>>Everyone. Welcome to the cube here. Live in San Francisco, California for AWS summit, 2022. We're live we're back with events. Also we're a virtual, we got hybrid all kinds of events this year, of course, summit in New York city happening this summer. We'll be there with the cube as well. I'm John, again, John host of the queue. Got a great guest here, Justin Colby, owner and CEO of innovative solutions. Their booth is right behind us. Justin, welcome to the cube. >>Thank you. Thank you for having me. >>So we're just chatting, uh, uh, off camera about some of the work you're doing the owner of and CEO. Yeah. Of innovative. Yeah. So tell us the story. What do you guys do? What's the elevator pitch. Yeah. >><laugh> so elevator pitch is we are, are, uh, a hundred percent focused on small to mid-size businesses that are moving to the cloud or have already moved to the cloud and really trying to understand how to best control, cost, security, compliance, all the good stuff, uh, that comes along with it. Um, exclusively focused on AWS and, um, you know, about 110 people, uh, based in Rochester, New York, that's where our headquarters is. Now. We have offices down in Austin, Texas, up in Toronto, uh, Canada, as well as Chicago. Um, and obviously in New York, uh, you know, the, the business was never like this, uh, five years ago, um, founded in 1989, made the decision in 2018 to pivot and go all in on the cloud. And, uh, I've been a part of the company for about 18 years, bought the company about five years ago. And yeah, it's been a great ride. >>It's interesting. The manages services are interesting with cloud cause a lot of the heavy liftings done by AWS. So we had Matt on your team on earlier talking about some of the edge stuff. Yeah. But you guys are a managed cloud service. You got cloud advisory, you know, the classic service that's needed, but the demand coming from cloud migrations and application modernization and obviously data is a huge part of it. Huge. How is this factoring into what you guys do and your growth cuz you guys are the number one partner on the SMB side for edge. Yeah. For AWS, you got results coming in. Where's the, where's the forcing function. What's the pressure point. What's the demand like? >>Yeah. It's a great question. Every CEO I talk to, it's a small to midsize business. They're all trying to understand how to leverage technology better to help either drive a revenue target for their own business, uh, help with customer service as so much has gone remote now. And we're all having problems or troubles or issues trying to hire talent. And um, you know, tech is really at the, at the forefront and the center of that. So most cut customers are coming to us and they're like, listen, we gotta move to the cloud or we move some things to the cloud and we want to do that better. And um, there's this big misnomer that when you move to the cloud, you gotta automatically modernize. Yeah. And what we try to help as many customers understand as possible is lifting and shifting, moving the stuff that you maybe currently have OnPrem and a data center to the cloud first is a first step. And then, uh, progressively working through a modernization strategy is always the better approach. And so we spent a lot of time with small to mid-size businesses who don't have the technology talent on staff to be able to do >>That. Yeah. And they want to get set up. But the, the dynamic of like latency is huge. We're seeing that edge product is a big part of it. This is not a one-off it's happening around everywhere. It is. And it's not, it's manufacturing, it's the physical plant or location >>Literally. >>And so, and you're seeing more, I O T devices, what's that like right now from a channel engine problem statement standpoint, are the customers, not staff, is the it staff kind of old school? Is it new skills? What's the core problem you guys solve >>In the SMB space? The core issue nine outta 10 times is people get enamored with the latest and greatest. And the reality is not everything that's cloud based. Not all cloud services are the latest and greatest. Some things have been around for quite some time and are hardened solutions. And so, um, what we try to do with technology staff that has traditional on-prem, uh, let's just say skill sets and they're trying to move to a cloud-based workload is we try to help those customers through education and through some practical, let's just call it use case. Um, whether that's a proof of concept that we're doing or whether that's, we're gonna migrate a small workload over, we try to give them the confidence to be able to not, not necessarily go it alone, but to, to, to have the, uh, the Gusto and to really have the, um, the, the opportunity to, to do that in a wise way. Um, and what I find is that most CEOs that I tell, yeah, they're like, listen, at the end of the day, I'm gonna be spending money in one place or another, whether that's OnPrem or in the cloud. I just want to know that I'm doing that in a way that helps me grow as quickly as possible status quo. I think every, every business owner knows that COVID taught us anything that status quo is, uh, is no. No. Good. >>How about factoring in the, the agility and speed equation? Does that come up a lot? >>It does. I think, um, I think there's also this idea that if, uh, if we do a deep dive analysis and we really take a surgical approach to things, um, we're gonna be better off. And the reality is the faster you move with anything cloud based, the better you are. And so there's this assumption that we gotta get it right the first time in the cloud. If you start down your journey in one way and you realize midway that it's not the right, let's just say the right place to go. It's not like buying a piece of iron that you put in the closet and now you own it in the cloud. You can turn those services on and off. It's a, gives you a much higher density for making decisions and failing forward. >>Well actually shutting down the abandoning, the projects that early, not worrying about it, you got it. I mean, most people don't abandon stuff cuz they're like, oh, I own >>It. Exactly. And >>They get, they get used to it. Like, and then they wait too long. >>That's exactly. Yeah. >>Frog and boiling water, as we used to say, oh, it's a great analogy. So I mean, this, this is a dynamic that's interesting. I wanna get more thoughts on it because like I'm, if I'm a CEO of a company, like, okay, I gotta make my number. Yeah. I gotta keep my people motivated. Yeah. And I gotta move faster. So this is where you guys come. I get the whole thing. And by the way, great service, um, professional services in the cloud right now are so hot because so hot, you can build it and then have option optionality. You got path decisions, you got new services to take advantage of. It's almost too much for customers. It is. I mean, everyone I talk to at reinvent, that's a customer. Well, how many announcements did Andy Jessey announce or Adam, you know, the 5,000 announcement or whatever. They did huge amounts. Right. Keeping track of it all. Oh, is huge. So what's the, what's the, um, the mission of, of your company. How does, how do you talk to that alignment? Yeah. Not just product. I can get that like values as companies, cuz they're betting on you and your people. >>They are, they are what's >>What's the values. >>Our mission is, is very simple. We want to help every small to mid-size business, leverage the power of the cloud. Here's the reality. We believe wholeheartedly. This is our vision that every company is going to become a technology company. So we, the market with this idea that every customer's trying to leverage the power of the cloud in some way, shape or form, whether they know it or don't know it. And number two, they're gonna become a tech company in the process of that because everything is so tech-centric. And so when you talk about speed and agility, when you talk about the, the endless options and the endless permutations of solutions that a customer can buy in the out, how are you gonna ask a team of one or two people in your it department to make all of those decisions going it alone or trying to learn it as you go, it only gets you so far working with a partner. >>I'll just give you some perspective. We work with about a thousand small to midsize business customers. More than 50% of those customers are on our manage services. Meaning they know that we have their back and we're the safety net. So when a customer is saying, right, I'm gonna spend a couple thousand dollars a month in the cloud. They know that that bill, isn't gonna jump to $10,000 a month going in alone. Who's there to help protect that. Number two, if you have a security posture and let's just say your high profile and you're gonna potentially be more vulnerable to security attacks. If you have a partner that's offering you some managed services. Now you, again, you've got that backstop and you've got those services and tooling. We, we offer, um, seven different products, uh, that are part of our managed services that give the customer the tooling, that for them to go out and buy on their own for a customer to go out today and go buy a new Relic solution on their own, it would cost them a fortune. If >>The training alone would be insane, a risk factor not mean the cost. Yes, absolutely. Opportunity cost is huge, >>Huge, absolutely enormous training and development. Something. I think that is often, you know, it's often overlooked technologists. Typically they want to get their skills up. They, they love to get the, the stickers and the badges and the pins, um, at innovative in 28 team. When, uh, when we made the decision to go all in on the club, I said to the organization, you know, we have this idea that we're gonna pivot and be aligned with AWS in such a way that it's gonna really require us all to get certified. My executive assistant at the time looks at me. She said, even me, I said, yeah, even you, why can't you get certified? Yeah. And so we made, uh, a conscious decision. It wasn't requirement. It still isn't today to make sure everybody in the company has the opportunity to become certified. Even the people that are answering the phones at the front desk >>And she could be running the Kubernetes clusters. I >>Love it. >>It's amazing. But I'll tell >>You what, when that customer calls and they have a real Kubernetes issue, she'll be able to assist and get the right >>People involved. And that's a cultural factor that you guys have. So, so again, this is back to my whole point about SMBs and businesses in general, small and large, it staffs are turning over the gen Z and millennials are in the workforce. They were a provisioning top of rack switches. Right? First of all. And so if you're a business is also the, I call the buildout, um, uh, return factor, ROI piece. At what point in time as an owner or SMB, do I get the ROI? Yeah. I gotta hire a person to manage it. That person's gonna have five zillion job offers. Yep. Uh, maybe who knows? Right. I got cyber security issues. Where am I gonna find a cyber person? Yeah. A data compliance. I need a data scientist and a compliance person. Right. Maybe one and the same. Right. Good luck. Trying to find a data scientist. Who's also a compliance person. Yep. And the list goes on. I can just continue. Absolutely. I need an SRE to manage the, the, uh, the sock report and we can pen test. Right. >>Right. >>These are, these are like >>Critical issues. This is >>Just like, these are the table stakes. >>Yeah. And, and every, every business owner's thinking about this. So >>That's, that's what at least a million in loading, if not three or more Just to get that going. Yeah. Then it's like, where's the app. Yeah. So there's no cloud migration. There's no modernization on the app side though. No. And then remind AI and ML. That's >>Right. That's right. So to try to it alone, to me, it's hard. It it's incredibly difficult. And the other thing is, is there's not a lot of partners, so the partner, >>No one's raising their hand boss. I'll do all that. Exactly. An it department. >>Exactly. >>Like, can we just call up, uh, you know, our old vendor that's right, >>Right. Our old vendor. I like it. >><laugh> but that's so true. I mean, when I think about how, if I was a business owner starting a business today and I had to build my team, um, and the amount of investment that it would take to get those people skilled up and then the risk factor of those people now having the skills and being so much more in demand and being recruited away, that's a real, that's a real issue. And so how you build your culture around the, at is, is very important. And it's something that we talk about every, with every one of our small to mid-size >>Business. So just, I wanna get, I want to get your story as CEO. Okay. Take us through your journey. You said you bought the company and your progression to, to being the owner and CEO of innovative yeah. Award winning guys doing great. Uh, great bet on a good call. Yeah. Things a good tell your, your story. What's your journey. >>It's real simple. I was, uh, I was a sophomore at the Rochester Institute of technology in 2003. And, uh, I knew that I, I was going to school for it and I, I knew I wanted to be in tech. I didn't know what I wanted to do, but I knew I didn't wanna code or configure routers and switches. So I had this great opportu with the local it company that was doing managed services. We didn't call it at that time innovative solutions to come in and, uh, jump on the phone and dial for dollars. I was gonna cold call and introduce other, uh, small to midsize businesses locally in Rochester, New York go to Western New York, um, who innovative was now. We were 19 people at the time. And I came in, I did an internship for six months and I loved it. I learned more in those six months than I probably did in my first couple of years at, uh, at RT long story short. >>Um, for about seven years, I worked, uh, to really help develop, uh, process and methodology for the business so that we could grow and scale. And we grew to about 30 people. And, um, I went to the owners at the time in 2010 and I was like, Hey, I'm growing the value of this business. And who knows where you guys are gonna be another five years? What do you think about making me an owner? And they were like, listen, you got a long ways before you're gonna be an owner, but if you stick it out in your patient, we'll, um, we'll work through a succession plan with you. And I said, okay, there were four other individuals at the time that were gonna also buy into the business with me. >>And they were the owners, no outside capital, >>None zero, well, 2014 comes around. And, uh, the other folks that were gonna buy into the business with me that were also working at innovative for different reasons. They all decided that it wasn't for them. One started a family. The other didn't wanna put capital in. Didn't wanna write a check. Um, the other had a real big problem with having to write a check. If we couldn't make payroll, I'm like, well, that's kind of like if we're owners, we're gonna have to like cover that stuff. <laugh> well, so >>It's called the pucker factor. >>Exactly. So, uh, I sat down with the CEO in early 2015, and, uh, we made the decision that I was gonna buy the three partners out, um, go through an earn out process, uh, coupled with, uh, an interesting financial strategy that wouldn't strap the business, cuz they cared very much. The company still had the opportunity to keep going. So in 2016 I bought the business, um, became the sole owner. And, and at that point we, um, we really focused hard on what do we want this company to be had built this company to this point? Yeah. And, uh, and by 2018 we knew that pivoting all going all in on the cloud was important for us and we haven't looked back. >>And at that time, the proof points were coming clearer and clearer 2012 through 15 was the early adopters, the builders, the startups and early enterprises. Yes. The capital ones of the world. Exactly the, uh, and those kinds of big enterprises. The GA I don't wanna say gamblers, but ones that were very savvy. The innovators, the FinTech folks. Yep. The hardcore glass eating enterprises >>Agreed, agreed to find a small to midsize business, to migrate completely to the cloud as, as infrastructure was considered. That just didn't happen as often. Um, what we were seeing where a lot of our small to mid-size business customers, they wanted to leverage cloud based backup, or they wanted to leverage a cloud for disaster recovery because it lent itself. Well, early days, our most common cloud customer though, was the customer that wanted to move messaging and collaboration, the, the Microsoft suite to the cloud and that a lot of 'em dipped their toe in the water. But by 2017 we knew interest structure was around the corner. Yeah. Yeah. And so, uh, we only had two customers on AWS at the time. Um, and we, uh, we, we made the decision to go all in >>Justin. Great to have you on the cube. Thank you. Let's wrap up. Uh, tell me the hottest product that you have. Is it migrations? Is it the app? Modernization is the data. What's the hot product and then put a plugin for the company. Awesome. >>So, uh, there's no question. Every customer is looking to migrate workloads and try to figure out how to modernize for the future. We have very interesting, sophisticated yet elegant funding solutions to help customers with the cash flow, uh, constraints that come along with those migration. So any SMB that's thinking about migrating to the cloud, they should be talking innovative solutions. We know how to do it in a way that allows those customers not to be cash strapped and gives them an opportunity to move forward in a controlled, contained way so that they can modernize. >>So like insurance, basically for them not insurance classic in the classic sense, but you help them out on the, on the cash exposure. >>Absolutely. We are known for that and we're known for being creative with those customers and being empathetic to where they are in their >>Journey. And that's the cloud upside is all about doubling down on the variable wind. That's right. Seeing the value and doubling down on it. Absolutely not praying for it. Yeah. <laugh> all right, Justin. Thanks for coming on. You really appreciate it. >>Thank you very much for having me. Okay. >>This is the cube coverage here live in San Francisco, California for AWS summit tour 22. I'm John for your host. Thanks for watching. We're back with more great coverage for two days after this short break.

More bottoms up and have more technical early adopters. And generally speaking, they're free to use. They're free to try. They're very commonly community source or open source companies where you have a large technical community that's supporting them. So there's a, there's kind of a new normal now I think in great enterprise software and it starts with great technical founders with great products and great bottoms of emotions. And I think there's no better place to, uh, service those people than in the cloud and uh, in, in your community. >>Well, first of all, congratulations, and by the way, you got a great pedigree and great background, super smart, but Myer of your work and your, and, and your founding, but let's face it. Enterprise is hot because digital transformation is all companies there's no, I mean, consumer is enterprise now, everything is what was once a niche. No, I won't say niche category, but you know, not for the faint of heart, you know, investors, >>You know, it's so funny that you say that enterprise is hot because you, and I feel that way now. <laugh> but remember, like right now there's also a tech and VC conference in Miami <laugh> and it's covering cryptocurrencies and FCS and web three. So I think beauty is definitely in the eye of the beholder <laugh> but no, I, I will tell you, >>Ts is one big enterprise, cuz you gotta have imutability you got performance issues. You have, I IOPS issues. >>Well, and, and I think all of us here that are, uh, may maybe students of history and have been involved in open source in the cloud would say that we're, you know, much of what we're doing is, uh, the predecessors of the web web three movement. And many of us I think are contributors to the web three >>Movement. The hype is definitely one web three. Yeah. >>But, >>But you know, >>For sure. Yeah, no, but now you're taking us further east of Miami. So, uh, you know, look, I think, I, I think, um, what is unquestioned with the case now? And maybe it's, it's more obvious the more time you spend in this world is this is the fastest growing part of enterprise software. And if you include cloud infrastructure and cloud infrastructure spend, you know, it is by many measures over, uh, $500 billion in growing, you know, 20 to 30% a year. So it it's a, it's a just incredibly fast, well, >>Let's get, let's get into some of the cultural and the, the shifts that are happening, cuz again, you, you have the luxury of being in enterprise when it was hard, it's getting easier and more cooler. I get it and more relevant <laugh> but there's also the hype of like the web three, for instance, but you know, for, uh, um, um, the CEO snowflake, okay. Has wrote a book and Dave Valenti and I were talking about it and uh, Frank Luman has says, there's no playbooks. We always ask the CEOs, what's your playbook. And he's like, there's no playbook, situational awareness, always Trump's playbooks. So in the enterprise playbook, oh, higher, a direct sales force and SAS kind of crushed that now SAS is being redefined, right. So what is SAS is snowflake assassin or is that a platform? So again, new unit economics are emerging, whole new situation, you got web three. So to me there's a cultural shift, the young entrepreneurs, the, uh, user experience, they look at Facebook and say, ah, you know, they own all my data and you know, we know that that cliche, um, they, you know, the product. So as this next gen, the gen Z and the millennials come in and our customers and the founders, they're looking at things a little bit differently and the tech better. >>Yeah. I mean, I mean, I think we can, we can see a lot of common across all successful startups and the overall adoption of technology. Um, and, and I would tell you, this is all one big giant revolution. I call it the user driven revolution. Right. It's the rise of the user. Yeah. And you might say product like growth is currently the hottest trend in enterprise software. It's actually like growth, right. They're one and the same. So sometimes people think the product, uh, is what is driving growth. >>You just pull the product >>Through. Exactly, exactly. And so that's that I, that I think is really this revolution that you see, and, and it does extend into things like cryptocurrencies and web three and, you know, sort of like the control that is taken back by the user. Um, but you know, many would say that, that the origins of this, but maybe started with open source where users were contributors, you know, contributors were users and looking back decades and seeing how it, how it fast forward to today. I think that's really the trend that we're all writing. It's enabling these end users. And these end users in our world are developers, data engineers, cybersecurity practitioners, right. They're really the, and they're really the, the beneficiaries and the most, you know, kind of valued people in >>This. I wanna come back to the data engineers in a second, but I wanna make a comment and get your reaction to, I have a, I'm a gen Xer technically. So for not a boomer, but I have some boomer friends who are a little bit older than me who have, you know, experienced the sixties. And I have what been saying on the cube for probably about eight years now that we are gonna hit digital hippie revolution, meaning a rebellion against in the sixties was rebellion against the fifties and the man and, you know, summer of love. That was a cultural differentiation from the other one other group, the predecessors. So we're kind of having that digital moment now where it's like, Hey boomers, Hey people, we're not gonna do that anymore. You, we hate how you organize shit. >>Right. But isn't this just technology. I mean, isn't it, isn't it like there used to be the old adage, like, you know, you would never get fired for buying IBM, but now it's like, you obviously probably would get fired if you bought IBM. And I mean, it's just like the, the, I think, I think >>During the mainframe days, those renegades were breaking into Stanford, starting the home group. So what I'm trying to get at is that, do you see the young cultural revolution also, culturally, just, this is my identity NFTs to me speak volumes about my, I wanna associate with NFTs, not single sign on. Well, >>Absolutely. And, and I think like, I think you're hitting on something, which is like this convergence of, of, you know, societal it'll trends with technology trends and how that manifests in our world is yes. I think like there is unquestionably almost a religion yeah. Around the way in which a product is built. Right. And we can use open source, one example of that religion. Some people will say, look, I'll just never try a product in the cloud if it's not open source. Yeah. I think cloud, native's another example of that, right? It's either it's, you know, it either is cloud native or it's not. And I think a lot of people will look at a product and say, look, you know, you were not designed in the cloud era. Therefore I just won't try you. And sometimes, um, like it or not, it's a religious decision, right? Yeah. It's so it's something that people just believe to be true almost without, uh, necessarily caring >>About data. Data drives all decision making. Let me ask you this next question. As a VC. Now you look at pitch, well, you've been a VC for many years, but you also have the founder entrepreneurial mindset, but you can get empathize with the founders. You know, hustle is a big part of the, that first founder check, right? You gotta convince someone to part with their ch their money and the first money in which you do a lot of it's about believing in the person. So faking it till you make it is hard. Now you, the data's there, you either have it cloud native, you either have the adaption or traction. So honesty is a big part of that pitch. You can't fake it. >>Oh, AB absolutely. You know, there used to be this concept of like the persona of an entrepreneur. Right. And the persona of the entrepreneur would be, you know, so somebody who was a great salesperson or somebody who tell a great story, and I still think that that's important, right. It still is a human need for people to believe in narratives and stories. Yeah. But having said that you're right. The proof is in the pudding, right. At some point you click download and you try the product and it does what it says it gonna it's gonna do, or it doesn't, or it either stands up to the load test or it doesn't. And so I, I feel like in the new economy that we live in, really, it's a shift from maybe the storytellers and the creators to, to the builders, right. The people that know how to build great product. And in some ways the people that can build great product yeah. Stand out from the crowd. And they're the ones that can build communities around their products. And, you know, in some ways can, um, you know, kind of own more of the narrative of because their product begins exactly >>The volume you back to the user led growth. >>Exactly. And it's the religion of, I just love your product. Right. And I, I, I, um, Doug song is the founder of du security used to say, Hey, like, you know, the, the really like in today's world of like consumption based software, like the user is only gonna give you 90 seconds to figure out whether or not you're a company that's easy to do business with. Right. And so you can say, and do all the things that you want about how easy you are to work with. But if the product isn't easy to install, if it's not easy to try, if it's not, if, if the it's gotta speak to the, >>Speak to the user, but let me ask a question now that for the people watching, who are maybe entrepreneurial entre, preneurs, um, masterclass here in session. So I have to ask you, do you prefer, um, an entrepreneur come in and say, look at John. Here's where I'm at. Okay. First of all, storytelling's fine with you an extrovert or introvert, have your style, sell the story in a way that's authentic, but do you, what do you prefer to say? Here's where I'm at? Look, I have an idea. Here's my traction. I think here's my MVP prototype. I need help. Or do, do you wanna just see more stats? What's the, what's the preferred way that you like to see entrepreneurs come in and engage? >>There's tons of different styles, man. I think the single most important thing that every founder should know is that we, we don't invest in what things are today. We invest in what we think something will become. Right. And I think that's why we all get up in the morning and try to build something different, right? It's that we see the world a different way. We want it to be a different way. And we wanna work every single moment of the day to try to make that vision a reality. So I think the more that you can show people where you want to be the, of more likely somebody is gonna align with your vision and, and wanna invest in you and wanna be along for the ride. So I, I wholeheartedly believe in showing off what you got today, because eventually we all get down to like, where are we and what are we gonna do together? But, um, no, I, you gotta >>Show the >>Path. I think the single most important thing for any founder and VC relationship is that they have the same vision. Uh, if you have the same vision, you can, you can get through bumps in the road, you can get through short term spills. You can all sorts of things in the middle. The journey can happen. Yeah. But it doesn't matter as much if you share the same long term vision, >>Don't flake out and, and be fashionable with the latest trends because it's over before you can get there. >>Exactly. I think many people that, that do what we do for a living, we'll say, you know, ultimately the future is relatively easy to predict, but it's the timing that's impossible to predict. <laugh> so you, you know, you sort of have to balance the, you know, we, we know that the world is going in this way and therefore we're gonna invest a lot of money to try to make this a reality. Uh, but some times it happens in six months. Sometimes it takes six years. Sometimes it takes 16 years. Uh, >>What's the hottest thing in enterprise that you see the biggest wave that people should pay attention to that you're looking at right now with Bel partners, Tebel dot your site. What's the big wave. What's your big >>Wave. There's three big trends that we invest in. And the they're the only things we do day in, day out one is the explosion and open source software. So I think many people think that all software is unquestionably moving to an open source model in some form or another yeah. Tons of reasons to debate whether or not that is gonna happen, an alwa timeline >>Happening forever. >>But, uh, it is, it is accelerating faster than we've ever seen. So I, I think it's, it's one big, massive wave that we continue to ride. Um, second is the rise of data engineering. Uh, I think data engineering is in and of itself now, a category of software. It's not just that we store data. It's now we move data and we develop applications on data. And, uh, I think data is in and of itself as big of a market as any of the other markets that we invest in. Uh, and finally, it's the gift that keeps on giving. I've spent my entire career in it. We still feel that security is a market that is underinvested. It is, it continues to be the place where people need to continue to invest and spend more money. Yeah. Uh, and those are the three major trends that we run >>And security, you think we all need a dessert do over, right? I mean, do we need you do over in security or is what's the core problem? I, >>I, I keep using this word underinvested because I think it's the right way to think about the problem. I think if you, I think people generally speaking, look at cybersecurity as an add-on. Yeah. But if you think about it, the whole economy is moving online. And so in, in some ways like security is core to protecting the digital economy. And so it's, it shouldn't be an afterthought, right? It should be core to what everyone is doing. And that's why I think relative to the trillions of dollars that are at stake, uh, I believe the market size for cybersecurity is run $150 billion. And it still is a fraction of what we're, >>What we're and national security even boom is booming now. So you get the convergence of national security, geopolitics, internet digital that's >>Right. You mean arguably, right? I mean, arguably again, it's the area of the world that people should be spending more time and more money given what to stake. >>I love your thesis. I gotta, I gotta say, you gotta love your firm. Love. You're doing we're big supporters, your mission. Congratulations on your entrepreneurial venture. And, uh, we'll be, we'll be talking and maybe see a Cuban. Uh, absolutely not. Certainly EU maybe even north Americans in Detroit this year. >>Huge fan of what you guys are doing here. Thank you so much for helping me on the show. >>Guess be VC Johnson here on the cube. Check him out. Founder for founders here on the cube, more coverage from San Francisco, California. After this short break, stay with us. Everyone. Welcome to the cue here. Live in San Francisco. K warn you for AWS summit 2022 we're live we're back with events. Also we're virtual. We got hybrid all kinds of events. This year, of course, summit in New York city is happening this summer. We'll be there with the cube as well. I'm John. Again, John host of the cube. Got a great guest here, Justin Kobe owner, and CEO of innovative solutions. Their booth is right behind us. Justin, welcome to the cube. >>Thank you. Thank you for having me. >>So we're just chatting, uh, uh, off camera about some of the work you're doing. You're the owner of and CEO. Yeah. Of innovative. Yeah. So tell us the story. What do you guys do? What's the elevator pitch. >>Yeah. <laugh> so the elevator pitch is we are, uh, a hundred percent focused on small to mid-size businesses that are moving to the cloud, or have already moved to the cloud and really trying to understand how to best control security, compliance, all the good stuff that comes along with it. Um, exclusively focused on AWS and, um, you know, about 110 people, uh, based in Rochester, New York, that's where our headquarters is, but now we have offices down in Austin, Texas, up in Toronto, uh, Canada, as well as Chicago. Um, and obviously in New York, uh, you know, the business was never like this, uh, five years ago, um, founded in 1989, made the decision in 2018 to pivot and go all in on the cloud. And, uh, I've been a part of the company for about 18 years, bought the company about five years ago. And it's been a great ride. >>It's interesting. The manages services are interesting with cloud cause a lot of the heavy liftings done by a of us. So we had Matt on your team on earlier talking about some of the edge stuff. Yeah. But you guys are a managed cloud service. You got cloud advisory, you know, the classic service that's needed, but the demands coming from cloud migrations and application modernization, but obviously data is a huge part of it. Huge. How is this factoring into what you guys do and your growth cuz you guys are the number one partner on the SMB side for edge. Yeah. For AWS, you got results coming in. Where's the, where's the forcing function. What's the pressure point. What's the demand like? >>Yeah. It's a great question. Every CEO I talk to, that's a small mids to size business. They're all trying to understand how to leverage technology better to help either drive a revenue target for their own business, uh, help with customer service as so much has gone remote now. And we're all having problems or troubles or issues trying to hire talent. And um, you know, tech is really at the, at the forefront and the center of that. So most customers are coming to us and they're of like, listen, we gotta move to the cloud or we move some things to the cloud and we want to do that better. And um, there's this big misnomer that when you move to the cloud, you gotta automatically modernize. Yeah. And what we try to help as many customers understand as possible is lifting and shifting, moving the stuff that you maybe currently have OnPrem and a data center to the cloud first is a first step. And then so, uh, progressively working through a modernization strategy is always the better approach. And so we spend a lot of time with small to mid-size businesses who don't have the technology talent on staff to be able to do >>That. Yeah. And they want to get set up. But the, the dynamic of like latency is huge. We're seeing that edge product is a big part of it. This is not a one-off happening around everywhere. It is not it's manufacturing, it's the physical plant or location >>Literally. >>And so, and you're seeing more IOT devices. What's that like right now from a challenge and problem statement standpoint, are the customers, not staff, is the it staff kind of old school? Is it new skills? What's the core problem. And you guys solve >>In the SMB space. The core issue nine outta 10 times is people get enamored with the latest and greatest. And the reality is not everything that's cloud based. Not all cloud services are the latest and greatest. Some things have been around for quite some time and our hardened solutions. And so, um, what we try to do with, to technology staff that has traditional on-prem, uh, let's just say skill sets and they're trying to move to a cloud-based workload is we try to help those customers through education and through some practical, let's just call it use case. Um, whether that's a proof of concept that we're doing or whether that's, we're gonna migrate a small workload over, we try to give them the confidence to be able to not, not necessarily go it alone, but, but to, to, to have the, uh, the Gusto and to really have the, um, the, the opportunity to, to do that in a wise way. Um, and what I find is that most CEOs that I talk to yeah. Feel like, listen, at the end of the day, I'm gonna be spending money in one place or another, whether that's on primer in the cloud, I just want know that I'm doing that way. That helps me grow as quickly as possible status quo. I think every, every business owner knows that COVID taught us anything that status quo is, uh, is, is no. No. Good. >>How about factoring in the, the agility and speed equation? Does that come up a lot? It >>Does. I think, um, I think there's also this idea that if, uh, if we do a deep dive analysis and we really take a surgical approach to things, um, we're gonna be better off. And the reality is the faster you move with anything cloud based, the better you are. And so there's this assumption that we gotta get it right the first time. Yeah. In the cloud, if you start down your journey in one way and you realize midway that it's not the right, let's just say the right place to go. It's not like buying a piece of iron that you put in the closet and now you own it in the cloud. You can turn those services on and off. It's a, gives you a much higher density for making decisions and failing >>Forward. Well actually shutting down the abandoning, the projects that early, not worrying about it, you got it mean most people don't abandon stuff cuz they're like, oh, I own it. >>Exactly. >>And they get, they get used to it. Like, and then they wait too long. >>That's exactly. >>Yeah. Frog and boiling water, as we used to say, oh, it's a great analogy. So I mean, this, this is a dynamic. That's interesting. I wanna get more thoughts on it because like I'm a, if I'm a CEO of a company, like, okay, I gotta make my number. Yeah. I gotta keep my people motivated. Yeah. And I gotta move faster. So this is where you guys come in. I get the whole thing. And by the way, great service, um, professional services in the cloud right now are so hot because so hot, you can build it and then have option optionality. You got path decisions, you got new services to take advantage of. It's almost too much for customers. It is. I mean, everyone I talked to at reinvent, that's a customer. Well, how many announcements did Andy jazzy announcer Adam? You know, the 5,000 announcement or whatever. They did huge amounts. Right. Keeping track of it all. Oh, is huge. So what's the, what's the, um, the mission of, of your company. How does, how do you talk to that alignment? Yeah. Not just processes. I can get that like values as companies, cuz they're betting on you and your people. >>They are, they are >>Values. >>Our mission is, is very simple. We want to help every small to midsize business leverage the power of the cloud. Here's the reality. We believe wholeheartedly. This is our vision that every company is going to become a technology company. So we go to market with this idea that every customer's trying to leverage the power of the cloud in some way, shape or form, whether they know it or don't know it. And number two, they're gonna become a 10 a company in the process of that because everything is so tech-centric. And so when you talk about speed and agility, when you talk about the, the endless options and the endless permutations of solutions that a customer can buy in the cloud, how are you gonna ask a team of one or two people in your it department to make all those decisions going it alone or trying to learn it as you go, it only gets you so far working with a partner. >>I'll just give you some perspective. We work with about a thousand small to midsize business customers. More than 50% of those customers are on our managed services. Meaning they know that we have their back and we're the safety net. So when a customer is saying, right, I'm gonna spend a couple thousand and dollars a month in the cloud. They know that that bill, isn't gonna jump to $10,000 a month going in alone. Who's there to help protect that. Number two, if you have a security posture and let's just say your high profile and you're gonna potentially be more vulnerable to security attacks. If you have a partner that's offering you some managed services. Now you, again, you've got that backstop and you've got those services and tooling. We, we offer, um, seven different products, uh, that are part of our managed services that give the customer the tooling, that for them to go out and buy on their own for a customer to go out today and go buy a new Relic solution on their own. It, it would cost 'em a four, >>The training alone would be insane. A risk factor. I mean the cost. Yes, absolutely opportunity cost is huge, >>Huge, absolutely enormous training and development. Something. I think that is often, you know, it's often overlooked technologists. Typically they want to get their skills up. They, they love to get the, the stickers and the badges and the pins, um, at innovative in 2018. When, uh, when we, he made the decision to go all in on the club, I said to the organization, you know, we have this idea that we're gonna pivot and be aligned with AWS in such a way that it's gonna really require us all to get certified. My executive assistant at the time looks at me. She said, even me, I said, yeah, even you, why can't you get certified? Yeah. And so we made, uh, a conscious, it wasn't requirement. It still isn't today to make sure everybody in the company has the opportunity to become certified. Even the people that are answering the phones at the front >>Desk and she could be running the Kubernetes clusters. I >>Love it. It's >>Amazing. >>But I'll tell you what, when that customer calls and they have a real Kubernetes issue, she'll be able to assist and get >>The right people with. And that's a cultural factor that you guys have. So, so again, this is back to my whole point out SMBs and businesses in general, small and large it staffs are turning over the gen Z and millennials are in the workforce. They were provisioning top of rack switches. Right. First of all. And so if you're a business, there's also the, I call the buildout, um, uh, return factor, ROI piece. At what point in time as an owner, SMB, do I get to ROI? Yeah. I gotta hire a person to manage it. That person's gonna have five zillion job offers. Yep. Uh, maybe who knows? Right. I got cyber security issues. Where am I gonna find a cyber person? Yeah. A data compliance. I need a data scientist and a compliance person. Right. Maybe one in the same. Right. Good luck. Trying to find a data scientist. Who's also a compliance person. Yep. And the list goes on. I can just continue. Absolutely. I need an SRE to manage the, the, uh, the sock report and we can pen test. Right. >>Right. >>These are, these are >>Like critical issues. >>This is just like, these are the table stakes. >>Yeah. And, and every, every business owner's thinking about this, >>That's, that's what, at least a million in loading, if not three or more Just to get that app going. Yeah. Then it's like, where's the app. Yeah. So there's no cloud migration. There's no modernization on the app side. No. And they remind AI and ML. >>That's right. That's right. So to try to go it alone, to me, it's hard. It it's incredibly difficult. And the other thing is, is there's not a lot of partners, so the partner, >>No one's raising their hand boss. I'll do all that exactly. In the it department. >>Exactly. >>So like, can we just call up, uh, you know, our old vendor that's >>Right. <laugh> right. Our old vendor. I like it, >>But that's so true. I mean, when I think about how, if I was a business owner starting a business today and I had to build my team, um, and the amount of investment that it would take to get those people skilled up and then the risk factor of those people now having the skills and being so much more in demand and being recruited away, that's a real, that's a real issue. And so how you build your culture around that is, is very important. It's something that we talk about every, with every one of our small to mid-size >>Businesses. So just, I want get, I want to get your story as CEO. Okay. Take us through your journey. You said you bought the company and your progression to, to being the owner and CEO of innovative yeah. Award winning guys doing great. Uh, great bet on a good call. Yeah. Things are good. Tell your story. What's your journey? >>It's real simple. I was, uh, I was a sophomore at the Rochester Institute of technology in 2003. And, uh, I knew that I, I was going to school for it and I, I knew I wanted to be in tech. I didn't know what I wanted to do, but I knew I didn't wanna code or configure routers and switches. So I had this great opportunity with the local it company that was doing managed services. We didn't call it at that time innovative solutions to come in and, uh, jump on the phone and dial for dollars. I was gonna cold call and introduced other, uh, small to midsize businesses locally in Rochester, New York go to Western New York, um, who innovative was now. We were 19 people at the time. Yeah. I came in, I did an internship for six months and I loved it. I learned more in those six months than I probably did in my first couple of years at, uh, at RT long story short. >>Um, for about seven years, I worked, uh, to really help develop, uh, sales process and methodology for the business so that we could grow and scale. And we grew to about 30 people. And, um, I went to the owners at the time in 2000 and I was like, Hey, I'm growing the value of this business. And who knows where you guys are gonna be another five years? What do you think about making me an owner? And they were like, listen, you got long ways before you're gonna be an owner. But if you stick it out in your patient, we'll, um, we'll work through a succession plan with you. And I said, okay, there were four other individuals at the time that were gonna also buy the business with me. >>And they were the owners, no outside capital, >>None zero, well, 2014 comes around. And, uh, the other folks that were gonna buy into the business with me that were also working at innovative for different reasons. They all decided that it wasn't for them. One started a family. The other didn't wanna put capital in. Didn't wanna write a check. Um, the other had a real big problem with having to write a check. If we couldn't make payroll, I'm like, well, that's kind of like, if we're own, we're gonna have to like cover that stuff. <laugh> so >>It's called the pucker factor. >>Exactly. So, uh, I sat down with the CEO in early 2015 and, uh, we made the decision that I was gonna buy the three partners out, um, go through an earn out process, uh, coupled with, uh, an interesting financial strategy that wouldn't strap the BI cuz they cared very much. The company still had the opportunity to keep going. So in 2016 I bought the business, um, became the sole owner. And, and at that point we, um, we really focused hard on what do we want this company to be? We had built this company to this point. Yeah. And, uh, and by 2018 we knew that pivoting all going all in on the cloud was important for us. And we haven't looked back. >>And at that time, the proof points were coming clearer and clearer 2012 through 15 was the early adopters, the builders, the startups and early enterprises. Yes. The capital ones of the world. Exactly the, uh, and those kinds of big enterprises. The GA I don't wanna say gamblers, but ones that were very savvy. The innovators, the FinTech folks. Yep. The hardcore glass eating enterprises >>Agreed, agreed to find a small to midsize business to migrate completely to the cloud is as infrastructure was considered, that just didn't happen as often. Um, what we were seeing where the, a lot of our small to midsize business customers, they wanted to leverage cloud based backup, or they wanted to leverage a cloud for disaster recovery because it lent itself. Well, early days, our most common cloud customer though, was the customer that wanted to move messaging and collaboration. The, the Microsoft suite to the cloud. And a lot of 'em dipped their toe in the water. But by 2017 we knew infrastructure was around the corner. Yeah. And so, uh, we only had two customers on AWS at the time. Um, and we, uh, we, we made the decision to go all in >>Justin. Great to have you on the cube. Thank you. Let's wrap up. Uh, tell me the hottest product that you have. Is it migrations? Is the app modernization? Is it data? What's the hot product and then put a plugin for the company. Awesome. >>So, uh, there's no question. Every customer is looking migrate workloads and try to figure out how to modernize for the future. We have very interesting, sophisticated yet elegant funding solutions to help customers with the cash flow, uh, constraints that come along with those migrations. So any SMB that's thinking about migrating into the cloud, they should be talking innovative solutions. We know how to do it in a way that allows those customer is not to be cash strapped and gives them an opportunity to move forward in a controlled, contained way so they can modernize. So >>Like insurance, basically for them not insurance class in the classic sense, but you help them out on the, on the cash exposure. >>Absolutely. We are known for that and we're known for being creative with those customers and being empathetic to where they are in their journey. >>And that's the cloud upside is all about doubling down on the variable win that's right. Seeing the value and ING down on it. Absolutely not praying for it. Yeah. <laugh> all right, Justin. Thanks for coming on. You really appreciate >>It. Thank you very much for having me. >>Okay. This is the cube coverage here live in San Francisco, California for AWS summit, 2022. I'm John for your host. Thanks for watching. We're back with more great coverage for two days after this short break >>Live on the floor in San Francisco for Aus summit. I'm John for host of the cube here for the next two days, getting all the actual back in person we're at AWS reinvent a few months ago. Now we're back events are coming back and we're happy to be here with the cube. Bring all the action. Also virtual. We have a hybrid cube, check out the cube.net, Silicon angle.com for all the coverage. After the event. We've got a great guest ticking off here. Matthew Park, director of solutions, architecture with innovation solutions. The booth is right here. Matthew, welcome to the cube. >>Thank you very much. I'm glad to be here. >>So we're back in person. You're from Tennessee. We were chatting before you came on camera. Um, it's great to be back through events. It's >>Amazing. This is the first, uh, summit I've been to, to in what two, three >>Years. That's awesome. We'll be at the, uh, a AWS summit in New York as well. A lot of developers and the big story this year is as developers look at cloud going distributed computing, you got on premises, you got public cloud, you got the edge. Essentially the cloud operations is running everything devs sec ops, everyone kind of sees that you got containers, you got Benet, he's got cloud native. So the, the game is pretty much laid out. Mm-hmm <affirmative> and the edge is with the actions you guys are number one, premier partner at SMB for edge. >>That's >>Right. Tell us about what you guys doing at innovative and, uh, what you do. >>That's right. Uh, so I'm the director of solutions architecture. Uh, me and my team are responsible for building out the solutions. The at our around, especially the edge public cloud for us edge is anything outside of an AWS availability zone. Uh, we are deploying that in countries that don't have AWS infrastructure in region. They don't have it. Uh, give >>An example, >>Uh, example would be Panama. We have a customer there that, uh, needs to deploy some financial tech data and compute is legally required to be in Panama, but they love AWS and they want to deploy AWS services in region. Uh, so they've taken E EKS anywhere. We've put storage gateway and, uh, snowball, uh, in region inside the country and they're running or FinTech on top of AWS services inside Panama. >>You know, what's interesting, Matthew is that we've been covering Aw since 2013 with the cube about their events. And we watched the progression and jazzy was, uh, was in charge and became the CEO. Now Adam slaps in charge, but the edge has always been that thing they've been trying to avoid. I don't wanna say trying to avoid, of course, Amazon would listens to the customer. They work backwards from the customer. We all know that. Uh, but the real issue was they were they're bread and butters EC two and S three. And then now they got tons of services and the cloud is obviously successful and seeing that, but the edge brings up a whole nother level. >>It does >>Computing. >>It >>Does. That's not centralized in the public cloud now they got regions. So what is the issue with the edge what's driving? The behavior. Outpost came out as a reaction to competitive threats and also customer momentum around OT, uh, operational technologies. And it merging. We see with the data at the edge, you got five GM having. So it's pretty obvious, but there was a slow transition. What was the driver for the edge? What's the driver now for edge action for AWS >>Data in is the driver for the edge. Data has gravity, right? And it's pulling compute back to where the customer's generating that data and that's happening over and over again. You said it best outpost was a reaction to a competitive situation. Whereas today we have over 15 AWS edge services and those are all reactions to things that customers need inside their data centers on location or in the field like with media companies. >>Outpost is interesting. We always use the riff on the cube, uh, cause it's basically Amazon in a box, pushed in the data center, running native, all this stuff, but now cloud native operations are kind of becoming standard. You're starting to see some standard. Deepak syncs group is doing some amazing work with opensource Raul's team on the AI side, obviously, uh, you got SW who's giving the keynote tomorrow. You got the big AI machine learning big part of that edge. Now you can say, okay, outpost, is it relevant today? In other words, did outpost do its job? Cause EKS anywhere seems to be getting a lot of momentum. You see local zones, the regions are kicking ass for Amazon. This edge piece is evolving. What's your take on EKS anywhere versus say outpost? >>Yeah, I think outpost did its job. It made customers that were looking at outpost really consider, do I wanna invest in this hardware? Do I, do I wanna have, um, this outpost in my datas center, do I want to manage this over the long term? A lot of those customers just transitioned to the public cloud. They went into AWS proper. Some of those customers stayed on prem because they did have use cases that were, uh, not a good fit for outpost. They weren't a good fit. Uh, in the customer's mind for the public AWS cloud inside an availability zone now happening is as AWS is pushing these services out and saying, we're gonna meet you where you are with 5g. We're gonna meet you where you are with wavelength. We're gonna meet you where you are with EKS anywhere. Uh, I think it has really reduced the amount of times that we have conversations about outposts and it's really increased. We can deploy fast. We don't have to spin up outpost hardware can go deploy EKS anywhere in your VMware environment. And it's increasing the speed of adoption >>For sure. Right? So you guys are making a lot of good business decisions around managed cloud service. That's right. Innovative. Does that get the cloud advisory, the classic professional services for the specific edge piece and, and doing that outside of the availability zones and regions for AWS, um, customers in these new areas that you're helping out are they want cloud, like they want to have modernization a modern applications. Obviously they got data machine learning and AI, all part of that. What's the main product or, or, or gap that you're filling for AWS, uh, outside of their availability zones or their regions that you guys are delivering. What's the key is that they don't have a footprint. Is it that it's not big enough for them? What's the real gap. What's why, why are you so successful? >>So what customers want when they look towards the cloud is they want to focus on what's making them money as a business. They wanna focus on their applications. They wanna focus on their customers. So they look towards AWS cloud and a AWS. You take the infrastructure, you take, uh, some of the higher layers and we'll focus on our revenue generating business, but there's a gap there between infrastructure and revenue generating business that innovative slides into, uh, we help manage the AWS environment. Uh, we help build out these things in local data centers for 32 plus year old company. We have traditional on-premises people that know about deploying hardware that know about deploying VMware to host EKS anywhere. But we also have most of our company totally focused on the AWS cloud. So we're that gap in helping deploy these AWS services, manage them over the long term. So our customers can go to just primarily and totally focusing on their revenue generating business. So >>Basically you guys are basically building AWS edges, >>Correct? >>For correct companies, correct? Mainly because the, the needs are there, you got data, you got certain products, whether it's, you know, low latency type requirements, right. And then they still work with the regions, right. It's all tied together, right. Is that how it >>Works? Right. And, and our customers, even the ones in the edge, they also want us to build out the AWS environment inside the availability zone, because we're always gonna have a failback scenario. If we're gonna deploy fin in the Caribbean, we're gonna talk about hurricanes. And we're gonna talk about failing back into the AWS availability zones. So innovative is filling that gap across the board, whether it be inside the AWS cloud or on the AWS edge. >>All right. So I gotta ask you on the, since you're at the edge in these areas, I won't say underserved, but developing areas where now have data and you have applications that are tapping into that, that requirement. It makes total sense. We're seeing that across the board. So it's not like it's a, it's an outlier it's actually growing. Yeah. There's also the crypto angle. You got the blockchain. Are you seeing any traction at the edge with blockchain? Because a lot of people are looking at the web three in these areas like Panama, you mentioned FinTech. And in, in the islands there a lot of, lot of, lot of web three happening. What's your, what your view on the web three world right now, relative >>To we, we have some customers actually deploying crypto, especially, um, especially in the Caribbean. I keep bringing the Caribbean up, but it's, it's top of my mind right now we have customers that are deploying crypto. A lot of, uh, countries are choosing crypto to underlie parts of their central banks. Yeah. Um, so it's, it's up and coming. Uh, I, I have some, you know, personal views that, that crypto is still searching for a use case. Yeah. And, uh, I think it's searching a lot and, and we're there to help customers search for that use case. Uh, but, but crypto, as a, as a, uh, technology, um, lives really well on the AWS edge. Yeah. Uh, and, and we're having more and more people talk to us about that. Yeah. And ask for assistance in the infrastructure, because they're developing new cryptocurrencies every day. Yeah. It's not like they're deploying Ethereum or anything specific. They're actually developing new currencies and, and putting them out there on >>It's interesting. I mean, first of all, we've been doing crypto for many, many years. We have our own little, um, you know, project going on. But if you look talk to all the crypto people that say, look, we do a smart contract, we use the blockchain. It's kind of over a lot of overhead and it's not really their technical already, but it's a cultural shift, but there's underserved use cases around use of money, but they're all using the blockchain just for like smart contracts, for instance, or certain transactions. And they go to Amazon for the database. Yeah. <laugh> they all don't tell anyone we're using a centralized service. Well, what happened to decentralized? >>Yeah. And that's, and that's the conversation performance issue. Yeah. And, and it's a cost issue. Yeah. And it's a development issue. Um, so I think more and more as, as some of these, uh, currencies maybe come up, some of the smart contracts get into, uh, they find their use cases. I think we'll start talking about how does that really live on, on AWS and, and what does it look like to build decentralized applications, but with AWS hardware and services. >>Right. So take me through, uh, a use case of a customer Matthew around the edge. Okay. So I'm a customer, pretend I'm a customer, Hey, you know, I'm, we're in an underserved area. I want to modernize my business. And I got my developers that are totally peaked up on cloud, but we've identified that it's just a lot of overhead latency issues. I need to have a local edge and serve my a, I also want all the benefit of the cloud. So I want the modern, and I wanna migrate to the cloud for all those cloud benefits and the goodness of the cloud. What's the answer. >>Yeah. Uh, big thing is, uh, industrial manufacturing, right? That's, that's one of the best use cases, uh, inside industrial manufacturing, we can pull in many of the AWS edge services we can bring in, uh, private 5g, uh, so that all the, uh, equipment that, that manufacturing plant can be hooked up, they don't have to pay huge overheads to deploy 5g it's, uh, better than wifi for the industrial space. Um, when we take computing down to that industrial area, uh, because we wanna do pre-procesing on the data. Yeah. We want to gather some analytics. We deploy that with a regular commercially available hardware running VMware, and we deploy EKS anywhere on that. Inside of that manufacturing plant, we can do pre-procesing on things coming out of the robotics, depending on what we're manufacturing. Right. And then we can take those refined analytics and for very low cost with maybe a little bit longer latency transmit those back, um, to the AWS availability zone, the, the standard >>For data, data lake, or whatever, >>To the data lake. Yeah. Data lake house, whatever it might be. Um, and we can do additional data science on that once it gets to the AWS cloud. Uh, but a lot of that, uh, just in time business decisions, just time manufacturing decisions can all take place on an AWS service or services inside that manufacturing plant. And that's, that's one of the best use cases that we're >>Seeing. And I think, I mean, we've been seeing this on the queue for many, many years, moving data around is very expensive. Yeah. But also compute going to the data that saves that cost yeah. On the data transfer also on the benefits of the latency. So I have to ask you, by the way, that's standard best practice now for the folks watching don't move the data unless you have to. Um, but those new things are developing. So I wanna ask you what new patterns are you seeing emerging once this new architecture's in place? Love that idea, localize everything right at the edge, manufacturing, industrial, whatever, the use case, retail, whatever it is. Right. But now what does that change in the, in the core cloud? There's a, there's a system element here. Yeah. What's the new pattern. There's >>Actually an organizational element as well, because once you have to start making the decision, do I put this compute at the point of use or do I put this compute in the cloud? Uh, now you start thinking about where business decisions should be taking place. Uh, so not only are you changing your architecture, you're actually changing your organization because you're thinking, you're thinking about a dichotomy you didn't have before. Uh, so now you say, okay, this can take place here. Uh, and maybe, maybe this decision can wait. Right. And then how do I visualize that? By >>The way, it could be a bot tube doing the work for management. Yeah. <laugh> exactly. You got observability going, right. But you gotta change the database architecture on the back. So there's new things developing. You've got more benefit. There >>Are, there are, and we have more and more people that, that want to talk less about databases and want to talk about data lakes because of this. They want to talk more about customers are starting to talk about throwing away data. Uh, you know, for the past maybe decade. Yeah. It's been store everything. And one day we will have a data science team that we hire in our organization to do analytics on this decade of data. And well, >>I mean, that's, that's a great point. We don't have time to drill into, maybe we do another session this, but the one pattern we're seeing come of the past year is that throwing away data's bad. Even data lakes that so-called turn into data swamps, actually, it's not the case. You look at data, brick, snowflake, and other successes out there. And even time series data, which may seem irrelevant efforts over actually matters when people start retrain their machine learning algorithms. Yep. So as data becomes co as we call it in our last showcase, we did a whole whole an event on this. The data's good in real time and in the lake. Yeah. Because the iteration of the data feeds the machine learning training. Things are getting better with the old data. So it's not throw away. It's not just business benefits. Yeah. There's all kinds of new scale. There >>Are. And, and we have, uh, many customers that are running petabyte level. Um, they're, they're essentially data factories on, on, on premises, right? They're, they're creating so much data and they're starting to say, okay, we could analyze this, uh, in the cloud, we could transition it. We could move petabytes of data to AWS cloud, or we can run, uh, computational workloads on premises. We can really do some analytics on this data transition, uh, those high level and sort of raw analytics back to AWS run 'em through machine learning. Um, and we don't have to transition 10, 12 petabytes of data into AWS. >>So I gotta end the segment on a, on a, kind of a, um, fun, I was told to ask you about your personal background on premise architect, Aus cloud, and skydiving instructor. How does that all work together? What tell, what does this mean? >>Yeah. Uh, I, >>You jumped out a plane and got a job. You got a customer to jump >>Out kind of. So I was, you jumped out. I was teaching Scott eing, uh, before I, before I started in the cloud space, this was 13, 14 years ago. I was a, I still am a Scott I instructor. Uh, I was teaching Scott eing and I heard out of the corner of my ear, uh, a guy that owned an MSP that was lamenting about, um, you know, storing data and how his customers are working. And he can't find enough people to operate all these workloads. So I walked over and said, Hey, this is, this is what I went to school for. Like, I'd love to, you know, I was living in a tent in the woods, teaching skydiving. I was like, I'd love to not live in a tent in the woods. So, uh, I started in the first day there, we had a, and, uh, EC two had just come out <laugh> um, and, uh, like, >>This is amazing. >>Yeah. And so we had this discussion, we should start moving customers here. And, uh, and that totally revolutionized that business, um, that, that led to, uh, that that guy actually still owns a skydiving airport. But, um, but through all of that, and through being in on premises, migrated me and myself, my career into the cloud, and now it feels like, uh, almost, almost looking back and saying, now let's take what we learned in the cloud and, and apply those lessons and those services to premises. >>So it's such a great story. You know, I was gonna, you know, you know, the, the, the, the whole, you know, growth mindset pack your own parachute, you know, uh, exactly. You know, the cloud in the early days was pretty much will the shoot open. Yeah. It was pretty much, you had to roll your own cloud at that time. And so, you know, you, you jump on a plane, you gotta make sure that parachute is gonna open. >>And so was Kubernetes by the way, 2015 or so when, uh, when that was coming out, it was, I mean, it was, it was still, and I, maybe it does still feel like that to some people, right. Yeah. But, uh, it was, it was the same kind of feeling that we had in the early days of AWS, the same feeling we have when we >>It's much now with you guys, it's more like a tandem jump. Yeah. You know, but, but it's a lot of, lot of this cutting stuff like jumping out of an airplane. Yeah. You guys, the right equipment, you gotta do the right things. Exactly. >>Right. >>Matthew, thanks for coming on the cube. Really appreciate it. Absolutely great conversation. Thanks for having me. Okay. The cubes here, lot in San Francisco for AWS summit, I'm John for your host of the cube. Uh, we'll be at a summit in New York coming up in the summer as well. Look up for that. Look at this calendar for all the cube, actually@thecube.net. We'll right back with our next segment after this break. >>Okay. Welcome back everyone to San Francisco live coverage here, we're at the cube, a summit 2022. We're back in person. I'm John furry host of the cube. We'll be at the, a us summit in New York city this summer, check us out then. But right now, two days in San Francisco getting all coverage, what's going on in the cloud, we got a cube alumni and friend of the cube, my dos car CEO, investor, a Sierra, and also an investor and a bunch of startups, angel investor. Gonna do great to see you. Thanks for coming on the cube. Good to see you. Good to see you, Pam. Cool. How are you? Good. >>How are you? >>So congratulations on all your investments. Uh, you've made a lot of great successes, uh, over the past couple years, uh, and your company raising, uh, some good cash as Sarah so give us the update. How much cash have you guys raised? What's the status of the company product what's going on? First >>Of all, thank you for having me. We're back to be business with you never while after. Great to see you. Um, so is a company started around four years back. I invested with a few of the investors and now I'm the CEO there. Um, we have raised close to a hundred million there. Uh, the investors are people like nor west Menlo, true ventures, coast, lo ventures, Ram Shera, and all those people, all known guys that Antibe chime Paul Mayard web. So a whole bunch of operating people and, uh, Silicon valley vs are involved. >>And has it gone? >>It's going well. We are doing really well. We are going almost 300% year over year. Uh, for last three years, the space ISR is going after is what I call the applying AI for customer service. It operations, it help desk the same place I used to work at ServiceNow. We are partners with ServiceNow to take, how can we argument for employees and customers, Salesforce, and ServiceNow to take it to the next stage? Well, >>I love having you on the cube, Dave and I, and Dave Valenti as well loves having you on too, because you not only bring the entrepreneurial CEO experience, you're an investor. You're like a, you're like a guest analyst. <laugh>, >>You know, >>You >>Get, the comment is fun to talk to you though. >>You get the commentary, you, your, your finger on the pulse. Um, so I gotta ask you obviously, AI and machine learning, machine learning AI, or you want to phrase it. Isn't every application. Now, AI first, uh, you're seeing a lot of that going on. You're starting to see companies build the modern applications at the top of the stack. So the cloud scale has hit. We're seeing cloud out scale. You predicted that we talked about in the cube many times. Now you have that past layer with a lot more services and cloud native becoming a standard layer. Containerizations growing Docker just raised a hundred million on our $2 billion valuation back from the dead after they pivoted from an enterprise services. So open source developers are booming. Um, where's the action. I mean, is there data control, plane emerging, AI needs data. There's a lot of challenges around this. There's a lot of discussions and a lot of companies being funded observability there's 10 million observability companies. Data is the key. This is what's your angle on this. What's your take. Yeah, >>No, look, I think I'll give you the view that I see, right? I, from my side, obviously data is very clear. So the things that room system of record that you and me talked about, the next layer is called system of intelligence. That's where the AI will play. Like we talk cloud native, it'll be called AI. NA NA is a new buzzword and using the AI for customer service, it operations. You talk about observability. I call it AI ops, applying AOPs for good old it operation management, cloud management. So you'll see the AOPs applied for whole list of, uh, application from observability doing the CMDB, predicting the events insurance. So I see a lot of work clicking for AIOps and AI service desk. What needs to be helped desk with ServiceNow BMC <inaudible> you see a new ALA emerging as a system of intelligence. Uh, the next would be is applying AI with workflow automation. So that's where you'll see a lot of things called customer workflows, employee workflows. So think of what UI path automation, anywhere ServiceNow are doing, that area will be driven with AI workflows. So you'll see AI going >>Off is RPA a company is AI, is RPA a feature of something bigger? Or can someone have a company on RPA UI S one will be at their event this summer? Um, or is it a product company? I mean, I mean, RPA is almost, should be embedded in everything. >>It's a feature. It is very good point. Very, very good thinking. So one is, it's a category for sure. Like, as we thought, it's a category, it's an area where RPA may change the name. I call it much more about automation, workflow automation, but RPA and automation is a category. Um, it's a company also, but that automation should be a, in every area. Yeah. Like we call cloud NA and AI NATO it'll become automation. NA yeah. And that's your thinking. >>It's almost interesting me. I think about the, what you're talking about what's coming to mind is I'm kind having flashbacks to the old software model of middleware. Remember at middleware, it was very easy to understand it was middleware. It sat between two things and then the middle and it was software was action. Now you have all kinds of workflows abstractions everywhere. Right? So multiple databases, it's not a monolithic thing. Right? Right. So as you break that down, is this the new modern middleware? Because what you're talking about is data workflows, but they might be siloed or they integrated. I mean, these are the challenges. This is crazy. What's the, >>So don't about the databases become all polyglot databases. I call this one polyglot automation. So you need automation as a layer, as a category, but you also need to put automation in every area, like, as you were talking about, it should be part of ServiceNow. It should be part of ISRA, like every company, every Salesforce. So that's why you see MuleSoft and Salesforce buying RPA companies. So you'll see all the SaaS companies could cloud companies having an automation as a core. So it's like how you have a database and compute and sales and networking. You'll also will have an automation as a layer <inaudible> inside every stack. >>All right. So I wanna shift gears a little bit and get your perspective on what's going on behind us. You can see, uh, behind us, you got the expo hall. You got, um, we're back to vents, but you got, you know, am Clume Ove, uh, Dynatrace data dog, innovative all the companies out here that we know, we interview them all. They're trying to be suppliers to this growing enterprise market. Right. Okay. But now you also got the entrepreneurial equation. Okay. We're gonna have John Sado on from Deibel later today. He's a former NEA guy and we always talk to Jerry, Jen, we know all the, the VCs. What does the startups look like? What does the state of the, in your mind, cause you, I know you invest the entrepreneurial founder situation. Cloud's bigger. Mm-hmm <affirmative> global, right? Data's part of it. You mentioned data's. Yes. Basically. Data's everything. What's it like for a first an entrepreneur right now who's starting a company. What's the white space. What's the attack plan. How do they get in the market? How do they engineer everything? >>Very good. So I'll give it to, uh, two things that I'm seeing out there. Remember leaders, how Amazon created the startups 15 years back, everybody built on Amazon now, Azure and GCP. The next layer would be is people don't just build on Amazon. They're gonna build it on top of snowflake. Companies are snowflake becomes a data platform, right? People will build on snowflake. Right? So I see my old boss flagman try to build companies on snowflake. So you don't build it just on Amazon. You build it on Amazon and snowflake. Snowflake will become your data store. Snowflake will become your data layer. Right? So I think that's the next level of <inaudible> trying to do that. So if I'm doing observability AI ops, if I'm doing next level of Splunk SIM, I'm gonna build it on snowflake, on Salesforce, on Amazon, on Azure, et cetera. >>It's interesting. You know, Jerry Chan has it put out a thesis of a couple months ago called castles in the cloud where your Mo is what you do in the cloud. Not necessarily in, in the, in the IP. Um, Dave LAN and I had last reinvent, coined the term super cloud, right? He's got a lot of traction and a lot of people throwing, throwing mud at us, but we were, our thesis was, is that what Snowflake's doing? What Goldman S Sachs is doing. You starting to see these clouds on top of clouds. So Amazon's got this huge CapEx advantage, and guys, Charles Fitzgerald out there who we like was kind of shitting on us saying, Hey, you guys terrible, they didn't get it. Like, yeah, I don't think he gets it, but that's a whole, can't wait to debate him publicly on this. <laugh> cause he's cool. Um, but snowflake is on Amazon. Now. They say they're on Azure now. Cause they've got a bigger market and they're public, but ultimately without a AWS snowflake doesn't exist. And, and they're reimagining the data warehouse with the cloud, right? That's the billion dollar opportunity. It >>Is. It is. They both are very tight. So imagine what Frank has done at snowflake and Amazon. So if I'm a startup today, I want to build everything on Amazon where possible whatever is, I cannot build. I'll make the pass layer. Remember the middle layer pass will be snowflake so I can build it on snowflake. I can use them for data layer if I really need to size build it on force.com Salesforce. Yeah. Right. So I think that's where you'll see. So >>Basically the, if you're an entrepreneur, the, the north star in terms of the, the outcome is be a super cloud. >>It is, >>That's the application on another big CapEx ride, the CapEx of AWS or cloud, >>And that reduce your product development, your go to market and you get use the snowflake marketplace to drive your engagement. Yeah. >>Yeah. How are, how is Amazon and the clouds dealing with these big whales, the snowflakes of the world? I mean, I know they got a great relationship, uh, but snowflake now has to run a company they're public. Yeah. So, I mean, I'll say, I think they had Redshift. Amazon has got Redshift. Um, but Snowflake's a big customer in the, they're probably paying AWS, I think big bills too. So >>Joe on very good. Cause it's like how Netflix is and Amazon prime, right. Netflix runs on Amazon, but Amazon has Amazon prime that co-optation will be there. So Amazon will have Redshift, but Amazon is also partnering with, uh, snowflake to have native snowflake data warehouses or data layer. So I think depending on the application use case, you have to use each of the above. I think snowflake is here for a long term. Yeah. Yeah. So if I'm building an application, I want to use snowflake then writing from stats. >>Well, I think that it comes back down to entrepreneurial hustle. Do you have a better product? Right. Product value will ultimately determine it as long as the cloud doesn't, you know, foreclose, your, you that's right with some sort of internal hack. Uh, but I think, I think the general question that I have is that I, I think it's okay to have a super cloud like that because the rising tide is still happening at some point, when does the rising tide stop and do the people shopping up their knives, it gets more competitive or is it just an infinite growth? So >>I think it's growth. You call it cloud scale, you invented the word cloud scale. So I think look, cloud will continually agree, increase. I think there's as long as there more movement from on, uh, OnPrem to the classical data center, I think there's no reason at this point, the rumor, the old lift and shift that's happening in like my business. I see people lift and shifting from the it operations. It helpless, even the customer service service now and, uh, ticket data from BMCs CAS like Microfocus, all those workloads are shifted to the cloud, right? So cloud ticketing system is happening. Cloud system of record is happening. So I think this train has still a long way to go >>Made. I wanna get your thoughts for the folks watching that are, uh, enterprise buyers are practitioners, not suppliers to the more market, feel free to text me or DMing. The next question's really about the buying side, which is if I'm a customer, what's the current, um, appetite for startup products, cuz you know, the big enterprises now and you know, small, medium, large and large enterprise are all buying new companies cuz a startup can go from zero to relevant very quickly. So that means now enterprises are engaging heavily with startups. What's it like what's is there a change in order of magnitude of the relationship between the startup selling to, or growing startup selling to an enterprise? Um, have you seen changes there? I mean I'm seeing some stuff, but why don't get your thoughts on that? What, >>No, it is. If I growing by or 2007 or eight, when I used to talk to you back then and Amazon started very small, right? We are an Amazon summit here. So I think enterprises on the average used to spend nothing with startups. It's almost like 0% or 1% today. Most companies are already spending 20, 30% with startups. Like if I look at a CIO or line of business, it's gone. Yeah. Can it go more? I think it can in the next four, five years. Yeah. Spending on the startups. >>Yeah. And check out, uh, AWS startups.com. That's a site that we built for the startup community for buyers and startups. And I want to get your reaction because I reference the URL cause it's like, there's like a bunch of companies we've been promoting because the solutions that startups have actually are new stuff. Yes. It's bending, it's shifting for security or using data differently or um, building tools and platforms for data engineering. Right. Which is a new persona that's emerging. So you know, a lot of good resources there. Um, and goes back now to the data question. Now, getting back to your, what you're working on now is what's your thoughts around this new, um, data engineering persona, you mentioned AIOps, we've been seeing AIOps IOPS booming and that's creating a new developer paradigm that's right. Which we call coin data as code data as code is like infrastructure is code, but it's for data, right? It's developing with data, right? Retraining machine learnings, going back to the data lake, getting data to make, to do analysis, to make the machine learning better post event or post action. So this, this data engineers like an SRE for data, it's a new, scalable role we're seeing. Do you see the same thing? Do you agree? Um, do you disagree or can you share >>Yourself a lot of first is I see the AIOP solutions in the future should be not looking back. I need to be like we are in San Francisco bay. That means earthquake prediction. Right? I want AOPs to predict when the outages are gonna happen. When there's a performance issue. I don't think most AOPs vendors have not gone there yet. Like I spend a lot of time with data dog, Cisco app Dyna, right? Dynatrace, all this solution. We will go future towards predict to proactive solution with AOPs. But what you bring up a very good point on the data side. I think like we have a Amazon marketplace and Amazon for startup, there should be data exchange where you want to create for AOPs and AI service desk. Customers are give the data, share the data because we thought the data algorithms are useless. I can them, but I gotta train them, modify them, tweak them, make them >>Better, >>Make them better. Yeah. And I think their whole data exchange is the industry has not thought through something you and me talk many times. Yeah. Yeah. I think the whole, that area is very important. >>You've always been on, um, on the Vanguard of data because, uh, it's been really fun. Yeah. >>Going back to big data days back in 2009, you know, >>Look at, look how much data Rick has grown. >>It is. They doubled the >>Key cloud air kinda went private. So good stuff, man. What are you working on right now? Give a, give a, um, plug for what you're working on. You'll still investing. >>I do still invest, but look, I'm a hundred percent on ISRA right now. I'm the CEO there. Yeah. Okay. So right. ISRA is my number one baby right now. So I'm looking at that growing customers and my customers are some of them, you like it's zoom auto desk McAfee, uh, grand to so all the top customers, um, mainly for it help desk customer service. AIOps those are three product lines and going after enterprise and commercial deals. >>And when should someone buy your product? What's what's their need? What category is it? >>I think they look whenever somebody needs to buy the product is if you need AOP solution to predict, keep your lights on predict is one area. If you want to improve employee experience, you are using a slack teams and you want to automate all your workflows. That's another value problem. Third is customer service. You don't want to hire more people to do it. Some of the areas where you want to scale your company, grow your company, eliminate the cost customer service. >>Great stuff, man. Great to see you. Thanks for coming on. Congratulations on the success of your company and your investments. Thanks for coming on the cube. Okay. I'm John fur here at the cube live in San Francisco for day one of two days of coverage of Aish summit 2022. And we're gonna be at Aus summit in San, uh, in New York in the summer. So look for that on this calendar, of course go to eight of us, startups.com. I mentioned that it's decipher all the hot startups and of course the cube.net and Silicon angle.com. Thanks for watching. We'll be back more coverage after this short break. >>Okay. Welcome back everyone. This the cubes coverage here in San Francisco, California, a Davis summit, 2022, the beginning of the event season, as it comes back, little bit smaller footprint, a lot of hybrid events going on, but this is actually a physical event, a summit in new York's coming in the summer. We'll be there too with the cube on the set. We're getting back in the groove psych to be back. We were at reinvent, uh, as well, and we'll see more and more cube, but you're can see a lot of virtual cube outta hybrid cube. We wanna get all those conversations, try to get more interviews, more flow going. But right now I'm excited to have Corey Quinn here on the back on the cube chief cloud economists with bill group. He's the founder, uh, and chief content person always got great angles, fun comedy, authoritative Corey. Great to see you. Thank >>You. Thanks. Coming on. Sure is a lot of words to describe is shit posting, which is how I describe what I tend to do. Most days, >>Shit posting is an art form now. And if you look at mark, Andrew's been doing a lot of shit posting lately. All a billionaires are shit hosting, but they don't know how to do it. Like they're not >>Doing it right? So there's something opportunity there. It's like here's how to be even more obnoxious and incisive. It's honestly the most terrifying scenario for anyone is if I have that kind of budget to throw at my endeavors, it's like, I get excited with a nonsense I can do with a $20 gift card for an AWS credit compared to, oh well, if I could buy a midsize island, do begin doing this from, oh, then we're having fun. >>This shit posting trend. Interesting. I was watching a thread go on about, saw someone didn't get a job because of their shit posting and the employer didn't get it. And then someone on this side I'll hire the guy cuz I get that's highly intelligent shit posting. So for the audience that doesn't know what shit posting is, what is shit posting? >>It's more or less talking about the world of enter prize technology, which even that sentence is hard to finish without falling asleep and toppling out of my chair in front of everyone on the livestream. But it's doing it in such a way that brings it to life that says the quiet part. A lot of the audience is thinking, but generally doesn't say either because they're polite or not a jackass or more prosaically are worried about getting fired for better or worse. I don't don't have that particular constraint, >>Which is why people love you. So let's talk about what you, what you think is, uh, worthy and not worthy in the industry right now, obviously, uh, coupons coming up in Spain, which they're having a physical event, you see the growth of cloud native Amazon's of all the Adams, especially new CEO. Andy's move on to be the chief of all Amazon. Just so I'm the cover of was it time met magazine? Um, he's under a lot of stress. Amazon's changed. Invoice has changed. What's working. What's not, what's rising, what's falling. What's hot. What's not, >>It's easy to sit here and criticize almost anything. These folks do. They're they're effectively in a fishbowl, but I have trouble imagining the logistics. It takes to wind up handling the catering for a relatively downscale event like this one this year, let alone running a 1.7 million employee company having to balance all the competing challenges and pressures and the rest. I, I just can't fathom what it would be like to look at all of AWS. And it's, it's sprawling immense that dominates our entire industry and say, okay, this is a good start, but I, I wanna focus on something with a broader remit. What is that? How do you even get into that position? And you can't win once you're there. All you can do is hold onto the tiger and hope you don't get mold. >>Well, there's a lot of force for good conversations. Seeing a lot of that going on, Amazon's trying to port eight of us is trying to portray themselves as you know, the Pathfinder, you know, you're the pioneer, um, force for good. And I get that and I think that's a good angle as cloud goes mainstream. There's still the question of, we had a guy on just earlier, who was a skydiving instructor and we were joking about the early days of cloud. Like that was like skydiving, build a parachute open, you know, and now same kind of thing. As you move to edge, things are like reliable in some areas, but still new, new fringe, new areas. That's crazy. Well, >>Since the last time we've spoken, uh, Steve Schmidt is now the CISO for all of Amazon and his backfill replacement. The AWS CISO is CJ. Moses who as a hobby races, a as a semi-pro race car driver to my understanding, which either, I don't know what direction to take that in either. This is what he does to relax or ultimately, or ultimately it's. Huh? That, that certainly says something about risk assessment. I'm not entirely sure what, but okay. <laugh> either way, sounds like more exciting. Like I better >>Have a replacement ready <laugh> I, in case something goes wrong on the track, highly >>Available >>CSOs. I gotta say one of the things I do like in the recent trend is that the tech companies are getting into the formula one, which I was never a fan of until I watched that Netflix series. But when you look at the formula one, it's pretty cool. Cause it's got some tech angles, I get the whole data instrumentation thing, but the most coolest thing about formula one is they have these new rigs out. Yeah. Where you can actually race in east sports with other people in pure simulation of the race car. You gotta get the latest and videographic card, but it's basically a tricked out PC with amazing monitors and you have all the equipment of F1 and you're basically simulating racing. >>Oh, it's great too. And I can see the appeal of these tech companies getting into it because these things are basically rocket shifts. When those cars go, like they're sitting there, we can instrument every last part of what is going on inside that vehicle. And then AWS crops up. And we can bill on every one of those dimensions too. And it's like slow down their hasty pudding one step at a time. But I do see the appeal. >>So I gotta ask you about, uh, what's going on in your world. I know you have a lot of great success. We've been following you in the queue for many, many years. Got a great newsletter, check out Corey Quinn's newsletter, uh, screaming in the cloud program. Uh, you're on the cutting edge and you've got a great balance between really being snarky and, and, and really being delivering content. That's exciting, uh, for people, uh, with a little bit of an edge, um, how's that going? Uh, what's the blowback, any blowback late? Has there been uptick? What was, what are some of the things you're hearing from your audience, more Corey, more Corey. And then of course the, the PR team's calling you >>The weird thing about having an audience beyond a certain size is far and away as a landslide. The most common response I get is silence where it's high. I'm emailing an awful lot of people at last week in AWS every week and okay. They must not have heard me it. That is not actually true. People just generally don't respond to email because who responds to email newsletters. That sounds like something, a lunatic might do same story with response to live streams and podcasts. It's like, I'm gonna call into that am radio show and give them a piece of my mind. People generally don't do >>That. We should do that. Actually. I think you're people would call in, oh, >>I, I think >>I guarantee we had that right now. People would call in and say, Corey, what do you think about X? >>Yeah. It not, everyone understands the full context of what I do. And in fact, increasingly few people do and that's fine. I, I keep forgetting that sometimes people do not see what I'm doing in the same light that I do. And that's fine. Blowback has been largely minimal. Honestly, I am surprised about anything by how little I have gotten over the last five years of doing this, but it would be easier to dismiss me if I weren't generally. Right. When, okay, so you launch this new service and it seems pretty crappy to me cuz when I try and build something, it falls over and begs for help. And people might not like hearing that, but it's what customers are finding too. Yeah. I really am the voice of the >>Customer. You know, I always joke with Dave Alane about how John Fort's always at, uh, um, reinvent getting the interview with jazzy now, Andy we're there, you're there. And so we have these rituals at the events. It's all cool. Um, one of the rituals I like about your, um, your content is you like to get on the naming product names. Um, and, and, and, and, and kind of goof on that. Now why I like is because I used to work at ETT Packard where they used to name things as like engineers, HP 1 0, 0 5, or we can't call, we >>Have a new monitor. How are we gonna name it? Throw the wireless keyboard down the stairs again. And then there you go. Yeah. >>It's and the old joke at HP was if they, if they invented SU sushi, they'd say, yeah, we can't call sushi. It's cold, dead fish. That's what it is. And so the joke was cold. Dead fish is a better name than sushi. So you know is fun. So what's the, what are the, how's the Amazon doing in there? Have they changed their naming, uh, strategy, uh, on some of their, their >>Producting. So they're going in different directions. When they named Amazon Aurora, they decided to explore a new theme of Disney princesses as they go down those paths. And some things are more descriptive. Some people are clearly getting bonused on number of words, they can shove into it. Like the better a service is the longer it's name. Like AWS systems manager, session manager is a great one. I love the service ridiculous name. They have a systems manager, parameter store, which is great. They have secrets manager, which does the same thing. It's two words less, but that one costs money in a way that systems manage your parameter store does not. It's fun. >>What's your, what's your favorite combination of acronyms >>Combination >>Of gots. You got EMR, you got EC two, you got S3 SQS. Well, RedShift's not an acronym you >>Gets is one of my personal favorites because it's either elastic block store or elastic bean stock, depending entirely on the context of the conversation, they >>Shook up bean stock or is that still around? Oh, >>They never turn anything off. They're like the anti Google, Google turns things off while they're still building it. Whereas Amazon is like, well, we built this thing in 2005 and everyone hates it, but while we certainly can't change it, now it has three customers on it. John three <laugh>. Okay. Simple BV still haunts our dreams. >>I, I actually got an email on, I saw one of my, uh, servers, all these C twos were being deprecated and I got an email I'm I couldn't figure out. Why can you just like roll it over? Why, why are you telling me? Just like, give me something else. All right. Okay. So let me talk about, uh, the other things I want to ask you, is that like, okay. So as Amazon better in some areas where do they need more work in your opinion? Because obviously they're all interested in new stuff and they tend to like put it out there for their end to end customers. But then they've got ecosystem partners who actually have the same product. Yes. And, and this has been well documented. So it's, it's not controversial. It's just that Amazon's got a database Snowflake's got out database service. So Redshift, snowflake data breach is out there. So you got this co-op petition. Yes. How's that going? And what do you hearing about the reaction to any of that stuff? >>Depends on who you ask. They love to basically trot out a bunch of their partners who will say nice things about them. And it very much has heirs of, let's be honest, a hostage video, but okay. Cuz these companies do partner with, and they cannot afford to rock the boat too far. I'm not partnered with anyone. I can say what I want. And they're basically restricted to taking away my birthday at worse so I can live with that. >>All right. So I gotta ask about multicloud. Cause obviously the other cloud shows are coming up. Amazon hated that word multicloud. Um, a lot of people though saying, you know, it's not a real good marketing word. Like multicloud sounds like, you know, root canal. Mm-hmm <affirmative> right. So is there a better description for multicloud? >>Multiple single >>Cloudant loves that term. Yeah. >>You know, you're building in multiple single points of failure, do it for the right reasons or don't do it as a default. I believe not doing it is probably the right answer. However, and if I were, if I were Amazon, I wouldn't want to talk about my multi-cloud either as the industry leader, let's talk about other clouds, bad direction to go in from a market cap perspective. It doesn't end well for you, but regardless of what they want to talk about, or don't want to talk about what they say, what they don't say, I tune all of it out. And I look at what customers are doing and multi-cloud exists in a variety of forms. Some brilliant, some brain dead. It depends a lot on, but my general response is when someone gets on stage from a company and tells me to do a thing that directly benefits their company. I am skeptical at best. Yeah. When customers get on stage and say, this is what we're doing because it solves problems. That's when I shut up and listen. >>Yeah, course. Awesome. Corey, I gotta ask you a question cause I know you we've been, you know, fellow journeyman and the, and the cloud journey going to all the events and then the pandemic hit. We now in the third year, who knows what it's gonna gonna end. Certainly events are gonna look different. They're gonna be either changing footprint with the virtual piece, new group formations. Community's gonna emerge. You've got a pretty big community growing and it's growing like crazy. What's the weirdest or coolest thing or just big changes you've seen with the pandemic, uh, from your perspective, cuz you've been in the you're in the middle of the whitewater rafting. You've seen the events you circle offline. You saw the online piece, come in, you're commentating, you're calling balls and strikes in the industry. You got a great team developing over there. Duck build group. What's the big aha moment that you saw with the pandemic. Weird, funny, serious, real in the industry and with customers what's >>Accessibility. Reinvent is a great example. When in the before times it's open to anyone who wants to attend, who can pony up two grand and a week in Las Vegas and get to Las Vegas from wherever they happen to be by moving virtually suddenly it, it embraces the reality that talent is evenly. Distributed. Opportunity is not. And that means that suddenly these things are accessible to a wide swath of audience and potential customer base and the rest that hadn't been invited to the table previously, it's imperative that we not lose that. It's nice to go out and talk to people and have people come up and try and smell my hair from time to time, I smelled delightful. Let me assure you. But it was, but it's also nice to be. >>I have a product for you if you want, you know? Oh, >>Oh excellent. I look forward to it. What is it? Pudding? Why not? <laugh> >>What else have you seen? So when accessibility for talent. Yes. Which by the way is totally home run. What weird things have happened that you've seen? Um, that's >>Uh, it's, it's weird, but it's good that an awful lot of people giving presentation have learned to tighten their message and get to the damn point because most people are not gonna get up from a front row seat in a conference hall, midway through your Aing talk and go somewhere else. But they will change a browser tab and you won't get them back. You've gotta be on point. You've gotta be compelling if it's going to be a virtual discussion. Yeah. >>And you turn off your iMessage too. >>Oh yes. It's always fun in the, in the meetings when you're ho to someone and their colleague is messaging them about, should we tell 'em about this? And I'm sitting there reading it and it's >>This guy is really weird. Like, >>Yes I am and I bring it into the conversation and then everyone's uncomfortable. It goes, wow. Why >>Not? I love when my wife yells at me over I message. When I'm on a business call, like, do you wanna take that about no, I'm good. >>No, no. It's better off. I don't the only entire sure. It's >>Fine. My kids text. Yeah, it's fine. Again, that's another weird thing. And, and then group behavior is weird. Now people are looking at, um, communities differently. Yes. Very much so, because if you're fatigued on content, people are looking for the personal aspect. You're starting to see much more of like yeah. Another virtual event. They gotta get better. One and two who's there. >>Yeah. >>The person >>That's a big part of it too is the human stories are what are being more and more interesting. Don't get up here and tell me about your product and how brilliant you are and how you built it. That's great. If I'm you, or if I wanna work with you or I want to compete with you or I want to put on my engineering hat and build it myself. Cause why would I buy anything? That's more than $8. But instead, tell me about the problem. Tell me about the painful spot that you specialize in. Yeah. Tell me a story there. >>I, I think >>That gets a glimpse in a hook and makes >>More, more, I think you nailed it. Scaling storytelling. Yes. And access to better people because they don't have to be there in person. I just did a thing. I never, we never would've done the queue. We did. Uh, Amazon stepped up in sponsors. Thank you, Amazon for sponsoring international women's day, we did 30 interviews, APAC. We did five regions and I interviewed this, these women in Asia, Pacific eight, PJ, they call for in this world. And they're amazing. I never would've done those interviews cuz I never, would've seen 'em at an event. I never would've been in pan or Singapore, uh, to access them. And now they're in the index, they're in the network. They're collaborating on LinkedIn. So a threads are developing around connections that I've never seen before. Yes. Around the content. >>Absolutely >>Content value plus and >>Effecting. And that is the next big revelation of this industry is going to realize you have different companies. And, and I Amazon's case different service teams all competing with each other, but you have the container group and you have the database group and you have the message cuing group. But customers don't really want to build things from spare parts. They want a solution to a problem. I want to build an app that does Twitter for pets or whatever it is I'm trying to do. I don't wanna basically have to pick and choose and fill my shopping cart with all these different things. I want something that's gonna basically give me what I'm trying to get as close to turnkey as possible. Moving up the stack. That is the future. And just how it gets here is gonna be >>Well we're here at Corey Quinn, the master of the master of content here in the a ecosystem. Of course we we've been following up from the beginning. His great guy, check out his blog, his site, his newsletter screaming podcast. Corey, final question for, uh, what are you here doing? What's on your agenda this week in San Francisco and give a plug for the duck build group. What are you guys doing? I know you're hiring some people what's on the table for the company. What's your focus this week and put a plug in for the group. >>I'm here as a customer and basically getting outta my cage cuz I do live here. It's nice to actually get out and talk to folks who are doing interesting things at the duck bill group. We solved one problem. We fixed the horrifying AWS bill, both from engineering and architecture, advising as well as negotiating AWS contracts because it turns out those things are big and complicated. And of course my side media projects last week in aws.com, we are, it it's more or less a content operation where I in my continual and ongoing love affair with the sound of my own voice. >><laugh> and you're good. It's good content it's on, on point fun, Starky and relevant. So thanks for coming to the cube and sharing with us. Appreciate it. No >>Thank you button. >>You. Okay. This the cube covers here in San Francisco, California, the cube is back going to events. These are the summits, Amazon web services summits. They happen all over the world. We'll be in New York and obviously we're here in San Francisco this week. I'm John fur. Keep, keep it right here. We'll be back with more coverage after this short break. Okay. Welcome back everyone. This's the cubes covers here in San Francisco, California, we're live on the show floor of AWS summit, 2022. I'm John for host of the cube and remember AWS summit in New York city coming up this summer, we'll be there as well. And of course reinvent the end of the year for all the cube coverage on cloud computing and AWS two great guests here from the APN global APN Sege chef Jenko and Jeff Grimes partner lead Jeff and Sege is doing partnerships global APN >>AWS global startup program. Yeah. >>Okay. Say that again. >>AWS. We'll start >>Program. That's the official name. >>I love >>It too long, too long for me. Thanks for coming on. Yeah, >>Of course. >>Appreciate it. Tell us about what's going on with you guys. What's the, how was you guys organized? You guys we're obviously we're in San Francisco bay area, Silicon valley, zillions of startups here, New York. It's got another one we're gonna be at tons of startups. A lot of 'em getting funded, big growth and cloud big growth and data secure hot in all sectors. >>Absolutely. >>So maybe, maybe we could just start with the global startup program. Um, it's essentially a white glove service that we provide to startups that are built on AWS. And the intention there is to help identify use cases that are being built on top of AWS. And for these startups, we want to pro vibe white glove support in co building products together. Right. Um, co-marketing and co-selling essentially, um, you know, the use cases that our customers need solved, um, that either they don't want to build themselves or are perhaps more innovative. Um, so the, a AWS global startup program provides white glove support. Dedicat at headcount for each one of those pillars. Um, and within our program, we've also provided incentives, programs go to market activities like the AWS startup showcase that we've built for these startups. >>Yeah. By the way, AWS startup, AWS startups.com is the URL, check it out. Okay. So partnerships are key. Jeff, what's your role? >>Yeah. So I'm responsible for leading the overall effort for the AWS global startup program. Um, so I've got a team of partner managers that are located throughout the us, uh, managing a few hundred startup ISVs right now. <laugh> >>Yeah, you got a >>Lot. We've got a lot. >>There's a lot. I gotta, I gotta ask a tough question. Okay. I'm I'm a startup founder. I got a team. I just got my series a we're grown. I'm trying to hire people. I'm super busy. What's in it for me. Yeah. What do you guys bring to the table? I love the white glove service, but translate that what's in it for what do I get out of it? What's >>A story. Good question. I focus, I think. Yeah, because we get, we get to see a lot of partners building their businesses on AWS. So, you know, from our perspective, helping these partners focus on what, what do we truly need to build by working backwards from customer feedback, right? How do we effectively go to market? Because we've seen startups do various things, um, through trial and error, um, and also just messaging, right? Because oftentimes partners or rather startups, um, try to boil the ocean with many different use cases. So we really help them, um, sort of laser focus on what are you really good at and how can we bring that to the customer as quickly as possible? >>Yeah. I mean, it's truly about helping that founder accelerate the growth of their company, right. And there's a lot that you can do with AWS, but focus is truly the key word there because they're gonna be able to find their little piece of real estate and absolutely deliver incredible outcomes for our customers. And then they can start their growth curve there. >>What are some of the coolest things you've seen with the APN that you can share publicly? I know you got a lot going on there, a lot of confidentiality. Um, but you know, we're here a lot of great partners on the floor here. I'm glad we're back at events. Uh, a lot of stuff going on digitally with virtual stuff and, and hybrid. What are some of the cool things you guys have seen in the APN that you can point to? >>Yeah, absolutely. I mean, I can point to few, you can take them. So, um, I think what's been fun over the years for me personally, I came from a startup brand sales at an early stage startup and, and I went through the whole thing. So I have a deep appreciation for what these guys are going through. And what's been interesting to see for me is taking some of these early stage guys, watching them progress, go public, get acquired and see that big day mm-hmm <affirmative>, uh, and being able to point to very specific items that we help them to get to that point. Uh, and it's just a really fun journey to watch. >>Yeah. I, and part of the reason why I really, um, love working at the AWS, uh, global startup program is working with passionate founders. Um, I just met with a founder today that it's gonna, he's gonna build a very big business one day, um, and watching them grow through these stages and supporting that growth. Um, I like to think of our program as a catalyst for enterprise is sort of scale. Yeah. Um, and through that we provide visibility, credibility and growth opportunities. >>Yeah. A lot, a lot of partners too. What I found talking to staff founders is when they have that milestone, they work so hard for it. Whether it's a B round C round Republic or get bought. Yeah. Um, then they take a deep breath and they look back at wow, what a journey it's been. So it's kind of emotional for sure. But still it's a grind. Right? You gotta, I mean, when you get funding, it's still day one. You don't stop. It's no celebrate, you got a big round or valuation. You still gotta execute >>And look it's hypercompetitive and it's brutally difficult. And our job is to try to make that a little less difficult and navigate those waters. Right. Where ever everyone's going after similar things. >>Yeah. And I think as a group element too, I observe that startups that I, I meet through the APN has been interesting because they feel part of AWS. Yeah, totally. As a group of community, as a vibe there. Um, I know they're hustling, they're trying to make things happen. But at the same time, Amazon throws a huge halo effect. I mean, that's a huge factor. I mean, you guys are the number one cloud in the business, the growth in every sector is booming. Yeah. And if you're a startup, you don't have that luxury yet. And look at companies like snowflake that built on top of AWS. I mean, people are winning by building on AWS. >>Yeah. And our, our, our program really validates their technology first. So we have, what's all the foundation's technical review that we put all of our startups through before we go to market. So that when enterprise customers are looking at startup technology, they know that it's already been vetted. And, um, to take that a step further and help these partners differentiate, we use programs like the competency programs, the DevOps competencies, the security competency, which continues to help, um, provide sort of a platform for these startups, help them differentiate. And also there's go to market benefits that are associated with that. >>Okay. So let me ask the, the question that's probably on everyone's mind, who's watching, certainly I asked this a lot. There's a lot of companies startups out there who makes the cut, is there a criteria cut? It's not like it's sports team or anything, but like sure. Like there's activate program, which is like, there's hundreds of thousands of startups out there. Not everyone is at the APN. Right? Correct. So ISVs again, that's a whole nother, that's a more mature partner that might have, you know, huge market cap or growth. How, how do you guys focus? How do you guys focus? I mean, you got a good question, you know, thousand flowers blooming all the time. Is there a new way you guys are looking at it? I know there's been some talk about restructure or, or new focus. What's the focus. >>Yeah. It's definitely not an easy task by any means. Um, but you know, I recently took over this role and we're really trying to establish focus areas, right. So obviously a lot of the ISVs that we look after are infrastructure ISVs. That's what we do. Uh, and so we have very specific pods that look after different type of partners. So we've got a security pod, we've got a DevOps pod, we've got core infrastructure, et cetera. And really, we're trying to find these ISVs that can solve, uh, really interesting AWS customer. >>You guys have a deliberate, uh, focus on these pillars. So what infrastructure, >>Security, DevOps, and data and analytics, and then line of business >>Line, business line business, like web >>Marketing, business apps, >>Owner type thing. Exactly. >>Yeah, exactly. >>So solutions there. Yeah. More solutions and the other ones are like hardcore. So infrastructure as well, like storage back up ransomware kind of stuff, or, >>Uh, storage, networking. >>Okay. Yeah. The classic >>Database, et cetera. Right. >>And so there's teams on each pillar. >>Yep. So I think what's, what's fascinating for the startups that we cover is that they've got, they truly have support from a build market sell perspective, right. So you've got someone who's technical to really help them get the technology, figured out someone to help them get the marketing message dialed and spread, and then someone to actually do the co-sell, uh, day to day activities to help them get in front of customers. >>Probably the number one request that we always ask for Amazon is can wish that sock report, oh, download it on the console, which we use all the time. <laugh> exactly. But security's a big deal. I mean, you know, ask the res are evolving, that role of DevOps is taking on dev SecOps. Um, I, I can see a lot of customers having that need for a relationship to move things faster. Do you guys provide like escalation or is that a part of a service or that not part of, uh, uh, >>Yeah, >>So the partner development manager can be an escalation for absolutely. Think of that. 'em as an extension of your business inside of AWS. >>Great. And you guys, how is that partner managers, uh, measure >>On those three pillars? Right. Got it. Are we billing, building valuable use cases? So product development go to market, so go to market activities, think blog, posts, webinars, case studies, so on and so forth. And then co-sell not only are we helping these partners win their current opportunities that they are sourcing, but can we also help them source net new deals? Yeah. Right. That's very, >>I mean, top asked from the partners is get me in front of customers. Right. Um, not an easy task, but that's a huge goal of ours to help them grow their top line. >>Right. Yeah. In fact, we had some interviews here on the cube earlier talking about that dynamic of how enterprise customers are buying. And it's interesting, a lot more POCs. I have one partner here that you guys work with, um, on observability, they got a huge POC with capital one mm-hmm <affirmative> and the enterprises are engaging the star ups and bringing them in. So the combination of open source software enterprises are leaning into that hard and bringing young growing startups in mm-hmm <affirmative>. Yep. So I could see that as a huge service that you guys can bring people in. >>Right. And they're bringing massively differentiated technology to the table. The challenge is they just might not have the brand recognition. The, at the big guys have mm-hmm <affirmative>. And so that's, our job is how do you get that great tech in front of the right situations? >>Okay. So my next question is about the show here, and then we'll talk globally. So here in San Francisco sure. You know, Silicon valley bay area, San Francisco bay area, a lot of startups, a lot of VCs, a lot of action. Mm-hmm <affirmative> so probably a big market for you guys. Yeah. So what's exciting here in SF. And then outside of SF, you guys have a global pro, have you see any trends that are geography based or is it sure areas more mature? There's certain regions that are better. I mean, I just interviewed a company here. That's doing, uh, a AWS edge really well in these cases. It's interesting that these, the partners are filling a lot of holes and gaps in the opportunities with a AWS. So what's exciting here. And then what's the global perspective. >>Yeah, totally. So obviously see a ton of partners from the bay area that we support. Um, but we're seeing a lot of really interesting technology come out of AMEA specifically. Yeah. Uh, and making a lot of noise here in the United States, which is great. Um, and so, you know, we definitely have that global presence and, and starting to see super differentiated technology come out of those regions. >>Yeah. Especially Tel Aviv. Yeah. >>Amy and real quick before you get into surge. It's interesting. The VC market in, in Europe is hot. They've got a lot of unicorns coming in. We've seen a lot of companies coming in. They're kind of rattling their own, you know, cage right now. Hey, look at us. Let's see if they crash, you know, but we don't see that happening. I mean, people have been predicting a crash now in, in the startup ecosystem for least a year. It's not crashing. In fact, funding's up. >>Yeah. The pandemic was hard on a lot of startups for sure. Yeah. Um, but what we've seen is many of these startups, they, as quickly as they can grow, they can also pivot as, as, as well. Um, and so I've actually seen many of our startups grow through the demo because their use cases are helping customers either save money, become more operationally efficient and provide value to leadership teams that need more visibility into their infrastructure during a pandemic. >>It's an interesting point. I talked to Andy jazzy and Adam Celski both say the same thing during the pandemic. Necessity's the mother of all invention. Yep. And startups can move fast. So with that, you guys are there to assist if I'm a startup and I gotta pivot cuz remember iterate and pivot, iterate and pivot. So you get your economics, that's the playbook of the ventures and the models. >>Exactly. How >>Do you guys help me do that? Give me an example of what me through. Pretend me, I'm a start up. Hey, I'm on the cloud. Oh my God. Pandemic. They need video conferencing. Hey cube. Yeah. What do I need? Search? What, what do >>I do? That's a good question. First thing is just listen. Yeah. I think what we have to do is a really good job of listening to the partner. Um, what are their needs? What is their problem statement? Where do they want to go at the end of the day? Um, and oftentimes because we've worked with, so how many successful startups that have come out of our program, we have, um, either through intuition or a playbook determined what is gonna be the best path forward and how do we get these partners to stop focusing on things that will eventually, um, just be a waste of time. Yeah. And, or not provide, or, you know, bring any fruit to the table, which, you know, essentially revenue. >>Well, we love startups here in the cube because one, um, they have good stories, they're oil and cutting edge, always pushing the envelope and they're kind of disrupting someone else. Yeah. And so they, they have an opinion. They don't mind sharing on camera. So love talking to startups. We love working with you guys on our startups. Showcases startups.com. Check out AWS startups.com and she got the showcase. So is, uh, final word. I'll give you guys the last word. What's the bottom line bumper sticker for AP globe. The global APN program summarize the opportunity for startups, what you guys bring to the table and we'll close it out. Totally. We'll start >>With you. Yeah. I think the AWS global startup programs here to help companies truly accelerate their business full stop. Right. And that's what we're here for. Love it. >>It's a good way to, it's a good way to put it. Dato yeah. >>All right. Thanks for coming out. Thanks John. Great to see you love working with you guys. Hey, startups need help. And the growing and huge market opportunities, the shift cloud scale data engineering, security infrastructure, all the markets are exploding in growth because of the digital transformation of realities here, open source and cloud. I'll making it happen here in the cube in San Francisco, California. I'm John furrier, your host. Thanks for >>Watching Cisco, John. >>Hello and welcome back to the Cube's live coverage here in San Francisco, California for AWS summit, 2022. I'm John for host of the cube. Uh, two days of coverage, AWS summit, 2022 in New York city coming up this summer will be there as well. Events are back. The cube is back of course, with the cube virtual cube hybrid, the cube.net. Check it out a lot of content this year more than ever a lot more cloud data cloud native, modern applic is all happening. Got a great guest here. Jeremy Burton, Cub alumni, uh, CEO of observe Inc in the middle of all the cloud scale, big data observability, Jeremy. Great to see you. Thanks. >>Coming on. Always great to come and talk to you on the queue, man. It's been been a few years, so, >>Um, well you, you got your hands. You're in the trenches with great startup, uh, good funding, great board, great people involved in the observability Smith hot area, but also you've been a senior executive president of Dell EMC. Um, 11 years ago you had a vision and you actually had an event called cloud meets big data. Um, yeah. And it's here, you predicted it 11 years ago. Um, look around it's cloud meets big data. >>Yeah. I mean the, the cloud thing I think, you know, was, was probably already a thing, but the big data thing I do claim credit for, for sort of catching that bus early, um, you know, we, we were on the, the, the bus early and, and I think it was only inevitable. Like, you know, if you could bring the economics and the compute of cloud to big data, you, you could find out things you could never possibly imagine. >>So you're close to a lot of companies that we've been covering deeply snowflake, obviously you involved, uh, at the board level, the other found, you know, the people there, uh, cloud, you know, Amazon, you know, what's going on here? Yeah. You're doing a startup as the CEO at the helm, uh, chief of observ, Inc, which is an observability, which is to me in the center of this confluence of data engineering, large scale integrations, um, data as code integrating into applications. I mean, it's a whole nother world developing, like you see with snowflake, it means snowflakes is super cloud as we call it. So a whole nother wave is here. What's your, what's this wave we're on what's how would you describe the wave? >>Well, a couple of things, I mean, people are, I think right in more software than, than ever before are why? Because they've realized that if, if you don't take your business online and offer a service, then you become largely irrelevant. And so you you've got a whole set of new applications. I think, I think more applications now than any point. Um, not, not just ever, but the mid nineties, I always looked at as the golden age of application development. Now, back then people were building for windows. Well, well now they're building for things like AWS is now the platform. Um, so you've got all of that going on. And then at the same time, the, the side effect of these applications is they generate data and lots of data. And the, you know, there's sort of the transactions, you know, what you bought today are something like that. But then there's what we do, which is all the telemetry, all the exhaust fumes. And I think people really are realizing that their differentiation is not so much their application. It's their understanding of the data. Can, can I understand who my best customers are, what I sell today. If people came to my website and didn't buy, then why not? Where did they drop off all of that? They wanna analyze. And, and the answers are all in the data. The question is, can you understand it >>In our last startup showcase, we featured data as code one of the insights that we got out of that, and I wanna get your opinion on our reaction to is, is that data used to be put into a data lake and turns into a data swamp or throw into the data warehouse. And then we'll do some queries, maybe a report once in a while. And so data, once it was done, unless it was real time, even real time was not good anymore after real time. That was the old way. Now you're seeing more and more, uh, effort to say, let's go look at the data, cuz now machine learning is getting better. Not just train once mm-hmm <affirmative> they're iterating. Yeah. This notion of iterating and then pivoting, iterating and pivoting. Yeah, that's a Silicon valley story. That's like how startups work, but now you're seeing data being treated the same way. So now you have another, this data concept that's now yeah. Part of a new way to create more value for the apps. So this whole, this whole new cycle of >>Yeah. >>Data being reused and repurposed and figured out and yeah, >>Yeah. I'm a big fan of, um, years ago. Uh, uh, just an amazing guy, Andy McAfee at the MIT C cell labs I spent time with and he, he had this line, which still sticks to me this day, which is look I'm I'm. He said I'm part of a body, which believes that everything is a matter of data. Like if you have enough data, you can answer any question. And, and this is going back 10 years when he was saying these kind of things and, and certainly, you know, research is on the forefront. But I think, you know, starting to see that mindset of the, the sort of MIT research be mainstream, you know, in enterprises, they they're realizing that. Yeah, it is about the data. You know, if I can better understand my data better than my competitor, then I've got an advantage. And so the question is is, is how, what, what technologies and what skills do I need in my organization to, to allow me to do that. >>So let's talk about observing you the CEO of, okay. Given you've seen the ways before you're in the front lines of observability, which again is in the center of all this action what's going on with the company. Give a quick minute to explain, observe for the folks who don't know what you guys do. What's the company doing? What's the funding status, what's the product status and what's the customer status. Yeah. >>So, um, we realized, you know, a handful of years ago, let's say five years ago that, um, look, the way people are building applications is different. They they're way more functional. They change every day. Uh, but in some respects they're a lot more complicated. They're distributed. They, you know, microservices architectures and when something goes wrong, um, the old way of troubleshooting and solving problems was not gonna fly because you had SA so much change going into production on a daily basis. It was hard to tell like where the problem was. And so we thought, okay, it's about time. Somebody looks at the exhaust fumes from this application and all the telemetry data and helps people troubleshoot and make sense of the problems that they're seeing. So, I mean, that's observability, it's actually a term that goes back to the 1960s. It was a guy called, uh, Rudolph like, like everything in tech, you know, it's, it's a reinvention of something from years gone by. >>Um, there's a guy called, um, Rudy Coleman in 1960s coiner term and, and, and the term was being able to determine the state of a system by looking at its external outputs. And so we've been going on this for, uh, the best part of four years now. Um, it took us three years just to build the product. I think, I think what people don't appreciate these days often is the barrier to entry in a lot of these markets is quite high. You, you need a lot of functionality to have something that's credible with a customer. Um, so yeah, this last year we, we, we did our first year selling, uh, we've got about 40 customers now. Um, we just we've got great investors for the hill ventures. Uh, I mean, Mike SP who was, you know, the, the guy who was the, really, the first guy in it snowflake and the, the initial investor were fortunate enough to, to have Mike and our board. And, um, you know, part of the observed story is closely knit with snowflake all of that time with your data, you know, we, we store in there. >>So I want to get, uh, yeah. Pivot to that. Mike SP snowflake, Jeremy Burton, the cube kind of, kind of same thinking this idea of a super cloud or what snowflake became. Yeah. Snowflake is massively successful on top of AWS. Mm-hmm <affirmative> and now you're seeing startups and companies build on top of snowflake. Yeah. So that's become an entrepreneurial story that we think that to go big in the cloud, you can have a cloud on a cloud, uh, like as Jerry, Jerry Chan and Greylock calls it, castles in the cloud where there are moats in the cloud. So you're close to it. I know you, you're doing some stuff with snowflake. So as a startup, what's your view on building on top of say a snowflake or an AWS, because again, you gotta go where the data is. You need all the data. >>Yeah. So >>What's your take on that? I mean, >>Having enough gray hair now, um, you know, again, in tech, I think if you wanna predict the future, look at the past. And, uh, you know, 20 years ago, 25 years ago, I was at a, a smaller company called Oracle and an Oracle was the database company. And, uh, their, their ambition was to manage all of the world's transactional data. And they built on a platform or a couple of platforms, one, one windows, and the other main one was Solaris. And so at that time, the operating system was the platform. And, and then that was the, you know, ecosystem that you would compete on top of. And then there were companies like SAP that built applications on top of Oracle. So then wind the clock forward 25 years gray hairs. <laugh> the platform, isn't the operating system anymore. The platform is AWS, you know, Google cloud. I gotta probably look around if I say that in. Yeah, >>It's okay. Columbia, but hyperscale. Yeah. CapX built out >>That is the new platform. And then snowflake comes along. Well, their aspiration is to manage all of the, not just human generated data, but machine generated data in the world of cloud. And I think they they've done an amazing job are doing for the, I'd say, say the, the big data world, what Oracle did for the relational data world, you know, way back 25 years ago. And then there are folks like us come along and, and of course my ambition would be, look, if, if we can be as successful as an SAP building on top of snowflake, uh, as, as they were on top of Oracle, then, then we'd probably be quite happy, >>Happy. So you're building on top of snowflake, >>We're building on top of snowflake a hundred percent. And, um, you know, I've had folks say to me, well, aren't you worried about that? Isn't that a risk? It's like, well, that that's a risk. You're >>Still on the board. >>Yeah. I'm still on the board. Yeah. That's a risk I'm prepared to take. I am more on snowing. >>It sounds well, you're in a good spot. Stay on the board, then you'll know what's going on. Okay. No, yeah. Serious one. But the, this is a real dynamic. It is. It's not a one off its >>Well, and I do believe as well that the platform that you see now with AWS, if you look at the revenues of AWS is in order of magnitude, more than Microsoft was 25 years ago with windows mm-hmm <affirmative>. And so I've believe the opportunity for folks like snowflake and, and folks like observe it. It's an order of magnitude more than it was for the Oracle and the SAPs of the old world. >>Yeah. And I think this is really, I think this is something that this next generation of entrepreneurship is the go big scenario is you gotta be on a platform. Yeah. >>It's quite easy >>Or be the platform, but it's hard. There's only like how seats were at that table left >>Well value migrates up over time. So, you know, when the cloud thing got going, there were probably 10, 20, 30, you know, rack space and there's 1,000,001 infrastructure, a service platform as a service. My, my old, uh, um, employee EMC, we had pivotal, you know, pivotal was a platform as a service. Don't hear so much about it these days, but initially there's a lot of players and then it consolidates. And then to, to like extract, uh, a real business, you gotta move up, you gotta add value, you gotta build databases, then you gotta build applications. So >>It's interesting. Moving from the data center of the cloud was a dream for starters within if the provision, the CapEx. Yeah. Now the CapEx is in the cloud. Then you build on, on top of that, you got snowflake. Now you got on top of that. >>The assumption is almost that compute and storage is free. I know it's not quite free. Yeah. It's almost free, but you can, you know, as an application vendor, you think, well, what can I do if I assume compute and storage is free, that's the mindset you've gotta get >>Into. And I think the platform enablement to value. So if I'm an entrepreneur, I'm gonna get a series us multiple of value in what I'm paying. Yeah. Most people don't even blanket their Avis pills unless they're like massively huge. Yeah. Then it's a repatriation question or whatever discount question, but for most startups or any growing company, the Amazon bill should be a small factor. >>Yeah. I mean, a lot of people, um, ask me, uh, like, look you build in on snowflake. Um, you, you know, you, you, you're gonna be, you're gonna be paying their money. How, how, how, how does that work with your business model? If you're paying their money, you know, do, do you have a viable business? And it's like, well, okay. I, we could build a database as well and observe, but then I've got half the development team working on something that will never be as good as snowflake. And so we made the call early on that. No, no, we, we want a eight above the database. Yeah. Right. Snowflake are doing a great job of innovating on the database and, and the same is true of something like Amazon, like, like snowflake could have built their own cloud and their own platform, but they didn't. >>Yeah. And what's interesting is that Dave <inaudible> and I have been pointing this out and he's obviously a more on snowflake. I've been looking at data bricks, um, and the same dynamics happening, the proof is the ecosystem. Yeah. I mean, if you look at Snowflake's ecosystem right now and data bricks it's exploding. Right. I mean, the shows are selling out the floor. Space's book. That's the old days at VMware. Yeah. The old days at AWS. >>Well, and for snowflake and, and any platform from VI, it's a beautiful thing because, you know, we build on snowflake and we pay them money. They don't have to sell to us. Right. And we do a lot of the support. And so the, the economics work out really, really well. If you're a platform provider and you've got a lot of >>Ecosystems. Yeah. And then also you get, you get a, um, a trajectory of, uh, economies of scale with the institutional knowledge of snowflake integrations, right. New product, you're scaling a step function with them. >>Yeah. I mean, we manage 10 petabytes of data right now. Right. When I, when I, when I arrived at EMC in 2010, we had, we had one petabyte customer. And, and so at observe, we've been only selling the product for a year. We have 10 petabytes of data under management. And so been able to rely on a platform that can manage that is inve >>You know, well, Jeremy great conversation. Thanks for sharing your insights on the industry. Uh, we got a couple minutes left, um, put a plug in for observe. What do you guys know? You got some good funding, great partners. I don't know if you can talk about your, your, your POC customers, but you got a lot of high ends folks that are working with you. You getting in traction. >>Yeah. Yeah. Scales >>Around the corner. Sounds like, are you, is that where you are scale? >>We've got a big that that's when coming up in two or three weeks, we've got, we've got new funding, um, which is always great. Um, the product is, uh, really, really close. I think, as a startup, you always strive for market fit, you know, which is at which point can you just start hiring salespeople? And the revenue keeps going. We're getting pretty close to that right now. Um, we've got about 40 SaaS companies that run on the platform. They're almost all AWS Kubernetes, uh, which is our sweet spot to begin with, but we're starting to get some really interesting, um, enterprise type customers. We're, we're, you know, F five networks we're POC in right now with capital one, we got some interest in news around capital one coming up. I, I can't share too much, but it's gonna be exciting. And, and like I said, so hill continue to, to, >>I think capital one's a big snowflake customer as well. Right. >>They were early in one of the things that attracted me to capital one was they were very, very good with snowflake early on. And, and they put snowflake in a position in the bank where they thought that snowflake could be successful. And, and today that, that is one of Snowflake's biggest accounts, >>Capital, one, very innovative cloud, obviously Atos customer, and very innovative, certainly in the CISO and CIO, um, on another point on where you're at. So you're, Prescale meaning you're about to scale, >>Right? >>So you got POCs, what's that trajectory look like? Can you see around the corner? What's, what's going on? What's on, around the corner. That you're, that you're gonna hit this straight and narrow and, and gas it fast. >>Yeah. I mean, the, the, the, the key thing for us is we gotta get the product. Right. Um, the nice thing about having a guy like Mike Pfizer on the board is he doesn't obsess about revenue at this stage. His questions that the board are always about, like is the product, right? Is the product right? Is the product right? Have you got the product right? And cuz we know when the product's right, we can then scale the sales team and, and the revenue will take care of itself. Yeah. So right now all the attention is on the product. Um, the, this year, the exciting thing is we we're, we're adding all the tracing visualizations. So people will be able to the kind of things that by in the day you could do with the new relics and AppDynamics, the last generation of, of APM tools, you're gonna be able to do that within observe. And we've already got the logs and the metrics capability in there. So for us this year is a big one, cuz we sort of complete the trifecta, you know, the, the >>Logs, what's the secret sauce observe. What if you had the, put it into a, a, a sentence what's the secret sauce? >>I, I, I think, you know, an amazing founding engineering team, uh, number one, I mean, at the end of the day, you have to build an amazing product and you have to solve a problem in a different way. And we've got great long term investors and, and the biggest thing our investors give is it actually, it's not just money. It gives us time to get the product, right. Because if we get the product right, then we can get the growth. >>Got it. Final question. While I got you here, you've been on the enterprise business for a long time. What's the buyer landscape out there. You got people doing POCs on capital one scale. So we know that goes on. What's the appetite at the buyer side for startups and what are their requirements that you're seeing? Uh, obviously we're seeing people go in and dip into the startup pool because new ways to refactor their, this restructure. So, so a lot of happening in cloud, what's the criteria. How are enterprises engaging in with startups? >>Yeah. I mean, enterprises, they know they've gotta spend money transforming the business. I mean, this was, I almost feel like my old Dell or EMC self there, but, um, what, what we were saying five years ago is happening. Um, everybody needs to figure out a way to take their business to this digital world. Everybody has to do it. So the nice thing from a startup standpoint is they know at times they need to risk or, or take a bet on new technology in order to, to help them do that. So I think you've got buyers that a have money, uh, B it prepared to take risks and it's, it's a race against time to you'll get their, their offerings in this, a new digital footprint. >>Final, final question. What's the state of AWS. Where do you see them going next? Obviously they're continuing to be successful. How does cloud 3.0, or they always say it's day one, but it's more like day 10, but what's next for Aw. Where do they go from here? Obviously they're doing well. They're getting bigger and bigger. Yeah, >>Better. It's an amazing story. I mean, you know, we're, we're on AWS as well. And so I, I think if they keep nurturing the builders and the ecosystem, then that is their superpower. They, they have an early leads. And if you look at where, you know, maybe the likes of Microsoft lost the plot in the, in the late nineties, it was, they stopped, uh, really caring about developers in the folks who were building on top of their ecosystem. In fact, they started buying up their ecosystem and competing with people in their ecosystem. And I see with AWS, they, they have an amazing headstart and if they did more, you know, if they do more than that, that's, what's gonna keep this juggernaut rolling for many years to come. >>Yeah. They got the Silicon and got the stack. They're developing Jeremy Burton inside the cube, great resource for commentary, but also founding with the CEO of a company called observing in the middle of all the action on the board of snowflake as well. Um, great startup. Thanks for coming on the cube. Always a pleasure. Okay. Live from San Francisco. It's to cube. I'm John for your host. Stay with us more coverage from San Francisco, California after the short break. >>Hello. Welcome back to the cubes coverage here live in San Francisco, California. I'm John furrier, host of the cubes cube coverage of AWS summit 2022 here in San Francisco. We're all the developers are the bay air at Silicon valley. And of course, AWS summit in New York city is coming up in the summer. We'll be there as well. SF and NYC cube coverage. Look for us. Of course, reinforcing Boston and re Mars with the whole robotics, AI. They all coming together. Lots of coverage stay with us today. We've got a great guest from Bel VC. John founding partner, entrepreneurial venture is a venture firm. Your next act, welcome to the cube. Good to see you. >>Good to see you, man. I feel like it's been forever since we've been able to do something in person. Well, >>I'm glad you're here because we run into each other all the time. We've known each other for over decade. Um, >>It's been at least 10 years, >>At least 10 years more. And we don't wanna actually go back as bring back the old school web 1.0 days. But anyway, we're in web three now. So we'll get to that in a second. We, >>We are, it's a little bit of a throwback to the path though, in my opinion, >>It's all the same. It's all distributed computing and software. We ran each other in cube con. You're investing in a lot of tech startup founders. Okay. This next level, next gen entrepreneurs have a new makeup and it's software. It's hardcore tech in some cases, not hardcore tech, but using software to take an old something old and make it better new, faster. So tell us about Bel what's the firm. I know you're the founder, uh, which is cool. What's going on. Explain >>What you, I mean, you remember I'm a recovering entrepreneur, right? So of course I, I, >>No, you're never recovering. You're always entrepreneur >>Always, but we are also always recovering. So I, um, started my first company when I was 24. If you remember, before there was Facebook and friends, there was instant messaging. People were using that product at work every day, they were creating a security vulnerability between their network and the outside world. So I plugged that hole and built an instant messaging firewall. It was my first company. The company was called IM logic and we were required by Symantec. Uh, then spent 12 years investing in the next generation of software companies, uh, early investor in open source companies and cloud companies and spent a really wonderful years, uh, at a firm called NEA. So I, I feel like my whole life I've been either starting enterprise software companies or helping founders start enterprise software companies. And I'll tell you, there's never been a better time than right now to start an enterprise software company. >>So, uh, the passion for starting a new firm was really a recognition that founders today that are starting an enterprise software company, they, they tend to be, as you said, a more technical founder, right? Usually it's a software engineer or a builder mm-hmm <affirmative>, uh, they are building that are serving a slightly different market than what we've traditionally seen in enterprise software. Right? I think traditionally we've seen it buyers or CIOs that have agendas and strategies, which, you know, purchase software that is traditionally bought and sold tops down. But you know, today I think the most successful enterprise software companies are the ones that are built more bottoms up and have more technical early adopters. And generally speaking, they're free to use. They're free to try. They're very commonly community source or open source companies where you have a large technical community that's supporting them. So there's a, there's kind of a new normal now I think in great enterprise software. And it starts with great technical founders with great products and great bottoms of motions. And I think there's no better place to, uh, service those people than in the cloud and uh, in, in your community. >>Well, first of all, congratulations, and by the way, you got a great pedigree and great background. You're super smart admire of your work and your, and, and your founding, but let's face it. Enterprise is hot because digital transformation is, is all companies there's no, I mean, consumer is enterprise now. Everything is what was once a niche, not, I won't say niche category, but you know, not for the faint of heart, you know, investors, >>You know, it's so funny that you say that enterprise is hot because you, and I feel that way now. But remember, like right now, there's also a giant tech in VC conference in Miami <laugh> and it's covering cryptocurrencies and FCS and web three. So I think beauty is definitely in the eye of the beholder <laugh> but no, I, I will tell you, well, >>MFTs is one big enterprise, cuz you gotta have imutability you got performance issues. You have, I IOPS issues. >>Well, and, and I think all of us here that are of may, maybe students of his stream have been involved in open source in the cloud would say that we're, you know, much of what we're doing is, uh, the predecessors of the web web three movement. And many of us I think are contributors to the web three >>Movement. The hype is definitely web >>Three. Yeah. But, >>But you know, >>For sure. Yeah, no, but now you're taking us further east to Miami. So, uh, you know, look, I think, I, I think, um, what is unquestioned with the case and maybe it's, it's more obvious the more time you spend in this world is this is the fastest growing part of enterprise software. And if you include cloud infrastructure and cloud infrastructure spend, you know, it is by many measures over, uh, $500 billion in growing, you know, 20 to 30 a year. So it it's a, it's a just incredibly fast >>Let's getting, let's get into some of the cultural and the, the shifts that are happening, cuz again, you, you have the luxury of being in enterprise when it was hard, it's getting easier and more cooler. I get it and more relevant <laugh> but there's also the hype of like the web three, for instance, but you know, for, uh, um, um, the CEO snowflake, okay. Has wrote a book and Dave Valenti and I were talking about it and uh, Frank Lutman has says, there's no playbooks. We always ask the CEOs, what's your playbook. And he's like, there's no playbook, situational awareness, always Trump's playbooks. So in the enterprise playbook, oh, hire a direct sales force and sass kind of crushed that now SAS is being redefined, right. So what is SAS? Is snowflake a SAS or is that a platform? So again, new unit economics are emerging, whole new situation, you got web three. So to me there's a cultural shift, the young entrepreneurs, the, uh, user experience, they look at Facebook and say, ah, you know, and they own all my data. And you know, we know that that cliche, um, they, you know, the product. So as this next gen, the gen Z and the millennials come in and our customers and the founders, they're looking at things a little bit differently and the tech better. >>Yeah. I mean, I mean, I think we can, we can see a lot of commonalities across all six of startups and the overall adoption of technology. Uh, and, and I would tell you, this is all one big giant revolution. I call it the user driven revolution. Right. It's the rise of the user. Yeah. And you might say product like growth is currently the hottest trend in enterprise software. It's actually user like growth, right. They're one in the same. So sometimes people think the product, uh, is what is driving. >>You just pull the product >>Through. Exactly, exactly. And so that's that I, that I think is really this revolution that you see, and, and it does extend into things like cryptocurrencies and web three and, you know, sort of like the control that is taken back by the user. Um, but you know, many would say that, that the origins of this movement may be started with open source where users were contributors, you know, contributors were users and looking back decades and seeing how it, how it fast forward to today. I think that's really the trend that we're all writing and it's enabling these end users. And these end users in our world are developers, data engineers, cybersecurity practitioners, right. They're really the users. And they're really the, the offic and the most, you know, kind of valued people in >>This. I wanna come back to the data engineers in a second, but I wanna make a comment and get your reaction to, I have a, I'm a gen Xer technically. So for not a boomer, but I have some boomer friends who are a little bit older than me who have, you know, experienced the sixties. And I've, I've been saying on the cube for probably about eight years now that we are gonna hit a digital hippie Revolut, meaning a rebellion against in the sixties was rebellion against the fifties and the man and, you know, summer of love. That was a cultural differentiation from the other one of group, the predecessors. So we're kind of having that digital moment now where it's like, Hey boomers, Hey people, we're not gonna do that anymore. We hate how you organize shit. >>Right. But isn't this just technology. I mean, isn't it, isn't it like there used to be the old adage, like, you know, you would never get fired for buying IBM, but now it's like, you obviously probably would get fired if you bought IBM. And I mean, it's just like the, the, I think, I think >>During the mainframe days, those renegades were breaking into Stanford, starting the home brew club. So what I'm trying to get at is that, do you see the young cultural revolution also, culturally, just, this is my identity NFTs to me speak volumes about my, I wanna associate with NFTs, not single sign on like, well, >>Absolutely. And, and I think like, I think you're hitting on something, which is like this convergence of, of, you know, societal trends with technology trends and how that manifests in our world is yes. I think like there is unquestionably almost a religion around the way in which a product is built. Right. And we can use open source. One example of that religion. Some people say, look, I'll just never try a product in the cloud if it's not open source. Yeah. I think cloud, native's another example of that, right? It's either it's, you know, it either is cloud native or it's not. And I think a lot of people will look at a product and say, look, you know, you were not designed in the cloud era. Therefore I just won't try you. And sometimes, um, like it or not, it's a religious decision, right? It's, it's something that people just believe to be true almost without, uh, necessarily. I mean, >>The data drives all decision making. Let me ask you this next question. As a VC. Now you look at pitch, well, you've been a VC for many years, but you also have the founder entrepreneurial mindset, but you can empathize with the founders. You know, hustle is a big part of the, that first founder check, right? You gotta convince someone to part with their ch their money and the first money in which you do a lot of is about believing in the first. So faking it till you make it is hard. Now you, the data's there, you either have it cloud native, you either have the adaption or traction. So honesty is a big part of that pitch. You can't fake it. Oh, >>AB absolutely. You know, there used to be this concept of like the persona of an entrepreneur, right. And the persona of the entrepreneur would be, you know, somebody who was a great salesperson or somebody who tell a great story. And I still think that that's important, right. It still is a human need for people to believe in narratives and stories. Yeah. But having said that you're right. The proof is in the pudding, right. At some point you click download and you try the product and it does what it says it's gonna, it's gonna do, or it doesn't, or it either stands up to the load test or it doesn't. And so I, I feel like in this new economy, that're, we live in really, it's a shift from maybe the storytellers and the creators to, to the builders, right. The people that know how to build great product. And in some ways the people that can build great product yeah. Stand out from the crowd. And they're the ones that can build communities around their products. And, you know, in some ways can, um, you know, kind of own more of the narrative because their product begin for exactly >>The volume you back to the user led growth. >>Exactly. And it's the religion of, I just love your product. Right. And I, I, I, um, Doug song is the founder of du security used to say, Hey, like, you know, the, the really like in today's world of like consumption based software, like the user is only gonna give you 90 seconds to figure out whether or not you're a company that's easy to do business with for right. And so you can say, and do all the things that you want about how easy you are to work with. But if the product isn't easy to install, if it's not easy to try, if it's not, if, if the it's gotta speak to the, >>Exactly. Speak to the user. But let me ask a question now that for the people watching, who are maybe entrepreneurial entre entrepreneurs, um, masterclass here is in session. So I have to ask you, do you prefer, um, an entrepreneur to come in and say, look at John. Here's where I'm at. Okay. First of all, storytelling's fine. Whether you're an extrovert or introvert, have your style, sell the story in a way that's authentic, but do you, what do you prefer to say? Here's where I'm at? Look, I have an idea. Here's my traction. I think here's my MVP prototype. I need help. Or do you wanna just see more stats? What's the, what's the preferred way that you like to see entrepreneurs come in and engage? >>There's tons of different styles, man. I think the single most important thing that every founder should know is that we, we don't invest in what things are today. We invest in what we think will become, right. And I think that's why we all get up in the morning and try to build something different, right? It's that we see the world a different way. We want it to be a different way, and we wanna work every single moment of the day to try to make that vision a reality. So I think the more that you can show people where you want to be, the more likely somebody is gonna to align with your vision and, and want to invest in you and wanna be along for the ride. So I, I wholeheartedly believe in showing off what you got today, because eventually we all get down to like, where are we and what are we gonna do together? But, um, no, I, you gotta show the path. I think the single most important thing for any founder and VC relationship is that they have the same vision. Uh, if you have the same vision, you can, you can get through bumps in the road, you can get through short term spills. You can all sorts of things in the middle of the journey can happen. Yeah. But it doesn't matter as much if you share the same long term vision, >>Don't flake out and, and be fashionable with the, the latest trends because it's over before you even get there. >>Exactly. I think many people that, that do what we do for a living will say, you know, ultimately the future is relatively easy to predict, but it's the timing that's impossible to predict. So you, you know, you sort of have to balance the, you know, we, we know that the world is going this way and therefore we're gonna invest a lot of money to try to make this a reality. Uh, but sometimes it happens ins six months. Sometimes it takes six years. Sometimes it takes 16 years. Uh, >>What's the hottest thing in enterprise that you see the biggest wave that people should pay attention to that you're looking at right now with Tebel partners, Tebel dot your site. What's the big wave. What's your big >>Wave. There there's three big trends that we invest in. And then the, the only things we do day in day out one is the explosion at open source software. So I think many people think that all software is unquestionably moving to an open source model in some form or another yeah. Tons of reasons to debate whether or not that is gonna happen an alwa timeline happening forever, but it is, it is accelerating faster than we've ever seen. So I, I think it's its one big mass of wave that we continue to ride. Um, second is the rise of data engineering. Uh, I think data engineering is in and of itself now a category of software. It's not just that we store data. It's now we move data and we develop applications on data. And, uh, I think data is in and of itself as big of a market as any of the other markets that we invest in. Uh, and finally it's the gift that keeps on giving. I've spent my entire career in it. We still feel that security is a market that is underinvested. It is, it continues to be the place where people need to continue to invest and spend more money. Yeah. Uh, and those are the three major trends that we run >>And security, you think we all need a do over, right? I mean, do we need a do over in security or is what's the core problem? I, >>I, I keep using this word underinvested because I think it's the right way to think about the problem. I think if you, I think people generally speaking, look at cyber security as an add-on. Yeah. But if you think about it, the whole like economy is moving online. And so in, in some ways like security is core to protecting the digital economy. And so it's, it shouldn't be an afterthought, right? It should be core to what everyone is doing. And that's why I think relative to the trillions of dollars that are at stake, uh, I believe the market size for cybersecurity is around 150 billion and it still is a fraction of what >>We're, what we're and even boom is booming now. So you get the convergence of national security, geopolitics, internet digital >>That's right. You mean arguably, right. Arguably again, it's the area of the world that people should be spending more time and more money given what to stake. >>I love your thesis. I gotta, I gotta say you gotta love your firm. Love who you're doing. We're big supporters of your mission. Congrat is on your entrepreneurial venture. And uh, we'll be, we'll be talking and maybe see a Cuban. Uh, >>Absolutely >>Not. Certainly EU maybe even north America's in Detroit this year. >>Huge fan of what you guys are doing here. Thank you so much for helping me on the show. >>Des bell VC Johnson here on the cube. Check him out. Founder for founders here on the cube, more coverage from San Francisco, California, after the short break, stay with us. Hey everyone. Welcome to the cue here. Live in San Francisco, California for AWS summit, 2022 we're live we're back with events. Also we're virtual. We got hybrid all kinds of events. This year, of course, 80% summit in New York city is happening this summer. We'll be there with the cube as well. I'm John. Again, John host of the cube. Got a great guest here. Justin Colby, owner and CEO of innovative solutions they booth is right behind us. Justin, welcome to the cube. >>Thank you. Thank you for having me. >>So we're just chatting, uh, off camera about some of the work you're doing. You're the owner of and CEO. Yeah. Of innovative. Yeah. So tell us the story. What do you guys do? What's the elevator pitch. Yeah. >><laugh> so the elevator pitch is we are, uh, a hundred percent focused on small to midsize businesses that are moving to the cloud or have already moved to the cloud and really trying to understand how to best control, cost, security, compliance, all the good stuff, uh, that comes along with it. Um, exclusively focused on AWS and, um, you know, about 110 people, uh, based in Rochester, New York, that's where our headquarters is. But now we have offices down in Austin, Texas up in Toronto, uh, Canada, as well as Chicago. Um, and obviously in New York, uh, you know, the, the business was never like this, uh, five years ago, um, founded in 1989, made the decision in 2018 to pivot and go all in on the cloud. And, uh, I've been a part of the company for about 18 years, bought the company about five years ago. And it's been a great ride. >>It's interesting. The manages services are interesting with cloud cause a lot of the heavy liftings done by AWS. So we had Matt on your team on earlier talking about some of the edge stuff. Yeah. But you guys are a managed cloud service. You got cloud advisory, you know, the classic service that's needed, but the demands coming from cloud migrations and application modernization and obviously data is a huge part of it. Huge. How is this factoring into what you guys do and your growth cuz you guys are the number one partner on the SMB side for edge. Yeah. For AWS, you got results coming in. Where's the, where's the forcing function. What's the pressure point. What's the demand like? Yeah. >>It's a great question. Every CEO I talk to, that's a small to mid-size business. I'll try and understand how to leverage technology better to help either drive a revenue target for their own business, uh, help with customer service as so much has gone remote now. And we're all having problems or troubles or issues trying to hire talent. And um, you know, tech is really at the, at the forefront and the center of that. So most customers are coming to us and they're like, listen, we gotta move to the out or we move some things to the cloud and we want to do that better. And um, there's this big misnomer that when you move to the cloud, you gotta automatically modernize. Yeah. And what we try to help as many customers understand as possible is lifting and shifting, moving the stuff that you maybe currently have OnPrem and a data center to the cloud first is a first step. And then, uh, progressively working through a modernization strategy is always the better approach. And so we spend a lot of time with small to midsize businesses who don't have the technology talent on staff to be able to do >>That. Yeah. They want to get set up. But the, the dynamic of like latency is huge. We're seeing that edge product is a big part of it. This is not a one-off happening around everywhere. It is. And it's not, it's manufacturing, it's the physical plant or location >>Literally. >>And so, and you're seeing more IOT devices. What's that like right now from a challenge and problem statement standpoint, are the customers, not staff, is the it staff kind of old school? Is it new skills? What's the core problem you guys solve >>The SMB space. The core issue nine outta 10 times is people get enamored with the latest and greatest. And the reality is not everything that's cloud based. Not all cloud services are the latest and greatest. Some things have been around for quite some time and are hardened solutions. And so, um, what we try to do with technology staff that has additional on-prem, uh, let's just say skill sets and they're trying to move to a cloud-based workload is we try to help those customers through education and through some practical, let's just call it use case. Um, whether that's a proof of concept that we're doing or whether that's, we're gonna migrate a small workload over, we try to give them the confidence to be able to not, not necessarily go it alone, but to, to, to have the, uh, the Gusto and to really have the, um, the, the opportunity to, to do that in a wise way. Um, and what I find is that most CEOs that I talk to, yeah, they're like, listen, the end of the day, I'm gonna be spending money in one place or another, whether that's OnPrem or in the cloud. I just want to know that I'm doing that in a way that helps me grow as quickly as possible status quo. I think every, every business owner knows that COVID taught us anything that status quo is, uh, is, is no. No. Good. >>How about factoring in the, the agility and speed equation? Does that come up a lot? It >>Does. I think, um, I think there's also this idea that if, uh, if we do a deep dive analysis and we really take a surgical approach to things, um, we're gonna be better off. And the reality is the faster you move with anything cloud based, the better you are. And so there's this assumption that we gotta get it right the first time. Yeah. In the cloud, if you start the, on your journey in one way, and you realize midway that it's not the right, let's just say the right place to go. It's not like buying a piece of iron that you put in the closet and now you own it in the cloud. You can turn those services on and off. It's a, gives you a much higher density for making decisions and failing >>Forward. Well actually shutting down the abandoning, the projects that early and not worrying about it, you got it. I mean, most people don't abandon stuff cuz they're like, oh, I own it. >>Exactly. >>And they get, they get used to it. Like, and then they wait too long. >>That's exactly. Yeah. >>Frog and boiling water as we used to say so, oh, it's a great analogy. So I mean this, this is a dynamic that's interesting. I wanna get more thoughts on it because like I'm a, if I'm a CEO of a company, like, okay, I gotta make my number. Yeah. I gotta keep my people motivated. Yeah. And I gotta move faster. So this is where you guys come in. I get the whole thing. And by the way, great service, um, professional services in the cloud right now are so hot because so hot, you can build it and then have option optionality. You got path decisions, you got new services to take advantage of. It's almost too much for customers. It is. I mean, everyone I talk to at reinvent, that's a customer. Well, how many announcements did Andy jazzy announcer Adam, you know, five, a thousand announcement or whatever they did with huge amounts. Right. Keeping track of it all. Oh, is huge. So what's the, what's the, um, the mission of, of your company. How does, how do you talk to that alignment? Yeah. Not just product. I can get that like values as companies, cuz they're betting on you and your people. >>They are, they are >>The values. >>Our mission is, is very simple. We want to help every small to mid-size business, leverage the power of the cloud. Here's the reality. We believe wholeheartedly. This is our vision that every company is going to become a technology company. So we go to market with this idea that every customer's trying to leverage the power of the cloud in some way, shape or form, whether they know it or don't know it. And number two, they're gonna become a tech company in the pro of that because everything is so tech-centric. And so when you talk about speed and agility, when you talk about the, the endless options and the endless permutations of solutions that a customer can buy in the cloud, how are you gonna ask a team of one or two people in your it department to make all those decisions going it alone or trying to learn it as you go, it only gets you so far working with a partner. >>I'll just give you some perspective. We work with about a thousand small to midsize business customers. More than 50% of those customers are on our managed services. Meaning know that we have their back and we're the safety net. So when a customer is saying, all right, I'm gonna spend a couple thousand dollars a month in the cloud. They know that that bill, isn't gonna jump to $10,000 a month going on loan. Who's there to help protect that. Number two, if you have a security posture and let's just say you're high profile and you're gonna potentially be more vulnerable to security attack. If you have a partner that's offering you some managed services. Now you, again, you've got that backstop and you've got those services and tooling. We, we offer, um, seven different products that are part of our managed services that give the customer the tooling, that for them to go out and buy on their own for a customer to go out today and go buy a new Relic solution on their own, it would cost 'em a fortune. If >>It's training alone would be insane. A risk factor not mean the cost. Yes, absolutely. Opportunity cost is huge, >>Huge, absolutely enormous training and development. Something. I think that is often, you know, it's often overlooked technologists. Typically they want to get their skills up. Yeah. They, they love to get the, the stickers and the badges and the pins, um, at innovative in 2018, when, uh, when we made the decision to go all on the club, I said to the organization, you know, we have this idea that we're gonna pivot and be aligned with AWS in such a way that it's gonna really require us all to get certified. My executive assistant at the time looks at me. She said, even me, I said, yeah, even you, why can't you get certified? Yeah. And so we made, uh, a conscious decision. It wasn't requirement isn't today to make sure everybody in the company has the opportunity to become certified. Even the people that are answering the phones at the front desk >>And she could be running the Kubernetes clusters. I >>Love it. It's amazing. So I'll tell you what, when that customer calls and they have a real Kubernetes issue, she'll be able to assist and get the right >>People involved. And that's a cultural factor that you guys have. So, so again, this is back to my whole point about SMBs and BIS is in general, small and large. It staffs are turning over the gen Z and millennials are in the workforce. They were provisioning top of rack switches. Right. First of all. And so if you're a business, there's also the, I call the build out, um, uh, return factor, ROI piece. At what point in time as an owner or SMB, do I get the why? Yeah. I gotta hire a person to manage it. That person's gonna have five zillion job offers. Yep. Uh, maybe who knows? Right. I got cyber security issues. Where am I gonna find a cyber person? Yeah. A data compliance. I need a data scientist and a compliance person. Right. Maybe one in the same. Right. Good luck. Trying to find a data scientist. Who's also a compliance person. Yep. And the list goes on. I can just continue. Absolutely. I need an SRE to manage the, the, uh, the sock report and we can pen test. Right. >>Right. >>These are, these are >>Like critical issues. This >>Is just like, these are the table stakes. >>Yeah. And, and every, every business owner's thinking about this, that's, >>That's what, at least a million in bloating, if not three or more Just to get that going. Yeah. Then it's like, where's the app. Yeah. So there's no cloud migration. There's no modernization on the app side now. Yeah. No. And nevermind AI and ML. That's >>Right. That's right. So to try to go it alone, to me, it's hard. It's incredibly difficult. And the other thing is, is there's not a lot of partners, so the partner, >>No one's raising their hand boss. I'll do all that exactly. In the it department. >>Exactly. >>Like, can we just call up, uh, you know, our old vendor that's >>Right. <laugh> right. Our old vendor. I like >>It, >>But that's so true. I mean, when I think about how, if I were a business owner starting a business today and I had to build my team, um, and the amount of investment that it would take to get those people skilled up and then the risk factor of those people now having the skills and being so much more in demand and being recruited away, that's a real, that's a real issue. And so how you build your culture around that is, is very important. And it's something that we tell, talk about every, with every one of our small to mid-size >>Businesses. So just, I wanna get, I want to get your story as CEO. Okay. Take us through your journey. You said you bought the company and your progression to, to being the owner and CEO of innovative yeah. Award winning guys doing great. Uh, great bet on a good call. Yeah. Things are good. Tell your story. What's your journey? >>It's real simple. I was, uh, I was a sophomore at the Rochester Institute of technology in 2003. And, uh, I knew that I, I was going to school for it and I, I knew I wanted to be in tech. I didn't know what I wanted to do, but I knew I didn't wanna code or configure routers and switches. So I had this great opportunity with the local it company that was doing managed services. We didn't call it at that time innovative solutions to come in and, uh, jump on the phone and dial for dollars. I was gonna cold call and introduce other, uh, small to midsize businesses locally in Rochester, New York go to Western New York, um, who innovative was now. We were 19 people at the time. And I came in, I did an internship for six months and I loved it. I learned more in those six months that I probably did in my first couple of years at, uh, at RT long story short. >>Um, for about seven years, I worked, uh, to really help develop, uh, sales process and methodology for the business so that we could grow and scale. And we grew to about 30 people. And, um, I went to the owners at the time in 2010 and I was like, Hey, on the value of this business and who knows where you guys are gonna be another five years, what do you think about making me an owner? And they were like, listen, you got long ways before you're gonna be an owner, but if you stick it out in your patient, we'll, um, we'll work through a succession plan with you. And I said, okay, there were four other individuals at the time that were gonna also buy into the business with me. >>And they were the owners, no outside capital, none >>Zero, well, 2014 comes around. And, uh, the other folks that were gonna buy into the business with me that were also working at innovative for different reasons, they all decided that it wasn't for them. One started a family. The other didn't wanna put capital in. Didn't wanna write a check. Um, the other had a real big problem with having to write a check. If we couldn't make payroll, I'm like, well, that's kind of like if we're owners, we're gonna have to like cover that stuff. <laugh> so >>It's called the pucker factor. >>Exactly. So, uh, I sat down with the CEO in early 2015, and, uh, we made the decision that I was gonna buy the three partners out, um, go through an early now process, uh, coupled with, uh, an interesting financial strategy that wouldn't strap the business, cuz they cared very much. The company still had the opportunity to keep going. So in 2016 I bought the business, um, became the sole owner. And, and at that point we, um, we really focused hard on what do we want this company to be? We had built this company to this point. Yeah. And, uh, and by 2018 we knew that pivoting going all in on the cloud was important for us and we haven't looked back. >>And at that time the proof points were coming clearer and clearer 2012 through 15 was the early adopters, the builders, the startups and early enterprises. Yes. The capital ones of the world. Exactly. And those kinds of big enterprises, the GA I don't wanna say gamblers, but ones that were very savvy. The innovators, the FinTech folks. Yep. The hardcore glass eating enterprises >>Agreed, agreed to find a small to mid-size business, to migrate completely to the cloud as, as infrastructure was considered. That just didn't happen as often. Um, what we were seeing where a lot of our small to mid-size as customers, they wanted to leverage cloud-based backup or they wanted to leverage a cloud for disaster recovery because it lent itself. Well, early days, our most common cloud customer though, was the customer that wanted to move messaging and collaboration, the Microsoft suite to the cloud. And a lot of 'em dipped their toe in the water. But by 2017 we knew infrastructure was around the corner. Yeah. And so, uh, we only had two customers on AWS at the time. Um, and we, uh, we, we made the decision to go all in >>Justin. Great to have you on the cube. Thank you. Let's wrap up. Uh, tell me the hottest product that you have. Is it migrations? Is it the app modernization? Is it data? What's the hot product and then put a plug in for the company. Awesome. >>So, uh, there's no question. Every customer is looking to migrate workloads and try to figure out how to modernize for the future. We have very interesting, sophisticated yet elegant funding solutions to help customers with the cash flow, uh, constraints that come along with those migrations. So any SMB that's thinking about migrating to the cloud, they should be talking innovative solutions. We know how to do it in a way that allows those customers not to be cash strap and gives them an opportunity to move forward in a controlled, contained way so that they can modernize. >>So like insurance, basically for them not insurance class in the classic sense, but you help them out on the, on the cash exposure. >>Absolutely. We are known for that and we're known for being creative with those customers and being empathetic to where they are in their journey. >>And that's the cloud upside is all about doubling down on the variable wind. That's right. Seeing the value and Ling down on it. Absolutely not praying for it. Yeah. <laugh> all right, Justin. Thanks for coming on. You really appreciate it. >>Thank you very much for having me. >>Okay. This is the cube coverage here live in San Francisco, California for AWS summit, 2022. I'm John for your host. Thanks for watching. We're back with more great coverage for two days after this short break, >>Live on the floor and see San Francisco for a AWS summit. I'm John ferry, host of the cube here for the next two days, getting all the action we're back in person. We're at a AWS reinvent a few months ago. Now we're back. Events are coming back and we're happy to be here with the cube. Bring all the action. Also virtual. We have a hybrid cube. Check out the cube.net, Silicon angle.com for all the coverage. After the event. We've got a great guest ticking off here. Matthew Park, director of solutions, architecture with innovation solutions. The booth is right here. Matthew, welcome to the cube. >>Thank you very much. I'm glad to be >>Here. So we're back in person. You're from Tennessee. We were chatting before you came on camera. Um, it's great to have to be back through events. >>It's amazing. This is the first, uh, summit I've been to and what two, three years. >>It's awesome. We'll be at the UHS summit in New York as well. A lot of developers and a big story this year is as developers look at cloud going distributed computing, you got on premises, you got public cloud, you got the edge. Essentially the cloud operations is running everything dev sec ops, everyone kind of sees that you got containers, you got Kubernetes, you got cloud native. So the game is pretty much laid out mm-hmm <affirmative> and the edge is with the actions you guys are number one, premier partner at SMB for edge. >>That's right. >>Tell us about what you guys doing at innovative and, uh, what you do. >>That's right. Uh, so I'm the director of solutions architecture. Uh, me and my team are responsible for building out the solutions that are around, especially the edge public cloud for us edge is anything outside of an AWS availability zone. Uh, we are deploying that in countries that don't have AWS infrastructure in region. They don't have it. Uh, give an example, uh, example would be Panama. We have a customer there that, uh, needs to deploy some financial tech and compute is legally required to be in Panama, but they love AWS and they want to deploy AWS services in region. Uh, so they've taken E EKS anywhere. We've put storage gateway and, uh, snowball, uh, in region inside the country and they're running their FinTech on top of AWS services inside Panama. >>You know, it's interesting, Matthew is that we've been covering a, since 2013 with the cube about their events. And we watched the progression and jazzy was, uh, was in charge and became the CEO. Now Adam's in charge, but the edge has always been that thing they've been trying to avoid. I don't wanna say trying to avoid, of course, Amazon would listen to the customers. They work backwards from the customer. We all know that. Uh, but the real issue was they were they're bread and butters EC two and S three. And then now they got tons of services and the cloud is obviously successful and seeing that, but the edge brings up a whole nother level. >>It does computing. It >>Does. That's not centralized in the public cloud now they got regions. So what is the issue at the edge what's driving the behavior. Outpost came out as a reaction to competitive threats and also customer momentum around OT, uh, operational technologies. And it merging. We see that the data at the edge, you got 5g having. So it's pretty obvious, but there's a slow transition. What was the driver for the edge? What's the driver now for edge action for AWS >>Data is the driver for the edge. Data has gravity, right? And it's pulling compute back to where the customer's generating that data and that's happening over and over again. You said it best outpost was a reaction to a competitive situation where today we have over 15 AWS edge services and those are all reactions to things that customers need inside their data centers on location or in the field like with media companies. >>Outpost is interesting. We always used to riff on the cube cause it's basically Amazon and a box pushed in the data center, running native, all the stuff, but now cloud native operations are kind of becoming standard. You're starting to see some standard Deepak syncs. Group's doing some amazing work with open source Rauls team on the AI side, obviously, uh, you got SW, he was giving the keynote tomorrow. You got the big AI machine learning big part of that edge. Now you can say, okay, outpost, is it relevant today? In other words, did outpost do its job? Cause EKS anywhere seems to be getting a lot of momentum. You see local zones, the regions are kicking ass for Amazon. This edge piece is evolving. What's your take on EKS anywhere versus say outpost? >>Yeah, I think outpost did its job. It made customers that were looking at outpost really consider, do I wanna invest in this hardware? Do I, do I wanna have, um, this outpost in my data center, do I want to manage this over the long term? A lot of those customers just transitioned to the public cloud. They went into AWS proper. Some of those customers stayed on prem because they did have use cases that were, uh, not a good fit for outposts. They weren't a good fit. Uh, in the customer's mind for the public AWS cloud inside an availability zone. Now what's happening is as AWS is pushing these services out and saying, we're gonna meet you where you are with 5g. We're gonna meet you where you are with wavelength. We're gonna meet you where you are with EKS anywhere. Uh, I think it has really reduced the amount of times that we have conversations about outposts and it's really increased. We can deploy fast. We don't have to spin up outpost hardware. We can go deploy EKS anywhere or in your VMware environment. And it's increasing the speed of adoption >>For sure. Right? So you guys are making a lot of good business decisions around managed cloud service. That's right. Innovative as that you get the cloud advisory, the classic professional services for the specific edge piece and, and doing that outside of the availability zones and regions for AWS, um, customers in, in these new areas that you're helping out are, they want cloud, like they want to have modernization a modern applications. Obviously they got data machine learning and AI, all part of that. What's the main product or, or, or gap that you're filling for AWS, uh, outside of their availability zones or their regions that you guys are delivering. What's the key is it. They don't have a footprint. Is it that it's not big enough for them? What's the real gap. What's why, why are you so successful? >>So what customers want when they look towards the cloud is they want to focus on, what's making them money as a business. They want on their applications. They want to focus on their customers. So they look towards AWS cloud and say, AWS, you take the infrastructure. You take, uh, some of the higher layers and we'll focus on our revenue generating business, but there's a gap there between infrastructure and revenue generating business that innovative slides into, uh, we help manage the AWS environment. Uh, we help build out these things in local data centers for 32 plus year old company. We have traditional on-premises people that know about deploying hardware that know about deploying VMware to host EKS anywhere. But we also have most of our company totally focused on the AWS cloud. So we're filling that gap in helping of these AWS services, manage them over the long term. So our customers can go to just primarily and totally focusing on their revenue generating business. So >>Basically you guys are basically building AWS edges, >>Correct? >>For correct companies, correct? Mainly because the, the needs are there, you got data, you got certain products, whether it's, you know, low latency type requirements, right. And then they still work with the regions, right. It's all tied together, right. Is that how it works? Right. >>And, and our customers, even the ones in the edge, they also want us to build out the AWS environment inside the availability zone, because we're always gonna have a failback scenario. If we're gonna deploy FinTech in the Caribbean, we talk about hurricanes and we're gonna talk about failing back into the AWS availability zones. So innovative is filling that gap across the board, whether it be inside the AWS cloud or on the AWS edge. >>All right. So I gotta ask you on the, since you're at the edge in these areas, I won't say underserved, but developing areas where you now have data and you have applications that are tapping into that, that required. It makes total sense. We're seeing that across the board. So it's not like it's, it's an outlier it's actually growing. Yeah. There's also the crypto angle. You got the blockchain. Are you seeing any traction at the edge with blockchain? Because a lot of people are looking at the web three in these areas like Panama, you mentioned FinTech. And in, in the islands there a lot of, lot of, lot of web three happening. What's your, what's your view on the web three world right now, relative >>To we, we have some customers actually deploying crypto, especially, um, especially in the Caribbean. I keep bringing the Caribbean up, but it's, it's top of my mind right now we have customers that are deploying crypto. A lot of, uh, countries are choosing crypto to underlie parts of their central banks. Yeah. Um, so it's, it's up and coming a, uh, I, I have some, you know, personal views that, that crypto is still searching for a use case. Yeah. And, uh, I think it's searching a lot and, and we're there to help customers search for that use case. Uh, but, but crypto, as a, as a, uh, technology, um, lives really well on the AWS edge. Yeah. Uh, and, and we're having more and more people talk to us about that. Yeah. And ask for assistance in the infrastructure, because they're developing new cryptocurrencies every day. Yeah. It's not like they're deploying Ethereum or anything specific. They're actually developing new currencies and, and putting them out there on it's >>Interesting. I mean, first of all, we've been doing crypto for many, many years. We have our own little, um, you know, projects going on. But if you look talk to all the crypto people that say, look, we do a smart concept. We use the blockchain. It's kind of over a lot of overhead and it's not really their technical already, but it's a cultural shift, but there's underserved use cases around use of money, but they're all using the blockchain, just for this like smart contracts for instance, or certain transactions. And they go into Amazon for the database. Yeah. <laugh> they all don't tell anyone we're using a centralized service, but what happened to decentralized. >>Yeah. And that's, and that's the conversation performance issue. Yeah. And, and it's a cost issue. Yeah. And it's a development issue. Um, so I think more and more as, as some of these, uh, currencies maybe come up, some of the smart contracts get into, uh, they find their use cases. I think we'll start talking about how does that really live on, on AWS and, and what does it look like to build decentralized applications, but with AWS hardware and services. >>Right. So take me through, uh, a use case of a customer, um, Matthew around the edge. Okay. So I'm a customer, pretend I'm a customer, Hey, you know, I'm, we're in an underserved area. I want to modernize my business. And I got my developers that are totally peaked up on cloud. Um, but we've identified that it's just a lot of overhead latency issues. I need to have a local edge and serve my ad. And I also want all the benefit of the cloud. So I want the modernization and I wanna migrate to the cloud for all those cloud benefits and the goodness of the cloud. What's the answer. Yeah. >>Uh, big thing is, uh, industrial manufacturing, right? That's, that's one of the best use cases, uh, inside industrial manufacturing, we can pull in many of the AWS edge services we can bring in, uh, private 5g, uh, so that all the, uh, equipment inside that, that manufacturing plant can be hooked up. They don't have to pay huge overheads to deploy 5g it's, uh, better than wifi for the industrial space. Um, when we take computing down to that industrial area, uh, because we wanna do pre-procesing on the data. Yeah. We want to gather some analytics. We deploy that with, uh, regular commercial available hardware running VMware, and we deploy EKS anywhere on that. Uh, inside of that manufacturing plant, uh, we can do pre-procesing on things coming out of the, uh, the robotics that depending on what we're manufacturing, right. Uh, and then we can take those refined analytics and for very low cost with maybe a little bit longer latency transmit those back, um, to the AWS availability zone, the, the standard for >>Data, data lake, or whatever, to >>The data lake. Yeah. Data lake house, whatever it might be. Um, and we can do additional data science on that once it gets to the AWS cloud. Uh, but a lot of that, uh, just in time business decisions, just in time, manufacturing decisions can all take place on an AWS service or services inside that manufacturing plant. And that's, that's one of the best use cases that we're >>Seeing. And I think, I mean, we've been seeing this on the queue for many, many years, moving data around is very expensive. Yeah. But also compute going to the data that saves that cost yep. On the data transfer also on the benefits of the latency. So I have to ask you, by the way, that's standard best practice now for the folks watching don't move the data, unless you have to, um, those new things are developing. So I wanna ask you what new patterns are you seeing emerging once this new architecture's in place? Love that idea, localize everything right at the edge, manufacturing, industrial, whatever, the use case, retail, whatever it is. Right. But now what does that change in the, in the core cloud? This is a, there's a system element here. Yeah. What's the new pattern. There's >>Actually an organizational element as well, because once you have to start making the decision, do I put this compute at the point of use or do I put this compute in the cloud out? Uh, now you start thinking about where business decisions should be taking place. Uh, so not only are you changing your architecture, you're actually changing your organization because you're thinking, you're thinking about a dichotomy you didn't have before. Uh, so now you say, okay, this can take place here. Uh, and maybe maybe decision can wait. Right? Yeah. Uh, and then how do I visualize that? By >>The way, it could be a bot too, doing the work for management. Yeah. <laugh> exactly. You got observability going, right. But you gotta change the database architecture on the back. So there's new things developing. You've got more benefit. There >>Are, there are. And, and we have more and more people that, that want to talk less about databases and want to talk more about data lakes because of this. They want to talk more about customers are starting to talk about throwing away data, uh, you know, for the past maybe decade. Yeah. It's been store everything. And one day we will have a data science team that we hire in our organization to do analytics on this decade of data. And >>Well, I mean, that's, that's a great point. We don't have time to drill into, maybe we do another session on this, but the one pattern was income of the past year is that throwing away data's bad. Even data lakes that so-called turn into data swamps, actually, it's not the case. You look at data, brick, snowflake, and other successes out there. And even time series data, which may seem irrelevant efforts over actually matters when people start retrain their machine learning algorithms. Yep. So as data becomes code, as we call it our lab showcase, we did a whole, whole, that event on this. The data's good in real time and in the lake. Yeah. Because the iteration of the data feeds the machine learning training. Things are getting better with the old data. So it's not throw away. It's not just business benefits. Yeah. There's all kinds of new scale. There >>Are. And, and we have, uh, many customers that are run petabyte level. Um, they're, they're essentially data factories on, on, uh, on premises, right? They're, they're creating so much data and they're starting to say, okay, we could analyze this, uh, in the cloud, we could transition it. We could move petabytes of data to the AWS cloud, or we can run, uh, computational workloads on premises. We can really do some analytics on this data transition, uh, those high level and sort of raw analytics back to AWS run 'em through machine learning. Um, and we don't have to transition 10, 12 petabytes of data into AWS. >>So I gotta end the segment on a, on a kind of a, um, fun note. I was told to ask you about your personal background on premise architect, a cloud and skydiving instructor. <laugh> how does that all work together? What tell, what does this mean? Yeah. >>Uh, you >>Jumped out a plane and got a job. You, you got a customer to jump out >>Kind of. So I was jump, I was teaching Scott eing, uh, before I, before I started in the cloud space, this was 13, 14 years ago. I was a, I still am a Scott I instructor. Yeah. Uh, I was teaching Scott eing and I heard out of the corner of my ear, uh, a guy that owned an MSP that was lamenting about, um, you know, storing data and, and how his cus customers are working. And he can't find enough people to operate all these workloads. So I walked over and said, Hey, this is, this is what I went to school for. Like, I'd love to, you know, uh, I was living in a tent in the woods teaching scout. I think I was like, I'd love to not live in a tent in the woods. So, uh, uh, I started in the first day there, uh, we had a, a discussion, uh, EC two, just come out <laugh> um, and, uh, like, >>This is amazing. >>Yeah. And so we had this discussion, we should start moving customers here. And, uh, and that totally revolutionized that business, um, that, that led to, uh, that that guy actually still owns a skydiving airport. But, um, but through all of that and through being an on premises migrated me and myself, my career into the cloud, and now it feels like, uh, almost, almost looking back and saying, now let's take what we learned in the cloud and, and apply those lessons and those services to >>It's. So it's such a great story, you know, I was gonna, you know, you know, the, the, the, the whole, you know, growth mindset pack your own parachute, you know, uh, exactly. You know, the cloud in the early day was pretty much will the shoot open. Yeah. It was pretty much, you had to roll your own cloud at that time. And so, you know, you, you jump on a plane, you gotta make sure that parachute is gonna open. >>And so was Kubernetes by the way, 2015 or so when, um, when that was coming out, it was, I mean, it was, it was still, and I, maybe it does still feel like that to some people. Right. But, uh, it was, it was the same kind of feeling that we had in the early days, AWS, the same feeling we have when we >>It's pretty much now with you guys, it's more like a tandem jump. Yeah. You know, but, but it's a lot of, lot of this cutting edge stuff, like jumping out of an airplane. Yeah. You guys, the right equipment, you gotta do the right things. Exactly. >>Right. >>Matthew, thanks for coming on the cube. Really appreciate it. Absolutely great conversation. Thanks for having me. Okay. The cubes here live and San Francisco for summit. I'm John Forry host of the cube. Uh, we'll be at a summit in New York coming up in the summer as well. Look up for that. look@thiscalendarforallthecubeactionatthecube.net. We'll be right back with our next segment after this break. >>Okay. Welcome back everyone to San Francisco live coverage here, we're at the cube a be summit 2022. We're back in person. I'm John fury host to the cube. We'll be at the eight of his summit in New York city. This summer, check us out then. But right now, two days in San Francisco, getting all the coverage what's going on in the cloud, we got a cube alumni and friend of the cube, my dudes, car CEO, investor, a Sierra, and also an investor and a bunch of startups, angel investor. Gonna do great to see you. Thanks for coming on the cube. Good to see you. Good to see you, sir. Chris. Cool. How are, are you >>Good? How are you? >>So congratulations on all your investments. Uh, you've made a lot of great successes, uh, over the past couple years, uh, and your company raising, uh, some good cash as Sarah. So give us the update. How much cash have you guys raised? What's the status of the company product what's going on? First >>Of all, thank you for having me back to be business with you. Never great to see you. Um, so is a company started around four years back. I invested with a few of the investors and now I'm the CEO there. Um, we have raised close to a hundred million there. Uh, the investors are people like Norwes Menlo, Tru ventures, coast, lo ventures, Ram Sheam and all those people, all well known guys. The Andy Beckel chime, Paul Mo uh, main web. So a whole bunch of operating people and, uh, Silicon valley VCs are involved >>And has it come? >>It's going well. We are doing really well. We are going almost 300% year over year. Uh, for last three years, the space ISR is going after is what I call the applying AI for customer service. It operations, it help desk, uh, the same place I used to work at ServiceNow. We are partners with ServiceNow to take, how can we argument for employees and customers, Salesforce, and ServiceNow to take it to the next stage? >>Well, I love having you on the cube, Dave and I, Dave Valenti as well loves having you on too, because you not only bring the entrepreneurial CEO experience, you're an investor. You're like a GE, you're like a guest analyst. <laugh> >>You know who you >>Get to call this fun to talk. You though, >>You got the commentary, you, your, your finger on the pulse. Um, so I gotta ask you obviously, AI and machine learning, machine learning AI, or you want to phrase it. Isn't every application. Now, AI first, uh, you're seeing a lot of that going on. You're starting to see companies build the modern applications at the top of the stack. So the cloud scale has hit. We're seeing cloud scale. You predicted that we talked about on cube many times. Now you have that past layer with a lot more services and cloud native becoming a standard layer. Containerizations growing DACA just raised a hundred million on a 2 billion valuation back from the dead after they pivoted from an enterprise services. So open source developers are booming. Um, where's the action. I mean, is there data control, plane emerging, AI needs data. There's a lot of challenges around this. There's a lot of discussions and a lot of companies being funded, observability there's 10 million observability companies. Data is the key. What's your angle on this? What's your take. Yeah, >>No, look, I think I'll give you the view that I see right from my side. Obviously data is very clear. So the things that remember system of recorded you and me talked about the next layer is called system of intelligence. That's where the AI will play. Like we talk cloud NA it'll be called AI, NA AI native is a new buzzword and using the AI customer service it operations. You talk about observability. I call it, AIOps applying AOPs for good old it operation management, cloud management. So you'll see the AOPs applied for whole list of, uh, application from observability doing the CMDB, predicting the events insurance. So I see a lot of work clicking for AIOps and service desk. What needs to be helped us with ServiceNow BMC G you see a new ELA emerging as a system of intelligence. Uh, the next would be is applying AI with workflow automation. So that's where you'll see a lot of things called customer workflow, employee workflows. So think of what UI path automation, anywhere ServiceNow are doing, that area will be driven with a AI workflows. So you'll see AI going >>Off is RPA a company is AI, is RPA a feature of something bigger? Or can someone have a company on RPA UI pass? One will be at their event this summer? Um, is it a product company? I mean, I mean, RPA is almost, should be embedded in everything. It's >>A feature. It is very good point. Very, very good thinking. So one is, it's a category for sure. Like, as we thought, it's a category, it's an area where RPA may change the name. I call it much more about automation, workflow automation, but RPA and automation is a category. Um, it's a company, or, but that automation should be embedded in every area. Yeah. Like we call cloud NA and AI NATO it'll become automation. NA yeah. And that's your thinking. >>It's almost interesting me. I think about the, what you're talking about what's coming to mind is I'm kinda having flashbacks to the old software model of middleware. Remember at middleware, it was very easy to understand it. It was middleware. It sat between two things and then the middle, and it was software abstraction. Now you have all, all kinds of workflows, abstractions everywhere. So multiple databases, it's not a monolithic thing. Right? Right. So as you break that down, is this the new modern middleware? Because what you're talking about is data workflows, but they might be siloed or they integrated. I mean, these are the challenges. This is crazy. What's the, >>So don't about the databases become called poly databases. Yeah. I call this one polyglot automation. So you need automation as a layer, as a category, but you also need to put automation in every area like you were talking about. It should be part of service. Now it should be part of ISRA, like every company, every Salesforce. So that's why you see MuleSoft and Salesforce buying RPA companies. So you'll see all the SaaS companies, cloud companies having an automation as a core. So it's like how you have a database and compute and sales and networking. You'll also have an automation as a layer <inaudible> inside every stack. >>All right. So I wanna shift gears a little bit and get your perspective on what's going on behind us. You can see, uh, behind us, you've got the expo hall. We got, um, we're back to vents, but you got, you know, AMD, Clum, Ove, uh, Dynatrace data, dog, innovative, all the companies out here that we know, we interview them all. They're trying to be suppliers to this growing enterprise market. Right. Okay. But now you also got the entrepreneurial equation. Okay. We're gonna have John Sado on from Bel later today. He's a former NEA guy and we always talk to Jerry, Jen. We know all the, the VCs. What does the startups look like? What does the state of the, in your mind, cause you, I know you invest the entrepreneurial founder situation, clouds bigger. Mm-hmm <affirmative> global, right? Data's part of it. You mentioned data's code. Yes. Basically data is everything. What's it like for a first an entrepreneur right now who's starting a company. What's the white space. What's the attack plan. How do they get in the market? How do they engineer everything? >>Very good. So I'll give it to, uh, two things that I'm seeing out there. Remember leaders of Amazon created the startups 15 years back. Everybody built on Amazon now, Azure and GCP. The next layer would be is people don't just build on Amazon. They're going to build it on top of snowflake. Companies are snowflake becomes a data platform, right? People will build on snowflake. Right? So I see my old boss flagman try to build companies on snowflake. So you don't build it just on Amazon. You build it on Amazon and snowflake. Snowflake will become your data store. Snowflake will become your data layer. Right? So I think that's in the of, <inaudible> trying to do that. So if I'm doing observability AI ops, if I'm doing next level of Splunk SIM, I'm gonna build it on snowflake, on Salesforce, on Amazon, on Azure, et cetera. >>It's interesting. You know, Jerry Chan has it put out a thesis a couple months ago called castles in the cloud where your moat is, what you do in the cloud. Not necessarily in the, in the IP. Um, Dave LAN and I had last reinvent, coined the term super cloud, right? He's got a lot of traction and a lot of people throwing, throwing mud at us, but we were, our thesis was, is that what Snowflake's doing? What Goldman S Sachs is doing. You starting to see these clouds on top of clouds. So Amazon's got this huge CapEx advantage. And guys like Charles Fitzgeral out there, who we like was kind of shit on us saying, Hey, you guys terrible, they didn't get it. Like, yeah. I don't think he gets it, but that's a whole, can't wait to debate him publicly on this. <laugh> if he's cool. Um, but snowflake is on Amazon. Yes. Now they say they're on Azure now. Cause they've got a bigger market and they're public, but ultimately without a AWS snowflake doesn't exist. And, and they're reimagining the data warehouse with the cloud, right? That's the billion dollar opportunity. >>It is. It is. They both are very tight. So imagine what Frank has done at snowflake and Amazon. So if I'm a startup today, I want to build everything on Amazon where possible whatever is, I cannot build. I'll make the pass layer. Remember the middle layer pass will be snowflake. So can build it on snowflake. I can use them for data layer. If I really need to size, I'll build it on four.com Salesforce. So I think that's where you'll see. So >>Basically if you're an entrepreneur, the north star in terms of the outcome is be a super cloud. >>It is, >>That's the application on another big CapEx ride, the CapEx of AWS or cloud, >>And that reduce your product development, your go to market and you get use the snowflake marketplace to drive your engagement. >>Yeah. Yeah. How are, how is Amazon and the clouds dealing with these big whales? The snowflakes of the world? I mean, I know they got a great relationship, uh, but snowflake now has to run a company they're public. Yeah. So, I mean, I'll say, I think got Redshift. Amazon has got red, um, but Snowflake's a big customer. They're probably paying AWS think big bills too. >>So John, very good. Cause it's like how Netflix is and Amazon prime, right. Netflix runs on Amazon, but Amazon has Amazon prime that co-option will be there. So Amazon will have Redshift, but Amazon is also partnering with, uh, snowflake to have native snowflake data warehouse as a data layer. So I think depending on the application use case, you have to use each of the above. I think snowflake is here for a long term. Yeah. Yeah. So if I'm building an application, I want to use snowflake then writing from stats. >>Well, I think that comes back down to entrepreneurial hustle. Do you have a better product? Right. Product value will ultimately determine it as long as the cloud doesn't, You know, foreclose your value that's right. But some sort of internal hack, but I think, I think the general question that I have is that I think it's okay to have a super cloud like that because the rising tide is still happening at some point. When does the rising tide stop >>And >>Do the people shopping up their knives, it gets more competitive or is it just an infinite growth cycle? I >>Think it's growth. You call it cloud scale. You invented the word cloud scale. So I think look, cloud will continually agree, increase. I think there's, as long as there are more movement from on, uh, OnPrem to the classical data center, I think there's no reason at this point, the rumor, the old lift and shift that's happening in like my business. I see people lift and shifting from the it operations. It helpless, even the customer service service now and, uh, ticket data from BMCs CAS like Microfocus, all those workloads are shifted to the cloud, right? So cloud ticketing system is happening. Cloud system of record is happening. So I think this train has still a long way to go made. >>I wanna get your thoughts for the folks watching that are, uh, enterprise buyers or practitioners, not suppliers to the market, feel free to, to XME or DMing. Next question's really about the buying side, which is if I'm a customer, what's the current, um, appetite for startup products. Cause you know, the big enterprises now and, you know, small, medium, large, and large enterprise are all buying new companies cuz a startup can go from zero to relevant very quickly. So that means now enterprises are engaging heavily with startups. What's it like what's is there a change in order of magnitude of the relationship between the startup selling to, or a growing startup selling to an enterprise? Um, have you seen changes there? I mean I'm seeing some stuff, but why don't we get your thoughts on that? What, no, it is. >>If I remember going back to our 2007 or eight, it, when I used to talk to you back then when Amazon started very small, right? We are an Amazon summit here. So I think enterprises on the average used to spend nothing with startups. It's almost like 0% or 1% today. Most companies are already spending 20, 30% with startups. Like if I look at a CIO line business, it's gone. Yeah. Can it go more? I think it can double in the next four, five years. Yeah. Spending on the startups. >>Yeah. And check out, uh, AWS startups.com. That's a site that we built for the startup community for buyers and startups. And I want to get your reaction because I reference the URL cause it's like, there's like a bunch of companies we've been promoting because the solutions that startups have actually are new stuff. Yes. It's bending, it's shifting left for security or using data differently or um, building tools and platforms for data engineering. Right. Which is a new persona that's emerging. So you know, a lot of good resources there, um, and gives back now to the data question. Now, getting back to your, what you're working on now is what's your thoughts around this new, um, data engineering persona, you mentioned AIOps, we've been seeing AIOps IOPS booming and that's creating a new developer paradigm that's right. Which we call coin data as code data as code is like infrastructure as code, but it's for data, right? It's developing with data, right? Retraining machine learnings, going back to the data lake, getting data to make, to do analysis, to make the machine learning better post event or post action. So this, this data engineers like an SRE for data, it's a new, scalable role we're seeing. Do you see the same thing? Do you agree? Um, do you disagree or can you share >>Yourself? No, I have a lot of thoughts that plus I see AIOP solutions in the future should be not looking back. I need to be like we are in San Francisco bay. That means earthquake prediction. Right? I want AOPs to predict when the outages are gonna happen. When there's a performance issue. I don't think most AOPs vendors have not gone there yet. Like I spend a lot of time with data dog, Cisco app Dyna, right? Dynatrace, all this solution will go future towards to proactive solution with AOPs. But what you bring up a very good point on the data side. I think like we have a Amazon marketplace and Amazon for startup, there should be data exchange where you want to create for AOPs and AI service that customers are give the data, share the data because we thought the data algorithms are useless. I can come the best algorithm, but I gotta train them, modify them, tweak them, make them better, make them better. Yeah. And I think their whole data exchange is the industry has not thought through something you and me talk many times. Yeah. Yeah. I think the whole, that area is very important. >>You've always been on, um, on the Vanguard of data because, uh, it's been really fun. Yeah. >>Going back to our big data days back in 2009, you know, >>Look at, look how much data bricks has grown. >>It is uh, double, the key >>Cloud kinda went private, so good stuff. What are you working on right now? Give a, give a, um, plug for what you're working on. You'll still investing. >>I do still invest, but look, I'm a hundred percent on ISRA right now. I'm the CEO there. Yeah. Okay. So right. ISRA is my number one baby right now. So I'm looking at that growing customers and my customers are some of them, you like it's zoom auto desk, Mac of fee, uh, grandchildren, all the top customers. Um, mainly for it help desk customer service. AIOps those are three product lines and going after enterprise and commercial deals. >>And when should someone buy your product? What's what's their need? What category is it? >>I think they look whenever somebody needs to buy the product is if you need AOP solution to predict, keep your lights on predict S one area. If you want to improve employee experience, you are using a slack teams and you want to automate all your workflows. That's another value problem. Third is customer service. You don't want to hire more people to do it. Some of the areas where you want to scale your company, grow your company, eliminate the cost customer service, >>Great stuff, man. Doing great to see you. Thanks for coming on. Congratulations on the success of your company and your investments. Thanks for coming on the cube. Okay. I'm John fur here at the cube live in San Francisco for day one of two days of coverage of 80 summit, 2022. And we're gonna be at 80 summit in San, uh, in New York and the summer. So look for that on this calendar, of course go to eight of us, startups.com. I mentioned that it's a site for all the hot startups and of course the cube.net and Silicon angle.com. Thanks for watching. We'll be back more coverage after this short break. >>Okay. Welcome back everyone. This to cubes coverage here in San Francisco, California, a Davis summit, 2022, the beginning of the event season, as it comes back a little bit smaller footprint, a lot of hybrid events going on, but this is actually a physical event, a summit new York's coming in the summer. We'll be there too with the cube on the set. We're getting back in the groove, psyched to be back. We were at reinvent, uh, as well, and we'll see more and more cube, but you're gonna see a lot of virtual cube, a lot of hybrid cube. We wanna get all those conversations, try to get more interviews, more flow going. But right now I'm excited to have Corey Quinn here on the back on the cube chief cloud economists with duck, bill groove, he founder, uh, and chief content person always got great angles, fun comedy, authoritative Corey. Great to see you. Thank you. >>Thanks. Coming on. Sure is a lot of words to describe as shit posting, which is how I describe what I tend to do. Most days, >>Shit posting is an art form now. And if you look at Mark's been doing a lot of shit posting lately, all a billionaires are shit posting, but they don't know how to do it. Like they're not >>Doing it right. Something opportunity there. It's like, here's how to be even more obnoxious and incisive. It's honestly the most terrifying scenario for anyone is if I have that kind of budget to throw at my endeavors, it's like, I get excited with a nonsense I can do with a $20 gift card for an AWS credit compared to, oh well, if I could buy a mid-size island to begin doing this from, oh, then we're having fun. This >>Shit posting trend. Interesting. I was watching a thread go on about, saw someone didn't get a job because of their shit posting and the employer didn't get it. And then someone on the other side, I'll hire the guy cuz I get that's highly intelligent shit posting. So for the audience that doesn't know what shit posting is, what is shit posting? >>It's more or less talking about the world of enterprise tech, which even that sentence is hard to finish without falling asleep and toppling out of my chair in front of everyone on the livestream. But it's doing it in such a way that brings it to life that says the quiet part. A lot of the audience is thinking, but generally doesn't say either because they're polite or not a jackass or more prosaically are worried about getting fired for better or worse. I don't have that particular constraint, >>Which is why people love you. So let's talk about what you, what you think is, uh, worthy and not worthy in the industry right now, obviously, uh, coupons coming up in Spain, which they're having a physical event, you can see the growth of cloud native Amazons, all, all the Adams let see new CEO, Andy move on to be the chief of all. Amazon just saw him. The cover of was it time magazine. Um, he's under a lot of stress. Amazon's changed. Invoice has changed. What's working. What's not, what's rising, what's falling. What's hot. What's not, >>It's easy to sit here and criticize almost anything these folks do. They they're effectively in a fishbowl, but I have trouble imagining the logistics. It takes to wind up handling the catering for a relatively downscale event like this one this year, let alone running a 1.7 million employee company having to balance all the competing challenges and pressures and the rest. I, I just can't fathom what it would be like to look at all of AWS. It's, it's sprawling, immense that dominates our entire industry and say, okay, this is a good start, but I, I wanna focus on something with a broader remit. What is that? How do you even get into that position? And you can't win once you're there. All you can do is hold onto the tiger and hope you don't get mold. Well, >>There's a lot of force for good conversations, seeing a lot of that going on, Amazon's trying to port and he was trying to portray themselves as you know, the Pathfinder, you know, you're the pioneer, um, force for good. And I get that and I think that's a good angle as cloud goes mainstream. There's still the question of, we had a guy on just earlier, who was a skydiving instructor and we were joking about the early days of cloud. Like that was like skydiving, build a parachute open, you know, and now it same kind of thing. As you move to edge, things are like reliable in some areas, but still new, new fringe, new areas. That's crazy. Well, >>Since the last time we've spoken, uh, Steve Schmidt is now the CISO for all of Amazon and his backfill replacement. The AWS CISO is CJ. Moses who as a hobby races, a as a semi-pro race car driver to my understanding, which either, I don't know what direction to take that in either. This is what he does to relax or ultimately, or ultimately it's. Huh? That, that certainly says something about risk assessment. I'm not entirely sure what, but okay. Either way, sounds like more exciting >>Replacement ready <laugh> in case something goes wrong. I, the track highly >>Available >>CSOs. I gotta say one of the things I do like in the recent trend is that the tech companies are getting into the formula one, which I was never a fan of until I watched that Netflix series. But when you look at the formula one, it's pretty cool. Cause it's got some tech angles, I get the whole data instrumentation thing, but the most coolest thing about formula one is they have these new rigs out. Yeah. Where you can actually race in e-sports with other, in pure simulation of the race car. You gotta get the latest and video graphics card, but it's basically a tricked out PC with amazing monitors and you have all the equipment of F1 and you're basically simulating racing. >>Oh, it's great too. And I can see the appeal of these tech companies getting into it because these things are basically rocket shifts. When those cars go, like they're sitting there, we can instrument every last part of what is going on inside that vehicle. And then AWS crops up. And we can bill on every one of those dimensions too. And it's like slow down their hasty pudding one step at a time. But I do see the appeal. >>So I gotta ask you about, uh, what's going in your world. I know you have a lot of great success. We've been following you in the queue for many, many years. Got a great newsletter. Check out Corey Quinn's newsletter, uh, screaming in the cloud program. Uh, you're on the cutting edge and you've got a great balance between really being snarky and, and, and really being delivering content. That's exciting, uh, for people, uh, with a little bit of an edge, um, how's that going? Uh, what's back any blow back late there been uptick. What was, what are some of the things you're hearing from your audience, more Corey, more Corey. And then of course the, the PR team's calling you >>The weird thing about having an audience beyond a certain size is far and away as a landslide. The most common response I get is silence where it's high. I'm emailing an awful lot of people at last week in AWS every week and okay. They must not have heard me it. That is not actually true. People just generally don't respond to email because who responds to email newsletters. That sounds like something, a lunatic might do same story with response to live streams and podcasts. It's like, I'm gonna call into that am radio show and give them a piece of my mind. People generally don't do that. >>We should do that. Actually. I think sure would call in. Oh, I, >>I think >>Chief, we had that right now. People would call in and say, Corey, what do you think about X? >>Yeah. It not, everyone understands the full context of what I do. And in fact, increasingly few people do and that's fine. I, I keep forgetting that sometimes people do not see what I'm doing in the same light that I do. And that's fine. Blowback has been largely minimal. Honestly, I am surprised anything by how little I have gotten over the last five years of doing this, but it would be easier to dismiss me if I weren't generally. Right. When, okay, so you launch this new service and it seems pretty crappy to me cuz when I try and build something, it falls over and begs for help. And people might not like hearing that, but it's what customers are finding too. Yeah. I really am the voice of the customer. >>You know, I always joke with Dave ante about how John Fort's always at, uh, reinvent getting the interview with jazzy now, Andy we're there, you're there. And so we have these rituals at the events. It's all cool. Um, one of the rituals I like about your, um, your content is you like to get on the naming product names. Um, and, and, and, and, and kind of goof on that. Now why I like is because I used to work at ETT Packard where they used to name things as like engineers, HP 1 0 5, or we can't, >>We have a new monitor. How are we gonna name it? Throw the wireless keyboard down the stairs again. And there you go. Yeah. >>It's and the old joke at HP was if they, if they invented sushi, they'd say, yeah, we can't call sushi. It's cold, dead fish. That's what it is. And so the joke was cold. Dead fish is a better name than sushi. So you know is fun. So what's the, what are the, how's the Amazon doing in there? Have they changed their naming, uh, strategy, uh, on some of their, their >>Producting, they're going in different directions. When they named Amazon Aurora, they decided to explore a new theme of Disney princesses as they go down those paths. And some things are more descriptive. Some people are clearly getting bonused on a number of words. They can shove into it. Like the better a service is the longer it's name. Like AWS systems manager, session manager is a great one. I love the service, ridiculous name. They have systems manager, parameter store, which is great. They have secrets manager, which does the same thing. It's two words less, but that one costs money in a way that systems manage your parameter store does not. It's >>Fun. What's your, what's your favorite combination of acronyms >>Combination of you >>Got Ks. You got EMR, you got EC two. You got S three SQS. Well, Redshift the on an acronym, you >>Gots is one of my personal favorites because it's either elastic block store or elastic bean stock, depending entirely on the context of the conversation. >>They still up bean stalk. Or is that still around? Oh, >>They never turn anything off. They're like the anti Google, Google turns things off while they're still building it. Whereas Amazon is like, wow, we built this thing in 2005 and everyone hates it. But while we certainly can't change it, now it has three customers on it. John three <laugh>. >>Okay. >>Simple BV still haunts our dreams. >>I, I actually got an email. I saw one of my, uh, servers, all these C two S were being deprecated and I got an email I'm like, I couldn't figure out. Why can you just like roll it over? Why, why are you telling me just like, give me something else. Right. Okay. So let me talk about, uh, the other things I want to ask you is that like, okay. So as Amazon gets better in some areas, where do they need more work in your opinion? Because obviously they're all interested in new stuff and they tend to like put it out there for their end to end customers. But then they've got ecosystem partners who actually have the same product. Yes. And, and this has been well documented. So it's, it's not controversial. It's just that Amazon's got a database, Snowflake's got a database service. So Redshift, snowflake database is, so you got this co-op petition. Yes. How's that going? And what are you hearing about the reaction to any of that stuff? >>Depends on who you ask. They love to basically trot out a bunch of their partners who will say nice things about them. And it very much has heirs of, let's be honest, a hostage video, but okay. Cuz these companies do partner with Amazon and they cannot afford to rock the boat too far. I'm not partnered with anyone. I can say what I want and they're basically restricted to taking away my birthday at worse so I can live with that. >>All right. So I gotta ask about multi-cloud cause obviously the other cloud shows are coming up. Amazon hated that word. Multi-cloud um, a lot of people are saying, you know, it's not a real good marketing word, like multi sounds like, you know, root canal. Mm-hmm <affirmative> right. So is there a better description for multi-cloud >>Multiple single points? >>Dave loves that term. Yeah. >>Yeah. You're building in multiple single points of failure. Do it for the right reasons or don't do it as a default. I believe not doing it is probably the right answer. However, and if I were, if I were Amazon, I wouldn't want to talk about multi-cloud either as the industry leader, talk about other clouds, bad direction to go in from a market cap perspective, it doesn't end well for you, but regardless of what they want to talk about, or don't want to talk about what they say, what they don't say, I tune all of it out. And I look at what customers are doing and multi-cloud exists in a variety of forms. Some brilliant, some brain dead. It depends a lot on context. But my general response is when someone gets on stage from a company and tells me to do a thing that directly benefits their company. I am skeptical at best. Yeah. When customers get on stage and say, this is what we're doing, because it solves problems. That's when I shut up and listen. Yeah. >>Cool. Awesome. Corey, I gotta ask you a question, cause I know you, we you've been, you know, fellow journeymen and the, and the cloud journey going to all the events and then the pandemic hit where now in the third year, who knows what it's gonna gonna end. Certainly events are gonna look different. They're gonna be either changing footprint with the virtual piece, new group formations. Community's gonna emerge. You got a pretty big community growing and it's throwing like crazy. What's the weirdest or coolest thing, or just big chain angels. You've seen with the pandemic, uh, from your perspective, cuz you've been in the you're in the middle of the whitewater rafting. You've seen the events you circle offline. You saw the online piece, come in, you're commentating. You're calling balls and strikes in the industry. You got a great team developing over there. Duck bill group. What's the big aha moment that you saw with the pandemic. Weird, fun, serious, real in the industry and with customers what's >>Accessibility. Reinvent is a great example. When in the before times it's open to anyone who wants to attend, who can pony up two grand and a week in Las Vegas and get to Las Vegas from wherever they happen to be by moving virtually suddenly it, it embraces the reality that talent is even distributed. Opportunity is not. And that means that suddenly these things are accessible to a wide swath of audience and potential customer base and the rest that hadn't been invited to the table previously, it's imperative that we not lose that. It's nice to go out and talk to people and have people come up and try and smell my hair from time to time, I smell delightful. Let make assure you, but it was, but it's also nice to be. >>I have a product for you if you want, you know. >>Oh, excellent. I look forward to it. What is it putting? Why not? <laugh> >>What else have you seen? So when accessibility for talent, which by the way is totally home run. What weird things have happened that you've seen? Um, that's >>Uh, it's, it's weird, but it's good that an awful lot of people giving presentations have learned to tighten their message and get to the damn point because most people are not gonna get up from a front row seat in a conference hall, midway through your Aing talk and go somewhere else. But they will change a browser tab and you won't get them back. You've gotta be on point. You've gotta be compelling if it's going to be a virtual discussion. >>Yeah. And also turn off your IMEs too. >>Oh yes. It's always fun in the, in the meetings when you're talking to someone and their co is messaging them about, should we tell 'em about this? And I'm sitting there reading it and it's >>This guy is really weird. Like, >>Yes I am and I bring it into the conversation and then everyone's uncomfortable. It goes, wow. >>Why not? I love when my wife yells at me over I message. When I'm on a business call, like, do you wanna take that about no, I'm good. >>No, no. It's better off. I don't. No, the only encourager it's fine. >>My kids. Excellent. Yeah. That's fun again. That's another weird thing. And, and then group behavior is weird. Now people are looking at, um, communities differently. Yes. Very much so, because if you're fatigued on content, people are looking for the personal aspect. You're starting to see much more of like yeah. Another virtual event. They gotta get better. One and two who's there. >>Yeah. >>The person >>That's a big part of it too is the human stories are what are being more and more interesting. Don't get up here and tell me about your product and how brilliant you are and how you built it. That's great. If I'm you, or if I wanna work with you or I want to compete with you, or I wanna put on my engineering hat and build it myself. Cause why would I buy anything? That's more than $8. But instead, tell me about the problem. Tell me about the painful spot that you specialize in. Tell me a story there. >>I, I >>Think that gets a glimpse in a hook and >>Makes more, more, I think you nailed it. Scaling storytelling. Yes. And access to better people because they don't have to be there in person. I just did it thing. I never, we never would've done the queue. We did. Uh, Amazon stepped up in sponsors. Thank you, Amazon for sponsoring international women's day, we did 30 interviews, APAC. We did five regions and I interviewed this, these women in Asia, Pacific eight, PJ, they called for in this world. And they're amazing. I never would've done those interviews cuz I never, would've seen 'em at an event. I never would've been in Japan or Singapore to access them. And now they're in the index. They're in the network. They're collaborating on LinkedIn. So a threads are developing around connections that I've never seen before. Yes. Around the content, >>Absolutely >>Content value plus >>The networking. And that is the next big revelation of this industry is going to realize you have different companies. And in Amazon's case, different service teams, all, all competing with each other, but you have the container group and you have the database group and you have the message cuing group. But customers don't really want to build things from spare parts. They want a solution to a problem. I want to build an app that does Twitter for pets or whatever it is I'm trying to do. I don't wanna basically have to pick and choose and fill my shopping cart with all these different things. I want something that's gonna give me what I'm trying to get as close to turnkey as possible. Moving up the stack. That is the future. And just how it gets here is gonna be >>Well we're here with Corey Quinn, the master of the master of content here in the a ecosystem. Of course we we've been following up in the beginnings. Great guy. Check out his blog, his site, his newsletter screaming podcast. Cory, final question for you. Uh, what do you hear doing what's on your agenda this week in San Francisco and give a plug for the duck build group. What are you guys doing? I know you're hiring some people what's on the table for the company. What's your focus this week and put a plug in for the group. >>I'm here as a customer and basically getting outta my cage cuz I do live here. It's nice to actually get out and talk to folks who are doing interesting things at the duck build group. We solve one problem. We fixed the horrifying AWS bill, both from engineering and architecture, advising as well as negotiating AWS contracts because it turns out those things are big and complicated. And of course my side media projects last week in aws.com, we are, it it's more or less a content operation where I indulge my continual and ongoing law of affair with the sound of my own voice. >><laugh> and you good. It's good content. It's on, on point fun, Starky and relevant. So thanks for coming to the cube and sharing with us. Appreciate it. No, thank you. Fun. You. Okay. This the cube covers here in San Francisco, California, the cube is back at to events. These are the summits, Amazon web services summits. They happen all over the world. We'll be in New York and obviously we're here in San Francisco this week. I'm John furry. Keep, keep it right here. We'll be back with more coverage after this short break. Okay. Welcome back everyone. This's the cubes covers here in San Francisco, California, we're live on the show floor of AWS summit, 2022. I'm John for host of the cube and remember AWS summit in New York city coming up this summer, we'll be there as well. And of course reinvent the end of the year for all the cube coverage on cloud computing and AWS. The two great guests here from the APN global APN se Jenko and Jeff Grimes partner leader, Jeff and se is doing partnerships global APN >>AWS global startup program. Yeah. >>Okay. Say that again. >>AWS global startup program. >>That's the official name. >>I love >>It too long, too long for me. Thanks for coming on. Yeah, of course. Appreciate it. Tell us about what's going on with you guys. What's the, how was you guys organized? You guys we're obviously were in San Francisco bay area, Silicon valley, zillions of startups here, New York. It's got another one we're gonna be at tons of startups. Lot of 'em getting funded, big growth and cloud big growth and data security, hot and sectors. >>Absolutely. >>So maybe, maybe we could just start with the global startup program. Um, it's essentially a white glove service that we provide to startups that are built on AWS. And the intention there is to help identify use cases that are being built on top of AWS. And for these startups, we want to provide white glove support in co building products together. Right. Um, co-marketing and co-selling essentially, um, you know, the use cases that our customers need solved, um, that either they don't want to build themselves or are perhaps more innovative. Um, so the, a AWS global startup program provides white glove support, dedicated headcount for each one of those pillars. Um, and within our program, we've also provided incentives, programs go to market activities like the AWS startup showcase that we've built for these startups. >>Yeah. By the way, start AWS startups.com is the URL, check it out. Okay. So partnerships are key. Jeff, what's your role? >>Yeah. So I'm responsible for leading the overall F for, for the AWS global startup program. Um, so I've got a team of partner managers that are located throughout the us, uh, managing a few hundred startup ISVs right now. <laugh> >>Yeah, I got >>A lot. We've got a lot. >>There's a lot. I gotta, I gotta ask the tough question. Okay. I'm I'm a startup founder. I got a team. I just got my series a we're grown. I'm trying to hire people. I'm super busy. What's in it for me. Yeah. What do you guys bring to the table? I love the white glove service, but translate that what's in it. What do I get out of it? What's >>A good story. Good question. I focus, I think. Yeah, because we get, we get to see a lot of partners building their businesses on AWS. So, you know, from our perspective, helping these partners focus on what, what do we truly need to build by working backwards from customer feedback, right? How do we effectively go to market? Because we've seen startups do various things, um, through trial and error, um, and also just messaging, right? Because oftentimes partners or rather startups, um, try to boil the ocean with many different use cases. So we really help them, um, sort of laser focus on what are you really good at and how can we bring that to the customer as quickly as possible? >>Yeah. I mean, it's truly about helping that founder accelerate the growth of their company. Yeah. Right. And there's a lot that you can do with AWS, but focus is truly the key word there because they're gonna be able to find their little piece of real estate and absolutely deliver incredible outcomes for our customers. And then they can start their growth curve there. >>What are some of the coolest things you've seen with the APN that you can share publicly? I know you got a lot going on there, a lot of confidentiality. Um, but you know, we're here lot of great partners on the floor here. I'm glad we're back at events. Uh, a lot of stuff going on digitally with virtual stuff and, and hybrid. What are some of the cool things you guys have seen in the APN that you can point to? >>Yeah, absolutely. I mean, I can point to few, you can take them. Sure. So, um, I think what's been fun over the years for me personally, I came from a startup, ran sales at an early stage startup and, and I went through the whole thing. So I have a deep appreciation for what these guys are going through. And what's been interesting to see for me is taking some of these early stage guys, watching them progress, go public, get acquired, and see that big day mm-hmm <affirmative>, uh, and being able to point to very specific items that we help them to get to that point. Uh, and it's just a really fun journey to watch. >>Yeah. I, and part of the reason why I really, um, love working at the AWS, uh, global startup program is working with passionate founders. Um, I just met with a founder today that it's gonna, he's gonna build a very big business one day, um, and watching them grow through these stages and supporting that growth. Um, I like to think of our program as a catalyst for enterprise sort of scale. Yeah. Um, and through that we provide visibility, credibility and growth opportunities. >>Yeah. A lot, a lot of partners too. What I found talking to staff founders is when they have that milestone, they work so hard for it. Whether it's a B round C round Republic or get bought. Yeah. Um, then they take a deep breath and they look back at wow, what a journey it's been. So it's kind of emotional for sure. Yeah. Still it's a grind. Right? You gotta, I mean, when you get funding, it's still day one. You don't stop. It's no celebrate, you got a big round or valuation. You still gotta execute >>And look it's hypercompetitive and it's brutally difficult. And our job is to try to make that a little less difficult and navigate those waters right. Where everyone's going after similar things. >>Yeah. I think as a group element too, I observe that startups that I, I meet through the APN has been interesting because they feel part of AWS. Yeah, totally. As a group of community, as a vibe there. Um, I know they're hustling, they're trying to make things happen. But at the same time, Amazon throws a huge halo effect. I mean, that's a huge factor. I mean, yeah. You guys are the number one cloud in the business, the growth in every sector is booming. Yeah. And if you're a startup, you don't have that luxury yet. And look at companies like snowflake, they're built on top of AWS. Yeah. I mean, people are winning by building on AWS. >>Yeah. And our, our, our program really validates their technology first. So we have, what's called a foundation's technical review that we put all of our startups through before we go to market. So that when enterprise customers are looking at startup technology, they know that it's already been vetted. And, um, to take that a step further and help these partners differentiate, we use programs like the competency programs, the DevOps compet, the, the security competency, which continues to help, um, provide sort of a platform for these startups, help them differentiate. And also there's go to market benefits that are associated with that. >>Okay. So let me ask the, the question that's probably on everyone's mind, who's watching. Certainly I asked this a lot. There's a lot of companies startups out there who makes the, is there a criteria? Oh God, it's not like his sports team or anything, but like sure. Like there's activate program, which is like, there's hundreds of thousands of startups out there. Not everyone is at the APN. Right? Correct. So ISVs again, that's a whole nother, that's a more mature partner that might have, you know, huge market cap or growth. How do you guys focus? How do you guys focus? I mean, you got a good question, you know, a thousand flowers blooming all the time. Is there a new way you guys are looking at it? I know there's been some talk about restructure or, or new focus. What's the focus. >>Yeah. It's definitely not an easy task by any means. Um, but you know, I recently took over this role and we're really trying to establish focus areas, right. So obviously a lot of the fees that we look after our infrastructure ISVs, that's what we do. Uh, and so we have very specific pods that look after different type of partners. So we've got a security pod, we've got a DevOps pod, we've got core infrastructure, et cetera. And really we're trying to find these ISVs that can solve, uh, really interesting AWS customer challenges. >>So you guys have a deliberate, uh, focus on these pillars. So what infrastructure, >>Security, DevOps, and data and analytics, and then line of business >>Line of business line, like web marketing >>Solutions, business apps, >>Business, this owner type thing. Exactly. >>Yeah, exactly. >>So solutions there. Yeah. More solutions and the other ones are like hardcore. So infrastructure as well, like storage, backup, ransomware of stuff, or, >>Uh, storage, networking. >>Okay. Yeah. The classic >>Database, et cetera. Right. >>And so there's teams on each pillar. >>Yep. So I think what's, what's fascinating for the startup that we cover is that they've got, they truly have support from a build market sell perspective. Right. So you've got someone who's technical to really help them get the technology, figured out someone to help them get the marketing message dialed and spread, and then someone to actually do the co-sell, uh, day to day activities to help them get in front of customers. >>Probably the number one request that we always ask for Amazon is can we waste that sock report? Oh, download it, the console, which we use all the time. Exactly. But security's a big deal. I mean, you know, SREs are evolving, that role of DevOps is taking on dev SecOps. Um, I, I could see a lot of customers having that need for a relationship to move things faster. Do you guys provide like escalation or is that a part of a service or not, not part of a, uh, >>Yeah, >>So the partner development manager can be an escalation point. Absolutely. Think of them as an extension of your business inside of AWS. >>Great. And you guys how's that partner managers, uh, measure >>On those three pillars. Right. Got it. Are we billing, building valuable use cases? So product development go to market, so go to market activities, think blog, posts, webinars, case studies, so on and so forth. And then co-sell not only are we helping these partners win their current opportunities that they are sourcing, but can we also help them source net new deals? Yeah. Right. That's >>Very important. I mean, top asked from the partners is get me in front of customers. Right. Um, not an easy task, but that's a huge goal of ours to help them grow their top >>Line. Right. Yeah. In fact, we had some interviews here on the cube earlier talking about that dynamic of how enterprise customers are buying. And it's interesting, a lot more POCs. I have one partner here that you guys work with, um, on observability, they got a huge POC with capital one mm-hmm <affirmative> and the enterprises are engaging the startups and bringing them in. So the combination of open source software enterprises are leaning into that hard and bringing young growing startups in mm-hmm <affirmative>. Yep. So I could see that as a huge service that you guys can bring people in. >>Right. And they're bringing massively differentiated technology to the table. Mm-hmm <affirmative> the challenge is they just might not have the brand recognition that the big guys have. And so that it's our job is how do you get that great tech in front of the right situations? >>Okay. So my next question is about the show here, and then we'll talk globally. So here in San Francisco sure. You know, Silicon valley bay area, San Francisco bay area, a lot of startups, a lot of VCs, a lot of action. Mm-hmm <affirmative> so probably a big market for you guys. Yeah. So what's exciting here in SF and then outside SF, you guys have a global program, you see any trends that are geography based or is it sure areas more mature? There's certain regions that are better. I mean, I just interviewed a company here that's doing, uh, AWS edge really well in these cases. It's interesting that these, the partners are filling a lot of holes and gaps in the opportunities with AWS. So what's exciting here. And then what's the global perspective. >>Yeah, totally. So obviously a ton of partners, I, from the bay area that we support. Um, but we're seeing a lot of really interesting technology coming out of AMEA specifically. Yeah. Uh, and making a lot of noise here in the United States, which is great. Um, and so, you know, we definitely have that global presence and, and starting to see super differentiated technology come out of those regions. >>Yeah. Especially Tel Aviv. Yeah. >>Amy real quick, before you get in the surge. It's interesting. The VC market in, in Europe is hot. Yeah. They've got a lot of unicorns coming in. We've seen a lot of companies coming in. They're kind of rattling their own, you know, cage right now. Hey, look at us. We'll see if they crash, you know, but we don't see that happening. I mean, people have been projecting a crash now in, in the startup ecosystem for at least a year. It's not crashing. In fact, funding's up. >>Yeah. The pandemic was hard on a lot of startups for sure. Yeah. Um, but what we've seen is many of these startups, they, as quickly as they can grow, they can also pivot as, as, as well. Um, and so I've actually seen many of our startups grow through the pandemic because their use cases are helping customers either save money, become more operationally efficient and provide value to leadership teams that need more visibility into their infrastructure during a pandemic. >>It's an interesting point. I talked to Andy jazzy and Adam Leski both say the same thing during the pandemic necessity, the mother of all invention. Yep. And startups can move fast. So with that, you guys are there to assist if I'm a startup and I gotta pivot cuz remember iterate and pivot, iterate and pivot. So you get your economics, that's the playbook of the ventures and the models. >>Exactly. How >>Do you guys help me do that? Give me an example of walk me through, pretend me I'm a startup. Hey, I am on the cloud. Oh my God. Pandemic. They need video conferencing. Hey cube. Yeah. What do I need? Surge? What, what do I do? >>That's a good question. First thing is just listen. Yeah. I think what we have to do is a really good job of listening to the partner. Um, what are their needs? What is their problem statement and where do they want to go at the end of the day? Um, and oftentimes because we've worked with so many successful startups, they have come out of our program. We have, um, either through intuition or a playbook, determined what is gonna be the best path forward and how do we get these partners to stop focusing on things that will eventually, um, just be a waste of time yeah. And, or not provide, or, you know, bring any fruit to the table, which, you know, essentially revenue. >>Well, we love star rights here in the cube because one, um, they have good stories. They're oil and cutting edge, always pushing the envelope and they're kind of disrupting someone else. Yeah. And so they have an opinion. They don't mind sharing on camera. So love talking to startups. We love working with you guys on our startup showcases startups.com. Check out AWS startups.com and you got the showcases, uh, final. We I'll give you guys the last word. What's the bottom line bumper sticker for AP the global APN program. Summarize the opportunity for startups, what you guys bring to the table and we'll close it out. Totally start >>With you. Yeah. I think the AWS global startup program's here to help companies truly accelerate their business full stop. Right. And that's what we're here for. I love it. >>It's a good way to, it's a good way to put it Dito. >>Yeah. All right, sir. Thanks for coming on. Thanks John. Great to see you love working with you guys. Hey, startups need help. And the growing and huge market opportunities, the shift cloud scale data engineering, security infrastructure, all the markets are exploding in growth because of the digital transformation of the realities here. Open source and cloud all making it happen here in the cube in San Francisco, California. I'm John furrier, your host. Thanks for watching >>John. >>Hello and welcome back to the cubes live coverage here in San Francisco, California for AWS summit, 2022. I'm John for host of the cube. Uh, two days of coverage, AWS summit, 2022 in New York city. Coming up this summer, we'll be there as well at events are back. The cube is back of course, with the cube virtual cube hybrid, the cube.net, check it out a lot of content this year, more than ever, a lot more cloud data cloud native, modern applic is all happening. Got a great guest here. Jeremy Burton, Cub alumni, uh, CEO of observe Inc in the middle of all the cloud scale, big data observability Jeremy. Great to see you. Thanks >>Always great to come and talk to you on the queue, man. It's been been a few years, so, >>Um, well you, you got your hands. You're in the trenches with great startup, uh, good funding, great board, great people involved in the observability hot area, but also you've been a senior executive president of Dell, uh, EMC, uh, 11 years ago you had a, a vision and you actually had an event called cloud meets big data. Um, yeah. And it's here. You predicted it 11 years ago. Um, look around it's cloud meets big data. >>Yeah. I mean the, the cloud thing I think, you know, was, was probably already a thing, but the big data thing I do claim credit for, for, for sort of catching that bus out, um, you know, we, we were on the, the, the bus early and, and I think it was only inevitable. Like, you know, if you could bring the economics and the compute of cloud to big data, you, you could find out things you could never possibly imagine. >>So you're close to a lot of companies that we've been covering deeply. Snowflake obviously are involved, uh, the board level, you know, the founders, you know, the people there cloud, you know, Amazon, you know, what's going on here? Yeah. You're doing a startup as the CEO at the helm, uh, chief of observ, Inc, which is an observability, which is to me in the center of this confluence of data engineering, large scale integrations, um, data as code integrating into applic. I mean, it's a whole nother world developing, like you see with snowflake, it means snowflake is super cloud as we call it. So a whole nother wave is here. What's your, what's this wave we're on what's how would you describe the wave? >>Well, a couple of things, I mean, people are, I think riding more software than, than ever fall. Why? Because they've realized that if, if you don't take your business online and offer a service, then you become largely irrelevant. And so you you've got a whole set of new applications. I think, I think more applications now than any point. Um, not, not just ever, but the mid nineties, I always looked at as the golden age of application development. Now back then people were building for windows. Well, well now they're building for things like AWS is now the platform. Um, so you've got all of that going on. And then at the same time, the, the side effect of these applications is they generate data and lots of data and the, you know, the sort of the transactions, you know, what you bought today or something like that. But then there's what we do, which is all the telemetry data, all the exhaust fumes. And I think people really are realizing that their differentiation is not so much their application. It's their understanding of the data. Can, can I understand who my best customers are, what I sell today. If people came to my website and didn't buy, then I not, where did they drop off all of that they wanna analyze. And, and the answers are all in the data. The question is, can you understand it >>In our last startup showcase, we featured data as code. One of the insights that we got out of that I wanna get your opinion on our reaction to is, is that data used to be put into a data lake and turns into a data swamp or throw into the data warehouse. And then we'll do some query, maybe a report once in a while. And so data, once it was done, unless it was real time, even real time was not good anymore after real time. That was the old way. Now you're seeing more and more, uh, effort to say, let's go look at the data cuz now machine learning is getting better. Not just train once mm-hmm <affirmative> they're iterating. Yeah. This notion of iterating and then pivoting, iterating and pivoting. Yeah, that's a Silicon valley story. That's like how startups work, but now you're seeing data being treated the same way. So now you have another, this data concept that's now yeah. Part of a new way to create more value for the apps. So this whole, this whole new cycle of >>Yeah. >>Data being reused and repurposed and figured out and >>Yeah, yeah. I'm a big fan of, um, years ago. Uh, uh, just an amazing guy, Andy McAfee at the MIT C cell labs I spent time with and he, he had this line, which still sticks to me this day, which is look I'm I'm. He said I'm part of a body, which believes that everything is a matter of data. Like if you, of enough data, you can answer any question. And, and this is going back 10 years when he was saying these kind of things and, and certainly, you know, research is on the forefront. But I, I think, you know, starting to see that mindset of the, the sort of MIT research be mainstream, you know, in enterprises, they they're realizing that yeah, it is about the data. You know, if I can better understand my data better than my competitor than I've got an advantage. And so the question is is, is how, what, what technologies and what skills do I need in my organization to, to allow me to do that. So >>Let's talk about observing you the CEO of, okay. Given you've seen the wave before you're in the front lines of observability, which again is in the center of all this action what's going on with the company. Give a quick minute to explain, observe for the folks who don't know what you guys do. What's the company doing? What's the funding status, what's the product status and what's the customer status. Yeah. >>So, um, we realized, you know, a handful of years ago, let's say five years ago that, um, look, the way people are building applications is different. They they're way more functional. They change every day. Uh, but in some respects they're a lot more complicated. They're distributed. They, you know, microservices architectures and when something goes wrong, um, the old way of troubleshooting and solving problems was not gonna fly because you had SA so much change going into production on a daily basis. It was hard to tell like where the problem was. And so we thought, okay, it's about time. Somebody looks at the exhaust fumes from this application and all the telemetry data and helps people troubleshoot and make sense of the problems that they're seeing. So, I mean, that's observability, it's actually a term that goes back to the 1960s. It was a guy called, uh, Rudolph like, like everything in tech, you know, it's, it's a reinvention of, of something from years gone by. >>But, um, there's a guy called, um, Rudy Coleman in 1960s, kinder term. And, and, and the term was been able to determine the state of a system by looking at its external outputs. And so we've been going on this for, uh, the best part of the all years now. Um, it took us three years just to build the product. I think, I think what people don't appreciate these days often is the barrier to entry in a lot of these markets is quite high. You, you need a lot of functionality to have something that's credible with a customer. Um, so yeah, this last year we, we, we did our first year selling, uh, we've got about 40 customers now. <affirmative> um, we just we've got great investors for the hill ventures. Uh, I mean, Mike SP who was, you know, the, the guy who was the, really, the first guy in it snowflake and the, the initial investor were fortunate enough to, to have Mike on our board. And, um, you know, part of the observed story yeah. Is closely knit with snowflake because all of that time data know we, we still are in there. >>So I want to get, uh, >>Yeah. >>Pivot to that. Mike Pfizer, snowflake, Jeremy Burton, the cube kind of, kind of same thinking this idea of a super cloud or what snowflake became snowflake is massively successful on top of AWS. Mm-hmm <affirmative> and now you're seeing startups and companies build on top of snowflake. Yeah. So that's become an entrepreneurial story that we think that to go big in the cloud, you can have a cloud on a cloud, uh, like as Jerry, Jerry Chan and Greylock calls it castles in the cloud where there are moats in the cloud. So you're close to it. I know you're doing some stuff with snowflake. So a startup, what's your view on building on top of say a snowflake or an AWS, because again, you gotta go where the data is. You need all the data. >>Yeah. So >>What's your take on that? >>I mean, having enough gray hair now, um, you know, again, in tech, I think if you wanna predict the future, look at the past. And, uh, you know, to many years ago, 25 years ago, I was at a, a smaller company called Oracle and an Oracle was the database company. And, uh, their, their ambition was to manage all of the world's transactional data. And they built on a platform or a couple of platforms, one, one windows, and the other main one was Solaris. And so at that time, the operator and system was the platform. And, and then that was the, you know, ecosystem that you would compete on top of. And then there were companies like SAP that built applications on top of Oracle. So then wind the clock forward 25 years gray hairs. <laugh> the platform, isn't the operating system anymore. The platform is AWS, you know, Google cloud. I gotta probably look around if I say that in. Yeah. It's >>Okay. But hyperscale, yeah. CapX built out >>That is the new platform. And then snowflake comes along. Well, their aspiration is to manage all of the, not just human generator data, but machine generated data in the world of cloud. And I think they they've done an amazing job doing for the, I'd say, say the, the big data world, what Oracle did for the relational data world, you know, way back 25 years ago. And then there are folks like us come along and, and of course my ambition would be, look, if, if we can be as successful as an SAP building on top of snow snowflake, uh, as, as they were on top of Oracle, then, then we'd probably be quite happy. >>So you're building on top of snowflake. >>We're building on top of snowflake a hundred percent. And, um, you know, I've had folks say to me, well, aren't you worried about that? Isn't that a risk? It's like, well, that that's a risk. You >>Still on the board. >>Yeah. I'm still on the board. Yeah. That that's a risk I'm prepared to take <laugh> I am long on snowflake you, >>Well, you're in a good spot. Stay on the board, then you'll know what's going on. Okay. No know just doing, but the, this is a real dynamic. It is. It's not a one off it's. >>Well, and I do believe as well that the platform that you see now with AWS, if you look at the revenues of AWS is an order of magnitude more than Microsoft was 25 years ago with windows mm-hmm <affirmative>. And so I believe the opportunity for folks like snowflake and folks like observe it's an order of magnitude more than it was for the Oracle and the SAPs of the old >>World. Yeah. And I think this is really, I think this is something that this next generation of entrepreneurship is the go big scenario is you gotta be on a platform. Yeah. >>It's quite >>Easy or be the platform, but it's hard. There's only like how many seats are at that table left. >>Well, value migrates up over time. So, you know, when the cloud thing got going, there were probably 10, 20, 30, you know, Rackspace and there's 1,000,001 infrastructure, a service platform as a service, my, my old, uh, um, employee EMC, we had pivotal, you know, pivotal was a platform as a service. You don't hear so much about it, these, but initially there's a lot of players and then it consolidates. And then to, to like extract, uh, a real business, you gotta move up, you gotta add value, you gotta build databases, then you gotta build applications. So >>It's interesting. Moving from the data center of the cloud was a dream for starters. Cause then if the provision, the CapEx, now the CapEx is in the cloud. Then you build on top of that, you got snowflake you on top of that, the >>Assumption is almost that compute and storage is free. I know it's not quite free. Yeah. It's >>Almost free, >>But, but you can, you know, as an application vendor, you think, well, what can I do if I assume compute and storage is free, that's the mindset you've gotta get into. >>And I think the platform enablement to value. So if I'm an entrepreneur, I'm gonna get a serious, multiple of value in what I'm paying. Yeah. Most people don't even blanket their Avis pills unless they're like massively huge. Yeah. Then it's a repatriation question or whatever discount question, but for most startups or any growing company, the Amazon bill should be a small factor. >>Yeah. I mean, a lot of people, um, ask me like, look, you're building on snowflake. Um, you, you know, you are, you are, you're gonna be, you're gonna be paying their money. How, how, how, how does that work with your business model? If you're paying them money, you know, do, do you have a viable business? And it's like, well, okay. I, we could build a database as well in observe, but then I've got half the development team working on in that will never be as good as snowflake. And so we made the call early on that. No, no, we, we wanna innovate above the database. Yeah. Right. Snowflake are doing a great job of innovating on the database and, and the same is true of something like Amazon, like, like snowflake could have built their own cloud and their own platform, but they didn't. >>Yeah. And what's interesting is that Dave <inaudible> and I have been pointing this out and he's actually more on snowflake. I I've been looking at data bricks, um, and the same dynamics happening, the proof is the ecosystem. Yeah. I mean, if you look at Snowflake's ecosystem right now and data bricks it's exploding. Right. I mean, the shows are selling out the floor. Space's book. That's the old days at VMware. Yeah. The old days at AWS >>One and for snowflake and, and any platform provider, it's a beautiful thing. You know, we build on snowflake and we pay them money. They don't have to sell to us. Right. And we do a lot of the support. And so the, the economics work out really, really well. If you're a platform provider and you've got a lot of ecosystems. >>Yeah. And then also you get, you get a, um, a trajectory of, uh, economies of scale with the institutional knowledge of snowflake integrations, right. New products. You're scaling that function with the, >>Yeah. I mean, we manage 10 petabytes of data right now. Right. When I, when I, when I arrived at EMC in 2010, we had, we had one petabyte customer. And, and so at observe, we've been only selling the product for a year. We have 10 petabytes of data under management. And so been able to rely on a platform that can manage that is invaluable, >>You know, but Jeremy Greek conversation, thanks for sharing your insights on the industry. Uh, we got a couple minutes left. Um, put a plug in for observe. What do you guys, I know you got some good funding, great partners. I don't know if you can talk about your, your, your POC customers, but you got a lot of high ends folks that are working with you. You getting traction. Yeah. >>Yeah. >>Scales around the corner. Sounds like, are you, is that where you are scale? >>Got, we've got a big announcement coming up in two or weeks. We've got, we've got new funding, um, which is always great. Um, the product is, uh, really, really close. I think, as a startup, you always strive for market fit, you know, which is at which point can you just start hiring salespeople? And the revenue keeps going. We're getting pretty close to that right now. Um, we've got about 40 SaaS companies run on the platform. They're almost all AWS Kubernetes, uh, which is our sweet spot to begin with, but we're starting to get some really interesting, um, enterprise type customers. We're, we're, you know, F five networks we're POC in right now with capital one, we got some interest in news around capital one coming up. I, I can't share too much, uh, but it's gonna be exciting. And, and like I saids hill continued to, to, to stick, >>I think capital one's a big snowflake customer as well. Right. They, >>They were early in one of the things that attracted me to capital one was they were very, very good with snowflake early on. And, and they put snowflake in a position in the bank where they thought that snowflake could be successful. Yeah. And, and today that, that is one of Snowflake's biggest accounts. >>So capital one, very innovative cloud, obviously AIOS customer and very innovative, certainly in the CISO and CIO, um, on another point on where you're at. So you're, Prescale meaning you're about to scale, right? So you got POCs, what's that trick GE look like, can you see around the corner? What's, what's going on? What's on, around the corner. That you're, that you're gonna hit the straight and narrow and, and gas it >>Fast. Yeah. I mean, the, the, the, the key thing for us is we gotta get the product. Right. Um, the nice thing about having a guy like Mike Pfizer on the board is he doesn't obsess about revenue at this stage is questions that the board are always about, like, is the product, right? Is the product right? Is the product right? If you got the product right. And cuz we know when the product's right, we can then scale the sales team and, and the revenue will take care of itself. Yeah. So right now all the attention is on the product. Um, the, this year, the exciting thing is we were, we're adding all the tracing visualizations. So people will be able to the kind of things that back in the day you could do with the new lakes and, and AppDynamics, the last generation of, of APM tools, you're gonna be able to do that within observe. And we've already got the logs and the metrics capability in there. So for us, this year's a big one, cuz we sort of complete the trifecta, you know, the, the logs, >>What's the secret sauce observe. What if you had the, put it into a, a sentence what's the secret sauce? I, >>I, I think, you know, an amazing founding engineering team, uh, number one, I mean, at the end of the day, you have to build an amazing product and you have to solve a problem in a different way. And we've got great long term investors. And, and the biggest thing our investors give is actually it's not just money. It gives us time to get the product, right. Because if we get the product right, then we can get the growth. >>Got it. Final question. Why I got you here? You've been on the enterprise business for a long time. What's the buyer landscape out there. You got people doing POCs on capital one scale. So we know that goes on. What's the appetite at the buyer side for startups and what are their requirements that you're seeing? Uh, obviously we're seeing people go in and dip into the startup pool because new ways to refactor their business restructure. So a lot happening in cloud. What's the criteria. How are enterprises engaging in with startups? >>Yeah. I mean, enterprises, they know they've gotta spend money transforming the business. I mean, this was, I almost feel like my old Dell or EMC self there, but, um, what, what we were saying five years ago is happening. Um, everybody needs to figure out out a way to take their, this to this digital world. Everybody has to do it. So the nice thing from a startup standpoint is they know at times they need to risk or, or take a bet on new technology in order to, to help them do that. So I think you've got buyers that a have money, uh, B prepared to take risks and it's, it's a race against time to, you know, get their, their offerings in this. So a new digital footprint, >>Final, final question. What's the state of AWS. Where do you see them going next? Obviously they're continuing to be successful. How does cloud 3.0, or they always say it's day one, but it's more like day 10. Uh, but what's next for Aw. Where do they go from here? Obviously they're doing well. They're getting bigger and bigger. >>Yeah. They're, they're, it's an amazing story. I mean, you know, we we're, we're on AWS as well. And so I, I think if they keep nurturing the builders in the ecosystem, then that is their superpower. They, they have an early leads. And if you look at where, you know, maybe the likes of Microsoft lost the plot in the, in the late it was, they stopped, uh, really caring about developers and the folks who were building on top of their ecosystem. In fact, they started buying up their ecosystem and competing with people in their ecosystem. And I see with AWS, they, they have an amazing head start and if they did more, you know, if they do more than that, that's, what's gonna keep the jut rolling for many years to come. Yeah, >>They got the silicone and they got the staff act, developing Jeremy Burton inside the cube, great resource for commentary, but also founding with the CEO of a company called observing in the middle of all the action on the board of snowflake as well. Um, great start. Thanks for coming on the cube. >>Always a pleasure. >>Okay. Live from San Francisco to cube. I'm John for your host. Stay with us more coverage from San Francisco, California after the short break. >>Hello. Welcome back to the cubes coverage here live in San Francisco, California. I'm John furrier, host of the cubes cube coverage of AWS summit 2022 here in San Francisco. We're all the developers of the bay area at Silicon valley. And of course, AWS summit in New York city is coming up in the summer. We'll be there as well. SF and NYC cube coverage. Look for us. Of course, reinforcing Boston and re Mars with the whole robotics AI thing, all coming together. Lots of coverage stay with us today. We've got a great guest from Deibel VC. John Skoda, founding partner, entrepreneurial venture is a venture firm. Your next act, welcome to the cube. Good to see you. >>Good to see you, Matt. I feel like it's been forever since we've been able to do something in person. Well, >>I'm glad you're here because we run into each other all the time. We've known each other for over a decade. Um, >><affirmative>, it's been at least 10 years now, >>At least 10 years more. And we don't wanna actually go back as frees back, uh, the old school web 1.0 days. But anyway, we're in web three now. So we'll get to that in >>Second. We, we are, it's a little bit of a throwback to the path though, in my opinion, >><laugh>, it's all the same. It's all distributed computing and software. We ran each other in cube con you're investing in a lot of tech startup founders. Okay. This next level, next gen entrepreneurs have a new makeup and it's software. It's hardcore tech in some cases, not hardcore tech, but using software is take old something old and make it better, new, faster. <laugh>. So tell us about Deibel what's the firm. I know you're the founder, uh, which is cool. What's going on. Explain >>What you're doing. I mean, you remember I'm a recovering entrepreneur, right? So of course I, I, I, >>No, you're never recovering. You're always entrepreneur >>Always, but we are also always recovering. So I, um, started my first company when I was 24. If you remember, before there was Facebook and friends, there was instant messaging. People were using that product at work every day, they were creating a security vulnerability between their network and the outside world. So I plugged that hole and built an instant messaging firewall. It was my first company. The company was called, I am logic and we were required by Symantec. Uh, then spent 12 years investing in the next generation of our companies, uh, early investor in open source companies and cloud companies and spent a really wonderful 12 years, uh, at a firm called NEA. So I, I feel like my whole life I've been either starting enterprise software companies or helping founders start enterprise software companies. And I'll tell you, there's never been a better time than right now to start enter price software company. >>So, uh, the passion for starting a new firm was really a recognition that founders today that are starting in an enterprise software company, they, they tend to be, as you said, a more technical founder, right? Usually it's a software engineer or a builder mm-hmm <affirmative>, uh, they are building products that are serving a slightly different market than what we've traditionally seen in enterprise software. Right? I think traditionally we've seen it buyers or CIOs that have agendas and strategies, which, you know, purchased software that has traditionally bought and sold tops down. But, you know, today I think the most successful enterprise software companies are the ones that are built more bottoms up and have more technical early opts. And generally speaking, they're free to use. They're free to try. They're very commonly community source or open source companies where you have a large technical community that's supporting them. So there's a, there's kind of a new normal now I think in great enterprise software. And it starts with great technical founders with great products and great and emotions. And I think there's no better place to, uh, service those people than in the cloud and uh, in, in your community. >>Well, first of all, congratulations, and by the way, you got a great pedigree and great background, super smart admire of your work and your, and, and your founding, but let's face it. Enterprise is hot because digital transformation is all companies. The is no, I mean, consumer is enterprise. Now everything is what was once a niche. No, I won't say niche category, but you know, not for the faint of heart, you know, investors, >>You know, it's so funny that you say that enterprise is hot because you, and I feel that way now. But remember, like right now, there's also a giant tech in VC conference in Miami <laugh> it's covering cryptocurrencies and FCS and web three. So I think beauty is definitely in the eye of the beholder <laugh> but no, I, I will tell you, >>Ts is one big enterprise, cuz you gotta have imutability you got performance issues. You have, I IOPS issues. Well, and, >>And I think all of us here that are, uh, maybe students of history and have been involved in, open in the cloud would say that we're, you know, much of what we're doing is, uh, the predecessors of the web web three movement. And many of us I think are contributors to the web three movement. >>The hype is definitely that three. >>Yeah. But, but >>You know, for >>Sure. Yeah, no, but now you're taking us further east to Miami. So, uh, you know, look, I think, I, I think, um, what is unquestioned with the case now? And maybe it's, it's more obvious the more time you spend in this world is this is the fastest growing part of enterprise software. And if you include cloud infrastructure and cloud infrastructure spend, you know, it is by many men over, uh, 500 billion in growing, you know, 20 to 30% a year. So it it's a, it's a just incredibly fast, >>Let's getting, let's get into some of the cultural and the, the shifts that are happening, cuz again, you, you have the luxury of being in enterprise when it was hard, it's getting easier and more cooler. I get it and more relevant, but it's also the hype of like the web three, for instance. But you know, uh, um, um, the CEO snowflake, okay. Has wrote a book and Dave Valenti and I were talking about it and uh, Frank Luman has says, there's no playbooks. We always ask the CEOs, what's your playbook. And he's like, there's no playbook, situational awareness, always Trump's playbooks. So in the enterprise playbook, oh, higher direct sales force and SAS kind of crushed the, at now SAS is being redefined, right. So what is SAS? Is snowflake a SAS or is that a platform? So again, new unit economics are emerging, whole new situation, you got web three. So to me there's a cultural shift, the young entrepreneurs, the, uh, user experience, they look at Facebook and say, ah, you know, they own all my data. You know, we know that that cliche, um, they, you know, the product. So as this next gen, the gen Z and the millennials come in and our customers and the founders, they're looking at things a little bit differently and the tech better. >>Yeah. I mean, I mean, I think we can, we can see a lot of commonalities across all successful startups and the overall adoption of technology. Uh, and, and I would tell you, this is all one big giant revolution. I call it the user driven revolution. Right. It's the rise of the user. Yeah. And you might say product like growth is currently the hottest trend in enterprise software. It's actually user like growth, right. They're one in the same. So sometimes people think the product, uh, is what is driving. You >>Just pull the >>Product through. Exactly, exactly. And so that's that I, that I think is really this revolution that you see, and, and it does extend into things like cryptocurrencies and web three and, you know, sort of like the control that is taken back by the user. Um, but you know, many would say that, that the origins of this movement maybe started with open source where users were, are contributors, you know, contributors, we're users and looking back decades and seeing how it, how it fast forward to today. I think that's really the trend that we're all writing and it's enabling these end users. And these end users in our world are developers, data engineers, cybersecurity practitioners, right. They're really the users. And they're really the, the beneficiaries and the most, you know, kind of valued people in >>This. I wanna come back to the data engineers in a second, but I wanna make a comment and get your reaction to, I have a, I'm a GenXer technically, so for not a boomer, but I have some boomer friends who are a little bit older than me who have, you know, experienced the sixties. And I've, I've been staying on the cube for probably about eight years now that we are gonna hit a digital hippie revolution, meaning a rebellion against in the sixties was rebellion against the fifties and the man and, you know, summer of love. That was a cultural differentiation from the other one other group, the predecessors. So we're kind of having that digital moment now where it's like, Hey boomers, Hey people, we're not gonna do that anymore. We hate how you organize shit. >>Right. But isn't this just technology. I mean, isn't it, isn't it like there used to be the old adage, like, you know, you would never get fired for buying IBM, but now it's like, you obviously probably would get fired if you bought IBM. And I mean, it's just like the, the, I think, I think >>It's the main for days, those renegades were breaking into Stanford, starting the home brew club. So what I'm trying to get at is that, do you see the young cultural revolution also, culturally, just, this is my identity NFTs to me speak volumes about my, I wanna associate with NFTs, not single sign on. Well, >>Absolutely. And, and I think like, I think you're hitting on something, which is like this convergence of, of, you know, societal trends with technology trends and how that manifests in our world is yes. I think like there is unquestionably almost a religion around the way in which a product is built. Right. And we can use open source, one example of that religion. Some people will say, look, I'll just never try a product in the cloud if it's not open source. Yeah. I think cloud, native's another example of that, right? It's either it's, you know, it either is cloud native or it's not. And I think a lot of people will look at a product and say, look, you know, you were not designed in the cloud era. Therefore I just won't try you. And sometimes, um, like it or not, it's a religious decision, right? It's, it's something that people just believe to be true almost without, uh, necessarily. I mean >>The decision making, let me ask you this next question. As a VC. Now you look at pitch, well, you've made a VC for many years, but you also have the founder, uh, entrepreneurial mindset, but you can get empathize with the founders. You know, hustle is a big part of the, that first founder check, right? You gotta convince someone to part with their ch their money and the first money in which you do a lot of is about believing in the person. So fing, so you make, it is hard. Now you, the data's there, you either have it cloud native, you either have the adaption or traction. So honesty is a big part of that pitch. You can't fake it. Oh, >>AB absolutely. You know, there used to be this concept of like the persona of an entrepreneur, right. And the persona of the entrepreneur would be, you know, somebody who was a great salesperson or somebody who tell a great story. You, I still think that that's important, right? It still is a human need for people to believe in narratives and stories. But having said that you're right, the proof is in the pudding, right? At some point you click download and you try the product and it does what it says it it's gonna do, or it doesn't, or it either stands up to the load test or it doesn't. And so I, I feel like in this new economy that we live in, it's a shift from maybe the storytellers and the creators to, to the builders, right. The people that know how to build great product. And in some ways the people that can build great product yeah. Stand out from the crowd. And they're the ones that can build communities around their products. And, you know, in some ways can, um, you know, kind of own more of the narrative because their products exactly >>The volume back to the user led growth. >>Exactly. And it's the religion of, I just love your product. Right. And I, I, I, um, Doug song was the founder of du security used to say, Hey, like, you know, the, the really like in today's world of like consumption based software, the user is only gonna give you 90 seconds to figure out whether or not you're a company that's easy to do business with. Right. And so you can say, and do all the things that you want about how easy you are to work with. But if the product isn't easy to install, if it's not easy to try, if it's not, if, if the, you know, it's gotta speak to >>The, speak to the user, but let me ask a question now that the people watching who are maybe entrepreneurial entrepreneur, um, masterclass here is in session. So I have to ask you, do you prefer, um, an entrepreneur to come in and say, look at John. Here's where I'm at. Okay. First of all, storytelling's fine. Whether you're an extrovert or introvert, have your style, sell the story in a way that's authentic, but do you, what do you prefer to say? Here's where I'm at? Look, I have an idea. Here's my traction. I think here's my MVP prototype. I need help. Or do you wanna just see more stats? What's the, what's the preferred way that you like to see entrepreneurs come in and engage, engage? >>There's tons of different styles, man. I think the single most important thing that every founder should know is that we, we don't invest in what things are today. We invest in what we think something will become. Right. And I think that's why we all get up in the morning and try to build something different, right? It's that we see the world a different way. We want it to be a different way, and we wanna work every single moment of the day to try to make that vision a reality. So I think the more that you can show people where you want to be, the more likely somebody is gonna align with your vision and, and want to invest in you and wanna be along for the ride. So I, I wholeheartedly believe in showing off what you got today, because eventually we all get down to like, where are we and what are we gonna do together? But, um, no, I >>Show >>The path. I think the single most important thing for any founder and VC relationship is that they have the same vision, uh, have the same vision. You can, you can get through bumps in the road, you can get through short term spills. You can all sorts of things in the middle of the journey can happen. Yeah. But it doesn't matter as much if you share the same long term vision, >>Don't flake out and, and be fashionable with the latest trends because it's over before you can get there. >>Exactly. I think many people that, that do what we do for a living will say, you know, ultimately the future is relatively easy to predict, but it's the timing that's impossible to predict. So you, you know, you sort of have to balance the, you know, we, we know that the world is going this way and therefore we're gonna invest a lot of money to try to make this a reality. Uh, but sometimes it happens in six months. Sometimes it takes six years is sometimes like 16 years. >>Uh, what's the hottest thing in enterprise that you see the biggest wave that people should pay attention to that you're looking at right now with Desel partners, Tebel dot your site. What's the big wave. What's your big >>Wave. There, there's three big trends that we invest in. And they're the, they're the only things we do day in, day out. One is the explosion and open source software. So I think many people think that all software is unquestionably moving to an open source model in some form or another yeah. Tons of reasons to debate whether or not that is gonna happen and on what timeline happening >>Forever. >>But it is, it is accelerating faster than we've ever seen. So I, I think it's, it's one big, massive wave that we continue to ride. Um, second is the rise of data engineering. Uh, I think data engineering is in and of itself now, a category of software. It's not just that we store data. It's now we move data and we develop applications on data. And, uh, I think data is in and of itself as big of a, a market as any of the other markets that we invest in. Uh, and finally, it's the gift that keeps on giving. I've spent my entire career in it. We still feel that security is a market that is under invested. It is, it continues to be the place where people need to continue to invest and spend more money. Yeah. Uh, and those are the three major trends that we run >>And security, you think we all need a dessert do over, right? I mean, do we need a do over in security or is what's the core problem? I, >>I, I keep using this word underinvested because I think it's the right way to think about the problem. I think if you, I think people generally speaking, look at cyber security as an add-on. Yeah. But if you think about it, the whole economy is moving online. And so in, in some ways like security is core to protecting the digital economy. And so it's, it shouldn't be an afterthought, right? It should be core to what everyone is doing. And that's why I think relative to the trillions of dollars that are at stake, uh, I believe the market size for cybersecurity is around 150 billion. And it still is a fraction of what we're, what >>We're and security even boom is booming now. So you get the convergence of national security, geopolitics, internet digital >>That's right. You mean arguably, right? I mean, arguably again, it's the area of the world that people should be spending more time and more money given what to stake. >>I love your thesis. I gotta, I gotta say, you gotta love your firm. Love. You're doing we're big supporters of your mission. Congratulations on your entrepreneurial venture. And, uh, we'll be, we'll be talking and maybe see a Cub gone. Uh, >>Absolutely. >>Certainly EU maybe even north America's in Detroit this year. >>Huge fan of what you guys are doing here. Thank you so much for having me on >>The show. Guess bell VC Johnson here on the cube. Check him out. Founder for founders here on the cube, more coverage from San Francisco, California. After the short break, stay with us. Everyone. Welcome to the queue here. Live in San Francisco, California for AWS summit, 2022 we're live we're back with the events. Also we're virtual. We got hybrid all kinds of events. This year, of course, 80% summit in New York city is happening this summer. We'll be there with the cube as well. I'm John. Again, John host of the cube got a great guest here. Justin Coby owner and CEO of innovative solutions. Their booth is right behind us. Justin, welcome to the cube. >>Thank you. Thank you for having me. >>So we're just chatting, uh, uh, off camera about some of the work you're doing. You're the owner of and CEO. Yeah. Of innovative. Yeah. So tell us a story. What do you guys do? What's the elevator pitch. >>Yeah. <laugh> so the elevator pitch is we are, uh, a hundred percent focused on small to midsize businesses that are moving into the cloud or have already moved to the cloud and really trying to understand how to best control, cost, security, compliance, all the good stuff, uh, that comes along with it. Um, exclusively focused on AWS and, um, you know, about 110 people, uh, based in Rochester, New York, that's where our headquarters is, but now we have offices down in Austin, Texas up in Toronto, uh, key Canada, as well as Chicago. Um, and obviously in New York, uh, you know, the, the business was never like this, uh, five years ago, um, founded in 1989, made the decision in 2018 to pivot and go all in on the cloud. And, uh, I've been a part of the company for about 18 years, bought the company about five years ago and it's been a great ride. It >>It's interesting. The manages services are interesting with cloud cause a lot of the heavy liftings done by AWS. So we had Matt on your team on earlier talking about some of the edge stuff. Yeah. But you guys are a managed cloud service. You got cloud advisory, you know, the classic service that's needed, but the demands coming from cloud migrations and application modernization and obviously data is a huge part of it. Huge. How is this factoring into what you guys do and your growth cuz you guys are the number one partner on the SMB side for edge. Yeah. For AWS, you got results coming in. Where's the, where's the forcing function. What's the pressure point. What's the demand like? >>Yeah. It's a great question. Every CEO I talk to, that's a small to midsize business. They're trying to understand how to leverage technology. It better to help either drive a revenue target for their own business, uh, help with customer service as so much has gone remote now. And we're all having problems or troubles or issues trying to hire talent. And um, you know, tech ISNT really at the, at the forefront and the center of that. So most customers are coming to us and they're like, listen, we gotta move to the cloud or we move some things to cloud and we want to do that better. And um, there's this big misnomer that when you move to the cloud, you gotta automatically modernize. Yeah. And what we try to help as many customers understand as possible is lifting and shifting, moving the stuff that you maybe currently have OnPrem and a data center to the cloud first is a first step. And then, uh, progressively working through a modernization strateg, always the better approach. And so we spend a lot of time with small to midsize businesses who don't have the technology talent on staff to be able to do >>That. Yeah. They want get set up. But then the dynamic of like latency is huge. We're seeing that edge product is a big part of it. This is not a one-off happening around everywhere. It is. And it's not, it's manufacturing, it's the physical plant or location >>Literally. >>And so, and you're seeing more IOT devices. What's that like right now from a challenge and problem statement standpoint, are the customers, not staff, is the it staff kind of old school? Is it new skills? What's the core problem you guys solve >>In the SMB space? The core issue nine outta 10 times is people get enamored with the latest and greatest. And the reality is not everything that's cloud based. Not all cloud services are the latest and greatest. Some things have been around for quite some time and are hardened solutions. And so, um, what we try to do with technology staff that has traditional on-prem, uh, let's just say skill sets and they're trying to move to a cloud-based workload is we try to help those customers through education and through some practical, let's just call it use case. Um, whether that's a proof of concept that we're doing or whether we're gonna migrate a small workload over, we try to give them the confidence to be able to not, not necessarily go it alone, but to, to, to have the, uh, the Gusto and to really have the, um, the, the opportunity to, to do that in a wise way. Um, and what I find is that most CEOs that I talk to, yeah, they're like, listen, the end of the day, I'm gonna be spending money in one place or another, whether that's OnPrem or in the cloud. I just want to know that I'm doing that in a way that helps me grow as quickly as possible status quo. I think every, every business owner knows that COVID taught us anything that status quo is, uh, is, is no. No. >>Good. How about factoring in the, the agility and speed equation? Does that come up a lot? It >>Does. I think, um, I, there's also this idea that if, uh, if we do a deep dive analysis and we really take a surgical approach to things, um, we're gonna be better off. And the reality is the faster you move with anything cloud based, the better you are. And so there's this assumption that we gotta get it right the first time. Yeah. In the cloud, if you start down your journey in one way and you realize midway that it's not the right, let's just say the right place to go. It's not like buying a piece of iron that you put in the closet and now you own it in the cloud. You can turn those services on and off. It's gives you a much higher density for making decisions and failing >>Forward. Well actually shutting down the abandoning the projects that early and not worrying about it, you got it. I mean, most people don't abandon cause like, oh, I own it. >>Exactly. And >>They get, they get used to it. Like, and then they wait too long. >>That's exactly. Yeah. >>Frog and boiling water as we used to say. So, oh, it's a great analogy. So I mean, this is a dynamic that's interesting. I wanna get more thoughts on it because like I'm a, if I'm a CEO of a company, like, okay, I gotta make my number. Yeah. I gotta keep my people motivated. Yeah. And I gotta move faster. So this is where you, I get the whole thing. And by the way, great service, um, professional services in the cloud right now are so hot because so hot, you can build it and then have option optionality. You got path decisions, you got new services to take advantage of. It's almost too much for customers. It is. I mean, everyone I talked to at reinvent, that's a customer. Well, how many announcements did am jazzy announce or Adam, you know, the 5,000 announcement or whatever. They do huge amounts. Right. Keeping track of it all. Oh, is huge. So what's the, what's the, um, the mission of, of your company. How does, how do you talk to that alignment? Yeah. Not just processes. I can get that like values as companies, cuz they're betting on you and your people. >>They are, they are, >>What's the values. >>Our mission is, is very simple. We want to help every small to midsize business leverage the power of the cloud. Here's the reality. We believe wholeheartedly. This is our vision that every company is going to become a technology company. So we go to market with this idea that every customer's trying to leverage the power of the cloud in some way, shape or form, whether they know it or don't know it. And number two, they're gonna become a tech company in the process of that because everything is so tech-centric. And so when you talk about speed and agility, when you talk about the, the endless options and the endless permutations of solutions that a customer can buy in the cloud, how are you gonna ask a team of one or two people in your, or it department to make all those decisions going it alone or trying to learn it as you go, it only gets you so far working with a partner. >>I'll just give you some perspective. We work with about a thousand small to midsize business customers. More than 50% of those customers are on our managed services. Meaning they know that we have their back Andre or the safety net. So when a customer is saying, all right, I'm gonna spend a couple thousand dollars a month in the cloud. They know that that bill, isn't gonna jump to $10,000 a month going in alone. Who's there to help protect that. Number two, if you have a security posture and let's just say you're high profile and you're gonna potentially be more vulnerable to security attack. If you have a partner, that's all offering you some managed services. Now you, again, you've got that backstop and you've got those services and tooling. We, we offer, um, seven different products, uh, that are part of our managed services that give the customer the tooling, that for them to go out and buy on their own for a customer to go out today and go buy a new Relic solution on their own. It, it would cost 'em a fortune. If >>Training alone would be insane, a factor and the cost. Yes, absolutely. Opportunity cost is huge, >>Huge, absolutely enormous training and development. Something. I think that is often, you know, it's often overlooked technologists. Typically they want to get their skills up. Yeah. They, they love to get the, the stickers and the badges and the pins, um, at innovative in 2018, when, uh, when we made the decision to go all in on the club, I said to the organization, you know, we have this idea that we're gonna pivot and be aligned with AWS in such a way that it's gonna really require us all to get certified. My executive assistant at the time looks at me. She said, even me, I said, yeah, even you, why can't you get certified? Yeah. And so we made, uh, a conscious decision. It wasn't requirement and still isn't today to make sure everybody in the company has the opportunity to become certified. Even the people that are answering the phones at the front desk >>And she could be running the Kubernetes clusters. I love it. It's amazing. >>But I'll tell you what, when that customer calls and they have a real Kubernetes issue, she'll be able to assist and get >>The right people involved. And that's a cultural factor that you guys have. So, so again, this is back to my whole point about SMBs and businesses in general, small en large, it staffs are turning over the gen Z and millennials are in the workforce. They were provisioning top of rack switches. Right. First of all. And so if you're a business, there's also the, I call the build out, um, uh, return factor, ROI piece. At what point in time as an owner or SMB, do I get the ROI? Yeah. I gotta hire a person to manage it. That person's gonna have five zillion job offers. Yep. Uh, maybe who knows? Right. I got cybersecurity issues. Where am I gonna find a cyber person? Yeah. A data compliance. I need a data scientist and a compliance person. Right. Maybe one and the same. Right. Good luck. Trying to find a data scientist. Who's also a compliance person. Yep. And the list goes on. I can just continue. Absolutely. I need an SRE to manage the, the, uh, the sock report and we can pen test. Right. >>Right. >>These are, these are >>Critical issues. This >>Is just like, these are the table stakes. >>Yeah. And, and every, every business owner's thinking about. So that's, >>That's what, at least a million in bloating, if not three or more Just to get that going. Yeah. Then it's like, where's the app. Yeah. So there's no cloud migration. There's no modernization on the app side though. Yeah. No. And nevermind AI and ML. That's >>Right. That's right. So to try to go it alone, to me, it's hard. It it's incredibly difficult. And, and the other thing is, is there's not a lot of partners, so the partner, >>No one's raising their hand boss. I'll >>Do all that >>Exactly. In it department. >>Exactly. >>Like, can we just call up, uh, you know, <laugh> our old vendor. That's >>Right. <laugh> right. Our old vendor. I like it, but that's so true. I mean, when I think about how, if I was a business owner, starting a business to today and I had to build my team, um, and the amount of investment that it would take to get those people skilled up and then the risk factor of those people now having the skills and being so much more in demand and being recruited away, that's a real, that's a real issue. And so how you build your culture around that is, is very important. And it's something that we talk about every, with every one of our small to midsize business. >>So just, I want to get, I want to get your story as CEO. Okay. Take us through your journey. You said you bought the company and your progression to, to being the owner and CEO of innovative award winning guys doing great. Uh, great bet on a good call. Yeah. Things are good. Tell your story. What's your journey? >>It's real simple. I was, uh, was a sophomore at the Rochester Institute of technology in 2003. And, uh, I knew that I, I was going to school for it and I, I knew I wanted to be in tech. I didn't know what I wanted to do, but I knew I didn't wanna code or configure routers and switches. So I had this great opportunity with the local it company that was doing managed services. We didn't call it at that time innovative solutions to come in and, uh, jump on the phone and dial for dollars. I was gonna cold call and introduce other, uh, small to midsize businesses locally in Rochester, New York go to Western New York, um, who innovative was now. We were 19 people at the time. And I came in, I did an internship for six months and I loved it. I learned more in those six months that I probably did in my first couple of years at, uh, at R I T long story short. >>Um, for about seven years, I worked, uh, to really help develop, uh, sales process and methodology for the business so that we could grow and scale. And we grew to about 30 people. And, um, I went to the owners at the time in 2010 and I was like, Hey, I'm growing the value of this business. And who knows where you guys are gonna be another five years? What do you think about making me an owner? And they were like, listen, you got long ways before you're gonna be an owner, but if you stick it out in your patient, we'll, um, we'll work through a succession plan with you. And I said, okay, there were four other individuals at the time that we're gonna also buy the business with >>Me. And they were the owners, no outside capital, >>None zero, well, 2014 comes around. And, uh, the other folks that were gonna buy into the business with me that were also working at innovative for different reasons. They all decided that it wasn't for them. One started a family. The other didn't wanna put capital in. Didn't wanna write a check. Um, the other had a real big problem with having to write a check. If we couldn't make payroll, I'm like, well, that's kind of like if we're owners, we're gonna have to like cover that stuff. <laugh> so >>It's called the pucker factor. >>Exactly. So, uh, I sat down with the CEO in early 2015, and, uh, we made the decision that I was gonna buy the three partners out, um, go through an earn out process, uh, coupled with, uh, an interesting financial strategy that wouldn't strap the business, cuz they care very much. The company still had the opportunity to keep going. So in 2016 I bought the business, um, became the sole owner. And, and at that point we, um, we really focused hard on what do we want this company to be? We had built this company to this point. Yeah. And, uh, and by 2018 we knew that pivoting all going all in on the cloud was important for us and we haven't looked back. >>And at that time, the proof points were coming clearer and clearer 2012 through 15 was the early adopters, the builders, the startups and early enterprises. Yes. The capital ones of the world. Exactly the, uh, and those kinds of big enterprises. The game don't, won't say gamblers, but ones that were very savvy. The innovators, the FinTech folks. Yep. The hardcore glass eating enterprises >>Agreed, agreed to find a small to midsize business, to migrate completely to the cloud as, as infrastructure was considered. That just didn't happen as often. Um, what we were seeing were a lot of our small to midsize business customers, they wanted to leverage cloud based backup, or they wanted to leverage a cloud for disaster recovery because it lent itself. Well, early days, our most common cloud customer though, was the customer that wanted to move messaging and collaboration. The, the Microsoft suite to the cloud and a lot of 'em dipped their toe in the water. But by 2017 we knew infrastructure was around the corner. Yeah. And so, uh, we only had two customers on eight at the time. Um, and we, uh, we, we made the decision to go all in >>Justin. Great to have you on the cube. Thank you. Let's wrap up. Uh, tell me the hottest product that you have. Is it migrations? Is the app modernization? Is it data? What's the hot product and then put a plug in for the company. Awesome. >>So, uh, there's no question. Every customer is looking to migrate workloads and try to figure out how to modernize for the future. We have very interesting, sophisticated yet elegant funding solutions to help customers with the cash flow, uh, constraints that come along with those migrations. So any SMB that's thinking about migrating to the cloud, they should be talking innovative solutions. We know how to do it in a way that allows those customers not to be cash strapped and gives them an opportunity to move forward in a controlled, contained way so that they can modernize. >>So like insurance, basically for them not insurance class in the classic sense, but you help them out on the, on the cash exposure. >>Absolutely. We are known for that and we're known for being creative with those customers, empathetic to where they are in their journey. And >>That's the cloud upside is all about doubling down on the variable wind. That's right. Seeing the value and doubling down on it. Absolutely not praying for it. Yeah. <laugh> all right, Justin. Thanks for coming on. You really appreciate it. Thank >>You very much for having >>Me. Okay. This is the cube coverage here live in San Francisco, California for AWS summit, 2022. I'm John for your host. Thanks for watching with back with more great coverage for two days after this short break >>Live on the floor in San Francisco for 80 west summit, I'm John ferry, host of the cube here for the next two days, getting all the action we're back in person. We're at AWS reinvent a few months ago. Now we're back events are coming back and we're happy to be here with the cube, bringing all the action. Also virtual, we have a hybrid cube, check out the cube.net, Silicon angle.com for all the coverage. After the event. We've got a great guest ticketing off here. Matthew Park, director of solutions, architecture with innovation solutions. The booth is right here. Matthew, welcome to the cube. >>Thank you very much. I'm glad >>To be here. So we're back in person. You're from Tennessee. We were chatting before you came on camera. Um, it's great to have to be back through events. >>It's amazing. This is the first, uh, summit I've been to and what two, three years. >>It's awesome. We'll be at the, uh, New York as well. A lot of developers and a big story this year is as developers look at cloud going distributed computing, you got on premises, you got public cloud, you got the edge. Essentially the cloud operations is running everything dev sec ops, everyone kind of sees that you got containers, you got Kubernetes, you got cloud native. So the, the game is pretty much laid out. Mm. And the edge is with the actions you guys are number one, premier partner at SMB for edge. >>That's right. >>Tell us about what you guys doing at innovative and, uh, what you do. >>That's right. Uh, so I'm the director of solutions architecture. Uh, me and my team are responsible for building out the solutions that are around, especially the edge public cloud out for us edge is anything outside of an AWS availability zone. Uh, we are deploying that in countries that don't have AWS infrastructure in region. They don't have it. Uh, give >>An example, >>Uh, example would be Panama. We have a customer there that, uh, needs to deploy some financial tech data and compute is legally required to be in Panama, but they love AWS and they want to deploy AWS services in region. Uh, so they've taken E EKS anywhere. We've put storage gateway and, uh, snowball, uh, in region inside the country and they're running their FinTech on top of AWS services inside Panama. >>You know, what's interesting, Matthew is that we've been covering Aw since 2013 with the cube about their events. And we watched the progression and jazzy was, uh, was in charge and then became the CEO. Now Adam Slosky is in charge, but the edge has always been that thing they've been trying to, I don't wanna say, trying to avoid, of course, Amazon would listen to customers. They work backwards from the customers. We all know that. Uh, but the real issue was they were they're bread and butters EC two and S three. And then now they got tons of services and the cloud is obviously successful and seeing that, but the edge brings up a whole nother level. >>It does >>Computing. It >>Does. >>That's not central lies in the public cloud. Now they got regions. So what is the issue with the edge what's driving? The behavior. Outpost came out as a reaction to competitive threats and also customer momentum around OT, uh, operational technologies. And it merging. We see with the data at the edge, you got five GM having. So it's pretty obvious, but there was a slow transition. What was the driver for the <affirmative> what's the driver now for edge action for AWS >>Data is the driver for the edge. Data has gravity, right? And it's pulling compute back to where the customer's generating that data and that's happening over and over again. You said it best outpost was a reaction to a competitive situation. Whereas today we have over fit 15 AWS edge services, and those are all reactions to things that customers need inside their data centers on location or in the field like with media companies. >>Outpost is interesting. We always used to riff on the cube, uh, cuz it's basically Amazon in a box, pushed in the data center, uh, running native, all the stuff, but now cloud native operations are kind of become standard. You're starting to see some standard Deepak sings group is doing some amazing work with open source Rauls team on the AI side, obviously, uh, you got SW who's giving the keynote tomorrow. You got the big AI machine learning big part of that edge. Now you can say, okay, outpost, is it relevant today? In other words, did outpost do its job? Cause EKS anywhere seems to be getting a lot of momentum. You see low the zones, the regions are kicking ass for Amazon. This edge piece is evolving. What's your take on EKS anywhere versus say outpost? >>Yeah, I think outpost did its job. It made customers that were looking at outpost really consider, do I wanna invest in this hardware? Do I, do I wanna have, um, this outpost in my data center, do I wanna manage this over the long term? A lot of those customers just transitioned to the public cloud. They went into AWS proper. Some of those customers stayed on prem because they did have use cases that were, uh, not a good fit for outpost. They weren't a good fit. Uh, in the customer's mind for the public AWS cloud inside an availability zone. Now what's happening is as AWS is pushing these services out and saying, we're gonna meet you where you are with 5g. We're gonna meet you where you are with wavelength. We're gonna meet you where you are with EKS anywhere. Uh, I think it has really reduced the amount of times that we have conversations about outposts and it's really increased. We can deploy fast. We don't have to spin up outpost hardware. We can go deploy EKS anywhere in your VMware environment and it's increasing the speed of adoption >>For sure. So you guys are making a lot of good business decisions around managed cloud service. Innovative does that. You have the cloud advisory, the classic professional services for the specific edge piece and, and doing that outside of the availability zones and regions for AWS, um, customers in, in these new areas that you're helping out are they want cloud, like they want to have modernization a modern applications. Obviously they got data machine learning and AI, all part of that. What's the main product or, or, or gap that you're filling for AWS, uh, outside of their available ability zones or their regions that you guys are delivering. What's the key is it. They don't have a footprint. Is it that it's not big enough for them? What's the real gap. What's why, why are you so successful? >>So what customers want when they look towards the cloud is they want to focus on, what's making them money as a business. They wanna focus on their applications. They want focus on their customers. So they look towards AWS cloud and say, AWS, you take the infrastructure. You take, uh, some of the higher layers and we'll focus on our revenue generating business, but there's a gap there between infrastructure and revenue generating business that innovative slides into, uh, we help manage the AWS environment. We help build out these things in local data centers for 32 plus year old company, we have traditional on-premises people that know about deploying hardware that know about deploying VMware to host EKS anywhere. But we also have most of our company totally focused on the AWS cloud. So we're filling that gap in helping deploy these AWS services, manage them over the long term. So our customers can go to just primarily and totally focusing on their revenue generating business. >>So basically you guys are basically building AWS edges, >>Correct? >>For correct companies, correct? Mainly because the, the needs are there, you got data, you got certain products, whether it's, you know, low latency type requirements, right. And then they still work with the regions, right. It's all tied together, right. Is that how it works? Right. >>And, and our customers, even the ones in the edge, they also want us to build out the AWS environment inside the availability zone, because we're always gonna have a failback scenario. If we're gonna deploy FinTech in the Caribbean, we're gonna talk about hurricanes and gonna talk about failing back into the AWS availability zones. So innovative is filling that gap across the board, whether it be inside the AWS cloud or on the AWS edge. >>All right. So I gotta ask you on the, since you're at the edge in these areas, I won't say underserved, but developing areas where now have data, you have applications that are tapping into that, that requirement. It makes total sense. We're seeing across the board. So it's not like it's, it's an outlier it's actually growing. Yeah. There's also the crypto angle. You got the blockchain. Are you seeing any traction at the edge with blockchain? Because a lot of people are looking at the web three in these areas like Panama, you mentioned FinTech in, in the islands. There are a lot of, lot of, lot of web three happening. What's your, what's your view on the web three world right now, relative >>To we, we have some customers actually deploying crypto, especially, um, especially in the Caribbean. I keep bringing the Caribbean up, but it's, it's top of my mind right now we have customers that are deploying crypto. A lot of, uh, countries are choosing crypto underly parts of their central banks. Yeah. Um, so it's, it's up and coming. Uh, I, I have some, you know, personal views that, that crypto is still searching for a use case. Yeah. And, uh, I think it's searching a lot and, and we're there to help customers search for that use case. Uh, but, but crypto, as a, as a tech technology, um, lives really well on the AWS edge. Yeah. Uh, and, and we're having more and more people talk to us about that. Yeah. And ask for assistance in the infrastructure because they're developing new cryptocurrencies every day. Yeah. It's not like they're deploying Ethereum or anything specific. They're actually developing new currencies and, and putting them out there on it's >>Interesting. And I mean, first of all, we've been doing crypto for many, many years. We have our own little, um, you know, projects going on. But if you look talk to all the crypto people that say, look, we do a smart contract, we use the blockchain. It's kind of over a lot of overhead. It's not really their technical already, but it's a cultural shift, but there's underserved use cases around use of money, but they're all using the blockchain, just for this like smart contracts for instance, or certain transactions. And they go into Amazon for the database. Yeah. <laugh> they all don't tell anyone we're using a centralized service, but what happened to decent centralized. >>Yeah. And that's, and that's the conversation performance. >>Yeah. >>And, and it's a cost issue. Yeah. And it's a development issue. Um, so I think more and more as, as some of these, uh, currencies maybe come up, some of the smart contracts get into, uh, they find their use cases. I think we'll start talking about how does that really live on, on AWS and, and what does it look like to build decentralized applications, but with AWS hardware and services. >>Right. So take me through a, a use case of a customer, um, Matthew around the edge. Okay. So I'm a customer, pretend I'm a customer, Hey, you know, I'm, we're in an underserved area. I want to modernize my business. And I got my developers that are totally peaked up on cloud. Um, but we've identified that it's just a lot of overhead latency issues. I need to have a local edge and serve my a and I also want all the benefits of the cloud. So I want the modernization and I wanna migrate to the cloud for all those cloud benefits and the good this of the cloud. What's the answer. Yeah. >>Uh, big thing is, uh, industrial manufacturing, right? That's, that's one of the best use cases, uh, inside industrial manufacturing, we can pull in many of the AWS edge services we can bring in, uh, private 5g, uh, so that all the, uh, equipment inside that, that manufacturing plant can be hooked up. They don't have to pay huge overheads to deploy 5g it's, uh, better than wifi for the industrial space. Um, when we take computing down to that industrial area, uh, because we wanna do pre-procesing on the data. Yeah. We want to gather some analytics. We deploy that with, uh, regular commercially available hardware running VMware, and we deploy EKS anywhere on that. Uh, inside of that manufacturing plant, uh, we can do pre-processing on things coming out of the, uh, the robotics that depending on what we're manufacturing, right. Uh, and then we can take the, those refined analytics and for very low cost with maybe a little bit longer latency transmit those back, um, to the AWS availability zone, the, the standard >>For data lake or whatever, >>To the data lake. Yeah. Data Lakehouse, whatever it might be. Um, and we can do additional data science on that once it gets to the AWS cloud. Uh, but I'll lot of that, uh, just in time business decisions, just in time, manufacturing decisions can all take place on an AWS service or services inside that manufacturing plant. And that's, that's one of the best use cases that we're >>Seeing. And I think, I mean, we've been seeing this on the queue for many, many years, moving data around is very expensive. Yeah. But also compute going of the data that saves that cost yep. On the data transfer also on the benefits of the latency. So I have to ask you, by the way, that's standard best practice now for the folks watching don't move the data unless you have to. Um, but those new things are developing. So I wanna ask you, what new patterns are you seeing emerging once this new architecture's in place? Love that idea, localize everything right at the edge, manufacture, industrial, whatever the use case, retail, whatever it is. Right. But now what does that change in the, in the core cloud? There's a, there's a system element here. Yeah. What's the new pattern. There's >>Actually an organizational element as well, because once you have to start making the decision, do I put this compute at the point of use or do I put this compute in the cloud? Uh, now you start thinking about where business decisions should be taking place. Uh, so not only are you changing your architecture, you're actually changing your organization because you're thinking, you're thinking about a dichotomy you didn't have before. Uh, so now you say, okay, this can take place here. Uh, and maybe, maybe this decision can wait. Yeah. Uh, and then how do I visualize that? By >>The way, it could be a bot tube doing the work for management. Yeah. <laugh> exactly. You got observability going, right. But you gotta change the database architecture in the back. So there's new things developing. You've got more benefit. There >>Are, there are. And, and we have more and more people that, that want to talk less about databases and want to talk more about data lakes because of this. They want to talk more about out. Customers are starting to talk about throwing away data, uh, you know, for the past maybe decade. Yeah. It's been store everything. And one day we will have a data science team that we hire in our organization to do analytics on this decade of data. And well, >>I mean, that's, that's a great point. We don't have time to drill into, maybe we do another session on this, but the one pattern we're seeing of the past year is that throwing away data's bad, even data lakes that so-called turn into data swamps, actually, it's not the case. You look at data, brick, snowflake, and other successes out there. And even time series data, which may seem irrelevant efforts over actually matters when people start retraining their machine learning algorithms. Yep. So as data becomes code, as we call it in our last showcase, we did a whole whole event on this. The data's good in real time and in the lake. Yeah. Because the iteration of the data feeds the machine learning training. Things are getting better with the old data. So it's not throw it away. It's not just business better. Yeah. There's all kinds of new scale. >>There are. And, and we have, uh, many customers that are running pay Toby level. Um, they're, they're essentially data factories on, on, uh, on premises, right? They're, they're creating so much data and they're starting to say, okay, we could analyze this, uh, in the cloud, we could transition it. We could move Aytes of data to the AWS cloud, or we can run, uh, computational workloads on premises. We can really do some analytics on this data transition, uh, those high level and sort of raw analytics back to AWS run 'em through machine learning. Um, and we don't have to transition 10, 12 petabytes of data into AWS. >>So I gotta end the segment on a, on a kind of a, um, fun note. I was told to ask you about your personal background, OnPrem architect, Aus cloud, and skydiving instructor. <laugh> how does that all work together? What tell, what does this mean? Yeah. >>Uh, you >>Jumped out a plane and got a job. You got a customer to jump out >>Kind of. So I was, you jumped out. I was teaching having, uh, before I, before I started in the cloud space, this was 13, 14 years ago. I was a, I still am a sky. I instructor, uh, I was teaching skydiving and I heard out of the corner of my ear, uh, a guy that owned an MSP that was lamenting about, um, you know, storing data and, and how his customers are working. And he can't find an enough people to operate all these workloads. So I walked over and said, Hey, this is, this is what I went to school for. Like, I'd love to, you know, uh, I was living in a tent in the woods, teaching skydiving. I was like, I'd love to not live in a tent in the woods. So, uh, uh, I started and the first day there, uh, we had a, a discussion, uh, EC two had just come out <laugh> and, uh, like, >>This is amazing. >>Yeah. And so we had this discussion, we should start moving customers here. And, uh, and that totally revolutionized that business, um, that, that led to, uh, that that guy actually still owns a skydiving airport. But, um, but through all of that, and through being in on premises, migrated me and myself, my career into the cloud, and now it feels like, uh, almost, almost looking back and saying, now let's take what we learned in the cloud and, and apply those lessons and those services tore >>It's. So it's such a great story, you know, was gonna, you know, you know, the whole, you know, growth mindset pack your own parachute, you know, uh, exactly. You know, the cloud in the early days was pretty much will the shoot open. Yeah. It was pretty much, you had to roll your own cloud at that time. And so, you know, you, you jump on a plane, you gotta make sure that parachute is gonna open. >>And so was Kubernetes by the way, 2015 or so when, uh, when that was coming out, it was, I mean, it was, it was still, and maybe it does still feel like that to some people. Right. But, uh, it was, it was the same kind of feeling that we had in the early days of AWS, the same feeling we have when we >>It's now with you guys, it's more like a tandem jump. Yeah. You know, but, but it's a lot of, lot of this cutting edge stuff, like jumping out of an airplane. Yeah. You got the right equipment. You gotta do the right things. Exactly. >>Right. >>Yeah. Thanks for coming. You really appreciate it. Absolutely great conversation. Thanks for having me. Okay. The cubes here live in San Francisco for eight of us summit. I'm John for host of the cube. Uh, we'll be at a summit in New York coming up in the summer as well. Look up for that. Look up this calendar for all the cube, actually@thecube.net. We'll right back with our next segment after this break. >>Okay. Welcome back everyone to San Francisco live coverage here, we're at the cube a be summit 2022. We're back in person. I'm John fury host of the cube. We'll be at the eighties summit in New York city this summer, check us out then. But right now, two days in San Francisco, getting all the coverage what's going on in the cloud, we got a cube alumni and friend of the cube, my dos car CEO, investor, a Sierra, and also an investor in a bunch of startups, angel investor. Gonna do great to see you. Thanks for coming on the cube. Good to see you. Good to see you. Cool. How are you? Good. >>How hello you. >>So congratulations on all your investments. Uh, you've made a lot of great successes, uh, over the past couple years, uh, and your company raising, uh, some good cash as Sarah. So give us the update. How much cash have you guys raised? What's the status of the company product what's going on? >>First of all, thank you for having me. We're back to be business with you, never after to see you. Uh, so is a company started around four years back. I invested with a few of the investors and now I'm the CEO there. We have raised close to a hundred million there. The investors are people like Norwes Menlo ventures, coastal ventures, Ram Shera, and all those people, all well known guys. And Beckel chime Paul me Mayard web. So whole bunch of operating people and, uh, Silicon valley VCs are involved >>And has it gone? >>It's going well. We are doing really well. We are going almost 300% year over year. Uh, for last three years, the space ISRA is going after is what I call the applying AI for customer service. It operations, it help desk, uh, the same place I used to work at ServiceNow. We are partners with ServiceNow to take, how can we argument for employees and customers, Salesforce, and service now to take you to the next stage? Well, >>I love having you on the cube, Dave and I, Dave LAN as well loves having you on too, because you not only bring the entrepreneurial CEO experience, you're an investor. You're like a, you're like a guest analyst. <laugh> >>You know, who does >>You, >>You >>Get the call fund to talk to you though. You >>Get the commentary, your, your finger in the pulse. Um, so I gotta ask you obviously, AI and machine learning, machine learning AI, or you want to phrase it. Isn't every application. Now, AI first, uh, you're seeing a lot of that going on. You're starting to see companies build the modern applications at the top of the stack. So the cloud scale has hit. We're seeing cloud scale. You predicted that we talked about in the cube many times. Now you have that past layer with a lot more services and cloud native becoming a standard layer. Containerizations growing Docker just raised a hundred million on a $2 billion valuation back from the dead after they pivoted from enterprise services. So open source developers are booming. Um, where's the action. I mean, is there data control plan? Emerging AI needs data. There's a lot of challenges around this. There's a lot of discussions and a lot of companies being funded, observability there's 10 billion observability companies. Data is the key. This is what's your end on this. What's your take. >>Yeah, look, I think I'll give you the few that I see right from my side. Obviously data is very clear. So the things that rumor system of recorded you and me talked about the next layer is called system of intelligence. That's where the AI will play. Like we talk cloud native, it'll be called AI. NA AI enable is a new buzzword and using the AI for customer service. It, you talk about observability. I call it, AIOps applying AOPs for good old it operation management, cloud management. So you'll see the AOPs applied for whole list of, uh, application from observability doing the CMDB, predicting the events insurance. So I see a lot of work clicking for AIOps and AI services. What used to be desk with ServiceNow BMC GLA you see a new ALA emerging as a system of intelligence. Uh, the next would be is applying AI with workflow automation. So that's where you'll see a lot of things called customer workflows, employee workflows. So think of what UI path automation, anywhere ServiceNow are doing, that area will be driven with AI workflows. So you, you see AI going >>Off is RPA. A company is AI, is RPA a feature of something bigger? Or can someone have a company on RPA UI S one will be at their event this summer? Um, is it a product company? I mean, or I mean, RPA is, should be embedded in everything. It's a >>Feature. It is very good point. Very, very good thinking. So one is, it's a category for sure. Like, as we thought, it's a category, it's an area where RPA may change the name. I call it much more about automation, workflow automation, but RPA and automation is a category. Um, it's a company also, but that automation should be embedded in every area. Yeah. Like we call cloud NATO and AI. They it'll become automation data. Yeah. And that's your, thinking's >>Interesting me. I think about the, what you're talking about what's coming to mind is I'm kinda having flashbacks to the old software model of middleware. Remember at middleware, it was very easy to understand it was middleware. It sat between two things and then the middle, and it was software abstraction. Now you have all kinds of workflows, abstractions everywhere. So multiple databases, it's not a monolithic thing. Right? Right. So as you break that down, is this the new modern middleware? Because what you're talking about is data workflows, but they might be siloed. Are they integrated? I mean, these are the challenges. This is crazy. What's the, >>So remember the databases became called polyglot databases. Yeah. I call this one polyglot automation. So you need automation as a layer, as a category, but you also need to put automation in every area like you, you were talking about, it should be part of service. Now it should be part of ISRA. Like every company, every Salesforce. So that's why you see it MuleSoft and sales buying RPA companies. So you'll see all the SaaS companies, cloud companies having an automation as a core. So it's like how you have a database and compute and sales and networking. You'll also have an automation as a layer embedded inside every stack. >>All right. So I wanna shift gears a little bit and get your perspective on what's going on behind us. You can see, uh, behind, as you got the XPO hall got, um, we're back to vis, but you got, you know, AMD, Clum, Dynatrace data, dog, innovative, all the companies out here that we know, we interview them all. They're trying to be suppliers to this growing enterprise market. Right? Okay. But now you also got the entrepreneurial equation. Okay. We're gonna have John Sado on from Deibel later. He's a former NEA guy and we always talk to Jerry, Jen, we know all the, the VCs, what does the startups look like? What does the state of the, in your mind, cause you, I know you invest the entrepreneurial founder situation. Cloud's bigger. Mm-hmm <affirmative> global, right? Data's part of it. You mentioned data's code. Yes. Basically. Data's everything. What's it like for a first an entrepreneur right now who's starting a company. What's the white space. What's the attack plan. How do they get in the market? How do they engineer everything? >>Very good. So I'll give it to, uh, two things that I'm seeing out there. Remember leaders of Amazon created the startups 15 years back. Everybody built on Amazon now, Azure and GCP. The next layer would be people don't just build on Amazon. They're going to build it on top of snow. Flake companies are snowflake becomes a data platform, right? People will build on snowflake, right? So I see my old boss playing ment, try to build companies on snowflake. So you don't build it just on Amazon. You build it on Amazon and snowflake. Snowflake will become your data store. Snowflake will become your data layer, right? So I think that's the next level of companies trying to do that. So if I'm doing observability AI ops, if I'm doing next level of Splunk SIM, I'm gonna build it on snowflake, on Salesforce, on Amazon, on Azure, et cetera. >>It's interesting. You know, Jerry Chan has it put out a thesis a couple months ago called castles in the cloud where your moat is, what you do in the cloud. Not necessarily in the, in the IP. Um, Dave LAN and I had last re invent, coined the term super cloud, right? It's got a lot of traction and a lot of people throwing, throwing mud at us, but we were, our thesis was, is that what Snowflake's doing? What Goldman S Sachs is doing. You're starting to see these clouds on top of clouds. So Amazon's got this huge CapEx advantage. And guys like Charles Fitzgeral out there, who we like was kind of hitting on us saying, Hey, you guys terrible, they didn't get him. Like, yeah, I don't think he gets it, but that's a whole, can't wait to debate him publicly on this. <laugh> cause he's cool. Um, but snowflake is on Amazon. Yes. Now they say they're on Azure now. Cause they've got a bigger market and they're public, but ultimately without a AWS snowflake doesn't exist and, and they're reimagining the data warehouse with the cloud, right? That's the billion dollar opportunity. >>It is. It is. They both are very tight. So imagine what Frank has done at snowflake and Amazon. So if I'm a startup today, I want to build everything on Amazon where possible whatever is, I cannot build. I'll make the pass layer room. The middle layer pass will be snowflake. So I cannot build it on snowflake. I can use them for data layer if I really need to size, I'll build it on force.com Salesforce. Yeah. Right. So I think that's where you'll >>See. So basically the, the, if you're an entrepreneur, the, the north star in terms of the, the outcome is be a super cloud. It >>Is, >>That's the application on another big CapEx ride, the CapEx of AWS or cloud, >>And that reduce your product development, your go to market and you get use the snowflake marketplace to drive your engagement. Yeah. >>Yeah. How are, how is Amazon and the clouds dealing with these big whales, the snowflakes of the world? I mean, I know they got a great relationship, uh, but snowflake now has to run a company they're public. Yeah. So, I mean, I'll say, I think got Redshift. Amazon has got Redshift. Um, but snowflake big customer. The they're probably paying AWS big, >>I >>Think big bills too. >>So John, very good. Cause it's like how Netflix is and Amazon prime, right. Netflix runs on Amazon, but Amazon has Amazon prime that co-option will be there. So Amazon will have Redshift, but Amazon is also partnering with the snowflake to have native snowflake data warehouse as a data layer. So I think depending on the use case you have to use each of the above, I think snowflake is here for a long term. Yeah. Yeah. So if I'm building an application, I want to use snowflake then writing from stats. >>Well, I think that comes back down to entrepreneurial hustle. Do you have a better product? Right. Product value will ultimately determine it as long as the cloud doesn't, you know, foreclose your value. That's right. With some sort of internal hack, but I've think, I think the general question that I have is that I think it's okay to have a super cloud like that because the rising tide is still happening at some point, when does the rising tide stop and the people shopping up their knives, it gets more competitive or is it just an infinite growth cycle? I >>Think it's growth. You call it closed skill you the word cloud scale. So I think look, cloud will continually agree, increase. I think there's as long as there more movement from on, uh, on-prem to the classical data center, I think there's no reason at this point, the rumor, the old lift and shift that's happening in like my business. I see people lift and shifting from the it operations, it helpless. Even the customer service service. Now the ticket data from BMCs CAS like Microfocus, all those workloads are shifted to the cloud, right? So cloud ticketing system is happening. Cloud system of record is happening. So I think this train has still a long way to go made. >>I wanna get your thoughts for the folks watching that are, uh, enterprise buyers are practitioners, not suppliers to the market. Feel free to text me or DMing. Next question is really about the buying side, which is if I'm a customer, what's the current, um, appetite for startup products. Cause you know, the big enterprises now and you know, small, medium, large, and large enterprise, they're all buying new companies cuz a startup can go from zero to relevant very quickly. So that means now enterprises are engaging heavily with startups. What's it like what's is there a change in order of magnitude of the relationship between the startup selling to, or growing startup selling to an enterprise? Um, have you seen changes there? I mean seeing some stuff, but why don't we get your thoughts on that? What it >>Is you, if I remember going back to our 2007 or eight, when I used to talk to you back then when Amazon started very small, right? We are an Amazon summit here. So I think enterprises on the average used to spend nothing with startups. It's almost like 0% or one person today. Most companies are already spending 20, 30% with startups. Like if I look at a C I will line our business, it's gone. Yeah. Can it go more? I think it can double in the next four, five years. Yeah. Spending on the startups. Yeah. >>And check out, uh, AWS startups.com. That's a site that we built for the startup community for buyers and startups. And I want to get your reaction because I, I reference the URL causes like there's like a bunch of companies we've been promoting because the solution that startups have actually are new stuff. Yes. It's bending, it's shifting left for security or using data differently or um, building tools and platforms for data engineering. Right. Which is a new persona that's emerging. So you know, a lot of good resources there. Um, and goes back now to the data question. Now, getting back to your, what you're working on now is what's your thoughts around this new, um, data engineering persona, you mentioned AIOps, we've been seeing AIOps IOPS booming and that's creating a new developer paradigm that's right. Which we call coin data as code data as code is like infrastructure as code, but it's for data, right? It's developing with data, right? Retraining machine learnings, going back to the data lake, getting data to make, to do analysis, to make the machine learning better post event or post action. So this, this data engineers like an SRE for data, it's a new, scalable role we're seeing. Do you see the same thing? Do you agree? Um, do you disagree or can you share? >>I, a lot of thoughts that Fu I see the AI op solutions in the futures should be not looking back. I need to be like we are in San Francisco bay. That means earthquake prediction. Right? I want AOPs to predict when the outages are gonna happen. When there's a performance issue. I don't think most AOPs vendors have not gone there yet. Like I spend a lot of time with data dog, Cisco app dynamic, right? Dynatrace, all this solution will go future towards predict to pro so solution with AOPs. But what you bring up a very good point on the data side. I think like we have a Amazon marketplace and Amazon for startup, there should be data exchange where you want to create for AOPs and AI service that customers give the data, share the data because we thought the data algorithms are useless. I can give the best algorithm, but I gotta train them, modify them, make them better, make them better. Yeah. And I think their whole data exchange is the industry has not thought through something you and me talk many times. Yeah. Yeah. I think the whole, that area is very important. >>You've always been on, um, on the Vanguard of data because, uh, it's been really fun. Yeah. >>Going back to big data days back in 2009, you know that >>Look at, look how much data bricks has grown. >>It is doubled. The key cloud >>Air kinda went private, so good stuff. What are you working on right now? Give a, give a, um, plug for what you're working on. You'll still investing. >>I do still invest, but look, I'm a hundred percent on ISRA right now. I'm the CEO there. Yeah. Okay. So right. ISRA is my number one baby right now. So I'm looking year that growing customers and my customers, or some of them, you like it's zoom auto desk, McAfee, uh, grand <inaudible>. So all the top customers, um, mainly for it help desk customer service. AIOps those are three product lines and going after enterprise and commercial deals. >>And when should someone buy your product? What's what's their need? What category is it? >>I think they look whenever somebody needs to buy the product is if you need AOP solution to predict, keep your lights on, predict ours. One area. If you want to improve employee experience, you are using a slack teams and you want to automate all your workflows. That's another value problem. Third is customer service. You don't want to hire more people to do it. Some of the areas where you want to scale your company, grow your company, eliminate the cost customer service, >>Great stuff, man. Doing great to see you. Thanks for coming on. Congratulations on the success of your company and your investments. Thanks for coming on the cube. Okay. I'm John fur here at the cube live in San Francisco for day one of two days of coverage of a us summit 2022. And we're gonna be at Aus summit in San, uh, in New York in the summer. So look for that on the calendar, of course, go to a us startups.com. That's a site for all the hot startups and of course the cube.net and Silicon angle.com. Thanks for watching. We'll be back more coverage after this short break. >>Okay. Welcome back everyone. This the cubes coverage here in San Francisco, California, a Davis summit, 2022, the beginning of the event season, as it comes back, little bit smaller footprint, a lot of hybrid events going on, but this is actually a physical event, a summit in new York's coming in the summer. We'll be two with the cube on the set. We're getting back in the Groove's psych to be back. We were at reinvent, uh, as well, and we'll see more and more cube, but you're gonna see a lot of virtual cube outta hybrid cube. We wanna get all those conversations, try to get more interviews, more flow going. But right now I'm excited to have Corey Quinn here on the back on the cube chief cloud economist with duck bill groove, he's the founder, uh, and chief content person always got great angles, fun comedy, authoritative Corey. Great to see you. Thank you. >>Thanks. Coming on. Sure is a lot of words to describe is shit posting, which is how I describe what I tend to do. Most days, >>Shit posting is an art form now. And if you look at mark, Andrew's been doing a lot of shit posting lately. All a billionaires are shit posting, but they don't know how to do it. They're >>Doing it right. There's something opportunity there. It's like, here's how to be even more obnoxious and incisive. It's honestly the most terrifying scenario for anyone is if I have that kind of budget to throw at my endeavors, it's like, I get excited with a nonsense I can do with a $20 gift card for an AWS credit compared to, oh well, if I could buy a mid-size island to begin doing this from, oh, then we're having fun. >>This shit posting trend. Interesting. I was watching a thread go on about, saw someone didn't get a job because of their shit posting and the employer didn't get it. And then someone on this side I'll hire the guy cuz I get that's highly intelligent shit posting. So for the audience that doesn't know what shit posting is, what, what is shitposting >>It's more or less talking about the world of enterprise technology, which even that sentence is hard to finish without falling asleep and toppling out of my chair in front of everyone on the livestream, but it's doing it in such a way that brings it to life that says the quiet part. A lot of the audience is thinking, but generally doesn't say either because they're polite or not a Jack ass or more prosaically are worried about getting fired for better or worse. I don't have that particular constraint, >>Which is why people love you. So let's talk about what you, what you think is, uh, worthy and not worthy in the industry right now, obviously, uh, Cuban coming up in Spain, which they're having a physical event, you see the growth of cloud native Amazon's evolving Atos, especially new CEO. Andy move on to be the chief of all. Amazon just saw him the cover of was it time magazine. Um, he's under a lot of stress. Amazon's changed. Invoice has changed. What's working. What's not, what's rising, what's falling. What's hot. What's not, >>It's easy to sit here and criticize almost anything. These folks do. They're they're effectively in a fishbowl, but I have trouble. Imagine the logistics, it takes to wind up handling the catering for a relatively downscale event like this one this year, let alone running a 1.7 million employee company having to balance all the competing challenges and pressures and the rest. I, I just can't fathom what it would be like to look at all of AWS. And it's, it's sprawling immense, the nominates our entire industry and say, okay, this is a good start, but I, I wanna focus on something with a broader remit. What is that? How do you even get into that position? And you can't win once you're there. All you can do is hold onto the tiger and hope you don't get mold. >>Well, there's a lot of force for good conversations. Seeing a lot of that going on, Amazon's trying to a, is trying to portray themselves, you know, the Pathfinder, you know, you're the pioneer, um, force for good. And I get that and I think that's a good angle as cloud goes mainstream. There's still the question of, we had a guy on just earlier, who was a skydiving instructor and we were joking about the early days of cloud. Like that was like skydiving, build a parachute open, you know, and now it's same kind of thing. As you move to edge, things are like reliable in some areas, but still new, new fringe, new areas. That's crazy. Well, >>Since the last time we've spoken, uh, Steve Schmidt is now the CISO for all of Amazon and his backfill replacement. The AWS CISO is CJ. Moses who as a hobby races, a as a semi-pro race car, our driver to my understanding, which either, I don't know what direction to take that in either. This is what he does to relax or ultimately, or ultimately it's. Huh? That, that certainly says something about risk assessment. I'm not entirely sure what, but okay. Either way, it sounds like more exciting. Like they >>Better have a replacement ready in case something goes wrong on the track, highly >>Available >>CSOs. I gotta say one of the things I do like in the recent trend is that the tech companies are getting into the formula one, which I was never a fan of until I watched that Netflix series. But when you look at the formula one, it's pretty cool. Cause it's got some tech angles, I get the whole data instrumentation thing, but the most coolest thing about formula, the one is they have these new rigs out. Yeah. Where you can actually race in e-sports with other people in pure simulation of the race car. You gotta get the latest and video graphics card, but it's basically a tricked out PC with amazing monitors and you have all the equipment of F1 and you're basically simulating racing. Oh, >>It's great too. And I can see the appeal of these tech companies getting it into it because these things are basically rocket shifts. When those cars go, like they're sitting there, we can instrument every last part of what is going on inside that vehicle. And then AWS crops up. And we can bill on every one of those dimensions too. And it's like slow down their hasty pudding one step at a time. But I do see the appeal. >>So I gotta ask you about, uh, what's going on in your world. I know you have a lot of great SA we've been following you in the queue for many, many years. Got a great newsletter. Check out Corey Quinn's newsletter, uh, screaming in the cloud program. Uh, you're on the cutting edge and you've got a great balance between really being snarky and, and, and really being delivering content. That's exciting, uh, for people, uh, with a little bit of an edge, um, how's that going? Uh, what's the blowback, any blowback late leads there been tick? What was, what are some of the things you're hearing from your audience, more Corey, more Corey. And then of course the, the PR team's calling you >>The weird thing about having an audience beyond a certain size is far and away as a landslide. The most common response I get is silence where it's hi, I'm emailing an awful lot of people at last week in AWS every week and okay. They not have heard me. It. That is not actually true. People just generally don't respond to email because who responds to email newsletters. That sounds like something, a lunatic might do same story with response to live streams and podcasts. It's like, I'm gonna call into that am radio show and give them a piece of my mind. People generally don't do that. >>We should do that. Actually. I think sure would call in. Oh, I, I >>Think >>I guarantee if we had that right now, people would call in and Corey, what do you think about X? >>Yeah. It not, everyone understands the full context of what I do. And in fact, increasingly few people do and that's fine. I, I keep forgetting that sometimes people do not see what I'm doing in the same light that I do. And that's fine. Blowback has been largely minimal. Honestly, I am surprised anything by how little I have gotten over the last five years of doing this, but it would be easier to dismiss me if I weren't generally. Right. When, okay, so you launch this new service and it seems pretty crappy to me cuz when I try and build something, it falls over and begs for help. And people might not like hearing that, but it's what customers are finding too. Yeah. I really am the voice of the customer. >>You know, I always joke with Dave Avante about how John Fort's always at, uh, um, reinvent getting the interview with jazzy now, Andy we're there, you're there. And so we have these rituals at the events. It's all cool. Um, one of the rituals I like about your, um, your content is you like to get on the naming product names. Um, and, and, and, and, and kind of goof on that. Now why I like is because I used to work at ETT Packard where they used to name things as like engineers, HP 1 0, 0 5, or we can't, we >>Have a new monitor. How are we gonna name it? Throw the wireless keyboard down the stairs again. And then there you go. Yeah. >>It's and the old joke at HP was if they, if they invented sushi, they'd say, yeah, we can't call sushi. It's cold, dead fish, but that's what it is. And so the joke was cold. Dead fish is a better name than sushi. So you know is fun. So what's the, what are the, how's the Amazon doing in there? Have they changed their naming, uh, strategy, uh, on some of their, their product >>They're going in different directions. When they named Aurora, they decided to explore a new theme of Disney princesses as they go down those paths. And some things are more descriptive. Some people are clearly getting bonus on number of words, they can shove into it. Like the better a service is the longer it's name. Like AWS systems manager, a session manager is a great one. I love the service ridiculous name. They have a systems manager, parameter store with is great. They have secrets manager, which does the same thing. It's two words less, but that one costs money in a way that systems manage through parameter store does not. It's fun. >>What's your, what's your favorite combination of acronyms >>Combination of you >>Got Ks. You got EMR, you got EC two. You got S three SQS. Well, RedShift's not an acronym. You got >>Gas is one of my personal favorites because it's either elastic block store or elastic bean stock, depending entirely on the context of the conversation, >>They still got bean stock or is that still >>Around? Oh, they never turn anything off. They're like the anti Google, Google turns things off while they're still building it. Whereas Amazon is like, wow, we built this thing in 2005 and everyone hates it. But while we certainly can't change it, now it has three customers on it, John. >>Okay. >>Simple BV still haunts our >>Dreams. I, I actually got an email on, I saw one of my, uh, servers, all these C twos were being deprecated and I got an email I'm like, I couldn't figure out. Why can you just like roll it over? Why, why are you telling me just like, gimme something else. Right. Okay. So let me talk about, uh, the other things I want to ask you is that like, okay, so as Amazon gets better in some areas where do they need more work? And you, your opinion, because obviously they're all interested in new stuff and they tend to like put it out there for their end to end customers. But then they've got ecosystem partners who actually have the same product. Yes. And, and this has been well documented. So it's, it's not controversial. It's just that Amazon's got a database Snowflake's got out database service. So, you know, Redshift, snowflake database is out there. So you've got this optician. Yes. How's that going? And what are you hearing about the reaction to any of that stuff? >>Depends on who you ask. They love to basically trot out a bunch of their partners who will say nice things about them. And it very much has heirs of, let's be honest, a hostage video, but okay. Cuz these companies do partner with Amazon and they cannot afford to rock the boat too far. I'm not partnered with anyone. I can say what I want. And they're basically restricted to taking away my birthday at worse so I can live with that. >>All right. So I gotta ask about multi-cloud cause obviously the other cloud shows are coming up. Amazon hated that word. Multi-cloud um, a lot of people are saying, you know, it's not a real good marketing word. Like multicloud sounds like, you know, root canal. Mm-hmm <affirmative> right. So is there a better description for multicloud? >>Multiple single >>Loves that term. Yeah. >>You're building in multiple single points of failure. Do it for the right reasons or don't do it as a default. I believe not doing it is probably the, the right answer. However, and if I were, if I were Amazon, I wouldn't want to talk about multi-cloud either as the industry leader, let's talk about other clouds, bad direction to go in from a market cap perspective. It doesn't end well for you, but regardless of what they want to talk about, or don't want to talk about what they say, what they don't say, I tune all of it out. And I look at what customers are doing and multi-cloud exists in a variety of some brilliant, some brain dead. It depends a lot on context. But my general response is when someone gets on stage from a company and tells me to do a thing that directly benefits their company. I am skeptical at best. Yeah. When customers get on stage and say, this is what we're doing because it solves problems. That's when I shut up and listen. >>Yeah. Cool. Awesome. Corey, I gotta ask you a question cause I know you we've been, you know, fellow journey mean in the, in the cloud journey, going to all the events and then the pandemic hit where now in the third year, who knows what it's gonna end, certainly events are gonna look different. They're gonna be either changing footprint with the virtual piece, new group formations community's gonna emerge. You've got a pretty big community growing and it's growing like crazy. What's the weirdest or coolest thing, or just big changes you've seen with the pan endemic, uh, from your perspective, cuz you've been in the you're in the middle of the whitewater rafting. You've seen the events you circle offline. You saw the online piece come in, you're commentating, you're calling balls and strikes in the industry. You got a great team developing over there. Duck bill group. What's the big aha moment that you saw with the pandemic. Weird, funny, serious, real in the industry and with customers what's >>Accessibility. Reinvent is a great example. When in the before times it's open to anyone who wants to attend, who >>Can pony. >>Hello and welcome back to the live cube coverage here in San Francisco, California, the cube live coverage. Two days, day two of a summit, 2022 Aish summit, New York city coming up in summer. We'll be there as well. Events are back. I'm the host, John fur, the Cub got great guest here. Johnny Dallas with Ze. Um, here is on the queue. We're gonna talk about his background. Uh, little trivia here. He was the youngest engineer ever worked at Amazon at the age. 17 had to get escorted into reinvent in Vegas cause he was underage <laugh> with security, all good stories. Now the CEO of company called Z know DevOps kind of focus, managed service, a lot of cool stuff, Johnny, welcome to the cube. >>Thanks John. Great. >>So tell a story. You were the youngest engineer at AWS. >>I was, yes. So I used to work at a company called Bebo. I got started very young. I started working when I was about 14, um, kind of as a software engineer. And when I, uh, it was about 16. I graduated out of high school early, um, working at this company Bebo, still running all of the DevOps at that company. Um, I went to reinvent in about 2018 to give a talk about some of the DevOps software I wrote at that company. Um, but you know, as many of those things were probably familiar with reinvent happens in a casino and I was 16. So was not able to actually go into the, a casino on my own. Um, so I'd have <inaudible> security as well as casino security escort me in to give my talk. >>Did Andy jazzy, was he aware of >>This? Um, you know, that's a great question. I don't know. <laugh> >>I'll ask him great story. So obviously you started a young age. I mean, it's so cool to see you jump right in. I mean, I mean you never grew up with the old school that I used to grew up in and loading package software, loading it onto the server, deploying it, plugging the cables in, I mean you just rocking and rolling with DevOps as you look back now what's the big generational shift because now you got the Z generation coming in, millennials on the workforce. It's changing like no one's putting and software on servers. Yeah, >>No. I mean the tools keep getting better, right? We, we keep creating more abstractions that make it easier and easier. When I, when I started doing DevOps, I could go straight into E two APIs. I had APIs from the get go and you know, my background was, I was a software engineer. I never went through like the CIS admin stack. I, I never had to, like you said, rack servers, myself. I was immediately able to scale. I was managing, I think 2,500 concurrent servers across every Ables region through software. It was a fundamental shift. >>Did you know what an SRE was at that time? >>Uh, >>You were kind of an SRE on >>Yeah, I was basically our first SRE, um, was familiar with the, with the phrasing, but really thought of myself as a software engineer who knows cloud APIs, not a SRE. All >>Right. So let's talk about what's what's going on now as you look at the landscape today, what's the coolest thing that's going on in your mind in cloud? >>Yeah, I think the, I think the coolest thing is, you know, we're seeing the next layer of those abstraction tools exist and that's what we're doing with Z is we've basically gone and we've, we're building an app platform that deploys onto your cloud. So if you're familiar with something like Carku, um, where you just click a GitHub repo, uh, we actually make it that easy. You click a GI hub repo and it will deploy on ALS using a AWS tools. So, >>Right. So this is Z. This is the company. Yes. How old's the company about >>A year and a half old now. >>All right. So explain what it does. >>Yeah. So we make it really easy for any software engineer to deploy on a AWS. It's not SREs. These are the actual application engineers doing the business logic. They don't really want to think about Yamo. They don't really want to configure everything super deeply. They want to say, run this API on S in the best way possible. We've encoded all the best practices into software and we set it up for you. Yeah. >>So I think the problem you're solving is that there's a lot of want be DevOps engineers. And then they realize, oh shit, I don't wanna do this. Yeah. And some people want to do it. They loved under the hood. Right. People love to have infrastructure, but the average developer needs to actually be as agile on scale. So that seems to be the problem you solve. Right? >>Yeah. We, we, we give way more productivity to each individual engineer, you know? >>All right. So let me ask you a question. So let me just say, I'm a developer. Cool. I build this new app. It's a streaming app or whatever. I'm making it up cube here, but let's just say I deploy it. I need your service. But what happens about when my customers say, Hey, what's your SLA? The CDN went down from this it's flaky. Does Amazon have, so how do you handle all that SLA reporting that Amazon provides? Cuz they do a good job with sock reports all through the console. But as you start getting into DevOps <affirmative> and sell your app, mm-hmm <affirmative> you have customer issues. How do you, how do you view that? Yeah, >>Well, I, I think you make a great point of AWS has all this stuff already. AWS has SLAs. AWS has contract. Aw has a lot of the tools that are expected. Um, so we don't have to reinvent the wheel here. What we do is we help people get to those SLAs more easily. So Hey, this is AWS SLA as a default. Um, Hey, we'll fix you your services. This is what you can expect here. Um, but we can really leverage S's reliability of you. Don't have to trust us. You have to trust ALS and trust that the setup is good there. >>Do you handle all the recovery or mitigation between, uh, identification say downtime for instance? Oh, the server's not 99% downtime. Uh, went down for an hour, say something's going on? And is there a service dashboard? How does it get what's the remedy? Do you have a, how does all that work? >>Yeah, so we have some built in remediation. You know, we, we basically say we're gonna do as much as we can to keep your endpoint up 24 7 mm-hmm <affirmative>. If it's something in our control, we'll do it. If it's a disc failure, that's on us. If you push bad code, we won't put out that new version until it's working. Um, so we do a lot to make sure that your endpoint stay is up, um, and then alert you if there's a problem that we can't fix. So cool. Hey S has some downtime, this thing's going on. You need to do this action. Um, we'll let you know. >>All right. So what do you do for fun? >>Yeah, so, uh, for, for fun, um, a lot of side projects. <laugh> uh, >>What's your side hustle right now. You got going on >>The, uh, it's >>A lot of tools playing tools, serverless. >>Yeah, painless. A lot of serverless stuff. Um, I think there's a lot of really cool WAM stuff as well. Going on right now. Um, I love tools is, is the truest answer is I love building something that I can give to somebody else. And they're suddenly twice as productive because of it. Um, >>It's a good feeling, isn't it? >>Oh yeah. There's >>Nothing like tools were platforms. Mm-hmm <affirmative>, you know, the expression, too many tools in the tool. She becomes, you know, tools for all. And then ultimately tools become platforms. What's your view on that? Because if a good tool works and starts to get traction, you need to either add more tools or start building a platform platform versus tool. What's your, what's your view on a reaction to that kind of concept debate? >>Yeah, it's a good question. Uh, we we've basically started as like a, a platform. First of we've really focused on these, uh, developers who don't wanna get deep into the DevOps. And so we've done all of the pieces of the stacks. We do C I C D management. Uh, we do container orchestration, we do monitoring. Um, and now we're, spliting those up into individual tools so they can be used. Awesome in conjunction more. >>All right. So what are some of the use cases that you see for your service? It's DevOps basically nano service DevOps. So people who want a DevOps team, do clients have a DevOps person and then one person, two people what's the requirements to run >>Z. Yeah. So we we've got teams, um, from no DevOps is kind of when they start and then we've had teams grow up to about, uh, five, 10 men DevOps teams. Um, so, you know, as is more infrastructure people come in because we're in your cloud, you're able to go in and configure it on top you're we can't block you. Uh, you wanna use some new AWS service. You're welcome to use that alongside the stack that we deploy >>For you. How many customers do you have now? >>So we've got about 40 companies that are using us for all of their infrastructure, um, kind of across the board, um, as well as >>What's the pricing model. >>Uh, so our pricing model is we, we charge basically similar to an engineering salary. So we charge a monthly rate. We have plans at 300 bucks a month, a thousand bucks a month, and then enterprise plan for >>The requirement scale. Yeah. So back into the people cost, you must have her discounts, not a fully loaded thing, is it? >>Yeah, there's a discounts kind of asking >>Then you pass the Amazon bill. >>Yeah. So our customers actually pay for the Amazon bill themselves. So >>Have their own >>Account. There's no margin on top. You're linking your, a analyst account in, um, got it. Which is huge because we can, we are now able to help our customers get better deals with Amazon. Um, got it. We're incentivized on their team to drive your costs down. >>And what's your unit main unit of economics software scale. >>Yeah. Um, yeah, so we, we think of things as projects. How many services do you have to deploy as that scales up? Um, awesome. >>All right. You're 20 years old now you not even can't even drink legally. <laugh> what are you gonna do when you're 30? We're gonna be there. >>Well, we're, uh, we're making it better, better, >>Better the old guy on the queue here. <laugh> >>I think, uh, I think we're seeing a big shift of, um, you know, we've got these major clouds. ALS is obviously the biggest cloud and it's constantly coming out with new services, but we're starting to see other clouds have built many of the common services. So Kubernetes is a great example. It exists across all the clouds and we're starting to see new platforms come up on top that allow you to leverage tools for multiple times. At the same time. Many of our customers actually have AWS as their primary cloud and they'll have secondary clouds or they'll pull features from other clouds into AWS, um, through our software. I think that's, I'm very excited by that. And I, uh, expect to be working on that when I'm 30. <laugh> awesome. >>Well, you gonna have a good future. I gotta ask you this question cuz uh, you know, I always, I was a computer science undergrad in the, in the, and um, computer science back then was hardcore, mostly systems OS stuff, uh, database compiler. Um, now there's so much compi, right? Mm-hmm <affirmative> how do you look at the high school college curriculum experience slash folks who are nerding out on computer science? It's not one or two things. You've got a lot of, lot of things. I mean, look at Python, data engineering and emerging as a huge skill. What's it, what's it like for college kids now and high school kids? What, what do you think they should be doing if you had to give advice to your 16 year old self back a few years ago now in college? Um, I mean Python's not a great language, but it's super effective for coding and the datas were really relevant, but it's, you've got other language opportunities you've got tools to build. So you got a whole culture of young builders out there. What should, what should people gravitate to in your opinion and stay away from or >>Stay away from? That's a good question. I, I think that first of all, you're very right of the, the amount of developers is increasing so quickly. Um, and so we see more specialization. That's why we also see, you know, these SREs that are different than typical application engineering. You know, you get more specialization in job roles. Um, I think if, what I'd say to my 16 year old self is do projects, um, the, I learned most of my, what I've learned just on the job or online trying things, playing with different technologies, actually getting stuff out into the world, um, way more useful than what you'll learn in kind of a college classroom. I think classroom's great to, uh, get a basis, but you need to go out and experiment actually try things. >>You know? I think that's great advice. In fact, I would just say from my experience of doing all the hard stuff and cloud is so great for just saying, okay, I'm done, I'm banning the project. Move on. Yeah. Cause you know, it's not gonna work in the old days. You have to build this data center. I bought all this, you know, people hang on to the old, you know, project and try to force it out there. Now you >>Can launch a project now, >>Instant gratification, it ain't working <laugh> or this is shut it down and then move on to something new. >>Yeah, exactly. Instantly you should be able to do that much more quickly. Right. So >>You're saying get those projects and don't be afraid to shut it down. Mm-hmm <affirmative> that? Do you agree with that? >>Yeah. I think it's ex experiment. Uh, you're probably not gonna hit it rich on the first one. It's probably not gonna be that idea is the genius idea. So don't be afraid to get rid of things and just try over and over again. It's it's number of reps >>That'll win. I was commenting online. Elon Musk was gonna buy Twitter, that whole Twitter thing. And someone said, Hey, you know, what's the, I go look at the product group at Twitter's been so messed up because they actually did get it right on the first time. And we can just a great product. They could never change it because people would freak out and the utility of Twitter. I mean, they gotta add some things, the added button and we all know what they need to add, but the product, it was just like this internal dysfunction, the product team, what are we gonna work on? Don't change the product so that you kind of have there's opportunities out there where you might get the lucky strike right outta the gate. Yeah. Right. You don't know. >>It's almost a curse too. It's you're not gonna hit curse Twitter. You're not gonna hit a rich the second time too. So yeah. >><laugh> Johnny Dallas. Thanks for coming on the cube. Really appreciate it. Give a plug for your company. Um, take a minute to explain what you're working on. What you're look looking for. You hiring funding. Customers. Just give a plug, uh, last minute and kind the last word. >>Yeah. So, um, John Dallas from Ze, if you, uh, need any help with your DevOps, if you're a early startup, you don't have DevOps team, um, or you're trying to deploy across clouds, check us out z.com. Um, we are actively hiring. So if you are a software engineer excited about tools and cloud, or you're interested in helping getting this message out there, hit me up. Um, find us on z.co. >>Yeah. LinkedIn Twitter handle GitHub handle. >>Yeah. I'm the only Johnny on a LinkedIn and GitHub and underscore Johnny Dallas underscore on Twitter. All right. Um, >>Johnny Dallas, the youngest engineer working at Amazon, um, now 20 we're on great new project here in the cube. Builders are all young. They're growing into the business. They got cloud at their, at their back it's tailwind. I wish I was 20. Again, this is a I'm John for your host. Thanks for watching. Thanks. >>Welcome >>Back to the cubes. Live coverage of a AWS summit in San Francisco, California events are back, uh, ADAS summit in New York cities. This summer, the cube will be there as well. Check us out there lot. I'm glad we have events back. It's great to have everyone here. I'm John furry host of the cube. Dr. Matt wood is with me cube alumni now VP of business analytics division of AWS. Matt. Great to see you. Thank >>You, John. Great to be here. >>Appreciate it. I always call you Dr. Matt wood, because Andy jazzy always says Dr. Matt, we >>Would introduce you on the he's the one and only the one and >>Only Dr. Matt wood >>In joke. I love it. >>Andy style. And I think you had walkup music too on, you know, >>Too. Yes. We all have our own personalized walk. >>So talk about your new role. I not new role, but you're running up, um, analytics, business or AWS. What does that consist of right now? >>Sure. So I work, I've got what I consider to be the one of the best jobs in the world. Uh, I get to work with our customers and, uh, the teams at AWS, uh, to build the analytics services that millions of our customers use to, um, uh, slice dice, pivot, uh, better understand their day data, um, look at how they can use that data for, um, reporting, looking backwards and also look at how they can use that data looking forward. So predictive analytics and machine learning. So whether it is, you know, slicing and dicing in the lower level of, uh Hado and the big data engines, or whether you're doing ETR with glue or whether you're visualizing the data in quick side or building models in SageMaker. I got my, uh, fingers in a lot of pies. >>You know, one of the benefits of, uh, having cube coverage with AWS since 2013 is watching the progression. You were on the cube that first year we were at reinvent 2013 and look at how machine learning just exploded onto the scene. You were involved in that from day one is still day one, as you guys say mm-hmm <affirmative>, what's the big thing now. I mean, look at, look at just what happened. Machine learning comes in and then a slew of services come in and got SageMaker became a hot seller, right outta the gate. Mm-hmm <affirmative> the database stuff was kicking butt. So all this is now booming. Mm-hmm <affirmative> that was the real generational changeover for <inaudible> what's the perspective. What's your perspective on, yeah, >>I think how that's evolved. No, I think it's a really good point. I, I totally agree. I think for machine machine learning, um, there was sort of a Renaissance in machine learning and the application of machine learning machine learning as a technology has been around for 50 years, let's say, but, uh, to do machine learning, right? You need like a lot of data, the data needs to be high quality. You need a lot of compute to be able to train those models and you have to be able to evaluate what those mean as you apply them to real world problems. And so the cloud really removed a lot of the constraints. Finally, customers had all of the data that they needed. We gave them services to be able to label that data in a high quality way. There's all the compute. You need to be able to train the models <laugh> and so where you go. >>And so the cloud really enabled this Renaissance with machine learning, and we're seeing honestly, a similar Renaissance with, uh, with data, uh, and analytics. You know, if you look back, you know, five, 10 years, um, analytics was something you did in batch, like your data warehouse ran a analysis to do, uh, reconciliation at the end of the month. And then was it? Yeah. And so that's when you needed it, but today, if your Redshift cluster isn't available, uh, Uber drivers don't turn up door dash deliveries, don't get made. It's analytics is now central to virtually every business and it is central to every virtually every business is digital transformation. Yeah. And be able to take that data from a variety of sources here, or to query it with high performance mm-hmm <affirmative> to be able to actually then start to augment that data with real information, which usually comes from technical experts and domain experts to form, you know, wisdom and information from raw data. That's kind of, uh, what most organizations are trying to do when they kind of go through this analytics journey. It's >>Interesting, you know, Dave LAN and I always talk on the cube, but out, you know, the future and, and you look back, the things we were talking about six years ago are actually happening now. Yeah. And it's not a, a, a, you know, hyped up statement to say digital transformation. It actually's happening now. And there's also times where we bang our fist on the table, say, I really think this is so important. And Dave says, John, you're gonna die on that hill <laugh>. >>And >>So I I'm excited that this year, for the first time I didn't die on that hill. I've been saying data you're right. Data as code is the next infrastructure as code mm-hmm <affirmative>. And Dave's like, what do you mean by that? We're talking about like how data gets and it's happening. So we just had an event on our 80 bus startups.com site mm-hmm <affirmative>, um, a showcase with startups and the theme was data as code and interesting new trends emerging really clearly the role of a data engineer, right? Like an SRE, what an SRE did for cloud. You have a new data engineering role because of the developer on, uh, onboarding is massively increasing exponentially, new developers, data science, scientists are growing mm-hmm <affirmative> and the, but the pipelining and managing and engineering as a system. Yeah. Almost like an operating system >>And as a discipline. >>So what's your reaction to that about this data engineer data as code, because if you have horizontally scalable data, you've gotta be open that's hard. <laugh> mm-hmm <affirmative> and you gotta silo the data that needs to be siloed for compliance and reasons. So that's got a very policy around that. So what's your reaction to data as code and data engineering and >>Phenomenon? Yeah, I think it's, it's a really good point. I think, you know, like with any, with any technology, uh, project inside an organization, you know, success with analytics or machine learning is it's kind of 50% technology and then 50% cultural. And, uh, you have often domain experts. Those are, could be physicians or drug experts, or they could be financial experts or whoever they might be got deep domain expertise. And then you've got technical implementation teams and it's kind of a natural often repulsive force. I don't mean that rudely, but they, they just, they don't talk the same language. And so the more complex the domain and the more complex the technology, the stronger that repulsive force, and it can become very difficult for, um, domain experts to work closely with the technical experts, to be able to actually get business decisions made. And so what data engineering does and data engineering is in some cases team, or it can be a role that you play. >>Uh, it's really allowing those two disciplines to speak the same language it provides. You can think of it as plumbing, but I think of it as like a bridge, it's a bridge between like the technical implementation and the domain experts. And that requires like a very disparate range of skills. You've gotta understand about statistics. You've gotta understand about the implementation. You've gotta understand about the, it, you've gotta understand and understand about the domain. And if you could pull all of that together, that data engineering discipline can be incredibly transformative for an organization, cuz it builds the bridge between those two >>Groups. You know, I was advising some, uh, young computer science students at the sophomore junior level, uh, just a couple weeks ago. And I told 'em, I would ask someone at Amazon, this questions I'll ask you since you're, you've been in the middle of of it for years, they were asking me and I was trying to mentor them on. What, how do you become a data engineer from a practical standpoint, uh, courseware projects to work on how to think, um, not just coding Python cause everyone's coding in Python mm-hmm <affirmative> but what else can they do? So I was trying to help them and I didn't really know the answer myself. I was just trying to like kind of help figure it out with them. So what is the answer in your opinion or the thoughts around advice to young students who want to be data engineers? Cuz data scientists is pretty clear in what that is. Yeah. You use tools, you make visualizations, you manage data, you get answers and insights and apply that to the business. That's an application mm-hmm <affirmative>, that's not the, you know, sta standing up a stack or managing the infrastructure. What, so what does that coding look like? What would your advice be to >>Yeah, I think >>Folks getting into a data engineering role. >>Yeah. I think if you, if you believe this, what I said earlier about like 50% technology, 50% culture, like the, the number one technology to learn as a data engineer is the tools in the cloud, which allow you to aggregate data from virtually any source into something which is incrementally more valuable for the organization. That's really what data engineering is all about. It's about taking from multiple sources. Some people call them silos, but silos indicates that the, the storage is kind of fungible or UND differentiated. That that's really not the case. Success requires you to really purpose built well crafted high performance, low cost engines for all of your data. So understanding those tools and understanding how to use 'em, that's probably the most important technical piece. Um, and yeah, Python and programming and statistics goes along with that, I think. And then the most important cultural part, I think is it's just curiosity. >>Like you want to be able to, as a data engineer, you want to have a natural curiosity that drives you to seek the truth inside an organization, seek the truth of a particular problem and to be able to engage, cuz you're probably, you're gonna have some choice as you go through your career about which domain you end up in, like maybe you're really passionate about healthcare. Maybe you're really just passionate about your transportation or media, whatever it might be. And you can allow that to drive a certain amount of curiosity, but within those roles, like the domains are so broad, you kind of gotta allow your curiosity to develop and lead, to ask the right questions and engage in the right way with your teams. So because you can have all the technical skills in the world, but if you're not able to help the team's truths seek through that curiosity, you simply won't be successful. >>We just had a guest on 20 year old, um, engineer, founder, Johnny Dallas, who was 16 when he worked at Amazon youngest engineer at >>Johnny Dallas is a great name by the that's fantastic. It's his real name? >>It sounds like a football player. Rockstar. I should call Johnny. I have Johnny Johnny cube. Uh it's me. Um, so, but he's young and, and he, he was saying, you know, his advice was just do projects. >>Yeah. That's get hands on. >>Yeah. And I was saying, Hey, I came from the old days though, you get to stand stuff up and you hugged onto the assets. Cause you didn't wanna kill the cause you spent all this money and, and he's like, yeah, with cloud, you can shut it down. If you do a project that's not working and you get bad data, no one's adopting it or you don't want like it anymore. You shut it down. Just something >>Else. Totally >>Instantly abandoned it. Move onto something new. >>Yeah. With progression. Totally. And it, the, the blast radius of, um, decisions is just way reduced, gone. Like we talk a lot about like trying to, you know, in the old world trying to find the resources and get the funding. And it's like, right. I wanna try out this kind of random idea that could be a big deal for the organization. I need 50 million in a new data center. Like you're not gonna get anywhere. You, >>You do a proposal working backwards, document >>Kinds, all that, that sort of stuff got hoops. So, so all of that is gone, but we sometimes forget that a big part of that is just the, the prototyping and the experimentation and the limited blast radius in terms of cost. And honestly, the most important thing is time just being able to jump in there, get fingers on keyboards, just try this stuff out. And that's why at AWS, we have part of the reason we have so many services because we want, when you get into AWS, we want the whole toolbox to be available to every developer. And so, as your ideas developed, you may want to jump from, you know, data that you have, that's already in a database to doing realtime data. Yeah. And then you can just, you have the tools there. And when you want to get into real time data, you don't just have kineses, but you have real time analytics and you can run SQL again, that data is like the, the capabilities and the breadth, like really matter when it comes to prototyping and, and >>That's culture too. That's the culture piece, because what was once a dysfunctional behavior, I'm gonna go off the reservation and try something behind my boss's back or cause now as a side hustle or fun project. Yeah. So for fun, you can just code something. Yeah, >>Totally. I remember my first Haddo project, I found almost literally a decommissioned set of servers in the data center that no one was using. They were super old. They're about to be literally turned off. And I managed to convince the team to leave them on for me for like another month. And I installed her DUP on them and like, got them going. It's like, that just seems crazy to me now that I, I had to go and convince anybody not to turn these service off, but what >>It was like for that, when you came up with elastic map produce, because you said this is too hard, we gotta make it >>Easier. Basically. Yes. <laugh> I was installing Haddo version, you know, beta nor 0.9 or whatever it was. It's like, this is really hard. This is really hard. >>We simpler. All right. Good stuff. I love the, the walk down memory lane and also your advice. Great stuff. I think culture's huge. I think. And that's why I like Adam's keynote to reinvent Adam. Lesky talk about path minds and trail blazers because that's a blast radius impact. Mm-hmm <affirmative> when you can actually have innovation organically just come from anywhere. Yeah, that's totally cool. Totally. Let's get into the products. Serverless has been hot mm-hmm <affirmative> uh, we hear a lot about EKS is hot. Uh, containers are booming. Kubernetes is getting adopted. There's still a lot of work to do there. Lambda cloud native developers are booming, serverless Lambda. How does that impact the analytics piece? Can you share the hot, um, products around how that translates? Sure, absolutely. Yeah, the SageMaker >>Yeah, I think it's a, if you look at kind of the evolution and what customers are asking for, they're not, you know, they don't just want low cost. They don't just want this broad set of services. They don't just want, you know, those services to have deep capabilities. They want those services to have as lower operating cost over time as possible. So we kind of really got it down. We got built a lot of muscle, lot of services about getting up and running and experimenting and prototyping and turning things off and turn turning them on and turning them off. And like, that's all great. But actually the, you really only most projects start something once and then stop something once. And maybe there's an hour in between, or maybe there's a year, but the real expense in terms of time and, and complexity is sometimes in that running cost. Yeah. And so, um, we've heard very loudly and clearly from customers that they want, that, that running cost is just undifferentiated to them and they wanna spend more time on their work and in analytics that is, you know, slicing the data, pivoting the data, combining the data, labeling the data, training their models, uh, you know, running inference against their models, uh, and less time doing the operational pieces. >>So is that why the servers focus is there? >>Yeah, absolutely. It, it dramatically reduces the skill required to run these, uh, workloads of any scale. And it dramatically reduces the UND differentiated, heavy lifting, cuz you get to focus more of the time that you would've spent on the operation on the actual work that you wanna get done. And so if you look at something just like Redshift serverless that we launched a reinvent, you know, there's a kind of a, we have a lot of customers that want to run like a, uh, the cluster and they want to get into the, the weeds where there is benefit. We have a lot of customers that say, you know, I there's no benefit for me though. I just wanna do the analytics. So you run the operational piece, you're the experts we've run. You know, we run 60 million instant startups every single day. Like we do this a lot. Exactly. We understand the operation. I >>Want the answers come on. So >>Just give the answers or just let, give me the notebook or just give the inference prediction. So today for example, we announced, um, you know, serverless inference. So now once you've trained your machine learning model, just, uh, run a few, uh, lines of code or you just click a few buttons and then yeah, you got an inference endpoint that you do not have to manage. And whether you're doing one query against that endpoint, you know, per hour or you're doing, you know, 10 million, but we'll just scale it on the back end. You >>Know, I know we got not a lot of time left, but I want, wanna get your reaction to this. One of the things about the data lakes, not being data swamps has been from what I've been reporting and hearing from customers is that they want to retrain their machine learning algorithm. They want, they need that data. They need the, the, the realtime data and they need the time series data, even though the time has passed, they gotta store in the data lake mm-hmm <affirmative>. So now the data lakes main function is being reusing the data to actually retrain. Yeah, >>That's >>Right. It worked properly. So a lot of, lot of postmortems turn into actually business improvements to make the machine learning smarter, faster. You see that same way. Do you see it the same way? Yeah, >>I think it's, I think it's really interesting. No, I think it's really interesting because you know, we talk it's, it's convenient to kind of think of analytics as a very clear progression from like point a point B, but really it's, you are navigating terrain for which you do not have a map and you need a lot of help to navigate that terrain. Yeah. And so, you know, being, having these services in place, not having to run the operations of those services, being able to have those services be secure and well governed, and we added PII detection today, you know, something you can do automatically, uh, to be able to use their, uh, any unstructured data run queries against that unstructured data. So today we added, you know, um, text extract queries. So you can just say, well, uh, you can scan a badge for example, and say, well, what's the name on this badge? And you don't have to identify where it is. We'll do all of that work for you. So there's a often a, it's more like a branch than it is just a, a normal, uh, a to B path, a linear path. Uh, and that includes loops backwards. And sometimes you gotta get the results and use those to make improvements further upstream. And sometimes you've gotta use those. And when you're downstream, you'll be like, ah, I remember that. And you come back and bring it all together. So awesome. It's um, it's, uh, uh, it's a wonderful >>Work for sure. Dr. Matt wood here in the queue. Got just take the last word and give the update. Why you're here. What's the big news happening that you're announcing here at summit in San Francisco, California, and update on the, the business analytics >>Group? Yeah, I think, you know, one of the, we did a lot of announcements in the keynote, uh, encouraged everyone to take a look at that. Uh, this morning was Swami. Uh, one of the ones I'm most excited about, uh, is the opportunity to be able to take, uh, dashboards, visualizations. We're all used to using these things. We see them in our business intelligence tools, uh, all over the place. However, what we've heard from customers is like, yes, I want those analytics. I want their visualization. I want it to be up to date, but you know, I don't actually want to have to go my tools where I'm actually doing my work to another separate tool to be able to look at that information. And so today we announced, uh, one click public embedding for quick side dashboards. So today you can literally, as easily as embedding a YouTube video, you can take a dashboard that you've built inside, quick site cut and paste the HTML, paste it into your application and that's it. That's all you have to do. It takes seconds and >>It gets updated in real time. >>Updated in real time, it's interactive. You can do everything that you would normally do. You can brand it like this is there's no power by quick site button or anything like that. You can change the colors, make it fit in perfectly with your, with your applications. So that's sitting incredibly powerful way of being able to take a, uh, an analytics capability that today sits inside its own little fiefdom and put it just everywhere. It's, uh, very transformative. >>Awesome. And the, the business is going well. You got the serverless and your tailwind for you there. Good stuff, Dr. Matt with thank you. Coming on the cube >>Anytime. Thank >>You. Okay. This is the cubes cover of eight summit, 2022 in San Francisco, California. I'm John host cube. Stay with us with more coverage of day two after this short break.

>> There's a movement that's sweeping across businesses everywhere here in this country and around the world. And it's all about data. Today businesses are being inundated with data. To the tune of over two and a half million gigabytes that'll be generated in the next 60 seconds alone. What do you do with all that data? To extract insights you typically turn to a data scientist. But not necessarily anymore. At least not exclusively. Today the ability to extract value from data is becoming a shared mission. A team effort that spans the organization extending far more widely than ever before. Today, data science is being democratized. >> Data Sciences for All: It's a Whole New Game. >> Welcome everyone, I'm Katie Linendoll. I'm a technology expert writer and I love reporting on all things tech. My fascination with tech started very young. I began coding when I was 12. Received my networking certs by 18 and a degree in IT and new media from Rochester Institute of Technology. So as you can tell, technology has always been a sure passion of mine. Having grown up in the digital age, I love having a career that keeps me at the forefront of science and technology innovations. I spend equal time in the field being hands on as I do on my laptop conducting in depth research. Whether I'm diving underwater with NASA astronauts, witnessing the new ways which mobile technology can help rebuild the Philippine's economy in the wake of super typhoons, or sharing a first look at the newest iPhones on The Today Show, yesterday, I'm always on the hunt for the latest and greatest tech stories. And that's what brought me here. I'll be your host for the next hour and as we explore the new phenomenon that is taking businesses around the world by storm. And data science continues to become democratized and extends beyond the domain of the data scientist. And why there's also a mandate for all of us to become data literate. Now that data science for all drives our AI culture. And we're going to be able to take to the streets and go behind the scenes as we uncover the factors that are fueling this phenomenon and giving rise to a movement that is reshaping how businesses leverage data. And putting organizations on the road to AI. So coming up, I'll be doing interviews with data scientists. We'll see real world demos and take a look at how IBM is changing the game with an open data science platform. We'll also be joined by legendary statistician Nate Silver, founder and editor-in-chief of FiveThirtyEight. Who will shed light on how a data driven mindset is changing everything from business to our culture. We also have a few people who are joining us in our studio, so thank you guys for joining us. Come on, I can do better than that, right? Live studio audience, the fun stuff. And for all of you during the program, I want to remind you to join that conversation on social media using the hashtag DSforAll, it's data science for all. Share your thoughts on what data science and AI means to you and your business. And, let's dive into a whole new game of data science. Now I'd like to welcome my co-host General Manager IBM Analytics, Rob Thomas. >> Hello, Katie. >> Come on guys. >> Yeah, seriously. >> No one's allowed to be quiet during this show, okay? >> Right. >> Or, I'll start calling people out. So Rob, thank you so much. I think you know this conversation, we're calling it a data explosion happening right now. And it's nothing new. And when you and I chatted about it. You've been talking about this for years. You have to ask, is this old news at this point? >> Yeah, I mean, well first of all, the data explosion is not coming, it's here. And everybody's in the middle of it right now. What is different is the economics have changed. And the scale and complexity of the data that organizations are having to deal with has changed. And to this day, 80% of the data in the world still sits behind corporate firewalls. So, that's becoming a problem. It's becoming unmanageable. IT struggles to manage it. The business can't get everything they need. Consumers can't consume it when they want. So we have a challenge here. >> It's challenging in the world of unmanageable. Crazy complexity. If I'm sitting here as an IT manager of my business, I'm probably thinking to myself, this is incredibly frustrating. How in the world am I going to get control of all this data? And probably not just me thinking it. Many individuals here as well. >> Yeah, indeed. Everybody's thinking about how am I going to put data to work in my organization in a way I haven't done before. Look, you've got to have the right expertise, the right tools. The other thing that's happening in the market right now is clients are dealing with multi cloud environments. So data behind the firewall in private cloud, multiple public clouds. And they have to find a way. How am I going to pull meaning out of this data? And that brings us to data science and AI. That's how you get there. >> I understand the data science part but I think we're all starting to hear more about AI. And it's incredible that this buzz word is happening. How do businesses adopt to this AI growth and boom and trend that's happening in this world right now? >> Well, let me define it this way. Data science is a discipline. And machine learning is one technique. And then AI puts both machine learning into practice and applies it to the business. So this is really about how getting your business where it needs to go. And to get to an AI future, you have to lay a data foundation today. I love the phrase, "there's no AI without IA." That means you're not going to get to AI unless you have the right information architecture to start with. >> Can you elaborate though in terms of how businesses can really adopt AI and get started. >> Look, I think there's four things you have to do if you're serious about AI. One is you need a strategy for data acquisition. Two is you need a modern data architecture. Three is you need pervasive automation. And four is you got to expand job roles in the organization. >> Data acquisition. First pillar in this you just discussed. Can we start there and explain why it's so critical in this process? >> Yeah, so let's think about how data acquisition has evolved through the years. 15 years ago, data acquisition was about how do I get data in and out of my ERP system? And that was pretty much solved. Then the mobile revolution happens. And suddenly you've got structured and non-structured data. More than you've ever dealt with. And now you get to where we are today. You're talking terabytes, petabytes of data. >> [Katie] Yottabytes, I heard that word the other day. >> I heard that too. >> Didn't even know what it meant. >> You know how many zeros that is? >> I thought we were in Star Wars. >> Yeah, I think it's a lot of zeroes. >> Yodabytes, it's new. >> So, it's becoming more and more complex in terms of how you acquire data. So that's the new data landscape that every client is dealing with. And if you don't have a strategy for how you acquire that and manage it, you're not going to get to that AI future. >> So a natural segue, if you are one of these businesses, how do you build for the data landscape? >> Yeah, so the question I always hear from customers is we need to evolve our data architecture to be ready for AI. And the way I think about that is it's really about moving from static data repositories to more of a fluid data layer. >> And we continue with the architecture. New data architecture is an interesting buzz word to hear. But it's also one of the four pillars. So if you could dive in there. >> Yeah, I mean it's a new twist on what I would call some core data science concepts. For example, you have to leverage tools with a modern, centralized data warehouse. But your data warehouse can't be stagnant to just what's right there. So you need a way to federate data across different environments. You need to be able to bring your analytics to the data because it's most efficient that way. And ultimately, it's about building an optimized data platform that is designed for data science and AI. Which means it has to be a lot more flexible than what clients have had in the past. >> All right. So we've laid out what you need for driving automation. But where does the machine learning kick in? >> Machine learning is what gives you the ability to automate tasks. And I think about machine learning. It's about predicting and automating. And this will really change the roles of data professionals and IT professionals. For example, a data scientist cannot possibly know every algorithm or every model that they could use. So we can automate the process of algorithm selection. Another example is things like automated data matching. Or metadata creation. Some of these things may not be exciting but they're hugely practical. And so when you think about the real use cases that are driving return on investment today, it's things like that. It's automating the mundane tasks. >> Let's go ahead and come back to something that you mentioned earlier because it's fascinating to be talking about this AI journey, but also significant is the new job roles. And what are those other participants in the analytics pipeline? >> Yeah I think we're just at the start of this idea of new job roles. We have data scientists. We have data engineers. Now you see machine learning engineers. Application developers. What's really happening is that data scientists are no longer allowed to work in their own silo. And so the new job roles is about how does everybody have data first in their mind? And then they're using tools to automate data science, to automate building machine learning into applications. So roles are going to change dramatically in organizations. >> I think that's confusing though because we have several organizations who saying is that highly specialized roles, just for data science? Or is it applicable to everybody across the board? >> Yeah, and that's the big question, right? Cause everybody's thinking how will this apply? Do I want this to be just a small set of people in the organization that will do this? But, our view is data science has to for everybody. It's about bring data science to everybody as a shared mission across the organization. Everybody in the company has to be data literate. And participate in this journey. >> So overall, group effort, has to be a common goal, and we all need to be data literate across the board. >> Absolutely. >> Done deal. But at the end of the day, it's kind of not an easy task. >> It's not. It's not easy but it's maybe not as big of a shift as you would think. Because you have to put data in the hands of people that can do something with it. So, it's very basic. Give access to data. Data's often locked up in a lot of organizations today. Give people the right tools. Embrace the idea of choice or diversity in terms of those tools. That gets you started on this path. >> It's interesting to hear you say essentially you need to train everyone though across the board when it comes to data literacy. And I think people that are coming into the work force don't necessarily have a background or a degree in data science. So how do you manage? >> Yeah, so in many cases that's true. I will tell you some universities are doing amazing work here. One example, University of California Berkeley. They offer a course for all majors. So no matter what you're majoring in, you have a course on foundations of data science. How do you bring data science to every role? So it's starting to happen. We at IBM provide data science courses through CognitiveClass.ai. It's for everybody. It's free. And look, if you want to get your hands on code and just dive right in, you go to datascience.ibm.com. The key point is this though. It's more about attitude than it is aptitude. I think anybody can figure this out. But it's about the attitude to say we're putting data first and we're going to figure out how to make this real in our organization. >> I also have to give a shout out to my alma mater because I have heard that there is an offering in MS in data analytics. And they are always on the forefront of new technologies and new majors and on trend. And I've heard that the placement behind those jobs, people graduating with the MS is high. >> I'm sure it's very high. >> So go Tigers. All right, tangential. Let me get back to something else you touched on earlier because you mentioned that a number of customers ask you how in the world do I get started with AI? It's an overwhelming question. Where do you even begin? What do you tell them? >> Yeah, well things are moving really fast. But the good thing is most organizations I see, they're already on the path, even if they don't know it. They might have a BI practice in place. They've got data warehouses. They've got data lakes. Let me give you an example. AMC Networks. They produce a lot of the shows that I'm sure you watch Katie. >> [Katie] Yes, Breaking Bad, Walking Dead, any fans? >> [Rob] Yeah, we've got a few. >> [Katie] Well you taught me something I didn't even know. Because it's amazing how we have all these different industries, but yet media in itself is impacted too. And this is a good example. >> Absolutely. So, AMC Networks, think about it. They've got ads to place. They want to track viewer behavior. What do people like? What do they dislike? So they have to optimize every aspect of their business from marketing campaigns to promotions to scheduling to ads. And their goal was transform data into business insights and really take the burden off of their IT team that was heavily burdened by obviously a huge increase in data. So their VP of BI took the approach of using machine learning to process large volumes of data. They used a platform that was designed for AI and data processing. It's the IBM analytics system where it's a data warehouse, data science tools are built in. It has in memory data processing. And just like that, they were ready for AI. And they're already seeing that impact in their business. >> Do you think a movement of that nature kind of presses other media conglomerates and organizations to say we need to be doing this too? >> I think it's inevitable that everybody, you're either going to be playing, you're either going to be leading, or you'll be playing catch up. And so, as we talk to clients we think about how do you start down this path now, even if you have to iterate over time? Because otherwise you're going to wake up and you're going to be behind. >> One thing worth noting is we've talked about analytics to the data. It's analytics first to the data, not the other way around. >> Right. So, look. We as a practice, we say you want to bring data to where the data sits. Because it's a lot more efficient that way. It gets you better outcomes in terms of how you train models and it's more efficient. And we think that leads to better outcomes. Other organization will say, "Hey move the data around." And everything becomes a big data movement exercise. But once an organization has started down this path, they're starting to get predictions, they want to do it where it's really easy. And that means analytics applied right where the data sits. >> And worth talking about the role of the data scientist in all of this. It's been called the hot job of the decade. And a Harvard Business Review even dubbed it the sexiest job of the 21st century. >> Yes. >> I want to see this on the cover of Vogue. Like I want to see the first data scientist. Female preferred, on the cover of Vogue. That would be amazing. >> Perhaps you can. >> People agree. So what changes for them? Is this challenging in terms of we talk data science for all. Where do all the data science, is it data science for everyone? And how does it change everything? >> Well, I think of it this way. AI gives software super powers. It really does. It changes the nature of software. And at the center of that is data scientists. So, a data scientist has a set of powers that they've never had before in any organization. And that's why it's a hot profession. Now, on one hand, this has been around for a while. We've had actuaries. We've had statisticians that have really transformed industries. But there are a few things that are new now. We have new tools. New languages. Broader recognition of this need. And while it's important to recognize this critical skill set, you can't just limit it to a few people. This is about scaling it across the organization. And truly making it accessible to all. >> So then do we need more data scientists? Or is this something you train like you said, across the board? >> Well, I think you want to do a little bit of both. We want more. But, we can also train more and make the ones we have more productive. The way I think about it is there's kind of two markets here. And we call it clickers and coders. >> [Katie] I like that. That's good. >> So, let's talk about what that means. So clickers are basically somebody that wants to use tools. Create models visually. It's drag and drop. Something that's very intuitive. Those are the clickers. Nothing wrong with that. It's been valuable for years. There's a new crop of data scientists. They want to code. They want to build with the latest open source tools. They want to write in Python or R. These are the coders. And both approaches are viable. Both approaches are critical. Organizations have to have a way to meet the needs of both of those types. And there's not a lot of things available today that do that. >> Well let's keep going on that. Because I hear you talking about the data scientists role and how it's critical to success, but with the new tools, data science and analytics skills can extend beyond the domain of just the data scientist. >> That's right. So look, we're unifying coders and clickers into a single platform, which we call IBM Data Science Experience. And as the demand for data science expertise grows, so does the need for these kind of tools. To bring them into the same environment. And my view is if you have the right platform, it enables the organization to collaborate. And suddenly you've changed the nature of data science from an individual sport to a team sport. >> So as somebody that, my background is in IT, the question is really is this an additional piece of what IT needs to do in 2017 and beyond? Or is it just another line item to the budget? >> So I'm afraid that some people might view it that way. As just another line item. But, I would challenge that and say data science is going to reinvent IT. It's going to change the nature of IT. And every organization needs to think about what are the skills that are critical? How do we engage a broader team to do this? Because once they get there, this is the chance to reinvent how they're performing IT. >> [Katie] Challenging or not? >> Look it's all a big challenge. Think about everything IT organizations have been through. Some of them were late to things like mobile, but then they caught up. Some were late to cloud, but then they caught up. I would just urge people, don't be late to data science. Use this as your chance to reinvent IT. Start with this notion of clickers and coders. This is a seminal moment. Much like mobile and cloud was. So don't be late. >> And I think it's critical because it could be so costly to wait. And Rob and I were even chatting earlier how data analytics is just moving into all different kinds of industries. And I can tell you even personally being effected by how important the analysis is in working in pediatric cancer for the last seven years. I personally implement virtual reality headsets to pediatric cancer hospitals across the country. And it's great. And it's working phenomenally. And the kids are amazed. And the staff is amazed. But the phase two of this project is putting in little metrics in the hardware that gather the breathing, the heart rate to show that we have data. Proof that we can hand over to the hospitals to continue making this program a success. So just in-- >> That's a great example. >> An interesting example. >> Saving lives? >> Yes. >> That's also applying a lot of what we talked about. >> Exciting stuff in the world of data science. >> Yes. Look, I just add this is an existential moment for every organization. Because what you do in this area is probably going to define how competitive you are going forward. And think about if you don't do something. What if one of your competitors goes and creates an application that's more engaging with clients? So my recommendation is start small. Experiment. Learn. Iterate on projects. Define the business outcomes. Then scale up. It's very doable. But you've got to take the first step. >> First step always critical. And now we're going to get to the fun hands on part of our story. Because in just a moment we're going to take a closer look at what data science can deliver. And where organizations are trying to get to. All right. Thank you Rob and now we've been joined by Siva Anne who is going to help us navigate this demo. First, welcome Siva. Give him a big round of applause. Yeah. All right, Rob break down what we're going to be looking at. You take over this demo. >> All right. So this is going to be pretty interesting. So Siva is going to take us through. So he's going to play the role of a financial adviser. Who wants to help better serve clients through recommendations. And I'm going to really illustrate three things. One is how do you federate data from multiple data sources? Inside the firewall, outside the firewall. How do you apply machine learning to predict and to automate? And then how do you move analytics closer to your data? So, what you're seeing here is a custom application for an investment firm. So, Siva, our financial adviser, welcome. So you can see at the top, we've got market data. We pulled that from an external source. And then we've got Siva's calendar in the middle. He's got clients on the right side. So page down, what else do you see down there Siva? >> [Siva] I can see the recent market news. And in here I can see that JP Morgan is calling for a US dollar rebound in the second half of the year. And, I have upcoming meeting with Leo Rakes. I can get-- >> [Rob] So let's go in there. Why don't you click on Leo Rakes. So, you're sitting at your desk, you're deciding how you're going to spend the day. You know you have a meeting with Leo. So you click on it. You immediately see, all right, so what do we know about him? We've got data governance implemented. So we know his age, we know his degree. We can see he's not that aggressive of a trader. Only six trades in the last few years. But then where it gets interesting is you go to the bottom. You start to see predicted industry affinity. Where did that come from? How do we have that? >> [Siva] So these green lines and red arrows here indicate the trending affinity of Leo Rakes for particular industry stocks. What we've done here is we've built machine learning models using customer's demographic data, his stock portfolios, and browsing behavior to build a model which can predict his affinity for a particular industry. >> [Rob] Interesting. So, I like to think of this, we call it celebrity experiences. So how do you treat every customer like they're a celebrity? So to some extent, we're reading his mind. Because without asking him, we know that he's going to have an affinity for auto stocks. So we go down. Now we look at his portfolio. You can see okay, he's got some different holdings. He's got Amazon, Google, Apple, and then he's got RACE, which is the ticker for Ferrari. You can see that's done incredibly well. And so, as a financial adviser, you look at this and you say, all right, we know he loves auto stocks. Ferrari's done very well. Let's create a hedge. Like what kind of security would interest him as a hedge against his position for Ferrari? Could we go figure that out? >> [Siva] Yes. Given I know that he's gotten an affinity for auto stocks, and I also see that Ferrari has got some terminus gains, I want to lock in these gains by hedging. And I want to do that by picking a auto stock which has got negative correlation with Ferrari. >> [Rob] So this is where we get to the idea of in database analytics. Cause you start clicking that and immediately we're getting instant answers of what's happening. So what did we find here? We're going to compare Ferrari and Honda. >> [Siva] I'm going to compare Ferrari with Honda. And what I see here instantly is that Honda has got a negative correlation with Ferrari, which makes it a perfect mix for his stock portfolio. Given he has an affinity for auto stocks and it correlates negatively with Ferrari. >> [Rob] These are very powerful tools at the hand of a financial adviser. You think about it. As a financial adviser, you wouldn't think about federating data, machine learning, pretty powerful. >> [Siva] Yes. So what we have seen here is that using the common SQL engine, we've been able to federate queries across multiple data sources. Db2 Warehouse in the cloud, IBM's Integrated Analytic System, and Hortonworks powered Hadoop platform for the new speeds. We've been able to use machine learning to derive innovative insights about his stock affinities. And drive the machine learning into the appliance. Closer to where the data resides to deliver high performance analytics. >> [Rob] At scale? >> [Siva] We're able to run millions of these correlations across stocks, currency, other factors. And even score hundreds of customers for their affinities on a daily basis. >> That's great. Siva, thank you for playing the role of financial adviser. So I just want to recap briefly. Cause this really powerful technology that's really simple. So we federated, we aggregated multiple data sources from all over the web and internal systems. And public cloud systems. Machine learning models were built that predicted Leo's affinity for a certain industry. In this case, automotive. And then you see when you deploy analytics next to your data, even a financial adviser, just with the click of a button is getting instant answers so they can go be more productive in their next meeting. This whole idea of celebrity experiences for your customer, that's available for everybody, if you take advantage of these types of capabilities. Katie, I'll hand it back to you. >> Good stuff. Thank you Rob. Thank you Siva. Powerful demonstration on what we've been talking about all afternoon. And thank you again to Siva for helping us navigate. Should be give him one more round of applause? We're going to be back in just a moment to look at how we operationalize all of this data. But in first, here's a message from me. If you're a part of a line of business, your main fear is disruption. You know data is the new goal that can create huge amounts of value. So does your competition. And they may be beating you to it. You're convinced there are new business models and revenue sources hidden in all the data. You just need to figure out how to leverage it. But with the scarcity of data scientists, you really can't rely solely on them. You may need more people throughout the organization that have the ability to extract value from data. And as a data science leader or data scientist, you have a lot of the same concerns. You spend way too much time looking for, prepping, and interpreting data and waiting for models to train. You know you need to operationalize the work you do to provide business value faster. What you want is an easier way to do data prep. And rapidly build models that can be easily deployed, monitored and automatically updated. So whether you're a data scientist, data science leader, or in a line of business, what's the solution? What'll it take to transform the way you work? That's what we're going to explore next. All right, now it's time to delve deeper into the nuts and bolts. The nitty gritty of operationalizing data science and creating a data driven culture. How do you actually do that? Well that's what these experts are here to share with us. I'm joined by Nir Kaldero, who's head of data science at Galvanize, which is an education and training organization. Tricia Wang, who is co-founder of Sudden Compass, a consultancy that helps companies understand people with data. And last, but certainly not least, Michael Li, founder and CEO of Data Incubator, which is a data science train company. All right guys. Shall we get right to it? >> All right. >> So data explosion happening right now. And we are seeing it across the board. I just shared an example of how it's impacting my philanthropic work in pediatric cancer. But you guys each have so many unique roles in your business life. How are you seeing it just blow up in your fields? Nir, your thing? >> Yeah, for example like in Galvanize we train many Fortune 500 companies. And just by looking at the demand of companies that wants us to help them go through this digital transformation is mind-blowing. Data point by itself. >> Okay. Well what we're seeing what's going on is that data science like as a theme, is that it's actually for everyone now. But what's happening is that it's actually meeting non technical people. But what we're seeing is that when non technical people are implementing these tools or coming at these tools without a base line of data literacy, they're often times using it in ways that distance themselves from the customer. Because they're implementing data science tools without a clear purpose, without a clear problem. And so what we do at Sudden Compass is that we work with companies to help them embrace and understand the complexity of their customers. Because often times they are misusing data science to try and flatten their understanding of the customer. As if you can just do more traditional marketing. Where you're putting people into boxes. And I think the whole ROI of data is that you can now understand people's relationships at a much more complex level at a greater scale before. But we have to do this with basic data literacy. And this has to involve technical and non technical people. >> Well you can have all the data in the world, and I think it speaks to, if you're not doing the proper movement with it, forget it. It means nothing at the same time. >> No absolutely. I mean, I think that when you look at the huge explosion in data, that comes with it a huge explosion in data experts. Right, we call them data scientists, data analysts. And sometimes they're people who are very, very talented, like the people here. But sometimes you have people who are maybe re-branding themselves, right? Trying to move up their title one notch to try to attract that higher salary. And I think that that's one of the things that customers are coming to us for, right? They're saying, hey look, there are a lot of people that call themselves data scientists, but we can't really distinguish. So, we have sort of run a fellowship where you help companies hire from a really talented group of folks, who are also truly data scientists and who know all those kind of really important data science tools. And we also help companies internally. Fortune 500 companies who are looking to grow that data science practice that they have. And we help clients like McKinsey, BCG, Bain, train up their customers, also their clients, also their workers to be more data talented. And to build up that data science capabilities. >> And Nir, this is something you work with a lot. A lot of Fortune 500 companies. And when we were speaking earlier, you were saying many of these companies can be in a panic. >> Yeah. >> Explain that. >> Yeah, so you know, not all Fortune 500 companies are fully data driven. And we know that the winners in this fourth industrial revolution, which I like to call the machine intelligence revolution, will be companies who navigate and transform their organization to unlock the power of data science and machine learning. And the companies that are not like that. Or not utilize data science and predictive power well, will pretty much get shredded. So they are in a panic. >> Tricia, companies have to deal with data behind the firewall and in the new multi cloud world. How do organizations start to become driven right to the core? >> I think the most urgent question to become data driven that companies should be asking is how do I bring the complex reality that our customers are experiencing on the ground in to a corporate office? Into the data models. So that question is critical because that's how you actually prevent any big data disasters. And that's how you leverage big data. Because when your data models are really far from your human models, that's when you're going to do things that are really far off from how, it's going to not feel right. That's when Tesco had their terrible big data disaster that they're still recovering from. And so that's why I think it's really important to understand that when you implement big data, you have to further embrace thick data. The qualitative, the emotional stuff, that is difficult to quantify. But then comes the difficult art and science that I think is the next level of data science. Which is that getting non technical and technical people together to ask how do we find those unknown nuggets of insights that are difficult to quantify? Then, how do we do the next step of figuring out how do you mathematically scale those insights into a data model? So that actually is reflective of human understanding? And then we can start making decisions at scale. But you have to have that first. >> That's absolutely right. And I think that when we think about what it means to be a data scientist, right? I always think about it in these sort of three pillars. You have the math side. You have to have that kind of stats, hardcore machine learning background. You have the programming side. You don't work with small amounts of data. You work with large amounts of data. You've got to be able to type the code to make those computers run. But then the last part is that human element. You have to understand the domain expertise. You have to understand what it is that I'm actually analyzing. What's the business proposition? And how are the clients, how are the users actually interacting with the system? That human element that you were talking about. And I think having somebody who understands all of those and not just in isolation, but is able to marry that understanding across those different topics, that's what makes a data scientist. >> But I find that we don't have people with those skill sets. And right now the way I see teams being set up inside companies is that they're creating these isolated data unicorns. These data scientists that have graduated from your programs, which are great. But, they don't involve the people who are the domain experts. They don't involve the designers, the consumer insight people, the people, the salespeople. The people who spend time with the customers day in and day out. Somehow they're left out of the room. They're consulted, but they're not a stakeholder. >> Can I actually >> Yeah, yeah please. >> Can I actually give a quick example? So for example, we at Galvanize train the executives and the managers. And then the technical people, the data scientists and the analysts. But in order to actually see all of the RY behind the data, you also have to have a creative fluid conversation between non technical and technical people. And this is a major trend now. And there's a major gap. And we need to increase awareness and kind of like create a new, kind of like environment where technical people also talks seamlessly with non technical ones. >> [Tricia] We call-- >> That's one of the things that we see a lot. Is one of the trends in-- >> A major trend. >> data science training is it's not just for the data science technical experts. It's not just for one type of person. So a lot of the training we do is sort of data engineers. People who are more on the software engineering side learning more about the stats of math. And then people who are sort of traditionally on the stat side learning more about the engineering. And then managers and people who are data analysts learning about both. >> Michael, I think you said something that was of interest too because I think we can look at IBM Watson as an example. And working in healthcare. The human component. Because often times we talk about machine learning and AI, and data and you get worried that you still need that human component. Especially in the world of healthcare. And I think that's a very strong point when it comes to the data analysis side. Is there any particular example you can speak to of that? >> So I think that there was this really excellent paper a while ago talking about all the neuro net stuff and trained on textual data. So looking at sort of different corpuses. And they found that these models were highly, highly sexist. They would read these corpuses and it's not because neuro nets themselves are sexist. It's because they're reading the things that we write. And it turns out that we write kind of sexist things. And they would sort of find all these patterns in there that were sort of latent, that had a lot of sort of things that maybe we would cringe at if we sort of saw. And I think that's one of the really important aspects of the human element, right? It's being able to come in and sort of say like, okay, I know what the biases of the system are, I know what the biases of the tools are. I need to figure out how to use that to make the tools, make the world a better place. And like another area where this comes up all the time is lending, right? So the federal government has said, and we have a lot of clients in the financial services space, so they're constantly under these kind of rules that they can't make discriminatory lending practices based on a whole set of protected categories. Race, sex, gender, things like that. But, it's very easy when you train a model on credit scores to pick that up. And then to have a model that's inadvertently sexist or racist. And that's where you need the human element to come back in and say okay, look, you're using the classic example would be zip code, you're using zip code as a variable. But when you look at it, zip codes actually highly correlated with race. And you can't do that. So you may inadvertently by sort of following the math and being a little naive about the problem, inadvertently introduce something really horrible into a model and that's where you need a human element to sort of step in and say, okay hold on. Slow things down. This isn't the right way to go. >> And the people who have -- >> I feel like, I can feel her ready to respond. >> Yes, I'm ready. >> She's like let me have at it. >> And the people here it is. And the people who are really great at providing that human intelligence are social scientists. We are trained to look for bias and to understand bias in data. Whether it's quantitative or qualitative. And I really think that we're going to have less of these kind of problems if we had more integrated teams. If it was a mandate from leadership to say no data science team should be without a social scientist, ethnographer, or qualitative researcher of some kind, to be able to help see these biases. >> The talent piece is actually the most crucial-- >> Yeah. >> one here. If you look about how to enable machine intelligence in organization there are the pillars that I have in my head which is the culture, the talent and the technology infrastructure. And I believe and I saw in working very closely with the Fortune 100 and 200 companies that the talent piece is actually the most important crucial hard to get. >> [Tricia] I totally agree. >> It's absolutely true. Yeah, no I mean I think that's sort of like how we came up with our business model. Companies were basically saying hey, I can't hire data scientists. And so we have a fellowship where we get 2,000 applicants each quarter. We take the top 2% and then we sort of train them up. And we work with hiring companies who then want to hire from that population. And so we're sort of helping them solve that problem. And the other half of it is really around training. Cause with a lot of industries, especially if you're sort of in a more regulated industry, there's a lot of nuances to what you're doing. And the fastest way to develop that data science or AI talent may not necessarily be to hire folks who are coming out of a PhD program. It may be to take folks internally who have a lot of that domain knowledge that you have and get them trained up on those data science techniques. So we've had large insurance companies come to us and say hey look, we hire three or four folks from you a quarter. That doesn't move the needle for us. What we really need is take the thousand actuaries and statisticians that we have and get all of them trained up to become a data scientist and become data literate in this new open source world. >> [Katie] Go ahead. >> All right, ladies first. >> Go ahead. >> Are you sure? >> No please, fight first. >> Go ahead. >> Go ahead Nir. >> So this is actually a trend that we have been seeing in the past year or so that companies kind of like start to look how to upscale and look for talent within the organization. So they can actually move them to become more literate and navigate 'em from analyst to data scientist. And from data scientist to machine learner. So this is actually a trend that is happening already for a year or so. >> Yeah, but I also find that after they've gone through that training in getting people skilled up in data science, the next problem that I get is executives coming to say we've invested in all of this. We're still not moving the needle. We've already invested in the right tools. We've gotten the right skills. We have enough scale of people who have these skills. Why are we not moving the needle? And what I explain to them is look, you're still making decisions in the same way. And you're still not involving enough of the non technical people. Especially from marketing, which is now, the CMO's are much more responsible for driving growth in their companies now. But often times it's so hard to change the old way of marketing, which is still like very segmentation. You know, demographic variable based, and we're trying to move people to say no, you have to understand the complexity of customers and not put them in boxes. >> And I think underlying a lot of this discussion is this question of culture, right? >> Yes. >> Absolutely. >> How do you build a data driven culture? And I think that that culture question, one of the ways that comes up quite often in especially in large, Fortune 500 enterprises, is that they are very, they're not very comfortable with sort of example, open source architecture. Open source tools. And there is some sort of residual bias that that's somehow dangerous. So security vulnerability. And I think that that's part of the cultural challenge that they often have in terms of how do I build a more data driven organization? Well a lot of the talent really wants to use these kind of tools. And I mean, just to give you an example, we are partnering with one of the major cloud providers to sort of help make open source tools more user friendly on their platform. So trying to help them attract the best technologists to use their platform because they want and they understand the value of having that kind of open source technology work seamlessly on their platforms. So I think that just sort of goes to show you how important open source is in this movement. And how much large companies and Fortune 500 companies and a lot of the ones we work with have to embrace that. >> Yeah, and I'm seeing it in our work. Even when we're working with Fortune 500 companies, is that they've already gone through the first phase of data science work. Where I explain it was all about the tools and getting the right tools and architecture in place. And then companies started moving into getting the right skill set in place. Getting the right talent. And what you're talking about with culture is really where I think we're talking about the third phase of data science, which is looking at communication of these technical frameworks so that we can get non technical people really comfortable in the same room with data scientists. That is going to be the phase, that's really where I see the pain point. And that's why at Sudden Compass, we're really dedicated to working with each other to figure out how do we solve this problem now? >> And I think that communication between the technical stakeholders and management and leadership. That's a very critical piece of this. You can't have a successful data science organization without that. >> Absolutely. >> And I think that actually some of the most popular trainings we've had recently are from managers and executives who are looking to say, how do I become more data savvy? How do I figure out what is this data science thing and how do I communicate with my data scientists? >> You guys made this way too easy. I was just going to get some popcorn and watch it play out. >> Nir, last 30 seconds. I want to leave you with an opportunity to, anything you want to add to this conversation? >> I think one thing to conclude is to say that companies that are not data driven is about time to hit refresh and figure how they transition the organization to become data driven. To become agile and nimble so they can actually see what opportunities from this important industrial revolution. Otherwise, unfortunately they will have hard time to survive. >> [Katie] All agreed? >> [Tricia] Absolutely, you're right. >> Michael, Trish, Nir, thank you so much. Fascinating discussion. And thank you guys again for joining us. We will be right back with another great demo. Right after this. >> Thank you Katie. >> Once again, thank you for an excellent discussion. Weren't they great guys? And thank you for everyone who's tuning in on the live webcast. As you can hear, we have an amazing studio audience here. And we're going to keep things moving. I'm now joined by Daniel Hernandez and Siva Anne. And we're going to turn our attention to how you can deliver on what they're talking about using data science experience to do data science faster. >> Thank you Katie. Siva and I are going to spend the next 10 minutes showing you how you can deliver on what they were saying using the IBM Data Science Experience to do data science faster. We'll demonstrate through new features we introduced this week how teams can work together more effectively across the entire analytics life cycle. How you can take advantage of any and all data no matter where it is and what it is. How you could use your favorite tools from open source. And finally how you could build models anywhere and employ them close to where your data is. Remember the financial adviser app Rob showed you? To build an app like that, we needed a team of data scientists, developers, data engineers, and IT staff to collaborate. We do this in the Data Science Experience through a concept we call projects. When I create a new project, I can now use the new Github integration feature. We're doing for data science what we've been doing for developers for years. Distributed teams can work together on analytics projects. And take advantage of Github's version management and change management features. This is a huge deal. Let's explore the project we created for the financial adviser app. As you can see, our data engineer Joane, our developer Rob, and others are collaborating this project. Joane got things started by bringing together the trusted data sources we need to build the app. Taking a closer look at the data, we see that our customer and profile data is stored on our recently announced IBM Integrated Analytics System, which runs safely behind our firewall. We also needed macro economic data, which she was able to find in the Federal Reserve. And she stored it in our Db2 Warehouse on Cloud. And finally, she selected stock news data from NASDAQ.com and landed that in a Hadoop cluster, which happens to be powered by Hortonworks. We added a new feature to the Data Science Experience so that when it's installed with Hortonworks, it automatically uses a need of security and governance controls within the cluster so your data is always secure and safe. Now we want to show you the news data we stored in the Hortonworks cluster. This is the mean administrative console. It's powered by an open source project called Ambari. And here's the news data. It's in parquet files stored in HDFS, which happens to be a distributive file system. To get the data from NASDAQ into our cluster, we used IBM's BigIntegrate and BigQuality to create automatic data pipelines that acquire, cleanse, and ingest that news data. Once the data's available, we use IBM's Big SQL to query that data using SQL statements that are much like the ones we would use for any relation of data, including the data that we have in the Integrated Analytics System and Db2 Warehouse on Cloud. This and the federation capabilities that Big SQL offers dramatically simplifies data acquisition. Now we want to show you how we support a brand new tool that we're excited about. Since we launched last summer, the Data Science Experience has supported Jupyter and R for data analysis and visualization. In this week's update, we deeply integrated another great open source project called Apache Zeppelin. It's known for having great visualization support, advanced collaboration features, and is growing in popularity amongst the data science community. This is an example of Apache Zeppelin and the notebook we created through it to explore some of our data. Notice how wonderful and easy the data visualizations are. Now we want to walk you through the Jupyter notebook we created to explore our customer preference for stocks. We use notebooks to understand and explore data. To identify the features that have some predictive power. Ultimately, we're trying to assess what ultimately is driving customer stock preference. Here we did the analysis to identify the attributes of customers that are likely to purchase auto stocks. We used this understanding to build our machine learning model. For building machine learning models, we've always had tools integrated into the Data Science Experience. But sometimes you need to use tools you already invested in. Like our very own SPSS as well as SAS. Through new import feature, you can easily import those models created with those tools. This helps you avoid vendor lock-in, and simplify the development, training, deployment, and management of all your models. To build the models we used in app, we could have coded, but we prefer a visual experience. We used our customer profile data in the Integrated Analytic System. Used the Auto Data Preparation to cleanse our data. Choose the binary classification algorithms. Let the Data Science Experience evaluate between logistic regression and gradient boosted tree. It's doing the heavy work for us. As you can see here, the Data Science Experience generated performance metrics that show us that the gradient boosted tree is the best performing algorithm for the data we gave it. Once we save this model, it's automatically deployed and available for developers to use. Any application developer can take this endpoint and consume it like they would any other API inside of the apps they built. We've made training and creating machine learning models super simple. But what about the operations? A lot of companies are struggling to ensure their model performance remains high over time. In our financial adviser app, we know that customer data changes constantly, so we need to always monitor model performance and ensure that our models are retrained as is necessary. This is a dashboard that shows the performance of our models and lets our teams monitor and retrain those models so that they're always performing to our standards. So far we've been showing you the Data Science Experience available behind the firewall that we're using to build and train models. Through a new publish feature, you can build models and deploy them anywhere. In another environment, private, public, or anywhere else with just a few clicks. So here we're publishing our model to the Watson machine learning service. It happens to be in the IBM cloud. And also deeply integrated with our Data Science Experience. After publishing and switching to the Watson machine learning service, you can see that our stock affinity and model that we just published is there and ready for use. So this is incredibly important. I just want to say it again. The Data Science Experience allows you to train models behind your own firewall, take advantage of your proprietary and sensitive data, and then deploy those models wherever you want with ease. So summarize what we just showed you. First, IBM's Data Science Experience supports all teams. You saw how our data engineer populated our project with trusted data sets. Our data scientists developed, trained, and tested a machine learning model. Our developers used APIs to integrate machine learning into their apps. And how IT can use our Integrated Model Management dashboard to monitor and manage model performance. Second, we support all data. On premises, in the cloud, structured, unstructured, inside of your firewall, and outside of it. We help you bring analytics and governance to where your data is. Third, we support all tools. The data science tools that you depend on are readily available and deeply integrated. This includes capabilities from great partners like Hortonworks. And powerful tools like our very own IBM SPSS. And fourth, and finally, we support all deployments. You can build your models anywhere, and deploy them right next to where your data is. Whether that's in the public cloud, private cloud, or even on the world's most reliable transaction platform, IBM z. So see for yourself. Go to the Data Science Experience website, take us for a spin. And if you happen to be ready right now, our recently created Data Science Elite Team can help you get started and run experiments alongside you with no charge. Thank you very much. >> Thank you very much Daniel. It seems like a great time to get started. And thanks to Siva for taking us through it. Rob and I will be back in just a moment to add some perspective right after this. All right, once again joined by Rob Thomas. And Rob obviously we got a lot of information here. >> Yes, we've covered a lot of ground. >> This is intense. You got to break it down for me cause I think we zoom out and see the big picture. What better data science can deliver to a business? Why is this so important? I mean we've heard it through and through. >> Yeah, well, I heard it a couple times. But it starts with businesses have to embrace a data driven culture. And it is a change. And we need to make data accessible with the right tools in a collaborative culture because we've got diverse skill sets in every organization. But data driven companies succeed when data science tools are in the hands of everyone. And I think that's a new thought. I think most companies think just get your data scientist some tools, you'll be fine. This is about tools in the hands of everyone. I think the panel did a great job of describing about how we get to data science for all. Building a data culture, making it a part of your everyday operations, and the highlights of what Daniel just showed us, that's some pretty cool features for how organizations can get to this, which is you can see IBM's Data Science Experience, how that supports all teams. You saw data analysts, data scientists, application developer, IT staff, all working together. Second, you saw how we support all tools. And your choice of tools. So the most popular data science libraries integrated into one platform. And we saw some new capabilities that help companies avoid lock-in, where you can import existing models created from specialist tools like SPSS or others. And then deploy them and manage them inside of Data Science Experience. That's pretty interesting. And lastly, you see we continue to build on this best of open tools. Partnering with companies like H2O, Hortonworks, and others. Third, you can see how you use all data no matter where it lives. That's a key challenge every organization's going to face. Private, public, federating all data sources. We announced new integration with the Hortonworks data platform where we deploy machine learning models where your data resides. That's been a key theme. Analytics where the data is. And lastly, supporting all types of deployments. Deploy them in your Hadoop cluster. Deploy them in your Integrated Analytic System. Or deploy them in z, just to name a few. A lot of different options here. But look, don't believe anything I say. Go try it for yourself. Data Science Experience, anybody can use it. Go to datascience.ibm.com and look, if you want to start right now, we just created a team that we call Data Science Elite. These are the best data scientists in the world that will come sit down with you and co-create solutions, models, and prove out a proof of concept. >> Good stuff. Thank you Rob. So you might be asking what does an organization look like that embraces data science for all? And how could it transform your role? I'm going to head back to the office and check it out. Let's start with the perspective of the line of business. What's changed? Well, now you're starting to explore new business models. You've uncovered opportunities for new revenue sources and all that hidden data. And being disrupted is no longer keeping you up at night. As a data science leader, you're beginning to collaborate with a line of business to better understand and translate the objectives into the models that are being built. Your data scientists are also starting to collaborate with the less technical team members and analysts who are working closest to the business problem. And as a data scientist, you stop feeling like you're falling behind. Open source tools are keeping you current. You're also starting to operationalize the work that you do. And you get to do more of what you love. Explore data, build models, put your models into production, and create business impact. All in all, it's not a bad scenario. Thanks. All right. We are back and coming up next, oh this is a special time right now. Cause we got a great guest speaker. New York Magazine called him the spreadsheet psychic and number crunching prodigy who went from correctly forecasting baseball games to correctly forecasting presidential elections. He even invented a proprietary algorithm called PECOTA for predicting future performance by baseball players and teams. And his New York Times bestselling book, The Signal and the Noise was named by Amazon.com as the number one best non-fiction book of 2012. He's currently the Editor in Chief of the award winning website, FiveThirtyEight and appears on ESPN as an on air commentator. Big round of applause. My pleasure to welcome Nate Silver. >> Thank you. We met backstage. >> Yes. >> It feels weird to re-shake your hand, but you know, for the audience. >> I had to give the intense firm grip. >> Definitely. >> The ninja grip. So you and I have crossed paths kind of digitally in the past, which it really interesting, is I started my career at ESPN. And I started as a production assistant, then later back on air for sports technology. And I go to you to talk about sports because-- >> Yeah. >> Wow, has ESPN upped their game in terms of understanding the importance of data and analytics. And what it brings. Not just to MLB, but across the board. >> No, it's really infused into the way they present the broadcast. You'll have win probability on the bottom line. And they'll incorporate FiveThirtyEight metrics into how they cover college football for example. So, ESPN ... Sports is maybe the perfect, if you're a data scientist, like the perfect kind of test case. And the reason being that sports consists of problems that have rules. And have structure. And when problems have rules and structure, then it's a lot easier to work with. So it's a great way to kind of improve your skills as a data scientist. Of course, there are also important real world problems that are more open ended, and those present different types of challenges. But it's such a natural fit. The teams. Think about the teams playing the World Series tonight. The Dodgers and the Astros are both like very data driven, especially Houston. Golden State Warriors, the NBA Champions, extremely data driven. New England Patriots, relative to an NFL team, it's shifted a little bit, the NFL bar is lower. But the Patriots are certainly very analytical in how they make decisions. So, you can't talk about sports without talking about analytics. >> And I was going to save the baseball question for later. Cause we are moments away from game seven. >> Yeah. >> Is everyone else watching game seven? It's been an incredible series. Probably one of the best of all time. >> Yeah, I mean-- >> You have a prediction here? >> You can mention that too. So I don't have a prediction. FiveThirtyEight has the Dodgers with a 60% chance of winning. >> [Katie] LA Fans. >> So you have two teams that are about equal. But the Dodgers pitching staff is in better shape at the moment. The end of a seven game series. And they're at home. >> But the statistics behind the two teams is pretty incredible. >> Yeah. It's like the first World Series in I think 56 years or something where you have two 100 win teams facing one another. There have been a lot of parity in baseball for a lot of years. Not that many offensive overall juggernauts. But this year, and last year with the Cubs and the Indians too really. But this year, you have really spectacular teams in the World Series. It kind of is a showcase of modern baseball. Lots of home runs. Lots of strikeouts. >> [Katie] Lots of extra innings. >> Lots of extra innings. Good defense. Lots of pitching changes. So if you love the modern baseball game, it's been about the best example that you've had. If you like a little bit more contact, and fewer strikeouts, maybe not so much. But it's been a spectacular and very exciting World Series. It's amazing to talk. MLB is huge with analysis. I mean, hands down. But across the board, if you can provide a few examples. Because there's so many teams in front offices putting such an, just a heavy intensity on the analysis side. And where the teams are going. And if you could provide any specific examples of teams that have really blown your mind. Especially over the last year or two. Because every year it gets more exciting if you will. I mean, so a big thing in baseball is defensive shifts. So if you watch tonight, you'll probably see a couple of plays where if you're used to watching baseball, a guy makes really solid contact. And there's a fielder there that you don't think should be there. But that's really very data driven where you analyze where's this guy hit the ball. That part's not so hard. But also there's game theory involved. Because you have to adjust for the fact that he knows where you're positioning the defenders. He's trying therefore to make adjustments to his own swing and so that's been a major innovation in how baseball is played. You know, how bullpens are used too. Where teams have realized that actually having a guy, across all sports pretty much, realizing the importance of rest. And of fatigue. And that you can be the best pitcher in the world, but guess what? After four or five innings, you're probably not as good as a guy who has a fresh arm necessarily. So I mean, it really is like, these are not subtle things anymore. It's not just oh, on base percentage is valuable. It really effects kind of every strategic decision in baseball. The NBA, if you watch an NBA game tonight, see how many three point shots are taken. That's in part because of data. And teams realizing hey, three points is worth more than two, once you're more than about five feet from the basket, the shooting percentage gets really flat. And so it's revolutionary, right? Like teams that will shoot almost half their shots from the three point range nowadays. Larry Bird, who wound up being one of the greatest three point shooters of all time, took only eight three pointers his first year in the NBA. It's quite noticeable if you watch baseball or basketball in particular. >> Not to focus too much on sports. One final question. In terms of Major League Soccer, and now in NFL, we're having the analysis and having wearables where it can now showcase if they wanted to on screen, heart rate and breathing and how much exertion. How much data is too much data? And when does it ruin the sport? >> So, I don't think, I mean, again, it goes sport by sport a little bit. I think in basketball you actually have a more exciting game. I think the game is more open now. You have more three pointers. You have guys getting higher assist totals. But you know, I don't know. I'm not one of those people who thinks look, if you love baseball or basketball, and you go in to work for the Astros, the Yankees or the Knicks, they probably need some help, right? You really have to be passionate about that sport. Because it's all based on what questions am I asking? As I'm a fan or I guess an employee of the team. Or a player watching the game. And there isn't really any substitute I don't think for the insight and intuition that a curious human has to kind of ask the right questions. So we can talk at great length about what tools do you then apply when you have those questions, but that still comes from people. I don't think machine learning could help with what questions do I want to ask of the data. It might help you get the answers. >> If you have a mid-fielder in a soccer game though, not exerting, only 80%, and you're seeing that on a screen as a fan, and you're saying could that person get fired at the end of the day? One day, with the data? >> So we found that actually some in soccer in particular, some of the better players are actually more still. So Leo Messi, maybe the best player in the world, doesn't move as much as other soccer players do. And the reason being that A) he kind of knows how to position himself in the first place. B) he realizes that you make a run, and you're out of position. That's quite fatiguing. And particularly soccer, like basketball, is a sport where it's incredibly fatiguing. And so, sometimes the guys who conserve their energy, that kind of old school mentality, you have to hustle at every moment. That is not helpful to the team if you're hustling on an irrelevant play. And therefore, on a critical play, can't get back on defense, for example. >> Sports, but also data is moving exponentially as we're just speaking about today. Tech, healthcare, every different industry. Is there any particular that's a favorite of yours to cover? And I imagine they're all different as well. >> I mean, I do like sports. We cover a lot of politics too. Which is different. I mean in politics I think people aren't intuitively as data driven as they might be in sports for example. It's impressive to follow the breakthroughs in artificial intelligence. It started out just as kind of playing games and playing chess and poker and Go and things like that. But you really have seen a lot of breakthroughs in the last couple of years. But yeah, it's kind of infused into everything really. >> You're known for your work in politics though. Especially presidential campaigns. >> Yeah. >> This year, in particular. Was it insanely challenging? What was the most notable thing that came out of any of your predictions? >> I mean, in some ways, looking at the polling was the easiest lens to look at it. So I think there's kind of a myth that last year's result was a big shock and it wasn't really. If you did the modeling in the right way, then you realized that number one, polls have a margin of error. And so when a candidate has a three point lead, that's not particularly safe. Number two, the outcome between different states is correlated. Meaning that it's not that much of a surprise that Clinton lost Wisconsin and Michigan and Pennsylvania and Ohio. You know I'm from Michigan. Have friends from all those states. Kind of the same types of people in those states. Those outcomes are all correlated. So what people thought was a big upset for the polls I think was an example of how data science done carefully and correctly where you understand probabilities, understand correlations. Our model gave Trump a 30% chance of winning. Others models gave him a 1% chance. And so that was interesting in that it showed that number one, that modeling strategies and skill do matter quite a lot. When you have someone saying 30% versus 1%. I mean, that's a very very big spread. And number two, that these aren't like solved problems necessarily. Although again, the problem with elections is that you only have one election every four years. So I can be very confident that I have a better model. Even one year of data doesn't really prove very much. Even five or 10 years doesn't really prove very much. And so, being aware of the limitations to some extent intrinsically in elections when you only get one kind of new training example every four years, there's not really any way around that. There are ways to be more robust to sparce data environments. But if you're identifying different types of business problems to solve, figuring out what's a solvable problem where I can add value with data science is a really key part of what you're doing. >> You're such a leader in this space. In data and analysis. It would be interesting to kind of peek back the curtain, understand how you operate but also how large is your team? How you're putting together information. How quickly you're putting it out. Cause I think in this right now world where everybody wants things instantly-- >> Yeah. >> There's also, you want to be first too in the world of journalism. But you don't want to be inaccurate because that's your credibility. >> We talked about this before, right? I think on average, speed is a little bit overrated in journalism. >> [Katie] I think it's a big problem in journalism. >> Yeah. >> Especially in the tech world. You have to be first. You have to be first. And it's just pumping out, pumping out. And there's got to be more time spent on stories if I can speak subjectively. >> Yeah, for sure. But at the same time, we are reacting to the news. And so we have people that come in, we hire most of our people actually from journalism. >> [Katie] How many people do you have on your team? >> About 35. But, if you get someone who comes in from an academic track for example, they might be surprised at how fast journalism is. That even though we might be slower than the average website, the fact that there's a tragic event in New York, are there things we have to say about that? A candidate drops out of the presidential race, are things we have to say about that. In periods ranging from minutes to days as opposed to kind of weeks to months to years in the academic world. The corporate world moves faster. What is a little different about journalism is that you are expected to have more precision where people notice when you make a mistake. In corporations, you have maybe less transparency. If you make 10 investments and seven of them turn out well, then you'll get a lot of profit from that, right? In journalism, it's a little different. If you make kind of seven predictions or say seven things, and seven of them are very accurate and three of them aren't, you'll still get criticized a lot for the three. Just because that's kind of the way that journalism is. And so the kind of combination of needing, not having that much tolerance for mistakes, but also needing to be fast. That is tricky. And I criticize other journalists sometimes including for not being data driven enough, but the best excuse any journalist has, this is happening really fast and it's my job to kind of figure out in real time what's going on and provide useful information to the readers. And that's really difficult. Especially in a world where literally, I'll probably get off the stage and check my phone and who knows what President Trump will have tweeted or what things will have happened. But it really is a kind of 24/7. >> Well because it's 24/7 with FiveThirtyEight, one of the most well known sites for data, are you feeling micromanagey on your people? Because you do have to hit this balance. You can't have something come out four or five days later. >> Yeah, I'm not -- >> Are you overseeing everything? >> I'm not by nature a micromanager. And so you try to hire well. You try and let people make mistakes. And the flip side of this is that if a news organization that never had any mistakes, never had any corrections, that's raw, right? You have to have some tolerance for error because you are trying to decide things in real time. And figure things out. I think transparency's a big part of that. Say here's what we think, and here's why we think it. If we have a model to say it's not just the final number, here's a lot of detail about how that's calculated. In some case we release the code and the raw data. Sometimes we don't because there's a proprietary advantage. But quite often we're saying we want you to trust us and it's so important that you trust us, here's the model. Go play around with it yourself. Here's the data. And that's also I think an important value. >> That speaks to open source. And your perspective on that in general. >> Yeah, I mean, look, I'm a big fan of open source. I worry that I think sometimes the trends are a little bit away from open source. But by the way, one thing that happens when you share your data or you share your thinking at least in lieu of the data, and you can definitely do both is that readers will catch embarrassing mistakes that you made. By the way, even having open sourceness within your team, I mean we have editors and copy editors who often save you from really embarrassing mistakes. And by the way, it's not necessarily people who have a training in data science. I would guess that of our 35 people, maybe only five to 10 have a kind of formal background in what you would call data science. >> [Katie] I think that speaks to the theme here. >> Yeah. >> [Katie] That everybody's kind of got to be data literate. >> But yeah, it is like you have a good intuition. You have a good BS detector basically. And you have a good intuition for hey, this looks a little bit out of line to me. And sometimes that can be based on domain knowledge, right? We have one of our copy editors, she's a big college football fan. And we had an algorithm we released that tries to predict what the human being selection committee will do, and she was like, why is LSU rated so high? Cause I know that LSU sucks this year. And we looked at it, and she was right. There was a bug where it had forgotten to account for their last game where they lost to Troy or something and so -- >> That also speaks to the human element as well. >> It does. In general as a rule, if you're designing a kind of regression based model, it's different in machine learning where you have more, when you kind of build in the tolerance for error. But if you're trying to do something more precise, then so much of it is just debugging. It's saying that looks wrong to me. And I'm going to investigate that. And sometimes it's not wrong. Sometimes your model actually has an insight that you didn't have yourself. But fairly often, it is. And I think kind of what you learn is like, hey if there's something that bothers me, I want to go investigate that now and debug that now. Because the last thing you want is where all of a sudden, the answer you're putting out there in the world hinges on a mistake that you made. Cause you never know if you have so to speak, 1,000 lines of code and they all perform something differently. You never know when you get in a weird edge case where this one decision you made winds up being the difference between your having a good forecast and a bad one. In a defensible position and a indefensible one. So we definitely are quite diligent and careful. But it's also kind of knowing like, hey, where is an approximation good enough and where do I need more precision? Cause you could also drive yourself crazy in the other direction where you know, it doesn't matter if the answer is 91.2 versus 90. And so you can kind of go 91.2, three, four and it's like kind of A) false precision and B) not a good use of your time. So that's where I do still spend a lot of time is thinking about which problems are "solvable" or approachable with data and which ones aren't. And when they're not by the way, you're still allowed to report on them. We are a news organization so we do traditional reporting as well. And then kind of figuring out when do you need precision versus when is being pointed in the right direction good enough? >> I would love to get inside your brain and see how you operate on just like an everyday walking to Walgreens movement. It's like oh, if I cross the street in .2-- >> It's not, I mean-- >> Is it like maddening in there? >> No, not really. I mean, I'm like-- >> This is an honest question. >> If I'm looking for airfares, I'm a little more careful. But no, part of it's like you don't want to waste time on unimportant decisions, right? I will sometimes, if I can't decide what to eat at a restaurant, I'll flip a coin. If the chicken and the pasta both sound really good-- >> That's not high tech Nate. We want better. >> But that's the point, right? It's like both the chicken and the pasta are going to be really darn good, right? So I'm not going to waste my time trying to figure it out. I'm just going to have an arbitrary way to decide. >> Serious and business, how organizations in the last three to five years have just evolved with this data boom. How are you seeing it as from a consultant point of view? Do you think it's an exciting time? Do you think it's a you must act now time? >> I mean, we do know that you definitely see a lot of talent among the younger generation now. That so FiveThirtyEight has been at ESPN for four years now. And man, the quality of the interns we get has improved so much in four years. The quality of the kind of young hires that we make straight out of college has improved so much in four years. So you definitely do see a younger generation for which this is just part of their bloodstream and part of their DNA. And also, particular fields that we're interested in. So we're interested in people who have both a data and a journalism background. We're interested in people who have a visualization and a coding background. A lot of what we do is very much interactive graphics and so forth. And so we do see those skill sets coming into play a lot more. And so the kind of shortage of talent that had I think frankly been a problem for a long time, I'm optimistic based on the young people in our office, it's a little anecdotal but you can tell that there are so many more programs that are kind of teaching students the right set of skills that maybe weren't taught as much a few years ago. >> But when you're seeing these big organizations, ESPN as perfect example, moving more towards data and analytics than ever before. >> Yeah. >> You would say that's obviously true. >> Oh for sure. >> If you're not moving that direction, you're going to fall behind quickly. >> Yeah and the thing is, if you read my book or I guess people have a copy of the book. In some ways it's saying hey, there are lot of ways to screw up when you're using data. And we've built bad models. We've had models that were bad and got good results. Good models that got bad results and everything else. But the point is that the reason to be out in front of the problem is so you give yourself more runway to make errors and mistakes. And to learn kind of what works and what doesn't and which people to put on the problem. I sometimes do worry that a company says oh we need data. And everyone kind of agrees on that now. We need data science. Then they have some big test case. And they have a failure. And they maybe have a failure because they didn't know really how to use it well enough. But learning from that and iterating on that. And so by the time that you're on the third generation of kind of a problem that you're trying to solve, and you're watching everyone else make the mistake that you made five years ago, I mean, that's really powerful. But that doesn't mean that getting invested in it now, getting invested both in technology and the human capital side is important. >> Final question for you as we run out of time. 2018 beyond, what is your biggest project in terms of data gathering that you're working on? >> There's a midterm election coming up. That's a big thing for us. We're also doing a lot of work with NBA data. So for four years now, the NBA has been collecting player tracking data. So they have 3D cameras in every arena. So they can actually kind of quantify for example how fast a fast break is, for example. Or literally where a player is and where the ball is. For every NBA game now for the past four or five years. And there hasn't really been an overall metric of player value that's taken advantage of that. The teams do it. But in the NBA, the teams are a little bit ahead of journalists and analysts. So we're trying to have a really truly next generation stat. It's a lot of data. Sometimes I now more oversee things than I once did myself. And so you're parsing through many, many, many lines of code. But yeah, so we hope to have that out at some point in the next few months. >> Anything you've personally been passionate about that you've wanted to work on and kind of solve? >> I mean, the NBA thing, I am a pretty big basketball fan. >> You can do better than that. Come on, I want something real personal that you're like I got to crunch the numbers. >> You know, we tried to figure out where the best burrito in America was a few years ago. >> I'm going to end it there. >> Okay. >> Nate, thank you so much for joining us. It's been an absolute pleasure. Thank you. >> Cool, thank you. >> I thought we were going to chat World Series, you know. Burritos, important. I want to thank everybody here in our audience. Let's give him a big round of applause. >> [Nate] Thank you everyone. >> Perfect way to end the day. And for a replay of today's program, just head on over to ibm.com/dsforall. I'm Katie Linendoll. And this has been Data Science for All: It's a Whole New Game. Test one, two. One, two, three. Hi guys, I just want to quickly let you know as you're exiting. A few heads up. Downstairs right now there's going to be a meet and greet with Nate. And we're going to be doing that with clients and customers who are interested. So I would recommend before the game starts, and you lose Nate, head on downstairs. And also the gallery is open until eight p.m. with demos and activations. And tomorrow, make sure to come back too. Because we have exciting stuff. I'll be joining you as your host. And we're kicking off at nine a.m. So bye everybody, thank you so much. >> [Announcer] Ladies and gentlemen, thank you for attending this evening's webcast. If you are not attending all cloud and cognitive summit tomorrow, we ask that you recycle your name badge at the registration desk. Thank you. Also, please note there are two exits on the back of the room on either side of the room. Have a good evening. Ladies and gentlemen, the meet and greet will be on stage. Thank you.

>> Announcer: Live, from Manhattan, it's theCUBE. Covering AWS Summit New York City 2017. Brought to you by Amazon Web Services. >> John Walls: Welcome back here at the Javits Center. We're in midtown, New York, with Stu Miniman, I'm John Walls, here on theCUBE, continuing our coverage here all day, livestreaming from AWS Summit. Thanks for being with us here. Aaron Newman now joins us, he's the co-founder and CEO of CrowdCheckr, and... CloudCheckr rather, and Aaron, the first employee of the company, period, to be on theCUBE, so you're really breaking out in a big way today. >> Yeah, thanks for having us here, and we're excited to be a part of this. >> I see your tag, first I thought it was "I love AWS," and then I saw it closer, "I CloudChecked AWS." >> Absolutely, but also we love AWS. So it works either way. >> So, CloudCheckr, first off tell us a little bit about you, and then how did you get here? >> Okay so, CloudCheckr is a software company. I am the CEO and one of the founders of it. Been around about six years. We build software to help enable, um, enable you to move workloads into the cloud and then manage them successfully. So there's lots of challenges as you move, and how you're going to deal with those is a little different than you did in your data center, so it's important you have the right tools, and processes, and people in place, to manage that move. >> So is the game changing any in that respect? Has it changed any in the last year or two? Is it just that you've got more options now? >> Well, I mean absolutely, this is the disruption for our generation, right? This idea of moving from the data center into the cloud is that disruption. Previously, it was the internet was the big disruption. The cloud is really this generation's disruption, and it's really a matter of how quickly are people moving workloads. Every year AWS gets more mature, they offer more services and more regions, you know, more robust service, so it's just a case of how quickly can people move workloads over. If you go back to a couple years, people thought this was for test workloads, dev workloads. It's just not the case. It's for production workloads, and the people who are taking advantage of it have a competitive advantage today. >> This is a real complex space, so last year at re:Invent I believe, Amazon gave a presentation, they were like, the eight R's to get from where you were to where you want to be. There's lift-and-shift was replatform, there was refactoring, you know, to completely building from scratch, to kind of just trying to move the whole piece. What are you seeing from customers, I'm sure it's a lot of everything, but what are kind of some of the main challenges, what's really slowing things down, and what is changing over the last couple of years? >> Yeah, absolutely, I mean change never comes fast enough, and we'd all love to be able to rewrite all our apps to work in the cloud the way that it was meant to, and that's the right and the best way to do it, you're just going to get way more return in terms of cost and security, and all the other great things that come out of the cloud, but the fact is most people are still lifting and shifting, right? They're taking their apps the way that it ran at the data center, moving into the cloud. And so you see some advantages, but you just clearly don't see the real 10x advantages. So most people are doing that, and it's just that it's expensive. New workloads, as they go in, are architected with this cloud in mind, and that's really powerful, and that's great, but it's going to take time, and it's not going to take five years, it's not going to take ten years, it's going to take 20, 30, 40 years to really get rid of all this old architecture, and convert it over. The same way nobody's putting anything on a mainframe today, but there's a whole lot of the world that's still run by mainframes, right? But you would never put a new app on a mainframe. >> Yeah, if you look at refresh cycles, you know, your server, your network takes a certain amount of time, it's your applications that's a huge amount of time, and the problem we had is, I think back and most of your applications, they kind of suck, and your users of those applications would love for you to update them. So the migration costs are so high, how do we get over that hump? >> Well, it is just going to take time for the refresh cycles, but even more important, I think we need to start looking at going back to the universities. Are universities teaching the right architectures for how to build this stuff? And I can go for hours and hours on some of the minute details, but the idea was, I used to have an application, I'd buy 20 servers, and that's what I ran it on. Now it's like, I build an application, and I don't know where it's really going to sit, it's going to sit on a server somewhere, and that server may use it for minutes or hours, and then it may be on a different server, and all of a sudden you have to think about, how am I going to architect, how am I going to write the code, how am I going to deploy that code? All that stuff is a little different than when you had 20 servers. How am I going to patch it for security holes? So we need to be educating people about that. We need to show them how to do that, back to universities, continuing education programs, all of that, needs to get brought up to date. >> A couple years ago, it seemed like security was the thing that would stop a lot of people, to say, "I'm not ready to go into it." We were talking to one of the Amazon spokespeople about security, and it seems that it's almost a driver now, because I know I need to stay up to date, I need to manage my security much closer, and in many ways, if you're running on Amazon, if you're running on Azure, if you're running on a public hub, they're going to manage some of the patching and testing and everything. So what are you seeing in kind of the security landscape? Is it an opportunity, is it still a challenge? Is it still some of both? >> I think you're absolutely right, security was the biggest fear factor that people were like, and I'm from Rochester, New York, and there are some more older, old-school technology companies there that, their attitude was, "We're not going to go to the cloud, because we don't know where the data sits," and there's a lot of server huggers, that if I can't see the server, it's not secure, and that's just not the case. Let me start with, Amazon has way better security people than you could hire, right? They just have a scale, caliber, programs, all of that that's so much better than anyone else. And you know what, if you had any question about it, the day the head of technology, the CIO for the CIA, stood on stage at an Amazon conference, and said we are going to the cloud, it's like if you think your security needs to be higher than the CIA's, you're wrong. So, it absolutely does, if you do things in the cloud properly, it can be 10 times more secure than what you're in your own data center, right? But you need to do things like think about, how am I doing deployment, so I can get out patches, right? What's the big problem with security in the data center is I have a patch, it hits, and it's going to take me a year to get that out to my 10,000 servers. In the cloud, if I've done things where I have this idea of no-patching strategies, and redeploying instantaneously, then you could fix a patch in a day, right? And all of a sudden it can create a much more secure world, where we don't have these ransomware problems. You don't have all these worms and such causing havoc. >> Go ahead, John. >> You touched on something just a few minutes ago, and you're talking about 20, 30, 40 years, right, catching up, and legacy systems, and people who can leapfrog, and I'm thinking, that's like this perpetual cycle of never catching up, because the technology innovates so quickly, and things are moving so fast. So somebody that might feel like they're really behind? How do they ever just relax and get there if they feel like they really can't catch up? >> Well, so I guess I'll start by saying that people in this room are on the leading edge, and I like to say if you're not bleeding, you're not leading, right? If you're on that leading edge, you're going to have more challenges, you're not going to be able to relax and take it easy. The question is, you know, do you want to be a firm that's trying to take advantage of every competitive edge they can, trying to drive a little bit more, then you're not going to be relaxed. That's just the state of technology today is, it is a marathon, it's not a sprint. But that means you have to find a pace that's appropriate for you, and if you're a brand new software company, like CloudCheckr, I've never bought a server, I built everything in the cloud day-one, so I never have the old legacy architecture. That makes my life much easier. If I am the postal service, it's going to take me a long time to get off the system, and that's just the fact of life, you know. You don't have to throw away your old apps, they'll be around for a long time, but be proactive about saying, "I'm going to build something new," do it the right way so you don't have to wait for a refresh cycle for that. >> Walls: Right, gotcha. >> I mean think about, on the mainframe, remember some of the problems with getting apps off the mainframe was? Nobody had the source code anymore. You couldn't fix Y2K bugs, because you didn't have source code, so you couldn't redeploy it, because they wrote code, and the person that wrote it retired 15 years ago, and now what do I do? I'm stuck. So we're going to be in that same scenario for a long time. >> The other place where you're involved is, once we'd actually got in the cloud, how do we make sure my expenses don't just run away? So you know, maybe talk to us a little bit about that. Amazon's always an interesting one. I was talking in our intro this morning, early in this year, I was talking to a lot of SMB customers that were just like, Google's really attractive, and Amazon doesn't seem to be listening to us, and a week after the Google conference, Amazon changed their pricing, to be able to really match what Google's doing. So what are the some of the biggest challenges in pricing, how are you helping customers, where are some of the pitfalls that they're seeing? >> I mean, absolutely, AWS is the smartest people out there, they know when they need to change and pivot, and somehow they're a billion dollar company that can still pivot, which is a miracle. I don't know how they do it, but they are amazing at that. But let me start by giving you a little of the analogy of, think back to in the 1850's when you had power plants. Everybody built their own power plant, right? And it would cost a million dollars to build a power plant, and then most of your power would be free, right? And then they decided, let's build power plants, I'll spend 50 million dollars to build it, and then everyone will use that, right? We're in the same place now, 150 years later, but it's just different, it's technology. Instead of building a data center and spending millions of dollars on it, instead Amazon has built a data center that's designed for everybody to use, and it's so much more efficient to do that, just like, God, who would build their own power plant anymore? That's the analogy. But think about the other side of it, though, is now if I'm getting my power from a power plant, well I got to start putting in a meter, and understanding turning off the lights at night, and I got to put windows in to keep the heat in the house, and put insulation, right? So we're in the same situation. Yes, Amazon is cheaper, except if you turn all of your servers on, you leave them on, and you don't meter it, you don't understand it, you don't try to put insulation in. So you got to do those things in the cloud. It was easy before, because I just paid for the servers and I was done. Now it's complicated, but it's complicated because you're going to save a lot of money if you do it right. But you know, I love to make that analogy of the physical world, we're no different. You got to actually do things to get your build out. >> Are you starting to see many customers looking at Lambda, because that's something, at least many customers we've talked to, significantly reduced the cost of your infrastructure, because it's not just, I'm choosing when to use it, but only when the function calls it. >> So I think, AWS, you can effectively drive your cost to zero by using the cloud, and by effectively, it never gets to zero, but you can really keep driving it down the more work you put into it. But there's a balance, right? If you put too much work, you offset the savings you're going to have, right? So you go to the cloud, and you start doing work, more work to reduce costs by rightsizing, turning things off, and then you say, let me go to Lambda, because that's even cheaper, but today Lambda still, it doesn't have all the bells and whistles, it's still very much the bleeding edge. So, if you can do it, if you have a fresh application, the expertise to do it, it's a great place to go, and I think in 20 years, everybody's going to be doing everything serverless, all new stuff. We're very early though, right now. We're still inventing this stuff, we're still figuring it out, we're still trying to understand how do I structure an entire application using this serverless architecture? It's trickier than doing it, when you go out there and you try to find 20 programmers to run a project, to get ones that know how to build serverless is very hard, so that's the real challenge. It's not the technology challenge, it's the people, where am I going to find the resources, how much is it going to cost me, all of that. >> I'm still thinking about the power plant. I'm still back in 1850 right now. (laughs) Thanks for being with us. >> You're welcome. >> I appreciate the time here on theCUBE, and best of luck down the road, and glad to see that you are cloudchecking with AWS. >> Check your cloud before you wreck your cloud, right? >> There you go, alright. Aaron Newman, CloudCheckr. Continuing our coverage, we are just a moment here from AWS Summit 2017, we are live at the Javits Center, in New York City. (electronic music)