the processor performance every 18 months, doubling the number of transistors, you know, within, uh, the footprint that's no longer what's driving innovation in the it and technology industry today. It's a combination of data with machine intelligence applied to that data and cloud. So data we've been collecting data, we've always talked about all this data that we've collected and over the past 10 years with the advent of lower costs, warehousing technologies in file stores like Hadoop, um, with activity going on at the edge with new databases and lower cost data stores that can handle unstructured data as well as structured data. We've amassed this huge amount of, of data that's growing at a, at a nonlinear rate. It's, you know, this, the curve is steepening is exponential. >>So there's all this data and then applying machine intelligence or artificial intelligence with machine learning to that data is the sort of blending of a new cocktail. And then the third piece of that third leg of that stool is the cloud. Why is the cloud important? Well, it's important for several reasons. One is that's where a lot of the data lives too. It's where agility lives. So cloud, cloud, native of dev ops, and being able to spin up infrastructure as code really started in the cloud and it's sort of seeping to to on prem, slowly and hybrid and multi-cloud, ACC architectures. But cloud gives you not only that data access, not only the agility, but also scale, global scale. So you can test things out very cheaply. You can experiment very cheaply with cloud and data and AI. And then once your POC is set and you know it's going to give you business value and the business outcomes you want, you can then scale it globally. >>And that's really what what cloud brings. So this forum here today where the big keynotes, uh, Rob Thomas kicked it off. He uh, uh, actually take that back. A gentleman named Ray Zahab, he's an adventure and ultra marathon or kicked it off. This Jude one time ran 4,500 miles in 111 days with two ultra marathon or colleagues. Um, they had no days off. They traveled through six countries, they traversed Africa, the continent, and he took two showers in a 111 days. And his whole mission is really talking about the power of human beings, uh, and, and the will of humans to really rise above any challenge would with no limits. So that was the sort of theme that, that was set for. This, the, the tone that was set for this conference that Rob Thomas came in and invoked the metaphor of superheroes and superpowers of course, AI and data being two of those three superpowers that I talked about in addition to cloud. >>So Rob talked about, uh, eliminating the good to find the great, he talked about some of the experiences with Disney's ward. Uh, ward Kimball and Stanley, uh, ward Kimball went to, uh, uh, Walt Disney with this amazing animation. And Walter said, I love it. It was so funny. It was so beautiful, was so amazing. Your work 283 days on this. I'm cutting it out. So Rob talked about cutting out the good to find, uh, the great, um, also talking about AI is penetrated only about four to 10% within organizations. Why is that? Why is it so low? He said there are three things that are blockers. They're there. One is data and he specifically is referring to data quality. The second is trust and the third is skillsets. So he then talked about, you know, of course dovetailed a bunch of IBM products and capabilities, uh, into, you know, those, those blockers, those challenges. >>He talked about two in particular, IBM cloud pack for data, which is this way to sort of virtualize data across different clouds and on prem and hybrid and and basically being able to pull different data stores in, virtualize it, combine join data and be able to act on it and apply a machine learning and AI to it. And then auto AI a way to basically machine intelligence for artificial intelligence. In other words, AI for AI. What's an example? How do I choose the right algorithm and that's the best fit for the use case that I'm using. Let machines do that. They've got experience and they can have models that are trained to actually get the best fit. So we talked about that, talked about a customer, a panel, a Miami Dade County, a Wunderman Thompson, and the standard bank of South Africa. These are incumbents that are using a machine intelligence and AI to actually try to super supercharge their business. We heard a use case with the Royal bank of Scotland, uh, basically applying AI and driving their net promoter score. So we'll talk some more about that. Um, and we're going to be here all day today, uh, interviewing executives, uh, from, uh, from IBM, talking about, you know, what customers are doing with a, uh, getting the feedback from the analysts. So this is what we do. Keep it right there, buddy. We're in Miami all day long. This is Dave Olanta. You're watching the cube. We'll be right back right after this short break..

(techy music playing) >> Hello, everyone, welcome to theCUBE Studios here in Palo Alto, California. I'm John Furrier, the cofounder of SiliconANGLE Media Inc., also cohost of theCUBE. We're here for a CUBE Conversation on Thought Leader Thursday and I'm here with Chris Cummings, who's a senior manager, advisor, big-time industry legend, but he's also the Chasm Group, right now, doer, Crossing the Chasm, famous books and it's all about the future. Formerly an exec at Netapp, been in the storage and infrastructure cloud tech business, also friends of Stanford. Season tickets together to go to the tailgates, but big Cal game coming up of course, but more importantly a big-time influence in the industry and we're going to do some drill down on what's going on with cloud computing, all the buzzword bingo going on in the industry. Also, AWS, Amazon Web Services re:Invent is coming up, do a little preview there, but really kind of share our views on what's happening in the industry, because there's a lot of noise out there. We're going to try to get the signal from the noise, thanks for watching. Chris, thanks for coming in. >> Thank you so much for having me, glad to be here. >> Great to see you, so you know, you have seen a lot of waves of innovation and right now you're working with a lot of companies trying to figure out the future. >> That's right. >> And you're seeing a lot of significant industry shifts. We talk about it on theCUBE all the time. Blockchain from decentralization all the way up to massive consolidation with hyper-convergence in the enterprise. >> Mm-hmm. >> So a lot of action, and because of the day the people out in the marketplace, whether it's a developer or a CXO, CIO, CDO, whatever enterprise leader's doing the transformations. >> Chris Collins: We got all of them. >> They're trying to essentially not go out of business. A lot of great things are happening, but at the same time a lot of pressure on the business is happening. So, let's discuss that, I mean, you are doing this for work at the Chasm Group. Talk about your role, you were formerly at Netapp, so I know you know the storage business. >> Right. >> So we're going to have a great conversation about storage and impact infrastructure, but at the Chasm Group how are you guys framing the conversation? >> Yeah, Chasm Group is really all about helping these companies process their thinking, think about if they're going to get to be a platform out in the industry. You can't just go and become a platform in the industry, you got to go knock down problem, problem, problem, solution, solution, solution. So we help them prioritize that and think about best practices for achieving that. >> You know, Dave Alante, my co-CEO, copartner, co-founder at SiliconANGLE Media and I always talk about this all the time, and the expression we use is if you don't know what check mate looks like you shouldn't be playing chess, and a lot of the IT folks and CIOs are in that mode now where the game has changed so much that sometimes they don't even know what they're playing. You know, they've been leaning on this Magic Quadrant from Gartner and all these other analyst firms and it's been kind of a slow game, a batch kind of game, now it's real time. Whatever metaphor you want to use, the game has changed so the chessboard has changed. >> Chris: Mm-hmm. >> So I got to get your take on this because you've been involved in strategy, been on product, you worked at growth companies, big companies, start-ups, and now looking at the bigger picture, what is the game? I mean, right now if you could lay out the chessboard, what are people looking at, what is the game? >> So, we deal a lot with customer conversations and that's where it all kind of begins, and I think what we found is this era of pushing product and just throwing stuff out there. It worked for a while but those days are over. These folks are so overwhelmed. The titles you mentioned, CIO, CDO, all the dev ops people, they're so overwhelmed with what's going on out there. What they want is people to come in and tell them about what's happening out there, what are their peers doing and what problems are they trying to solve in order and drive it that way. >> And there's a lot of disruption on the product side. >> Yes. >> So tech's changing, obviously the business models are changing, that's a different issue. Let's consider the tech things, you have-- >> Mm-hmm. >> A tech perspective, let's get into the tech conversation. You got cloud, you got private cloud, hybrid cloud, multi-cloud, micro-machine learning, hyper-machine learning, hyper-cloud, all these buzzwords are out there. It's buzzwords bingo. >> Chris: Right. >> But also the reality is you got Amazon Web Services absolutely crushing it, no doubt about it. I mean, I've been looking at Oracle, I've been looking at Google, I've been looking at SAP, looking at IBM, looking at Alibaba, looking at Microsoft, the game is really kind of a cloak and dagger situation going on here. >> That's right. >> A lot of things shifting on the provider side, but no doubt scale is the big issue. >> Chris: That's right. >> So how does a customer squint through all this? >> The conversations that I've had, especially with the larger enterprises, is they know that they've got to be able to adopt and utilize the public cloud capabilities, but they also want to retain that degree of control, so they want to maintain, whether it's their apps, their dev ops, some pieces of their infrastructure on prem, and as you talked about that transition it used to be okay, well we thought about cloud was equal to private cloud, then it became public cloud. Hybrid cloud, people are hanging on to hybrid cloud, sometimes for the right reasons and sometimes for the wrong reasons. Right reasons are because it's critical for their business. You look at somebody, for instance, in media and entertainment. They can't just push everything out there. They've got to retain control and really have their hands around that content because they've got to be able to distribute it, right? But then you look at some others that are hanging on for the wrong reasons, and the wrong reasons are they want to have their control and they want to have their salary and they want to have their staff, so boy, hybrid sounds like a mix that works. >> So I'm going to be having a one-on-one with Andy Jassy next week, exclusive. I do that every year as part of theCUBE. He's a great guy, good friend, become a good friend, because we've been a fan of him when no one loved Amazon. We saw the early, obviously at SiliconANGLE, now he's the king of the industry, but he's a great manager, great executive, and has done a great job on his ethos of Bezos and Amazon. Ship stuff faster, lower prices, the flywheel that Amazon uses. Everything's kind of on that-- And they own Twitch, which we stream, too, and we love. But if you could ask Andy any questions what questions would you ask him if you get to have that one-on-one? >> Yeah, well, it stems from conversations I've had with customers, which was probably once a week I would be talking to a CIO or somebody on that person's staff, and they'd slide the piece of paper across and say this is my bill. I had no idea that this was what AWS was going to drive me from a billing perspective, and I think we've seen... You know, we've had all kinds of commentary out there about ingress fees, egress fees, all of that sort of stuff. I think the question for Andy, when you look at the amount of revenue and operating margin that they are generating in that business, how are they going to start diversifying that pricing strategy so that they can keep those customers on without having them rethink their strategy in the future. >> So are you saying that when they slide that piece of paper over that the fees are higher than expected or not... Or low and happy, they're happy with the prices. >> Oh, they're-- I think they're-- I think it's the first time they've ever thought that it could be as expensive as on-premise infrastructure because they just didn't understand when they went into this how much it was going to cost to access that data over time, and when you're talking about data that is high volume and high frequency data, they are accessing it quite a bit, as opposed to just stale, cold, dead stuff that they want to put off somewhere else and not have to maintain. >> Yeah, and one of the things we're seeing that we pointed at the Wikibon team is a lot of these pricings are... The clients don't know that they're being billed for something that they may not be using, so AI or machine learning could come in potentially. So this is kind of what you're getting at. >> Exactly. >> The operational things that Amazon's doing to keep prices low for the customer, not get bill shock. >> Chris: That's right. >> Okay, so that's cool. What else would you ask him about culture or is there anything you would ask him about his plans... What else would you ask him? >> I think another big thing would be just more plans on what's going to be done around data analytics and big data. We can call it whatever we want, but they've been so good at the semi-structured or unstructured content, you know, when we think about AWS and where AWS was going with S3, but now there's a whole new phenomenon going on around this and companies are as every bit as scared about that transition as they were about the prior cloud transition, so what really are their plans there when they think about that, and for instance, things like how does GPU processing come into play versus CPU processing. There's going to be a really interesting discussion I think you're going to have with him on that front. >> Awesome, let's talk about IT. IT and information technology departments formerly known as DP, data processing, information-- All that stuff's changed, but there were still guys that were buying hardware, buying Netapp tries that you used to work for, buying EMC, doing data domain, doing a lot of stuff. These guys are essentially looking at potentially a role where-- I mean, for instance, we use Amazon. We're a big customer, happy customer. >> Chris: Mm-hmm. >> We don't have those guys. >> Chris: Right. >> So if I'm an IT guy I might be thinking shit, I could be out of a job, Amazon's doing my job, so I'm not saying that's the case but that's certainly a fear. >> Chris: Absolutely. >> But the business models have to shift from old IT to new IT. >> Chris: Mm-hmm. >> What does that game look like? What is this new IT game? Is it more, not a department view, is it more of a holistic view, and what's the sentiment around the buyers and your customers that you talk to around how do they message to the IT guys, like, look, there's higher valued jobs you could go to. >> Right. >> You mention analytics... >> That's right. >> What's the conversation? Certainly some guys won't make the transition and might not make it, but what's the narrative? >> Well, I think that's where it just starts with what segment are you talking about, so if you look at it and say just break it down between the large enterprise, the uber enterprise that we've seen for so long, mid-size and smaller, the mid-size and smaller are gone, okay. Outside of just specific industries where they really need that control, media and entertainment might be an example. That mid-size business is gone for those vendors, right? So those vendors are now having to grab on and say I'm part of that cloud phenomenon, my hyper-cloud of the future. I'm part of that phenomenon, and that becomes really the game that they have to play, but when you look at those IT shops I think they really need to figure out where are they adding value and where are they just enabling value that's being driven by cloud providers, and really that's all they are is a facilitator, and they've got to shift their energy towards where am I adding value, and that becomes more that-- >> That's differentiation, that's where differentiation is, so non-differentiated labor is the term that Wikibon analysts use. >> Oh, okay. >> That's going down, the differentiated labor is either revenue generating or something operationally more efficient, right? >> That's right, and it's all going to be revenue generating now. I mean, I used to be out there talking about things like archiving, and archiving's a great idea. It's something where I'm going to save money, okay, but I got this many projects on my list if I'm a CIO of where I can save money. I'm being under pressure about how am I going to go generate money, and that's where I think people are really shifting their eyeballs and their attention, is more towards that. >> And you got IOT coming down the pike. I mean, we're hearing is from what I hear from CIOs when we have a few in-depth conversations is look, I got to get my development team ramped up and being more cloud native, more microservice and I got to get more app development going that drives revenue for my business, more efficiency. >> Chris: Right. >> I have a digital transformation across the company in terms of hiring culture and talent. >> Chris: Mm-hmm. >> And then I got pressure to do IOT. >> Chris: Right. >> And I got security, so of those five things, IOT tends to fall out, security takes preference because of the security challenges, and then that's already putting their plate full right there. >> That's right, that's real time and those people are-- >> Those are core issues. >> Putting too much pressure on that right now and then you're thinking about IT and in the meantime, by the way, most of these places don't have the dev ops shop that's operating on a flywheel, right? So you're not... What's it, Goldman Sachs has 5,000 developers, right? That's bigger than most tech companies, so as a consequence you start thinking about well, not everybody looks like that. What the heck are they going to do in the future. They're going to have to be thinking about new ways of accessing that type of capability. >> This is where the cloud really shines in my mind. I think in the cloud, too, it's starting to fragment the conversations. People will try to pigeonhole Amazon. I see Microsoft-- I've been very critical of Microsoft in their cloud because-- First of all, I love the move that they're making. I think it's a smart move business-wise, but they bundle in 365 Office, that's not really cloud, it's just SAS, so then you start getting into the splitting of the hairs of well, SAS is not included in cloud. But come on, SAS is cloud. >> Chris: Mm-hmm. >> Well, maybe Amazon should include their ecosystem that would be a trillion dollar revenue number, so all companies don't look the same. >> That's right. >> And so from an enterprise that's a challenge. >> Chris: Mm-hmm. >> Do I got to hire developers for Asger, do I got to hire developers for Amazon, do I got to hire developers for Google. >> Chris: Mm-hmm. >> There's no stack consistency across private enterprises to cloud. >> Chris: So I have-- >> Because I'm a storage guy, I've got Netapp drives and now I've got an Amazon thing. I like Amazon, but now I got to go Asger, what the hell do I do? >> I got EMCs here and I got Nimbles there and HP and I've still got tape from IBM from five decades ago, so, John, I got a great term for you that's going to be a key one, I think, in the ability. It's called histocompatibility, and this is really about... >> Oh, here we go. Let's get nerdy with the tape glasses on. >> It's really about the ability to be able to inter-operate with all this system and some of these systems are live systems, they're current systems. Some of it's garbage that should've been thrown out a long time ago and actually recycled. So I think histocompatibility is going to be a really, really big deal. >> Well, keep the glasses on. Let's get down in the weeds here. >> Okay. >> I like the-- With the pocket protector, if you had the pocket protector we'd be in good shape. >> Yep. >> So, vendors got to compete with these buzzwords, become buzzword bingo, but there are trends that you're seeing. You've done some analysis of how the positionings and you're also a positioning guru as well. There's ways to do it and that's a challenge is for suppliers, vendors who want to serve customers. They got to rise above the noise. >> Chris: That's right. >> That's a huge problem. What are you seeing in terms of buzzword bingo-- >> Oh, my goodness. >> Because like I said, I used to work for HP in the old days and they used to have an expression, you know, don't call it what it is because that's boring and make it exciting, so the analogy they used was sushi is basically cold, dead fish. (laughing) So, sushi is a name for cold, dead fish. >> Chris: Yeah. >> So you don't call your product cold, dead fish, you call it sushi. >> Chris: Right. >> That was the analogy, so in our world-- >> Chris: That was HP-UX. >> That was HP-UX, you know, HP was very engineering. >> Yes. >> That's not-- Sushi doesn't mean anything. It's cold, dead fish, that's what it is. >> Right. >> That's what it does. >> That's right. >> So a lot of vendors can error in that they're accurate and their engineers, they call it what it is, but there's more sex appeal with some better naming. >> Totally. >> What are you seeing in terms of the fashion, if you will, in terms of the naming conventions. Which ones are standing out, what's the analysis. >> Well, I think the analysis is this, you start with your adjectives with STEM words, John, and what I mean by that is things like histocompatibility. It could start with things like agility, flexibility, manageability, simplicity, all those sorts of things, and they've got to line those terms up and go out there, but I think the thing that right now-- >> But those are boring, I saw a press release saying we're more agile, we're the most effective software platform with agility and dev ops, like what the hell does that mean? >> Yeah, I think you also have to combine it with a heavy degree of hyperbole, right? So hyperbole, an off-the-cuff statement that is so extreme that you'd never really want to be tested on it, so an easy way to do that is to add hyper in front of all that. So it's hyper-manageability, right, and so I think we're going to see a whole new class of words. There are 361 great adjectives with STEMs, but-- >> Go through the list. >> Honestly. >> Go through the list that you have. >> I mean, there's so many, John, it's... >> So hyper is an easy one, right? >> Hyper is easy, I think that's a very simple one. I think now we also see that micro is so big, right, because we're talking about microservices and that's really the big buzzword in the industry right now. So everything's going to be about micro-segmenting your apps and then allowing those apps to be manifest and consumed by an uber app, and ultimately that uber app is an ultra app, so I think ultra is going to be another term that we see heading into the spectrum as well. >> And so histocompatibility is a word you mentioned, just here in my notes. >> Yep. >> You mentioned, so histo means historical. >> Exactly. >> So it means legacy. >> Chris: That's right. >> So basically backwards compatible would be the boring kind of word. >> Chris: That's right. >> And histocompatibility means we got you covered from legacy to cloud, right. >> Uh-huh. >> Or whatever. >> You bet. >> Micro-segmentility really talks to the granularity of data-driven things, right? >> That's right, another one would be macro API ability, it's kind of a mouthful, but everyone needs an API. I think we've seen that and because they're consuming so many different pieces and trying to assemble those they've got to have something that sits above. So macro API ability, I think, is another big one, and then lastly is this notion of mobility, right. We talk about-- As you said earlier, we talked about clouds and going from-- It's not just good enough to talk about hybrid cloud now, it's about multi-cloud. Well, multi-cloud means we're thinking about how we can place these apps and the data in all kinds of different spaces, but I've got to be able to have those be mobile, so hyper-mobility becomes a key for these applications as well. >> So hyper-scale we've seen, we've seen hyper-convergence. Hyper is the most popular-- >> Chris: Absolutely. >> Adjective with STEM, right? >> Chris: It's big. >> STEM words, okay, micro makes sense because, you know, micro-targeting, micro-segmentation, microservices, it speaks to the level of detail. >> Chris: Right. >> I love that one. >> Chris: Right. >> Which ones aren't working in your mind? We see anything that's so dead on arrival... >> Sure, I think there's a few that aren't working anymore. You got your agility, you got your flexibility, you got your manageability, and you got your simplicity. Okay, I could take all four of those and toss those over there in the trash because every vendor will say that they have those capabilities for you, so how does that help you distinguish yourself from anyone else. >> So that's old hat. >> It's just gone. >> Yeah, never fight fashion, as Jeremy Burton at EMC, now at Dell Technologies, said on theCUBE. I love that, so these are popular words. This is a way to stand out and be relevant. >> That's right. >> This is the challenge for vendors. Be cool and relevant but not be offensive. >> Yeah. >> All right, so what's your take on the current landscape for things like how do companies market themselves. Let's say they get the hyper in all the naming and the STEM words down. They have something compelling. >> Chris: Right. >> Something that's differentiated, something unique, how do companies stand out above the crowd, because the current way is advertising's not working. We're seeing fake news, you're seeing the analyst firms kind of becoming more old, slower, not relevant. I mean, does the Magic Quadrant really solve that problem or are they just putting that out there? If I'm a marketer, I'm a B2B marketer. >> Yeah. >> Obviously besides working with theCUBE and our team, so obviously great benefits. Plug there, but seriously, what do you advise? >> Yeah, I think the biggest thing is, you know, you think about marketing as not only reaching your target market, but also enabling your sales force and your channel partners, and frankly, the best thing that I've found in doing that, John, is starting every single piece that we would come up with with a number. How much value are we generating, whether it's zero clicks to get this thing installed. It's 90% efficiency, and then prove it. Don't just throw it out there and say isn't that good enough, but numbers matter because they're meaningful and they stimulate the conversation, and that's ultimately what all of this is. It's a conversation about is this going to be relevant for you, so that's the thing that I start with. >> So you're say being in the conversation matters. >> Absolutely. >> Yeah. >> Absolutely. >> What's the thought leadership view, what's your vision on how a company should be looking at thought leadership. Obviously you're seeing more of a real-time-- I call it the old world was batch marketing. >> Chris: Mm-hmm. >> E-mail marketing, do the normal things, get the white papers, do those things. You know, go to events, have a booth, and then the new way is real-time. >> Chris: Mm-hmm. >> Things are happening very fast-- >> That's right. >> In the market, people are connected now. It's a global, basically, message group. >> That's right. >> Twitter, LinkedIn, Facebook and all this stuff. >> It's really an unfulfilled need that you guys are really looking to fill, which is to provide that sort of real-time piece of it, but I think vendors trip over themselves and they think about I need a 50 page vision. They don't need a 50 page vision. What they need is here are a couple of dimensions on which this industry is going to change, and then commit to them. I think the biggest problem that many vendors have is they won't commit, they hedge, as opposed to they go all in behind those and one thing we talk about at Chasm Institute is if you're going to fail, fail fast, and that really means that you commit full time behind what you're pushing. >> Yeah, and of course what the Chasm, what it's based upon, you got to get to mainstream, get to early pioneers, cross the chasm. The other paradigm that I always loved from Jeffrey Moore was inside the tornado. Get inside the tornado because if you don't get in you're going to be spun out, so you've got to kind of get in the game, if you will. >> Chris: That's right. >> Don't overthink it, and this is where the iteration mindset comes in, "agile" start-up or "agile" venture. Okay, cool, so let's take a step back and reset to end the segment here. >> Mm-hmm. >> Re:Invent's coming up, obviously that's the big show of the year. VMworld, someone was commenting on Facebook VMworld 2008 was the big moment where they're comparing Amazon now to VMworld in 2008. >> Chris: Right. >> But you know, Pat Gelsinger essentially cut a great deal with Andy Jassy on Vmware. >> Chris: Right. >> And everything's clean, everything's growing, they're kicking ass. >> Chris: Mm-hmm. >> They got a private cloud and they got the hybrid cloud with Amazon. >> Yeah, it's that VMcloud on Amazon, that really seems to be the thing that's really driving their move into the future, and I think we're going to see from both of those folks, you are going to see so much on containers. Containerization, ultra-containers, hyper-containers, whatever it may be. If you're not speaking container language, then you are yesterday's news, right? >> And Kubernetes' certainly the orchestration piece right underneath it to kind of manage it. Okay, final point, what's in store for the legacy, because you're seeing a few major trends that we're pointing out and we're watching very closely, which really I put into two buckets. I know Wikibon's a more disciplined approach, I'm more simple about that. The decentralization trend we're seeing with Blockchain, which is kind of crazy and bubbly but very infrastructure relevant, this decentralized, disrupting, non-decentralized incumbence, so that's one trend and the other one is what cloud's doing to legacy IT vendors, Oracle, you know, these traditional manufacturers like that HP and Dell and all these guys, and Netapp which is transforming. So you've got disruption on both sides, cloud and like a decentralized model, apps, what's the position, view, from your standpoint, for these legacy guys? >> It's going to be quite an interesting one. I think they have to ride the wave, and I'll steal this from Peter Levine, from Andreessen, right? He talks about the end of cloud computing, and really what that is is just basically saying everything is going to be moving to the edge and there's going to be so much more compute at the edge with IOT and you can think about autonomous vehicles as the ultimate example of that, where you're talking about more powerful computers, certainly, than this that are sitting in cars all over the place, so that's going to be a big change, and those vendors that have been selling into the core data center for so long are going to have to figure out their way of being relevant in that universe and move towards that. And like we were talking about before, commit to that. >> Yeah. >> Right, don't just hedge, but commit to it and move. >> What's interesting is that I was talking with some executives at Alibaba when I was in China for part of the Alibaba Cloud Conference and Amazon had multiple conversations with Andy Jassy and his team over the years. It's interesting, a lot of people don't understand the nuances of kind of what's going on in cloud, and what I'm seeing is it's essentially, to your point, it's a compute game. >> Chris: Yeah. >> Right, so if you look at Intel for instance, Alibaba told me on my interview, they don't view Intel as a chip company anymore, they're a compute company, right, and CJ Bruno, one of the executives there, reaffirmed that. So Intel's looking at the big picture saying the cloud's a computer. Intel Inside is a series of compute, and you mentioned that the edge, Jassy is building a set of services with his team around core compute, which has storage, so this is essentially hyper-converged cloud. >> That's right. >> This is a pretty big thing. What's the one thing that people might not understand about this. If you could kind of illuminate this trend. I mean, the old Intel now turned into the new Intel, which is a monster franchise continuing to grow. >> Mm-hmm. >> Amazon, people see the numbers, they go oh, my god, they're a leader, but they have so much more headroom. >> Chris: Right, right. >> And they've got everyone else playing catch up. >> Yeah. >> What's the real phenomenon going on here? >> I think you're going to see more of this aggregation phenomenon where one vendor can't solve this entire problem. I mean, look at most recently, in the last two weeks, Intel and AMD getting together. Who would've thought that would happen? But they're just basically admitting we got a real big piece of the equation, Intel, and then AMD can fulfill this niche because they're getting killed by NVIDIA, but you're going to see just more of these industry conglomerations getting together to try and solve the problem. >> Just to end the segment, this is a great point. NVIDIA had a niche segment, graphics, now competing head to head with Intel. >> Chris: That's right. >> So essentially what's happening is the landscape is completely changing. Once competitors no longer-- New entrants, new competitors coming in. >> Chris: Mm-hmm. >> So this is a massive shift. >> Chris: It is. >> Okay, Chris Cummings here inside theCUBE. I'm John Furrier of CUBE Conversation. There's a massive shift happening, the game has changed and it's incumbent upon start-ups, venture capital, you know, Blockchain, ICOs or whatever's going on. Look at the new chessboard, look at the game and figure it out. Of course, we'll be broadcasting live at AWS re:Invent in a couple weeks. Stay tuned, more coverage, thanks for watching. (techy music playing)