>> 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)

(upbeat music) >> Hey everyone. Welcome to theCUBE's coverage of VMware Explore 2022. We are at Moscone West. Lisa Martin and Dave Nicholson here. Excited, really excited, whereas they were saying in the VMware keynote, pumped and jacked and jazzed to be back in-person with a lot of folks here. Keynote with standing room only. We've just come from that. We've got a couple of guests here from Expedient, going to unpack their relationship with VMware. Please welcome Brian Smith, the Senior Vice President and Chief Strategy Officer at Expedient. And Brent Meadows, the Vice President of Advanced Solution Architecture at Expedient. Guys it's great to have you on the program. >> Appreciate it bringing us on. >> Yep, welcome. >> Isn't it great to be back in person? >> It is phenomenal to be back. >> So let's talk about obviously three years since the last, what was called VMworld, so many dynamics in the market. Talk to us about what's going on at Expedient, we want to dig into Cloud Different, but kind of give us a lay of the land of what's going on and then we're going to uncrack the VMware partnership as well. >> Sure, so Expedient we're a full stack cloud service provider. So we have physical data centers that we run and then have VMware-based cloud and we've seen a huge shift from the client perspective during the pandemic in how they've really responded from everything pre-pandemic was very focused with Cloud First and trying to go that route only with hyper scaler. And there's been a big evolution with how people have to change how they think about their transformation to get the end result they're looking for. >> Talk about Cloud Different and what it's helping customers to achieve as everyone's in this accelerated transformation. >> Yeah. So, Cloud Different is something that Expedient branded. It's really about how the transformation works. And traditionally, companies thought about doing their transformation, at first they kept everything in house that they were doing and they started building their new applications out into a hyper scale cloud. And what that really is like is, a good analogy would be, it's like living in a house while you're renovating it. And I know what that's like from my relationship versus if you build a new house, or move to a new property that's completed already. And that's really the difference in that experience from a Cloud Different approach from transformation is you think of all the things that you have internally, and there's a lot of technical debt there, and that's a lot of weight that you're carrying when you're trying to do that transformation. So if you kind of flip that around and instead look to make that transformation and move all that technical debt into a cloud that's already built to run those same types of applications, a VMware-based cloud, now you can remove all of that noise, move into a curated stack of technology and everything just works. It has the security in place, your teams know how to run it, and then you can take that time you really reclaim and apply that towards new applications and new things that are strategic to the business. >> That's really critical, Brent, to get folks in the IT organization across the business, really focused on strategic initiatives rather than a lot of the mundane tasks that they just don't have time for. Brent, what are you hearing in the last couple of years with the dynamics we talked about, what are you hearing from the customer? >> Right. So, one of the big things and the challenges in the current dynamic is kind of that staffing part. So as people have built their infrastructure over the years, there's a lot of tribal knowledge that's been created during that process and every day more and more of that knowledge is walking out the door. So taking some of that technical debt that Brian mentioned and kind of removing that so you don't have to have all that tribal knowledge, really standardizing on the foundational infrastructure pieces, allows them to make that transition and not have to carry that technical debt along with them as they make their digital transformations. >> We heard a lot this morning in the keynote guys about customers going, most of them still being in cloud chaos, but VMware wanting them to get to cloud smart. What does that mean, Brian, from Expedient's perspective? What does cloud smart look like to Expedient and its customers? >> Yeah, we completely agree with that message. And it's something we've been preaching for a couple years in part of that Cloud Different story. And it's really about having a consistent wrapper across all of your environments. It doesn't matter if it's things that you're running on-premises that's legacy to things that are in a VMware-based cloud, like an Expedient cloud or things that are in a hyper scale, but having one consistent security, one consistent automation, one consistent cost management, really gives you the governance so that you can get the value out of cloud that you are hoping for and remove a lot of the noise and think less about the technology and more about what the business is getting out of the technology. >> So what does that look like as a practical matter? I imagine you have customers whose on-premises VMware environments look different than what you've created within Expedient data centers. I'm thinking of things like the level of adoption of NSX, how well a customer may embrace VSAN on-prem as an example. Is part of this transmogrification into your data center, kind of nudging people to adopt frameworks that are really necessary for success in the future? >> It's less of a nudge because a lot of times as a service provider, we don't talk about the technology, we talk more about the outcome. So the nice thing with VMware is we can move that same virtual machine or that container into the platform and the client doesn't always know exactly what's underneath because we have that standardized VMware stack and it just works. And that's part of the beauty of the process. I dunno if you want to talk about a specific client or... >> Yeah, so one of the ones we worked with is Bob Evans Foods. So they were in that transformation stage of refreshing, not only their office space and their data center, but also their VMware environment. So we helped them go through and first thing is looking at their existing environment, figuring out what they currently have, because you can't really make a good decision of what you need to change until you know where you're starting from. So we worked with them through that process, completely evacuated their data center. And from a business perspective, what that allowed them to do as well is have more flexibility in the choice of their next corporate office, because they didn't have to have a data center attached to it. So just from that data center perspective, we gave them some flexibility there. But then from an operations perspective, really standardize that process, offloaded some of those menial tasks that you mentioned earlier, and allow them to really look more towards business-driving projects, instead of just trying to keep those lights on, keeping the backups running, et cetera. >> Brian, question for you, here we are, the theme of the event is "The Center of the Multi-cloud Universe" which seems like a Marvel movie, I haven't seen any new superheroes yet, but I suspect there might be some here. But as customers end up and land in multi-cloud by default not by strategy, how does Expedient and VMware help them actually take the environment that they have and make it strategic so that the business can achieve the outcomes, improving revenue, finding new revenue streams, new products, new routes to market to delight those customers. How do you turn that kind of cloud chaos into a strategy? >> Yeah. I'd say there's a couple different components. One is really time. How can you give them time back for things that are creating noise and aren't really strategic to the business? And so if you can give that time back, that's the first way that you can really impact the business. And the second is through that standardization, but also a lot of times when people think of that new standard, they're only thinking if you're building from scratch. And what VMware has really helped is by taking those existing workloads and giving a standard that works for those applications and what you're building new and brings those together under a common platform and so had a really significant impact to the speed that somebody can get to that cloud operating model, that used to be a multi-year process and most of our clients can go from really everything or almost everything on-prem and a little bit in a cloud to a complete cloud operating model, on average, in four to six months. >> Wow! >> So if I have an on-premises environment and some of my workloads are running in a VMware context, VMware would make the pitch in an agnostic way that, "Well, you can go and deploy that "on top of a stack of infrastructure "and anybody and anywhere now." Why do customers come to you instead of saying, "Oh, we'll go to "pick your flavor of hyper scale cloud provider." What's kind of your superpower? You've mentioned a couple of things, but really hone it in on, why would someone want to go to Expedient? >> Yeah. In a single word, service. I mean, we have a 99% client retention rate and have for well over a decade. So it's really that expertise that wraps around all the different technology so that you're not worried about what's happening and you're not worried about trying to keep the lights on and doing the firefighting. You're really focused on the business. And the other way to, I guess another analogy is, if you think about a lot of the technology and the way people go to cloud, it's like if you got a set of Legos without the box or the instructions. So you can build stuff, it could be cool, but you're not going to get to that end state-- >> Hold on. That's how Legos used to work. Just maybe you're too young to remember a time-- >> You see their sales go up because now you buy a different set for this-- >> I build those sets with my son, but I do it grudgingly. >> Do you ever step on one? >> Of course I do. >> Yeah, there's some pain involved. Same thing happens in the transformation. So when they're buying services from an Expedient, you're buying that box set where you have a picture of what your outcome's going to be, the instructions are there. So you also have confidence that you're going to get to the end outcome much faster than you would if you're trying to assemble everything yourself. (David laughing) >> In my mind, I'm imagining the things that I built with Lego, before there were instructions. >> No death star? >> No. Nothing close with the death star. Definitely something that you would not want your information technology to depend upon. >> Got it. >> Brent, we've seen obviously, it seems like every customer these days, regardless of industry has a cloud first initiative. They have competitors in the rear view mirror who are, if they're able to be more agile and faster to market, are potential huge competitive threat. As we see the rise of multi-cloud in the last 12 months, there's also been a lot of increased analyst coverage for alternate specialty hybrid cloud. Talk to us about, Expedient was in the recent Gartner market guide for specialty cloud. How are these related? What's driving this constant change out in the customer marketplace? >> Sure. So a lot of that agility that clients are getting and trying to do that digital transformation or refactor their applications requires a lot of effort from the developers and the internal IT practitioners. So by moving to a model with an enterprise kind of like Expedient, that allows them to get a consistent foundational level for those technical debt, the 'traditional workloads' where they can start focusing their efforts more on that refactoring of their applications, to get that agility, to get the flexibility, to get the market advantage of time to market with their new refactored applications. That takes them much faster to market, allows them to get ahead of those competitors, if they're not already ahead of them, get further ahead of them or catch up the ones that may have already made that transition. >> And I would add that the analyst coverage you've seen in the last 9 to 12 months, really accelerate for our type of cloud because before everything was hyper scale, everything's going to be hyper scale and they realized that companies have been trying to go to the cloud really for over a decade, really 15 years, that digital transformation, but most companies, when you look at the analysts say they're about 30% there, they've hit a plateau. So they need to look at a different way to approach that. And they're realizing that a VMware-based cloud or the specialty cloud providers give a different mode of cloud. Because you had of a pendulum that everything was on-premises, everything swung to cloud first and then it swung to multi-cloud, which meant multiple hyper scale providers and now it's really landing at that equilibrium where you have different modes of cloud. So it's similar like if you want to travel the world, you don't use one mode of transportation to get from one continent to the other. You have to use different modes. Same thing to get all the way to that cloud transformation, you need to use different modes of cloud, an enterprise cloud, a hyper scale cloud, working them together with that common management plan. >> And with that said Brian, where have customer conversations gone in the last couple of years? Obviously this has got to be an executive level, maybe even a board level conversation. Talk to us about how your customer conversations have changed. Have the stakeholders changed? Has things gone up to stack? >> Yeah. The business is much more involved than what it's been in the past and some of the drivers, even through the pandemic, as people reevaluate office space, a lot of times data centers were part of the same building. Or they were added into a review that nobody ever asked, "Well, why are you only using 20% of your data center?" So now that conversation is very active and they're reevaluating that and then the conversation shifts to "Where's the best place?" And that's a lot of, the conference also talks about the best place for your application for the workload in the right location. >> My role here is to dive down into the weeds constantly to stay away from business outcomes and things like that. But somewhere in the middle there's this question of how what you provide is consumed. So fair to assume that often people are moving from CapEx model to an OPEX model where they're consuming by the glass, by the drink. What does that mean organizationally for your customers? And do you help them work through that journey, reorganizing their internal organization to take advantage of cloud? Is that something that Expedient is a part of, or do you have partners that help them through that? How does that work? >> Yeah. There's some unique things that an enterprise doesn't understand when they think about what they've done on-prem versus a service provider is. There's whole models that they can purchase with us in consumption, not just the physical hardware, but licensing as well. Do you want to talk about how clients actually step in and start to do that evaluation? >> Sure. So it really kind of starts on the front end of evaluating what they have. So going through an assessment process, because traditionally, if you have a big data center full of hardware, you've already paid for it. So as you're deploying new workloads, it's "free to deploy." But when you go to that cloud operating model, you're paying for each drink that you're taking. So we want to make sure that as they're going into that cloud operating model, that they are right sized on the front end. They're not over-provisioned on anything that they're going to just waste money and resources on after they make that transition. So it's really about giving them great data on the front end, doing all that collection from a foundational level, from a infrastructure level, but also from a business and IT operations perspective and figuring out where they're spending, not just their money, but also their time and effort and helping them streamline and simplify those IT operations. >> Let's talk about one of the other elephants in the room and that is the remote hybrid workforce. Obviously it's been two and a half years, which is hard to believe. I think I'm one of the only people that hates working from home. Most people, do you too? Okay, good. Thank you, we're normal. >> Absolutely. (Lisa laughing) But VMware was talking about desktop as a service, there was so much change and quick temporary platform set up to accommodate offsite workers during the pandemic. What are some of the experiences that your clients are having and how is Expedient plus VMware helping businesses adapt and really create them the right hybrid model for them going forward? >> Sure. So as part of being that full sack cloud service provider, desktop in that remote user has to be part of that consideration. And one of the biggest things we saw with the pandemic was people stood up what we call pandemic VDI, very temporary solutions. And you saw the news articles that they said, "We did it in 10 days." And how many big transformational events do people plan and execute in 10 days that transform their workforce? So now they're having to come back and say, "Okay, what's the right way to deploy it?" And do you want to talk about some of the specifics of what we're seeing in the adjustments that they're doing? >> Sure. So it is, when you look at it from the end user perspective, it's how they're operating, how they're getting their tools through their day to day job, but it's also the IT administrators that are having to provide that service to the end users. So it's really kind of across the board, it's affecting everyone. So it's really kind of going through and helping them figure out how they're going to support their users going forward. So we've spun up things like VMware desktop as a service providing that multi-tenant ability to consume on a per desktop basis, but then we've also wrapped around with a lot of security features. So one of the big things is as people are going and distributing where they're working from, that data and access to data is also opened up to those locations. So putting those protections in place to be able to protect the environment and then be able, if something does get in, to be able to detect what's going on. And then of course, with a lot of the other components, being able to recover those environments. So building the desktops, the end user access into the disaster recovery plans. >> And talk more, a little bit Brent, about the security aspect. We've seen the threat landscape change dramatically in the last couple of years, ransomware is a household word. I'm pretty sure even my mom knows what that means, to some degree. Where is that in customer conversations? I can imagine in certain industries like financial services and healthcare with PII, it's absolutely critical to ensure that that data is, they know where it is. It's protected and it's recoverable, 'cause everyone's talking about cyber resilience these days. >> Right. And if it's not conversation 1, it's conversation 1A. So it's really kind of core to everything that we do when we're talking to clients. It's whether it's production DR or the desktops, is building that security in place to help them build their security practice up. So when you think about it, it's doing it at layers. So starting with things like more advanced antivirus to see what's actually going on the desktop and then kind of layering above there. So even up to micro-segmentation, where you can envelop each individual desktop in their own quasi network, so that they're only allowed kind of that zero trust model where, Hey, if you can get to a file share, that's the only place you should be going or do I need web apps to get my day to day job done, but really restricting that access and making sure that everything is more good traffic versus unknown traffic. >> Yeah. >> And also on the, you asked about the clouds smarter earlier. And you can really weave the desktop into that because when you're thinking of your production compute environment and your remote desktop environment, and now you can actually share storage together, you can share security together and you start to get economies of scale across those different environments as well. >> So as we are in August, I think still yeah, 2022, barely for a couple more days, lot of change going on at VMware. Expedient has been VMware America's partner of the year before. Talk to us about some of the things that you think from a strategic perspective are next for the partnership. >> That it's definitely the multi-cloud world is here. And it's how we can go deeper, how we're going to see that really mature. You know, one of the things that we've actually done together this year was we worked on a project and evaluated over 30 different companies of what they spend on IT. Everything from the physical data center to the entire stack, to people and actually build a cloud transformation calculator that allows you to compare strategies, so that if you look at Strategy A over a five year period, doing your current transformation, versus that Cloud Different approach, it can actually help quantify the number of hours difference that you can get, the total cost of ownership and the speed that you can get there. So it's things like that that help people make easier decisions and simplify information are going to be part of it. But without a doubt, it's going to be how you can have that wrapper across all of your different environments that really delivers that cloud-like environment that panacea people have been looking for. >> Yeah. That panacea, that seems like it's critical for every organization to achieve. Last question for you. When customers come to you, when they've hit that plateau. They come to Expedient saying, "Guys, with VMware, help us accelerate past this. "We don't have the time, we need to get this done quickly." How do you advise them to move forward? >> Sure. So it goes back to that, what's causing them to hit that plateau? Is it more on the development side of things? Is it the infrastructure teams, not being able to respond fast enough to the developers? And really putting a plan in place to really get rid of those plateaus. It could be getting rid of the technical debt. It could be changing the IT operations and kind of that, the way that they're looking at a cloud transformation model, to help them kind of get accelerated and get them back on the right path. >> Back on the right path. I think we all want to get back on the right path. Guys, thank you so much for joining David and me on theCUBE today, talking about Expedient Cloud Different, what you're seeing in the marketplace, and how Expedient and VMware are helping customers to succeed. We appreciate your time. >> Yep. >> Thanks for having us. >> For our guests and Dave Nicholson, I'm Lisa Martin. You're watching theCUBE live from VMware Explorer '22, stick around, Dave and I will be back shortly with our next guest. (gentle upbeat music)

(electronic music) (background party chatter) >> All right. Hello, everyone. Welcome to The Cube. Coming up, Bryan Talebi will be here with Ahura A.I? >> Ahura A.I. >> Ahura A.I. Bryan Talebi here with Ahura A.I. We are at The Cube post party networking event, special on the ground, extended coverage. Bryan, we were at The Futurist, not The Futurist Conference, The Future of Blockchain which was the Monaco Crypto Summit over at the Grimaldi Center. Now we're at the VIP gala, the prince is here, a lot of action's happening. You had a chance to look all the presentations we have all the heavy hitters here, kind of a movement going on, right? >> Absolutely. Well, first of all, I think it's absolutely amazing that Prince Albert II put this all together. He obviously understands the future and understands technology. It's absolutely brilliance. And Julio as well, I mean is incredible. So I take off my hat to all the people that put this event together and the speakers were brilliant. I mean, did you see all the speakers the technologies that they've built have the potential to radically transform billions of people's lives. >> It's interesting, you know, I've been covering crypto for a very long time and watched it emerge and then start exploding. And there's always been, and I saw this with the web too early on, legit versus not legit. And all early markets have the hype cycles go down and up, and you always kind of have that but now you're starting to see legitimate tie-in between physical digital assets where, and the confluence of the business value, societal value, government value, all across the spectrum. Every vertical, every use case is got a decentralized vibe going on right now because it's a forcing function. And, and here in Monaco, the price and the king they're leaning into it cause I think they see the future because they could answer their legacy. >> Yeah. Absolutely. And look, you're absolutely right about this because this downturn that we're facing, especially this new crypto winter, I think is the best thing that could possibly have happened to the crypto space because what it's doing is pushing out the let's call them the less than honest brokers within the crypto community, the people that were just in it for a buck, the pump and dumpers and so forth it's really pushing those folks out. And the companies that remain are the true technologists that aren't looking at crypto as just a speculative asset, but rather an underlying technology that can transform the way that we engage with the world in a decentralized way. >> Bryan, you know, we didn't mention in the intro but you also do investment. >> I do. >> You also have a lot of things going on. You got a great history, great pedigree of seeing the waves of innovation the best. That's something, an investment question, like are you in it for the money or are you in it for the make it happen mission? That becomes kind of like the probing question. Someone comes to the table, "Hey, I need some cash. We do funding." What's your exit strategy? "I want to make an exit in two years." Okay. You're out. (Bryan laughs) (John) But it's almost that easy now, right? >> Sure. >> (John) To figure out who's in it for the money. >> Sure. >> (John) Who's in it for the mission. Yeah, the mission's successful. You make a lot of money. >> That's exactly right. Look, one of my mentors once taught me is, money like power is only amassed in great amount if indirectly sought because money by itself is not intrinsically a motivator. And so, what we do at our AB+ Ventures, my venture capital fund, is we only invest, not only in companies that are impact driven and have the capacity to impact a billion people, but we invest in founders that are climbing their third or fourth mountain. So these are people who've already made their money. They either had a couple big exits at over a hundred million dollars or they became rock stars or they became astronauts. They did things where they achieved the highest levels of achievement. And now are building technologies because they believe that they're going to impact the world in a meaningful way. >> They kind of know it's important, right? They made some money, they've been successful. They have scar tissue and experience to apply almost I want to say for the legacy of it, but more for value. >> Yeah. >> For everybody. >> Absolutely. >> All right. So I got to ask about what your current venture, I know you got some good action going on. It's growing pretty good. As they say in golf, it's middle of the fairway. It's growing, got momentum. It's a turbine market. You probably has some offers on the table. I mean, I could imagine all the AI you got going on. Blockchain, very attracted. It's a hard problem, but it's the first inning. Not even. >> Yeah. >> What going on with the company? >> We're very early. Look, we've been building our technologies, the deep tech platform we've been building for four and a half years. There's a whole bunch of offers on the table to buy us. But look, the reality is right now is a fantastic hiring opportunity. There's a lot of amazing talent out there that now wants to come to us, which is great. Number one, number two, if you look back to the 2000 Dot-com bubble, what you saw is all of the companies that didn't really solve real problems went away and it left a more oxygen in the room for the companies that were really solving problems that needed to be solved. And those are now all trillion dollar companies. So, >> Well, Brian, you and I both got a little gray hair. So let's talk about the Dot-com bubble. The other thing, I'll add to that, by the way great commentary, is that everything that was like bullshit actually happened. People bought pet food online, >> Right. >> Groceries delivered to their house. So to your point, the things actually happen. See the visions and the aspirations were correct, timing and capital markets spree. >> Sure. >> Is there similarities going on in crypto? Is it the crypto winter, weeding out those pretenders? Is that what you're saying? >> Well, there's definitely a lot of similarities there but if you look at the example that you use, right, pets.com versus Amazon, people are still buying pet food online. I buy all my pet supplies for my two puppies online. However, if you look at the reason that Amazon works is because of their supply chain and the innovations that they created on being able to deliver anything to you within a day or two days in an extremely cost effective manner. It wasn't just because they had a website and they did some hand wavy stuff to say isn't this a good idea. You actually have to have the underlying operational capability and innovation from a technology standpoint to make it happen. And so, when we talk about crypto over the past number of years, and I've been in the crypto space for a long time, as you have there's been a lot of hand wavy stuff. There's been a lot of people like, "wouldn't this be a good idea?" but then you have the true operators that are able to find the underlying competitive advantages that actually make it work. And that's what I'm interested in. >> I'd love to get your thoughts on that. First of all, great point if you look at like, I was just commentating earlier I was asked the question what I think, and I said, well, I do a lot of lot of reporting and analysis on cloud computing. I watch what Amazon Web Service has done from many, many years ago. And all the followers now. Scale data, higher level services, they're all happening and it's creating a lot of value. Okay? That's going to come to crypto. And so, okay, the dots aren't connected there yet, but you've got this, but one of the things that has proven to be a success criteria, ecosystems. When you have enabling technology like DigitalBits, for instance, is kind the main powering of this ecosystem here, the value that's being created on top of it has to be a step function or multiple of the cost or operational cost to deploy the platform. Okay, so that's kind of in concert with everyone else. You product decentralized, what's your thoughts on that? Because now you have a lot of potential ecosystems that could connect together cause there's no one centralized ecosystem. >> (Bryan) Absolutely. >> But what is, what, how do you get that? How do you square that circle? So to speak. What's your take on that? How does ecosystems play into defi, decentralization, de-apps blockchain? >> So what you really talking about is interoperable, right? So again, if we use an analogy, if we look back to the late nineties, when Web 1.0 was really flourishing and then in the 2000s where everybody created their own websites, people went to the world wide web, but every company had their own website. They had their own social media platform. They had their entire Salesforce platform or what have you. So everyone had their entire separate organization. And so, I suspect that the future of crypto is going to be very similar, where there's going to be a bunch of different metaverses, a bunch of different ecosystems, but someone's going to come along, and I think there's a number of people on the back end that are actually working on this, Some of them are really brilliant, that are going to create an interoperable mechanism for people that jump from metaverse to metaverse from chain to chain in a completely easy experience from a user experience standpoint where you don't have to have a PhD in crypto, so to speak, that doesn't exist, but you don't have to have that level. >> Well, if you're working on crypto for the past five years you've got a PhD. >> Basically. >> The thesis is, you're still alive producing. (Brian laughs) Well, that's a good point. So I'm looking for like, this defacto enabler, right? Because TCP/IP was an example in the old days, you know, the levels of the stack that never, TCP/IP is part of the OSI model. It's just interconnect. That layer, nothing got above it, was open. It was just hard and top that TCP/IP the rest was all standard. Ethernet, token ring add that data layer and then cards. That worked, the industry could galvanize around that. I'm waiting for the crypto moment now, where, what is going to be that cloud (indistinct), Kubernetes and service matches and whatnot. What, is there anything on the horizon that you see that has that kind of coalescent ecosystem, let's get, if we all get behind this, we all win. Rather than chasing crumbs. >> Sure. >> You know, the bigger pie, rising tide, all that stuff. >> Well, so I think there's a really interesting analogy from a couple of hundred years ago on this. So most people don't realize that when the United States first had their railroad system which was the innovative infrastructure play at the time each state or each region had their own systems they had different size railroad. So what would happen if you were trying to ship a bunch of grain from one part of the country to the other you would take it by a train. You get to a train station, you'd have to take everything off, put it on a different train, on a different set of train tracks. You would go a couple states over. You'd have to do that again, go a couple states over. You have to do that again. Eventually what happened is the federal government came in and said, hey, we need to create a system of policies around one set of rules for all trains and all logistics across the country. And so, I do think there's a role for governments to come together, along with the operators and the companies to work collaboratively together to say, hey, what are the regulations? What are the rules of the road? How do we make sure we get all the scam artists out of the system? How do we create a system that actually works for everybody? Now, there's always dangers there, right? You have regulatory capture. Sometimes the government, oftentimes they're slow, they don't understand the technology. So they come down with a heavy hand. And so if it's done properly, and it's not just the United States alone, by the way, it's all the countries in the world. Now at this point, it's a global effort. >> There's money involved, too. >> Exactly. But if we are able to bring together people that are much smarter than me from the public and private sectors as well as the nonprofit sectors, together to come up with one set of rules I think that will enable crypto to massively expand across the entire globe. >> What are you passionate about right now? I know you got the investment fund for, you know, helping society and the planet, you get your project with your startup company, AI is in a hot area. What's going on? What's your top goals for the year? >> So there's two things. Number one, my company, Ahura A.I. is my baby. It's where I spend 70, 80 hours a week. We invent a technology that enables people to learn three to five times faster than traditional education. >> (John) Is that so? >> Because I believe that education is the first step. It's the first variable, that impacts all of the sustainable development goals, impacts the world in a very real way. >> And you're not wearing your UA pin. >> I'm not wearing my pin, I always point to it. >> I wanted to grab it, I saw it earlier. >> But then the second thing I'm super focused on is existential risk. Look, so I throw a lot of events where I bring together four categories of people, CEOs of impact driven companies, investors, whether they're VCs or billionaires or family offices, global experts, and celebrities that want to use their influence for good in the world. And one of the speakers that I had at one of my events is a guy at Stanford who runs their lab on existential risk and what he told the group, and what he told me, is according to Stanford and all the researchers, there's a one in six chance that we're all going to go extinct by 2050. One in six, that's a dice roll. And so to me, the most important thing I can do is bring people together that have capacity, have resources, have capabilities, to address these drivers of existential risk because selfishly, I don't want to live in a dystopian Hellscape. >> Exactly, yeah. Bryan, thanks for coming on. We're going to get back into dinner. Great to see you. >> Thank you very much. >> The Cube after dark, extended hours. Look at us, we're going the whole day. VIP gala, Prince Albert, the team, DigitalBits, The Cube, all here at the Yacht Club in Monaco. I'm John Furrier. Thanks for watching.

>>The cube presents HPE discover 2022 brought to you by HPE. >>Hi everybody. Welcome back to the Cube's coverage of HPE. Discover 2022 from the Venetian convention center, formerly the sand convention center in Las Vegas, Dave ante, with John furrier. We're here with Brad parks. Who's the chief product officer at morphia data and Brian Thompson. Who's the vice president of GreenLake cloud product management at Hewlett Packard enterprise gentlemen. Great to see you first time on the queue first time. Wow. I just assumed we've known each other for, so >>We've been around a long time now. I'm happy to be here and thanks for, thanks for making the >>Time. Yeah, you've put a lot of people on the queue, but Morpheus data, when we, you know, we first met, I mean, with your new role here several years ago, tell, give us the update what's Morpheus do, why are you, so why does people, why do people need Morpheus? Think >>People need Morpheus, cuz it is messy, right? Technology promise, you know, simple, better, faster, but it's only gotten more complex, more heterogeneous over the last decade. We are a unified orchestration and automation platform that makes kind of the, the messy labyrinth that is enterprise. It kind of simpler to navigate primary use case. Self-service for developers who wanna push a button, get a database and an abstract deployed into their on-prem or their public cloud without having to wait on it. >>So you've, you've, you've been through the hyper-converged world. You've seen all that hardware come together. The VMware Nutanix of the world's kind of hardware. Now you got this software abstraction where you got operations, you've got AI, you got all kinds of ops AI ops dev ops data ops ops machine, >>You >>Know, they're all there. And so you got developer environments, you got operating environments. It's just getting more complicated at scale. Yep. This is a huge challenge. You guys are tackling this and then by the way, throw in automation in there too. Right? So, so all that's kind of coming together. How does self-service work put all that complication? >>Well, so I was just talking about Robert Christiansen. I know he's probably think he's been on the Q he's on S team and the ven diagram that we see in hundreds of enterprises we talk to is there's a need for central platform engineering at an enterprise to enable developers, to hit a button, get their database, run an I API line, you know, get their app stack deployed. They also wanna do the same thing with Kubernetes, right? Micro clusters deployed, you know, at a service, same thing with Terraform and Ansible. And they're just there aren't enough skilled operators who have moved up that stack. So you have to automate and canonize that knowledge and, and make it easy. >>Brian, one of the sort of pillars of GreenLake is, and as a service is data and we see a change in the way data is data platforms are being architected, data organizations. And one of the things that is a critical principle of sort of what we see as the new data era is self-service infrastructure where the operation of the technical details are an operational detail, not the be all end, all, you have to go beg and get data out. Okay. So you guys are building out, I think, consistent with that principle self-service infrastructure. That's right. So where does Morpheus fit in, in terms of that objective, what's your relationship like and, and help us understand >>That. Yeah. Within GreenLake, specifically think of this as a broad portfolio of different as a service offerings. Part of that key is meeting customers where they are and where they want to be. So we have that array of things which are fully self-service if you will, but serving an it admin type of persona. So it's where as a enterprise, I still have those resources. I want that granular of control all the way through, how do we deliver some of our more advanced cloud services, really trying to serve the end user to your point, how do I empower application owners, developers to, to bring in and, and work with those services? This is key in, in some of those cloud services, we're delivering more of VSC is a key component that we work as we bring to again, provide those interfaces. How do I provide everything from API CLI through a gooey experience that can span across multiple form factors, bring together that more of a homogenous experience? >>What, what options are out there to solve this problem today? I mean, what are the best practices? Is it do it yourself? Is it, you know, a little bit of VMware here, a little bit of, you know, other tooling there, what, what do you see out there in the marketplace? >>I'll give kinda my perspective kind of yeah. Outside the, the tools that we see when we walk into an enterprise, you've got a company that's got a lot of VMware, maybe a little Nutanix, we've got some AWS, they wanna use OpenShift for their clusters. They got Terraform Ansible, and they got service now. And there's a, there's a poor it ops team in the middle, trying to wire all that together. And each of those domains have tried to go up this hill, right. VMware's done with vRealize automation, you know? Yeah. OpenShift will say no where the way, and you use cube vert to >>Do your virtual service now will say the same thing. Right? >>So our goal is, you know, we started in the middle right. Middle out, right. We started unifying that for self-service for developers and finance teams. And we're we're agnostic. We don't have a dog in the fight, right. We don't have a hypervisor business, a hardware business, an ITSM business. We're all about bringing the pieces together. But that said, we work with partners like HP, you have a footprint of thousands of customers who are solving that same problem and need to need to move up stack. So it's been a good win-win. So >>You're not trying to be the cloud operating system per se. I mean, right. The way, the way a VMware wants to be, or you could even argue, well, I guess open, you >>Got, you got the hyperscalers coming down, you got VMware moving up. But again, they all at the end of the day are trying to control their cash cow, right. Their hardcore business. We wanna make them all transparent. So >>Your bet is it's gonna be all of the above. Yeah. That's not gonna change. Right. That's the complexity is, is that right? Or do you think they're gonna consolidate? >>No, I think there's definitely something to that. I also think there's enough. Disparate. Technology's not gonna be one size fits all or one to rule them all. In fact, I think that's part of the examples in the past, like private cloud is we announced yesterday private cloud for enterprise. It's not a new term. People are doing that for quite a while now, but they are typically fairly brittle hand rolled disparate technologies, some poor it team trying to hold it together. So where we can provide that kind of life cycle management in a cloud operating model, remove that complexity and provide that stability. And in that experience across what will be interchangeable parts at times, I think that's really that direction in, >>Yeah. You guys talk about this whole starting in the middle. I like that because there's a skills gap as well. Right. Not only is there for a challenge on it that transforms, there's not enough. People actually know how to manage a Kubernetes cluster spin one up. Yeah. So there's been a rise of managed services. We're seeing come outta the woodwork almost in all areas where it's complex. Yeah. How does that fit into the makeup of as customers, engineer or rearchitect or, or just evolve to edge on premises and public cloud? Yeah. In a cloud operating way, because if I got managed service, do they just plug in, I mean, new orchestrating services, managed services all the above, take us through this dynamic because we're seeing more and more customers saying, just gimme the service. Yeah. >>I, I know manage perspective. This, this kinda goes back to that portfolio of meeting customers where they are. There are some that, that have that expertise in house they're opinionated. They just want a different consumption model. But on the other side of that, it's difficult to attract and retain that type of talent. And if I have limited resources, am I gonna focus on the care and feeding of that underlying infrastructure? Or am I gonna try to up level and focus on things more strategic to the business? So that's where we've certainly been focusing. And I think this type of management capability is what feeds into that. Right? >>Talk about the trust aspect, because if I'm gonna go manage service, it better work. I need to trust it. It's not a zero trust environment. It's actually a trust and verified, but you're seeing the software supply chain is a big discussion point. Developers don't wanna have to get back off their CDC pipeline to go in and manage stuff. So a managed service has to be verified. Yeah. There's a huge trust factor in there. How does what's the status of this now? Is it real? >>I think one of the, one of the pieces we see in terms of trust organizationally, I mean, people in process is always harder than the tech usually. And, and a lot of the trust is just internal. You get, you know, developers don't trust the ops team, right? Security doesn't trust anybody, you know, finance doesn't trust, you know, who's billing them. Part of what we do as a stack is we give each of those stakeholder groups, the ability to get their core needs met without getting each other's way. And from a delivery perspective where we partner with HPE is we are, you know, we're a platform framework, we're a technology provider we're inside, you know, products like the private cloud. We work their GMs team, the manage services team. If they wanna take on more of that operational concern, right. They use us or if the customer wants to manage it themselves. So we we're all about enabling them at the end of the day. And, and HP brings >>And how hard bread is it to unify? UN unification is a great word. I love let's unify everybody. Right. So how, how hard is that? Can you scope that problem statement for us? What does that mean? >>I'll separate it from a technology perspective and then the people process. So a lot of the traditional people that have played in that space that do it yourself, you mention right. Scripting it all together is hard, right? And if you change from cloud a to cloud B, you're set back six months, like why we exist is we wanna very quickly pull the pieces together. We can usually get a POC up and running in about two hours, right? That's a, self-service VMware private cloud, right? That doesn't mean you've solved the organizational inertia. You know, that's, that takes time, weeks, months. And that's where people are like Accenture GreenLake, other SI other channel partners bring that together to, to help make that change happen. >>How mature is the platform? Where are you in terms of determining product market fit? Are you, are you scaling at this point? >>Well, the, the great part about our origin story, right? We got our start as an internal tool set inside a two and a half billion dollar private equity firm that was transforming it at dozens of companies. So we were built for the use case product market fit happened, cuz a bunch of guys needed to get their jobs done. So we've been an outbound since 2015, right? We were top of the stack ranking, you know, all the MQs, all the quadrants, all the analysis. So we think we're their product market fit. The nice thing is customers have actually moved to where we are. Right? Five years ago, cloud management meant cleaning up the lift and shift mess. Now it's automation platform engineering. So it it's a fun time. >>It's it's operational. Yeah. It's they're operationalizing it. >>What's your go to market model. Maybe you could double click on those through >>Partners. So honestly through HP is a big one. We're small, right? We want to be the best unified platform we can be. Our go to market is via technology partners like HPE, right? The other systems integrators, other channel partners globally. So, so yeah. It's >>So then you've got kind of a tiger team overly. Yep. Salesforce is that, that >>Yeah, we've got teams globally. So we've got about 700,000 workloads under management around the world. About 70% of those are OnPrem VMware Nutanix. The rest are up in the public cloud. So we work with partners, solution providers, services, engines to, to help deliver that to >>Customers. What do you make of the 61 billion acquisition of VMware from Broadcom? >>We're, you know, I think your analysis was spot on. It is gonna be a, a war of, you know, what is the, the most profitable to that new Broadcom business and things like vRealize automation, some of these fringe products that are core at a customer use cases, but may not be driving a lot of bottom revenue for VMware, I think are gonna be gonna be on the bubble. And we've seen more interest in the last few weeks from people who just want to hedge their bets. Right. They want to be able to switch from hypervisor a to hypervisor B or cloud a to cloud B without being locked into anyone's stack. And that is, that is why we exist. Mm. >>You wanna comment on that? >>I mean, it's, you know, for HP and from a GreenLake and even just historically, right. It's about customer choice. Mm. We have a strong relationship with VMware. Sure. We have, I don't know how many bajillions of servers out there running VMware that we, we support with. So, you know, it's, it's, it's all just looking at that ecosystem and helping deliver those customer solutions and outcomes is our focus. Yeah. >>Thank >>You. Brian. Talk about the GreenLake success with partners. We're seeing ecosystem is a big part of that and we know the formula for ecosystems create value. What is the pitch that green lakes making to the marketplace right now to attract more folks to build and or integrate into the >>Platform? Yeah. I mean, GreenLake started with a, a vehicle of how do I start to deliver an OPEX model, a consumption model for traditional infrastructure that we've been providing more and more as the services and solutions really have emerged and evolved. It's gone from, how do I just give you kit and a consumption model for it to now looking at embedded solutions with third party ISV software building or wrapping those services around it, really delivering outcomes and solutions you're seeing. And hopefully you'll solve just from announcement more and more of that, where we have kind of turnkey solutions with key partners, how do we bring a marketplace ecosystem together? How do we help enable those kind of full solutions? Because we're not gonna build it all ourselves, right. We wanna make sure that we can deliver those outcomes. >>So marketing is often and should be ahead of the actual product, early days of GreenLake. It was really a, you know, financial model. Sure. Right. Where do, where do you see GreenLake today? How far is it matured? We saw some of the, the announcements yesterday. We saw some demos. Where are we at? >>Yeah. So this actually, I think really the exciting part is you might have heard Antonio refer to as that journey to one each of our different businesses within green or within HPE, they've all been building these cloud services in GreenLake enabled services. But as you saw Alma share the path to the HPE GreenLake cloud platform that really is bringing these services together into a functional platform, right? Common identity, common telemetry services, bringing these together as now, integrable interoperable services. Like you're starting to see that come together and you can really see the Chrome trail of, of where we're going with a very powerful hybrid cloud experience, right? Spanning private public on-prem colo and a, and a full solution set within there. So it that's, that's the exciting part >>For me and Brad Morpheus will be a capability inside of GreenLake that a customer can consume. Do you have to write to GreenLake APIs to enable that? Or is it, is it more just certify that you work inside a GreenLake? What has to get done? I'll say a lot >>Of what they've done is actually written into, into our APIs. Like we've normalized hybrid it. We have a, a database model of every load balance or a cloud endpoint automation tool. So we are, we're all about making it easier to consume it. And the vision that Alma and HP has around GreenLake fits very well with why we exist. So they're able to extract metering data from our, you know, from our API, we know who provisioned what, where how much they spent. So we're a good repository and platform partner for them to, to build on. It's >>Great for that console that you guys have. Yeah. >>You got the, you got the open APIs, you publish those, you guys take advantage of 'em and then sure. Boom. Then you can consume. Got it. All right, guys. Hey, great to see you again, red. Thanks for, for >>Coming on. Thanks. Thanks for having us on >>Our pleasure. Great stuff. Congratulations. Okay. Keep it right there. This is Dave Valante for John furrier. Are you watching the cubes coverage of HPE discover 2022 from Las Vegas? We'll be right back.

(upbeat music) >> Hello everyone, welcome back to the manager risk across the extended attack surface with Armis. I'm John Furrier, your host of theCUBE. Got the demo. Got here, Bryan Inman sales engineer at Armis. Bryan, thanks for coming on. We're looking forward to the demo. How you doing? >> I'm doing well, John, thanks for having me. >> We heard from Nadir describing Armis' platform, lot of intelligence. It's like a search engine meets data at scale, intelligent platform around laying out the asset map, if you will, the new vulnerability module among other things that really solves CISCO's problems. A lot of great customer testimonials and we got the demo here that you're going to give us. What's the demo about? What are we going to see? >> Well, John, thanks. Great question. And truthfully, I think as Nadir has pointed out what Armis as a baseline is giving you is great visibility into every asset that's communicating within your environment. And from there, what we've done is we've layered on known vulnerabilities associated with not just the device, but also what else is on the device. Is there certain applications running on that device, the versions of those applications, and what are the vulnerabilities known with that? So that's really gives you great visibility in terms of the devices that folks aren't necessarily have visibility into now, unmanaged devices, IoT devices, OT, and critical infrastructure, medical devices things that you're not necessarily able to actively scan or put an agent on. So not only is Armis telling you about these devices but we're also layering on those vulnerabilities all passively and in real time. >> A lot of great feedback we've heard and I've talked to some of your customers. Rhe agentless is a huge deal. The discoveries are awesome. You can see everything and just getting real time information. It's really, really cool. So I'm looking forward to the demo for our guests. Take us on that tour. Let's go with the demo for the guests today. >> All right. Sounds good. So what we're looking at here is within the Armis console is just a clean representation of the passive reporting of what Armis has discovered. So we see a lot of different types of devices from your virtual machines and personal computers, things that are relatively easy to manage. But working our way down, you're able to see a lot of different types of devices that are not necessarily easy to get visibility into, things like your up systems, IT cameras, dash cams, et cetera, lighting systems. And today's day and age where everything is moving to that smart feature, it's great to have that visibility into what's communicating on my network and getting that, being able to layer on the risk factors associated with it as well as the vulnerabilities. So let's pivot over to our vulnerabilities tab and talk about the the AVM portion, the asset vulnerability management. So what we're looking at is the dashboard where we're reporting another clean representation with customizable dashlets that gives you visuals and reporting and things like new vulnerabilities as they come in. What are the most critical vulnerabilities, the newest as they roll in the vulnerabilities by type? We have hardware. We have application. We have operating systems. As we scroll down, we can see things to break it down by vulnerabilities, by the operating system, Windows, Linux, et cetera. We can create dashlets that show you views of the number of devices that are impacted by these CVEs. And scrolling down, we can see how long have these vulnerabilities been sitting within my environment? So what are the oldest vulnerabilities we have here? And then also of course, vulnerabilities by applications. So things like Google Chrome, Microsoft Office. So we're able to give a good representation of the amount of vulnerabilities as they're associated to the hardware and applications as well. So we're going to dig in and take a a deeper look at one of these vulnerabilities here. So I'm excited to talk today about of where Armis AVM is, but also where it's going as well. So we're not just reporting on things like the CVSS score from NIST NVD. We're also able to report on things like the exploitability of that. How actively is this CVE being exploited in the wild? We're reporting EPSS scores. For example, we're able to take open source information as well as a lot of our partnerships that we have with other vendors that are giving us a lot of great value of known vulnerabilities associated with the applications and with hardware, et cetera. But where we're going with this is in very near future releases, we're going to be able to take an algorithm approach of, what are the most critical CVSS that we see? How exploitable are those? What are common threat actors doing with these CVEs? Have they weaponized these CVEs? Are they actively using those weaponized tools to exploit these within other folks' environments? And who's reporting on these? So we're going to take all of these and then really add that Armis flavor of we already know what that device is and we can explain and so can the users of it, the business criticality of that device. So we're able to pivot over to the matches as we see the CVEs. We're able to very cleanly view, what exactly are the devices that the CVE resides on. And as you can see, we're giving you more than just an IP address or a lot more context and we're able to click in and dive into what exactly are these devices. And more importantly, how critical are these devices to my environment? If one of these devices were to go down if it were to be a server, whatever it may be, I would want to focus on those particular devices and ensuring that that CVE, especially if it's an exploitable CVE were to be addressed earlier than say the others and really be able to manage and prioritize these. Another great feature about it is, for example, we're looking at a particular CVE in terms of its patch and build number from Windows 10. So the auto result feature that we have, for example, we've passively detected what this particular personal computer is running Windows 10 and the build and revision numbers on it. And then once Armis passively discovers an update to that firmware and patch level, we can automatically resolve that, giving you a confidence that that has been addressed from that particular device. We're also able to customize and look through and potentially select a few of these, say, these particular devices reside on your guest network or an employee wifi network where we don't necessarily, I don't want to say care, but we don't necessarily value that as much as something internally that holds significantly, more business criticality. So we can select some of these and potentially ignore or resolve for determining reasons as you see here. Be able to really truly manage and prioritize these CVEs. As I scroll up, I can pivot over to the remediation tab and open up each one of these. So what this is doing is essentially Armis says, through our knowledge base been able to work with the vendors and pull down the patches associated with these. And within the remediation portion, we're able to view, for example, if we were to pull down the patch from this particular vendor and apply it to these 60 devices that you see here, right now we're able to view which patches are going to gimme the most impact as I prioritize these and take care of these affected devices. And lastly, as I pivot back over. Again, where we're at now is we're able to allow the users to customize the organizational priority of this particular CVE to where in terms of, this has given us a high CVSS score but maybe for whatever reasons it may be, maybe this CVE in terms of this particular logical segment of my network, I'm going to give it a low priority for whatever the use case may be. We have compensating controls set in place that render this CVE not impactful to this particular segment of my environment. So we're able to add that organizational priority to that CVE and where we're going as you can see that popped up here but where we're going is we're going to start to be able to apply the organizational priority in terms of the actual device level. So what we'll see is we'll see a column added to here to where we'll see the the business impact of that device based on the importance of that particular segment of your environment or the device type, be it critical networking device or maybe a critical infrastructure device, PLCs, controllers, et cetera, but really giving you that passive reporting on the CVEs in terms of what the device is within your network. And then finally, we do integrate with your vulnerability management and scanners as well. So if you have a scanner actively scanning these, but potentially they're missing segments of your net network, or they're not able to actively scan certain devices on your network, that's the power of Armis being able to come back in and give you that visibility of not only what those devices are for visibility into them, but also what vulnerabilities are associated with those passive devices that aren't being scanned by your network today. So with that, that concludes my demo. So I'll kick it back over to you, John. >> Awesome. Great walk through there. Take me through what you think the most important part of that. Is it the discovery piece? Is it the interaction? What's your favorite? >> Honestly, I think my favorite part about that is in terms of being able to have the visibility into the devices that a lot of folks don't see currently. So those IoT devices, those OT devices, things that you're not able to run a scan on or put an agent on. Armis is not only giving you visibility into them, but also layering in, as I said before, those vulnerabilities on top of that, that's just visibility that a lot of folks today don't have. So Armis does a great job of giving you visibility and vulnerabilities and risks associated with those devices. >> So I have to ask you, when you give this demo to customers and prospects, what's the reaction? Falling out of their chair moment? Are they more skeptical? It's almost too good to be true and end to end vulnerability management is a tough nut to crack in terms of solution. >> Honestly, a lot of clients that we've had, especially within the OT and the medical side, they're blown away because at the end of the day when we can give them that visibility, as I've said, Hey, I didn't even know that those devices resided in that portion, but not only we showing them what they are and where they are and enrichment on risk factors, et cetera, but then we show them, Hey, we've worked with that vendor, whatever it may be and Rockwell, et cetera, and we know that there's vulnerabilities associated with those devices. So they just seem to be blown away by the fact that we can show them so much about those devices from behind one single console. >> It reminds me of the old days. I'm going to date myself here. Remember the old Google Maps mashup days. Customers talk about this as the Google Maps for their assets. And when you have the Google Maps and you have the Ubers out there, you can look at the trails, you can look at what's happening inside the enterprise. So there's got to be a lot of interest in once you get the assets, what's going on those networks or those roads, if you will, 'cause you got in packet movement. You got things happening. You got upgrades. You got changing devices. It's always on kind of living thing. >> Absolutely. Yeah, it's what's on my network. And more importantly at times, what's on those devices? What are the risks associated with the the applications running on those? How are those devices communicating? And then as we've seen here, what are the vulnerabilities associated with those and how can I take action with them? >> Real quick, put a plug in for where I can find the demo. Is it online? Is it on YouTube? On the website? Where does someone see this demo? >> Yeah, the Armis website has a lot of demo content loaded. Get you in touch with folks like engineers like myself to provide demos whenever needed. >> All right, Bryan, thanks for coming on this show. Appreciate, Sales Engineer at Armis, Bryan Inman. Given the demo God award out to him. Good job. Thanks for the demo. >> Thanks, thanks for having me. >> Okay. In a moment, we're going to have my closing thoughts on this event and really the impact to the business operations side, in a moment. I'm John Furrier of theCUBE. Thanks for watching. (upbeat music)

>>Hello, welcome back to the manager risk across the extended attack surface with Armas I'm John fair host of the cube. Got the demo. God here, Brian Inman sales engineer at Armit. Brian. Thanks for coming on. We're looking forward to the demo, how you doing? >>I'm doing well, John, thanks for having me, >>You know, we heard from Nair, you know, describing arm's platform, a lot of intelligence. It's like a search engine meets data at scale intelligent platform around laying out the asset map. If you will, the new vulnerability module among other things that really solves CISO's problems, a lot of great customer testimonials. And we, we got the demo here that you're gonna give us, what's the demo about what are we, what are we gonna see? >>Well, John, thanks. Great question. And truthfully, I think as NAIA has pointed out what AIS as a baseline is giving you is, is great visibility into every asset on your that's communicating within your, within your environment. And from there, what we've done is we've layered on known vulnerabilities associated with not just the device, but also what else is on the device. What's is there certain applications running on that device, the versions of those applications and what are the vulnerabilities known with that? So that's really gives you great visibility in, in terms of the devices that folks aren't necessarily have visibility into now, unmanaged devices, OT devices, OT, and critical infrastructure, medical devices, things that you're not necessarily able to actively scan or put an agent on. So not only is Armas telling you about these devices, but we're also layer layering on those vulnerabilities all passively and in real time, >>A lot of great feedback we've heard and I've talked to some of your customers, the agent list is a huge deal. The Discover's at awesome. You can see everything and, and just getting real time information. It's really, really cool. So I'm looking forward to, for the demo for our guests, take us on that tour. Let's go with the demo for the guests today. >>All right. Sounds good. So what we're looking at here is within the Armas console is just a clean representation of the passive reporting of what Armas has discovered. So we see a lot of different types of devices, you know, from your virtual machines and personal computers, things that are relatively easy to manage, but working our way down, you're able to see a lot of different of the different types of devices that are not necessarily easy to, to get visibility into things like your up systems, IP cameras, dash cams, et cetera, lighting systems, and, and today's day and age, where everything is moving to the, that smart feature. You know, it's, it's great to have that visibility into, you know, what's communicating on my network and getting that, being able to layer on the risk factors associated with it, as well as the vulnerabilities. So let's pivot over to our vulnerabilities tab and talk about the, the ADM portion, the asset vulnerability management. >>So what we're looking at is the dashboard where we're reporting a, a, another clean representation with customizable dashboards that gives you visuals and reporting and things like new vulnerabilities as they come in, you know, what are the most critical vulnerabilities that are the, the newest as they roll in the vulnerabilities by type, we have hardware, we have application, we have operating systems. As we scroll down, we can see things to break it down by vulnerabilities, by the operating system, windows, Linux, et cetera. We can take, you know, create dashes that show you views of the, the number of, of devices that are impacted by these CVEs and scrolling down. We can see, you know, what, how long have these vulnerabilities been sitting within my environment? So how, what are the oldest vulnerabilities we have here? And then also of course, vulnerabilities by applications. So things like Google Chrome, Microsoft office. >>So we're able to give a, a good representation of the amount of vulnerabilities as they're associated to the hardware and applications as well. So we're gonna dig in and take a, a deeper look at one of these vulnerabilities here. So I'm excited to talk today about where Armas ABM is, but also where it's going as well. So we're not just reporting on things like the CVSs score from, from N N VD. We're also able to report on things like the exploitability of that, right? How, how actively is this, this CVE being exploited in the wild, right? We're reporting E EPSS scores. For example, we're able to take open source information as well as a lot of our partnerships that we have with other vendors that are giving us a lot of great value of known vulnerabilities associated with the applications and with hardware, et cetera. >>But we're where we're going with. This is we're in Fu very near future releases. We're gonna be able to, to take sort of an algorithm approach of what are the most critical CVSs that we see, how exploitable are those, what are common threat actors doing with these, these CVEs have they weaponized these CVS? Are they actively using those weaponized tools to exploit these within, within other folks' environments? And who's reporting on these. So we're gonna take all of these and then really add that Armas flavor of we already know what that device is, and we can explain. And, and so can the users of it, the business criticality of that device, right? So we're able to pivot over to the matches as we see the CVEs, we're able to very cleanly view, what are, what exactly are the devices that the CVE resides on, right? >>And as you can see, we're giving you more than just an IP address or more, you know, a lot more context, and we're able to click in and dive into what exactly are these devices and how, and more importantly, how critical are these devices to, to my, my environment, if one of these devices were to go down, if it were to be a server, if you know, whatever it may be, I would wanna focus on those particular devices and ensuring that that CVE, especially if it's an exploitable CVE were to be addressed or early, earlier than, than say the others, and really be able to manage and prioritize these another great feature about it is, you know, for example, we're looking at a, a particular CVE in terms of its its patch and build number from windows 10. So the AutoSol feature that we have, for example, we've passively detected what this particular personal computer is running windows 10 and the build and revision numbers on it. >>And then once Armas passively discovers an update to that firmware and patch level, we can automatically resolve that, giving you a, a confidence that that has been addressed from that particular device. We're also able to customize and look through and potentially select a few of these, say, you know, these particular devices reside on your guest network or an employee wifi network where we don't necessarily, I don't wanna say care, but we don't necessarily value that as much as something in, you know, internally that has holds significantly more business criticality. So we can select some of these and potentially ignore or resolve for determining reasons. As you see here, be able to really truly manage and prioritize these, these CVEs. As I scroll up, I can pivot over to the remediation tab and open up each one of these. So what this is doing is essentially Arma says, you know, through our knowledge base, been able to work with the vendors and, and pull down the patches associated with these. >>And within the remediation portion, we're able to view, for example, if we were to pull down the patch from this particular vendor and apply it to these 60 devices that you see here, right now, we're able to F to view, you know, which patches are gonna gimme the most impact as I prioritize these and take care of these affected devices. And lastly, as I pivot back, go again, where we're at now is we're able to allow the, the users to customize the organizational priority of this particular CVE, to where in terms of, you know, this has, has given us a high CVSs score, but maybe for whatever reasons it may be maybe this CVE in terms of this particular logical segment of my network, I'm gonna give it a low priority for whatever the use case may be. We have compensating controls set in place that, that render this CVE, not impactful to this particular segment of my environment. >>So we're able to add that organizational priority to that CVE and where we're going, as you can see that that popped up here, but where we're going is we're gonna start to be able to apply the, the organizational priority in terms of the actual device level. Right? So what we'll see is we'll see a, a column added to here to where we'll see the, the business impact of that device, based on the importance of that particular segment of your environment or the device type, be it, you know, critical networking device, or maybe a, a critical infrastructure device, PLCs controllers, et cetera, but really giving you that passive reporting on the CVEs in terms of what the device is within your network. And then finally we do integrate with your vulnerability, vulnerability management, and scanners as well. So if you have a scanner actively scanning these, but potentially they're missing segments of your net network, or they're not able to actively scan certain devices on your network, that's the power of Armas being able to come back in and give you that visibility of not only what those devices are for visibility into them, but also what vulnerabilities are associated with those passive devices that aren't being scanned by your network today. >>So with that that's, that concludes my demo. So I'll kick it back over to you, John. >>Awesome. Great, great walk through there. Take me through what you think the most important part of that. Is it the discovery piece? Is it the interaction what's your favorite? >>Honestly, I think my favorite part about that is, you know, in terms of being able to have the visibility into the devices, that a lot of folks don't see currently. So those OT devices, those OT devices, things that you're not able to, to run a scan on or put an agent on Armas is not only giving you visibility into them, but also layering in, as I said before, those vulnerabilities on top of that, that's just visibility that a lot of folks today don't have. So Armas does a great job of giving you visibility and vulnerabilities and risks associated with those devices. >>So I have to ask you, when you give this demo to customers and prospects, what's the reaction falling outta their chair moment? Are they more skeptical? It's almost too good to be true. And the end to end vulnerability management's is a tough nut to crack in terms of solution. >>Well, honestly, a lot of clients that we've had, you know, especially within the OT and the medical side, they're, they're blown away because at the end of the day, when we can give them that visibility, as I've said, you know, Hey, I, I didn't even know that those devices resided in that, that portion, but not only are we showing them what they are and where they are and enrichment on risk factors, et cetera. But then we show them, Hey, there's a known, you know, we've worked with that vendor, whatever it may be and, you know, Rockwell, et cetera. And we know that there's vulnerabilities associated with those devices. So they just seem to be blown away by the fact that we can show them so much about those devices from behind one single console. >>You know, it reminds me of the old days. I'm gonna date myself here. Remember the old Google maps, mashup days. This is customers. Talk about this as the Google maps for their assets. And when you have the Google maps and you have the Ubers out there, you can look at the trails, you can look at what's happening inside the, inside the enterprise. So there's gotta be a lot of interest in once you get the assets what's going on, on those, on, in those, on those networks or those roads, if you will, cuz you got in packet movement, you got things happening, you got upgrades, you got changing devices. It's always on kind of living thing. >>Absolutely. Yeah. It's what's on my network. And more importantly at times what's on those devices, right? Are the, what are the risks associated with the, the applications running on those? How are those devices communicating? And then as we've seen here, what are the vulnerabilities associated with those and how can I take action with them? >>All right. Real quick, put a plug in for where I can find the demo. Is it online is on YouTube, on the website. Where does someone see this demo? >>Yeah, the Amis website has a lot of demo content loaded. Get you in touch with folks like engineers like myself to, to provide demos whenever, whenever needed. >>All right, Brian, thanks for coming on this show. Appreciate sales engineer, Armas Brian Inman, given the demo God award out to him. Good job. Thanks for the demo. >>Thanks. Thanks for having me. >>Okay. You know, in a moment we're gonna have my closing thoughts on this event and really the impact to the business operation side. In a moment I'm John fur the cube. Thanks for watching.

>>Thanks. Hey, everyone, welcome back to the cubes. Continuing coverage of snowflakes. Summit 22 live from Caesars Forum in Las Vegas. Lisa Martin here. I have three guests here with me. We're gonna be talking about Snowflake Ventures and the snowflakes start up Challenge. That's in its second year. I've got Harry Glaser with me. Co founder and CEO of Model Bit Start Up Challenge finalist Damon Bryan joins us as well. The CTO and co founder of Hyper Affinity. Also a startup Challenge Finalists. And Stephane Williams to my left here, VP of Corporate development and snowflake Ventures. Guys, great to have you all on this little mini panel this morning. >>Thank you. >>Thank you. >>Let's go ahead, Harry, and we'll start with you. Talk to the audience about model. But what do you guys do? And then we'll kind of unpack the snowflake. The Snowflakes challenge >>Model bit is the easiest way for data scientists to deploy machine learning models directly into Snowflake. We make use of the latest snowflake functionality called Snow Park for python that allows those models to run adjacent to the data so that machine learning models can be much more efficient and much more powerful than they were before. >>Awesome. Damon. Give us an overview of hyper affinity. >>Yes, so hyper affinity were Decision Intelligence platform. So we helped. Specifically retailers and brands make intelligent decisions through the use of their own customer, data their product data and put data science in a I into the heart of the decision makers across their business. >>Nice Step seven. Tell us about the startup challenge. We talked a little bit about it yesterday with CMO Denise Pearson, but I know it's in its second year. Give us the idea of the impetus for it, what it's all about and what these companies embody. >>Yeah, so we This is the second year that we've done it. Um, we it was really out of, um Well, it starts with snowflake Ventures when we started to invest in companies, and we quickly realised that there's there's a massive opportunity for companies to be building on top of the Lego blocks, uh, of snowflake. And so, um, open up the competition. Last year it was the inaugural competition overlay analytics one, Um, and since then, you've seen a number of different functionalities and features as part of snowflakes snow part. Being one of them native applications is a really exciting one going forward. Um, the companies can really use to accelerate their ability to kind of deliver best in class applications using best in class technology to deliver real customer outcomes and value. Um, so we've we've seen tremendous traction across the globe, 250 applicants across 50. I think 70 countries was mentioned today, so truly global in nature. And it's really exciting to see how some of the start ups are taking snowflake to to to new and interesting use cases and new personas and new industries. >>So you had 200 over 250 software companies applied for this. How did you did you narrow it down to three? >>We did. Yeah, >>you do that. >>So, behind the scenes, we had a sub judging panel, the ones you didn't see up on stage, which I was luckily part of. We had kind of very distinct evaluation criteria that we were evaluating every company across. Um and we kind of took in tranches, right? We we took the first big garden, and we kind of try to get that down to a top 50 and top 50. Then we really went into the details and we kind of across, um, myself in ventures with some of my venture partners. Um, some of the market teams, some of the product and engineering team, all kind of came together and evaluated all of these different companies to get to the top 10, which was our semifinalists and then the semi finalists, or had a chance to present in front of the group. So we get. We got to meet over Zoom along the way where they did a pitch, a five minute pitch followed by a Q and A in a similar former, I guess, to what we just went through the startup challenge live, um, to get to the top three. And then here we are today, just coming out of the competition with with With folks here on the table. >>Wow, Harry talked to us about How did you just still down what model bit is doing into five minutes over Zoom and then five minutes this morning in person? >>I think it was really fun to have that pressure test where, you know, we've only been doing this for a short time. In fact model. It's only been a company for four or five months now, and to have this process where we pitch and pitch again and pitch again and pitch again really helped us nail the one sentence value proposition, which we hadn't done previously. So in that way, very grateful to step on in the team for giving us that opportunity. >>That helps tremendously. I can imagine being a 4 to 5 months young start up and really trying to figure out I've worked with those young start ups before. Messaging is challenging the narrative. Who are we? What do we do? How are we changing or chasing the market? What are our customers saying we are? That's challenging. So this was a good opportunity for you, Damon. Would you say the same as well for hyper affinity? >>Yeah, definitely conquer. It's really helped us to shape our our value proposition early and how we speak about that. It's quite complicated stuff, data science when you're trying to get across what you do, especially in retail, that we work in. So part of what our platform does is to help them make sense of data science and Ai and implement that into commercial decisions. So you have to be really kind of snappy with how you position things. And it's really helped us to do that. We're a little bit further down the line than than these guys we've been going for three years. So we've had the benefit of working with a lot of retailers to this point to actually identify what their problems are and shape our product and our proposition towards. >>Are you primarily working with the retail industry? >>Yes, Retail and CPG? Our primary use case. We have seen any kind of consumer related industries. >>Got it. Massive changes right in retail and CPG the last couple of years, the rise of consumer expectations. It's not going to go back down, right? We're impatient. We want brands to know who we are. I want you to deliver relevant content to me that if I if I bought a tent, go back on your website, don't show me more tense. Show me things that go with that. We have this expectation. You >>just explain the whole business. But >>it's so challenging because the brothers brands have to respond to that. How do you what is the value for retailers working with hyper affinity and snowflake together. What's that powerhouse? >>Yeah, exactly. So you're exactly right. The retail landscape is changing massively. There's inflation everywhere. The pandemic really impacted what consumers really value out of shopping with retailers. And those decisions are even harder for retailers to make. So that's kind of what our platform does. It helps them to make those decisions quickly, get the power of data science or democratise it into the hands of those decision makers. Um, so our platform helps to do that. And Snowflake really underpins that. You know, the scalability of snowflake means that we can scale the data and the capability that platform in tangent with that and snowflake have been innovating a lot of things like Snow Park and then the new announcements, announcements, uni store and a native APP framework really helping us to make developments to our product as quick as snowflakes are doing it. So it's really beneficial. >>You get kind of that tailwind from snowflakes acceleration. It sounds like >>exactly that. Yeah. So as soon as we hear about new things were like, Can we use it? You know, and Snow Park in particular was music to our ears, and we actually part of private preview for that. So we've been using that while and again some of the new developments will be. I'm on the phone to my guys saying, Can we use this? Get it, get it implemented pretty quickly. So yeah, >>fantastic. Sounds like a great aligned partnership there, Harry. Talk to us a little bit about model bit and how it's enabling customers. Maybe you've got a favourite customer example at model bit plus snowflake, the power that delivers to the end user customer? >>Absolutely. I mean, as I said, it allows you to deploy the M L model directly into snowflake. But sometimes you need to use the exact same machine learning model in multiple endpoints simultaneously. For example, one of our customers uses model bit to train and deploy a lead scoring model. So you know when somebody comes into your website and they fill out the form like they want to talk to a sales person, is this gonna be a really good customer? Do we think or maybe not so great? Maybe they won't pay quite as much, and that lead scoring model actually runs on the website using model bit so that you can deploy display a custom experience to that customer we know right away. If this is an A, B, C or D lead, and therefore do we show them a salesperson contact form? Do we just put them in the marketing funnel? Based on that lead score simultaneously, the business needs to know in the back office the score of the lead so that they can do things like routed to the appropriate salesperson or update their sales forecasts for the end of the quarter. That same model also runs in the in the snowflake warehouse so that those back office systems can be powered directly off of snowflake. The fact that they're able to train and deploy one model into two production environment simultaneously and manage all that is something they can only do with bottled it. >>Lead scoring has been traditionally challenging for businesses in every industry, but it's so incredibly important, especially as consumers get pickier and pickier with. I don't want I don't want to be measured. I want to opt out. What sounds like what model but is enabling is especially alignment between sales and marketing within companies, which is That's also a big challenge at many companies face for >>us. It starts with the data scientist, right? The fact that sales and marketing may not be aligned might be an issue with the source of truth. And do we have a source of truth at this company? And so the idea that we can empower these data scientists who are creating this value in the company by giving them best in class tools and resources That's our dream. That's our mission. >>Talk to me a little bit, Harry. You said you're only 4 to 5 months old. What were the gaps in the market that you and your co founders saw and said, Guys, we've got to solve this. And Snowflake is the right partner to help us do it. >>Absolutely. We This is actually our second start up, and we started previously a data Analytics company that was somewhat successful, and it got caught up in this big wave of migration of cloud tools. So all of data tools moved and are moving from on premise tools to cloud based tools. This is really a migration. That snowflake catalyst Snowflake, of course, is the ultimate in cloud based data platforms, moving customers from on premise data warehouses to modern cloud based data clouds that dragged and pulled the rest of the industry along with it. Data Science is one of the last pieces of the data industry that really hasn't moved to the cloud yet. We were almost surprised when we got done with our last start up. We were thinking about what to do next. The data scientists were still using Jupiter notebooks locally on their laptops, and we thought, This is a big market opportunity and we're We're almost surprised it hasn't been captured yet, and we're going to get in there. >>The other thing. I think it's really interesting on your business that we haven't talked about is just the the flow of data, right? So that the data scientist is usually taking data out of a of a of a day like something like Smoke like a data platform and the security kind of breaks down because then it's one. It's two, it's three, it's five, it's 20. Its, you know, big companies just gets really big. And so I think the really interesting thing with what you guys are doing is enabling the data to stay where it's at, not copping out keeping that security, that that highly governed environment that big companies want but allowing the data science community to really unlock that value from the data, which is really, really >>cool. Wonderful for small startups like Model Bit. Because you talk to a big company, you want them to become a customer. You want them to use your data science technology. They want to see your fed ramp certification. They want to talk to your C. So we're two guys in Silicon Valley with a dream. But if we can tell them the data is staying in snowflake and you have that conversation with Snowflake all the time and you trust them were just built on top. That is an easy and very smooth way to have that conversation with the customer. >>Would you both say that there's credibility like you got street cred, especially being so so early in this stage? Harry, with the partnership with With Snowflake Damon, we'll start with you. >>Yeah, absolutely. We've been using Snowflake from day one. We leave from when we started our company, and it was a little bit of an unknown, I guess maybe 23 years ago, especially in retail. A lot of retailers using all the legacy kind of enterprise software, are really starting to adopt the cloud now with what they're doing and obviously snowflake really innovating in that area. So what we're finding is we use Snowflake to host our platform and our infrastructure. We're finding a lot of retailers doing that as well, which makes it great for when they wanted to use products like ours because of the whole data share thing. It just becomes really easy. And it really simplifies it'll and data transformation and data sharing. >>Stephane, talk about the startup challenge, the innovation that you guys have seen, and only the second year I can. I can just hear it from the two of you. And I know that the winner is back in India, but tremendous amount of of potential, like to me the last 2.5 days, the flywheel that is snowflake is getting faster and faster and more and more powerful. What are some of the things that excite you about working on the start up challenge and some of the vision going forward that it's driving. >>I think the incredible thing about Snowflake is that we really focus as a company on the data infrastructure and and we're hyper focused on enabling and incubating and encouraging partners to kind of stand on top of a best of breed platform, um, unlocked value across the different, either personas within I T organisations or industries like hypothermia is doing. And so it's it's it's really incredible to see kind of domain knowledge and subject matter expertise, able to kind of plug into best of breed underlying data infrastructure and really divide, drive, drive real meaningful outcomes for for for our customers in the community. Um, it's just been incredible to see. I mean, we just saw three today. Um, there was 250 incredible applications that past the initial. Like, do they check all the boxes and then actually, wow, they just take you to these completely different areas. You never thought that the technology would go and solve. And yet here we are talking about, you know, really interesting use cases that have partners are taking us to two >>150. Did that surprise you? And what was it last year. >>I think it was actually close to close to 2 to 40 to 50 as well, and I think it was above to 50 this year. I think that's the number that is in my head from last year, but I think it's actually above that. But the momentum is, Yeah, it's there and and again, we're gonna be back next year with the full competition, too. So >>awesome. Harry, what is what are some of the things that are next for model bed as it progresses through its early stages? >>You know, one thing I've learned and I think probably everyone at this table has internalised this lesson. Product market fit really is everything for a start up. And so for us, it's We're fortunate to have a set of early design partners who will become our customers, who we work with every day to build features, get their feedback, make sure they love the product, and the most exciting thing that happened to me here this week was one of our early design partner. Customers wanted us to completely rethink how we integrate with gets so that they can use their CI CD workflows their continuous integration that they have in their own get platform, which is advanced. They've built it over many years, and so can they back, all of model, but with their get. And it was it was one of those conversations. I know this is getting a little bit in the weeds, but it was one of those conversations that, as a founder, makes your head explode. If we can have a critical mass of those conversations and get to that product market fit, then the flywheel starts. Then the investment money comes. Then you're hiring a big team and you're off to the races. >>Awesome. Sounds like there's a lot of potential and momentum there. Damon. Last question for you is what's next for hyper affinity. Obviously you've got we talked about the street cred. >>Yeah, what's >>next for the business? >>Well, so yeah, we we've got a lot of exciting times coming up, so we're about to really fully launch our products. So we've been trading for three years with consultancy in retail analytics and data science and actually using our product before it was fully ready to launch. So we have the kind of main launch of our product and we actually starting to onboard some clients now as we speak. Um, I think the climate with regards to trying to find data, science, resources, you know, a problem across the globe. So it really helps companies like ours that allow, you know, allow retailers or whoever is to democratise the use of data science. And perhaps, you know, really help them in this current climate where they're struggling to get world class resource to enable them to do that >>right so critical stuff and take us home with your overall summary of snowflake summit. Fourth annual, nearly 10,000 people here. Huge increase from the last time we were all in person. What's your bumper sticker takeaway from Summit 22 the Startup Challenge? >>Uh, that's a big closing statement for me. It's been just the energy. It's been incredible energy, incredible excitement. I feel the the products that have been unveiled just unlock a tonne, more value and a tonne, more interesting things for companies like the model bit I profanity and all the other startups here. And to go and think about so there's there's just this incredible energy, incredible excitement, both internally, our product and engineering teams, the partners that we have spoke. I've spoken here with the event, the portfolio companies that we've invested in. And so there's there's there's just this. Yeah, incredible momentum and excitement around what we're able to do with data in today's world, powered by underlying platform, like snowflakes. >>Right? And we've heard that energy, I think, through l 30 plus guests we've had on the show since Tuesday and certainly from the two of you as well. Congratulations on being finalist. We wish you the best of luck. You have to come back next year and talk about some of the great things. More great >>things hopefully will be exhibited next year. >>Yeah, that's a good thing to look for. Guys really appreciate your time and your insights. Congratulations on another successful start up challenge. >>Thank you so much >>for Harry, Damon and Stefan. I'm Lisa Martin. You're watching the cubes. Continuing coverage of snowflakes. Summit 22 live from Vegas. Stick around. We'll be right back with a volonte and our final guest of the day. Mhm, mhm

>>Welcome to this cube conversation. I'm Lisa Martin. Joining me next is bran Kirschner, the vice president of strategy at DataStax Brian. Welcome to the program. Thank you. Glad to be here. Excited to unpack this survey that DataStax recently did. This is with 500 or so it executives, technology practitioners talking about data strategy. Talk to me, first of all, about the state of the data, racist, the name of the survey. Why did data sect students? What was the impetus behind that? >>Yeah. Great question. Thank you. So, you know, um, we are in a race for our company. Every organization is in a race to find ways to use data in new ways to move the business forward, satisfy your customers and so on. Um, it's okay to have a strategy to be a leader. It's probably okay to have a strategy, to be a fast follower. It might even be okay to say we stayed in touch with best practices and once they're proven we adopt them, but what's not okay, is one to lose track of where you need to be relative to how the market's moving most important than your competitors. But in general, customer expectations, your employee partner expectations are going to be set by companies potentially in different industries. So you need to be at the right spot in your journey. So that's why we do a lot of benchmarking, but as important is as your particular company's context and history and situation and technical architecture, um, kind of comes in contact with a strategy that looks great on paper, you have to understand is something slowing us down that we didn't expect because of our culture or unspoken incentives or, you know, what is our next best step for us? >>So in this, in this dataset, we really look to identify the leaders who are having the most success and then work back from the patterns and practices we saw with them to how different, different types of companies at different stages of their journey can find their next best step to make the right progress. >>So the showed that a lot of companies have a data strategy. The execution piece is a, is a different story. Talk to me about how this survey defines a data leader. What is it, what are some of the key characteristics? Yes, >>There are quite a few. Um, in fact, what we've done over the last year that we fed into the survey was, you know, in the course of my work and my colleagues work, we talked with lots of CEOs, hands-on techno practitioners, CTOs, and so on. And we put all that conversation and qualitative insight together into, uh, about 70 measures. Um, and so that was all in the survey. And once we got the data back, uh, we did a cluster analysis, bringing some data science, the data strategy, if you will. Um, and that's surface these segments. Um, and for example, how much revenue you were generating from data was not part of the, so then we mapped these segments and these practices against that, and we say, oh, the leaders generating the most revenue from data. So that gave us some confidence in using these patterns and practices to bucketize folks. >>And you found that the data, those companies in the data leadership category were able to attribute more than 20% of their data of two gives me 20% of their revenue to data and analytics. Talk to me about that 20% benchmark is that considered where a lot of organizations need to aim to be because there's still a lot of money on the table. Yeah, >>That's right. That's right. So, you know, in common industry parlance as a standard, you know, materiality on the balance sheet is 10%. Um, and we've seen a pretty significant number of companies hit that mark. Um, what we saw, which is interesting in our data was, you know, that's kind of a comfortable benchmark to pick it's an industry standard data's material, Hey, congratulations. Uh, but you're actually drill down further and you look at that 20% mark and you say, well, 10% is probably not aiming nearly high enough because a significant proportion of these leaders have already gotten to that 20% mark. Um, and so it's in part, you know, again about that benchmarking where you are, where's your destination, your destination, probably isn't we're on the board, your destination probably isn't its material. Your destination is probably, you know, it's big and it keeps getting bigger. >>And where are these data leaders with respect to deploying a hybrid data strategy? What is it about how they're organized and structurally what they're doing that is positioning them to actually really drive incremental revenue from data? >>Yes. Yeah. What stands out about the leaders, um, is, and, you know, we see this in our data, you can see this in any number of analysts, firms and other data sources hybrid cloud strategy is, you know, the dominant strategy for large enterprises, right? It's about preserving your flexibility to operate in multiple clouds. And on-prem, so that's pretty well understood. What we saw in this data was overwhelmingly almost a hundred percent of the data leaders also say they're pursuing a hybrid data strategy. So they're already doing that kind of same level of thoughtfulness and planning about how can we get and deploy apps and compute everywhere to how can we store and deploy and redeploy data everywhere. And there's a real steep curve to the extent where the folks who are just starting out, who may have a strategy, but have taken very little action. None of them strongly agree that they have that type of hybrid data strategy. Um, and so the pattern qualitative pattern we see is companies go down this hybrid cloud compute strategy for good reasons, and it pays off, starts to pay off. And then they realize, oh, we should be doing the same thing for data. Um, and that's giving these leaders, you know, a lot of agility control, flexibility, um, and opportunities. >>One of the things I found interesting in the report from a statistics perspective is that those data leaders that you talked about that are able to, or able to attribute more than 20% of their revenue to data and analytics twice as many of those are two. And they're two X likely to be using a robust open source data stack talking about that as it plays into the computing strategy and the ability to convert data into revenue. >>That's right. So they're, they're, they're almost a hundred percent comparable to the hybrid data strategy. Almost a hundred percent are also increasing their use of open source software. And I kind of think about this from, from two dimensions, right? The, the hybrid cloud and hybrid data strategy gives you agility, optionality flexibility for your infrastructure, for your compute, for your storage and so on. Um, then it's about really making sure you're using the best of breed tools for the job of creating value with data. Um, and if you look backwards, um, you know, the track record of open source technologies, Apache Cassandra Kafka spark at some of just like, you know, the applications and experiences that are, you know, have, have, you know, validated the massive impact data can have on a business. Um, the track record of open source is strong and you look at the cycle of innovation and you see, you know, Kafka having emerged and now pulse are emerging as sort of a, a newer, more cloud friendly version of Kafka and flank kind of emerging as potentially a successor to spark that cycle of innovation, arguably is accelerating. >>Um, and so as you think about what's, you know, what's unique to us as a company, um, it's the data you have, right? No one has the customer interactions. You have, nobody has a business processes you have. So what you want to do is take those best of breed tools and have flexibility about the infrastructure services to support them and focus your people on doing great things with the data. So don't try to solve a problem that the open source ecosystem has already solved, right? If you're, if you're writing that code, instead of focusing on what differentiates your business, that's a miss. Um, so when you see the leaders leaning hard into, um, open source, you know, it's because they've got the clarity about, we differentiate by using these best of breed tools on our data, not reinventing the wheel, >>Are these companies, you mentioned culture a minute ago, and that's always something that I find intriguing because it's very hard to change. We've been in the last 16, 18 months in an, in a very fast pace of change, as we know, but are you seeing these data leaders that are companies that are reorienting towards a data culture where data is part of everyone's job? >>Yeah, absolutely. Absolutely. So they, so it's interesting. Um, a majority of all companies said that reacting to the COVID crisis did increase their pace of innovation, but again, it's almost universal among, among those leaders. Um, and one of the patterns that stands out is indeed, when you say making it everyone's job, I'll put finer point on it. It's saying accountability for creating value, generating revenue with data is the line of business is accountability. I'm in conversations. I've literally had CEOs say, it's not my problem anymore. It's my problem to help them execute on the ideas, right? And that can even raise the bar because now they're coming up with bolder, bigger ideas, but it's not about it being the custodians of the data, trying to go to the business and say, Hey, could you use some data it's business, general managers, VPs now accountable for how have you used data to drive revenue? >>How can it change the way you sell or the way you service customers? Um, and so on. And, and that, um, in part, what we heard from some folks was in organizations with progressive CEOs, chief data officers, they have been going to the side to the business side of things and saying, Hey, I think we've got ways to do business better, but there wasn't pressure on the business. They're like our business is going fine. Uh, but once COVID hit, it was okay. We need to take out costs. We need to find new ways to grow. Um, and there's sort of that that drove and organic embrace of, ah, I see, I want to pick up the reins and, you know, work with my technology partners to make it happen, but now I see we should be driving it on the business side. >>And have you seen in the COVID era data strategy become really a board level initiative and, and to your point, one of the things that you've found is, is it's not just the culture of data being core to everyone's job, it's the accountability level at the line of business level. But I imagine that that data strategy is indeed a board level initiative. >>That's right. That's the biggest, when you mentioned culture, the biggest of the segments is a group whose biggest challenge is cultural change about almost a third of, of all organizations. Um, and you see there, there's this big drop, you know, compared to the leaders of whether the data strategy is a board level discussion, right. And you see this big drop in other metrics where, you know, do you have a data strategy, mild agreement like, oh yeah, we talk about data of everybody talks about data. Um, but it's really about getting that top down. This is a true corporate priority, which kind of circles back to our initial conversation, you know, if the goal is 20% or more of your revenue from data, it better be a board level conversation. Right. And, and, you know, if you have an effective board, you want the board to be helping to drive toward that. Um, so it really closes the loop on, you know, again, calibrating, what's our aspiration, um, what's at stake. And if we believe in the data, you know, we shouldn't be hesitating to elevate this to the board level and get their attention on >>It. Right. Give me an example of a, of a customer that's doing that. That's a data leader that's doing this really well. And one that pivoted to be able to, to use data and extract value and revenue from it during the last year and a half, >>I would say it's a little bit less of a pivot and more of an amazing success story. Um, uh, because of you look backwards a few years ago, um, home Depot made a significant board level, you know, top-down, company-wide commitment to a very bold digital and data strategy. And so, you know, by 2019, um, for one example, you know, Forester ranked them as a top retail app, um, uh, for customers, um, and all that work, which is already paying off, right. They're making big investments, but they're getting big payoffs. Um, when COVID hits home, Depot is able to deploy curbside delivery as a service. They did not have a feature they did not have in weeks at scale, um, which drove even more outsized returns during COVID. Um, and so it's, it's a little, uh, you know, it's a less of a pivot, but more about the value of making that commitment. >>Um, because you know, they, weren't planning on deploying curbside delivery to the app in weeks, but when COVID hit, they were able to, because they already had the cultural change, the infrastructure, the metrics, the technologies in place. Um, and so, you know, it's really a message about don't wait, right? If you are going to fast follow, if you are going to be away for proven best practices, you don't want to start off the blocks at zero. When something disruptive happens, you want to have some success stories, some practice at it under your belt. So, you know, even if you're, if you're, if you're fortunate enough not to have been pushed into radical action because of COVID, don't, don't let that stop you from seizing the day and actually starting to move. >>I now I've, I think I'll never have the same opinion of, of home Depot. Again, I will always go on there looking for light bulbs and batteries and flashlights thinking of them as a data company, but as a company, that, to your point, committed to it and push that accountability out into those lines of business. How does, what did the survey show in terms of those data leaders embracing, uh, open source, embracing a hybrid data strategy? How does that facilitate that, driving that accountability into the lines of business so that that revenue that's sitting on the table from data can be unpacked. >>Yeah, it's, it's almost, I think, you know, if I look at it from the technology side, um, imagine, you know, in the past, you're the custodian of data, you know, as a CIO and your job is to kind of make, make, make, you know, data's not lost. We comply with regulations, you know, for the kind of way we run the business yesterday and today doesn't break tomorrow. And so if I think about the shifts to where the lines of business are now accountable for finding new ways to use data, what are the, to come up with? Like, you know, if you think about like, you know, innovating in business, um, taking data under the wing, right? Your job now, as a manager is innovate, innovate your business model, deliver something we never delivered before deliver something. No one in our industry delivers. So on the tech side, you know, it should be exciting, but it also means you may be on the hook for delivering some capability that your company had never thought about. >>Um, so that really gets back to this idea of like, do you have access to, you know, the best infrastructure services through hybrid cloud and data strategy? Are you set up to use best of breed tools, even if, you know, last year we didn't have a scenario that uses best of breed tools. Well, now that the businesses, I think it really hard on how we differentiate with data. They're probably going to come up with some big bold ideas, um, again, which should be exciting, but you gotta be ready to invest in change and something new as opposed to keeping the lights on. >>Right. I think that pace of innovation, I don't know, maybe it's permanently altered because of the scenario was one that nobody ever expected to be in. As we saw so much transformation in the last year and a half, and the pace of innovation change and, and the, you know, the places that are like the home Depot being able to radically change so quickly. And so we saw a lot of other businesses that could not do that. What are some of the market trends that you're seeing as we're now coming around the corner into the second half of 2021? >>I mean, the acceleration is a great point because when you're using data to deliver value to customers or create value for your business, things actually build on them on each other. Right. So, you know, data doesn't get used up until the, the amazing things about digital data. It can be used and reuse and recombined. So if you saw, for example, you know, leaders are well on the way before COVID, do you have real time inventory we'll share. Uh, but then once COVID hit, do you have real time inventory? And can you make a recommendation for somebody that's out of stock became like, wow, we should get that done ASAP. So then as you see folks do some necessary things, um, you start to see, well, if we've got real-time inventory and we can make recommendations, why are we getting a 360 degree view of the customer from that data plus marketing data, right? >>And now the value gets unlocked. Whereas if you said, you know, two years ago, how can we justify creating a 360 degree view of the customer, some organizations might've been like, well, we can, you know, it's hard to do. We can't see the value. Whereas once you're doing a couple of these use cases, it becomes obvious that they'd be better together. Right. And so, um, if you see, you know, the home Depot, I think you're going to see, um, you know, essentially every retailer that wants to stay competitive is going to follow in that path. >>Do you think that those companies that become data leaders or are on the path to become data leaders that have the hybrid data strategy that are embracing OpenStack? Is that mentality in your opinion, going to separate the winners and the losers going forward in the next year plus? >>Yeah, I mean, I think, I think in a sense it has to, uh, because again, as I think, you know, there was a trend already in place for all of us as consumers, right. We love, for example, delightful recommendations, you know, uh, companies and applications that know us and just make our lives better because they're smart, like Netflix and Spotify, right. The classic examples. Um, but now you think about for anything. So Cengage is an education platform company, and they talk about being the Netflix of education. Um, and you know, retailers like home Depot, like target have gotten super smart about things like recommendations. Um, and you know, in the case of home Depot, like connecting me with the data that explains how to do DIY projects and use the tools I'm trying to buy. So, you know, the bar just keeps getting raised to the point where, you know, you look at, you know, you look at a, the e-commerce site of the past, we just sort of a dumb e-commerce site where it's, I can pick things, put them in a cart and buy, you know, that's not acceptable by any stretch of the imagination today, right. >>Are there user reviews? Are there, you know, recommendations? We expect all of this. Um, and I think you'll see it, you know, obviously retail's heavily disrupted by COVID pointing into the sphere, so to speak, but I mean, telehealth is another example where, you know, I think the writing is on the wall. If you can't do telehealth as a health system or a hospital, you know, very soon you're going to have a big problem. >>Yeah. The consumer demand is incredible for, I want whatever it is, if it's I'm shopping on Amazon or if it's going to be, but I want them to know what to recommend to me next, based on what I just thought we have that expectation that the Netflix is and the Spotify is to your point have set. And we also have that expectation in our business life. So when folks are buying it, interacting with software, they want the same thing, right. It's not just limited to healthcare retailers. That's >>Right. And I that's that there's a virtuous cycle, right? If you think about companies, you know, making that cultural change, leaning into using data to make things better, it's not just for customers, it's for your employees, it's for your partners, it's for your business processes. Right. And how are you going to be able to hire people who are super excited about making things better for customers, if you're also not, you know, internally making things better for your employees, right. There's just a real disconnect in terms of, you know, culture and personnel. There. >>That's a great point. Those are in my opinion, inextricably linked, Brian, it's been great to have you on the program. Thank you for sharing with us. The state of the data raised very interesting sort of that you guys have done. Folks can get their hands on that lot of opportunity and a lot of money on the table for organizations in any industry. Thanks so much for joining me today, brand thank you for Brian Kirschner. I'm Lisa Martin. You're watching a cube conversation.

>>Ly from San Diego, California. It's the cube covering to clock in cloud native con brought to you by red hat, the cloud native computing foundation and its ecosystem Marsh. >>Welcome back to San Diego. I'm Stewman and my cohost is Justin Warren. And coming back to our program, one of our cube alumni and be coach hair of this coupon cloud native con prion Lyles who is also a senior staff engineer at VMware. Brian, thanks so much for joining us. Thanks for having me on. And do you want to have a shout out of course to a Vicky Chung who is your coach hair. She has been doing a lot of work. She came to our studio ahead of it to do a preview and unfortunately she's supposed to be sitting here but a little under the weather. And we know there was nothing worse than, you know, doing travel and you know, fighting an illness. But she's a little sick today, but um, uh, she knows that we'll, we'll, we'll still handle it. Alright, so Brian, 12,000 people here in attendance. >>Uh, more keynotes than most of us can keep a track of. So, first of all, um, congratulations. Uh, things seem to be going well other than maybe, uh, choosing the one day of the year that it rained in, uh, you know, San Diego, uh, which we we can't necessarily plan for. Um, I'd love you to bring us a little bit insight as to some of the, the, the goals and the themes that, uh, you know, you and Vicki and the, the, the, the, the community we're, we're looking at for, for this coupon. So you're right, let's help thousand people and so many sponsors and so many ideas and so many projects, it's really hard to have a singular theme. But a few months ago we came up with was, well, if, if Kubernetes in this cloud software make us better or basically advances, then we can do more advanced things. >>And then our end users can be more advanced. And it was like a three pong thing. And if you look, go back and look at our keynotes, he would say, Hey, we're looking at our software. Hey, we're looking at an amazing things that we did, especially cat by that five G keynote yesterday. And the notice that we had, it was me talking about how we could look forward and then, and then notice we had in talking about security and then we had Walmart and target talking about how they're using it and, and that was all on purpose. It's trying to tell a story that people can go back and look at. Yeah, I liked the, the message that you were, you were trying to put out there around how we need to make Kubernetes a little bit easier, but how we need to change the way that we talk about it as well. >>So maybe you could, uh, fill us in a little bit more. Let's say, unfortunately, Kubernetes is not going to get an easier, um, that's like saying we wish Linux was easier to use. Um, Linux has a huge ABI and API interface. It's not going to get easier. So what we need to do is start doing what we did with Linux and Linux is the Colonel. Um, this should be some Wars happened over the years and you notice some distributions are easier to use. Another. So if you use the current fedora or you the current Ubuntu or even like mint, it's getting really easy to use. And I'm not suggesting that we need Kubernetes distributions. That's actually the furthest thing, but we do need to work on building our ecosystem on top of Kubernetes because I mentioned like CIS CD, um, observability security audit management and who knows what else we need to start thinking about those things as pretty much first-class items. >>Just as important as Kubernetes. Kubernetes is the Colonel. Yeah. Um, in the keynotes, there's, as you said, there's such a broad landscape here. Uh, uh, I've heard some horror stories that people like, Oh, Hey, where do I start? And they're like, Oh, here's the CNCF landscape. And they're like, um, I can't start there. There's too much there. Uh, you, you picked out and highlighted, um, some of the lesser known pieces. Uh, th there's some areas that are a little bit mature. What, what are some of the more exciting things that you've seen going on right now, your system and this ecosystem? >> Um, I'm not even gonna. I highlighted open policy agent as a, as an interesting product. I don't know if it's the right answer, actually. I kind of wish there was a competitor just so I could determine if it was the right answer. >>But things like OPA and then like open telemetry, um, two projects coming together and having even bigger goals. Uh, let's make a severability easy. What I would also like to see is a little bit more, more maturity and the workflow space. So, you know, the CII and CD space. And I know with Argo and flux merging to Argo flux, uh, that's very interesting. And just a little bit of a tidbit is that I, I also co-chair the CNCF SIG application delivery, uh, special interest group, but, uh, we're thinking about that, that space right there. So I would love to see more in the workflow space, but then also I would like to see more security tools and not just old school check, check, check, but, um, think about what Aqua security is doing. And I'm, I don't know if they're now Snick or S, I don't know how to say it, but, um, there's, there's companies out there rethinking security. >>Let's do that. Yeah. I spoke to Snick a couple of days ago and it's, I'm pretty sure it's sneak. Apparently it stands for, so now you know, which that was news to me that, so now I know interesting. But they have a lot of good projects coming up. Yeah. You mentioned that the ecosystem and that you like that there's competitors for particular projects to kind of explore which way is the right way of doing things. We have a lot of exhibitors here and we have a lot of competitors out there trying to come into this ecosystem. It seems to actually be growing even bigger. Are we going to see a period of consolidation where some of these competing options, we decided that actually no, we don't want to use that. We want to go over here. I mean according to crossing the chasm, yes, but we need to figure out where we are on the maturity chart for, for the whole ecosystem. >>So I think in a healthy, healthy ecosystem, people don't succeed and products go away, but then what we see is in maybe six months or a year or two later, those same founders are out there creating new products. So not everyone's going to win on their first shot. So I think that's fine because, you know, we've all had failures in the past, but we're still better for those failures. Yeah, I've heard it described as a kind of Cambridge and explosion at the moment. So hopefully we don't get an asteroid that comes in and, uh, and hopefully it is out cause yeah. Um, one of the things really, really noticed is, uh, if you went back a year or even two years ago, we were talking about very much the infrastructure, the building blocks of what we had. Uh, I really noticed front and center, especially in the keynote here, talking a lot about the workload. >>You're talking about the application. We're talking about, uh, you know, much more up the stack and uh, from kind of that application, uh, uh, piece down, even, uh, some friends of mine that were new to this ecosystem was like, I don't understand what language they're talking. I'm like, well, they're talking to the app devs. That's why, you know, they're not speaking to you. Is that, was that intentional? >> Well, I mean for me it is because I like to speak to the app devs and I realized that infrastructure comes and goes. I've been doing this for decades now and I've seen the rise of Cisco as, as a networking platform and I've seen their ups and downs. I've worked in security. But what I know is fundamentals are, are just that. And I would like to speak to the developers now because we need to get back to the developers because they create the value. >>I mean the only people who win at selling via our selling Kubernetes are vendors of Kubernetes. So, you know, I work for one and then there's the clouds and then there's other companies as well. So the thing that stays constant are people are building applications and ultimately if Kubernetes and the cloud native landscape can't take care of those application developers remember happened, remember, um, OpenStack, and not in like a negative way, but remember OpenStack, it got to be so hard that people couldn't even focus on what gave value. >> Unlike obvious fact leaves on it. It's still being used a lot in, in service providers and so on. So technology never really goes away completely. It just may fade off and live in a corner and then we move on to whatever's the next newest and greatest thing and then end up reinventing ourselves and having to do all of the same problems again. >>It feels a little bit like that with sometimes the Kubernetes way where haven't we already sold this? Linux is still here, Linux is still, and Linux is still growing. I mean Linux is over Virgin five right now and Linux is adapting and bringing in new things in a Colonel and moving things out to the user land. Kubernetes needs to figure out how to do that as well. Yeah, no Brian, I think it's a great point. You know, I'm an infrastructure guy and we know the only reason infrastructure exists is to serve up that application. What Matt managed to the business, my application, my data. Um, you and your team have some open source projects that you're involved in. Maybe give us a little bit about right? So oxen is a, so let me tell you the quick story. Joe Beda and I talked about how do we approach developers where they are. >>And one thing came up really early in that conversation was, well, why don't we just tell developers where things are broken? So come to find out using Kubernetes object model and a little bit of computer science, like just a tiny little bit. You can actually build this graph where everything is connected and then all you need to do then is determine if for any type of object, is it working or is it not working? So now look at this. Now I can actually show you what's broken and what's not broken. And what makes octane a little bit different is that we also wrapped it with a dashboard that shows everything inside of a Kubernetes cluster. And then we made it extensible. And just, just a crazy thing. I made a plugin API one weekend because I'm like, Oh, that would be kind of cool. And just at this conference alone, nine to 10 people to walk up to me and said, Oh, um, we use oxygen and we use your plugin system. >>And now we've done things that I can't imagine, and I think I might've said this, I know I've said it somewhere recently, but the hallmark of a good platform is when people start creating things you could never imagine on it. And that's what Linux did. That's what Kubernetes is doing. And octane is doing it in the small right now. So kudos to me and me really and my team that's really exciting. So fry, Oakton, Coobernetti's and Tansu both are seven sided. Uh, was, was that, that, that uh, uh, moving to, uh, to, to eight, uh, so no marketing. Okay. And I don't profess to understand what marketing is. Someone just named it. And I said, you know what, I'm a developer. I don't really mind w as long as you can call it something, that's fine. I do like the idea that we should evolve the number of platonic solids. >>There's another answer too. So if you think about what seven is, it, um, people were thinking ahead and said, well, someone could actually take that and use it as another connotation. So I was like, all right, we'll just get out of that. That's why it's called octane, but still nautical theme. Okay, great. Brian. So much going on. You know, even outside of this facility, there's things going on. Uh, any hidden gems that just the, you know, our audience that's watching or people that we'll look back at this event and say, Hey, you know, here's some cool little things there. I mean, they hit the Twitters, I'm sure they'll see the therapy dogs and whatnot, but you know, for the people geeking out, some of those hidden gems that you'd want to share. Um, some of the hidden gems or I'll, I'll throw up to, um, watch what these end-user companies are doing and watch what, like the advanced companies like Walmart and target and capital one are doing. >>I just think there's a lot of lessons to be learned and think about this. They have a crazy amount of money. They're actually investing time in this. It might be a good idea. And other hidden gyms are, are companies that are embracing the, the extension model of Kubernetes through custom resource definitions and building things. So the other day I had the tests on, on the stage, and they're not the only example of this, but running my sequel and Coobernetti's and it pretty much works all well, let's see what we can run with this. So I think that there's going to be a lot more companies that are going to invest in this space and, and, and actually deliver on these types of products. And, and I think that's a very interesting space. Yeah. We, we spoke to Bloomberg just before and uh, we talked to the tests, we spoke to Subaru from the test yesterday. >>Uh, seeing how people are using Kubernetes to build these systems, which can then be built upon themselves. Right. I think that's, that's probably for me, one of the more interesting things is that we end up with a platform and then we build more platforms on top of it. But we, we're creating these higher levels of abstraction, which actually gets us closer to just being able to do the work that we want to do as developers. I don't need to think about how all of the internals work, which again to your keynote today is like, I don't want to write machine code and I just want to solve this sort of business problem. If we can embed that into the, into this ecosystem, then it just makes everyone's lives much, much easier. So you basically, that is my secret. I'm really, I know people hate it for attractions and they say they will, but no one hates an abstraction. >>You don't actually turn the crank in your motor to make the car run. You press the accelerator and it goes. Yeah. Um, so we need to figure out the correct attractions and we do that through iteration and failure, but I'm liking that people are pushing the boundaries and uh, like Joe beta and Kelsey Hightower said is that Kubernetes is a platform of platforms. It is basically an API for writing API APIs. Let's take advantage of that and write API APIs. All right. Well, Brian, thank you. Thank Vicky. Uh, please, uh, you know, share, congratulations to the team for everything done here. And while you might be stepping down as, or we do hope you'll come and join us back on the cube at a future event. No, I enjoyed talking to you all, so thank you. Alright, thanks so much Brian for Justin Warren we'll be back with more of our water wall coverage. CubeCon cloud native con here in San Diego. Thanks for watching the queue.

>> live from Barcelona, Spain. It's the key covering KubeCon Cloud, Native Con Europe twenty nineteen by Red Hat, the Cloud, Native Computing Foundation and Ecosystem Partners. >> Welcome back to Barcelona, Spain >> were here of the era, and seventy seven hundred people are here for the KubeCon Cloud NativeCon, twenty, nineteen, Off student. My co host for the two days of coverage is Corey Quinn, and joining Me are the two co chairs of this CNC event. Janet Cooper, who is also thie, suffer engineer with Google and having done the wrap up on stage in the keynote this morning, find Lyle's a senior staff engineer with BM where thank you both for joining us, >> Thank you. >> Thanks for having me. >> So let's start. We're celebrating five years of Kubernetes as damn calm laid out this morning. You know, of course, you know came from Google board in over a decade of experience there. So it just helps out the state for us. >> Um, so I started working on communities since before the 1.4 release and then steal a project Montana today. And I feel so proud to see, uh, the progress off this project and its has grown exponentially. And today we have already thirty one thousand contributors and expect it to grow even more if you can. >> All right. So, Brian, you work with some of the original people that helped create who Burnett ease because you came to be and where, by way of the FTO acquisition, seventy seven hundred people here we said it. So it's, you know, just about the size of us feel that we had in Seattle a few months ago Way Expect that San Diego is going to be massive when we get there in the fall. But you know, talk to us is the co chair, you know, What's it mean to, you know, put something like this together? >> Well, so as ah is a long time open source person and seeing you know, all these companies move around for, you know, decades. Now it's nice to be a part of something that I saw from the sidelines for so, so long. I'm actually... it's kind of surreal because I didn't do anything special to get here. I just did what I was doing. And you know, Jan and I just wound up here together, so it's a great feeling, and it's the best part about it is whenever I get off stage and I walked outside and I walked back. It's like a ten minute walk each way. So many people are like, Yeah, you really made my morning And that's that's super special. >> Yeah. I mean, look, you know, we're we're huge fans of open source in general and, you know, communities, especially here. So look, there was no, you know, you both have full time jobs, and you're giving your time to support this. So thank you for what you did. And, you know, we know it takes an army to put together in a community. Some of these people, we're Brian, you know, you got upstate talk about all the various project. There's so many pieces here. We've only have a few minutes. Any kind of major highlights You wanna pull from the keynote? >> So the biggest. Actually, I I've only highlight won the open census open. Tracing merge is great, because not only because it's going to make a better product, but he had two pretty good pieces of software. One from Google, actually, literally both from Google. Ultimately, But they realize that. Hey, we have the same goals. We have similar interfaces. And instead of going through this arms race, what they did is sable. This is what we'LL do. We'LL create a new project and will merge them. That is, you know, that is one of the best things about open source. You know, you want to see this in a lot of places, but people are mature enough to say, Hey, we're going to actually make something bigger and better for everyone. And that was my favorite update. >> Yeah, well, I tell you, and I'm doing my job well, because literally like during the keynote, I reached out to Ben. And Ben and Morgan are going to come on the program to talk about that merging later today. That was interested. >> I've often been accused of having that first language being snark, and I guess in that light, something that I'm not particularly clear on, and this is not the setup for a joke. But one announcement that was made on stage today was that Tiller is no longer included in the current version of Wasn't Helm. Yes, yes, And everyone clapped and applauded, and my immediate response was first off. Wow, if you were the person that wrote Tiller, that probably didn't feel so good given. Everyone was copping and happy about it. But it seems that that was big and transformative and revelatory for a lot of the audience. What is Tiller and why is it perceived as being less than awesome? >> All right, so I will give you a disclaimer, >> please. >> The disclaimer is I do not work on the helm project... Wonderful >> ...so anything that I say should be fact checked. >> Excellent. >> So Well, so here's the big deal. When Tiller, when Helm was introduced, they had this thing called Tiller. And what tiller did was it ran at a basically a cluster wide level to make sure that it could coordinate software being installed and Kubernetes named Spaces or groups how Kubernetes applications are distributed. So what happens is is that that was the best vector for security problems. Basically, you had this root level piece of software running, and people were figuring out ways to get around it. And it was a big security hole. What >> they've done Just a component. It's an attack platform. It >> was one hundred percent. I mean, I remember bit. Nami actually wrote a block post. You know, disclaimer of'em were just bought that bit na me. >> Yes, I insisted It's called Bitten, am I? But we'LL get to that >> another. This's a disclaimer, You know, There Now you know there now my co workers But they wrote they were with very good article about a year and a half ago about just all the attack vectors, but and then also gave us solution around that. Now you don't need that solution. What you get by default. Now something is much more secure. And that's the most important piece. And I think the community really loves Helm, and now they have helm with better defaults. >> So, Janet, a lot of people at the show you talk about, you know, tens of thousands of contributors to it. But that being said, there's still a lot of the world that is just getting started. Part of the key note. And I knew you wrote something running workloads and cover Netease talk a little bit about how we're helping you know, those that aren't yet, you know, on board with you getting into the community ship. >> So I work on the C gaps. So she grabs one of the sub fracture that own is the work wells AP Eyes. That's why I had that. What post? About running for closing covered alleys. So basically, you you're using coronaries clarity, baby eyes to run a different type of application, and we call it were close. So you have stay full state wears or jobs and demons and you have different guys to run those clothes in the communities. And then for those who are just getting started, maybe start with, uh, stay last were close. That's the easiest one. And then for people who are looking Teo, contribute war I. I encouraged you to start with maybe small fixes, maybe take some documents or do some small P R's and you're reputations from there and star from small contributions and then feel all the way up. >> Yeah, so you know, one of one of the things when I look out there, you know, it's a complex ecosystem now, and, you know, there's a lot of pieces in there, you know, you know, trend we see is a lot of customers looking for manage services. A lot of you know, you know, I need opinions to help get me through all of these various pieces. You know what? What do you say to those people? And they're coming in And there's that, you know, paradox of choice When they, you know, come, come looking. You know, all the options out there. >> So I would say, Start with something simple that works. And then you can always ask others for advice for what works, What doesn't work. And you can hear from their success stories or failure stories. And then I think I recently he saw Block post about Some people in the community is collecting a potential failure stories. There is also a talk about humanity's fellow, the stories. So maybe you can go there and learn from the old those mistakes and then how to build a better system from there. >> I'd love that. We have to celebrate those failures that we hopefully can learn from them. Find anything on that, You know, from your viewpoint. >> Eso Actually, it's something I research is developer experience for you. Bernetti. So my communities is this whole big ping. I look on top of it and I'm looking at the outside in howto developers interact with Burnett, ese. And what we're seeing is that there's lots of room for opportunities and Mohr tools outside of the main community space that will help people actually interact with it because that's not really communities. Developers responsibility, you know, so one anything that I think that we're doing now is we're looking and this is something that we're doing and be aware that I can talk about is that we're looking at a P ice we're looking at. We realize that client go, which is the way that you burnett ese talks with sapi eyes, and a lot of people are using out externally were looking at. But what does it actually mean for human to use this and a lot of my work is just really around. Well, that's cool for computers. Now, what if a human has to use it? So what we're finding is that no. And I'm going to talk about this in my keynote tomorrow. You know, we're on this journey, and Kubernetes is not the destination. Coover Netease is the vehicle that is getting us to the destination that we don't even know what it is. So there's lots of spaces that we can look around to improve Kubernetes without even touching Cooper Netease itself, because actually, it's pretty good and it's fairly stable in a lot of cases. But it's hard, and that's the best part. So that's, you know, lots of work for us, the salt >> from my perspective. One of the turning points in Kou Burnett is a success. Story was when it got beyond just Google. Well, folks working on it. For better or worse, Google has a certain step of coding standards, and then you bring it to the real world, where there are people who are, Let's be honest, like me, where my coding standard is. I should try to right some some days, and not everything winds up having the same constraints. Not everything has the same approach. To some extent, it really feels like a tipping point for all of it was when you wind up getting to a position where people are bringing their real world workload that don't look like anything, anyone would be able to write a googol and keep their job. But still having to work with this, there was a wound up being sort of blossoming effect really accelerating the project. Conversely, other large infrastructure projects we need not mention when they had that tipping point in getting more people involved, they sort of imploded on themselves. I'm curious. Do you have any thoughts as to why you Burnett? He started thriving where other projects and failed trying to do the same things. >> I have something you go first. And >> I think the biggest thing about cybernetics is the really strong community and the ecosystem and also communities has the extensive bility for you to build on top of communities. We've seen people building from works, and then the platform is different platforms. Open source platforms on top of you. Burnett is so other people can use on other layers. Hyah. Layers off stacks on top of fraternities. Just use those open source. So, for example, we have the CRD. It's an A P I that allows you to feel your own customized, overnighted style FBI, so they're using some custom for couple databases. You could just create your own carbonated style FBI and call out your database or other stuffs, and then you can combine them into your own platform. And that's very powerful because everywhere. I can just use the same FBI, the Carbonari style idea to manage almost everything and that enables a Teo be able to, you know, on communities being adopted in different industry, such as I o t. A and Lord. >> So actually, this is perfect because the sleaze and so what I was going to say The secret of community is that we don't talk about actually job, Ada says. It's a lot, but it's a communities is a platform for creating platforms. So Kubernetes really is almost built on itself. You can extend Cooper. Netease like communities extends itself with the same semantics that it lets users extended. So Janet was talking about >> becoming the software that is eating the world. Yeah, it >> literally is. So Janet talked about the CRD sees custom resource definitions. It's the same. It's the same mechanism that Kubernetes uses to add new features. So whenever you're using these mechanisms, you're using Kou Burnett. He's basically the Cooper Nate's infrastructure to create. So really, what it is is that this is the tool kit for creating your solutions. What is why I say that Kubernetes is not an end point its its journey. >> So the cloud native system. >> So you know what? Yeah, and I like I like the limits analogy that people talk about. Like Coburn. Eighties is is like clinics. If you think about how Lennox you know little l. Lennox. Yeah. You know, I'm saying little l olynyk sub Let's put together. Yeah, you Burnett. He's like parts of communities would be system. And it's it's all these components come together the creature operating system, and that's the best part about it. >> Okay, so for me, the people that are not the seventy seven hundred that air here give them a little bit of, you know, walk around the show and some of the nooks and crannies that they might not know, like, you know, for myself having been to a number of these like Boy, there were so many half day and full day workshops yesterday there were, like, at least, like fifteen or seventeen or something like that that I saw, You know, obviously there's some of the big keynote. The Expo Hall is sprawling it, you know, I've been toe, you know, fifteen twenty thousand people show here This sex Bohol feels is bustling ahs that one is and well as tons of breakout session. So, you know, give us some of the things that people would have been missing if they didn't come to the show here. >> So just for the record, if you missed the show, you can still watch all the videos online. And then you can also watch the lifestream for keynotes so on. I personally love the applicant the different ways for a customizing covered at ease. So there's Ah, customizing overnight is track. And also there's the apple that applications track and I personally love that. And also I like the color case studies So you can't go to the case studies track to see on different users and users off Cooper, Natty shared. There were war stories, >> Yes, So I think that she will miss. There's a few things that you'll miss if you if you're not here in Barcelona right now, the first thing is that this convention center is huge. It's a ten minute walk from the door to where we're sitting right now, but more seriously, one. The things you'LL miss is that before the conference starts, there are there are a whole bunch of summits, Red had had a summit and fewer people had some. It's yesterday where they talk about things. There's the training sessions, which a lot of cases aren't recorded. And then another thing is that the special interest groups, the cigs. So Cooper ninety six, they all get together and they have faced the face discussions and then generally one from yesterday We're not. We're not recorded. So what you're missing is the people who actually make this big machine turn. They get together face to face and they first of all, they built from a rotary. But they get to discuss items that have require high bit of bandwith that you really can't do over again of issue or email, or even even a slack call like you can actually get this thing solved. And the best thing is watching these people. And then you watch the great ideas that in, you know, three, six months to a year become like, really big thing. So I bet yesterday, so something was discussed. Actually, I know of some things that we discussed yesterday that might fundamentally change how we deal with communities. So that's that is the value of being here and then the third thing is like when you come to a conference like this, where there's almost a thousand people, there's a lot of conversations that happened between, you know, the Expo Hall and the session rooms. And there's, um there's, you know, people are getting jobs here, People are finding new friends and people are learning. And before thing and I'll end with This is that I walk around looking for people who come in on the on the diversity scholarships, and I would not hear their stories if I did not come. So I met two people. I met a young lady from New Zealand who got the scholarship and flew here, you know, and super smart, but is in New Zealand and university, and I get to hear her insights with life. And then I get to share how you could be better in the same thing. I met a gentleman from Zimbabwe yesterday was going to school and take down, and what I hear is that there's so many smart people without opportunities, so if you're looking for opportunities, it's in these halls. There's a lot of people who have either money for you or they have re sources were really doesn't have a job or just you know what? Maybe there's someone you can call whenever you're stuck. So there is a lot of benefit to come into these. If you can get here, >> talent is evenly distributed. Opportunity is not. So I think the diversity scholarship program is one of the most inspirational things I saw mentioned out of a number of inspirational things that >> I know. It's It's my favorite part of communities. You know, I am super lucky that I haven't employees that our employer that can afford to send me here. Then I'm also super lucky that I probably couldn't afford to send myself here if I wanted to. And I do as much as I can to get people >> here. Well, Brian and Janet thank you so much for all you did to put this and sharing it with our community here. I'Ll repeat something that I said in Seattle. Actually, there was a lot of cloud shows out there. But if you're looking for you know, that independent cloud show that you know, lives in this multi hybrid cloud, whatever you wanna call it world you know this is one of the best out there. And the people? Absolutely. If you don't come with networking opportunities, we had into it on earlier, and they talked about how you know, this is the kind of place you come and you find a few people that you could hire to train the hundreds of people inside on all of the latest cloud native pieces. >> Can I say one thing, please? Brian S O, this is This is significant and it's significant for Janet and I. We are in the United States. We are, you know, Janet is a minority and I am a minority. This is the largest open source conference in the world. Siri's This is the largest open source conference in Europe. When we do, when we do, it ended a year. Whenever we do San Diego, it'Ll be the largest open source conference in the world. And look who's running it. You know, my new co chair is also a minority. This is amazing. And I love that. It shows that people who look like us we can come up here and do these things because like you said, opportunity is is, you know, opportunities the hard thing. Talent is everywhere. It's all over the place. And I'm glad we had a chance to do this. >> All right. Well, Brian, Janet, thank you so much for all of that. And Cory and I will be back with more coverage after this brief break. Thank you for watching the cues.

>> Announcer: Live, from Las Vegas, it's theCUBE covering Dell Technologies World 2018. Brought to you by Dell EMC and its ecosystem partners. >> Welcome back. We are live here in Las Vegas at the Sands, along with Stu Miniman. I'm John Walls. You're watching theCUBE, of course, Dell Technologies World 2018. It's now a pleasure to welcome to the set, we have Bryan Bond, director of IT Infrastructure at Siemens eMeter. Bryan, thank you for being with us. >> Thank you for having me. >> John: And Andre Leibovici, who is the Vice President of Solutions and Alliances at Datrium. Andre, good afternoon to you. Good to see you. >> Great to see you. >> Alright, Bryan, tell us about Siemens eMeter, first, just for viewers who might not be familiar with the company and your mission. >> eMeter, basically, is a software development company. We do enterprise-level software for utilities, so gas, power, water, just about anything that has a meter. We do not make meters, but we deal with all the data that comes from those meters. So, data acquisition, meter data management, loss prevention, all those types of things that come from that data that's leaving your house or your business. We deal with that for the utilities. So, back-in billing systems, longterm data analytics, all of those types of things, that's what we do. >> Yeah, so, Bryan, most companies I talk to, it's like your industry's changing so fast, digital transformation, software, everything. Utilities are considered by most to be one of the slower moving pieces, so what's the reality in your world? >> It's like selling to a rock. (Stu laughs) A rock, right? It's tough, historically, it is very tough. Especially in the United States, with PUC regulations, with the way you can charge customers and can't, it makes it very hard. And I wish I was a real expert at that type of stuff, but... It's a slow-moving process. The good news is most countries in the planet have decided that they need to go full-on smart grid and they need to do it fast. So, in a lot of countries in Europe, there's an edict out, we're going to do this and that has helped move this along. So it's very helpful to us, as a business. I also think it's very helpful to us in general, you know, on the planet, being able to manage grids better and more efficiently. >> Okay, so we're not going to be talking about power grids and all the things on the utility. You're an IT guy. And that's what we love talking about on theCUBE here. So, give us a thumbnail sketch of your environment, your purview. What's going on? >> All right. So, like I said, so we're a software development house. It's all developers: dev test QA, sales, support, you know, all that type of stuff. I'm fortunate to be part of a very large company, so I don't have to worry about e-mail, SharePoint sites, or any of that stuff. I get to deal with the real fun stuff, which is our product, how it's deployed, how it's developed and tested. We're a pretty much a 100% virtualized. VMware shop. We use VMware-based cloud services for the appropriate things for that. And we do all of that work ourself with our own team. So we have a small team in the U.S., we have a small team in India, and we handle all of that ourselves, we don't really outsource any of that. >> Alright, so Andre, I want to pull you in here. You're software development in VMware environment. Brings me back; I remember early days of VMware was always only for test dev. Today, I hear developers, I hear this stuff, and it's like, "Oh, isn't that kind of public cloud "and some of those things?" So, give us your viewpoint on customers like Bryan and what kind of things Datrium brings to that environment, obviously virtualized and all that. >> Yeah, no, that's a good point. So... All types of customers know suddenly looking at how they can leverage private cloud, but also public cloud. Create the ideal, hybrid cloud. What does that mean, right? So we have Fortune 100 companies like Siemens who are leveraging our technology to deploy the private cloud, run the VMware infrastructure on us. At the same time, create, you know, DR strategies to their secondary sites. But there is also those customers who are looking to, "How can I actually push workloads to the cloud? "How can I create a strategy around disaster recovery "to the cloud?" And I believe that, as part of our journey as a company, embracing private data centers, we got to embrace, also, the cloud. And this is the next big thing for us at Datrium. Where are we going to help customers on the journey to take their workloads running on-premise to the cloud, but at the same time enabling them to use as as DR and also move back when needed. I may as well just spill the beans here. I'm not sure if I'm getting trouble with marketing or not. >> John: I'm sure you're not. >> So we actually releasing very soon a fully orchestrated DR from our platform to the VMware cloud, to VMC. Fully orchestrated and enables you to fire over environment to the cloud and back, once your DR site or your primary site is actually back. There's a lot of promise on this market. There's a lot of companies doing, saying that they would do, but, you know, I see that's something that customers are really excited... >> You know, how does it work when you're dealing with a customer who is dealing with a customer, who's dealing with customers who... You know, privacy's essential, right? And there's a lot of concern... They have to be the customer of a utility. So how do you treat them, you know, because they have very unique needs, I would assume and that's a major consideration, because of their position with their customer. I mean, that's got to create a new dynamic, or an interesting dynamic, for both of you to handle. >> Yeah, it does. You know, from a development standpoint, you know, you may not be actually dealing with that particular customer's data, but you're helping that customer deal with that data. So, we're having to go through and make sure that our software doesn't have any holes in it and it's patchable, and that it follows, you know, simple guidelines. But, at the same time, we make recommendations to customers all the time, you know. "Well, how are you guys doing X, Y, Z in-house, "because you seem to be doing okay." And we say, "Well, we're using this particular platform." And, their encryption is probably the best there is right now out there. De-duped encryption, it's just fantastic. And across different storage arrays. And being able to that to the cloud and be encrypted there, and not have to worry about that is a big bonus. And that's definitely something that we look at. Obviously, we don't encrypt all of our data, because a lot of it's just nonsense. But, we do have stuff that we do that with. And we do it both for testing purposes and to prove that this meets the requirements of the customer. Because those requirements are different, not just in different countries, but in every state you go to. So, being able to provide that level of assurance of yeah you can meet your requirements with our software regardless of what platform you're running on. >> Bryan, you mentioned a couple of features there. But I wonder if you could back us up a second. You've got a virtualized environment. There's, you know, so many options that you can choose on there. Walk us a little bit through the problems that you were having, the decision process, and ultimately what led to Datrium. >> So... The set of primary goals for us was the typical thing you see in IT is you're doing the same thing for a long period of time. You're buying the same stuff, you buy more of it, you renew, and then they tell you that the price is going to go way up on support. So you buy a new one and start over again, right? The hockey stick approach. And so that's the time I like to actually stop and say, "Hey, am I doing this right, still?" Because what I did five years ago may not be right, you know, going forward, knowing what the changes are in the business. We were looking for great cost to capacity. Right? And ease of management and overall cost of the deployment. And when we started looking at all the different players in the space... For us, the big thing was going to NFS. So, single file system for management. Prior to that, we were either fibre channel on or iSCSCI. So, mini management points. Hundreds of LUNs. Hundreds of LUNs. We're managing storage, right? A small group of people, three, four guys? You're spending 20 hours a week managing storage? That's nuts, right? So, day one, we put these guys in in a POC. And my guys are like, "This stuff's never leaving." Because now I'm down to one management point, right? Six months, seven months later, I'm down six hundred LUNs from where I was with three management points. I don't manage storage anymore. None of my guys manage storage anymore. That's a hidden cost, you know? And I'm not suggesting reduction in FTE or anything like that. I'm saying, "Oh, now those guys can go work "on operating system patching." You know, the other paying points that you've got in the business, rather than managing, you know, that platform. So, all of those things rolled in together. And when we tried to compare them to other vendors, we couldn't get an apples to apples comparison. We had to go with multiple vendors to get the same performance, to get the same capacity, and we could never get the pricing. The best-case scenario we got for capacity and performance was three times the cost. Best-case scenario. And I still had to manage LUNs. >> Yeah, Andre, I used to always joke simplicity in the enterprise was an oxymoron, because there's so much happening. You hear, "Okay, get rid of one thing, I got to patch the other thing." There's no such thing as eliminating bottlenecks, you just move them. But, you know, sounds like some common problems we've been hearing out there. What's typical about his environment? What are you hearing from customers in general that Datrium's helping? >> So, I think the first point is simplicity. And it's something that I know we've been evolving, it's a journey not only for Datrium, but the whole data center industry, right? Went through ACI and now it's open conversions. So the whole simplification of the data center and make sure that most of the task can be automated. So some of the things that we do, that we simplify from a management perspective: we have no knobs, you don't decide if it's compression, the de-duplication enable, the erasure codings. Everything is owned by default and that's the way it's going to be because it doesn't make sense for an organization with thousands of virtual machines and applications to start tweaking every single knob to make sure they're going to get the best possible performance. Across the board, once we've actually verified, you might get like one or 2% CPU back. So, simplicity's a big point. Also, the other point that we mitigate in the organization, especially compared to ACI's solutions, is the data resiliency. So we actually offer enterprise-grade data resiliency that for ACI... And when talking about evolution with data center, you know, taking like putting SSDs into the servers, ACI clusters, and moving forward. So we actually make all the management of this SSDs much simpler. I forgot the line, where I was going to, but I... (laughs) I think the message is simplicity, skill ability, back data resiliency. Making sure you get enterprise-greater data resiliency in the data center. And you don't compromise on that. You get capacity, data resiliency, simplicity at the same time. >> Keep it simple, make it work. >> Andre: Exactly. >> Right. Faster. Gentleman, thanks for joining us. We appreciate the time. Thanks for telling the Siemens eMeter story. We look forward to seeing you down the road. And good luck, continue success at Datrium, as well. Thanks, Andre. >> Yeah, thank you. >> Alright, thanks for having us. >> Back with more. You're watching Dell Technologies World 2018 right here on theCUBE. (techno music)

>> Man: Live from Boston, Massachusetts, it's theCUBE! Covering Red Hat Summit 2017, brought to you by Red Hat. (energetic music) >> Bryan, good to see you again. >> Bryan: Thanks for having me. >> You're welcome. I said, "Good to see you again." We thought we had you on before, but maybe not. But anyway-- >> Bryan: I have lots of Rackers. >> We feel like Rackspace is one of ours, with theCUBE alone. Red Hat Summit, obviously a big show for the industry. Big show for Rackspace. But your focus is on OpenStack, you're the general manager of the OpenStack business. You guys started OpenStack, I mean, you and some others. But it was really the seed and the vision of Rackspace. So bring us up to date as to where you are now. >> Yeah, I see your point. It kind of goes back to 2010, where Rackspace and NASA essentially co-invented OpenStack and opened it up as a community project, and made it open source. Again, the intent was, how do you help leverage the innovation of a community to help build cloud infrastructure? At that time, it was really focused on public and private cloud. Rackspace over the years, certainly, our public cloud was built on OpenStack and we continue to do a lot of that focus in upstream innovation and contributing in, how do you make this platform scale very massively? Over the last several years, where we've seen great adoption of OpenStack specifically, though, is in private cloud solutions. We have built a practice over the last several years building, deploying, and operating private clouds for customers in our data centers, in their data centers, third party data centers. And that's where we've seen a lot of growth in that. >> Bryan, I wonder if you could help us unpack that a little bit. I know you and I are going to be back here in Boston down the road at the Hynes for OpenStack Summit next week. But when you hear the general discussion, OpenStack has changed a lot in the last few years. So there are people that throw stones and are like, "Oh, well, it's done, it's over." Sounds like you've got a good, robust business. Tell us where are people using it, how are they using it, what is it replacing, or helping them grow their business? >> OpenStack itself, if you think of this arc of an open source project in the rapid innovation, how quickly it's matured, over the last couple years OpenStack itself has really become a solid platform. Infrastructure as a service. In fact, I think I heard a comment as of the Barcelona summit where an analyst or media or somebody said, "OpenStack is now boring." Because a lot of the drama or rapid change has really come out of it, many of the core projects have very much matured. You do hear, "Is OpenStack dead? "Are people going straight to containers on bare metal? "Is this the end of the space?" In practice, we are seeing it is still, how am I consuming or building cloud-native apps? I'm consuming cloud services, and certainly in a private cloud context I'm looking for that power and agility that I see from a public cloud, but delivered in a private cloud form factor. We're still seeing huge adoption for OpenStack in that use case. >> Well, there's a lot of misconceptions about OpenStack over the years, and part of it is it was just sort of put out there and said, "Okay, let's see what happens." But I remember when it went public, John Furrier, other co-host of theCUBE, called it a Hail Mary against Amazon. >> Bryan: Yeah. >> Okay, well, in a way, people needed some kind of alternative. And it's really emerged as the only, correct me if I'm wrong, really the only open platform to build private clouds on. >> Bryan: Yeah. >> And when you say you hear, "Oh, is OpenStack it?", you hear that from a lot of the legacy enterprise companies who are sort of doing their own proprietary private cloud. To your point, it's become a platform with momentum. Further thoughts on that? >> Yeah, I think to your point that those that are really saying it's dead and they're doing their own proprietary cloud, that's really just virtualization at scale. They're not really consuming cloud services in the same framework that OpenStack delivers it. It is still a vibrant and growing platform. We're seeing it as the platform of choice for not just, how do I move virtualized workloads, but even for containers and other orchestrated solutions on top of that as well. It really is this underpinning technology that people are consuming for private and hybrid types of scenarios. >> Red Hat would argue, I wonder if you could weigh in on this, that in order for you to build a true hybrid cloud, we use the term true private cloud, we can extend that to true hybrid cloud, you've got to have a sort of modern infrastructure that's open on-prem. Or else you're going to be just force-fitting square pegs in round holes. >> I think there's a lot of validity to that. Especially when you think about the concept of portability or leveraging moving applications between different platforms. If I have a truly siloed infrastructure, I don't have that capability. Whereas if you look at leveraging these open platforms of OpenStack and the tooling that I could use on top of that, cloud forms and ether services, and certainly as I move into paths and containers, I now have much more portability on where I can deploy and operate these different technologies. >> Bryan, congratulations. You guys are an Innovation Award winner. Can you talk a little bit about the solutions and what you guys are working closely with Red Hat to give to your customers? >> It's really exciting. We were awarded one of their Innovator of the Year awards for cloud infrastructure. The way this came about is, Rackspace and Red Hat have a mutual customer that really came to us where they were looking for a private cloud delivered as a service. They're looking for the operational expertise that Rackspace brings in operating these technologies at scale, but were looking for a fully certified Red Hat stack. At that time, we didn't have an offering around the Red Hat OpenStack platform. We obviously have a long-standing relationship with Red Hat, and support a number of Red Hat technologies across our businesses, but in the OpenStack space we had not productized or brought to market a main service around the Red Hat OSP Platform. And so we partnered very closely with them to bring this solution to market. But it's not as simple as just saying, "Voilà, now we have our Red Hat offering." Our focus is really to bring the operators' perspective to it. So we spent eighteen months in total, if you think about from when we really kicked off this effort with them, deploying and operating and scaling and testing, and going through all the stages of patching, and upgrading and running different workload profiles and really, scalability testing. And feeding back a lot of innovation into the Red Hat team. It led to a number of enhancements that have come in later releases of RHEL OSP, which allowed us to really get to a platform that we could stand behind, provide as a main service and deliver a four nines availability SLA around it. This is the offering that we brought together. We're being recognized for some of those innovations that we fed back into it. We consume their Distributed Continuous Integration environment, so through the DCI platform we execute over 1500 tests on a daily basis, which allows us to deliver the latest release of RHEL OSP to our customers within two weeks of a given major release. We made a number of networking plainly-needed enhancements in how can we break out the bouncing from the control plane? Things that allow us to deploy and operate these solutions at a much larger scale. >> Maybe if you could speak to one of the challenges we've heard for OpenStack for years is, it's kind of complicated, and how do we do this? And I have to think, the Red Hat service and support model partnered with the fanatical support from Rackspace should be able to address some of those concerns for customers. >> That's honestly where I think we've found the most success with customers is, OpenStack itself is a very powerful tool. But it is complex. It's not something that you're just going to download and run on a VM in your laptop to gain experience with it. >> Stu: Built by rocket scientists! What do you expect? >> Literally, quite literally! So the complexity does continue to be a barrier to adoption for many enterprises. That's where our focus of being the operators and delivering it as a service has been so key for many customers. And then, given that fully compliant or certified stack from Red Hat, the software assurance that comes with that has been a great fit to a lot of customers who really grow. >> You mentioned platform as a service. Stu, earlier, you made the comment of The Platform Formerly Known As PaaS. There's a lot of discussion about PaaS, well, it's really not here anymore. Can you guys, at least start with Bryan, maybe Stu, you can chime in, what's happening with PaaS? Is it getting subsumed? I often say infrastructure's a service plus, or a SaaS minus. What's happening with PaaS? 'Cause when you talk to companies like Oracle, it's like, "Oh, our PaaS business is rockin'!" So what's really happening out there? >> I'm sure you have thoughts on this, too. I believe that PaaS is still a very strong plane. That's where many organizations, now they're embracing cloud and cloud-native development, are looking to move up the step and leverage more fabric-like services. Things that a PaaS can provide them, that integrated development environment. How do I make it easy to consume different data services? Removing the coarse-grained building blocks that I would otherwise have to orchestrate or manage myself. So we do see a lot of adoption for that. It's kind of that progression, as I'm moving up, I'm moving into cloud-native designs and architectures. Now I'm looking to really empower and enable my developers to consume these fabric services. Moving up the stack. >> Comment I'll make on it is, if you look at what's happening with the container space, you heard about what Red Hat talked, is how they take that piece. I want to be able to take my application, have how I built that and have some flexibility as to where that lives. And that was one of the core values of what PaaS was going to offer because, if I want to do Red Hat as the AMP with OpenShift, I want to do it on-premises, I want to do it in AWS, I want to do it with Google, I have that flexibility. Maybe we're just not calling it PaaS anymore. >> Yeah, I think that's good. I think if you look at the move to containerization, there are still those other components or services that I need to consume. How am I solving for identity and networking and storage and all these other components that go into it? This is where some of the PaaS frameworks can help that. >> Just one piece. Rackspace has a really interesting portfolio of services. You're partnering with all the big cloud guys. You've got private cloud. What do your customers think when you say hybrid cloud, or multi-cloud, how does that fit in to where they are today and where they're making their strategy for cloud going forward? >> Again, Rackspace does represent a very large portfolio. We are the managed cloud company. I obviously am very focused on our private cloud and OpenStack, but we have as practices, we help enable customers to either migrate to, deploy or operate on Amazon web services. Certainly, the Azure platform, and recently we announced Google Compute, providing support for that. We have customers that are coming to us looking for help in architecting or moving to these. But the reality is almost all customers, and they touched on that during the keynote here, we live in a multi-vendor strategy or multi-cloud strategy. Certain clouds, either geographically or feature-set-wise are better suited for certain applications or workloads. Many of our customers are living in that hybrid cloud world, where I'm leveraging multiple different platforms depending on workload placement or other rules to that. Where Rackspace has really stepped it is providing that cloud expertise and helping them leverage that, providing tooling to help them deploy and operate in these different environments. In some cases where it's portability, move the same application around, but oftentimes it's really workload placement and how do I more effectively use it. >> We were talking in our open about the bromide from Marc Andreessen in Software's Eating The World, and the implication, tying that into Benioff's statement that there'll be more SaaS companies coming out of non-tech companies than tech companies. You're seeing some big SaaS tech companies like Workday and Salesforce, and Infor's always been there, moving to the Amazon cloud. And others who are maybe saying, "Well, I'm not sure I want to move to the Amazon Cloud." So my specific question is, relative to SaaS takeup on things like OpenStack, what are you seeing there? >> Ironically, certainly in private cloud, that's probably one of our biggest areas of growth is companies that are launching SaaS platforms for all the same reasons that they would be using an AWS to back that, right? They have the agility and rapid growth and elasticity that they can build into it, but they're running their platform, and depending on HR, you mentioned Workday, we have another great example. Ultimate software. They run their platform. Again, it's HR management and other services they want to run in a private cloud context, but deploying that framework where they can leverage cloud-native deployment. OpenStack has been a great fit for that, and helped them grow and scale. >> What's next for you guys in your world of OpenStack? Can you give us a little road map, and what we should expect going forward? >> For us, very specifically, if you focus on the IaaS layer, we continue to be very focused on operational efficiencies. How are we helping customers get the right unit economics out of a private cloud? Getting to greater densities, higher performance, more optimal usage of their cloud as we bring more visibility to actual capacity planning and capacity management, and make sure they're really leveraging or growing their cloud as they can. And then certainly from a feature set where we continue to move up and adopt these other services. I know we touched on earlier on the PaaS. This is an area where we're starting to get a lot of customer demand saying, "Can you help us in this area? "Are there things that you could be doing?" Going straight to native Kubernetes or looking at the different PaaS frameworks like OpenShift or Cloud Foundry. These are areas that we're starting to work more and more to potentially bring services to help customers really leverage these platforms. >> Paul Cormier was talking about how, you know, early days of the Cloud everybody thought everything was going to Amazon and so forth. But everything is going to the Cloud. Whether it's a private cloud or a public cloud, I know somebody told me the other day they're running an application in VMS. Okay, so some stuff never dies. But generally, the world will be cloud. Maybe we'll stop using the words like cloud and digital. Look at a camera! It's not a digital camera. Your thoughts on that? You buy that? >> No, I think you're spot-on. There's a long tale, there's still a lot of AS/400 out there. Although with OpenPOWER, maybe you could make the argument it's coming to OpenStack anyway. It is. If you think about any greenfield development, it's all being done in cloud-native ways. If you look at folks coming out of school and new application development, nobody's developing in the context of bare metal or legacy client/server apps that are built in that framework. I think even as enterprises continue to replatform services, they're moving into that cloud way. So they can take the long-term benefits of agility and cost-savings they're looking for. So we'll become ubiquitous. You're right, at some point, we're going to stop calling it cloud. It's just the way you're consuming infrastructure. >> Final question I have for you. A piece that I hadn't heard enough about when it comes to OpenStack is that kind of application modernization and replatforming. How does OpenStack fit into that discussion with your customers? I'm worried we talked in the keynote this morning about, it's like, oh, okay. We're going to do new stuff, but we might move the old stuff. We're not just moving the old stuff and leaving it, right? >> You're absolutely right. If you think of enterprises that are adopting or going all-in on OpenStack, they have, if you go back to the pets vs. cattle analogy everybody knows, they have lots of pets that they need to care for. We've looked at it and we've actually worked very hard with many customers on, how do I leverage things like Ceph to back Nova, and help bring things like live migration and other services that help OpenStack still cater to those pets and not force them in a full cloud-native model. How can I still deliver some amount of resiliency and failover in the infrastructure so the app doesn't have to be aware of it, and that way they can have one environment to run both new cloud development, but also still care for those legacy apps. >> Excellent. Bryan, thanks very much for coming to theCUBE. It was great to have you. >> Thank you guys. >> Enjoy the rest of the show. >> Bryan: Thank you. >> Keep it right there, everybody. We'll be back with our next guest at theCUBE. We're live from Red Hat Summit in Boston. Be right back. (energetic music)

>> Announcer: Live from New York, it's theCUBE, covering the IBM Machine Learning Launch Event, brought to you by IBM. Now, here are your hosts, Dave Vellante and Stu Miniman. >> Welcome back to New York City, everybody. We're here at the Waldorf Astoria covering the IBM Machine Learning Launch Event, bringing machine learning to the IBM Z. Bryan Smith is here, he's the vice president of R&D and the CTO of Rocket Software, powering the path to digital transformation. Bryan, welcome to theCUBE, thanks for coming on. >> Thanks for having me. >> So, Rocket Software, Waltham, Mass. based, close to where we are, but a lot of people don't know about Rocket, so pretty large company, give us the background. >> It's been around for, this'll be our 27th year. Private company, we've been a partner of IBM's for the last 23 years. Almost all of that is in the mainframe space, or we focused on the mainframe space, I'll say. We have 1,300 employees, we call ourselves Rocketeers. It's spread around the world. We're really an R&D focused company. More than half the company is engineering, and it's spread across the world on every continent and most major countries. >> You're esstenially OEM-ing your tools as it were. Is that right, no direct sales force? >> About half, there are different lenses to look at this, but about half of our go-to-market is through IBM with IBM-labeled, IBM-branded products. We've always been, for the side of products, we've always been the R&D behind the products. The partnership, though, has really grown. It's more than just an R&D partnership now, now we're doing co-marketing, we're even doing some joint selling to serve IBM mainframe customers. The partnership has really grown over these last 23 years from just being the guys who write the code to doing much more. >> Okay, so how do you fit in this announcement. Machine learning on Z, where does Rocket fit? >> Part of the announcement today is a very important piece of technology that we developed. We call it data virtualization. Data virtualization is really enabling customers to open their mainframe to allow the data to be used in ways that it was never designed to be used. You might have these data structures that were designed 10, 20, even 30 years ago that were designed for a very specific application, but today they want to use it in a very different way, and so, the traditional path is to take that data and copy it, to ETL it someplace else they can get some new use or to build some new application. What data virtualization allows you to do is to leave that data in place but access it using APIs that developers want to use today. They want to use JSON access, for example, or they want to use SQL access. But they want to be able to do things like join across IMS, DB2, and VSAM all with a single query using an SQL statement. We can do that relational databases and non-relational databases. It gets us out of this mode of having to copy data into some other data store through this ETL process, access the data in place, we call it moving the applications or the analytics to the data versus moving the data to the analytics or to the applications. >> Okay, so in this specific case, and I have said several times today, as Stu has heard me, two years ago IBM had a big theme around the z13 bringing analytics and transactions together, this sort of extends that. Great, I've got this transaction data that lives behind a firewall somewhere. Why the mainframe, why now? >> Well, I would pull back to where I said where we see more companies and organizations wanting to move applications and analytics closer to the data. The data in many of these large companies, that core business-critical data is on the mainframe, and so, being able to do more real time analytics without having to look at old data is really important. There's this term data gravity. I love the visual that presents in my mind that you have these different masses, these different planets if you will, and the biggest, massivest planet in that solar system really is the data, and so, it's pulling the smaller satellites if you will into this planet or this star by way of gravity because data is, data's a new currency, data is what the companies are running on. We're helping in this announcement with being able to unlock and open up all mainframe data sources, even some non-mainframe data sources, and using things like Spark that's running on the platform, that's running on z/OS to access that data directly without having to write any special programming or any special code to get to all their data. >> And the preferred place to run all that data is on the mainframe obviously if you're a mainframe customer. One of the questions I guess people have is, okay, I get that, it's the transaction data that I'm getting access to, but if I'm bringing transaction and analytic data together a lot of times that analytic data might be in social media, it might be somewhere else not on the mainframe. How do envision customers dealing with that? Do you have tooling them to do that? >> We do, so this data virtualization solution that I'm talking about is one that is mainframe resident, but it can also access other data sources. It can access DB2 on Linux Windows, it can access Informix, it can access Cloudant, it can access Hadoop through IBM's BigInsights. Other feeds like Twitter, like other social media, it can pull that in. The case where you'd want to do that is where you're trying to take that data and integrate it with a massive amount of mainframe data. It's going to be much more highly performant by pulling this other small amount of data into, next to that core business data. >> I get the performance and I get the security of the mainframe, I like those two things, but what about the economics? >> Couple of things. One, IBM when they ported Spark to z/OS, they did it the right way. They leveraged the architecture, it wasn't just a simple port of recompiling a bunch of open source code from Apache, it was rewriting it to be highly performant on the Z architecture, taking advantage of specialty engines. We've done the same with the data virtualization component that goes along with that Spark on z/OS offering that also leverages the architecture. We actually have different binaries that we load depending on which architecture of the machine that we're running on, whether it be a z9, an EC12, or the big granddaddy of a z13. >> Bryan, can you speak the developers? I think about, you're talking about all this mobile and Spark and everything like that. There's got to be certain developers that are like, "Oh my gosh, there's mainframe stuff. "I don't know anything about that." How do you help bridge that gap between where it lives in the tools that they're using? >> The best example is talking about embracing this API economy. And so, developers really don't care where the stuff is at, they just want it to be easy to get to. They don't have to code up some specific interface or language to get to different types of data, right? IBM's done a great job with the z/OS Connect in opening up the mainframe to the API economy with ReSTful interfaces, and so with z/OS Connect combined with Rocket data virtualization, you can come through that z/OS Connect same path using all those same ReSTful interfaces pushing those APIs out to tools like Swagger, which the developers want to use, and not only can you get to the applications through z/OS Connect, but we're a service provider to z/OS Connect allowing them to also get to every piece of data using those same ReSTful APIs. >> If I heard you correctly, the developer doesn't need to even worry about that it's on mainframe or speak mainframe or anything like that, right? >> The goal is that they never do. That they simply see in their tool-set, again like Swagger, that they have data as well as different services that they can invoke using these very straightforward, simple ReSTful APIs. >> Can you speak to the customers you've talked to? You know, there's certain people out in the industry, I've had this conversation for a few years at IBM shows is there's some part of the market that are like, oh, well, the mainframe is this dusty old box sitting in a corner with nothing new, and my experience has been the containers and cool streaming and everything like that, oh well, you know, mainframe did virtualization and Linux and all these things really early, decades ago and is keeping up with a lot of these trends with these new type of technologies. What do you find in the customers that, how much are they driving forward on new technologies, looking for that new technology and being able to leverage the assets that they have? >> You asked a lot of questions there. The types of customers certainly financial and insurance are the big two, but that doesn't mean that we're limited and not going after retail and helping governments and manufacturing customers as well. What I find is talking with them that there's the folks who get it and the folks who don't, and the folks who get it are the ones who are saying, "Well, I want to be able "to embrace these new technologies," and they're taking things like open source, they're looking at Spark, for example, they're looking at Anaconda. Last week, we just announced at the Anaconda Conference, we stepped on stage with Continuum, IBM, and we, Rocket, stood up there talking about this partnership that we formed to create this ecosystem because the development world changes very, very rapidly. For a while, all the rage was JDBC, or all the rage was component broker, and so today it's Spark and Anaconda are really in the forefront of developers' minds. We're constantly moving to keep up with developers because that's where the action's happening. Again, they don't care where the data is housed as long as you can open that up. We've been playing with this concept that came up from some research firm called two-speed IT where you have maybe your core business that has been running for years, and it's designed to really be slow-moving, very high quality, it keeps everything running today, but they want to embrace some of their new technologies, they want to be able to roll out a brand-new app, and they want to be able to update that multiple times a week. And so, this two-speed IT says, you're kind of breaking 'em off into two separate teams. You don't have to take your existing infrastructure team and say, "You must embrace every Agile "and every DevOps type of methodology." What we're seeing customers be successful with is this two-speed IT where you can fracture these two, and now you need to create some nice integration between those two teams, so things like data virtualization really help with that. It opens up and allows the development teams to very quickly access those assets on the mainframe in this case while allowing those developers to very quickly crank out an application where quality is not that important, where being very quick to respond and doing lots of AB testing with customers is really critical. >> Waterfall still has its place. As a company that predominately, or maybe even exclusively is involved in mainframe, I'm struck by, it must've been 2008, 2009, Paul Maritz comes in and he says VMWare our vision is to build the software mainframe. And of course the world said, "Ah, that's, mainframe's dead," we've been hearing that forever. In many respects, I accredit the VMWare, they built sort of a form of software mainframe, but now you hear a lot of talk, Stu, about going back to bare metal. You don't hear that talk on the mainframe. Everything's virtualized, right, so it's kind of interesting to see, and IBM uses the language of private cloud. The mainframe's, we're joking, the original private cloud. My question is you're strategy as a company has been always focused on the mainframe and going forward I presume it's going to continue to do that. What's your outlook for that platform? >> We're not exclusively by the mainframe, by the way. We're not, we have a good mix. >> Okay, it's overstating that, then. It's half and half or whatever. You don't talk about it, 'cause you're a private company. >> Maybe a little more than half is mainframe-focused. >> Dave: Significant. >> It is significant. >> You've got a large of proportion of the company on mainframe, z/OS. >> So we're bullish on the mainframe. We continue to invest more every year. We invest, we increase our investment every year, and so in a software company, your investment is primarily people. We increase that by double digits every year. We have license revenue increases in the double digits every year. I don't know many other mainframe-based software companies that have that. But I think that comes back to the partnership that we have with IBM because we are more than just a technology partner. We work on strategic projects with IBM. IBM will oftentimes stand up and say Rocket is a strategic partner that works with us on hard problem-solving customers issues every day. We're bullish, we're investing more all the time. We're not backing away, we're not decreasing our interest or our bets on the mainframe. If anything, we're increasing them at a faster rate than we have in the past 10 years. >> And this trend of bringing analytics and transactions together is a huge mega-trend, I mean, why not do it on the mainframe? If the economics are there, which you're arguing that in many use cases they are, because of the value component as well, then the future looks pretty reasonable, wouldn't you say? >> I'd say it's very, very bright. At the Anaconda Conference last week, I was coming up with an analogy for these folks. It's just a bunch of data scientists, right, and during most of the breaks and the receptions, they were just asking questions, "Well, what is a mainframe? "I didn't know that we still had 'em, "and what do they do?" So it was fun to educate them on that. But I was trying to show them an analogy with data warehousing where, say that in the mid-'90s it was perfectly acceptable to have a separate data warehouse separate from your transaction system. You would copy all this data over into the data warehouse. That was the model, right, and then slowly it became more important that the analytics or the BI against that data warehouse was looking at more real time data. So then it became more efficiencies and how do we replicate this faster, and how do we get closer to, not looking at week-old data but day-old data? And so, I explained that to them and said the days of being able to do analytics against old data that's copied are going away. ETL, we're also bullish to say that ETL is dead. ETL's future is very bleak. There's no place for it. It had its time, but now it's done because with data virtualization you can access that data in place. I was telling these folks as they're talking about, these data scientists, as they're talking about how they look at their models, their first step is always ETL. And so I told them this story, I said ETL is dead, and they just look at me kind of strange. >> Dave: Now the first step is load. >> Yes, there you go, right, load it in there. But having access from these platforms directly to that data, you don't have to worry about any type of a delay. >> What you described, though, is still common architecture where you've got, let's say, a Z mainframe, it's got an InfiniBand pipe to some exit data warehouse or something like that, and so, IBM's vision was, okay, we can collapse that, we can simplify that, consolidate it. SAP with HANA has a similar vision, we can do that. I'm sure Oracle's got their vision. What gives you confidence in IBM's approach and legs going forward? >> Probably due to the advances that we see in z/OS itself where handling mixed workloads, which it's just been doing for many of the 50 years that it's been around, being able to prioritize different workloads, not only just at the CPU dispatching, but also at the memory usage, also at the IO, all the way down through the channel to the actual device. You don't see other operating systems that have that level of granularity for managing mixed workloads. >> In the security component, that's what to me is unique about this so-called private cloud, and I say, I was using that software mainframe example from VMWare in the past, and it got a good portion of the way there, but it couldn't get that last mile, which is, any workload, any application with the performance and security that you would expect. It's just never quite got there. I don't know if the pendulum is swinging, I don't know if that's the accurate way to say it, but it's certainly stabilized, wouldn't you say? >> There's certainly new eyes being opened every day to saying, wait a minute, I could do something different here. Muscle memory doesn't have to guide me in doing business the way I have been doing it before, and that's this muscle memory I'm talking about of this ETL piece. >> Right, well, and a large number of workloads in mainframe are running Linux, right, you got Anaconda, Spark, all these modern tools. The question you asked about developers was right on. If it's independent or transparent to developers, then who cares, that's the key. That's the key lever this day and age is the developer community. You know it well. >> That's right. Give 'em what they want. They're the customers, they're the infrastructure that's being built. >> Bryan, we'll give you the last word, bumper sticker on the event, Rocket Software, your partnership, whatever you choose. >> We're excited to be here, it's an exciting day to talk about machine learning on z/OS. I say we're bullish on the mainframe, we are, we're especially bullish on z/OS, and that's what this even today is all about. That's where the data is, that's where we need the analytics running, that's where we need the machine learning running, that's where we need to get the developers to access the data live. >> Excellent, Bryan, thanks very much for coming to theCUBE. >> Bryan: Thank you. >> And keep right there, everybody. We'll be back with our next guest. This is theCUBE, we're live from New York City. Be right back. (electronic keyboard music)

>> Announcer: Live from Boston, Massachusetts. This is "The Cube" covering Spark Summit East 2017. Brought to you by Databricks. Now here are your hosts Dave Volante and George Gilbert. >> Welcome back to snowy Boston everybody. This is "The Cube." The leader in live tech coverage. This is Spark Summit. Spark Summit East #SparkSummit. Bryan Duxbury's here. He's the vice president of engineering at StreamSets. Cleveland boy! Welcome to "The Cube." >> Thanks for having me. >> You've very welcome. Tell us, let's start with StreamSets. We're going to talk about Spark and some of the use cases that it's enabling and some of the integrations you're doing. But what does StreamSets do? >> Sure, StreamSets is a data movement software. So I like to think of it either the first mile or the last mile of a lot of different analytical or data movement workflows. Basically we build a product that allows you to build a workflow, or build a data pipeline that doesn't require you to code. It's a graphical user interphase for dropping an origin, several destinations, and then lightweight transformations onto a canvas. You click play and it runs. So this is kind of different than, a lot of the market today is a programming tool or a command line tool. That still requires your systems engineers or your unfortunate data scientists pretending to be systems engineers to do systems engineering. To do a science project to figure out how to move data. The challenge of data movement I think is often underplayed how challenging it is. But it's extremely tedious work. You know, you have to connect to dozens or hundreds of different data sources. Totally different schemas. Different database drivers, or systems altogether. And it break all the time. So the home-built stuff is really challenging to keep online. When it goes down, your business is not, you're not moving data. You can't actually get the insights you built in the first place. >> I remember I broke into this industry you know, in the days of mainframe. You used to read about them and they had this high-speed data mover. And it was this key component. And it had to be integrated. It had to be able to move, back then, it was large amounts of data fast. Today especially with the advent of Hadoop, people say okay don't move the data, keep it in place. Now that's not always practical. So talk about the sort of business case for starting a company that basically moves data. >> We handle basically the one step before. I agree with you completely. Many data analytical situations today where you're doing like the true, like business-oriented detail, where you're actually analyzing data and producing value, you can do it in place. Which is to say in your cluster, in your Spark cluster, all the different environments you can imagine. The problem is that if it's not there already, then it's a pretty monumental effort to get it there. I think we see. You know a lot of people think oh I can just write a SQL script, right? And that works for the first two to 20 tables you want to deploy. But for instance, in my background, I used to work at Square. I ran a data platform there. We had 500 tables we had to move on a regular basis. Coupled with a whole variety of other data sources. So at some point it becomes really impractical to hand-code these solutions. And even when you build your own framework, and you start to build tools internally, you know, it's not your job really, these companies, to build a world class data movement tool. It's their job to make the data valuable, right? And actually data movement is like utility, right. Providing the utility, really the thing to do is be productive and cost effective, right? So the reason why we build StreamSets, the reason why this thing is a thing in the first place, is because we think people shouldn't be in the business of building data movement tools. They should be in the business of moving their data and then getting on with it. Does that make sense? >> Yeah absolutely. So talk about how it all fits in with Spark generally and specifically Spark coming to the enterprise. >> Well in terms of how StreamSets connects to stuff, we deploy in every way you can imagine, whether you want to run your own premise, on your own machines, or in the Cloud. It's up to you to deploy however you like. We're not prescriptive about that. We often get deployed on the edge of clusters, wether it's your Hadoop cluster or your Spark cluster. And basically we try not to get in the way of these analysis tools. There are many great analytical tools out there like Spark is a great example. We focus really on the moving of data. So what you'll see is someone will build a Spark streaming application or some big Spark SQL thing that actually produces the reports. And we plug in ahead of that. So if you're data is being collected from, you know, Edge web logs or some thing or some Kafka thing or a third party AVI or scripting website. We do the first collection. And then it's usually picked up from there with the next tool. Whether it's Spark or other things. I'm trying to think about the right way to put this. I think that people who write Spark they should focus on the part that's like the business value for them. They should be doing the thing that actually is applying the machine learning model, or is producing the report that the CEO or CTO wants to see. And move away from the ingest part of the business. Does that make sense? >> [] Yeah. >> Yeah. When the Spark guys sort of aspire to that by saying you don't have to worry about exactly when's delivery. And you know you can make sure this sort of guarantee, you've got guarantees that will get from point A to point B. >> Bryan: Yeah. >> Things like that. But all those sources of data and all those targets, writing all those adapters is, I mean, that's been a La Brea tar pit for many companies over time. >> In essence that is our business. I think that you touch on a good point. Spark can actually do some of these things right. There's not complete, but significant overlap in some cases. But the important difference is that Spark is a cluster tool for working with cluster data. And we're not going to beat you running a Spark application for consuming from Kafka to do your analysis. But you want to use Spark for reading local files? Do you want to use Spark for reading from a mainframe? Like these are things that StreamSets is built for. And that library of connectors you're talking about, it's our bread and butter. It's not your job as a data scientist, you know, applying Spark, to build a library of connectors. So actually the challenge is not the difficulty of building any one connector, because we have that down to an art now. But we can afford to invest, we can build a portfolio of connectors. But you as a user of Spark, can only afford to do it on demand. Reactive. And so that turn around time, of the cost it might take you to build that connector is pretty significant. And actually I often see the flow side. This is a problem I faced at Square, which was that people asked me to integrate new data sources, I had to say no. Because it was too rare, it was too unusual for what we had to do. We had other things to support. So the problem with that is that I have no idea what kind of opportunity cost I left behind. Like what kind of data we didn't get, kind of analysis we couldn't do. And with an approach like StreamSets, you can solve that problem sort of up front even. >> So sort of two follow ups. One is it would seem to be an evergreen effort to maintain the existing connectors. >> Bryan: Certainly. >> And two, is there a way to leverage connectors that others have built, like the Kafka connect type stuff. >> Truthfully we are a heavy-duty user of open source software so our actual product, if you dig in to what you see, it's a framework for executing pipelines. And it's for connecting other software into our product. So it's not like when we integrate Kafka we built a build brand new blue sky Kafka connector. We actually integrate what stuff is out there. So our idea is to bring as much of that stuff in there as we can. And really be part of the community. You know, our product is also open source. So we play well with the community. We have had people contribute connectors. People who say we love the product, we need it to connect to this other database. And then they do it for us. So it's been a pretty exciting situation. >> We were talking earlier off-camera, George and I have been talking all week about the badge workloads, interactive workloads, now you've got this sort of new emerging workloads, continuous screening workloads, which is in the name. What are you seeing there? And what kind of use cases is that enabling? >> So we're focused on mostly the continuous delivery workload. We also deliver the batch stuff. We're finding is people are moving farther and farther away from batch in general. Because batch was not the goal it was a means to the end. People wanted to get their data into their environment, so they could do their analysis. They want to run their daily reports, things like that. But ask any data scientist, they would rather the data show up immediately. So we're definitely seeing a lot of customers who want to do things like moving data live from a log file into Hadoop they can read immediately, in the order of minutes. We're trying to do our best to enable those kind of use cases. In particular we're seeing a lot of interest in the Spark arena, obviously that's kind of why we're here today. You know people want to add their event processing, or their aggregation, and analysis, like Spark, especially like Spark SQL. And they want that to be almost happening at the time of ingest. Not once it landed, but like when it's happening. So we're starting to build integration. We have kind of our foot in the door there, with our Spark processor. Which allows you to put a Spark workflow right in the middle of your data pipeline. Or as many of them as you want in fact. And we all sort of manage the lifecycle of that. And do all those connections as required to make your pipeline pretend to have a Spark processor in the middle. We really think that with that kind of workload, you can do your ingest, but you can also capture your real-time analytics along the way. And that doesn't replace batch reporting for say that'll happen after the fact. Our your daily reports or what have you. But it makes it that much easier for your data scientists to have, you know, a piece of intelligence that they had in flight. You know? >> I love talking to someone who's a practitioner now sort of working for a company that's selling technology. What do you see, from both perspectives, as Spark being good at? You know, what's the best fit? And what's it not good at? >> Well I think that Spark is following the arc of like Hadoop basically. It started out as infrastructure for engineers, for building really big scary things. But it's becoming more and more a productivity tool for analysts, data scientist, machine-learning experts. And we see that popping up all the time. And it's really exciting frankly, to think about these streaming analytics that can happen. These scoring machine-learning models. Really bringing a lot more power into the hands of these people who are not engineers. People who are much more focused on the semantic value of the data. And not the garbage in garbage out value of the data. >> You were talking before about it's really hard, data movement and the data's not always right. Data quality continues to be a challenge. >> Bryan: Yeah. >> Maybe comment on that. State the data quality and how the industry is dealing with that problem. >> It is hard, it is hard. I think that the traditional approach to data quality is to try and specify a quality up front. We take the opposite approach. We basically say that it's impossible to know that your data will be correct at all times. So we have what we call schema drift tools. So we try to go, we say like intent-driven approach. We're interacting with your data. Rather then a schema driven approach. So of course your data has an implicit schema as it's passing through the pipeline. Rather than saying, let's transform com three, we want you to use the name. We want you to be aware of what it is you're trying to actually change and affect. And the rest just kind of flows along with it. There's no magic bullet for every kind of data-quality issue or schema change that could possibly come into your pipeline. We try to do the best to make it easy for you to do effectively the best practice. The easiest thing that will survive the future, build robust data pipelines. This is one of the biggest challenges I think with like home-grown solutions. Is that it's really easy to build something that works. It's not easy to build something that works all the time. It's very easy to not imagine the edge cases. 'Cause it might take you a year until you've actually encountered you know, the first big problem. The real, the gotcha that you didn't consider when you were building your own thing. And those of us at StreamSets who have been in the industry and on the user side, we've had some of these experiences. So we're trying to export that knowledge in the product. >> Dave: Who do you guys sell to? >> Everybody. (laughing) We see a lot of success today with, we call it Hadoop replatforming. Which is people who are moving from their huge variety of data sources environment into like a Hadoop data-like kind of environment. Also Cloud, people are moving into the Cloud. The need a way for their data to get from wherever it is to where they want it to be. And certainly people could script these things manually. They could build their own tools for this. But it's just so much more productive to do it quickly in a UI. >> Is it an architect who's buying your product? Is it a developer? >> It's a variety. So I think our product resonates greatly with a developer. But also people who are higher up in the chain. People who are trying to design their whole topology. I think the thing I love to talk about is everyone, when they start on a data project, they sit down and they draw this beautiful diagram with boxes and arrows that says here's where the data's going to go. But a month later, it works, kind of, but it's never that thing. >> Dave: Yeah because the data is just everywhere. >> Exactly. And the reality is that what you have to do to make it work correctly within SLA guidelines and things like that is so not what you imagined. But then you can almost never go backwards. You can never say based on what I have, give me the box scenarios, because it's a systems analysis effort that no one has the time to engage in. But since StreamSets is actually instruments, every step of the pipeline, and we have a view into how all your pipelines actually fit together. We can give you that. We can just generate it. So we actually have a product. We've been talking about the StreamSet data collector which is the core like data movement product. We have like our enterprise edition, which is called the Dataflow Performance Manager, or DPM, It basically gives you a lot of collaboration and enterprise grade authentication. And access control, and the commander control features. So it aggregates your metrics across all your data collectors. It helps you visualize your topology. So people like your director of analytics, or your CIO, who want to know is everything okay? We have a dashboard for them now. And that's really powerful. It's a beautiful UI. And it's really a platform for us to build visualizations with more intelligence. That looks across your whole infrastructure. >> Dave: That's good. >> Yeah. And then the thing is this is strangely kind of unprecedented. Because, you know, again, the engineer who wants to build this himself would say, I could just deploy Graphite. And all of a sudden I've got graphs it's fine right. But they're missing the details. What about the systems that aren't under your control? What about the failure cases? All these things, these are the things we tackle. 'Cause it's our business we can afford to invest massively and make this a really first-class data engineering environment. >> Would it be fair to say that Kafka sort of as it exists today is just data movement built on a log, but that it doesn't do the analytics. And it doesn't really yet, maybe it's just beginning to do some of the monitoring you know, with a dashboard, or that's a statement of direction. Would it be fair to say that you can layer on top of that? Or you can substitute on top of it with all the analytics? And then when you want the really fancy analytic soup, you know, call out to Spark. >> Sure, I would say that for one thing we definitely want to stay out of the analytics base. We think there's many great analytics tools out there like Spark. We also are not a storage tool. In fact, we're kind of like, we're queue-like but we view ourselves more like, if there's a pipe and a pump, we're the pump. And Kafka is the pipe. I think that from like a monitoring perspective, we monitor Kafka indirectly. 'Cause if we know what's coming out, and we know what's going in later, we can give you the stats. And that's actually what's important. This is actually one of the challenges of having sort of a home-grown or disconnected solution, is that stitching together so you understand the end to end is extremely difficult. 'Cause if you have a relational database, and a Kafka, and a Hadoop, and a Spark job, sure you can monitor all those things. They all have their own UIs. But if you can't understand what the is on the whole system you're left like with four windows open trying to figure out where things connect. And it's just too difficult. >> So just on a sort of a positioning point of view for someone who's trying to make sense out of all the choices they have, to what extent would you call yourself a management framework for someone who's building these pipelines, whether from Scratch, or buying components. And to what extent is it, I guess, when you talk about a pump, that would be almost like the run time part of it. >> Bryan: Yeah, yeah. >> So you know there's a control plane and then there's a data plane. >> Bryan: Sure. >> What's the mix? >> Yeah well we do both for sure. I mean I would say that the data point for us is StreamSet's data collector. We move data, we physically move the data. We have our own internal pipeline execution engine. So it doesn't presuppose any other existing technologies, not dependent on Hadoop or Spark or Kafka or anything. You know to some degree data collector is also the control plane for small deployments. Because it does give you start to stop commanding control. Some metrics monitoring, things like that. Now, what people need to expand beyond the realm of single data collector, when they have enterprises with more than one business unit, or data center, or security zone, things like that. You don't just deploy one data collector, you deploy a bunch, dozens or hundreds. And in that case, that's where dataflow performance manager again comes in, as that control plane. Now dataflow performance manager has no data in it. It does not pass your actual business data. But it does again aggregate all of your metrics from all your data collectors and gives you a unified view across your whole enterprise. >> And one more follow-up along those lines. When you have a multi-vendor stack, or a multi-vendor pipeline. >> Bryan: Yeah. >> What gives you the meta view? >> Well we're at the ins and outs. We see the interfaces. So in theory if someone were to consume data out of Kafka do something right. Then there's another job later, like a Spark job. >> George: Yeah. >> So we don't automatic visibility for that. But our plan in the future is to expand as dataflow performance manager to take third party metric sources effectively. To broaden the view of your entire enterprise. >> You've got a bunch of stuff on your website here which is kind of interesting. Talking about some of the things we talked about. You know taming data drift is one of your papers. The silent killer of data integrity. And some other good resources. So just in sort of closing, how do we learn more? What would you suggest? >> Sure, yeah please visit the website. The product is open source and free to download. Data collector is free to download. I would encourage people to try it out. It's really easy to take for a spin. And if you love it you should check out our community. We have a very active Slack channel and Google group, which you can find from the website as well. And there's also a blog full of tutorials. >> Yeah well you're solving gnarly problems that a lot of companies just don't want to deal with. That's good thanks for doing the dirty work, we appreciate it. >> Yeah my pleasure. >> Alright Bryan thanks for coming on "The Cube." >> Thanks for having me. >> Good to see you. You're welcome. Keep right there buddy we'll be back with our next guest. This is "The Cube" we're live from Boston Spark Summit. Spark Summit East #SparkSummit right back. >> Narrator: Since the dawn.

>>This past May, The Cube in collaboration with Influx data shared with you the latest innovations in Time series databases. We talked at length about why a purpose built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember the time series data is any data that's stamped in time, and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community, we talked about how in theory, those time slices could be taken, you know, every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors and other devices and IOT equipment. A time series databases have had to evolve to efficiently support realtime data in emerging use cases in iot T and other use cases. >>And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the smart Data platform, made possible by influx data and produced by the Cube. My name is Dave Valante and I'll be your host today. Now, in this program, we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're gonna hear from Brian Gilmore, who is the director of IOT and emerging technologies at Influx Data. And we're gonna talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program, you're gonna hear a lot about things like Rust, implementation of Apache Arrow, the use of par k and tooling such as data fusion, which powering a new engine for Influx db. >>Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices, if you will, from, for example, minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're gonna hear from Anna East Dos Georgio, who is a developer advocate at In Flux Data. And we're gonna get into the why of these open source capabilities and how they contribute to the evolution of the Influx DB platform. And then we're gonna close the program with Tim Yokum, he's the director of engineering at Influx Data, and he's gonna explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started. Okay, we're kicking things off with Brian Gilmore. He's the director of i t and emerging Technology at Influx State of Bryan. Welcome to the program. Thanks for coming on. >>Thanks Dave. Great to be here. I appreciate the time. >>Hey, explain why Influx db, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >>No, no, not at all. I mean, I think it's, for us, it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like sql, you know, query support, things like that, we have to figure out a way to, to execute those for them in a way that will scale long term. And then we also, we wanna make sure we're innovating, we're sort of staying ahead of the market as well and sort of anticipating those future needs. So, you know, this is really a, a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine, but you know, initially the customers who are using us are gonna see just great improvements in performance, you know, especially those that are working at the top end of the, of the workload scale, you know, the massive data volumes and things like that. >>Yeah, and we're gonna get into that today and the architecture and the like, but what was the catalyst for the enhancements? I mean, when and how did this all come about? >>Well, I mean, like three years ago we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product, you know, and, and sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was, that was, that was a long journey I guess, you know, phase one was, you know, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to, to optimize for like multi-tenant, multi-cloud, be able to, to host it in a truly like sass manner where we could use, you know, some type of customer activity or consumption as the, the pricing vector, you know, And, and that was sort of the birth of the, of the real first influx DB cloud, you know, which has been really successful. >>We've seen, I think, like 60,000 people sign up and we've got tons and tons of, of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a, on a daily basis, you know, and having that sort of big pool of, of very diverse and very customers to chat with as they're using the product, as they're giving us feedback, et cetera, has has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that and then also making these big leaps as we're doing with this, with this new engine. >>Right. So you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really wanna understand how much of a pivot this is and what, what does it take to make that shift from, you know, time series, you know, specialist to real time analytics and being able to support both? >>Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. You know, time series data is always gonna be fundamental and sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. You know, the time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics, if we're being honest though, I think our, our user base is well aware that the way we were architected was much more towards those sort of like backwards looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a, and a, you know, a time to response on the queries, and can we get that to the point where the results sets are coming back so quickly from the time of query that we can like limit that window down to minutes and then seconds. >>And now with this new engine, we're really starting to talk about a query window that could be like returning results in, in, you know, milliseconds of time since it hit the, the, the ingest queue. And that's, that's really getting to the point where as your data is available, you can use it and you can query it, you can visualize it, and you can do all those sort of magical things with it, you know? And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the, the real time queries, the, the multiple language query support, but, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a a limited number of customers, strategic customers and strategic availability zones to start. But you know, everybody over time. >>So you're basically going from what happened to in, you can still do that obviously, but to what's happening now in the moment? >>Yeah, yeah. I mean, if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the, by the underlying data collection, the architecture, the infrastructure, the, you know, the, the devices and you know, the sort of highly distributed nature of all of this. So yeah, I mean, getting, getting a customer or a user to be able to use the data as soon as it is available is what we're after here. >>I always thought, you know, real, I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >>Yeah, it's, it's, I mean it is operationally or operational real time is different, you know, and that's one of the things that really triggered us to know that we were, we were heading in the right direction, is just how many sort of operational customers we have. You know, everything from like aerospace and defense. We've got companies monitoring satellites, we've got tons of industrial users, users using us as a processes storing on the plant floor, you know, and, and if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're gonna do here is we're gonna start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems. Certainly not their, their historians and databases. >>I, is this available, these innovations to influx DB cloud customers only who can access this capability? >>Yeah. I mean, commercially and today, yes. You know, I think we want to emphasize that's a, for now our goal is to get our latest and greatest and our best to everybody over time. Of course. You know, one of the things we had to do here was like we double down on sort of our, our commitment to open source and availability. So like anybody today can take a look at the, the libraries in on our GitHub and, you know, can ex inspect it and even can try to, you know, implement or execute some of it themselves in their own infrastructure. You know, we are, we're committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. >>And so just, you know, being careful, maybe a little cautious in terms of, of, of how big we go with this right away. Just sort of both limits, you know, the risk of, of, you know, any issues that can come with new software rollouts. We haven't seen anything so far, but also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products, but once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's gonna be exciting time for the whole ecosystem. >>Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are gonna help deliver on this vision. What, what should we know there? >>Well, I mean, I think foundationally we built the, the new core on Rust. You know, this is a new very sort of popular systems language, you know, it's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well. And if it does find error conditions, I mean, we, we've loved working with Go and, you know, a lot of our libraries will continue to, to be sort of implemented in Go, but you know, when it came to this particular new engine, you know, that power performance and stability rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parque for persistence. I think for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our, our time series merged Trees, this is a big break from that, you know, arrow on the sort of in MI side and then Par K in the on disk side. >>It, it allows us to, to present, you know, a unified set of APIs for those really fast real time inquiries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that PARQUE format, which is also cool because there's an entire ecosystem sort of popping up around Parque in terms of the machine learning community, you know, and getting that all to work, we had to glue it together with aero flight. That's sort of what we're using as our, our RPC component. You know, it handles the orchestration and the, the transportation of the Coer data. Now we're moving to like a true Coer database model for this, this version of the engine, you know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like blurring that line between real time and historical data. It's, you know, it's, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >>Yeah. Again, I mean, it's funny you mentioned Rust. It is, it's been around for a long time, but it's popularity is, is, you know, really starting to hit that steep part of the S-curve. And, and we're gonna dig into to more of that, but give us any, is there anything else that we should know about Bryan? Give us the last word? >>Well, I mean, I think first I'd like everybody sort of watching just to like, take a look at what we're offering in terms of early access in beta programs. I mean, if, if, if you wanna participate or if you wanna work sort of in terms of early access with the, with the new engine, please reach out to the team. I'm sure you know, there's a lot of communications going out and, you know, it'll be highly featured on our, our website, you know, but reach out to the team, believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're, we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to because we can flip a lot of stuff on, especially in cloud through feature flags. >>But if there's something new that you wanna try out, we'd just love to hear from you. And then, you know, our goal would be that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to, to sort of build the next versions of your business. Because, you know, the whole database, the ecosystem as it expands out into to, you know, this vertically oriented stack of cloud services and enterprise databases and edge databases, you know, it's gonna be what we all make it together, not just, you know, those of us who were employed by Influx db. And then finally, I would just say please, like watch in ice in Tim's sessions, Like these are two of our best and brightest. They're totally brilliant, completely pragmatic, and they are most of all customer obsessed, which is amazing. And there's no better takes, like honestly on the, the sort of technical details of this, then there's, especially when it comes to like the value that these investments will, will bring to our customers and our communities. So encourage you to, to, you know, pay more attention to them than you did to me, for sure. >>Brian Gilmore, great stuff. Really appreciate your time. Thank you. >>Yeah, thanks Dave. It was awesome. Look forward to it. >>Yeah, me too. Looking forward to see how the, the community actually applies these new innovations and goes, goes beyond just the historical into the real time, really hot area. As Brian said in a moment, I'll be right back with Anna East Dos Georgio to dig into the critical aspects of key open source components of the Influx DB engine, including Rust, Arrow, Parque, data fusion. Keep it right there. You don't want to miss this.