from New York City it's the cube covering welcome to the new edge brought to you by pensando systems hey welcome back here ready Jeff Rick here with the cube we are in downtown Manhattan at the top of goldman sachs it was a beautiful day now the clouds coming in but that's appropriate because we're talking about cloud we're talking about edge and the launch of a brand new company is pensando and their event it's called welcome to the new edge and we're happy to have since we're goldman the guys who have the money we're barry Eggers a founding partner of Lightspeed ventures and randy pond the CFO a pensando gentlemen welcome thank you thank you so Barry let's start with you you think you were involved at this early on why did you get involved what what kind of sparked your interest we got involved in this round and the reason we got involved were mainly because we've worked with this team before at Cisco we know they're fantastic they're probably the most prolific team and the enterprise and they're going after a big opportunity so we were pleased when the company said hey you guys want to work with us on this as a financial investor and we did some diligence and dug in and found you know everything to our liking and jump right in didn't anybody tell them this startup is a young man's game they mixed up the twenty-something I think yeah they sort of turned the startup on its head if you will no pun intended that's going right yeah yeah and Randy you've joined him a CFO you've known them for a while I mean what is it about this group of people that execute kind of forward-looking transformation transformational technologies time and time again that's not a very common trait it's a it's a great question so you know the key for these guys have been well they've been together since the 80s so Mario look and primitive this is the 80s I work with them at their previous startup before Christian two ladies and they're the combination of their skills are phenomenal together so you know one of them has some of the vision of where they want to go the second guy is a substantive sort of engineer takes it from concept first drawing and then the Prem takes over the execution perspective and then drives this thing and they've really been incredible together and then we added Sony at crescendo as a as a product marketing person and she's really stepped up and become integral part on the team so they work together so well it just makes a huge difference yeah it's it's it's amazing that that a that they keep doing it and B that they want to keep doing it right because they've got a few bucks in the bank and they don't really need to do it but still to take on a big challenge and then to keep it under wraps for two and a half years that's pretty pretty amazing so curious Barry from your point of view venture investing you guys kind of see the future you get pitched by smart people all day when you looked at John Chambers kind of conversation of these ten-year kind of big cycles you know what did you think of that how do you guys kind of slice and dice your opportunities and looking at these big Nick's yeah going back going back to the team a little bit they've been pretty good at identifying a lot of these cycles they brought us land switching a long time ago with crescendo they sort of redefined the data center several times and so there's another opportunity what's driving this opportunity really is the fact that explosion of applications in the network and of course east-west traffic in the network so networks were more designed north-south and they're slowly becoming more east-west but because the applications are closer to the edge and networks today mostly provide services in the core the idea for pensando is well why don't we bring the service deliver the services closer to the applications improve performance better security and better monitoring yeah and then just the just the hyper acceleration of you know the amount of data the amount of applications and then this age-old it's we're going to use the data to the computer do you move the compute to the data now the answer is yes all the above so you got some money to work with we do you got a round that he could be around you guys are closing the C round so I think 180 people approximately I think somebody told me close enough so as you put some of this capital to work what are some of your priorities going forward so we will continue to hire both in the engineering side but more importantly now we're hiring in sales and service we've been waiting for the product quite frankly so we've just got our first few sales guys hired we've got a pretty aggressive ramp especially with the HP relationship to put people out into the field we've hired a couple guys in New York will continue to hire at the sales team we're ramping the supply chain and we've got a relative complicated supply chain model but that has to react now that we're going to market all that might be pretty used to do that we're changing facilities we need to grow we're sort of cramped in a one-story building open up one floor of a building right now so the money is going to be used sort of critically to really scale the business down they can go to market okay but a pretty impressive list of both partners and customers on launch day you don't see Goldman HPE Equinix I think it was quite a slide some of that is the uniqueness of the way we went to market and did the original due diligence on the product and bringing customers in early and then converting them to investors you end up with a customer investment model so they stayed with us Goldman's been through all three rounds we've been about HP and last model we had NetApp has been um two rounds now so we've we've continued to develop as a business with this small core group of customers and investors that we could try to expand every time we move to the next round and as Barry said earlier this is the first time we had a traditional financial investor in our rounds the rest of them have all been customers they've been friends and family for the most part did you join the board too right I did yeah so what are you what are you excited about what what do you see is I mean just clearly your side you invested but is there something just extra special here you know react chambers put in a 10-year 10-year cycle yeah we've talked about it I mean I'm excited to work with the team right there best-in-class working closely with John again is a lot of fun a chance to not exhaust yeah yeah you know a chance to read redefine the data center and be part of the next way even as a VC you love waves and build my Connick company right and I think we have a real opportunity in front of us it it takes a lot of money to do this and do it right and I think we have the team that proven they can execute on this kind of opportunities from I'm excited to see what the next five years hold for this company good well it was funny John teased him a little bit about you know all the M&A stuff that he was famous for at Cisco he's like I don't do that anymore now I'm an investor I want IPOs all the way what's all 18 thinks it is 18 companies in his portfolio their routes they're going to IPO all the way yeah that's that's a good point actually this team has been prolific and they've delivered products that have generated fifty billion dollars and any walk into any data center in the world you're gonna see a product this team has built however this team has not taken a company public so that's really the opportunity I think that's what excites them Randy's here it's why Jon's here that's why I'm here we want to build a company that can be an independent company be a lasting leader in a new category yeah so last word Randy for you for people that aren't familiar with the team that aren't familiar with with with what they've done what would you tell them about why you came to this opportunity and why you're excited about it well this there is no higher quality engineering team in the world didn't these people so it's to get re-engaged with them again with an entirely new concept that's catching a transition and the market was just too good an opportunity to pass I mean I had retired for 15 months and I came out of retirement to join this team much to the chagrin of my wife but I just couldn't pass up the opportunity high caliber talent it's um every day is is interesting I have to say well thanks for for sharing the story with us and and congratulations on a great day and in a terrific event thank you thank you very much all right he's berry he's Randy I'm Jeff you're watching the cube from the top of goldman sachs in Manhattan thanks for watching we'll see you next time

>> Live from Orlando, Florida, it's theCUBE covering Microsoft Ignite. Brought to you by Cohesity and theCUBE's ecosystem partners. >> Welcome back, everyone, to theCUBE's live coverage of Microsoft Ignite. I'm your host, Rebecca Knight, along with my cohost, Stu Miniman. We're joined by Patrick O'Reilly of O'Reilly Venture Partners based in San Francisco. Thanks so much for coming on theCUBE, Patrick. >> Thanks for having me. >> So, you are a serial entrepreneur now working as a VC, what are you doing here? Tell us why you came to Ignite. >> Yeah, well selfishly on the VC side we have a few of our portfolio companies here that have booths, and I wanted to kind of hear what people are asking, you know, why they're interested in the companies and how we're framing, you know, those companies to the end users. I think these type of events are really good to unlock hidden potential, or things that people can tell you that you wouldn't actually have thought about, yeah. >> Yeah, so Patrick, you know, I've known you for a number of years. Usually see you at the opensource shows. Microsoft, you know, publicly very embracing opensource. You know, they love Linux, partnering with Red Hat, even you know, partnering is a lot of things that Microsoft does. They were working with VMware. What's your viewpoint as to how you see Microsoft and the opensource world, and how about this ecosystem? Is this a vibrant ecosystem that, you know, VCs are investing in, or is it just that there's companies of yours that, you know, this is part of the story. >> No, and I think historically we've had the, you know, build versus buy, you know, kind of way of looking at it, but when I typically think of Microsoft, it's more people building glue, you know, code to kind of connect things together, and you tend to have blinders on and not think about what opensource components you can use. You know, you look for like what company has a solution you can buy, or license or OEM, and I think that's changing, you know, over time. You know, Microsoft does an amazing job with developers of giving them very easy to understand languages and amazing tooling, and along with that the documentation and the training, so I kind of felt like you came into development one of two ways. You either were like on the Microsoft track and using the cookie cutter approach, you know, to doing things and getting certified on something, or you were opensource, you learned the scripting language and you just looked at what you can cobble together in the opensource world, and there wasn't a lot of crosspollination, but now I see that those walls kind of dissolving. People are willing to mix and match. >> Yeah, it's interesting, you know, some places I've seen Microsoft, a lot in the Kubernetes show, so you know, first got to know you you were at Kismetic, you know, really the first company around Kubernetes that we knew. You know, I know you're doing a lot of different things but we love your viewpoint on, you know, anything on Microsoft in that space, as well as just what you've seen, you know, as a watcher of the Kubernetes space these days. >> Yeah, I mean I've been... You know, if I step back from Kubernetes, you know, back to like the Apache Mesos and the Mesosphere days, you know, if you rewind all the way back there you kind of had to do a lot of education of like, "What do you mean 'containerization?' "I have VMs, why do I need containers?" And now that we've gotten past that and people actually understand the value of containers, like having an orchestration system in place that works and works with everything, you know, is obviously more important than ever, and it's... I really credit the CNCF and the Linux Foundation for what they've done to kind of bring standards around Kubernetes and shepherd the project, and I think that, you know, the fairly recent announcement from Google that they're fully trusting, you know, CNCF to be the shepherd of that is huge, and it gives a framework for people, like Gabe at Microsoft, to work with, you know, some of the staff at Google, and like, in a collaborative way and move it forward for everyone, and I think, you know, historically containers made a ton of sense on Linux, but now that we have Windows server, you know, supporting containers and theCUBE working, you know, on Windows, I think in the 111... Or sorry, 113 release we'll have full Windows server, you know, support in Kubernetes, like that'll be huge. And just a quick aside, like the reason I even kind of honed in on containers and thought it was interesting is the average server utilization is still so low, but we're not really trained as technologists to care about that, and you know, we're really good at building data centers and tucking them off in places where no one sees, but when the average server's taking like... It's like running a hairdryer on high, you know, for electricity and then they run so hot you have to cool it. Like, we're really not helping the environment, so I think if we can move towards containerization, move towards efficient utilization of our hardware, you know, it'll be better for everyone, not just this ecosystem, so... >> So, talk to, tell our viewers a little bit about your portfolios and your portfolio companies that are here, and how they fit into the ecosystem. >> Yeah, so the one I'm most excited about, or shouldn't probably say it that way, I'll reframe that-- >> Can't have favorites, they're all your babies. (laughs) >> Yeah, they're all my babies. (laughs) >> But Ziften Technologies is great. I think their integration with the Windows, the vendor ATP, you know, advanced threat protection, you know, tool is great. They focus on the Mac and the Linux components and give you that same kind of pane of glass on the Microsoft side to see those endpoints, and like their utilization of AI, like they have an upcoming release where they're using AI to do things, and traditionally in that space it's been like the AB vendors, you know, doing everything and you had kind of, "Here's our signatures, "we're going to scan against those signatures," and it's a creative use of AI now to, like, look for just anomaly detections. These are the things we haven't seen before. Not sure what it is but it looks abnormal, and those are the kind of like spin-outs of companies that I'm looking for, too. Like I want to see people doing more meaningful things, you know, with AI. I think if we look at Azure and what they're offering now, like I don't need to have a bunch of data scientists at my startup. I can implement computer vision just using what off-the-shelf components, you know, from Microsoft and you know, Azure. I can do video indexing, you know, using their services. Like, if I rewind just back three years I would've had to have a team of like four data scientists. They'd be reading whitepapers, they'd be implementing code that like sort of half works, and they would probably take half a year to train some models to get, like, moderate results, and now in a matter of minutes, you know, I can use this off-the-shelf stuff. >> Yeah, it's fascinating, I think back to, you know, we were pretty early at theCUBE at watching the whole big data trend, and back then it was like, "Okay, we're going to "take that two-year project and you know, "drive it down to six months," and now we talk in the AI space is, you know, how can we drive that down even more. In big data there was concern, everything seemed to be custom. In AI we're starting to get to more templatized solutions, rolling out for a lot of industries, and it feels like it's taking off a lot faster than that space is, and I know there's a lot of investment going on in the space, and a lot there, so... Anything in particular, you know, what excites you, what makes a good, you know, AI investment versus, you know, there's just so much happening out there. >> Well, you know, I... I struggle with the name AI a little bit. >> Yeah, no, no, I understand, yeah. >> I'm working on a talk, and you know, I kind of like don't, I don't enjoy the artificial aspect of it because it's really just intelligence, and you know, right now it's a buzzword people are throwing into everything when really they mean, "We use an algorithm." (laughs) You know, it's not truly AI, but when we get to cognition we get, you know, to, you know, someday if we have quantum supremacy we'll have, you know, systems that actually can maybe have a consciousness, you know, and decide things. That's where I'm interested, I'm looking... Like on the devops side I'm looking for people using AI to get away with repetitive tasks. Like I would love to see, you know, someone have a system where it's like, "Hey, we've noticed, you know, 90 times "this week this guy's done this exact "same thing, you know, 99% the same way." Like, let's automate that away. You know, we've been really good in the space to kind of treat infrastructure like code, you know, and be able to tear things up. Like I mean, I've been incredibly excited to see, like just in my career, how we went from, "Okay, you're going to do something meaningful on the web. "You need to build a data center. "You need to, you know, get a bunch of servers, racks," and then you pay all this equipment and oh, by the way, 18 months from now it's going to be obsolete and you're going to have to spend money again, to where now I can just, you know, get some credits to start up in the cloud, you know, try things out and do like really meaningful things. So, just looking for anyone on AI that's going to do something that moves the needle. >> Yeah, now that, yeah, just on the terminology piece, I've lived through the cloud wars and the argument over what was and what isn't, so it's just, you know, the shorthand for this wave that we have there, where AI or ML, or you know, IBM has some interesting terms that they want to call it. We understand that there's intelligence that I can do with software, a lot of machine-to-machine things that are going on, and it's not a lot of, you know, shouldn't be a lot of heavy lifting by people to go in there. Oh, wait, I can train something, I can learn what's happening, so... >> Well, I wanted to ask when... I'm sure a lot of entrepreneurs ears are pricking up when they hear that you want to make these meaningful investments. What is it that you look for in a company, is it... In terms of the leadership team, in terms of any track record, what sort of makes your eyes light up? >> So, I try to go to as many conferences as I can, because I feel that's where, you know, the hallway track and I can meet people. I can see, you know, their talks, see what they're passionate about, so what I'm really looking for is investing more in the people than in the idea, because startups can always pivot, and you look at some of the greatest companies out there, they were pivots from, you know, a slightly different model and they realized that, "Oh, we should go chase down this other thing." So, to me, I'm looking for people that are doing something exciting where they are already, looking to make the leap. You know, for example, like you know, the Spinnaker team or people that do something, you know, like... You know, like if etcd wanted to move off and be a separate company, like things like that where they've done something, they've proven it, and now they want to go start a company around it, and I think right off the bat, like if you've built some interesting technology that people are starting to use you have a decent revenue stream just from support, you know, of that and helping those end users, and I think, you know, with O'Reilly we do something a little different than other people. Like I focus mostly on seed investment, very early stage. Our typical check size is around $500k, and I actually allow people to take us off the cap table and just pay us back. Like you know, I've done nine startups in my career, and it's... Fundraising is one of those things where you only get good at it once you don't need it anymore, (chuckles) and I felt the pain of being on that side of the desk and I want to be in the position where, you know, we can write the checks and not try to, like, have a lot of governance, not try to take a board seat, not give you down pressure, you know, on what you're doing but really be additive. I think moving forward I would love to be in the position where we can help incubate, you know, a lot of companies because we've found that, you know, you all kind of go through, every company goes through the same process like, "Now, we need a real CFO because "we need financial projections." Like, being able to, like, provide those services for portfolio companies where they don't have to go spend their resources chasing that down. >> I'm curious how much some of the big players, or just the gravity of what's happening in the space that you're looking at, so obviously we're here at the Microsoft show, but Google, Amazon, a lot of activity going on and we can call it AI or what you will, VMware even, Oracle, SalesForce, how much of the big players defining and you have to build around them, versus you know, we look at Kubernetes is supposed to make things independent, to be able to be opensource and be able to build solutions, you know, regardless of what platform they're on. >> Yeah, I mean, I think we're living in a world where people have a lot of choice, you know, and we look at even, like we take the example of cloud providers. Like, as long as I don't get vendor lock in and use, you know, their specific features, like I can move around to different cloud providers, I can now say I want to negotiate a better price here and migrate over, and I think just with any of the technologies, like trying to work in ways where companies can work together and be additive, I think that's where we actually move, you know, move down the field. I don't know what analogy's appropriate to use, but you know, I feel like there's a lot of really interesting stuff that we should be doing, and making... Every company doing a slightly different version of the same thing I don't think, you know, makes sense. Like, you know, even silly things like as we mature. Like, you know, back in the day everyone used to have broadcast television. We built all these antennas, we got all this range, you know, and then we moved to digital and we didn't need those antennas, we didn't need that range, so they started decommissioning them, but then companies came along and they're like, "Well, wait, now we have this "unlicensed spectrum we can use." So, now they're using it for internet. You know, you can get 20 megabit connectivity out to a rural farm where now they can put some cheap IoT sensors, and like, do really meaningful things with low cost technologies, like those are the things I'm interesting in. You know, so kids that want to cobble together, you know, IoT sensors and come up with a way to use, you know, what they have in rural areas, and like, and have technology actually help people in a meaningful way, and I think those are a lot of very viable startups, you know, in that space. I do think we live in a world where every company's going to end up graduating into one of the camps, be it, you know, SalesForce, Google, you know, Microsoft, but in that innovation spike, like when they're first starting improving out the companies I think they have a ton of choice, you know. >> You described a very beneficent approach to how you think about VC. Do you think, how would you describe the VC landscape right now? You said you want to be able to just incubate great ideas and help these young companies when they are not good at fundraising and they don't have the smooth, slick deck that will really impress the bigger VC firms. I mean, how, what's wrong with the VC landscape today and what else are you doing to make it better? >> Well, I think the incentives are a little off. You know, I can speak for myself, like when I was... You know, when I was looking to raise VC money and my previous companies, like you know, you get these great offers from people, but then you talk to other entrepreneurs and you're like, you know, I'm not going to call anyone out by name, but you're like, "Well, how is this VC's firm served you," and you start hearing of ways that it was additive, but also kind of put undue pressure on them, or they say things like, "Well, we really didn't "need to raise that round then. "We could've done bridge financing "or we could've figured out how to get a MVP product "out there and brought in some revenue." So, I just think it's the ultrahigh returns that VCs are looking for, and the promises that those VCs are making to their LPs, (chuckles) you know, in their funds to outperform everyone else, and you know, everyone talks to everyone, right? So, if anything's meaningful out there looking for investment kind of the back channel is very vibrant and it's dog-eat-dog, and some of it, I kind of reckon it to, you know, your alma mater, like where you went to school. Like, you know, if you're an MIT person, like MIT's the best place in the world. You know, if you're, you know, some other school, they're the best place in the world, and the VCs tend to kind of, like, fall in those camps, and what I'm looking to do-- >> And those are real biases that impact women and underrepresented minorities, to their detriment. >> Yeah, and you know, and that's the thing I've struggled with, too, when you look at the... Like, let's take Andreessen, you know, for example and you look at the portfolio companies, like you know, you kind of become locked into that ecosystem. Like if you want to go, you know, if I'm on Mesosphere and I want to go partner with someone that's not under that, or they have a company in that portfolio that does similar things, you're going to be pressured into working with the portfolio company over going off and maybe choosing the better, you know, choice for the industry, so I'd like to see, you know, those things change. >> Right, and so, Patrick, we talked a little bit about Ziften, security endpoint, you know, really hot space. I want to give the opportunity, other companies you have here that we should check out. >> Yeah, so we work closely with the team at Turbonomic. I think, you know, what they've done over time, you know, is amazing. I love products where you can just bolt it in and within a short period of time you're getting value. Like, you know, stepping back and just saying one thing about Ziften, like I think it's amazing, because I come from a software development, you know, background, and one thing as a software developer I've always found fascinating is like when you come in wearing the developer hat they give you the keys to the kingdom. They're like, "Oh, here's root access to the servers, "here's where all of our data is, "here's how you do a snapshot of production "to, you know, test it, you know, in staging," and I've always thought that it was a tremendous amount of risk, and you know, on average a company can be hacked for up to 100 days before they even realize that they've had a breach, and like, any kind of company, you know, be it Ziften or anyone in that space, that can showcase that to you. Like, you know, raise up things that you weren't aware of, you know, is really interesting, and then, you know, to the, like, Nico and Turbonomics and the things that they're doing there. Like, to actually get the most out of what you already have, like that's huge to me, because one of the, you know, one of the things I see in cloud computing that we didn't necessarily have, you know, directly owned physical infrastructure is it's almost too easy to spin things up. You know, you've got the guy clicking through the UIs like, "Oh, this instance looks great. "Oh, and it says it's only be $140 this month," and then they end up spinning up 1,000 of those, you know? (laughs) You get that first sticker shock of, like, here's that $250,000 bill that month, (chuckles) you know, for cloud, and companies like Turbonomics can, like, avoid you, you know, making those mistakes. >> Great, Patrick, thank you so much for coming on theCUBE. It was really fun talking. >> Yeah. >> We could talk to you for hours. >> Thanks for having me, I appreciate it. >> I'm Rebecca Knight for Stu Miniman. We will have more from theCUBE's live coverage of Microsoft Ignite coming up in just a little bit. (techy music)

(upbeat music) >> Hello and welcome back to theCUBE. We're here in Palo Alto, California. I'm your host, John Furrier with a special guest here in the studio. As part of our Cloud Native SecurityCon Coverage we had an opportunity to bring in Jon Turow who is the partner at Madrona Venture Partners formerly with AWS and to talk about machine learning, foundational models, and how the future of AI is going to be impacted by some of the innovation around what's going on in the industry. ChatGPT has taken the world by storm. A million downloads, fastest to the million downloads there. Before some were saying it's just a gimmick. Others saying it's a game changer. Jon's here to break it down, and great to have you on. Thanks for coming in. >> Thanks John. Glad to be here. >> Thanks for coming on. So first of all, I'm glad you're here. First of all, because two things. One, you were formerly with AWS, got a lot of experience running projects at AWS. Now a partner at Madrona, a great firm doing great deals, and they had this future at modern application kind of thesis. Now you are putting out some content recently around foundational models. You're deep into computer vision. You were the IoT general manager at AWS among other things, Greengrass. So you know a lot about data. You know a lot about some of this automation, some of the edge stuff. You've been in the middle of all these kind of areas that now seem to be the next wave coming. So I wanted to ask you what your thoughts are of how the machine learning and this new automation wave is coming in, this AI tools are coming out. Is it a platform? Is it going to be smarter? What feeds AI? What's your take on this whole foundational big movement into AI? What's your general reaction to all this? >> So, thanks, Jon, again for having me here. Really excited to talk about these things. AI has been coming for a long time. It's been kind of the next big thing. Always just over the horizon for quite some time. And we've seen really compelling applications in generations before and until now. Amazon and AWS have introduced a lot of them. My firm, Madrona Venture Group has invested in some of those early players as well. But what we're seeing now is something categorically different. That's really exciting and feels like a durable change. And I can try and explain what that is. We have these really large models that are useful in a general way. They can be applied to a lot of different tasks beyond the specific task that the designers envisioned. That makes them more flexible, that makes them more useful for building applications than what we've seen before. And so that, we can talk about the depths of it, but in a nutshell, that's why I think people are really excited. >> And I think one of the things that you wrote about that jumped out at me is that this seems to be this moment where there's been a multiple decades of nerds and computer scientists and programmers and data thinkers around waiting for AI to blossom. And it's like they're scratching that itch. Every year is going to be, and it's like the bottleneck's always been compute power. And we've seen other areas, genome sequencing, all kinds of high computation things where required high forms computing. But now there's no real bottleneck to compute. You got cloud. And so you're starting to see the emergence of a massive acceleration of where AI's been and where it needs to be going. Now, it's almost like it's got a reboot. It's almost a renaissance in the AI community with a whole nother macro environmental things happening. Cloud, younger generation, applications proliferate from mobile to cloud native. It's the perfect storm for this kind of moment to switch over. Am I overreading that? Is that right? >> You're right. And it's been cooking for a cycle or two. And let me try and explain why that is. We have cloud and AWS launch in whatever it was, 2006, and offered more compute to more people than really was possible before. Initially that was about taking existing applications and running them more easily in a bigger scale. But in that period of time what's also become possible is new kinds of computation that really weren't practical or even possible without that vast amount of compute. And so one result that came of that is something called the transformer AI model architecture. And Google came out with that, published a paper in 2017. And what that says is, with a transformer model you can actually train an arbitrarily large amount of data into a model, and see what happens. That's what Google demonstrated in 2017. The what happens is the really exciting part because when you do that, what you start to see, when models exceed a certain size that we had never really seen before all of a sudden they get what we call emerging capabilities of complex reasoning and reasoning outside a domain and reasoning with data. The kinds of things that people describe as spooky when they play with something like ChatGPT. That's the underlying term. We don't as an industry quite know why it happens or how it happens, but we can measure that it does. So cloud enables new kinds of math and science. New kinds of math and science allow new kinds of experimentation. And that experimentation has led to this new generation of models. >> So one of the debates we had on theCUBE at our Supercloud event last month was, what's the barriers to entry for say OpenAI, for instance? Obviously, I weighed in aggressively and said, "The barriers for getting into cloud are high because all the CapEx." And Howie Xu formerly VMware, now at ZScaler, he's an AI machine learning guy. He was like, "Well, you can spend $100 million and replicate it." I saw a quote that set up for 180,000 I can get this other package. What's the barriers to entry? Is ChatGPT or OpenAI, does it have sustainability? Is it easy to get into? What is the market like for AI? I mean, because a lot of entrepreneurs are jumping in. I mean, I just read a story today. San Francisco's got more inbound migration because of the AI action happening, Seattle's booming, Boston with MIT's been working on neural networks for generations. That's what we've found the answer. Get off the neural network, Boston jump on the AI bus. So there's total excitement for this. People are enthusiastic around this area. >> You can think of an iPhone versus Android tension that's happening today. In the iPhone world, there are proprietary models from OpenAI who you might consider as the leader. There's Cohere, there's AI21, there's Anthropic, Google's going to have their own, and a few others. These are proprietary models that developers can build on top of, get started really quickly. They're measured to have the highest accuracy and the highest performance today. That's the proprietary side. On the other side, there is an open source part of the world. These are a proliferation of model architectures that developers and practitioners can take off the shelf and train themselves. Typically found in Hugging face. What people seem to think is that the accuracy and performance of the open source models is something like 18 to 20 months behind the accuracy and performance of the proprietary models. But on the other hand, there's infinite flexibility for teams that are capable enough. So you're going to see teams choose sides based on whether they want speed or flexibility. >> That's interesting. And that brings up a point I was talking to a startup and the debate was, do you abstract away from the hardware and be software-defined or software-led on the AI side and let the hardware side just extremely accelerate on its own, 'cause it's flywheel? So again, back to proprietary, that's with hardware kind of bundled in, bolted on. Is it accelerator or is it bolted on or is it part of it? So to me, I think that the big struggle in understanding this is that which one will end up being right. I mean, is it a beta max versus VHS kind of thing going on? Or iPhone, Android, I mean iPhone makes a lot of sense, but if you're Apple, but is there an Apple moment in the machine learning? >> In proprietary models, here does seem to be a jump ball. That there's going to be a virtuous flywheel that emerges that, for example, all these excitement about ChatGPT. What's really exciting about it is it's really easy to use. The technology isn't so different from what we've seen before even from OpenAI. You mentioned a million users in a short period of time, all providing training data for OpenAI that makes their underlying models, their next generation even better. So it's not unreasonable to guess that there's going to be power laws that emerge on the proprietary side. What I think history has shown is that iPhone, Android, Windows, Linux, there seems to be gravity towards this yin and yang. And my guess, and what other people seem to think is going to be the case is that we're going to continue to see these two poles of AI. >> So let's get into the relationship with data because I've been emerging myself with ChatGPT, fascinated by the ease of use, yes, but also the fidelity of how you query it. And I felt like when I was doing writing SQL back in the eighties and nineties where SQL was emerging. You had to be really a guru at the SQL to get the answers you wanted. It seems like the querying into ChatGPT is a good thing if you know how to talk to it. Labeling whether your input is and it does a great job if you feed it right. If you ask a generic questions like Google. It's like a Google search. It gives you great format, sounds credible, but the facts are kind of wrong. >> That's right. >> That's where general consensus is coming on. So what does that mean? That means people are on one hand saying, "Ah, it's bullshit 'cause it's wrong." But I look at, I'm like, "Wow, that's that's compelling." 'Cause if you feed it the right data, so now we're in the data modeling here, so the role of data's going to be critical. Is there a data operating system emerging? Because if this thing continues to go the way it's going you can almost imagine as you would look at companies to invest in. Who's going to be right on this? What's going to scale? What's sustainable? What could build a durable company? It might not look what like what people think it is. I mean, I remember when Google started everyone thought it was the worst search engine because it wasn't a portal. But it was the best organic search on the planet became successful. So I'm trying to figure out like, okay, how do you read this? How do you read the tea leaves? >> Yeah. There are a few different ways that companies can differentiate themselves. Teams with galactic capabilities to take an open source model and then change the architecture and retrain and go down to the silicon. They can do things that might not have been possible for other teams to do. There's a company that that we're proud to be investors in called RunwayML that provides video accelerated, sorry, AI accelerated video editing capabilities. They were used in everything, everywhere all at once and some others. In order to build RunwayML, they needed a vision of what the future was going to look like and they needed to make deep contributions to the science that was going to enable all that. But not every team has those capabilities, maybe nor should they. So as far as how other teams are going to differentiate there's a couple of things that they can do. One is called prompt engineering where they shape on behalf of their own users exactly how the prompt to get fed to the underlying model. It's not clear whether that's going to be a durable problem or whether like Google, we consumers are going to start to get more intuitive about this. That's one. The second is what's called information retrieval. How can I get information about the world outside, information from a database or a data store or whatever service into these models so they can reason about them. And the third is, this is going to sound funny, but attribution. Just like you would do in a news report or an academic paper. If you can state where your facts are coming from, the downstream consumer or the human being who has to use that information actually is going to be able to make better sense of it and rely better on it. So that's prompt engineering, that's retrieval, and that's attribution. >> So that brings me to my next point I want to dig in on is the foundational model stack that you published. And I'll start by saying that with ChatGPT, if you take out the naysayers who are like throwing cold water on it about being a gimmick or whatever, and then you got the other side, I would call the alpha nerds who are like they can see, "Wow, this is amazing." This is truly NextGen. This isn't yesterday's chatbot nonsense. They're like, they're all over it. It's that everybody's using it right now in every vertical. I heard someone using it for security logs. I heard a data center, hardware vendor using it for pushing out appsec review updates. I mean, I've heard corner cases. We're using it for theCUBE to put our metadata in. So there's a horizontal use case of value. So to me that tells me it's a market there. So when you have horizontal scalability in the use case you're going to have a stack. So you publish this stack and it has an application at the top, applications like Jasper out there. You're seeing ChatGPT. But you go after the bottom, you got silicon, cloud, foundational model operations, the foundational models themselves, tooling, sources, actions. Where'd you get this from? How'd you put this together? Did you just work backwards from the startups or was there a thesis behind this? Could you share your thoughts behind this foundational model stack? >> Sure. Well, I'm a recovering product manager and my job that I think about as a product manager is who is my customer and what problem he wants to solve. And so to put myself in the mindset of an application developer and a founder who is actually my customer as a partner at Madrona, I think about what technology and resources does she need to be really powerful, to be able to take a brilliant idea, and actually bring that to life. And if you spend time with that community, which I do and I've met with hundreds of founders now who are trying to do exactly this, you can see that the stack is emerging. In fact, we first drew it in, not in January 2023, but October 2022. And if you look at the difference between the October '22 and January '23 stacks you're going to see that holes in the stack that we identified in October around tooling and around foundation model ops and the rest are organically starting to get filled because of how much demand from the developers at the top of the stack. >> If you look at the young generation coming out and even some of the analysts, I was just reading an analyst report on who's following the whole data stacks area, Databricks, Snowflake, there's variety of analytics, realtime AI, data's hot. There's a lot of engineers coming out that were either data scientists or I would call data platform engineering folks are becoming very key resources in this area. What's the skillset emerging and what's the mindset of that entrepreneur that sees the opportunity? How does these startups come together? Is there a pattern in the formation? Is there a pattern in the competency or proficiency around the talent behind these ventures? >> Yes. I would say there's two groups. The first is a very distinct pattern, John. For the past 10 years or a little more we've seen a pattern of democratization of ML where more and more people had access to this powerful science and technology. And since about 2017, with the rise of the transformer architecture in these foundation models, that pattern has reversed. All of a sudden what has become broader access is now shrinking to a pretty small group of scientists who can actually train and manipulate the architectures of these models themselves. So that's one. And what that means is the teams who can do that have huge ability to make the future happen in ways that other people don't have access to yet. That's one. The second is there is a broader population of people who by definition has even more collective imagination 'cause there's even more people who sees what should be possible and can use things like the proprietary models, like the OpenAI models that are available off the shelf and try to create something that maybe nobody has seen before. And when they do that, Jasper AI is a great example of that. Jasper AI is a company that creates marketing copy automatically with generative models such as GPT-3. They do that and it's really useful and it's almost fun for a marketer to use that. But there are going to be questions of how they can defend that against someone else who has access to the same technology. It's a different population of founders who has to find other sources of differentiation without being able to go all the way down to the the silicon and the science. >> Yeah, and it's going to be also opportunity recognition is one thing. Building a viable venture product market fit. You got competition. And so when things get crowded you got to have some differentiation. I think that's going to be the key. And that's where I was trying to figure out and I think data with scale I think are big ones. Where's the vulnerability in the stack in terms of gaps? Where's the white space? I shouldn't say vulnerability. I should say where's the opportunity, where's the white space in the stack that you see opportunities for entrepreneurs to attack? >> I would say there's two. At the application level, there is almost infinite opportunity, John, because almost every kind of application is about to be reimagined or disrupted with a new generation that takes advantage of this really powerful new technology. And so if there is a kind of application in almost any vertical, it's hard to rule something out. Almost any vertical that a founder wishes she had created the original app in, well, now it's her time. So that's one. The second is, if you look at the tooling layer that we discussed, tooling is a really powerful way that you can provide more flexibility to app developers to get more differentiation for themselves. And the tooling layer is still forming. This is the interface between the models themselves and the applications. Tools that help bring in data, as you mentioned, connect to external actions, bring context across multiple calls, chain together multiple models. These kinds of things, there's huge opportunity there. >> Well, Jon, I really appreciate you coming in. I had a couple more questions, but I will take a minute to read some of your bios for the audience and we'll get into, I won't embarrass you, but I want to set the context. You said you were recovering product manager, 10 plus years at AWS. Obviously, recovering from AWS, which is a whole nother dimension of recovering. In all seriousness, I talked to Andy Jassy around that time and Dr. Matt Wood and it was about that time when AI was just getting on the radar when they started. So you guys started seeing the wave coming in early on. So I remember at that time as Amazon was starting to grow significantly and even just stock price and overall growth. From a tech perspective, it was pretty clear what was coming, so you were there when this tsunami hit. >> Jon: That's right. >> And you had a front row seat building tech, you were led the product teams for Computer Vision AI, Textract, AI intelligence for document processing, recognition for image and video analysis. You wrote the business product plan for AWS IoT and Greengrass, which we've covered a lot in theCUBE, which extends out to the whole edge thing. So you know a lot about AI/ML, edge computing, IOT, messaging, which I call the law of small numbers that scale become big. This is a big new thing. So as a former AWS leader who's been there and at Madrona, what's your investment thesis as you start to peruse the landscape and talk to entrepreneurs as you got the stack? What's the big picture? What are you looking for? What's the thesis? How do you see this next five years emerging? >> Five years is a really long time given some of this science is only six months out. I'll start with some, no pun intended, some foundational things. And we can talk about some implications of the technology. The basics are the same as they've always been. We want, what I like to call customers with their hair on fire. So they have problems, so urgent they'll buy half a product. The joke is if your hair is on fire you might want a bucket of cold water, but you'll take a tennis racket and you'll beat yourself over the head to put the fire out. You want those customers 'cause they'll meet you more than halfway. And when you find them, you can obsess about them and you can get better every day. So we want customers with their hair on fire. We want founders who have empathy for those customers, understand what is going to be required to serve them really well, and have what I like to call founder-market fit to be able to build the products that those customers are going to need. >> And because that's a good strategy from an emerging, not yet fully baked out requirements definition. >> Jon: That's right. >> Enough where directionally they're leaning in, more than in, they're part of the product development process. >> That's right. And when you're doing early stage development, which is where I personally spend a lot of my time at the seed and A and a little bit beyond that stage often that's going to be what you have to go on because the future is going to be so complex that you can't see the curves beyond it. But if you have customers with their hair on fire and talented founders who have the capability to serve those customers, that's got me interested. >> So if I'm an entrepreneur, I walk in and say, "I have customers that have their hair on fire." What kind of checks do you write? What's the kind of the average you're seeing for seed and series? Probably seed, seed rounds and series As. >> It can depend. I have seen seed rounds of double digit million dollars. I have seen seed rounds much smaller than that. It really depends on what is going to be the right thing for these founders to prove out the hypothesis that they're testing that says, "Look, we have this customer with her hair on fire. We think we can build at least a tennis racket that she can use to start beating herself over the head and put the fire out. And then we're going to have something really interesting that we can scale up from there and we can make the future happen. >> So it sounds like your advice to founders is go out and find some customers, show them a product, don't obsess over full completion, get some sort of vibe on fit and go from there. >> Yeah, and I think by the time founders come to me they may not have a product, they may not have a deck, but if they have a customer with her hair on fire, then I'm really interested. >> Well, I always love the professional services angle on these markets. You go in and you get some business and you understand it. Walk away if you don't like it, but you see the hair on fire, then you go in product mode. >> That's right. >> All Right, Jon, thank you for coming on theCUBE. Really appreciate you stopping by the studio and good luck on your investments. Great to see you. >> You too. >> Thanks for coming on. >> Thank you, Jon. >> CUBE coverage here at Palo Alto. I'm John Furrier, your host. More coverage with CUBE Conversations after this break. (upbeat music)

>> From the SiliconANGLE media office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Dave Vellante. >> Hello everyone, and welcome to this CUBE Conversation. You know, theCUBE has been following the trends in the so-called big data space since 2010. And one of the things that we reported on for a number of years is the complexity involved in wrangling and making sense out of data. The allure of this idea of no schema on write and very low cost platforms like Hadoop became a data magnet. And for years, organizations would shove data into a data lake. And of course the joke was it was became a data swamp. And organizations really struggled to realize the promised return on their big data investments. Now, while the cloud certainly simplified infrastructure deployment, it really introduced a much more complex data environment and data pipeline, with dozens of APIs and a mind-boggling array of services that required highly skilled data engineers to properly ingest, shape, and prepare that data, so that it could be turned into insights. This became a real time suck for data pros, who spent 70 to 80% of their time wrestling data. A number of people saw the opportunity to solve this problem and automate the heavy lift of data, and simplify the process to adjust, synchronize, transform, and really prepare data for analysis. And one of the companies that is attacking this challenge is InfoWorks. And with me to talk about the evolving data landscape is Buno Pati, CEO of InfoWorks. Buno, great to see you, thanks for coming in. >> Well thank you Dave, thanks for having me here. >> You're welcome. I love that you're in Palo Alto, you come to MetroWest in Boston to see us (Buno laughs), that's great. Well welcome. So, you heard my narrative. We're 10 years plus into this big data theme and meme. What did we learn, what are some of the failures and successes that we can now build on, from your point of view? >> All right, so Dave, I'm going to start from the top, with why big data, all right? I think this big data movement really started with the realization by companies that they need to transform their customer experience and their operations, in order to compete effectively in this increasingly digital world, right? And in that context, they also realized very quickly that data was the key asset on which this transformation would be built. So given that, you look at this and say, "What is digital transformation really about?" It is about competing with digital disruption, or fending off digital disruption. And this has become, over time, an existential imperative. You cannot survive and be relevant in this world without leveraging data to compete with others who would otherwise disrupt your business. >> You know, let's stay on that for a minute, because when we started the whole big data, covering that big data space, you didn't really hear about digital transformation. That's sort of a more recent trend. So I got to ask you, what's the difference between a business and a digital business, in your view? >> That is the foundational question behind big data. So if you look at a digital native, there are many of them that you can name. These companies start by building a foundational platform on which they build their analytics and data programs. It gives them a tremendous amount of agility and the right framework within which to build a data-first strategy. A data-first strategy where business information is persistently collected and used at every level of the organization. Furthermore, they take this and they automate this process. Because if you want to collect all your data and leverage it at every part of the business, it needs to be a highly automated system, and it needs to be able to seamlessly traverse on-premise, cloud, hybrid, and multi-cloud environments. Now, let's look at a traditional business. In a traditional enterprise, there is no foundational platform. There are things like point tools for ETL, and data integration, and you can name a whole slew of other things, that need to be stitched together and somehow made to work to deliver data to the applications that consume. The strategy is not a data-first strategy. It is use case by use case. When there is a use case, people go and find the data, they gather the data, they transform that data, and eventually feed an application. A process that can take months to years, depending on the complexity of the project that they're trying. And they don't automate this. This is heavily dependent, as you pointed out, on engineering talent, highly skilled engineering talent that is scarce. And they have not seamlessly traversed the various clouds and on-premise environments, but rather fragmented those environments, where individual teams are focused on a single environment, building different applications, using different tools, and different infrastructure. >> So you're saying the digital native company puts data at the core. They organize around that data, as opposed to maybe around a bottling plant, or around people. And then they leverage that data for competitive advantage through a platform that's kind of table stakes. And then obviously there's cultural aspects and other skills that they need to develop, right? >> Yeah, they have an ability which traditional enterprises don't. Because of this choice of a data-first strategy with a foundational platform, they have the ability to rapidly launch analytics use cases and iterate all them. That is not possible in a traditional or legacy environment. >> So their speed to market and time to value is going to be much better than their competition. This gets into the risk of disruption. Sometimes we talk about cloud native and cloud naive. You could talk about digital native and digital naive. So it's hard for incumbents to fend off the disrupters, and then ultimately become disrupters themselves. But what are you seeing in terms of some of the trends where organizations are having success there? >> One of the key trends that we're seeing, or key attributes of companies that are seeing a lot of success, is when they have organized themselves around their data. Now, what do I mean by that? This is usually a high-level mandate coming down from the top of the company, where they're forming centralized groups to manage the data and make it available for the rest of the organization to use. There are a variety of names that are being used for this. People are calling it their data fabric. They're calling it data as a service, which is pretty descriptive of what it ends up being. And those are terms that are all sort of representing the same concept of a centralized environment and, ideally, a highly automated environment that serves the rest of the business with data. And the goal, ultimately, is to get any data at any time for any application. >> So, let's talk a little bit about the cloud. I mentioned up front that the cloud really simplified infrastructure deployment, but it really didn't solve this problem of, we talked about in terms of data wrangling. So, why didn't it solve that problem? And you got companies like Amazon and Google and Microsoft, who are very adept at data. They're some of these data-first companies. Why is it that the cloud sort of in and of itself has not been able to solve this problem? >> Okay, so when you say solve this problem, it sort of begs the question, what's the goal, right? And if I were to very simply state the goal, I would call it analytics agility. It is gaining agility with analytics. Companies are going from a traditional world, where they had to generate a handful of BI and other reporting type of dashboards in a year, to where they literally need to generate thousands of these things in a year, to run the business and compete with digital disruption. So agility is the goal. >> But wait, the cloud is all about agility, is it not? >> It is, when you talk about agility of compute and storage infrastructure. So, there are three layers to this problem. The first is, what is the compute and storage infrastructure? The cloud is wonderful in that sense. It gives you the ability to rapidly add new infrastructure and spin it down when it's not in use. That is a huge blessing, when you compare it to the six to nine months, or perhaps even longer, that it takes companies to order, install, and test hardware on premise, and then find that it's only partially used. The next layer on that is what is the operating system on which my data and analytics are going to be run? This is where Hadoop comes in. Now, Hadoop is inherently complex, but operating systems are complex things. And Spark falls in that category. Databricks has taken some of the complexity out of running Spark because of their sort of manage service type of offering. But there's still a missing layer, which leverages that infrastructure and that operating system to deliver this agility where users can access data that they need anywhere in the organization, without intensely deep knowledge of what that infrastructure is and what that operating system is doing underneath. >> So, in my up front narrative, I talked about the data pipeline a little bit. But I'm inferring from your comments on platform that it's more than just this sort of narrow data pipeline. There's a macro here. I wonder if you could talk about that a little bit. >> Yeah. So, the data pipeline is one piece of the puzzle. What needs to happen? Data needs to be ingested. It needs to be brought into these environments. It has to be kept fresh, because the source data is persistently changing. It needs to be organized and cataloged, so that people know what's there. And from there, pipelines can be created that ultimately generate data in a form that's consumable by the application. But even surrounding that, you need to be able to orchestrate all of this. Typical enterprise is a multi-cloud enterprise. 80% of all enterprises have more than one cloud that they're working on, and on-premise. So if you can't orchestrate all of this activity in the pipelines, and the data across these various environments, that's not a complete solution either. There's certainly no agility in that. Then there's governance, security, lineage. All of this has to be managed. It's not simply creation of the pipeline, but all these surrounding things that need to happen in order for analytics to run at-scale within enterprises. >> So the cloud sort of solved that layer one problem. And you certainly saw this in the, not early days, but sort of mid-days of Hadoop, where the cloud really became the place where people wanted to do a lot of their Hadoop workloads. And it was kind of ironic that guys like Hortonworks, and Cloudera and MapR really didn't have a strong cloud play. But now, it's sort of flipping back where, as you point out, everybody's multi-cloud. So you have to include a lot of these on-prem systems, whether it's your Oracle database or your ETL systems or your existing data warehouse, those are data feeds into the cloud, or the digital incumbent who wants to be a digital native. They can't just throw all that stuff away, right? So you're seeing an equilibrium there. >> An equilibrium between ... ? >> Yeah, between sort of what's in the cloud and what's on-prem. Let me ask it this way: If the cloud is not a panacea, is there an approach that does really solve the problem of different datasets, the need to ingest them from different clouds, on-prem, and bring them into a platform that can be analyzed and drive insights for an organization? >> Yeah, so I'm going to stay away from the word panacea, because I don't think there ever is really a panacea to any problem. >> That's good, that means we got a good roadmap for our business then. (both laugh) >> However, there is a solution. And the solution has to be guided by three principles. Number one, automation. If you do not automate, the dependence on skill talent is never going to go away. And that talent, as we all know, is very very scarce and hard to come by. The second thing is integration. So, what's different now? All of these capabilities that we just talked about, whether it's things like ETL, or cataloging, or ingesting, or keeping data fresh, or creating pipelines, all of this needs to be integrated together as a single solution. And that's been missing. Most of what we've seen is point tools. And the third is absolutely critical. For things to work in multi-cloud and hybrid environments, you need to introduce a layer of abstraction between the complexity of the underlying systems and the user of those systems. And the way to think about this, Dave, is to think about it much like a compiler. What does a compiler do, right? You don't have to worry about what Intel processor is underneath, what version of your operating system you're running on, what memory is in the system. Ultimately, you might-- >> As much as we love assembly code. >> As much as we love assembly code. Now, so take the analogy a little bit further, there was a time when we wrote assembly code because there was no compiler. So somebody had to sit back and say, "Hey, wouldn't it be nice if we abstracted away from this?" (both laugh) >> Okay, so this sort of sets up my next question, which is, is this why you guys started InfoWorks? Maybe you could talk a little bit about your why, and kind of where you fit. >> So, let me give you the history of InfoWorks. Because the vision of InfoWorks, believe it or not, came out of a rear view mirror. Looking backwards, not forwards. And then predicting the future in a different manner. So, Amar Arsikere is the founder of InfoWorks. And when I met him, he had just left Zynga, where he was the general manager of their gaming platform. What he told me was very very simple. He said he had been at Google at a time when Google was moving off of the legacy systems of, I believe it was Netezza, and Oracle, and a variety of things. And they had just created Bigtable, and they wanted to move and create a data warehouse on Bigtable. So he was given that job. And he led that team. And that, as you might imagine, was this massive project that required a high degree of automation to make it all come together. And he built that, and then he built a very similar system at Zynga, when he was there. These foundational platforms, going back to what I was talking about before digital days. When I met him, he said, "Look, looking back, "Google may have been the only company "that needed such a platform. "But looking forward, "I believe that everyone's going to need one." And that has, you know, absolute truth in it, and that's what we're seeing today. Where, after going through this exercise of trying to write machine code, or assembly code, or whatever we'd like to call it, down at the detailed, complex level of an operating system or infrastructure, people have realized, "Hey, I need something much more holistic. "I need to look at this from a enterprise-wide perspective. "And I need to eliminate all of this dependence on," kind of like the cloud plays a role because it eliminates some of the dependence, or the bottlenecks around hardware and infrastructure. "And ultimately gain a lot more agility "than I'm able to do with legacy methodology." So you were asking early on, what are the lessons learned from that first 10 years? And lot of technology goes through these types of cycles of hype and disillusionment, and we all know the curve. I think there are two key lessons. One is, just having a place to land your data doesn't solve your problem. That's the beginning of your problems. And the second is that legacy methodologies do not transfer into the future. You have to think differently. And looking to the digital natives as guides for how to think, when you're trying to compete with them is a wonderful perspective to take. >> But those legacy technologies, if you're an incumbent, you can't just rip 'em and throw 'em out and convert. You going to use them as feeders to your digital platform. So, presumably, you guys have products. You call this space Enterprise Data Ops and Orchestration, EDO2. Presumably you have products and a portfolio to support those higher layer challenges that we talked about, right? >> Yeah, so that's a really important question. No, you don't rip and replace stuff. These enterprises have been built over years of acquisitions and business systems. These are layers, one on top of another. So think about the introduction of ERP. By the way, ERP is a good analogy of to what happened, because those were point tools that were eventually combined into a single system called ERP. Well, these are point capabilities that are being combined into a single system for EDO2, or Enterprise Data Operations and Orchestration. The old systems do not go away. And we are seeing some companies wanting to move some of their workloads from old systems to new systems. But that's not the major trend. The major trend is that new things that get done, the things that give you holistic views of the company, and then analytics based on that holistic view, are all being done on the new platforms. So it's a layer on top. It's not a rip and replace of the layers underneath. What's in place stays in place. But for the layer on top, you need to think differently. You cannot use all the legacy methodologies and just say that's going to apply to the new platform or new system. >> Okay, so how do you engage with customers? Take a customer who's got, you know, on-prem, they've got legacy infrastructure, they don't want to get disrupted. They want to be a digital native. How do you help them? You know, what do I buy from you? >> Yeah, so our product is called DataFoundry. It is a EDO2 system. It is built on the three principles, founding principles, that I mentioned earlier. It is highly automated. It is integrated in all the capabilities that surround pipelines, perhaps. And ultimately, it's also abstracting. So we're able to very easily traverse one cloud to another, or on-premise to the cloud, or even back. There are some customers that are moving some workloads back from the cloud. Now, what's the benefit here? Well first of all, we lay down the foundation for digital transformation. And we enable these companies to consolidate and organize their data in these complex hybrid, cloud, multi-cloud environments. And then generate analytics use cases 10x faster with about tenth of the resource. And I'm happy to give you some examples on how that works. >> Please do. I mean, maybe you could share some customer examples? >> Yeah, absolutely. So, let me talk about Macy's. >> Okay. >> Macy's is a customer of ours. They've been a customer for about, I think about 14 months at this point in time. And they had built a number of systems to run their analytics, but then recognized what we're seeing other companies recognize. And that is, there's a lot of complexity there. And building it isn't the end game. Maintaining it is the real challenge, right? So even if you have a lot of talent available to you, maintaining what you built is a real challenge. So they came to us. And within a period of 12 months, I'll just give you some numbers that are just mind-blowing. They are currently running 165,000 jobs a month. Now, what's a job? A job is a ingestion job, or a synchronization job, or a transformation. They have launched 431 use cases over a period of 12 months. And you know what? They're just ramping. They will get to thousands. >> Scale. >> Yeah, scale. And they have ingested a lot of data, brought in a lot of DataSources. So to do that in a period of 12 months is unheard of. It does not happen. Why is it important for them? So what problem are they trying to solve? They're a retailer. They are being digitally disruptive like (chuckles) no one else. >> They have an Amazon war room-- >> Right. >> No doubt. >> And they have had to build themselves out as a omni-channel retailer now. They are online, they are also with brick and mortar stores. So you take a look at this. And the key to competing with digital disrupters is the customer experience. What is that experience? You're online, how does that meld with your in-store experience? What happens if I buy online and return something in a store? How does all this come together into a single unified experience for the consumer? And that's what they're chasing. So that was the first application that they came to us with. They said, "Look, let us go into a customer 360. "Let us understand the entirety "of that customer's interaction "and touchpoints with our business. "And having done so, we are in a position "to deliver a better experience." >> Now that's a data problem. I mean, different DataSources, and trying to understand 360, I mean, you got data all over the place. >> All over the place. (speaking simultaneously) And there's historical data, there's stuff coming in from, you know, what's online, what's in the store. And then they progress from there. I mean, they're not restricting it to customer experience and selling. They're looking at merchandising, and inventory, and fulfillment, and store operations. Simple problem. You order something online, where do I pull this from? A store or a warehouse? >> So this is, you know, big data 2.0, just to use a sort of silly term. But it's really taking advantage of all the investment. I've often said, you know, Hadoop, for all the criticism it gets, it did lower our cost of getting data into, you know, at least one virtual place. And it got us thinking about how to get insights out of data. And so, what you're describing is the ability to operationalize your data initiatives at scale. >> Yeah, you can absolutely get your insights off of Hadoop. And I know people have different opinions of Hadoop, given their experience. But what they don't have, what these customers have not achieved yet, most of them, is that agility, right? So, how easily can you get your insights off of Hadoop? Do I need to hire a boatload of consultants who are going to write code for me, and shovel data in, and create these pipelines, and so forth? Or can I do this with a click of a button, right? And that's the difference. That is truly the difference. The level of automation that you need, and the level of abstraction that you need, away from this complexity, has not been delivered. >> We did, in, it must have been 2011, I think, the very first big data market study from anybody in the world, and put it out on, you know, Wikibon, free research. And one of the findings was (chuckles) this is a huge services business. I mean, the professional service is where all the money was going to flow because it was so complicated. And that's kind of exactly what happened. But now we're entering, really it seems like a phase where you can scale, and operationalize, and really simplify, and really focus your attention on driving business value, versus making stuff work. >> You are absolutely correct. So I'll give you the numbers. 55% of this industry is services. About 30% is software, and the rest is hardware. Break it down that way. 55%. So what's going on? People will buy a big data system. Call it Hadoop, it could be something in the cloud, it could be Databricks. And then, this is welcome to the world of SIs. Because at this point, you need these SIs to write code and perform these services in order to get any kind of value out of that. And look, we have some dismal numbers that we're staring at. According to Gardner, only 17% of those who have invested in Hadoop have anything in production. This is after how many years? And you look at surveys from, well, pick your favorite. They all look the same. People have not been able to get the value out of this, because it is too hard. It is too complex and you need too many consultants (laughs) delivering services for you to make this happen. >> Well, what I like about your story, Buno, is you're not, I mean, a lot of the data companies have pivoted to AI. Sort of like, we have a joke, ya know, same wine, new bottle. But you're not talking about, I mean sure, machine intelligence, I'm sure, fits in here, but you're talking about really taking advantage of the investments that you've made in the last decade and helping incumbents become digital natives. That sounds like it's at least a part of your mission here. >> Not become digital natives, but rather compete with them. >> Yeah, right, right. >> Effectively, right? >> Yep, okay. >> So, yeah, that is absolutely what needs to get done. So let me talk for a moment about AI, all right? Way back when, there was another wave of AI in the late 80s. I was part of that, I was doing my PhD at the time. And that obviously went nowhere, because we didn't have any data, we didn't have enough compute power or connectivity. Pretty inert. So here it is again. Very little has changed. Except for we do have the data, we have the connectivity, and we have the compute power. But do we really? So what's AI without the data? Just A, right? There's nothing there. So what's missing, even for AI and ML to be, and I believe these are going to be powerful game changers. But for them to be effective, you need to provide data to it, and you need to be able to do so in a very agile way, so that you can iterate on ideas. No one knows exactly what AI solution is going to solve your problem or enhance your business. This is a process of experimentation. This is what a company like Google can do extraordinarily well, because of this foundational platform. They have this agility to keep iterating, and experimenting, and trying ideas. Because without trying them, you will not discover what works best. >> Yeah, I mean, for 50 years, this industry has marched to the cadence of Moore's Law, and that really was the engine of innovation. And today, it's about data, applying machine intelligence to that data. And the cloud brings, as you point out, agility and scale. That's kind of the new cocktail for innovation, isn't it? >> The cloud brings agility and scale to the infrastructure. >> In low risk, as you said, right? >> Yeah. >> Experimentation, fail fast, et cetera. >> But without an EDO2 type of system, that gives you a great degree of automation, you could spend six months to run one experiment with AI. >> Yeah, because-- >> In gathering data and feeding it to it. >> 'Cause if the answer is people and throwing people at the problem, then you're not going to scale. >> You're not going to scale, and you're never going to really leverage AI and ML capabilities. You need to be able to do that not in six months, in six days, right, or less. >> So let's talk about your company a little bit. Can you give us the status, you know, where you're at? As their newly minted CEO, what your sort of goals are, milestones that we should be watching in 2020 and beyond? >> Yeah, so newly minted CEO, I came in July of last year. This has been an extraordinary company. I started my journey with this company as an investor. And it was funded by actually two funds that I was associated with, first being Nexus Venture Partners, and then Centerview Capital, where I'm still a partner. And myself and my other two partners looked at the opportunity and what the company had been able to do. And in July of last year, I joined as CEO. My partner, David Dorman, who used to be CEO of AT&T, he joined as chairman. And my third partner, Ned Hooper, joined as President and Chief Operating Officer. Ned used to be the Chief Strategy Officer of Cisco. So we pushed pause on the funding, and that's about as all-in as a fund can get. >> Yeah, so you guys were operational experts that became investors, and said, "Okay, we're going to dive back in "and actually run the business." >> And here's why. So we obviously see a lot of companies as investors, as they go out and look for funding. There are three things that come together very rarely. One is a massive market opportunity combined with the second, which is the right product to serve that opportunity. But the third is pure luck, timing. (Dave chuckles) It's timing. And timing, you know, it's a very very challenging thing to try to predict. You can get lucky and get it right, but then again, it's luck. This had all three. It was the absolute perfect time. And it's largely because of what you described, the 10 years of time that had elapsed, where people had sort of run the experiment and were not going to get fooled again by how easy this supposed to be by just getting one piece or the other. They recognized that they need to take this holistic approach and deploy something as an enterprise-wide platform. >> Yeah, I mean, you talk about a large market, I don't even know how you do a TAM, what's the TAM? It's data. (laughs) You know, it's the data universe, which is just, you know, massive. So, I have to ask you a question as an investor. I think you've raised, what 50 million, is that right? >> We've raised 50 million. The last round was led by NEA. >> Right, okay. You got great investors, hefty amount. Although, you know, in this day and age, you know, you're seeing just outrageous amounts being raised. Software obviously is a capital efficient business, but today you need to raise a lot of money for promotion, right, to get your name out there. What's your thoughts on, as a Silicon Valley investor, as this wave, I mean, get it while you can, I guess. You know, we're in the 10th year of this boom market. But your thoughts? >> You're asking me to put on my other hat. (Dave laughs) I think companies have, in general, raised too much money at too high a value too fast. And there's a penalty for that. And the down round IPO, which has become fashionable these days, is one of those penalties. It's a clear indication. Markets are very rational, public markets are very rational. And the pricing in a public market, when it's significantly below the pricing of in a private market, is telling you something. So, we are a little old-fashioned in that sense. We believe that a company has to lay down the right foundation before it adds fuel to the mix and grows. You have to have evidence that the machinery that you build, whether it's for sales, or marketing, or other go-to-market activities, or even product development, is working. And if you do not see all of those signs, you're building a very fragile company. And adding fuel in that setting is like flooding the carburetor. You don't necessarily go faster. (laughs) You just-- >> Consume more. >> You consume more. So there's a little bit of, perhaps, old-fashioned discipline that we bring to the table. And you can argue against it. You can say, "Well, why don't you just raise a lot of money, "hire a lot of sales guys, and hope for the best?" >> See what sticks? (laughs) >> Yeah. We are fully expecting to build a large institution here. And I use that word carefully. And for that to happen, you need the right foundation down first. >> Well, that resonates with us east coast people. So, Buno, thanks very much for comin' on theCUBE and sharing with us your perspectives on the marketplace. And best of luck with InfoWorks. >> Thank you, Dave. This has been a pleasure. Thank you for having me here. >> All right, we'll be watching, thank you. And thank you for watching, everybody. This is Dave Vellante for theCUBE. We'll see ya next time. (upbeat music fades out)

(upbeat techno music) >> Narrator: Live, from San Jose, it's theCUBE! Presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partners. >> Welcome back to Big Data SV, everybody. My name is Dave Vellante and this is theCUBE, the leader in live tech coverage. You know, this is our 10th big data event. When we first started covering big data, back in 2010, it was Hadoop, and everything was a batch job. About four or five years ago, everybody started talking about real time and the ability to affect outcomes before you lose the customer. Lewis Kaneshiro was here. He's the CEO of Streamlio and he's joined by Karthik Ramasamy who's the chief product officer. They're both co-founders. Gentlemen, welcome to theCUBE. My first question is, why did you start this company? >> Sure, we came together around a vision that enterprises need to access the value around fast data. And so as you mentioned, enterprises are moving out of the slow data era, and looking for a fast data value to their data, to really deliver that back to their users or their use cases. And so, coming together around that idea of real time action what we did was we realized that enterprises can't all access this data with projects right now that are not meant to work together, that are very difficult, perhaps, to stitch together. So what we did was create an intelligent platform for fast data that's really accessible to enterprises of all sizes. What we do is we unify the core components to access fast data, which is messaging, compute and stream storage, accessing the best of breed open-source technology that's really open-source out of Twitter and Yahoo! >> It's a good thing I was going to ask why does the world need to know there are, you know, streaming platforms, but Lewis kind of touched on it, 'cause it's too hard. It's too complicated, so you guys are trying to simplify all that. >> Yep, the reason mainly we wanted to simplify it because, based on all our experiences at Twitter and Yahoo! one of the key aspects was to to simplify it so that it's conceivable by regular enterprise because Twitter and Yahoo! kind of our position can afford the talent and the expertise in order to do this real time platforms. But when it goes to normal enterprises, they don't have access to the expertise and the cost benefits that they might have to reincur. So, because of that we wanted to use these open-source projects, the Twitter and the Yahoo!'s provider, combine them, and make sure that you have a simple, easy, drag and drop kind of interface, so that it's easily conceivable for any enterprise. Essentially, what we are trying to do is reduce the (mumbles) for enterprises for real time, for all enterprises. >> Dave: Yeah, enterprises will pay up... >> Yes. >> For a solution. The companies that you used to work for, they all gladly throw engineering at the problem. >> Yeah. >> Sure. >> To save time, but most organizations, they don't have the resources and so. Okay, so how does it, would it work prior to Streamlio? Maybe take us through sort of how a company would attack this problem, the complexities of what they have to deal with, and what life is like with you guys. >> So, current state of the world is it's fragmented solution, today. So the state of the world is where you take multiple pieces of different projects and you'd assemble them together in formats so that you can do (mumbles) right? So the reason why people end up doing is each of these big data projects that people use was the same for completely different purpose. Like messaging is one, and compute is another one, and third one is storage one. So, essentially what we have done as company is to simplify this aspect by integrating this well-known, best-of-the-breed projects called, for messaging we use something called Apache Poser, for compute we use something called Apache Krem, from Twitter, and similarly for storage, for real time storage, we use something called Apache Bookkeeper, so and to unify them, so that, under the hoods, it may be three systems, but, as a user, when you are using it, it serves or functions as a single system. So you install the system, and ingest your data, express your computation, and get the results out, in one single system. >> So you've unified or converged these functions. If I understand it correctly, we talking off camera a little bit, the team, Lewis, that you've assembled actually developed a lot of these, or hugely committed to these open-source projects, right? >> Absolutely, co-creators of each of the projects and what that allows us to do is to really integrate, at a deep level, each project. For example, Pulsar is actually a pub/sub system that is built on Bookkeeper, and Bookkeeper, in our minds, is a pure list best-of-breed stream storage solution. So, fast and durable storage. That storage is also used in Apache Heron to store State. So, as you can see, enterprises, rather than stitching together multiple different solutions for queuing, streaming, compute, and storage, now have one option that they can install in a very small cluster, and operationally it's very simple to scale up. We simply add nodes if you get data spikes. And what this allows is enterprises to access new and exciting use cases that really weren't possible before. For example, machine learning model deployment to real time. So I'm a data scientist and what I found is in data science, you spend a lot of time training models in batch mode. It's a legacy type of approach, but once the model is trained, you want to put that model into production in real time so that you can deliver that value back to a user in real time. Let's call it under two second SLA. So, that has been a great use case for Streamlio because we are a ready made intelligent platform for fast data, for MLai deployment. >> And the use cases are typically stateful and your persisting data, is that right? >> Yes, use cases, it can be used for stateless use cases also, but the key advantage that we bring to a table is stateful storage. And since we ship along with the storage (mumbles) stateful storage becomes much easier because of the fact that it can be used to store a real intermediate state of the computation or it can be used for the staging (mumbles) data when it spills over from what the memory is it's automatically stored to disk or you can even in the data for as long as you want so that you can unlock the value later after the data has been processed for the fast data. You can access the lazy data later, in time. >> So give us the run-down on the company, funding, you know, VCs, head count. Give us the basics. >> Sure, we raise Series A from Lightspeed Venture Partners, lead by John Vrionis and Sudip Chakrabarti. We've raised seven and a half million and emerged from stealth back in August. That allowed us to ramp up our team to 17, now, mainly engineers, in order to really have a very solid product, but we launched post rev, prelaunch and some of our customers are really looking at geo replication across multiple data centers and so active, active geo replication is an open-source feature in Apache Pulsar, and that's been a huge draw, compared to some other solutions that are out there. As you can see, this theme of simplifying architecture is where Streamlio sits, so unifying, queuing and streaming allows us to replace a number of different legacy systems. So that's been one avenue to help growth. The other, obviously is on the compute piece. As enterprises are finding new and exciting use cases to deliver back to their users, the compute piece needs to scale up and down. We also announce Pulsar Functions, which is stream-native compute that allows very simple function computation in native Python and Java, so you spin out the Apache Python cluster or Streamlio platform, and you simply have compute functionality. That allows us to access edge use cases, so IOT is a huge, kind of exciting POC's for us right now where we have connected car examples that don't need heavyweight schedule or deployment at the edge. It's Pulsar Pulsar functions. What that allows us to do are things like fraud detection, anomaly detection at the edge, model deployment at the edge, interpolation, observability, and alerts. >> And, so how do you charge for this? Is it usage based. >> Sure. What we found is enterprise are more comfortable on a per node basis, simply because we have the ambition to really scale up and help enterprises really use Streamlio as their fast data platform across the entire enterprise. We found that having a per data charge rate actually would limit that growth, and so per node and shared architecture. So, we took an early investment in optimizing around Kubernetes. And so, as enterprises are adopting Kubernetes, we are the most simple installation on Kubernetes, so on-prem, multicloud, at the edge. >> I love it, so I mean for years we've just been talking about the complexity headwinds in this big data space. We certainly saw that with Hadoop. You know, Spark was designed to certainly solve some of those problems, but. Sounds like you're doing some really good work to take that further. Lewis and Karthik, thank you so much for coming on theCUBE. I really appreciate it. >> Thanks for having us, Dave. >> All right, thank you for watching. We're here at Big Data SV, live from San Jose. We'll be right back. (techno music)

>> Narrator: From the Silicon Angle Media Office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Stu Miniman. >> Hi, I'm Stu Miniman and we're here at the east coast studio for Silicon Angle Media. Happy to welcome back to the program, a many time guest, Chris Selland, who is now the Vice President of strategic growth with Unifi Software. Great to see you Chris. >> Thanks so much Stu, great to see you too. >> Alright, so Chris, we'd had you in your previous role many times. >> Chris: Yes >> I think not only is the first time we've had you on since you made the switch, but also first time we've had somebody from Unifi Software on. So, why don't you give us a little bit of background of Unifi and what brought you to this opportunity. >> Sure, absolutely happy to sort of open up the relationship with Unifi Software. I'm sure it's going to be a long and good one. But I joined the company about six months ago at this point. So I joined earlier this year. I actually had worked with Unifi for a bit as partners. Where when I was previously at the Vertica business inside of HP/HP, as you know for a number of years prior to that, where we did all the work together. I also knew the founders of Unifi, who were actually at Greenplum, which was a direct Vertica competitor. Greenplum is acquired by EMC. Vertica was acquired by HP. We were sort of friendly respected competitors. And so I have known the founders for a long time. But it was partly the people, but it was really the sort of the idea, the product. I was actually reading the report that Peter Burris or the piece that Peter Burris just did on I guess wikibon.com about distributed data. And it played so into our value proposition. We just see it's where things are going. I think it's where things are going right now. And I think the market's bearing that out. >> The piece you reference, it was actually, it's a Wikibon research meeting, we run those weekly. Internally, we're actually going to be doing them soon we will be broadcasting video. Cause, of course, we do a lot of video. But we pull the whole team together, and it was one, George Gilbert actually led this for us, talking about what architectures do I need to build, when I start doing distributed data. With my background really more in kind of the cloud and infrastructure world. We see it's a hybrid, and many times a multi-cloud world. And, therefore, one of the things we look at that's critical is wait, if I've got things in multiple places. I've got my SAS over here, I've got multiple public clouds I'm using, and I've got my data center. How do I get my arms around all the pieces? And of course data is critical to that. >> Right, exactly, and the fact that more and more people need data to do their jobs these days. Working with data is no longer just the area where data scientists, I mean organizations are certainly investing in data scientists, but there's a shortage, but at the same time, marketing people, finance people, operations people, supply chain folks. They need data to do their jobs. And as you said where it is, it's distributed, it's in legacy systems, it's in the data center, it's in warehouses, it's in SAS applications, it's in the cloud, it's on premise, It's all over the place, so, yep. >> Chris, I've talked to so many companies that are, everybody seems to be nibbling at a piece of this. We go to the Amazon show and there's this just ginormous ecosystem that everybody's picking at. Can you drill in a little bit for what problems do you solve there. I have talked to people. Everything from just trying to get the licensing in place, trying to empower the business unit to do things, trying to do government compliance of course. So where's Unifi's point in this. >> Well, having come out of essentially the data warehousing market. And now of course this has been going on, of course with all the investments in HDFS, Hadoop infrastructure, and open source infrastructure. There's been this fundamental thinking that, well the answer's if I get all of the data in one place then I can analyze it. Well that just doesn't work. >> Right. >> Because it's just not feasible. So I think really and its really when you step back it's one of these like ah-ha that makes total sense, right. What we do is we basically catalog the data in place. So you can use your legacy data that's on the main frame. Let's say I'm a marketing person. I'm trying to do an analysis of selling trends, marketing trends, marketing effectiveness. And I want to use some order data that's on the main frame, I want some click stream data that's sitting in HDFS, I want some customer data in the CRM system, or maybe it's in Sales Force, or Mercado. I need some data out of Workday. I want to use some external data. I want to use, say, weather data to look at seasonal analysis. I want to do neighborhooding. So, how do I do that? You know I may be sitting there with Qlik or Tableau or Looker or one of these modern B.I. products or visualization products, but at the same time where's the data. So our value proposition it starts with we catalog the data and we show where the data is. Okay, you've got these data sources, this is what they are, we describe them. And then there's a whole collaboration element to the platform that lets people as they're using the data say, well yes that's order data, but that's old data. So it's good if you use it up to 2007, but the more current data's over here. Do things like that. And then we also then help the person use it. And again I almost said IT, but it's not real data scientists, it's not just them. It's really about democratizing the use. Because business people don't know how to do inner and outer joins and things like that or what a schema is. They just know, I'm trying do a better job of analyzing sales trends. I got all these different data sources, but then once I found them, once I've decided what I want to use, how do I use them? So we answer that question too. >> Yea, Chris reminds me a lot of some the early value propositions we heard when kind of Hadoop and the whole big data wave came. It was how do I get as a smaller company, or even if I'm a bigger company, do it faster, do it for less money than the things it use to be. Okay, its going to be millions of dollars and it's going to take me 18 months to roll out. Is it right to say this is kind of an extension of that big data wave or what's different and what's the same? >> Absolutely, we use a lot of that stuff. I mean we basically use, and we've got flexibility in what we can use, but for most of our customers we use HDFS to store the data. We use Hive as the most typical data form, you have flexibility around there. We use MapReduce, or Spark to do transformation of the data. So we use all of those open source components, and as the product is being used, as the platform is being used and as multiple users, cause it's designed to be an enterprise platform, are using it, the data does eventually migrate into the data lake, but we don't require you to sort of get it there as a prerequisite. As I said, this is one of the things that we really talk about a lot. We catalog the data where it is, in place, so you don't have to move it to use it, you don't have to move it to see it. But at the same time if you want to move it you can. The fundamental idea I got to move it all first, I got to put it all in one place first, it never works. We've come into so many projects where organizations have tried to do that and they just can't, it's too complex these days. >> Alright, Chris, what are some of the organizational dynamics you're seeing from your customers. You mention data scientist, the business users. Who is identifying, whose driving this issues, whose got the budget to try to fix some of these challenges. >> Well, it tends to be our best implementations are driven really, almost all of them these days, are driven by used cases. So they're driven by business needs. Some of the big ones. I've sort of talked about customers already, but like customer 360 views. For instance, there's a very large credit union client of ours, that they have all of their data, that is organized by accounts, but they can't really look at Stu Miniman as my customer. How do I look at Stu's value to us as a customer? I can look at his mortgage account, I can look at his savings account, I can look at his checking account, I can look at his debit card, but I can't just see Stu. I want to like organize my data, that way. That type of customer 360 or marketing analysis I talked about is a great use case. Another one that we've been seeing a lot of is compliance. Where just having a better handle on what data is where it is. This is where some of the governance aspects of what we do also comes into play. Even though we're very much about solving business problems. There's a very strong data governance. Because when you are doing things like data compliance. We're working, for instance, with MoneyGram, is a customer of ours. Who this day and age in particular, when there's money flows across the borders, there's often times regulators want to know, wait that money that went from here to there, tell me where it came from, tell me where it went, tell me the lineage. And they need to be able to respond to those inquiries very very quickly. Now the reality is that data sits in all sorts of different places, both inside and outside of the organization. Being able to organize that and give the ability to respond more quickly and effectively is a big competitive advantage. Both helps with avoiding regulatory fines, but also helps with customers responsiveness. And then you've got things GDPR, the General Data Protection Regulation, I believe it is, which is being driven by the EU. Where its sort of like the next Y2K. Anybody in data, if they are not paying attention to it, they need to be pretty quick. At least if they're a big enough company they're doing business in Europe. Because if you are doing business with European companies or European customers, this is going to be a requirement as of May next year. There's a whole 'nother set of how data's kept, how data's stored, what customers can control over data. Things like 'Right to Be Forgotten'. This need to comply with regulatory... As data's gotten more important, as you might imagine, the regulators have gotten more interested in what organizations are doing with data. Having a framework with that, organizes and helps you be more compliant with those regulations is absolutely critical. >> Yeah, my understanding of GDPR, if you don't comply, there's hefty fines. >> Chris: Major Fines. >> Major Fines. That are going to hit you. Does Unifi solve that? Is there other re-architecture, redesign that customers need to do to be able to be compliant? [speaking at The same Time] >> No, no that's the whole idea again where being able to leave the data where it is, but know what it is and know where it is and if and when I need to use it and where it came from and where it's going and where it went. All of those things, so we provide the platform that enables the customers to use it or the partners to build the solutions for their customers. >> Curious, customers, their adoption of public cloud, how does that play into what you are doing? They deploy more SAS environments. We were having a conversation off camera today talking about the consolidation that's happening in the software world. What does those dynamics mean for your customers? >> Well public cloud is obviously booming and growing and any organization has some public cloud infrastructure at this point, just about any organization. There's some very heavily regulated areas. Actually health care's probably a good example. Where there's very little public cloud. But even there we're working with... we're part of the Microsoft Accelerator Program. Work very closely with the Azure team, for instance. And they're working in some health care environments, where you have to be things like HIPAA compliant, so there is a lot of caution around that. But none the less, the move to public cloud is certainly happening. I think I was just reading some stats the other day. I can't remember if they're Wikibon or other stats. It's still only about 5% of IT spending. And the reality is organizations of any size have plenty of on-prem data. And of course with all the use of SAS solutions, with Salesforce, Workday, Mercado, all of these different SAS applications, it's also in somebody else's data center, much of our data as well. So it's absolutely a hybrid environment. That's why the report that you guys put out on distributed data, really it spoke so much to what out value proposition is. And that's why you know I'm really glad to be here to talk to you about it. >> Great, Chris tell us a little bit, the company itself, how many employees you have, what metrics can you share about the number of customers, revenue, things like that. >> Sure, no, we've got about, I believe about 65 people at the company right now. I joined like I said earlier this year, late February, early March. At that point we we were like 40 people, so we've been growing very quickly. I can't get in too specifically to like our revenue, but basically we're well in the triple digit growth phase. We're still a small company, but we're growing quickly. Our number of customers it's up in the triple digits as well. So expanding very rapidly. And again we're a platform company, so we serve a variety of industries. Some of the big ones are health care, financial services. But even more in the industries it tends to be driven by these used cases I talked about as well. And we're building out our partnerships also, so that's a big part of what I do also. >> Can you share anything about funding where you are? >> Oh yeah, funding, you asked about that, sorry. Yes, we raised our B round of funding, which closed in March of this year. So we [mumbles], a company called Pelion Venture Partners, who you may know, Canaan Partners, and then most recently Scale Venture Partners are investors. So the companies raised a little over $32 million dollars so far. >> Partnerships, you mentioned Microsoft already. Any other key partnerships you want to call out? >> We're doing a lot of work. We have a very broad partner network, which we're building up, but some of the ones that we are sort of leaning in the most with, Microsoft is certainly one. We're doing a lot of work guys at Cloudera as well. We also work with Hortonworks, we also work with MapR. We're really working almost across the board in the BI space. We have spent a lot of time with the folks at Looker. Who was also a partner I was working with very closely during my Vertica days. We're working with Qlik, we're working with Tableau. We're really working with actually just about everybody in sort of BI and visualization. I don't think people like the term BI anymore. The desktop visualization space. And then on public cloud, also Google, Amazon, so really all the kind of major players. I would say that they're the ones that we worked with the most closely to date. As I mentioned earlier we're part of the Microsoft Accelerator Program, so we're certainly very involved in the Microsoft ecosystem. I actually just wrote a blog post, which I don't believe has been published yet, about some of the, what we call the full stack solutions we have been rolling out with Microsoft for a few customers. Where we're sitting on Azure, we're using HDInsight, which is essentially Microsoft's Hadoop cloud Hadoop distribution, visualized empower BI. So we've really got to lot of deep integration with Microsoft, but we've got a broad network as well. And then I should also mention service providers. We're building out our service provider partnerships also. >> Yeah, Chris I'm surprised we haven't talked about kind of AI yet at all, machine learning. It feels like everybody that was doing big data, now has kind pivoted in maybe a little bit early in the buzz word phase. What's your take on that? You've been apart of this for a while. Is big data just old now and we have a new thing, or how do you put those together? >> Well I think what we do maps very well until, at least my personal view of what's going on with AI/ML, is that it's really part of the fabric of what our product does. I talked before about once you sort of found the data you want to use, how do I use it? Well there's a lot of ML built into that. Where essentially, I see these different datasets, I want to use them... We do what's called one click functions. Which basically... What happens is these one click functions get smarter as more and more people use the product and use the data. So that if I've got some table over here and then I've got some SAS data source over there and one user of the product... or we might see field names that we, we grab the metadata, even though we don't require moving the data, we grab the metadata, we look at the metadata and then we'll sort of tell the user, we suggest that you join this data source with that data source and see what it looks like. And if they say: ah that worked, then we say oh okay that's part of sort of the whole ML infrastructure. Then we are more likely to advise the next few folks with the one click function that, hey if you trying to do a analysis of sales trends, well you might want to use this source and that source and you might want to join them together this way. So it's a combination of sort of AI and ML built into the fabric of what we do, and then also the community aspect of more and more people using it. But that's, going back to your original question, That's what I think that... There was quote, I'll misquote it, so I'm not going to directly say it, but it was just.. I think it might have John Ferrier, who was recently was talking about ML and just sort of saying you know eventually we're not going to talk about ML anymore than we talk about phone business or something. It's just going to become sort of integrated into the fabric of how organizations do business and how organizations do things. So we very much got it built in. You could certainly call us an AI/ML company if you want, its actually definitely part of our slide deck. But at the same time its something that will just sort of become a part of doing business over time. But it really, it depends on large data sets. As we all know, this is why it's so cheap to get Amazon Echoes and such these days. Because it's really beneficial, because the more data... There's value in that data, there was just another piece, I actually shared it on Linkedin today as a matter of fact, about, talking about Amazon and Whole Foods and saying: why are they getting such a valuation premium? They're getting such a valuation premium, because they're smart about using data, but one of the reasons they're smart about using the data is cause they have the data. So the more data you collect, the more data you use, the smarter the systems get, the more useful the solutions become. >> Absolutely, last year when Amazon reinvented, John Ferrier interviewed Andy Jassy and I had posited that the customer flywheel, is going to be replaced by that data flywheel. And enhanced to make things spin even further. >> That's exactly right and once you get that flywheel going it becomes a bigger and bigger competitive advantage, by the way that's also why the regulators are getting interested these days too, right? There's sort of, that flywheel going back the other way, but from our perspective... I mean first of all it just makes economic sense, right? These things could conceivably get out of control, that's at least what the regulators think, if you're not careful at least there's some oversight and I would say that, yes probably some oversight is a good idea, so you've got kind of flywheels pushing in both directions. But one way or another organizations need to get much smarter and much more precise and prescriptive about how they use data. And that's really what we're trying to help with. >> Okay, Chris want to give you the final word, Unify Software, you're working on kind of the strategic road pieces. What should we look for from you in your segment through the rest of 2017? >> Well, I think, I've always been a big believer, I've probably cited 'Crossing the Chasm' like so many times on theCUBE, during my prior HP 10 year and such but you know, I'm a big believer and we should be talking about customers, we should be talking about used cases. It's not about alphabet soup technology or data lakes, it's about the solutions and it's about how organizations are moving themselves forward with data. Going back to that Amazon example, so I think from us, yes we just released 2.O, we've got a very active blog, come by unifisoftware.com, visit it. But it's also going to be around what our customers are doing and that's really what we're going to try to promote. I mean if you remember this was also something, that for all the years I've worked with you guys I've been very much... You always have to make sure that the customer has agreed to be cited, it's nice when you can name them and reference them and we're working on our customer references, because that's what I think is the most powerful in this day and age, because again, going back to my, what I said before about, this is going throughout organizations now. People don't necessarily care about the technology infrastructure, but they care about what's being done with it. And so, being able to tell those customer stories, I think that's what you're going to probably see and hear the most from us. But we'll talk about our product as much as you let us as well. >> Great thing, it reminds me of when Wikibon was founded it was really about IT practice, users being able to share with their peers. Now when the software economy today, when they're doing things in software often that can be leveraged by their peers and that flywheel that they're doing, just like when Salesforce first rolled out, they make one change and then everybody else has that option. We're starting to see that more and more as we deploy as SAS and as cloud, it's not the shrink wrap software anymore. >> I think to that point, you know, I was at a conference earlier this year and it was an IT conference, but I was really sort of floored, because when you ask what we're talking about, what the enlightened IT folks and there is more and more enlightened IT folks we're talking about these days, it's the same thing. Right, it's how our business is succeeding, by being better at leveraging data. And I think the opportunities for people in IT... But they really have to think outside of the box, it's not about Hadoop and Sqoop and Sequel and Java anymore it's really about business solutions, but if you can start to think that way, I think there's tremendous opportunities and we're just scratching the surface. >> Absolutely, we found that really some of the proof points of what digital transformation really is for the companies. Alright Chris Selland, always a pleasure to catch up with you. Thanks so much for joining us and thank you for watching theCUBE. >> Chris: Thanks too. (techno music)