>> Live from San Diego, California It's the queue covering Sisqo live US 2019 Tio by Cisco and its ecosystem barters >> Welcome back. We're here, Cisco Live San Diego. You're watching the Cubans to minimum. My co host is Dave Volante and happy to welcome to the program. First of all, I have to tell Valente, no relation was the vice president of product management who are Cloud Platform in Solutions group at Cisco. And joining us is also Santana Dasgupta, who's a distinguished systems engineer at Cisco. We're gonna be talking about service Friday. Gentlemen, thanks so much for joining us. Of course. Alright, so jail let let's start the service. Freida Group, Of course. You know, we've heard for a long time how important service fighters are out there. Everything from service writers were going to become the new channel. A Sze Yu know customers less unless they're building their own data centers. You know, service fighters become a bigger environment. Tell us a little bit about you know your organ the latest What's going on in your customers? >> Yeah, So you know what? Cisco Obviously they are trying to help Ray in the transformation to actually multi cloud leveraging. Actually, the cloud benefits not only for enterprises and public sectors, but also for the service providers so that they can also reaped the benefit off the new actually trans technologies coming out, including five g on in that context. Obviously, if you really want to take advantage of Far Gina proper way going forward, starting actually with an evolution of architectures, you really have to look at the clouds and specifically what we call the telco cloud. >> Yeah, so the Espy market is going through a mass killed transformation, transformation in the business model and architecture and how you take the services to the market on one key. And it blew up the transformation that we believe is virtual elation, adopting the whole notion of telco cloud very virtualized your core functions for enabling the delivery of services in a more agile fashion into the market. But also it's all about transforming the court services construct itself. How do we push on the services element into the age of the net for being closer to the proximity of the Indians so that it enables much? Lord didn't see a new monitor visible applications, which is where service order to have a lot of open right now. >> So if I could just dig in on that for 1st 2nd you talk about services. So we watched that wave of network functions virtual ization, NFI where before it was I just had lots of appliances and rolling out each service individually, as opposed to what people want is they want, you know, the basically, you know, at market for the enterprise. And, you know, I just want to be able to get my services. You know, when I'm a consumer and you know, I want to do things well, I've got the Internet and I get those things. I need a similar environment from the service fighters going out to the Enterprise. Do I have that kind of high level, right? >> Yes, actually, we had on that bath. I mean, they're completely years as an industry were on the journey to actually get there on go. We initially talked about most of the core functions, like think ofthe armory packet corner policy or some some value added engine at the back end. But the world is evolving faster. To actually also think through that how we can add more consumer facing applications and services on top of it, like augmented reality, virtual reality, cloud gaming and all that sort >> of stuff. Dale, this is a real imperative for telcos, and it's a complicated situation, right, because they've got decades and decades of infrastructure built up. Don Tapscott famously said one time that God may have created the world in six days, but he didn't have an install base. And so the telcos they have of kind of a fossilized, hard installed based built around making sure it's up and not necessarily agile. Now you got all these over the top players coming in, and all these value other services on top of dumb pipe, the price is air coming down. The demand for data is going up, so they gotta change. That's right, right? So what? What do they have to do and what role this Cisco play? >> So again, it told about that software defined transformation and win that is required. And they, you know, we talked already a bit more about the record, an example that was actually even showcase briefly this morning because certainly, obviously it's a greenfield operator, so it's a bit of difference, but We think that there is a lot off applique ability to brown field as well tow the legacy. You have to actually chuck into the different domains what, that service provider environment and really start looking at how you can offer both consumer services and business services at a price point at a level of automation and agility that makes sense. And that is pretty much comparable to a large extent to what the cloud providers of the week. Um, you know, there are advantages the service providers hive in terms ofthe. Obviously, the services they deliver today thie assets that they own, the proximity, the locations as well, that they have the relationships. But really, there is a, as we said earlier, Nassif transformation that start with the network, but also with those pockets where you need to Software eyes will turn to software many of those assets >> essentially talking about a specialized telco cloud, if you will. So how is that different from you know, the clouds that we know the private clouds, the hybrid clouds, the public clouds, one of the attributes that are different in how do people get on the company's getting telcos? Get in that journey. >> Yeah, well, I mean, if you look at, uh, the telco industry in general, including ourselves, like the vendors. I mean, I call myself for ourselves as, like, you know, coming back from the era of dinosaurs, right? So, I mean, if you look at the access technology for last three decades, what have changed? Nothing way have been moving from one G Tito Tito treaty to 40. Now we're talking a five g without talking off. A fundamentally destructive are differentiated architecture. So that's something which is actually being coming up all in the front front at the moment on, that's changing the way the networks can be built. How you can build on how you can break the monolithic supplication and adopt a more decomposed, desegregated our conjecture and also, at the same time, drive all the services and applications in a more distributed manner with a flexible placement capability, so that you can enable all sort of new applications and services. And again, I mean at the other. And given the fact that this is mostly a brown Fillon moment, it is largely all about culture transformation, given the fact that you know, unless the people process on, the culture revolves. This would be a very tough journey. Moving for >> one of the point back to your question is wellies. Though there are nuances big ones between a 90 cloud, uh, today in the cloud that are generally club general purpose Cloud that offered, you know, buy are obviously partners ws Microsoft, Google it and really a telco clan based on the nature ofthe those network functions. The workload on the nature of this were close. The traffic demand that they have the understanding or cliff There are how the hardware itself or the underpinning the infrastructure needs to have some specific attributes to make this work at scale. But we're trying to mimic as much as possible the scaling capabilities, the flexibility, agility, the elasticity of a cloud so that service providers can read the prophet off pretty much a general cloud >> involvement. Conceptually, there are a lot of similar out similarities. I presume that from a developer standpoint, there's a Dev ops analog, analog, maybe a cloud native, maybe serve earless. Something like server list functions absolutely in Telco cloud. >> Absolutely, absolutely. So what we see is the idea under Telco World are actually coming together because I need a lot ofthe telco expertise were also at the same time. I need a lot of expertise because that's what exact exactly right now happening. I mean, there's some fundamental differences between a standard righty private, our hybrid Claude and tell the cloud like I deploy our thousands or hundreds of locations are set a few locations. The applications are different. It's highly Io intensive. You're dealing with a lot of packets like millions of packets which are mostly are transiting function going in and out. But having said that while this initial deployment wave is being targeted for mostly for those delicate type of obligations, we're seeing a very clear demand on a journey towards a common goal of setting up our one unified cloud, right so that you can host it and telco all in the same cloud on that's exactly what they want sexually takes a reality. >> Well, in one of the things I'm surprised we haven't touched on yet is EJ Computing is, you know, critical for these environment. And I can't just have bespoke solutions for all of them. From my corrida edge toe, you know, Telco, there need to be communications amongst all of these because data is going to flow between them and therefore, it can't be. You know, Moz, in between them, I need to be able to pass data and have my applications access these various pieces. >> Absolutely. In fact, the way we have he'll concede some of the systems is a unified architecture that is distributed as a Delco plowed. So that actually from the new service managers or the new ways says B. S s. They see, actually, one unified cloud with placement capabilities based on constraints where you can actually put the workload where they need to be based on Soleil is based on the requirements in technical resources that are available, you know, from forage to a central DC and all the way to actually a public cloud because we're starting seeing some of the customers around the world. It's really a massive transformation that is global. Some of them are starting to look at how they can leverage the public cloud for bursting purposes, for disaster recovery, or even for other functions for specific applications that maybe less demanding, actually on the side. >> Well, since I know you were talking about how that one of the differences that hell cozier more distributed, you know, greater io intensity. My question is, can we learn from the telco clouds from a security model standpoint? Because normally if they go tell coz we're kind of behind traditional i t. But from a security model astounds maybe more challenging. And you always hear the traditional i t. So we it's going to the edge, the telcos already there. So is the security model actually more advanced than what can we learn from that? And how is it >> evolving? Yeah, the security model is still evolving. So in fact, I would say for the total cloud which is being done at the more Court Central Data Center location, the security model is pretty advanced. But when things go towards the edge, especially its computing, which is huge, the security model is actually evolving. And we see a lot of promises with things such as, you know, secure chain of trust, or even block Chinn actually coming there and trying to play a huge role. So I think that's one area which we expected you all over the next few years. It's a lot of challenge but also you know, it's very exciting in that particular space. >> And actually those. This is a very key point because that infrastructure from service providers is actually usually many of the country's part of the national assets the cyber securities. The agencies in those countries work actually with Cisco Security Trust officer letters to really make sure that we do have a level of security that goes beyond maybe even the boundaries of what we've seen on enterprise. So yes, to your point, there is a lot of advances in that area as well. >> All right, so jail, half the shows I've been to this year have had a breakout for Telco. There's there's no denying that there's a lot of growth and a lot of change happening in that environment. What differentiates Cisco's approach from the rest of the people looking at the multi cloud and software pieces >> so more people are murky? Pool area is first. Obviously we have these murky cloud or this hybrid cloud view in which we have worked with the best out there. The Web scale providers, the cloud providers. In fact, if I look at racket and others there are even mimicking this notion off a sorry the Google approach to, you know, really the reliability enginering the transformation off those class cloud in a very specific way. Theater aspect is we're doing it. We have a holistic view at the Telco Cloud. It's not just the infrastructure, it's the automation. The automation is absolutely critical that there is absolutely no touch from humans to be able actually to manage of that scale even more so if you deploy it in 1,000 of edge points, it has to be completely actually automated. So the aspect ofthe automation, the aspect of security, the aspect of people transformation, organizational as well is something that, between the service component to this other solution and the products is very unique. And what we do, it's Cisco. >> Yeah, if I may just add one thing on top of that, just chill said right. So if you look at our playing the Espy or telco market, we have a comprehensive solution. We are solutions right from routing Optical Jacinto Compute Telco, Claude Watch television automation, melodic or being gcm. Here's a bunch of stuff, right? But what becomes very interesting is if you look at 55 g and we all are talking up. The five G is going to be all about enterprise services now. Think about it for a while, right? Who is the number one dominant player in the market for a better price, with the deepest portfolio absolution and the farthest reaching there? No price market that Cisco. So that's what we believe, that we can actually really, you know, creator right confluence of border side of the technology to create the right offer for our customers and held them to take to the market. >> In fact, we've taken a number off our very large enterprise customers that journey to understand, from their point of view as well how they could leverage five g wife like six in the context off a mobile first cloud first type environment. And it's across permeates, actually, obviously what those service providers need to offer to grow again beyond customer services, which is not where, actually the you know, the hyper growth will be as faras Service school sir, >> Well, jail in Santa Ana. Thank you so much for sharing the updates. What happened? Tell Cho service provider space. Thanks so much for joining us. Everybody alright, We'll be back with lots more water wall coverage here at Cisco alive. San Diego 2019 for David Dante on stew Minimum. And thank you for what? Thank you.

>> Narrator: theCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (bright music) >> Hey everyone! Welcome back. Good evening from Barcelona, Spain. It's theCUBE, the leader in live tech coverage. As you well know, Lisa Martin and Dave Nicholson. Day two of our coverage of MWC 23. Dave, we've been talking about sexy stuff all day. It's about to get, we're bringing sexy back. >> It's about to get hot. >> It's about to get hot. We've had two guests with us, two senior consultants from the product planning, networking and emerging server solutions group at Dell, Heather Raheel and James Bryan. Welcome guys. >> Thanks for having us. >> Thanks for having us. >> Really appreciate it. >> Lisa: Dude, you're bringing sexy back. >> I know. We are. We are. We wanted to bring it, yes. >> This is like XR8000 >> We've been talking about this all day. It's here... >> Yes. Yes. Talk to us about why this is so innovative. >> So, actually we wanted to bring this, getting a lot of attention here on site. Matter of fact, we even have a lot of our competition taking pictures of it. And why is it so innovative? So one of the things that we've done here is we've taken a lot of insights and feedback from our customers that are looking at 5G deployments and looking at how do they, basically, bring commercial off the shelf to a very proprietary industry. So what we've done is we've built a very flexible and scalable form factor in the XR8000. And so this is actually a product that we've purposely built for the telecommunications space. Specifically can be deployed for serving a virtual DU or DUC at a cell site for distributed ram. Or it can be put in a local data center, but outside a main data center to support centralized ram. We'll get into it, which is where the really excitement gets is it's sled-based in its design. And so because of that, it enables us to provide both functionality for telecommunications. Could be network, could be enterprise edge as well as being designed to be configured to whatever that workload is, and be cost-optimized for whatever that work. >> Ah, you're killing us! Let's see. Show, show it to us. >> Actually this is where I have to hand it off to my colleague Heather. But what I really want to show you here is the flexibility that we have and the scalability. So, right here what I'm going to show you first is a one U sled. So I'll set that out here, and I'll let Heather tell us all about it. >> Yeah. So XR8000. Let's talk about flexibility first. So the chassis is a two U chassis with a hot swap shared power supply on the right. Within it there are two form factors for the sleds. What James brought out here, this is the one U form factor. Each sled features one node or one CPU first sled. So we're calling the one U the highest, highest density sled right? Cause you can have up to four one node one U sleds in the chassis. The other form factor is a two U sled, on the right here. And that's just really building on top of the one U sled that adds two PCIe sleds on top. So this is really our general purpose sled. You could have up to two of these sleds within the chassis. So what's really cool about the flexibility is you can plug and play with these. So you could have two one Us, two two Us, or mix and match of each of those. >> Talk about the catalyst to build this for telco and some of the emerging trends that, that you guys have seen and said this needs to be purpose-built for the telco. There's so much challenge and complexity there, they need this. >> Want me to take this? So actually that, that's a great question by the way. It turns out that the market's growing. It's nascent right now. Different telecommunication providers have different needs. Their workloads are different. So they're looking for a form factor like this that, when we say flexible, they need to be able to configure it for theirs. They don't all configure the same way. And so they're looking for something that they can configure to their needs, but they also don't want to pay for things that they don't need. And so that's what led to the creation of, of this device the way we've created it. >> How is it specific for edge use cases, though? We think of the edge: it's emerging, it's burgeoning. What makes this so (pause) specific to edge use cases? >> Yeah, let's talk about some of the the ruggedized features of the product. So first of all, it is short depth. So only 430 millimeters. And this is designed for extreme temperatures, really for any environment. So the normal temperatures of operating are negative five to 55, but we've also developed an enhanced heat sink to get us even beyond that. >> Dave: That's Celsius? >> Celsius. Thank you. >> Lisa: Right. So this will get us all the way down to negative 20 boot in operating all the way up to 65 C. So this is one of the most extreme temperature edge offerings we've seen on the market so far. >> And so this is all outside the data center, so not your typical data center server. So not only are we getting those capabilities, but half the size when you look at a typical data center server. >> So these can go into a place where there's a rack, maybe, but definitely not, not doesn't have to be raised for... >> Could be a cell side cabinet. >> Yeah. Okay. >> Heather: Yeah. And we also have AC and DC power options that can be changed over time as well. >> So what can you pack into that one one U sled in terms of CPU cores and memory, just as an example? >> Yeah, great. So, each of the sleds will support the fourth generation of Intel Sapphire Rapids up to 32 corp. They'll also be supporting their new vRAN boost SKUs. And the benefit of those is it has an integrated FEC accelerator within the CPU. Traditionally, to get FEC acceleration, you would need a PCIe card that would take up one of the slots here. Now with it integrated, you're freeing up a PCIe slot, and there's also a power savings involved with that as well. >> So talk about the involvement of, of the telco customer here and then design, I know Dell is very tight with its customers. I imagine there was a lot of communications and collaboration with customers to, to deliver this. >> Interesting question. So it turns out that early on, we had had some initial insight, but it was actually through deep engagement with our customers that we actually redesigned the form factor to what you see here today. So we actually spent significant amount of time with various telecommunication customers from around the world, and they had a very strong influence in this form factor. Even to the point, like Lisa mentioned, we ended up redesigning it. >> Do, do you have a sense for how many of these, or in what kinds of configurations would you deploy in like the typical BBU? So if we're thinking about radio access network literally tran- tower transmitter receiver... somewhere down there (pause) in a cabinet, you have one of these, you have multiple units. I know, I know the answer is "it depends". >> You are right. >> But if, but if someone tells you, well you know, we have 20, 20 cellular sites, and we need (pause) we're we're moving to an open model, and we need the horsepower to do what we want to do. I'm trying to, I'm trying to gauge like what, one of these, what does that, what does that mean? Or is it more like four of these? >> So that, so we'll go >> It depends? >> Yeah it depends, you're absolutely right. However, we can go right there. So if you look in the two U >> Yeah. >> we have three PCIe slots, you know, as Heather mentioned. And so let's say you have a typical cell site, right? We could be able to support a cell site that could have it could have three radios in the configuration here, it could have a, multiply by three, right? It could have up to 18 radios, and we could actually support that. We could support multiple form factors or multiple deployments at a particular cell site. It really then to your point, it does depend, and that's one of the reasons that we've designed it the way we have. For example, if a customer says their initial deployment, they only need one compute node because maybe they're only going to have, you know, two or three carriers. So then, there, you've got maybe six or eight or nine radios. Well then, you put in a single node, but then they may want to scale over time. Well then, you actually have a chassis. They just come in, and they put in a new chassis. The other beauty of that is, is that maybe they wait, but then they want to do new technology. They don't even have to buy a whole new server. They can update to >> Heather: Yeah. the newest technology, same chassis put that in, connect to the radios, and keep going. >> But in this chassis, is it fair to say that most people will be shocked by how much traffic can go through something like this? In the sense that, if a tower is servicing 'n' number of conversations and data streams, going through something like this? I mean somehow blow, it blows my mind to think of thousands of people accessing something and having them all wrapped through something like this. >> It, it'll depend on what they're doing with that data. So you've probably talked a lot about a type of radios, right? Are we going to be massive MIMO or what type of radio? Is it going to be a mix of 4G or 5G? So it'll really depend on that type of radio, and then where this is located. Is it in a dense urban environment, or is it in a rural type of environment at that cell site shelter, but out in a suburban area. So will depend, but then, that's the beauty of this is then, (pause) I get the right CPU, I get the right number of adding cards to connect to the right radios. I purchase whatever, what I need. I may scale to that. I may be (pause) in a growing part of the city, like where we're from or where I'm from or in San Diego where Heather's from where she's in a new suburban, and they put out a new tower and the community grows rapidly. Well then, we may, they may put out one and then you may add another one and I can connect to more radios, more carriers. So it really just comes down to the type and what you're trying to put through that. It could edit a stadium where I may have a lot of people. I may have like, video streaming, and other things. Not only could I be a network connectivity, but I could do other functions like me, multi-axis axon point that you've heard about, talked about here. So I could have a GPU processing information on one side. I could do network on the other side. >> I do, I do. >> Go for it >> Yeah, no, no, I'm sorry. I'm sorry. I don't want to, don't want to hog all of the time. What about expansion beyond the chassis? Is there a scenario where you might load this chassis up with four of those nodes, but then because you need some type of external connectivity, you go to another chassis that has maybe some of these sleds? Or are these self-contained and independent of one another? >> They are all independent. >> Okay. >> So, and then we've done that for a reason. So one of the things that was clear from the customers, again and again and again, was cost, right? Total cost of ownership. So not only, how much does this cost when I buy it from you to what is it going to take to power and run it. And so basically we've designed that with that in mind. So we've separated the compute and isolated the compute from the chassis, from the power. So (pause) I can only deal with this. And the other thing is is it's, it's a sophisticated piece of equipment that people that would go out and service it are not used to. So they can just come out, pull it out without even bringing the system down. If they've got multiple nodes, pull it. They don't have to pull out a whole chassis or whole server. Put one in, connect it back up while the system is still running. If a power supply goes out, they can come and pull it out. We've got one, it's designed with a power infrastructure that if I lose one power supply, I'm not losing the whole system. So it's really that serviceability, total cost of ownership at the edge, which led us to do this as a configurable chassis. >> I was just going to ask you about TCO reduction but another thing that I'm curious about is: there seems to be like a sustainability angle here. Is that something that you guys talk with customers about in terms of reducing footprint and being able to pack more in with less reducing TCO, reducing storage, power consumption, that sort of thing? >> Go ahead. >> You want me to take that one as well? So yes, so it comes at me, varies by the customer, but it does come up and matter of fact one- in that vein, similar to this from a chassis perspective is, I don't, especially now with the technology changing so fast and and customers still trying to figure out well is this how we're really going to deploy it? You basically can configure, and so maybe that doesn't work. They reconfigure it, or, as I mentioned earlier, I purchased a single sled today, and I purchased a chassis. Well then the next generation comes. I don't have to purchase a new chassis. I don't have to purchase a new power supply. So we're trying to address those sustainability issues as we go, you know, again, back to the whole TCO. So they, they're kind of related to some extent. >> Right. Right, right. Definitely. We hear a lot from customers in every industry about ESG, and it's, and it's an important initiative. So Dell being able to, to help facilitate that for customers, I'm sure is part of what gives you that competitive advantage, but you talked about, James, that and, and we talked about it in an earlier segment that competitors are coming by, sniffing around your booth. What's going on? Talk about, from both of your lenses, the (pause) competitive advantage that you think this gives Dell in telco. Heather, we'll start with you. >> Heather: Yeah, I think the first one which we've really been hitting home with is the flexibility for scalability, right? This is really designed for any workload, from AI and inferencing on like a factory floor all the way to the cell site. I don't know another server that could say that. All in one box, right? And the second thing is, really, all of the TCO savings that will happen, you know, immediately at the point of sale and also throughout the life cycle of this product that is designed to have an extremely long lifetime compared to a traditional server. >> Yeah, I'll get a little geeky with you on that one. Heather mentioned that we'll be able to take this, eventually, to 65 C operating conditions. So we've even designed some of the thermal solutions enabling us to go there. We'll also help us become more power efficient. So, again, back to the flexibility even on how we cool it so it enables us to do that. >> So do, do you expect, you just mentioned maybe if I, if I heard you correctly, the idea that this might have a longer (pause) user-usable life than the average kind of refresh cycle we see in general IT. What? I mean, how often are they replacing equipment now in, kind of, legacy network environments? >> I believe the traditional life cycle of a of a server is, what? Three? Three to five years? Three to five years traditionally. And with the sled based design, like James said, we'll be designing new sleds, you know, every year two years that can just be plugged in, and swapped out. So the chassis is really designed to live much longer than, than just three to five years. >> James: We're having customers ask anywhere from seven to when it dies. So (pause) substantial increase in the life cycle as we move out because as you can, as you probably know, well, right? The further I get out on the edge, it, the more costly it is. >> Lisa: Yep. >> And, I don't want to change it if I don't have to. And so something has to justify me changing it. And so we're trying to build to support that both that longevity, but then with that longevity, things change. I mean, seven years is a long time in technology. >> Lisa: Yes it is. >> So we need to be there for those customers that are ready for that change, or something changed, and they want to still be able to, to adopt that without having to change a lot of their infrastructure. >> So customers are going to want to get their hands on this, obviously. We know, we, we can tell by your excitement. Is this GA now? Where is it GA, and where can folks go to learn more? >> Yeah, so we are here at Mobile World Congress in our booth. We've got a few featured here, and other booths throughout the venue. But if you're not here at Mobile World Congress, this will be launched live on the market at the end of May for Dell. >> Awesome. And what geographies? >> Worldwide. >> Worldwide. Get your hands on the XR8000. Worldwide in just a couple months. Guys, thank you >> James: Thank you very much. >> for the show and tell, talking to us about really why you're designing this for the telco edge, the importance there, what it's going to enable operators to achieve. We appreciate your time and your insights and your show and tell. >> Thanks! >> Thank you. >> For our guests and for Dave Nicholson, I'm Lisa Martin. You're watching theCUBE live, Spain in Mobile MWC 23. Be back with our sho- day two wrap with Dave Valente and some guests in just a minute. (bright music)

(gentle synth music) (music ends) >> Welcome back to Supercloud 2, an open industry collaboration between technologists, consultants, analysts, and of course practitioners to help shape the future of cloud. At this event, one of the key areas we're exploring is the intersection of cloud and data. And how building value on top of hyperscale clouds and across clouds is evolving, a concept of course we call "Supercloud". And we're pleased to welcome our friends from Enterprise Technology research, Erik Bradley and Darren Brabham. Guys, thanks for joining us, great to see you. we love to bring the data into these conversations. >> Thank you for having us, Dave, I appreciate it. >> Yeah, thanks. >> You bet. And so, let me do the setup on what is Supercloud. It's a concept that we've floated, Before re:Invent 2021, based on the idea that cloud infrastructure is becoming ubiquitous, incredibly powerful, but there's a lack of standards across the big three clouds. That creates friction. So we defined over the period of time, you know, better part of a year, a set of essential elements, deployment models for so-called supercloud, which create this common experience for specific cloud services that, of course, again, span multiple clouds and even on-premise data. So Erik, with that as background, I wonder if you could add your general thoughts on the term supercloud, maybe play proxy for the CIO community, 'cause you do these round tables, you talk to these guys all the time, you gather a lot of amazing information from senior IT DMs that compliment your survey. So what are your thoughts on the term and the concept? >> Yeah, sure. I'll even go back to last year when you and I did our predictions panel, right? And we threw it out there. And to your point, you know, there's some haters. Anytime you throw out a new term, "Is it marketing buzz? Is it worth it? Why are you even doing it?" But you know, from my own perspective, and then also speaking to the IT DMs that we interview on a regular basis, this is just a natural evolution. It's something that's inevitable in enterprise tech, right? The internet was not built for what it has become. It was never intended to be the underlying infrastructure of our daily lives and work. The cloud also was not built to be what it's become. But where we're at now is, we have to figure out what the cloud is and what it needs to be to be scalable, resilient, secure, and have the governance wrapped around it. And to me that's what supercloud is. It's a way to define operantly, what the next generation, the continued iteration and evolution of the cloud and what its needs to be. And that's what the supercloud means to me. And what depends, if you want to call it metacloud, supercloud, it doesn't matter. The point is that we're trying to define the next layer, the next future of work, which is inevitable in enterprise tech. Now, from the IT DM perspective, I have two interesting call outs. One is from basically a senior developer IT architecture and DevSecOps who says he uses the term all the time. And the reason he uses the term, is that because multi-cloud has a stigma attached to it, when he is talking to his business executives. (David chuckles) the stigma is because it's complex and it's expensive. So he switched to supercloud to better explain to his business executives and his CFO and his CIO what he's trying to do. And we can get into more later about what it means to him. But the inverse of that, of course, is a good CSO friend of mine for a very large enterprise says the concern with Supercloud is the reduction of complexity. And I'll explain, he believes anything that takes the requirement of specific expertise out of the equation, even a little bit, as a CSO worries him. So as you said, David, always two sides to the coin, but I do believe supercloud is a relevant term, and it is necessary because the cloud is continuing to be defined. >> You know, that's really interesting too, 'cause you know, Darren, we use Snowflake a lot as an example, sort of early supercloud, and you think from a security standpoint, we've always pushed Amazon and, "Are you ever going to kind of abstract the complexity away from all these primitives?" and their position has always been, "Look, if we produce these primitives, and offer these primitives, we we can move as the market moves. When you abstract, then it becomes harder to peel the layers." But Darren, from a data standpoint, like I say, we use Snowflake a lot. I think of like Tim Burners-Lee when Web 2.0 came out, he said, "Well this is what the internet was always supposed to be." So in a way, you know, supercloud is maybe what multi-cloud was supposed to be. But I mean, you think about data sharing, Darren, across clouds, it's always been a challenge. Snowflake always, you know, obviously trying to solve that problem, as are others. But what are your thoughts on the concept? >> Yeah, I think the concept fits, right? It is reflective of, it's a paradigm shift, right? Things, as a pendulum have swung back and forth between needing to piece together a bunch of different tools that have specific unique use cases and they're best in breed in what they do. And then focusing on the duct tape that holds 'em all together and all the engineering complexity and skill, it shifted from that end of the pendulum all the way back to, "Let's streamline this, let's simplify it. Maybe we have budget crunches and we need to consolidate tools or eliminate tools." And so then you kind of see this back and forth over time. And with data and analytics for instance, a lot of organizations were trying to bring the data closer to the business. That's where we saw self-service analytics coming in. And tools like Snowflake, what they did was they helped point to different databases, they helped unify data, and organize it in a single place that was, you know, in a sense neutral, away from a single cloud vendor or a single database, and allowed the business to kind of be more flexible in how it brought stuff together and provided it out to the business units. So Snowflake was an example of one of those times where we pulled back from the granular, multiple points of the spear, back to a simple way to do things. And I think Snowflake has continued to kind of keep that mantle to a degree, and we see other tools trying to do that, but that's all it is. It's a paradigm shift back to this kind of meta abstraction layer that kind of simplifies what is the reality, that you need a complex multi-use case, multi-region way of doing business. And it sort of reflects the reality of that. >> And you know, to me it's a spectrum. As part of Supercloud 2, we're talking to a number of of practitioners, Ionis Pharmaceuticals, US West, we got Walmart. And it's a spectrum, right? In some cases the practitioner's saying, "You know, the way I solve multi-cloud complexity is mono-cloud, I just do one cloud." (laughs) Others like Walmart are saying, "Hey, you know, we actually are building an abstraction layer ourselves, take advantage of it." So my general question to both of you is, is this a concept, is the lack of standards across clouds, you know, really a problem, you know, or is supercloud a solution looking for a problem? Or do you hear from practitioners that "No, this is really an issue, we have to bring together a set of standards to sort of unify our cloud estates." >> Allow me to answer that at a higher level, and then we're going to hand it over to Dr. Brabham because he is a little bit more detailed on the realtime streaming analytics use cases, which I think is where we're going to get to. But to answer that question, it really depends on the size and the complexity of your business. At the very large enterprise, Dave, Yes, a hundred percent. This needs to happen. There is complexity, there is not only complexity in the compute and actually deploying the applications, but the governance and the security around them. But for lower end or, you know, business use cases, and for smaller businesses, it's a little less necessary. You certainly don't need to have all of these. Some of the things that come into mind from the interviews that Darren and I have done are, you know, financial services, if you're doing real-time trading, anything that has real-time data metrics involved in your transactions, is going to be necessary. And another use case that we hear about is in online travel agencies. So I think it is very relevant, the complexity does need to be solved, and I'll allow Darren to explain a little bit more about how that's used from an analytics perspective. >> Yeah, go for it. >> Yeah, exactly. I mean, I think any modern, you know, multinational company that's going to have a footprint in the US and Europe, in China, or works in different areas like manufacturing, where you're probably going to have on-prem instances that will stay on-prem forever, for various performance reasons. You have these complicated governance and security and regulatory issues. So inherently, I think, large multinational companies and or companies that are in certain areas like finance or in, you know, online e-commerce, or things that need real-time data, they inherently are going to have a very complex environment that's going to need to be managed in some kind of cleaner way. You know, they're looking for one door to open, one pane of glass to look at, one thing to do to manage these multi points. And, streaming's a good example of that. I mean, not every organization has a real-time streaming use case, and may not ever, but a lot of organizations do, a lot of industries do. And so there's this need to use, you know, they want to use open-source tools, they want to use Apache Kafka for instance. They want to use different megacloud vendors offerings, like Google Pub/Sub or you know, Amazon Kinesis Firehose. They have all these different pieces they want to use for different use cases at different stages of maturity or proof of concept, you name it. They're going to have to have this complexity. And I think that's why we're seeing this need, to have sort of this supercloud concept, to juggle all this, to wrangle all of it. 'Cause the reality is, it's complex and you have to simplify it somehow. >> Great, thanks you guys. All right, let's bring up the graphic, and take a look. Anybody who follows the breaking analysis, which is co-branded with ETR Cube Insights powered by ETR, knows we like to bring data to the table. ETR does amazing survey work every quarter, 1200 plus 1500 practitioners that that answer a number of questions. The vertical axis here is net score, which is ETR's proprietary methodology, which is a measure of spending momentum, spending velocity. And the horizontal axis here is overlap, but it's the presence pervasiveness, and the dataset, the ends, that table insert on the bottom right shows you how the dots are plotted, the net score and then the ends in the survey. And what we've done is we've plotted a bunch of the so-called supercloud suspects, let's start in the upper right, the cloud platforms. Without these hyperscale clouds, you can't have a supercloud. And as always, Azure and AWS, up and to the right, it's amazing we're talking about, you know, 80 plus billion dollar company in AWS. Azure's business is, if you just look at the IaaS is in the 50 billion range, I mean it's just amazing to me the net scores here. Anything above 40% we consider highly elevated. And you got Azure and you got Snowflake, Databricks, HashiCorp, we'll get to them. And you got AWS, you know, right up there at that size, it's quite amazing. With really big ends as well, you know, 700 plus ends in the survey. So, you know, kind of half the survey actually has these platforms. So my question to you guys is, what are you seeing in terms of cloud adoption within the big three cloud players? I wonder if you could could comment, maybe Erik, you could start. >> Yeah, sure. Now we're talking data, now I'm happy. So yeah, we'll get into some of it. Right now, the January, 2023 TSIS is approaching 1500 survey respondents. One caveat, it's not closed yet, it will close on Friday, but with an end that big we are over statistically significant. We also recently did a cloud survey, and there's a couple of key points on that I want to get into before we get into individual vendors. What we're seeing here, is that annual spend on cloud infrastructure is expected to grow at almost a 70% CAGR over the next three years. The percentage of those workloads for cloud infrastructure are expected to grow over 70% as three years as well. And as you mentioned, Azure and AWS are still dominant. However, we're seeing some share shift spreading around a little bit. Now to get into the individual vendors you mentioned about, yes, Azure is still number one, AWS is number two. What we're seeing, which is incredibly interesting, CloudFlare is number three. It's actually beating GCP. That's the first time we've seen it. What I do want to state, is this is on net score only, which is our measure of spending intentions. When you talk about actual pervasion in the enterprise, it's not even close. But from a spending velocity intention point of view, CloudFlare is now number three above GCP, and even Salesforce is creeping up to be at GCPs level. So what we're seeing here, is a continued domination by Azure and AWS, but some of these other players that maybe might fit into your moniker. And I definitely want to talk about CloudFlare more in a bit, but I'm going to stop there. But what we're seeing is some of these other players that fit into your Supercloud moniker, are starting to creep up, Dave. >> Yeah, I just want to clarify. So as you also know, we track IaaS and PaaS revenue and we try to extract, so AWS reports in its quarterly earnings, you know, they're just IaaS and PaaS, they don't have a SaaS play, a little bit maybe, whereas Microsoft and Google include their applications and so we extract those out and if you do that, AWS is bigger, but in the surveys, you know, customers, they see cloud, SaaS to them as cloud. So that's one of the reasons why you see, you know, Microsoft as larger in pervasion. If you bring up that survey again, Alex, the survey results, you see them further to the right and they have higher spending momentum, which is consistent with what you see in the earnings calls. Now, interesting about CloudFlare because the CEO of CloudFlare actually, and CloudFlare itself uses the term supercloud basically saying, "Hey, we're building a new type of internet." So what are your thoughts? Do you have additional information on CloudFlare, Erik that you want to share? I mean, you've seen them pop up. I mean this is a really interesting company that is pretty forward thinking and vocal about how it's disrupting the industry. >> Sure, we've been tracking 'em for a long time, and even from the disruption of just a traditional CDN where they took down Akamai and what they're doing. But for me, the definition of a true supercloud provider can't just be one instance. You have to have multiple. So it's not just the cloud, it's networking aspect on top of it, it's also security. And to me, CloudFlare is the only one that has all of it. That they actually have the ability to offer all of those things. Whereas you look at some of the other names, they're still piggybacking on the infrastructure or platform as a service of the hyperscalers. CloudFlare does not need to, they actually have the cloud, the networking, and the security all themselves. So to me that lends credibility to their own internal usage of that moniker Supercloud. And also, again, just what we're seeing right here that their net score is now creeping above AGCP really does state it. And then just one real last thing, one of the other things we do in our surveys is we track adoption and replacement reasoning. And when you look at Cloudflare's adoption rate, which is extremely high, it's based on technical capabilities, the breadth of their feature set, it's also based on what we call the ability to avoid stack alignment. So those are again, really supporting reasons that makes CloudFlare a top candidate for your moniker of supercloud. >> And they've also announced an object store (chuckles) and a database. So, you know, that's going to be, it takes a while as you well know, to get database adoption going, but you know, they're ambitious and going for it. All right, let's bring the chart back up, and I want to focus Darren in on the ecosystem now, and really, we've identified Snowflake and Databricks, it's always fun to talk about those guys, and there are a number of other, you know, data platforms out there, but we use those too as really proxies for leaders. We got a bunch of the backup guys, the data protection folks, Rubric, Cohesity, and Veeam. They're sort of in a cluster, although Rubric, you know, ahead of those guys in terms of spending momentum. And then VMware, Tanzu and Red Hat as sort of the cross cloud platform. But I want to focus, Darren, on the data piece of it. We're seeing a lot of activity around data sharing, governed data sharing. Databricks is using Delta Sharing as their sort of place, Snowflakes is sort of this walled garden like the app store. What are your thoughts on, you know, in the context of Supercloud, cross cloud capabilities for the data platforms? >> Yeah, good question. You know, I think Databricks is an interesting player because they sort of have made some interesting moves, with their Data Lakehouse technology. So they're trying to kind of complicate, or not complicate, they're trying to take away the complications of, you know, the downsides of data warehousing and data lakes, and trying to find that middle ground, where you have the benefits of a managed, governed, you know, data warehouse environment, but you have sort of the lower cost, you know, capability of a data lake. And so, you know, Databricks has become really attractive, especially by data scientists, right? We've been tracking them in the AI machine learning sector for quite some time here at ETR, attractive for a data scientist because it looks and acts like a lake, but can have some managed capabilities like a warehouse. So it's kind of the best of both worlds. So in some ways I think you've seen sort of a data science driver for the adoption of Databricks that has now become a little bit more mainstream across the business. Snowflake, maybe the other direction, you know, it's a cloud data warehouse that you know, is starting to expand its capabilities and add on new things like Streamlit is a good example in the analytics space, with apps. So you see these tools starting to branch and creep out a bit, but they offer that sort of neutrality, right? We heard one IT decision maker we recently interviewed that referred to Snowflake and Databricks as the quote unquote Switzerland of what they do. And so there's this desirability from an organization to find these tools that can solve the complex multi-headed use-case of data and analytics, which every business unit needs in different ways. And figure out a way to do that, an elegant way that's governed and centrally managed, that federated kind of best of both worlds that you get by bringing the data close to the business while having a central governed instance. So these tools are incredibly powerful and I think there's only going to be room for growth, for those two especially. I think they're going to expand and do different things and maybe, you know, join forces with others and a lot of the power of what they do well is trying to define these connections and find these partnerships with other vendors, and try to be seen as the nice add-on to your existing environment that plays nicely with everyone. So I think that's where those two tools are going, but they certainly fit this sort of label of, you know, trying to be that supercloud neutral, you know, layer that unites everything. >> Yeah, and if you bring the graphic back up, please, there's obviously big data plays in each of the cloud platforms, you know, Microsoft, big database player, AWS is, you know, 11, 12, 15, data stores. And of course, you know, BigQuery and other, you know, data platforms within Google. But you know, I'm not sure the big cloud guys are going to go hard after so-called supercloud, cross-cloud services. Although, we see Oracle getting in bed with Microsoft and Azure, with a database service that is cross-cloud, certainly Google with Anthos and you know, you never say never with with AWS. I guess what I would say guys, and I'll I'll leave you with this is that, you know, just like all players today are cloud players, I feel like anybody in the business or most companies are going to be so-called supercloud players. In other words, they're going to have a cross-cloud strategy, they're going to try to build connections if they're coming from on-prem like a Dell or an HPE, you know, or Pure or you know, many of these other companies, Cohesity is another one. They're going to try to connect to their on-premise states, of course, and create a consistent experience. It's natural that they're going to have sort of some consistency across clouds. You know, the big question is, what's that spectrum look like? I think on the one hand you're going to have some, you know, maybe some rudimentary, you know, instances of supercloud or maybe they just run on the individual clouds versus where Snowflake and others and even beyond that are trying to go with a single global instance, basically building out what I would think of as their own cloud, and importantly their own ecosystem. I'll give you guys the last thought. Maybe you could each give us, you know, closing thoughts. Maybe Darren, you could start and Erik, you could bring us home on just this entire topic, the future of cloud and data. >> Yeah, I mean I think, you know, two points to make on that is, this question of these, I guess what we'll call legacy on-prem players. These, mega vendors that have been around a long time, have big on-prem footprints and a lot of people have them for that reason. I think it's foolish to assume that a company, especially a large, mature, multinational company that's been around a long time, it's foolish to think that they can just uproot and leave on-premises entirely full scale. There will almost always be an on-prem footprint from any company that was not, you know, natively born in the cloud after 2010, right? I just don't think that's reasonable anytime soon. I think there's some industries that need on-prem, things like, you know, industrial manufacturing and so on. So I don't think on-prem is going away, and I think vendors that are going to, you know, go very cloud forward, very big on the cloud, if they neglect having at least decent connectors to on-prem legacy vendors, they're going to miss out. So I think that's something that these players need to keep in mind is that they continue to reach back to some of these players that have big footprints on-prem, and make sure that those integrations are seamless and work well, or else their customers will always have a multi-cloud or hybrid experience. And then I think a second point here about the future is, you know, we talk about the three big, you know, cloud providers, the Google, Microsoft, AWS as sort of the opposite of, or different from this new supercloud paradigm that's emerging. But I want to kind of point out that, they will always try to make a play to become that and I think, you know, we'll certainly see someone like Microsoft trying to expand their licensing and expand how they play in order to become that super cloud provider for folks. So also don't want to downplay them. I think you're going to see those three big players continue to move, and take over what players like CloudFlare are doing and try to, you know, cut them off before they get too big. So, keep an eye on them as well. >> Great points, I mean, I think you're right, the first point, if you're Dell, HPE, Cisco, IBM, your strategy should be to make your on-premise state as cloud-like as possible and you know, make those differences as minimal as possible. And you know, if you're a customer, then the business case is going to be low for you to move off of that. And I think you're right. I think the cloud guys, if this is a real problem, the cloud guys are going to play in there, and they're going to make some money at it. Erik, bring us home please. >> Yeah, I'm going to revert back to our data and this on the macro side. So to kind of support this concept of a supercloud right now, you know Dave, you and I know, we check overall spending and what we're seeing right now is total year spent is expected to only be 4.6%. We ended 2022 at 5% even though it began at almost eight and a half. So this is clearly declining and in that environment, we're seeing the top two strategies to reduce spend are actually vendor consolidation with 36% of our respondents saying they're actively seeking a way to reduce their number of vendors, and consolidate into one. That's obviously supporting a supercloud type of play. Number two is reducing excess cloud resources. So when I look at both of those combined, with a drop in the overall spending reduction, I think you're on the right thread here, Dave. You know, the overall macro view that we're seeing in the data supports this happening. And if I can real quick, couple of names we did not touch on that I do think deserve to be in this conversation, one is HashiCorp. HashiCorp is the number one player in our infrastructure sector, with a 56% net score. It does multiple things within infrastructure and it is completely agnostic to your environment. And if we're also speaking about something that's just a singular feature, we would look at Rubric for data, backup, storage, recovery. They're not going to offer you your full cloud or your networking of course, but if you are looking for your backup, recovery, and storage Rubric, also number one in that sector with a 53% net score. Two other names that deserve to be in this conversation as we watch it move and evolve. >> Great, thank you for bringing that up. Yeah, we had both of those guys in the chart and I failed to focus in on HashiCorp. And clearly a Supercloud enabler. All right guys, we got to go. Thank you so much for joining us, appreciate it. Let's keep this conversation going. >> Always enjoy talking to you Dave, thanks. >> Yeah, thanks for having us. >> All right, keep it right there for more content from Supercloud 2. This is Dave Valente for John Ferg and the entire Cube team. We'll be right back. (gentle synth music) (music fades)

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

foreign welcome back to re invent 2022 we're wrapping up four days well one evening and three solid days wall-to-wall of cube coverage I'm Dave vellante John furrier's birthday is today he's on a plane to London to go see his nephew get married his his great Sister Janet awesome family the furriers uh spanning the globe and uh and John I know you wanted to be here you're watching in Newark or you were waiting to uh to get in the plane so all the best to you happy birthday one year the Amazon PR people brought a cake out to celebrate John's birthday because he's always here at AWS re invented his birthday so I'm really pleased to have two really special guests uh former Cube host Cube Alum great wikibon contributor Stu miniman now with red hat still good to see you again great to be here Dave yeah I was here for that cake uh the twitterverse uh was uh really helping to celebrate John's birthday today and uh you know always great to be here with you and then with this you know Awesome event this week and friend of the cube of many time Cube often Cube contributor as here's a cube analyst this week as his own consultancy sarbj johal great to see you thanks for coming on good to see you Dave uh great to see you stu I'm always happy to participate in these discussions and um I enjoy the discussion every time so this is kind of cool because you know usually the last day is a getaway day and this is a getaway day but this place is still packed I mean it's I mean yeah it's definitely lighter you can at least walk and not get slammed but I subjit I'm going to start with you I I wanted to have you as the the tail end here because cause you participated in the analyst sessions you've been watching this event from from the first moment and now you've got four days of the Kool-Aid injection but you're also talking to customers developers Partners the ecosystem where do you want to go what's your big takeaways I think big takeaways that Amazon sort of innovation machine is chugging along they are I was listening to some of the accessions and when I was back to my room at nine so they're filling the holes in some areas but in some areas they're moving forward there's a lot to fix still it doesn't seem like that it seems like we are done with the cloud or The Innovation is done now we are building at the millisecond level so where do you go next there's a lot of room to grow on the storage side on the network side uh the improvements we need and and also making sure that the software which is you know which fits the hardware like there's a specialized software um sorry specialized hardware for certain software you know so there was a lot of talk around that and I attended some of those sessions where I asked the questions around like we have a specialized database for each kind of workload specialized processes processors for each kind of workload yeah the graviton section and actually the the one interesting before I forget that the arbitration was I asked that like why there are so many so many databases and IRS for the egress costs and all that stuff can you are you guys thinking about reducing that you know um the answer was no egress cost is not a big big sort of uh um show stopper for many of the customers but but the from all that sort of little discussion with with the folks sitting who build these products over there was that the plethora of choice is given to the customers to to make them feel that there's no vendor lock-in so if you are using some open source you know um soft software it can be on the you know platform side or can be database side you have database site you have that option at AWS so this is a lot there because I always thought that that AWS is the mother of all lock-ins but it's got an ecosystem and we're going to talk about exactly we'll talk about Stu what's working within AWS when you talk to customers and where are the challenges yeah I I got a comment on open source Dave of course there because I mean look we criticized to Amazon for years about their lack of contribution they've gotten better they're doing more in open source but is Amazon the mother of all lock-ins many times absolutely there's certain people inside Amazon I'm saying you know many of us talk Cloud native they're like well let's do Amazon native which means you're like full stack is things from Amazon and do things the way that we want to do things and you know I talk to a lot of customers they use more than one Cloud Dave and therefore certain things absolutely I want to Leverage The Innovation that Amazon has brought I do think we're past building all the main building blocks in many ways we are like in day two yes Amazon is fanatically customer focused and will always stay that way but you know there wasn't anything that jumped out at me last year or this year that was like Wow new category whole new way of thinking about something we're in a vocals last year Dave said you know we have over 200 services and if we listen to you the customer we'd have over two thousand his session this week actually got some great buzz from my friends in the serverless ecosystem they love some of the things tying together we're using data the next flywheel that we're going to see for the next 10 years Amazon's at the center of the cloud ecosystem in the IT world so you know there's a lot of good things here and to your point Dave the ecosystem one of the things I always look at is you know was there a booth that they're all going to be crying in their beer after Amazon made an announcement there was not a tech vendor that I saw this week that was like oh gosh there was an announcement and all of a sudden our business is gone where I did hear some rumbling is Amazon might be the next GSI to really move forward and we've seen all the gsis pushing really deep into supporting Cloud bringing workloads to the cloud and there's a little bit of rumbling as to that balance between what Amazon will do and their uh their go to market so a couple things so I think I think we all agree that a lot of the the announcements here today were taping seams right I call it and as it relates to the mother of all lock-in the reason why I say that it's it's obviously very much a pejorative compare Oracle company you know really well with Amazon's lock-in for Amazon's lock-in is about bringing this ecosystem together so that you actually have Choice Within the the house so you don't have to leave you know there's a there's a lot to eat at the table yeah you look at oracle's ecosystem it's like yeah you know oracle is oracle's ecosystem so so that is how I think they do lock in customers by incenting them not to leave because there's so much Choice Dave I agree with you a thousand I mean I'm here I'm a I'm a good partner of AWS and all of the partners here want to be successful with Amazon and Amazon is open to that it's not our way or get out which Oracle tries how much do you extract from the overall I.T budget you know are you a YouTube where you give the people that help you create a large sum of the money YouTube hasn't been all that profitable Amazon I think is doing a good balance of the ecosystem makes money you know we used to talk Dave about you know how much dollars does VMware make versus there um I think you know Amazon is a much bigger you know VMware 2.0 we used to think talk about all the time that VMware for every dollar spent on VMware licenses 15 or or 12 or 20 were spent in the ecosystem I would think the ratio is even higher here sarbji and an Oracle I would say it's I don't know yeah actually 1 to 0.5 maybe I don't know but I want to pick on your discussion about the the ecosystem the the partner ecosystem is so it's it's robust strong because it's wider I was I was not saying that there's no lock-in with with Amazon right AWS there's lock-in there's lock-in with everything there's lock-in with open source as well but but the point is that they're they're the the circle is so big you don't feel like locked in but they're playing smart as well they're bringing in the software the the platforms from the open source they're picking up those packages and saying we'll bring it in and cater that to you through AWS make it better perform better and also throw in their custom chips on top of that hey this MySQL runs better here so like what do you do I said oh Oracle because it's oracle's product if you will right so they are I think think they're filing or not slenders from their go to market strategy from their engineering and they listen to they're listening to customers like very closely and that has sort of side effects as well listening to customers creates a sprawl of services they have so many services and I criticized them last year for calling everything a new service I said don't call it a new service it's a feature of a existing service sure a lot of features a lot of features this is egress our egress costs a real problem or is it just the the on-prem guys picking at the the scab I mean what do you hear from customers so I mean Dave you know I I look at what Corey Quinn talks about all the time and Amazon charges on that are more expensive than any other Cloud the cloud providers and partly because Amazon is you know probably not a word they'd use they are dominant when it comes to the infrastructure space and therefore they do want to make it a little bit harder to do that they can get away with it um because um yeah you know we've seen some of the cloud providers have special Partnerships where you can actually you know leave and you're not going to be charged and Amazon they've been a little bit more flexible but absolutely I've heard customers say that they wish some good tunning and tongue-in-cheek stuff what else you got we lay it on us so do our players okay this year I think the focus was on the upside it's shifting gradually this was more focused on offside there were less talk of of developers from the main stage from from all sort of quadrants if you will from all Keynotes right so even Werner this morning he had a little bit for he was talking about he he was talking he he's job is to Rally up the builders right yeah so he talks about the go build right AWS pipes I thought was kind of cool then I said like I'm making glue easier I thought that was good you know I know some folks don't use that I I couldn't attend the whole session but but I heard in between right so it is really adopt or die you know I am Cloud Pro for last you know 10 years and I think it's the best model for a technology consumption right um because of economies of scale but more importantly because of division of labor because of specialization because you can't afford to hire the best security people the best you know the arm chip designers uh you can't you know there's one actually I came up with a bumper sticker you guys talked about bumper sticker I came up with that like last couple of weeks The Innovation favorite scale they have scale they have Innovation so that's where the Innovation is and it's it's not there again they actually say the market sets the price Market you as a customer don't set the price the vendor doesn't set the price Market sets the price so if somebody's complaining about their margins or egress and all that I think that's BS um yeah I I have a few more notes on the the partner if you you concur yeah Dave you know with just coming back to some of this commentary about like can Amazon actually enable something we used to call like Community clouds uh your companies like you know Goldman and NASDAQ and the like where Industries will actually be able to share data uh and you know expand the usage and you know Amazon's going to help drive that API economy forward some so it's good to see those things because you know we all know you know all of us are smarter than just any uh single company together so again some of that's open source but some of that is you know I think Amazon is is you know allowing Innovation to thrive I think the word you're looking for is super cloud there well yeah I mean it it's uh Dave if you want to go there with the super cloud because you know there's a metaphor for exactly what you described NASDAQ Goldman Sachs we you know and and you know a number of other companies that are few weeks at the Berkeley Sky Computing paper yeah you know that's a former supercloud Dave Linthicum calls it metacloud I'm not really careful I mean you know I go back to the the challenge we've been you know working at for a decade is the distributed architecture you know if you talk about AI architectures you know what lives in the cloud what lives at the edge where do we train things where do we do inferences um locations should matter a lot less Amazon you know I I didn't hear a lot about it this show but when they came out with like local zones and oh my gosh out you know all the things that Amazon is building to push out to the edge and also enabling that technology and software and the partner ecosystem helps expand that and Pull It in it's no longer you know Dave it was Hotel California all of the data eventually is going to end up in the public cloud and lock it in it's like I don't think that's going to be the case we know that there will be so much data out at the edge Amazon absolutely is super important um there some of those examples we're giving it's not necessarily multi-cloud but there's collaboration happening like in the healthcare world you know universities and hospitals can all share what they're doing uh regardless of you know where they live well Stephen Armstrong in the analyst session did say that you know we're going to talk about multi-cloud we're not going to lead with it necessarily but we are going to actually talk about it and that's different to your points too than in the fullness of time all the data will be in the cloud that's a new narrative but go ahead yeah actually Amazon is a leader in the cloud so if they push the cloud even if they don't say AWS or Amazon with it they benefit from it right and and the narrative is that way there's the proof is there right so again Innovation favorite scale there are chips which are being made for high scale their software being tweaked for high scale you as a Bank of America or for the Chrysler as a typical Enterprise you cannot afford to do those things in-house what cloud providers can I'm not saying just AWS Google cloud is there Azure guys are there and few others who are behind them and and you guys are there as well so IBM has IBM by the way congratulations to your red hat I know but IBM won the award um right you know very good partner and yeah but yeah people are dragging their feet people usually do on the change and they are in denial denial they they drag their feet and they came in IBM director feed the cave Den Dell drag their feed the cave in yeah you mean by Dragon vs cloud deniers cloud deniers right so server Huggers I call them but they they actually are sitting in Amazon Cloud Marketplace everybody is buying stuff from there the marketplace is the new model OKAY Amazon created the marketplace for b2c they are leading the marketplace of B2B as well on the technology side and other people are copying it so there are multiple marketplaces now so now actually it's like if you're in in a mobile app development there are two main platforms Android and Apple you first write the application for Apple right then for Android hex same here as a technology provider as and I I and and I actually you put your stuff to AWS first then you go anywhere else yeah they are later yeah the Enterprise app store is what we've wanted for a long time the question is is Amazon alone the Enterprise app store or are they partner of a of a larger portfolio because there's a lot of SAS companies out there uh that that play into yeah what we need well and this is what you're talking about the future but I just want to make a point about the past you talking about dragging their feet because the Cube's been following this and Stu you remember this in 2013 IBM actually you know got in a big fight with with Amazon over the CIA deal you know and it all became public judge wheeler eviscerated you know IBM and it ended up IBM ended up buying you know soft layer and then we know what happened there and it Joe Tucci thought the cloud was Mosey right so it's just amazing to see we have booksellers you know VMware called them books I wasn't not all of them are like talking about how great Partnerships they are it's amazing like you said sub GC and IBM uh with the the GSI you know Partnership of the year but what you guys were just talking about was the future and that's what I wanted to get to is because you know Amazon's been leading the way I I was listening to Werner this morning and that just reminded me of back in the days when we used to listen to IBM educate us give us a master class on system design and decoupled systems and and IO and everything else now Amazon is you know the master educator and it got me thinking how long will that last you know will they go the way of you know the other you know incumbents will they be disrupted or will they you know keep innovating maybe it's going to take 10 or 20 years I don't know yeah I mean Dave you actually you did some research I believe it was a year or so ago yeah but what will stop Amazon and the one thing that worries me a little bit um is the two Pizza teams when you have over 202 Pizza teams the amount of things that each one of those groups needs to take care of was more than any human could take care of people burn out they run out of people how many amazonians only last two or three years and then leave because it is tough I bumped into plenty of friends of mine that have been you know six ten years at Amazon and love it but it is a tough culture and they are driving werner's keynote I thought did look to from a product standpoint you could say tape over some of the seams some of those solutions to bring Beyond just a single product and bring them together and leverage data so there are some signs that they might be able to get past some of those limitations but I still worry structurally culturally there could be some challenges for Amazon to keep the momentum going especially with the global economic impact that we are likely to see in the next year bring us home I think the future side like we could talk about the vendors all day right to serve the community out there I think we should talk about how what's the future of technology consumption from the consumer side so from the supplier side just a quick note I think the only danger AWS has has that that you know Fred's going after them you know too big you know like we will break you up and that can cause some disruption there other than that I think they they have some more steam to go for a few more years at least before we start thinking about like oh this thing is falling apart or anything like that so they have a lot more they have momentum and it's continuing so okay from the I think game is on retail by the way is going to get disrupted before AWS yeah go ahead from the buyer's side I think um the the future of the sort of Technology consumption is based on the paper uh use and they actually are turning all their services to uh they are sort of becoming serverless behind the scenes right all analytics service they had one service left they they did that this year so every service is serverless so that means you pay exactly for the amount you use the compute the iops the the storage so all these three layers of course Network we talked about the egress stuff and that's a problem there because of the network design mainly because Google has a flatter design and they have lower cost so so they are actually squeezing the their their designing this their services in a way that you don't waste any resources as a buyer so for example very simple example when early earlier In This Cloud you will get a VM right in Cloud that's how we started so and you can get 20 use 20 percent of the VM 80 is getting wasted that's not happening now that that has been reduced to the most extent so now your VM grows as you grow the usage and if you go higher than the tier you picked they will charge you otherwise they will not charge you extra so that's why there's still a lot of instances like many different types you have to pick one I think the future is that those instances will go away the the instance will be formed for you on the fly so that is the future serverless all right give us bumper sticker Stu and then Serb G I'll give you my quick one and then we'll wrap yeah so just Dave to play off of sharp G and to wrap it up you actually wrote about it on your preview post for here uh serverless we're talking about how developers think about things um and you know Amazon in many ways you know is the new default server uh you know for the cloud um and containerization fits into the whole serverless Paradigm uh it's the space that I live in uh you know every day here and you know I was happy to see the last few years serverless and containers there's a blurring a line and you know subject we're still going to see VMS for a long time yeah yeah we will see that so give us give us your book Instagram my number six is innovation favorite scale that's my bumper sticker and and Amazon has that but also I I want everybody else to like the viewers to take a look at the the Google Cloud as well as well as IBM with others like maybe you have a better price to Performance there for certain workloads and by the way one vendor cannot do it alone we know that for sure the market is so big there's a lot of room for uh Red Hats of the world and and and Microsoft's the world to innovate so keep an eye on them they we need the competition actually and that's why competition Will Keep Us to a place where Market sets the price one vendor doesn't so the only only danger is if if AWS is a monopoly then I will be worried I think ecosystems are the Hallmark of a great Cloud company and Amazon's got the the biggest and baddest ecosystem and I think the other thing to watch for is Industries building on top of the cloud you mentioned the Goldman Sachs NASDAQ Capital One and Warner media these all these industries are building their own clouds and that's where the real money is going to be made in the latter half of the 2020s all right we're a wrap this is Dave Valente I want to first of all thank thanks to our great sponsors AWS for for having us here this is our 10th year at the cube AMD you know sponsoring as well the the the cube here Accenture sponsor to third set upstairs upstairs on the fifth floor all the ecosystem partners that came on the cube this week and supported our mission for free content our content is always free we try to give more to the community and we we take back so go to thecube.net and you'll see all these videos go to siliconangle com for all the news wikibon.com I publish weekly a breaking analysis series I want to thank our amazing crew here you guys we have probably 30 35 people unbelievable our awesome last session John Walls uh Paul Gillen Lisa Martin Savannah Peterson John Furrier who's on a plane we appreciate Andrew and Leonard in our ear and all of our our crew Palo Alto Boston and across the country thank you so much really appreciate it all right we are a wrap AWS re invent 2022 we'll see you in two weeks we'll see you two weeks at Palo Alto ignite back here in Vegas thanks for watching thecube the leader in Enterprise and emerging Tech coverage [Music]

live from San Francisco it's the cube covering IBM thing 2019 brought to you by IBM good afternoon and welcome back to the cubes continuing coverage of IBM think 2019 I'm Lisa Martin and sake San Francisco with Dave Volante hey Dave hey Lisa we're staying dry though we are the most part exactly there are there looks like the Moscone notices maybe having a few little areas of improvement I think just running water through the pipes as we would say is a little trial that's true so we're welcoming back to the queue but guess that hasn't been with us for a while Paul is a couple of vice president of Big Data at cognitive systems at IBM Paul welcome back oh thank you and thanks for get my name right that was good so you are an accomplished author I talked to you on Twitter 19 books ever 350 articles I know you do a lot of speaking you've been IBM a long time this events massive great 30,000 people or so yesterday was standing room only in fact they shut the doors to Judy's keynote because there were so many people I'm curious some of the announcements that came out with cognitive yesterday what are some of what are some of the things that you saw yesterday that kind of piqued your interest well the Watson the Watson anywhere was I person have said that's a long time coming and they come on we got to have Watson on any cloud right not just the IBM cloud so that was I thought a big deal and then there were a bunch of announcements around enabling hybrid I think there were 20 plus services so you know it's not kind of vogue you know we're in this multi cloud world I need a way to get to hybrid so those are two standouts so your group's been busy basically that's right that's right I mean you hit it right Watson anywhere cloud everywhere so it's about AI in that drink I have to tell you that when I hear all the announcements there's tons of them right one of my favorite ones probably doesn't go as notice and it was Watson machine learning accelerator and that is really about looking at the journey for AI and clients over the next course of the years on that journey see most clients are just getting started there's some clients in the middle phase and there's some clients now that are hitting what I call the enterprise worthiness stage of AI right and so when we look at our announcements they're actually taking you from just getting started all the way to enterprise hardened explainable and algorithms and how to manage that because we're gonna go from this world where AI is sitting in the corner offices for the privileged few we have to democratize for the many but today it's like here's a little data science team they have their own server here's our programmer on their laptop you know and hanging out working there we want to bring this all together for enterprise so things like workload management which is what watching machine learning accelerated really does is how do I get everything together and working in a concurrent environment as organizations go from having 10 20 algorithms to trying to deploy thousands of them that's all they'll define themselves well you know when you get a bunch of data scientists in the room and you talk about citizens data scientists they kind of look at he like me there's no such thing but the fact is that if you can operationalize a you can open it up to a lot more people you know as a line of business person you'd much rather not have to go to a data scientist every time you want to do something with a because otherwise you're just kind of repeating the old decision-support world cells right what do you guys do when to operationalize yeah so it's a great question we're trying to taking the friction and so a lot of people will come and say oh gee p you acceleration so yeah it's about training stuff faster it's an open architecture and power and so you've seen the work with NVIDIA and that's unique to what Nvidia can do with with our cognitive systems is to accelerate the CPU GPU communications but there's a broader pipeline when you go to as the say I journey and we want to flatten that curve so one is how do I get up and running I don't know if you remember open source changes all the time so we're Enterprise hardening back testing getting you ready for here's the platform to deploy built on open source and where 80% of a data scientist time is spent right now is in what I call data preparation wrangling data labeling data gets stuff together now none of that is data science like none of that is data science at all and that's where the time and once I get the data ready I train the model ok so you've heard a lot about that and then the next thing I do is have to optimize the model so I think about where data scientist should be spending their time and that's on stage for we call that exploring the hyper parameter space another thing that Watson machine learning accelerator is all about how do we make the model perform now for data science geeks perform means how well is it classifying or how accurate is Hardware people often think performance means how fast you go right and then finally go to inference so we're looking at all five of those stages and one of them the biggest one is that 80% sink time we're trying to drop that to 20% and open it up for the rest of the enterprise so how do you democratize AI you mentioned that a lot of enterprises are really at the beginning of that journey yeah but when you're out talking with customers is there some sort of paralysis there where they're like Paul where do we start right right I think there's two areas where I see inertia or friction and so one is where do we start so let me say that start with the data you have you don't have to step up to the plate and hit a homerun you just get started and it's the things the little things you do every day not the big things you do once in a while and we always hear about disruption disruption you hear about uber and airbnb as the disruptors I actually believed they were the disruptors of yesterday I think right now we're in this list shift rift or cliff moment the disruptors of tomorrow will be those at the head of the analytics Renaissance that work with the data they have we know the outcomes we call that supervised learning and that's where you get started and the other piece is how do I get more people to participate talk about the lift shift rift or cliff intersection I saw that you've seen talked about that on social media can you break that down a little bit more and also talk to us about how you're helping customers actually kind of break through that or maybe it's avoid that altogether yeah well I mean you want to take two of those four and not take the other two right and I think that we do this lift if cliff moment in two ways one is as individuals so the people in the audience to people watching here all of us as practitioners we have got to get our skills moving forward I always say skill years are like dog years right like they age instantly and so you should be waking up every day like a newbie in this world and learning every single day and if you do that you'll have nothing to worry about as an individual and as organizations you had better put analytics at the forefront that means from the boardroom that means we encourage the culture of analytics everywhere and so those that's what I mean by lift chef rift or cliff moment so what comes back to sort of opening it up for average everyday line of business people you got a you got a demo yeah I'm gonna see what can I show to you all right so you know you were talking about the data scientist and citizen data scientist so I'm gonna propose to you this thing I call the wisdom of the crowd right today data scientists have to build things they're not domain experts imagine if I could invite the many to participate in this storyline and in this story line everyday line of business people could create an application based on an idea or a model and maybe we'd have thousands of them and out of those thousands we might vet I don't know 50 or hundred and out of that we would team up with data science deploy ten or twenty into production and then do the whole thing over again so let me show you how I could create this application here without building a single line of code and I actually use you Dave as an example because I wanted to see how much face time you get on the cube when John is up here with you doing this I get the short end of the stick the data tell the truth right so I had this intuition as a line of business user and I went to explore this so you can see here that we'll have two videos here and on the first video see where I put this here will say host screen time that's actually gonna measure the amount of time that you're on screen and I will be like that yeah and I actually built that in this modern way that democratizes for the many I'll just start it out here and on the bottom I built it the old-fashioned way so you can see we got John in there and they start out pretty good to start right there both recognizing both of them so let me go in pause these now the first thing you should notice is I've got a timer on the bottom I got a timer on the bottom cuz actually I had time to build that my dev ops team kind of put that in there for me so we'll continue this move it over here and let these things run now look at the accuracy of these models do you notice on the top you guys are both identified increasing this green counter and on the bottom I can't see you so in computer vision is very interesting if I wanted to teach a computer to tell me what the number eight was I could show it a picture of an eight but no more when I moved it sideways it would have no idea what it was I need to train it with lots and lots of data and so the bottom is the way the data scientists work so what did I have to do to do that I had to go collect some video had to reformat it had to put it down to a 480 and I had to write some code fire away and you see the code there now in order to get just to MVP so this model clearly doesn't score well Dave turns his head and it doesn't know who it is anymore all I said is your Dave Valente and if you're not then you're John so what do you do if you've got a third person in there all right and this is where we democratize it so this is our power I vision we've been talking a lot about this and I want to kind of invite everybody to take part in this kind of data science Renaissance all you do is you would go and upload some video here and you go capture some frames we could auto capture those frames every five seconds and let's say I wanted to add a new person like Arvin into this list here so I want to go develop and figure out how the algorithm can find out Arvin is now my last demo I showed you that was a linear classifier that wasn't easy here we'll go type in Arvind add Arvind and then I'm just gonna highlight it and box Arvind and now I've started to train the model there's no code at all you just train them all you just said this is Arvind when I see this so I'm leaving the model and then I'd have to go set it off to training and I'll look I'll do one other thing for you here I'll go and say well here's the think logo and maybe I want to track some logo detection that's it that's how I built the model now it's all about how much supervised label data you have so I asked I said who are the disruptors of the future and it's all about the compute power and the workload management power to train this stuff so economy systems is really all about both so we obviously know about the power in the workload management how do I go and actually generate the data so once I train this model I could click auto label it'll actually go through the rest of the video and go and find out from what it saw but here's where things get beautiful and everything I've showed you is someone writing lines of code now replaced with a clicker so I click on mint data we call these morphological operations I want you to notice something we have a hundred nineteen images labeled of Dave and John so as I click here I'm gonna apply these morphological operations Gaussian blurs sharpening blur that all means stuff to data scientists now I have four thousand two hundred and forty nine data points and I will generate that automatically that's all driven by line of business and finally we can come over here and go actually look at the model here's my model this model is actually scoring pretty really well but even if it wasn't scoring well and that's seventy percent this is now when I pass it to the data scientist team to do what their exceptional at the the hyper parameter tuning for the performance score the algorithm and so here I'll just finish this off by I think I had a picture of you I'll just drag it in here and now it's actually going out and scoring it we're scoring at 96% okay accuracy and I can expose this as a rest of API with the click of a button so I just have one thing the way I found out with the AI for you Dave at the end of it from what I can see John is getting about 50% more screen time than you and it's all good actually yeah oh you thought it was worse and if you notice your name here is Dave dapper Volante because we can't help but notice funny we can't even always tell well-dressed you a scientist you're well-dressed and it's pretty accurate but you're not getting the ROI on those outfits that you need for screen time that's what we found with it stuff with my business partner John but that's that's pretty good now you're saying you wrote the code right to identify either John or Dave and and at what point did you bring the data scientist in yeah so I didn't write any code on the top right on the bottom which the model did not perform well when he turns Ivy conceived that's the code we wrote now would take iterations iterations there was no code written there we built the model and then we brought a dev person in to try to build us a timer it was a couple lines of code took him about half an hour and in this case I didn't really bring the data scientist in yet because I'm scoring at 96% but I can easily pass it on into workflow and that's the story it's a pipeline workflow across so I'll pull the data scientists and I need to but 96% accuracy without a data scientist presence pretty good so a more complex use case you know you might not get 96 percent accuracy you might be at 50 percent forty percent more than 70 percent now you bring the data scientists in for the last mile absolutely let's say I was only scoring 50% and you don't think that's impressive I think it's pretty impressive that I did that in a half an hour and now this is engineer from the wisdom of the crowd I'm a line of business user and I'd like to know what kind of screen time you're getting maybe that's not a sporting event and I'd actually like a new business model where I charge Toyota by the second that they show up on the screen that's my idea data scientist never gonna think that I get it started and then they join the Renaissance that's how you democratize AI for the money yeah so maybe you could talk a little bit about how what was the compute power behind this the infrastructure behind this and then maybe we could talk about power and how you're applying that for AI infrastructure yeah that's a great great question so the bottom video actually trained on my laptop it ran for about a day and a half just so you know who's saying it is my laptop on the top of the video we actually leveraged our para AI architecture and ran that through with Watson machine learning accelerator and I gotta tell you the models train in about 30 minutes and in fact we had trained a model on your last show with your last guest in the amount of time it when you finish to when I came on stage 20 loads yeah so I mean that's the that's the accelerated compute and it's not and I hope what you're seeing here this isn't just a hardware component tree story this is a kind of coexistence in an almost synergy of software and hardware together and that's what's needed in the AI era well it's interesting I know when when you guys change the name of the you know power systems group to cognitive systems they had you know and I inferred of course we got a guy running it who used to run the software business so the different software component so it is clearly more than than software what are some of the sort of more interesting use cases that you guys are seeing with with clients specifically in terms of operationalizing this yeah for sure so in use cases of AI is I think it's we're in this world of precision so we're in precision agriculture precision risk or underwriting precision finance precision retail so the use cases are everywhere and it's really taking in all this kind of data in the operationalizing I think that we're helping people on all the levels you think about it I almost see three segments the first segment is we're not really sure what to do this AI and everyone says they're doing AI reminds me of the Hadoop days and the big data Lake and you know all that stuff turned out so how do we get you started so you can get down the path and build kind of MVPs and that's what I just showed you is the MVP the next group of people are the folks that have maybe one or two models deployed and now they're trying to say how do we scale out to hundreds and thousands of models what is the path now to make this bigger because we got it moving here and then the final phase with few people are at are those who are getting the challenge of I'm getting to a thousand algorithms deployed and now how do I get all this stuff running and so that entire path goes like this and our story line goes across that entire path how unique is this in the marketplace I'm interested in your commentary on IBM's competitive advantage is this so you guys have only guys who can do this and and how you know why are you winning in the market how how differentiable is this yeah so I think I'll answer that in two ways one is from the brand in which I participate in a larger company called IBM in terms of the acceleration there's nobody doing what we're doing and the reason is you took this kind of power processor and created the open power project and just like software evolved through open innovation that's what hardware is done so you look at Mellanox and Nvidia so I'll give you an example Dave the NV link exists on Intel and exists on power but they operate in two very different ways and nobody realizes that so envy link accelerates GPU to GPU communications does that an Intel does that on power but because of open power Envy link also allows the GPU to talk to the CPU so GPUs accelerate ai training because there's thousands of cores there right but they still got to talk to the CPU on top of that they don't have much memory so there's an example that's completely unique in the industry to make you train faster I think our workflow model is completely unique the tools that I showed you and around the workload management and then you look at the bigger part of IBM and how I can mix this with API calls to clouds clouds based Watson services or local but on top of that is now it's about how do you build the data that you can trust and how do you look at things like the explained ability of the model with their Watson open scale and that kind of stuff so it's a bigger story and nobody else has that end end story well and it's showing up in the in the in in the results we saw last quarter the your line of business was a bright star you know we're seeing some momentum obviously there's a lot of activity going on in Linux clearly you know cognitive is a big play there so congratulations that's it's exciting to see and of course maybe a lot of people don't realize it when you guys did the work to bring in little-endian compatibility and you know and tire you know software Suites now that it's you know it's not just this sort of niche proprietary platform anymore it's mainstream and so it's starting to show up in the business results so that's great to see yeah when I say democratize for the many I mean for the people for the enterprise and across the entire spectrum so well Paul thank you for confirming my suspicions here that John is my partner John Ferrier is sucking up all the camera time John I'm gonna have to elbow my way in a lot more so appreciate that having the data John's very data-driven so appreciate that yeah to have you on yeah as I see you again all right take deep right there everybody we'll be back with our next guest we're live from IBM think 2019 you're watching the cube

(uplifting music) >> Hi, I'm Stu Miniman and this is a CUBEConversation at SiliconANGLE's, Palo Alto studio, happy to welcome to the program, a first time guest and and new to Nutanix, the Chief Marketing Officer, Ben Gibson. Ben, thank you so much for joining us. >> Stu thanks for having me here. I love the studio and love the chance to chat with you. >> Alright, so our audience know Nutanix really well, so let's introduce them to you first of all. Give our audience a little bit about your background and, you know, we know Nutanix IPO'd not that long ago. Exciting. Growing real fast so, you know, I'm sure it makes sense but why for Ben? >> Yeah. For me, firs and foremost, it's such an exciting company. We're going through a lot of fairly strong growth right now. We've really created a category in the Space Realm Hyperconvergence and now we're really expanding from that position and to be in the middle of that, I think is really exciting. >> So your background is mostly in the networking space and it's one of those things, I'm a networking guy by background. >> Yeah. >> Networking is changing a lot, lately, but not nearly as fast as some of the other parts so, you know, tell us a little bit about your background, where you've been, what your skillset is going to bring to Nutanix, in that ecosystem. >> Yeah, you know, I did, like you, I hailed from the networking industry. I spent a good portion of my career at Sisco, I spent part of my career at Aruba Networks, which is in the Wi-Fi, wireless space, but I think you're right. I think the acceleration of innovation in Nutanix's industry a lot of stuff has changed so quickly in that space, so networking hasn't quite kept up with that level of change of things and for me it's kind of getting on a much faster train and moving forward with it. >> Alright, so we've had theCUBE at every single one of the DotNext conferences >> Ben: Thank you. >> They're fun. Your predecessor Howard Ting had a certain flair so let's talk a little bit about the show and, you know are there things that your going to be putting your stamp on at this show, coming up in New Orleans? >> Yeah, I mean first of all it great to succeed Howard, in the role. I used to be an advisor for the company and so Howard and I used to have periodic pancake breakfasts and I told him what I thought and he shared with me a lot about how exciting things were. What was happening at Nutanix and how they really move forward. And I knew about DotNext, I've seen DotNext I know what an exciting show it is. For a company like Nutanix to draw, this year, over 5000, attendees to this show, that's notable. And, what, to me, that signals is the level of intensity and the level of loyalty that we enjoy in our customer base, they're believers and they're coming to the show to learn from us, to connect with each other and really to accelerate some of their plans on how they continue to innovate. >> Yeah, you know, we travel to so many different shows and absolutely, it is at it's core, it's a user conference, it's users there wanting to learn how to use what they have even better. Learn about the cool net technologies. Dave Valente and I have talked about Nutanix is up there with companies like Service Now, that have, just, people that come and are just, you know, kind of fanatically supported. It reminds me of, you know, Vmware's a great company, doing well today. Many years ago everybody had the I love VMware bumper stickers. Vmware is a great show. We'll be there this year >> Ben: Yeah, so will we. >> And your company will too, so ah, what, what do people what brings them to that show from your standpoint and give us a little insight as to, kind of, some of the special things they'll be seeing this year in New Orleans. >> Yeah, you know, I think one of the biggest attractions of coming to this show is if you think about the role of an infrastructure professional, someone who's looking at hybrid Cloud environments and how do you manage those. Thinking about what applications run on what cloud platform. There's a lot of change and fluidity to that and I think the nature or the role of an IT or an Infrastructure professional is changing. Server Store J-Admin is quickly evolving because that's converging, just like the technology has and so, for me this show is about how do you get ahead of those trends. How do you position yourself to be as strategic as possible, within your own organization? And that's the way I like to think of Dotnext, it's a place that a professional can come to learn and to grow their career and their technology expertise. >> Yeah it's a great point, one of the things I've loved on theCUBE is there are speakers that aren't just from the tech industry, so everyone from Deepak Malhotra, who is from Harvard, um, thought leaders in the space to onstage at one of the shows it was David Blaine doing a magic trick while one of your engineers was configuring stuff. So there's some great speakers that'll be at the event this year to, not only learn the tech but as you said, you're thinking about the persons career and how do they embrace change and how do they help become more valuable to their company. >> Yeah, you know, there's two in particular that are going to be joining this year that I'm very excited about. One is Dr. Brene Brown. If you haven't seen her TED talk on YouTube I highly recommend you go and check it out. It's something different. It's about how do you be vulnerable, with yourself, with your career. How do you take chances? How do you take risk, and that's a lot of what's going on in our in our industry right now. The second one I think is going to be really fun and that's Anthony Bourdain. Known for his show, Parts Unknown and he's going to have colorful language, right, noted, some colorful language but also he's just a riot. He's the one I think is going to bring his special kind of flair to the show and get everyone really excited, laughing and maybe a little bit of gasping, in terms of what he's going to be sharing with us. >> Yeah, absolutely I mean, New Orleans is a great culinary destination and so I know everybody that's going there you'll want to check out the music, the culture and a lot going in there. So, excited about the show. We're going to have theCUBE there for two days. Last thing I want to talk to you about, Ben, just, Nutanix as a company, when I registered for the show there was one of the questions I thought was pretty interesting. They said, "What do you think Nutanix is?" You know, how do you, what's your relationship with Nutanix? And I'm trying to remember, I know one of the options was M: Is Nutanix a hypercovergence infrastructure player? Are they a cloud player? I think storage might even be in there, it's been one of those things as to, who is Nutanix? What are you today and what do you want to be in the next phase of growth? >> You know, it's a great question Stu, "Who is Nutanix?" We are focused on helping our customers build their enterprise cloud. Our taglines could be Your Enterprise Cloud. Enterprise Cloud is not only how you modernize your own data center but it's also, how do you embrace? How do you have a strategy? How do you govern? How do you bring together the right workload, for the right cloud platform at the right time? And that's the direction we're taking with our innovation. This is what more and more of our customers are looking to do. Not every application's going to a public cloud provider. Not every application is staying on premise. We're going to be living in the hybrid world, Enterprise Cloud is about how do you take the ingredients of hyperconvergent infrastructure? Take the ingredients for automation and management, over these different workloads, across these different environments and do so in a way that makes the complexity of infrastructure and multiple cloud management and make that all invisible. So for us that's our mission. It's building that Enterprise Cloud and making all that complexity go away. And that's the vision we're going to be talking about and that's what we think our attendees are really looking to get the guidance and, kind of the vision of how they move their careers forward and flourish in that space >> Ben, it's the barometer that I've been using for probably the last two years. If I spend a lot more time in kind of the Dev/Op Cloud native, you know, worlds these days. We were at an Amazon summit yesterday but, absolutely. It's heterogeneous world. IT has never, you know, let's throw out the old and start the new. Sure there's some new companies that might do that but it's a heterogeneous world, it's a multi-cloud world and big struggle for people is how do they get their arms around it? So if I look at a company that has started mostly on premises, it's like, oh how are you evolving? How are you working with the public Cloud? You know, Nutanix has been working very closely with Google over the last year or so. A new acquisition recently that I know plays into this whole story. Tell us a little bit about the acquisition and, you know, how does Nutanix look at itself, which is now, I mean, if you read the Wall Street reports, Nutanix is a software company. And you're getting great multiples on that and it's helping and you know I've been pretty vocal on this from it's early days, is Nutanix was never a hardware company. It, you know, building an appliance was a go to market choice to simplify and make it easy for customers but as the company matured, >> Yep. >> It made a lot more options and today it makes perfect sense that really software is where it goes, so you talked about a bunch of things there but specifically, kind of the multi-cloud and the acquisition first. >> You know Stu, we're really excited about this recent acquisition we made. It's a company called Minjar and the offering is essentially it's going to be integrated into our broader software platform, that allows customers to be able to assess on a realtime basis. What's the right Cloud platform for a particular workload from a costing perspective? From reliability standpoint. Derish talks about the law of the land. The laws of physics and the like, you need to apply these all to determine what you're going to run where. And what we got with this Minjar acquisition, is a really sophisticated way that our customers can embrace and is part of their enterprise cloud. Because part of this is taking back control over all this disaggregation of workloads running everywhere. You're losing control. Losing governance of your data and your applications if you don't really keep on top of it, this acquisition, I believe, is going to be a really key part of helping IT organizations regain that control. Yet still enjoy all the benefits of hybrid-cloud environments. Whether it be with an AWS an Azure a Google Cloud platform, like we're partnering very closely with, as well as what they're building with their Enterprise Cloud on premise. Whether it be, you know, with Nutanix. >> Yeah, it reminds me of, was it Progressive Insurance, I think has the, you know >> Yeah. >> We're going to give you quote on all of the things there. Cloud is complicated these days. Is there bias towards it, pushing towards, you know, Nutanix closer partners in the technology itself. >> Yeah, I mean it certainly has. There's a lot of complexity around that and to me the industry hasn't solved a lot of new complexity that has come out of the emerging trend of a different line of businesses starting to develop a new application now, on a certain Cloud platform and the like. And as you're seeing this demand for more application mobility between clouds, so all of a sudden the partners that are coming to us and the partners we're seeing and are demanded of that we work with in the market our players are looking at application automation. Players that are looking at dev/ops tools and the like and it both guides how we innovate on our Calm platform, which we introduced last year at DotNext as well it helps us expand our reach. So, we talked to our traditional buyers but there's a lot of new buyers now that are building those apps and managing those environments and we're going to start to see some of those come into the DotNext show. >> Alright. Ben I want to give you the final word. DotNext is one of the many events that I know you do lots of regional shows and the like, what should we expect to see from Nutanix through 2018? >> Yeah, you know the first thing is that the dialogue, the narrative for the company, that we're going out with we've moved to be a software only company and I think our customers tell us and I heard this from one of our largest retail industry customers just a few weeks ago, by moving to a software only model, it's given them freedom to take advantage of Nutanix, regardless of their hardware platforms. And like you said we've never been a hardware company, it's all been about software value and what you're going to see from us is a new narrative. An expansion of the branded Nutanix talking about the freedom we give for customers to build the data center they've always wanted to build. Freedom to run their application or the workload that they've wanted to run, where they choose to run it based on that insight I talked about. And in another realm, the freedom to play. Freedom to get their weekends back. A lot of our value proposition is because of all the complexity we've taken out of the equation, is that we give our customers their weekends back. This is a story that our DotNext attendees, I think, know better than other but we want to spread the word and so part of that is harnessing that freedom concept to build, to run, to play to invent and tell the world. And DotNext, whether is be in New Orleans, which I think is going to be a blast. When we take it to Europe, London and we do this all around the world, to me that's kind of ground zero for that story. For the community of what we've built together with our customers and partners and that what we take out to the world. >> Alright, well Ben Gibson, I'm glad we could introduce you to our community today because we're going to be seeing you at lots of other events, you and your team, of course, theCUBE will be at Nutanix, DotNext in New Orleans. Nutanix and you will be at Dell Technologies World in Las Vegas. Google Cloud Next happening this summer in San Fransisco. Lots of other shows so be sure to tune in to theCUBE.net, get the list of all the upcoming shows, Ben, Nutanix and of course lots of the Cloud and infrastructure ecosystem. Check it all out. I'm Stu Miniman and thanks for watching theCUBE. (uplifting music)

(Upbeat music) (Coughs) >> Hi, I'm Peter Burris with another Wikibooks action item quick take. Jim Kobielus, IBM's up to some good with new tooling for managing data. What's going on? >> Yes Peter, it's not brand new tooling but its important because it actually is a foreshadowing of what's going to be universal. I think it's a capability for programming the uni grade as we've been discussing. Essentially this week at the IBM Signature event Sam Whitestone of IBM discussed with Dave Valente a product they have called Queryplex which is on the market for money even more. Essentially it's a data virtualization environment for distributor query processing in a mesh fabric. And what's important about Queryplex to understand, in a uni grade context, is it enables link binding distributed computation to find the lowest latency path between... Across very fairly complex edge clouds. So to speed up queries no matter where the data may reside and so forth in a fairly real time dynamic fashion. So I think the important things to know about Queryplex are A- that it prioritizes connections with lowest latency based on ongoing computations that are performed and is able to distribute this computation to find the lowest path across the network to prevent the query... The computation controller from being a bottle neck. I think that's a fundamental, architectural capability we're going to see more of with the advent or the growth of the uni grade as a broad concept for building up a distributor cloud computing environment. >> And very importantly there are still a lot of applications that run the businesses on top of IBM machines. Jim Kabielus thanks very much talking about IBM Queryplex and some of the next steps coming. This is Peter Burris with another Wikibooks action item quick take. (upbeat music)

>> Hi welcome to Wikibon's weekly Friday research meeting. Here on the queue. (tech music) >> I'm Peter Burris. We've assembled a gus team of analysts to discuss a very very important topic. Block chain. Now block chain means a lot of things to a lot of different people. Partly because there hasn't been a lot of practical utilization of it. We've talked a lot about bitcoin and ethereum and some other applications of block chain related technologies. But it's very clear that what block chain will become is more than what it is. And to try to unpack that and really understand block chain from the perspective of business decision makers, CIOs and IT. And the IT industry, we want to talk a little bit about what block chain is. What some of the key applications are. And what's it's going to mean from a technology design and investment standpoint over the next few years. Now to kick us off, we've asked David Floyer to start with a little observation on. Let's talk a bit about what is block chain David? >> Okay well block chain is a very exciting set of new technologies. But at heart it's the shared immutable ledger. So lets us go down one level from that. It allows consensus, it allows all of the participants to agree on its validity. It allows provenance to know exactly what has happened. The history of what's happened. It allows immutability so that no participant can tamper with a transaction or an asset value. And is now allows finality. A single shared ledger provided in one place. So that they can track the ownership of an asset or the completion of transaction. So the second concept's really important. Where are we applying this? And where need to apply this in any sort of business network. Assets can be real or they can be virtual. They can be widgets that you count or they can be IP for example. So the core of it is a business network. So what's the problem that it solves? The key problem that it solves is that in order to have those characteristics. Of consensus, provenance, immutability, finality. You, society had to put together very complex systems indeed. So give a few example of those, stock exchanges need to be created. Around the value of stocks then they were sold and the transactions. Credit card companies, the Swift banking system. Diamond dealers for example have to had a system by which they could know the provenance and value of these assets. These systems were essentially centralized. They were centralized and controlled centrally. Or there was a very very sophisticated complex trust and honor system. Some of the systems that have been put in place particularly in the Middle East. And they're expensive. The transaction cost is high for doing that. And companies that have allowed themselves to be in control of these, can take a high percentage. A large amount of money out of this. But they make a lot of money by owning this right to manage. This provenance, this immutable ledger. So the value of block chain is that we can cut down that cost and we can create many more smaller business networks. Which can us focus on a small area and get the same result as this big complex thing we had before. >> But a crucial feature is that David, is going to be the question of design. We're going to have to set these things up and design them right. And that's going to have a lot of implications for how businesses work. So John if I take a look at some of the applications of this. David talked about immutability, finality, provenance etc. And how it's going to take transaction costs out of the system. Where do we envision block chain's going to end up within the application framework? >> I think the key thing that on the application. There's a many series of use. Cause there's low hanging fruit today and then ones that people are connecting the dots in the future. The fundamental application impact really comes down to. Where the confusion and clarity come from. The difference between decentralized and distributed. That's often confused and I think applications purpose. The outcome of applications is really how people work and engage and create value. And the measure of that is how authority and control are provisioned. Distributed and decentralized has a unique difference there. That's a fundamental architectural thing that David pointed out. When it comes to block chain people get confused. They think bitcoin, they think ethereum. That's kind of on the currency side and the crypto side. But the momentum around decentralization goes much farther than that. So you're seeing things like energy systems. Grid Plus had a presale that was over $40 million. They're changing the game on how energy may be used and managed. The government, political sovereignty is changing. A breakthrough in science for instance. Crypser and other labs make opportunities for decentralized labs. Crowdfunding is an obvious one. You see that really get a lot of traction. Space exploration is one. Open source software, you're going to see a lot of activity there. Personal health monitoring, online educational systems, security. These are tell signs that the game will shift in terms of the new architecture. And then the impact will be the creative destruction around that. And how things are done so we were talking before we went on. About the role of horse and buggy verses a car. A mechanic on a car is not the same person managing the horse and buggy. That's the role of the service provider market. A lawyer is going to be very instrumental or legal but in new context. So the applications are going to morph around that, you're going to see people who deal with. Used cases like tokens, example that's hence the token sale. But applications that are already solving some of these problems with their business. Block chain opens up the door for a lot more head room for competitive advantage and value creation. I think that's where the action is. >> So Dave Valente, I want to bring you into this conversation very quickly. And try to build upon what John just talked about, this notion of the difference between distributed and decentralized. Distributed is kind of where things are. Decentralized is more of a state about authority. What kind of observations do we initially make about how block chain is going to impact the whole concept of authority within communities and markets? >> I think that's right I do think there are some subtle but important differences between distributed and decentralized. If you look at the internet initially and today. It's distributed but power increasingly has become centralized and that's problematic. Because it exposes us to a number of things. High value breaches if that power is centralized. Manipulation, surveillance risks, etc. I think there are you know some characteristics to look at that are relevant here. The distributive nature of that block chain, the immutability, and the lack of need or no need for single trusted third parties. So the distributed nature of the block chain verses that decentralized internet if you will. To use that as an analogy dramatically decreases those exposures. And it's much more inclusive. >> So when you think about that notion of inclusivity. We do have to come back to the idea that, we have certain ways. David you mentioned about how we're doing things today. Relatively high transaction cost but a few parties making an enormous amount of money but administering those transaction costs. And now we're talking about going to something that does inherently look more like a peer to peer but requires an enormous amount of upfront design. James Kobielus, talk a little bit about how we envision the transition. From where we are today to where certain attributes of these applications are going to be in the future. Are we going to need things like PKI? What is going to be the near term implications at a business level? >> Yeah. You know I agree with everything that Dave and John said about the business environment. Word going is that, what's fundamentally innovative about block chain. The evolution of distributive collaboration is a really clever commerce. It builds upon immutable distributive public identity. PKI, that's what PKI is all about. PKI has been around for a while. And adds to it an immutable distributed public ledger. And in the public ledger itself then the block chain becomes the foundation for distributed decentralized market places. With that said. Where it's going is that, increasingly there will be. Layered onto block chain, more standard interchange formats to enable various types of collaboration or interaction amongst various types of entity. And various types of business networks. I guess it's just the foundation for really a truly distributed peer to peer environment. At it's very heart there is still. As it were more centralized infrastructure called PKI with certification authorities and root CAs. That's not going away. That's becoming ever more fundamental the whole PKI infrastructure that's been build up. >> So David Foyer if I were to listen to this conversation as a CIO. I might think that this is going to be somebody else's problem. Lets take this down inside the business. What is it that a CIO needs to think about? This notion of distributed networks of data that both represent data and it can represent other assets? And what're some of the things that I need to start thinking about, inside my business? Is block chain really just at an economy level? Or is it going to have an impact on how I think about architecting, building, conceiving, deploying, and managing systems? >> So there's no shortcut to good systems design. People design very complex centralized systems. And they're going to need to design systems that work together. Especially when you go real time. So it's relatively simple to have batch systems which can catch up and things like that. But if you want to get the real value of block chain, it's going to be doing things in real time. So it's for example, if you're in a car and you want to get data from other cars. And you want to be able to feed data into that, to optimize on where you should have lunch or the best route to take. All of that data has to be done in real time. So what needs to be done is to make sure. As in any design of system that you have sufficient power, you have the network which is fast enough. And these types of systems because of their encryption because there's a lot of work that needs to be done to make them immutable and all the other characteristics. These systems take a lot more power to drive. >> David let me, let me jump in for a second. So one of the key differences just so we're clear. Is that we build these centralized systems and historically we've created a data store. That in a centralized system is under centralized control. And we serialize all access to that data through that centralized control. Fundamentally what we're talking, and that creates latency. Both on what's on the wire but also latency in terms of the path link. Of handling that serialization software through the system. What we're fundamentally talking about here is decentralizing that control. Putting the data everywhere but decentralizing that control. So we're not serializing anything through a central authority. That's fundamentally what we're doing right? >> Yes but a little caution there. You still got to have processed it in all of the nodes and for you to be able to get it. And you still got to make sure that all of that work's been done. >> That's all decentralized. >> It is decentralized but you still if people aren't keeping up to date up to time. You will still have a serialization impact. Eventually yes. >> So George think about from a peer to peer standpoint. What does this mean from thinking not just about designing systems at a grand scale but on a smaller scale. Can envision how block chain might be used to better marry identity, authority, and incentives as we think about building systems within a business? >> Well you had talked about the upfront design requirements. You talked about the upfront design requirements in organizational design enabled by this. At the risk of sounding big picture, this technology makes it easier to have an ecosystem of peer to peer companies that cooperate. Typically in the past we've had like supply chain masters. And they've sort of disseminated demand signals and collective supply signals. That was the central coordination, central trust sort of clearing house. And having the data distributed. The data distributed but this one system of record which essentially is logically centralized. Makes it easier to have a new sort of a new ecosystem design. >> So fundamentally we're talking about the idea of design very very large. In the sene of the degree to which we have to diminish the expectation that we'll fix design problems later on. We're going to have to do a lot of design work upfront. So David I want to close this conversation by bringing it down to the middle so to speak. Because when we think about unigrid and the idea of highly elastic, highly plastic systems. Where data's flying around and five milliseconds away from any other data kind of thing. There's going to be a need to envision how we can manage all of those applications or user problems within a system. In a way that sustains integrity of the data. Does block chain have a role to play inside the system and how we allocate resources? How we allocate data? What do you think? >> I think that's a very astute observation because one of the issues at heart here is ensuring that the system itself is not tampered with. The chips or the any part of it. So there is a role here potentially for block chain to be the arbiter of truth within the system itself. Or within the systems themselves. Now that is not here yet and that's got to be something which works super suer fast. It has to work in a way which allows the rest of the system to do its work. So it's going to be extremely interesting technology change to put it in there. But the value of it would be enormous. If you can trust then that the system itself. The chips, all of the. Everything within that system. For example you can take a snapshot these days which are very quick indeed. And if you can track that track all of the activities. You will have much greater confidence in the system itself. But that's not here yet and I suspect that's going to be quite a few years before those are put into the microcode etc. >> So John Furrier. That has an implication where we start thinking about control, authority. What's this going to mean? >> I mean David talks about the network aspect in the system's level. >> The systems of control you guys are getting at. But the edge of the network is where the action is, if you look at all the accessible block chain. You're seeing the edge of the network really be the economies of scale. And that's where, people call this the future of work. All this nonsense out there is true but the action for the people getting value are the ones that have economies of scale that go beyond their current economies of scale centralized systems. So you're seeing edge of the network type things. Crowdsourcing, edge of the network, of autonomous vehicles. You mentioned that used case. So the edge of the network paradigm that we've been researching at Wikibon. In covering on SiliconANGLE and on the cube of the events. Fundamental in this new exploration area. So for CIOs and for businesses trying to grab block chain. Which is different than the crypto currency piece, working together with tokens and block chain, is an edge of the network value proposition. As you go beyond centralization. Hence decentralization and distributed working together that's where the action is. The people that are realizing the benefits there and so companies that are evaluating their position. These of the block chain and crypto should be evaluating. Our we exploring these kinds of things? And that's where the filter is. >> Yeah so I'd say here's what I'd say just before I summarize gentlemen. I think you're right, I think that block chain that we as we've written on our Wikibon research. Folks have to design around the edge whether block chain's there or not. But block chain is going to ultimately make it easier to enact those designs over a period of time. Okay let me summarize guys. Great conversation today about block chain and our objective here is to bring it down from the level of magic, the level of potential. The level of someday into the level of practical. And I think what we've done is we've talked about block chain in a couple of different ways. First off, block chain is an immutable ledger that is decentralized in the sense that. A lot of different agents can gain control of a piece of data in a way that everybody else knows where it is and who has it. And that opens up an enormous amount of new application forms. We talked about what some of those application forms are. They can be open source software having an enormous new way of thinking about it. How they monetize work day performed. We've talked about how business networks can be established at a large and small scale That are capable of now but having a centralized authority that becomes the clearing house but rather reduce the transaction cost of deploying and running those networks. However all this means ultimately that the issue of design becomes that much more important. Block chain is not a magic technology. You just don't establish a block chain. It absolutely requires upfront thinking about what is it that you're trying to perform what is the work, what is the context that the block chain is trying to manage from an overall security standpoint? That's going to require a lot of very collaborative work between the CIO, the IT organization, the business and very importantly the lawyers. And that's not going to go away. We will see near term a number of interesting efforts from existing authorities. Folks who are handling public infrastructure, Swift and other types of networks try to use block chain as a mechanism. And that's likely to have some important queues as to how this is going to play out. But ultimately what CIOs need to do is they need to turn to somebody and they need to say, go understand block chain in an architectural level. So we can think about how we're going to build applications for communities that operate differently. Now the final point that I want to make here is that it's likely that we will block chain or block chain like technologies. Actually go deeper into systems as a way of arbitrating access to data and other resources within some of these highly elastic very a large scale unigrid like systems that we're talking about building. Definitely something to watch, not here today but likely something that's going to start hitting the market next few years. What's the action item? CIOs need to understand that block chain is not magic, it's not something that somebody else is going to do. You have to get someone on the issue of block chain architecture right now. Understand block chain design issues right now so that you can deploy block chain in small ways. But absolutely participate in the process of your business starting to enter into business networks that are likely to be mediated by block chain like technologies. Don't worry so much about bitcoin or ethereum. Watch those currencies, they're going to be important. But that's not really where the action is going to be over the next few years. The action is going to be how we think about bringing data and authority and identity closer to the work that's going to be performed increasingly at the edge. Utilizing a decentralized authority mechanism. And block chain right now is the best option we have. Thanks very much for observing us once again have an open conversation about a crucial research matter. This is Wikibon's research meeting on the cube. Until next time. (techy music)

>> Narrator: Live from Las Vegas, it's theCUBE, covering Veritas Vision 2017. Brought to you by Veritas. >> Dave: We're here at Veritas Vision, #VtasVision, The Truth in Information. This is a company that was founded in 1983 and has gone through a very interesting history, acquired by Symantec for around 15 or 16 billion dollars and then spun back out and purchased by a private equity Carlyle Group in 2005 for about 7 billion net of cash; it's about a two and a half billion dollar company with a really interesting growth plan, one that involves transforming from what many consider to be a legacy backup company into a multi cloud, hyperscale, data protection, value of information organization. My name is Dave Valente and I'm here with Stu Miniman. Stu! Good to see you. >> Stu: Great to be here with you, Dave. It's interesting, yeah, Veritas Company, I've known for, I don't know, gosh, about 20 years and they kind of went under the radar a little bit, under the Symantec piece and now back at it, but you know gosh, felt like a time warp hearing about like Netbackup, you know? A product that you know well, entrenched in the market, has lots of customers, so you know, in talking to the people here, people on board Veritas, some, you know, very veteran to the company, a lot of new faces though, and you know, they say it's energy, innovation, bringing as Bill Coleman who we're going to have on shortly, it's about the software-defined, multi cloud, hyperscale word so you know, A for hitting all the buzzwords and excited to, in the next two days, to kind of dig in and see where the reality is. >> Dave: Yeah, and you know, Stu, you know me, Stu. I like to look at the structure and the organizational structure and the market caps and things like that, but I always felt like, you know Veritas kind of disappeared under Symantec's governance and now, it is breaking out. I love the new private equity play, I want to hear from Bill Coleman about that, what the relationship is with Carlyle, you know it used to be that private equity would come in and they would just suck all the cash out of a company, I mean the classic example was ZA, right? They would maybe do some acquiring companies, they would maybe buy cashflow positive companies, take on more debt, suck all of the cash out and leave the carcass. That's not the new private equity way. We see that with Riverbed, we see that with Infor, VMC, and many, many others have said, you know what, the public markets aren't going to give us the love that we need, we're going to go private, we're going to get a deal on the company, we're going to invest in that company, invest in R&D, build the asset value of that company, maybe even in some cases do acquisitions, grow it, and then maybe do another exit, and that is a great way, a better way in fact, for these private equity firms to really cash in and I think Veritas is an interesting asset from that regard. >> Stu: Yeah, absolutely, I think back, you know, Dave, when I worked at EMC, you know Veritas was one of those competitors that EMC was like, we got to keep an eye on them. Veritas would put out, you know, billboards and have people running around in shirts that said No Hardware Agenda. One of the reasons I think that Veritas also disappeared a little bit under Symantec, is while they were great for lots of environments, they didn't really hit hard that wave of virtualization. Interesting thing is that, you know, EMC bought VMware, everybody knows, but the company that almost bought VMware was Symantec, and lots of us say, what if? What if Symantec had bought Vmware, would they, as a software company, really kind of squash that, you know, could Veritas have then really, integrated very deep here, and now as, Dave, you and I were at the Veem show earlier this year, and they said Veem and VREN, you know, the tenures of virtualization, and now hopping on multi cloud, well, you know, a lot of that message I hear from companies like Veem, companies like NetApp, you know, software-based storage companies, if you're not living in that multi cloud world, you know, what is your future, so. >> Dave: Well, to your point. >> Stu: Microsoft and Google, Amazon, and how those all fit. >> Dave: To your point, with no hardware agenda, Veritas was always viewed as the company with that sort of open software glue to bring together the data management pieces, and as I said, it sort of got lost over the last several years under Symantec. When you hear the keynotes this morning, you hear a story of information, information value, leveraging that information, information governance, a lot of talk about GDPR, obviously a lot of talk about backup, multi cloud, really an entirely new vision from the brand that has frankly become Veritas over the last decade, and new management really trying to affect that brand and send a message to customers that we hear you, that we're self-deprecating, talking about their UX not being what it should be, listening to customers, and putting forth the vision around not just the backup, but data management, now, that's always been the Holy Grail. Can you use that data protection backup corpus of data to really leverage that, to turn information into an asset, that's something that we're going to be unpacking all week with executives, partners, customers, analysts and the like. Last thought before we get to our next guest. >> Stu: Yeah, Dave, absolutely, you know, a bunch of new products are out there, it's that balance of how do they build off of their brand, all of their customer adoption, and now they have a lot of new things going on, so how do they fit in that environment, how do they differentiate, because everyone's trying to partner with the mega clouds, and it's not just the big three that we talk about. IBM and Oracle are two big partners that Veritas is talking about here, and something like hyperconverged infrastructure, Veritas has a play there. They came out with an object story, you know, you're asking me like wait, is this an array, or is it, well no, it's Veritas, it's software, it's always going to be software. Joseph Skorupa who was giving one of the super sessions, we're going to have him on to say your infrastructure does not differentiate you, it is your data, and that is what they want to highlight to the top. I think a message that we in general agree with, and looking forward to digging into it. >> Dave: Okay, so we'll be here for the next two days and what we like to do in theCUBE is what we hear in the messaging, and then we like to test that messaging, poke at it a little bit with the executives, talk to the customers about it, see how well it aligns, and then opine on where we think this is going, but if you were at Vmworld, you knew that data protection was the hottest category, it's an exploding area, a lot of dynamism, because it's all about the data, so we'll be talking about that, digital business. Keep right there everybody, this is theCUBE. Veritas Vision, #VtasVision. We'll be right back with our next guest, right after this short break. (electronic music)

(mellow music) >> Announcer: Live, from New Orleans, it's theCUBE, covering Veeam ON, 2017, brought to you by Veeam. >> We're back at the big easy, this is theCUBE, the leader in live tech coverage Dave Valente with Stu Miniman, Jason Buffington, long time CUBE guest and lead analyst at ESG, Jason, great to see you again. >> Thanks for having me >> @JBuff you're welcome, it's always a pleasure. You are an icon in this business. Ratmeyer today on theCUBE brought you up, said my friend, Jason Buffington, made an observation about the industry, and it's great to see you again. >> Thank you. >> So, you got some good play in the keynotes this morning, you guys just recently did a study that you spearheaded, talking about the availability gap, tell us about that research. >> So, 24 countries, a little under 1100 enterprises. So all organizations, over 1000 employees, and what we wanted to look at was how often are you down, how much does it cost when you're down, what are the differences between what the business expects of you, versus what you can actually deliver. Right, and by the way, that's the definition of the gap. Right, so the business expects that we cannot tolerate more than 30 minutes of downtime, and yet your fail over window is two hours. You have the availability gap. If the business says I cannot tolerate more than an hour of data loss, but you only backup once per night, you have a protection gap, right. So, looking at those gaps between the business expectations, and what IT can deliver, via whatever tools they're using, it was an unbiased panel, is what we went off and quantified. There were some really interesting numbers in there. >> Were you able to go to the same firm and ask of business people and IT people at the same firm? >> No, in this case what we did is we looked for IT decision makers who were familiar with the data protection processes they were using, and as well as being able to speak to business issues. So kind of look for the director IT, VP IT, someone who already has the business grade conversation. Probably the person who is being held accountable by the business units when IT fails to deliver. >> Do you think that, we've had a bunch of conversations with the practitioners today about what's the business conversation like, "well we go to the business" "and say how much data are you willing to lose." "Well none!" and then they go back and say >> There's a price for that >> There's a price for that, right. And most are not doing charge backs, some are doing show backs, so it's up to IT to say okay, look, we know they can't afford it. We can't afford it, so this is the level of service that we're going to give them. Do you think that's where the availability gap exists? Or is it because people have the wrong architecture, the wrong processes? >> I think it's more the former than the latter. I did a breakout session on this report earlier today. There was a great question in Q & A, why is it backup is still broken? Why is it no one can fix these gaps? And, what I offered them was that there's a lot of folks that just underestimate backup. They think of it as a cost center. They think it's always broken. Well, backup is not broken, the problem is if we were all still using Windows server 2003 physical boxes and exchange and sequel were still on pram and file was just that, we'd have solved backup ten years ago, right. But every time that you modernize production, it forces a modernization of protection. If you do it reactively, it's because you put in this brand new shiny flex pod or v-block or whatever, and figured out oh that legacy backup doesn't work. If you do it proactively, then you're catching up with things. But the problem is if you underestimate the importance of that, you get these gaps, right? So, what I counseled to the room that I was in was the first thing you have to do is you have to stop talking about data protection, even availability as an IT problem. It is a business impact cause, period. Right, so the first thing you want to do is you want to get all the tech out of the conversation. So, I offer a formula up, I published a book back in 2010, and there's a free chapter. I'll get it to you, so you can put in online, but I basically breakdown the cost of downtime into four values. There's the cost of lost data, there's the cost of lost productivity, right. So there's time down and time you have to repeat. And you can equate those to R2 and RPO. But a parentheses around those and times what's the human cost plus the profitability cost. And that's overly simple, but the point is if you know how long you're down, if you know how much data you will have lost, multiply that times how many butts and seats are sitting idle and how much did the inside sales department not sell that hour, right. That tells you cost of outage. And then all you have to do at that point, and there's no tack in that, right. It's just what is your RPO in real, what's your RTO in real, how much do your humans cost, how much does your department lose? If you have those four things, you know how much the problem is. Then, all you have to do is just go back to your system log and say how many times did that happen this year. If you do that, you've turned an IT problem into a business problem. Anytime I get a hold of C-level executives, the first thing I talk to them about availability is downtime is not in your budget, right. The idea of doing nothing costs you money. That's not in your budget and I guarantee of you did a data protection and availability solution, that will cost you less to your bottom line than the downtime that's unplanned that you have not budgeted for. >> Jason, Ratmere in the keynote this morning talked about the last ten years and they launched a new logo, talked a lot about cloud and physical and the next 10 years. What's your take on the message? Veeam just changed the leadership up a little bit. Are they in a transitional phase? Where are they positioned for kind of that next wave? >> So, the whole market's kind of in a transitional phase. So, I've been in data protection for 28 years. The only thing I've done since before getting out of school. Every time that we've had a major IT platform shift, the leaders in data protection have not made that jump, right. I started when we were doing mid-range, going to netware and over to Windows. >> That was what Ratmeyer was saying today. I didn't want to steal your thunder, so I'm glad you've brought this up. He noted that you had observed this, so carry on. >> Yeah so in times passed, we went went from physical to virtual servers, those leaders didn't make the jump and Veeam did, right. Veeam kind of took the crown on that for this whole last run. Our platform is shifting again, right. Now the difference this time around is and by the way the reason that most people don't make the jump is because whatever made you great from a technology perspective the last time around, doesn't apply to the new platform, right. So, NLMs didn't apply to Windows, agents didn't apply to hosts. We're now moving into cloud, but it's not a cloud, right. Some folks want IS, some want SAS. Neither of those use the same approaches that Veeam's secret sauce for host-based protection will carry for. So, the industry is in kind of a flux, and the other thing which is different this time around is when I was helping people move on to Windows NT, the presumption was we we're going to shutdown all the netware when we were done, right. For most of us, as we move into virtual machines, the presumption was we'd get rid of the metal on the way out. In this case though, cloud is not necessarily the end state, the end state is hybrid. Some data will be on pram, most of that data will be virtualized, some of it will still be physical. Right, the data that's in the cloud. Some of it will just be cloud stores, some of it will be the IS hosted VM, some of it will be SAS. But that's not because it's a prolonged transition, it's because we shouldn't be talking about migration, we should be talking about agility, where some data starts in the cloud and comes home. Other data starts on pram and moves, or from cloud to cloud. Because of that multi-cloud hybrid architecture, if that's the new end state for what IT is going to be delivering on, then the rules change. There is no secret sauce that carries from the last generation over. Certainly, Veeam's going to continue to be thought of as the virtualization data protection solution. But, if you think about they've added agents for physical, they've added cloud-based support on the back end. They announced more support for Office 365 and SAS. They're not a virtualization only play anymore. So, the market is going to have to take a reset, where everybody is unified, the difference is you've got the legacy folks that are unified and trying to catch up on virtualization features. And you've got Veeam, who is unified, where their virtualization is their strong suit, and cloud hosted and physical are the catch ups. So they're flying in opposite directions. >> So, you're saying that Veeam's secret sauce doesn't and virtualization doesn't necessarily carry over, however, they're making moves that will allow them to bridge, is that right? >> Absolutely, so unlike everyone else, who is in that virtualization wave, who solved the end protection and then happily got sold for their IP and you don't know those brands anymore. In this case, Veeam has continually looked at what else do people need, let's go do that. So, 4 or 5 years ago they added snapshot support, which wasn't necessary, but added more scenarios. Then, they added tape, who adds tape in 2015? Right, but they did because they recognized that people needed tape out, and since then they've added cloud, a couple different versions of cloud. This week they announced continuous data protection. Now, I'm glad no one from SNIA is around, cause they have a very prescriptive definition of what CDP is supposed to look like, and this isn't exactly that, so it's really more like KCDP, Kind of CDP, kind of thing. But, they continue to arrow the edges. They added physical support, those agents walls will allow them for IS hosted. They're not unified anymore, and that forward motion, but the moment they've got coming off of the first strategy, that's what's going to keep them moving forward for the next ten years. >> What makes is not KCDP, and makes it pure CDP? Just an infinite granularity or? >> Well, if you ask SNIA folks, they would tell you it's not just about infinite granularity on the protection, it's also infinite recoverability on the way back. So every single microsecond, so-- >> Stu: That's CRR isn't it? >> Yeah, think more like sequel does with every given transaction, could we go back to a given point. >> You need a data base to be involved, to actually get there. >> Yeah, but again, what I think is interesting is it's not just about backup, so in the availability report we talked about the gap between how little downtime that an organization can tolerate, versus just backup can't meet that goal. You can't recover fast enough if the only thing you're going to do is restore from backup. So, being able to integrate snapshots, being able to have replication, which shrinks down that data loss window considerably, that's how you meet the rest of the story, that backup alone can't do. And kudos to Veeam for doing it. >> Jason, how should we think about some of these emerging players who are actually in Veeam's ecosystem? Like Rubric or Cohesity, or Datos. Datos is not here, These sort of new, emerging, they don't want to call themselves backup players, they want to call themselves data protection or availability. How should we think about those emerging players? >> So, I have a category in a slide. I put them all in the category that I lovingly call the disrupters, right, because it forces you to reconsider the conversation. If you kind of step back and I could put Veeam and some of the other legacy unified enterprise class data protection products in one category, and what all of them are saying is let's take the backups that you know and trust us with. We're going to add indexing, we're going to add orchestration, we're going to help you do more with your data along the way. The end result is what the industry is calling copy data management. What else can you do with that data, which is otherwise dormant, sitting away in a store. What the disrupter category would tell you, is instead of starting with backup, and trying to evolve it forward, start with new storage. Think of the things you could do with a new paradigm for storage. >> So, the storage that would automatically know where the footprints are, that would automatically back you up along the way, that would automatically allow for copy data management type scenarios. So, again, it's two ways to get there. There's the backup first approach, and building on who you trust, then, there's the, if you want to start over again, have I got a deal for you. And that's going to be really interesting. For the rest of 2017 and 2018, the whole space of copy data management, copy data virtualization, copy data fill in the blank, that whole idea of good, better, best. Good, keep all your data as long as you need. Better, and get rid of it a moment longer. Then best, what else can you do with it. Analytics, testing, reporting, et cetera. That'll be an interesting market to watch, and one that now that Veeam is broad enough, will start to play in now as the year moves forward. >> Jason, like us, you go to a lot of these conferences. You've been to the Veeam on trail, which was our first one here. For the audience it's not here. What differentiates this show from some of the other ones you go to. What excites you about the community, the show itself, anything surrounding it? >> Sure, Veeam has a wonderful sense of community that most of the other vendors have just not been able to capitalize on. You know, there's certainly, there are many many thousands of IT professionals that have made their career out of this storage platform, or that backup software platform, et cetera. And, they're all good for support. Veeam has somehow cracked that code like Microsoft MVPs. The difference between a post-sale's we'll help you if you want, to a pre-sale's advocates. They literally have a green army walking around on this floor, who is delighted to tell anybody who will listen how Veeam saved their bacon, gave them back their weekend, et cetera. That energy of community, that's what's different about not only Veeam ON, it's also what's different about like a Veeam party at Vmworld or a Microsoft event. That culture and community, they've tapped something special there, and it shows in their results. >> Alright, we've got to wrap there, but I'll give you the last word, any upcoming research we should be paying attention to, or you want to promote a little bit? >> Sure, my blog within ESG is technicaloptimist.com. I do primary research on a whole bunch of things. Next ones coming out are on data protection modernization. So, why are people staying put or changing. If so, why or why not, and then what features matter most. So that's the next one that'll come out for me, and then over the summer I'm going to look at appliances as form factor, there's a lot of those to look at this week. What the affect of the DVA and the VM are having in the market, and then also more on the availability study. What we did for Veeam was so interesting ESG is going to go and take a few other angles and look at it some more. >> Awesome, great research agenda you've got upcoming. We will be looking for that, so, Jason, thanks very much, it was a pleasure to see you. >> Thanks for having me. >> You're welcome, alright, keep it right there buddy. We'll be back, with our next guest at theCUBE. We're live from Veeam ON, 2017 in New Orleans, We'll be right back. (upbeat music)

>> Narrator: Live from Boston, Massachusetts, this is The Cube, covering Spark Summit East 2017. Brought to you by Data Bricks. Now, here are your hosts, Dave Valente and George Gilbert. >> Boston, everybody. Nick Pentry this year, he's a principal engineer a the IBM Spark Technology Center in South Africa. Welcome to The Cube. >> Thank you. >> Great to see you. >> Great to see you. >> So let's see, it's a different time of year, here that you're used to. >> I've flown from, I don't know the Fahrenheit's equivalent, but 30 degrees Celsius heat and sunshine to snow and sleet, so. >> Yeah, yeah. So it's a lot chillier there. Wait until tomorrow. But, so we were joking. You probably get the T-shirt for the longest flight here, so welcome. >> Yeah, I actually need the parka, or like a beanie. (all laugh) >> Little better. Long sleeve. So Nick, tell us about the Spark Technology Center, STC is its acronym and your role, there. >> Sure, yeah, thank you. So Spark Technology Center was formed by IBM a little over a year ago, and its mission is to focus on the Open Source world, particularly Apache Spark and the ecosystem around that, and to really drive forward the community and to make contributions to both the core project and the ecosystem. The overarching goal is to help drive adoption, yeah, and particularly enterprise customers, the kind of customers that IBM typically serves. And to harden Spark and to make it really enterprise ready. >> So why Spark? I mean, we've watched IBM do this now for several years. The famous example that I like to use is Linux. When IBM put $1 billion into Linux, it really went all in on Open Source, and it drove a lot of IBM value, both internally and externally for customers. So what was it about Spark? I mean, you could have made a similar bet on Hadoop. You decided not to, you sort of waited to see that market evolve. What was the catalyst for having you guys all go in on Spark? >> Yeah, good question. I don't know all the details, certainly, of what was the internal drivers because I joined HTC a little under a year ago, so I'm fairly new. >> Translate the hallway talk, maybe. (Nick laughs) >> Essentially, I think you raise very good parallels to Linux and also Java. >> Absolutely. >> So Spark, sorry, IBM, made these investments and Open Source technologies that had ceased to be transformational and kind of game-changing. And I think, you know, most people will probably admit within IBM that they maybe missed the boat, actually, on Hadoop and saw Spark as the successor and actually saw a chance to really dive into that and kind of almost leap frog and say, "We're going to "back this as the next generation analytics platform "and operating system for analytics "and big debt in the enterprise." >> Well, I don't know if you happened to watch the Super Bowl, but there's a saying that it's sometimes better to be lucky than good. (Nick laughs) And that sort of applies, and so, in some respects, maybe missing the window on Hadoop was not a bad thing for IBM >> Yeah, exactly because not a lot of people made a ton of dough on Hadoop and they're still sort of struggling to figure it out. And now along comes Spark, and you've got this more real time nature. IBM talks a lot about bringing analytics and transactions together. They've made some announcements about that and affecting business outcomes in near real time. I mean, that's really what it's all about and one of your areas of expertise is machine learning. And so, talk about that relationship and what it means for organizations, your mission. >> Yeah, machine learning is a key part of the mission. And you've seen the kind of big debt in enterprise story, starting with the kind of Hadoop and data lakes. And that's evolved into, now we've, before we just dumped all of this data into these data lakes and these silos and maybe we had some Hadoop jobs and so on. But now we've got all this data we can store, what are we actually going to do with it? So part of that is the traditional data warehousing and business intelligence and analytics, but more and more, we're seeing there's a rich value in this data, and to unlock it, you really need intelligent systems. You need machine learning, you need AI, you need real time decision making that starts transcending the boundaries of all the rule-based systems and human-based systems. So we see machine learning as one of the key tools and one of the key unlockers of value in these enterprise data stores. >> So Nick, perhaps paint us a picture of someone who's advanced enough to be working with machine learning with BMI and we know that the tool chain's kind of immature. Although, IBM with Data Works or Data First has a fairly broad end-to-end sort of suit of tools, but what are the early-use cases? And what needs to mature to go into higher volume production apps or higher-value production apps? >> I think the early-use cases for machine learning in general and certainly at scale are numerous and they're growing, but classic examples are, let's say, recommendation engines. That's an area that's close to my heart. In my previous life before IBM, I bought the startup that had a recommendation engine service targeting online stores and new commerce players and social networks and so on. So this is a great kind of example use case. We've got all this data about, let's say, customer behavior in your retail store or your video-sharing site, and in order to serve those customers better and make more money, if you can make good recommendations about what they should buy, what they should watch, or what they should listen to, that's a classic use case for machine learning and unlocking the data that is there, so that is one of the drivers of some of these systems, players like Amazon, they're sort of good examples of the recommendation use case. Another is fraud detection, and that is a classic example in financial services, enterprise, which is a kind of staple of IBM's customer base. So these are a couple of examples of the use cases, but the tool sets, traditionally, have been kind of cumbersome. So Amazon bought everything from scratch themselves using customized systems, and they've got teams and teams of people. Nowadays, you've got this bold into Apache Spark, you've got it in Spark, a machine learning library, you've got good models to do that kind of thing. So I think from an algorithmic perspective, there's been a lot of advancement and there's a lot of standardization and almost commoditization of the model side. So what is missing? >> George: Yeah, what else? >> And what are the shortfalls currently? So there's a big difference between the current view, I guess the hype of the machine learning as you've got data, you apply some machine learning, and then you get profit, right? But really, there's a hugely complex workflow that involves this end-to-end story. You've got data coming from various data sources, you have to feed it into one centralized system, transform and process it, extract your features and do your sort of hardcore data signs, which is the core piece that everyone sort of thinks about as the only piece, but that's kind of in the middle and it makes up a relatively small proportion of the overall chain. And once you've got that, you do model training and selection testing, and you now have to take that model, that machine-learning algorithm and you need to deploy it into a real system to make real decisions. And that's not even the end of it because once you've got that, you need to close the loop, what we call the feedback loop, and you need to monitor the performance of that model in the real world. You need to make sure that it's not deteriorating, that it's adding business value. All of these ind of things. So I think that is the real, the piece of the puzzle that's missing at the moment is this end-to-end, delivering this end-to-end story and doing it at scale, securely, enterprise-grade. >> And the business impact of that presumably will be a better-quality experience. I mean, recommendation engines and fraud detection have been around for a while, they're just not that good. Retargeting systems are too little too late, and kind of cumbersome fraud detection. Still a lot of false positives. Getting much better, certainly compressing the time. It used to be six months, >> Yes, yes. Now it's minutes or second, but a lot of false positives still, so, but are you suggesting that by closing that gap, that we'll start to see from a consumer standpoint much better experiences? >> Well, I think that's imperative because if you don't see that from a consumer standpoint, then the mission is failing because ultimately, it's not magic that you just simply throw machine learning at something and you unlock business value and everyone's happy. You have to, you know, there's a human in the loop, there. You have to fulfill the customer's need, you have to fulfill consumer needs, and the better you do that, the more successful your business is. You mentioned the time scale, and I think that's a key piece, here. >> Yeah. >> What makes better decisions? What makes a machine-learning system better? Well, it's better data and more data, and faster decisions. So I think all of those three are coming into play with Apache Spark, end-to-end's story streaming systems, and the models are getting better and better because they're getting more data and better data. >> So I think we've, the industry, has pretty much attacked the time problem. Certainly for fraud detection and recommendation systems the quality issue. Are we close? I mean, are we're talking about 6-12 months before we really sort of start to see a major impact to the consumer and ultimately, to the company who's providing those services? >> Nick: Well, >> Or is it further away than that, you think? >> You know, it's always difficult to make predictions about timeframes, but I think there's a long way to go to go from, yeah, as you mentioned where we are, the algorithms and the models are quite commoditized. The time gap to make predictions is kind of down to this real-time nature. >> Yeah. >> So what is missing? I think it's actually less about the traditional machine-learning algorithms and more about making the systems better and getting better feedback, better monitoring, so improving the end user's experience of these systems. >> Yeah. >> And that's actually, I don't think it's, I think there's a lot of work to be done. I don't think it's a 6-12 month thing, necessarily. I don't think that in 12 months, certainly, you know, everything's going to be perfectly recommended. I think there's areas of active research in the kind of academic fields of how to improve these things, but I think there's a big engineering challenge to bring in more disparate data sources, to better, to improve data quality, to improve these feedback loops, to try and get systems that are serving customer needs better. So improving recommendations, improving the quality of fraud detection systems. Everything from that to medical imaging and counter detection. I think we've got a long way to go. >> Would it be fair to say that we've done a pretty good job with traditional application lifecycle in terms of DevOps, but we now need the DevOps for the data scientists and their collaborators? >> Nick: Yeah, I think that's >> And where is BMI along that? >> Yeah, that's a good question, and I think you kind of hit the nail on the head, that the enterprise applied machine learning problem has moved from the kind of academic to the software engineering and actually, DevOps. Internally, someone mentioned the word train ops, so it's almost like, you know, the machine learning workflow and actually professionalizing and operationalizing that. So recently, IBM, for one, has announced what's in data platform and now, what's in machine learning. And that really tries to address that problem. So really, the aim is to simplify and productionize these end-to-end machine-learning workflows. So that is the product push that IBM has at the moment. >> George: Okay, that's helpful. >> Yeah, and right. I was at the Watson data platform announcement you call the Data Works. I think they changed the branding. >> Nick: Yeah. >> It looked like there were numerous components that IBM had in its portfolio that's now strung together. And to create that end-to-end system that you're describing. Is that a fair characterization, or is it underplaying? I'm sure it is. The work that went into it, but help us maybe understand that better. >> Yeah, I should caveat it by saying we're fairly focused, very focused at HTC on the Open Source side of things, So my work is predominately within the Apache Spark project and I'm less involved in the data bank. >> Dave: So you didn't contribute specifically to Watson data platform? >> Not to the product line, so, you know, >> Yeah, so its really not an appropriate question for you? >> I wouldn't want to kind of, >> Yeah. >> To talk too deeply about it >> Yeah, yeah, so that, >> Simply because I haven't been involved. >> Yeah, that's, I don't want to push you on that because it's not your wheelhouse, but then, help me understand how you will commercialize the activities that you do, or is that not necessarily the intent? >> So the intent with HTC particularly is that we focus on Open Source and a core part of that is that we, being within IBM, we have the opportunity to interface with other product groups and customer groups. >> George: Right. >> So while we're not directly focused on, let's say, the commercial aspect, we want to effectively leverage the ability to talk to real-world customers and find the use cases, talk to other product groups that are building this Watson data platform and all the product lines and the features, data sans experience, it's all built on top of Apache Apache Spark and platform. >> Dave: So your role is really to innovate? >> Exactly, yeah. >> Leverage and Open Source and innovate. >> Both innovate and kind of improve, so improve performance improve efficiency. When you are operating at the scale of a company such as IBM and other large players, your customers and you as product teams and builders of products will come into contact with all the kind of little issues and bugs >> Right. >> And performance >> Make it better. Problems, yeah. And that is the feedback that we take on board and we try and make it better, not just for IBM and their customers. Because it's an Apache product and everyone benefits. So that's really the idea. Take all the feedback and learnings from enterprise customers and product groups and centralize that in the Open Source contributions that we make. >> Great. Would it be, so would it be fair to say you're focusing on making the core Spark, Spark ML and Spark ML Lib capabilities sort of machine learning libraries and in the pipeline, more robust? >> Yes. >> And if that's the case, we know there needs to be improvements in its ability to serve predictions in real time, like high speed. We know there's a need to take the pipeline and sort of share it with other tools, perhaps. Or collaborate with other tool chains. >> Nick: Yeah. >> What are some of the things that the Enterprise customers are looking for along the lines? >> Yeah, that's a great question and very topical at the moment. So both from an Open Source community perspective and Enterprise customer perspective, this is one of the, if not the key, I think, kind of missing pieces within the Spark machine-learning kind of community at the moment, and it's one of the things that comes up most often. So it is a missing piece, and we as a community need to work together and decide, is this something that we built within Spark and provide that functionality? Is is something where we try and adopt open standards that will benefit everybody and that provides a kind of one standardized format, or way or serving models? Or is it something where there's a few Open Source projects out there that might serve for this purpose, and do we get behind those? So I don't have the answer because this is ongoing work, but it's definitely one of the most critical kind of blockers, or, let's say, areas that needs work at the moment. >> One quick question, then, along those lines. IBM, the first thing IBM contributed to the Spark community was Spark ML, which is, as I understand it, it was an ability to, I think, create an ensemble sort of set of models to do a better job or create a more, >> So are you referring to system ML, I think it is? >> System ML. >> System ML, yeah, yeah. >> What are they, I forgot. >> Yeah, so, so. >> Yeah, where does that fit? >> System ML started out as a IBM research project and perhaps the simplest way to describe it is, as a kind of sequel optimizer is to take sequel queries and decide how to execute them in the most efficient way, system ML takes a kind of high-level mathematical language and compiles it down to a execution plan that runs in a distributed system. So in much the same way as your sequel operators allow this very flexible and high-level language, you don't have to worry about how things are done, you just tell the system what you want done. System ML aims to do that for mathematical and machine learning problems, so it's now an Apache project. It's been donated to Open Source and it's an incubating project under very active development. And that is really, there's a couple of different aspects to it, but that's the high-level goal. The underlying execution engine is Spark. It can run on Hadoop and it can run locally, but really, the main focus is to execute on Spark and then expose these kind of higher level APRs that are familiar to users of languages like R and Python, for example, to be able to write their algorithms and not necessarily worry about how do I do large scale matrix operations on a cluster? System ML will compile that down and execute that for them. >> So really quickly, follow up, what that means is if it's a higher level way for people who sort of cluster aware to write machine-learning algorithms that are cluster aware? >> Nick: Precisely, yeah. >> That's very, very valuable. When it works. >> When it works, yeah. So it does, again, with the caveat that I'm mostly focused on Spark and not so much the System ML side of things, so I'm definitely not an expert. I don't claim to be an expert in it. But it does, you know, it works at the moment. It works for a large class of machine-learning problems. It's very powerful, but again, it's a young project and there's always work to be done, so exactly the areas that I know that they're focusing on are these areas of usability, hardening up the APRs and making them easier to use and easier to access for users coming from the R and Python communities who, again are, as you said, they're not necessarily experts on distributed systems and cluster awareness, but they know how to write a very complex machine-learning model in R, for example. And it's really trying to enable them with a set of APR tools. So in terms of the underlying engine, they are, I don't know how many hundreds of thousands, millions of lines of code and years and years of research that's gone into that, so it's an extremely powerful set of tools. But yes, a lot of work still to be done there and ongoing to make it, in a way to make it user ready and Enterprise ready in a sense of making it easier for people to use it and adopt it and to put it into their systems and production. >> So I wonder if we can close, Nick, just a few questions on STC, so the Spark Technology Centers in Cape Town, is that a global expertise center? Is is STC a virtual sort of IBM community, or? >> I'm the only member visiting Cape Town, >> David: Okay. >> So I'm kind of fairly lucky from that perspective, to be able to kind of live at home. The rest of the team is mostly in San Francisco, so there's an office there that's co-located with the Watson west office >> Yeah. >> And Watson teams >> Sure. >> That are based there in Howard Street, I think it is. >> Dave: How often do you get there? >> I'll be there next week. >> Okay. >> So I typically, sort of two or three times a year, I try and get across there >> Right. And interface with the team, >> So, >> But we are a fairly, I mean, IBM is obviously a global company, and I've been surprised actually, pleasantly surprised there are team members pretty much everywhere. Our team has a few scattered around including me, but in general, when we interface with various teams, they pop up in all kinds of geographical locations, and I think it's great, you know, a huge diversity of people and locations, so. >> Anything, I mean, these early days here, early day one, but anything you saw in the morning keynotes or things you hope to learn here? Anything that's excited you so far? >> A couple of the morning keynotes, but had to dash out to kind of prepare for, I'm doing a talk later, actually on feature hashing for scalable machine learning, so that's at 12:20, please come and see it. >> Dave: A breakout session, it's at what, 12:20? >> 20 past 12:00, yeah. >> Okay. >> So in room 302, I think, >> Okay. >> I'll be talking about that, so I needed to prepare, but I think some of the key exciting things that I have seen that I would like to go and take a look at are kind of related to the deep learning on Spark. I think that's been a hot topic recently in one of the areas, again, Spark is, perhaps, hasn't been the strongest contender, let's say, but there's some really interesting work coming out of Intel, it looks like. >> They're talking here on The Cube in a couple hours. >> Yeah. >> Yeah. >> I'd really like to see their work. >> Yeah. >> And that sounds very exciting, so yeah. I think every time I come to a Spark summit, they always need projects from the community, various companies, some of them big, some of them startups that are pushing the envelope, whether it's research projects in machine learning, whether it's adding deep learning libraries, whether it's improving performance for kind of commodity clusters or for single, very powerful single modes, there's always people pushing the envelope, and that's what's great about being involved in an Open Source community project and being part of those communities, so yeah. That's one of the talks that I would like to go and see. And I think I, unfortunately, had to miss some of the Netflix talks on their recommendation pipeline. That's always interesting to see. >> Dave: Right. >> But I'll have to check them on the video (laughs). >> Well, there's always another project in Open Source land. Nick, thanks very much for coming on The Cube and good luck. Cool, thanks very much. Thanks for having me. >> Have a good trip, stay warm, hang in there. (Nick laughs) Alright, keep it right there. My buddy George and I will be back with our next guest. We're live. This is The Cube from Sparks Summit East, #sparksummit. We'll be right back. (upbeat music) (gentle music)

>> Voiceover: Live, from Orlando, Florida, it's theCUBE. Covering SapphireNow. Headlines sponsored by SAP Hana Cloud, the leader in platform as a service, with support from Console, Inc., the Cloud internet company. Now, here are your hosts, John Furrier, and Peter Burris. >> Okay, welcome back everyone. We are here live in Orlando, Florida, for SAP Sapphire coverage from SiliconANGLE Media, theCUBE, our flagship program. We go out to the events, and extract the signal for the noise. Want to give a shout out to our sponsors, who allow us to get here, SAP Hana Cloud platform, Console, Inc., EMC, Cap Gemini, thanks for supporting us. We appreciate it. Our next guest is Mitch Kick, Global Vice President, Head of Strategy and Programs for SAP Global Ecosystem. We love strategy guys because, they get the chess board. And they look like they're always playing chess, 3-D chess. Been looking at the landscape, looking at the horse on the track. Welcome to The Cube. >> Thank you very much. Good to be here. >> It's an evolving ecosystem. It's fluid, but yet, active. The Apple announcement, certainly notable news for SAP. Certainly, the Cloud, mobile, social data trend, the confluence of those things, causing massive innovation surge. So you, got a lot going on. >> Absolutely. >> What is the current ecosystem? >> Well, you know, when you think about the way SAP looks at it's ecosystem, I mean certainly we have those traditional types of partners, who resell our product. But, when we talk about our global ecosystem, we're really talking about those partners who are either strategic service partners, technology partners, some emerging partners and names that you mentioned, like Apple, Uber, Facebook, some of these, they're not your grandfathers, SAP partners. And so, we're really moving to partner in new ways. To co-innovate new types of solutions, that take advantage of the trends in the digital landscape. >> John: Like what are you doing with Facebook? >> Well, Facebook is an example, it's something where we said, "Look, there's all this social data," "that's out there. How do we put that together with" "our Hybris, CEC, types of solutions," "our commerce solutions?". To basically allow marketers to do one-to-one marketing, that leverages the power of Facebook data, and your enterprise data, brings it together in a very manageable tool. >> That must've been a very hard deal, because they're very controlled about their data. And also, each person has their profile settings. So, that's awesome. >> Yeah, and it's something that allows for marketers to just do much more targeting, much more insightful targeting. You know, we announced that last year and over the course of the last year had a number of really interesting pilot examples. >> Can developers get involved in that Or is this more of SAP directly, kind of thing? >> Well that, is an example of where we are creating a solution that sort of packages it turnkey. But, you know when you think something like in Apple, the beauty of that one is, not only are we developing these beautiful industry applications, that are going to be in targeted industries, and I don't know if you saw them, they were out on the floor here. >> Yeah, impressive. >> With regard to retail, or with regard to.. >> Well start-ups will come out of the woodwork just in a short time, have hundreds of employees, with this ecosystem. >> Well, exactly. I guess the point I was making with the Apple deal, is not only are we working with to design some really incredible industry apps, but then we're also creating the software developer kit, making that into the Hana Cloud platform, so that if you're developing on Hana Cloud platform, it now becomes another compelling reason you can leverage these beautiful interfaces, and these beautiful tools, that take full advantage of native capabilities on the Apple devices. And so it's a way that our partnership not only delivers, kind of near-term solutions that matter for us, but enables our broader ecosystem of solution partners to capitalize. >> It's fastest to innovation. I mean, you're going to get more R and D, and then real production apps faster that way. >> Absolutely. >> From the developer. So that's Core. David Valente and I always talk about courses for horses, which is, you know, certain things fit certain ways. There seems to be now, with the Cloud platform, an opportunity for developers to come in. So I want you to explain how Hana fits in. 'Cause this, Hana Cloud and then this Hana Cloud platform. What's the difference between the two? Can you just quickly share what that means to the ecosystem? >> Well, Hana as a database, I mean, the thing about the Hana Cloud Platform is that, that creates platform for our solution partners to extend, and integrate, as well as build and develop on it. And you'd say, "Well, as a platform as a service," "are you guys using HCP, to go out there and win" "the past wars?" In the generic sense of the past, that's really not the intention. The intention is, we've got this huge installed base. We've got these service partners, who are working very closely with their customers to innovate on top of, so that once our customers move to that digital core of S4 Hana, they can use HCP as that extension and integration platform, to tie together a number of different things. And a lot of the things that are, you know, when you think about digital transformation, there is so much activity, and discussion around the customer experience, and architecting a beautiful customer experience, with mobile devices, with you know, targeted types of commerce on the front end. But, what people are coming to realize, I think, is the importance of having that end-to-end. Because, you aren't going to be able to deliver the beautiful experience. And so, the example with, you know I was on a panel yesterday with Uber and Tumi. As an example, Tumi, luxury retailer that wants to create, not only a compelling customer experience that embodies the best of its luxury brand, but also is facing the threat of Amazon Prime Same-Day delivery, in metropolitan areas. And the beauty is, by partnering with Uber, and SAP, we are able to incorporate that seamlessly, as an option for Same-Day delivery. They can deliver in 30 minutes, for seven dollars, it's game-changing. That's an example of where we provide, here at this event, an early window into the type of co-innovation that we are doing. It's sort of like, in the past where you'd think, "Well, SAP has a certain solution footprint," "and we're going to partner with other software companies," "who can plug-in to that footprint.". Now you have, in the new world, where there are industry ecosystems like Uber, platforms that you can capitalize on, it's the business network. You can plug-in business networks to, an overall solution to customers, that's really compelling and that delivers opportunities in ways that we couldn't have imagined a few years ago. >> I want to build on that. So, historically, strategy has been three to five years, tied to asset values, mainly fixed asset values, and how are we going to generate a return in those fixed asset, over an extended period of time. You're describing a world where, whereas especially as those assets become more programmable, they can be applied to a broader array of activities, and opportunities, where the horizon starts to shrink pretty dramatically, the strategic horizon. And it becomes more, "What capabilities do we have?", and "How do we improve those capabilities," "and drive them forward?". And that's a crucial way of thinking about partnerships, is partnerships, as capabilities. I think that's where you were going. >> Absolutely. >> Are you thinking now about partnerships in the ecosystem as crucial capabilities, not only for SAP, but for SAP customers? >> They've always been, in many ways, when you think about, customers need a whole solution. In the past, even when the on-prem software world, you didn't get the whole solution by just buying the software package, it required a lot of additional service. With the Cloud model's that are emerging, it's much more easy to consume the software functionality, but there still is a tremendous amount of on-going innovation, differentiation, customization. And that's why when you look at, a lot of where we're going with our solution, you can hear Mike Getlin talking about our success factors product, and the fact that, "Well, how do partners help us?", "Do our service partners help us in the same way" "of just implementing software?". No. There role is really in integrating and extending it, and creating micro-services on top of it, that then say, "This is a really unique capability" "that's essential for delivering value" "to this particular customer or client.". So, you're now finding that because of our ecosystem, that is getting plugged into these new ways of contributing, we can now have a broad array of contribution. People understand how they can plug-in and capitalize on that, and deliver real innovation and benefit to the end customer. >> So you look a lot at industry trends. As you walk the floor here, what trends are starting to emerge, for you, and what is getting you excited, as a strategist? >> From my standpoint, when you think about digital transformation, and honestly, we were joking a lot about this whole term, because when it first game out, it was sort of like, "I'm not familiar with anyone who's actually" "doing analogue transformation.". All IT is digital. We've been doing digital things for years. And transformation, I mean, I was involved in the early '90s and the big re-engineering wave. Right? Where you're re-engineering, using technology and what not, so what is really different here? And I think what we see, is that, through all these trends, there's sort of confluence of them, and people map out a dozen, two dozen different trends that are going to change the world, they speak breathlessly about all these things. But in the end, what difference does it really make? From my standpoint, it's really three. One is you're starting to see all these things change the customer experience, fundamentally. Right? To the real-time, mobile devices, one-to-one. That's being enabled now. You're also seeing the difference in how value is delivered, in terms of IOT, instrumenting the broader landscape, etc. And you're seeing a difference in business models, in terms of how value is captured. You can think about it as, "Well, how is value consumed?", "How is value being delivered?", "How is value being captured?". The real, so what, is that all these different individual technology trends are combining to make those differences happen, that enable completely different ways of making money, of growing of opportunity. >> It changes the analogue, where, the analogue piece used to be the transactional, digital then hands off to analogue, or vice versa. That whole thing, end-to-end you just talked about, is an end-to-end digital. But the analogue role of the person, is augmented differently. So what you said is interesting because, I think people look at it differently and say, "Hey, if it's digital end-to-end," "where does analogue fit in?". Well still, people walking around here at the show, we're face-to-face, so I think it's interesting when you look at the optimization of digital. I'll take sales leads, for instance or marketing automation. You know, get the form, pass the leads to the sales people, they go knock on the door, call, email, that's analogue transaction. That's now digital. >> Mitch: Right. >> But the still, analogue components. What's your thoughts on that? How do you look at it? 'Cause you still got to do business, the people still are going to be involved. >> That really hit home when we were talking about this Uber example, because everybody talks about Tumi, they were talking about, "Well, its a beautiful experience." for somebody to be able to then say, "I got a one-hour delivery.". We can all identify with going to a retail outlet and they say, "Oh, I'm sorry, we don't have any more" "of those in the store, but we've got one" "that's 40 minutes away, if you want to go drive there.". Well, what if now all of the sudden you can get the product in to this store, in the next 30 minutes? Or, deliver it to wherever you happen to be, in 30 minutes? That changes the game. >> John: And that's user experience. >> Yeah. But, the thing is, so that's nifty, that's great, it's really compelling. But, when you start thinking about what it would take to work this, okay? Well now, you're going to have to have an implication for those retail store people. And so, this notion of, "How are we making this" "a beautiful experience for the retail clerk?", who now, instead of just serving the store, is going to get pinged because, "Hey, wait a minute," "we've got some deliveries that you're going to have to" "pick and pack, to get ready for some Uber driver" "to come in." That's a change to them. So, when you talk about implication, that highlights all of the, "change management", all of the, "how does it make a difference" "in individuals work?", and there's always going to be that last mile engagement that is needed. And that's really when you start talking about trends, how do we see things changing, I think about our service partners, I see their role changing to enable the real business change. >> Well that's it, that's it. The impact is clear. Totally agree, 100%. It's the confluence that magnifies that change, and its massive. It's frickin' awesome. Everyone can look at it and say, "Damn, its going to be big!". My final question to you is, given that impact, what advice are you sharing with your ecosystem, in terms of how to prepare for it? How to be ready not to go out of business, or help your customers not go out of business? And enable them to actually compete, digitally, in the transformation. >> Well, when we look at it, part of the challenge is that the ecosystem is so diverse, that you know, often your guidelines are speaking to specific people. The one thing I would say is, everybody is going out and talking a digital message, we need to be on the same song sheet. So when your solution partner, or service partner, and you've got your own offerings, your own reference architecture's, et cetera, let's work together to make sure that we are all singing from the same sheet. Second thing is, it's really imperative that we, basically migrate our installed base, to the digital core. So, S4 Hana, getting enabled around that, making that change happen, that enables all sorts of other benefits. And the third thing would be, the importance of then leveraging Hana Cloud platform. Because, the integrations that were hard coded, from yesterday, are no longer valid. So, if you leverage Hana Cloud platform from integration standpoint, you're really allowing for this much more agile, and fluid, innovation cycle to happen, in a much faster clip. And that's really what our customers are going to need, and it's going to take all of us working together to deliver that promise, of digital transformation. >> Well the Apple deal puts you guys front and center, on the user experience side, consumerization of IT. The chess board, multiple dimensions of chess, going on at the SAP ecosystem. Mitch, thanks for coming on. >> Absolutely. >> Welcome to The Cube Alumni Club. This is The Cube here live at Sapphire, we'll be right back. You're watching, The Cube.