>>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.

>>Hey everyone. Welcome back to Chicago. The Cube is excited to be live on day two of Ansible Fest, 2022. Lisa Martin and John Fur. You're here having some great conversations, a lot of cube alumni, a lot of wisdom from the Ansible community coming at you on this program this week. You know, John, we've been, we've been hearing stories about the power and the capabilities and the collective wisdom of the Ansible community. You can feel it here. Yeah, there's no doubt about that. It's, Ansible is nothing, as Stephanie Chair said yesterday, if not a community and the significant contributions that it makes over and over again, or it's fuel. >>I mean the power of the community is what drives Ansible is gonna drive the future of, I think, cloud in our next generation modern application environment. And it's collective intelligence. It's a production system at the end of the day. And I think these guys have harnessed it. So it should be a really great segment to talk about all the contributor work that's been done. So I'm looking forward to it. >>We've got two great alumni here to talk about the contributor work, how you can get involved. Please welcome back to the cube. Carol Chen, principal community architect at Red Hat. Adam Miller joins us as well, fresh from the keynote stage senior principal software engineer at Red Hat. Guys, great to have you on the cube. Great to be here. Yeah, thank you. So we, we talked, we enjoyed your keynotes, Adam, and what you were talking about on stage, the Ansible contributor summit. That's, you guys have been doing what, this is the seven you've had seven so far in just a couple of years. >>Well, we had seven virtual contributor summits. >>Seven virtual. This is the first Monday was the first in person in. >>First in person since the pandemic and actually the 15th contributor summit overall >>15th overall. Talk about the contributor summits, what the contributors are able to do and the influence that it's having on Ansible Red Hat and what people are able to do with cloud. At the Edge automation. Yeah. >>So our community contributors have always had ways to influence and contribute to the project. But the contributor Summit is really a place where we can get people together, preferably in the same place so that we can, you know, have a really great dynamic conversations and interactions. But we also want to make sure that we don't leave out people who have been constantly online joining us. So this year we are so happy to be here in Chicago in person. We've had about 60 to 70 here joining us. And at first I thought maybe we'll have one third of the attendees joining online because about 30 to 40 people signed up to join online. But in the end, we have more than 100 per people watching our live stream. So that's more than half of the attendees overall, were joining us online. So that really shows where, you know, the contributors are interested in participating for >>Develop. Right. Yeah, it's been, it's been interesting. It's been since 2019, since the in-person Ansible Fest in Atlanta. Now we're in Chicago, we had the pandemic. Couple interesting observations from our side that I wanna get your reaction to Adam Carol. And that is one Ansible's relevance has grown significantly since then. Just from a cloud growth standpoint, developer open source standpoint, and how people work and collaborate has changed. So your contributor based in your community is getting more powerful in scope, in my opinion. Like in, as they become, have the keys to the kingdom in the, in their respective worlds as it gets bigger and larger. So the personas are changing, the makeup of the community's changing and also how you guys collaborate is changing. Can you share your, what's going on with those two dynamics? Cause I think that power dynamic is, is looking really good. How are you guys handling >>That? Yeah, so I mean, I, I had the opportunity to represent the community on stage yesterday as part of the keynote and talk to this point specifically is one of the things that we've seen is the project has had the opportunity to kind of grow and evolve. There's been certain elements that have had to kind of decompose from a technology perspective. We actually had to kind of break it apart and change the architecture a little bit and move things into what are called Ansible collections, which, you know, folks here are very familiar with No One Love. And we've seen a lot of community work in the form of working groups coalesce around those organically. However, they've done so in kind of different ways. They, they pick tools and collaboration platforms that are popular to their subject matter expertise audience and things like that. So we find ourselves in a place where kind of the, the community itself had more or less segmented naturally in a way. And we needed to find ways to, you know, kind of ke that >>Fragmentation by demographics or by expertise or both as >>A Mostly, mostly expertise. Yeah. And so there was a open source technology called Matrix. It is a open source, standardized, federated messaging platform that we're able to use to start to bridge back some of those communities that have kind of broken off and, and made their their own home elsewhere on the internet. So now we're able to, for example, the right, the docs organization, they had a, a group of people who was very interested in contributing to the Ansible documentation, but they'd already self-organized on Discord. And what was interesting there is the existing team for the Ansible documentation, they were already on internet Relay Chat, also known as irc. And Matrix allowed us to actually bring those two together and bridge that into the other matrix cha chat channels that we had. So now we're able to have people from all over the world in different areas and different platforms, coalesce and, and cross. It's like a festival cross pollinate. Yeah. >>And you're meeting the contributors exactly where they are and where they want to be, where they're comfortable. >>Yes. Yeah, we always say we, we reach out to where they are. So, >>And, and, and much in the way that Ansible has the capability to reach out to things in their own way, Right. And allow that subject matter expertise to, you know, cause the technology has the potential and possibility and capability to talk to anything over any protocol. We're able to do, you know, kind of the same thing with Matrix, allowing us to bridge into any chat platform that it has support for bridging and, and we're able to bring a lot of people >>Together. Yeah. And how's that, how's the feedback been on that so far? >>I, I think it has been very positive. For example, I want to highlight that the technical writers that we have contributing via Discord is actually a group from Nigeria. And Dave also participated in the contributor summit online virtually joining us in, in, you know, on the matrix platform. So that, that bridge that really helps to bring together people from different geographical regions and also different topics and arenas like that. >>So what were some of the outcomes of the contributor summit? The, the first in person in a while, great. That you guys were able to do seven virtually during the pandemic. That's hard. It's hard to get people together. You, there's so much greatness and innovation that comes when we're all together in person that just can't replicate by video. You can do a lot. Right. But talk about some of the outcomes from Monday. What were some of the feedback? What were some of the contributions that you think are really going to impact the community? >>I think for a lot of us, myself included, the fact that we are in person and meeting people face to face, it helps to really build the connections. And when we do talk about contribution, the connection is so important that you understand, well this person a little bit about their background, what they've done for the SPO project and or just generally what, what they're interested in that builds the rapport and connection that helps, you know, further, further collaboration in the future. Because maybe on that day we did not have any, you know, co contributions or anything, but the fact that we had a chance to sit together in the same place to discuss things and share new ideas, roadmaps is really the, the kind of a big step to the future for our community. Yes, >>Yes. And in a lot of ways we often online the project has various elements that are able to function asynchronously. So we work very well globally across many time zones. And now we were able to get a lot of people in the same place at the same time, synchronously in the same time zone. And then we had breakout sessions where the subject matter, you know, working groups were able to kind of go and focus on things that maybe have been taking a little while to discuss in, in that asynchronous form of communication and do it synchronously and, you know, be in the same room and work on things. It's been, it's been fantastic >>Developers there, like they, they take to asynchronous like fish to water. It's not a problem. But I do want to ask if there's any observations that you guys have had now that we're kind of coming out of that one way, but the pandemic, but the world's changed. It's hybrid, hybrid work environment, steady state. So we see that. Any observations on your end on what's new that you observed that people are gravitating to? Is there a pattern of styles is or same old self-governing, or what's new? What do you see that's coming out of the pandemic that might be a norm? >>I I think that even though people are excited to get back in person, there are, things have changed, like you said, and we have to be more aware of, there are people who think that not be in person, it's okay. And that's how they want to do it. And we have to make sure that they, they are included. So we, we did want to make a high priority for online participation in this event. And like I said, even though only 4 30, 40 people signed up to join us online initially, so that it was what we were expecting, but in the end, more than 100 people were watching us and, and joining participation in >>Actually on demand consumption be good too, >>Right? Yeah. So, you know, I think going forward that is probably the trend. And as, as much as we, we love being in person, we, we want this to continue that we, we take care of people who are, has been constantly participating online and contributing you >>Meaning again, meaning folks where they are, but also allowing the, the, those members that want to get together to, to collaborate in person. I can only imagine the innovation that's gonna come even from having part of the back, Right. >>And, and not to continue to harp on the matrix point, but it, it's been very cool because Matrix has the ability to do live video sessions using open source another to open source technology called jy. So we're able to actually use the same place that we normally find ourselves, you know, congregating and collaborating for the project itself in an asynchronous and, you know, somewhat synchronous way to also host these types of things that are, are now hybrid that used to be, you know, all one way or all the other. Yeah. And it's been, it's >>Been incredible. Integration is, the integration is have been fascinating to watch how you guys do that. And also, you know, with q we've been virtual too. It's like, it's like people don't want another microsite, but they want a more of a festival vibe, a hub, right? Like a place to kind of check in and have choice, not get absolutely jammed into a, you know, forum or, you know, or whatever. Hey, if you wanna be on Discord, be on Discord, right? Why >>Not? And we still, you know, we do still have our asynchronous forms of >>Work through >>Our get GitHub. We have our projects, we have our issues, we have our, you know, wiki, we have various elements there that everybody can continue to collaborate on. And it's all been, it's all been very good. >>Speaking of festivals, octoberfest that's going on, not to be confused with Octoberfest, that was last month. Talk about how the Ansible project and the Ansible community is involved in Octoberfest. Give us the dates, Carol. So >>YesTo Fest is a annual thing in October. So October Octoberfest, I think it's organized by Digital Ocean for the past eight or nine years. And it's really a, a way to kind of encourage people to contribute to open source projects. So it's not anal specific, but we as an Ansible project encourage people to take this opportunity to, you know, a lot of them doing their first contributions during this event. And when, when we first announced, we're participating in Octoberfest within the first four days of October, which is over a weekend actually. We've had 24 contributions, it, 24 issues fixed, which is like amazing, like, you know, just the interest and the, the momentum that we had. And so far until I just checked with my teammates this morning that we've had about 35 contributions so far during the month, which is, and I'm sorry, I forgot to mention this is only for Ansible documentation. So yeah, specifically. And, and that's also one thing we want to highlight, that contributions don't just come in code in, you know, kind of software side, but really there's many ways to contribute and documentation is such a, a great way for first time users, first time contributors to get involved. So it's really amazing to see these contributions from all over the world. And also partly thanks to the technical writers in Nigeria kind of promoting and sharing this initiative. And it's just great to see the, the results from that. Can >>You double click on the different ways of contribution? You mentioned a couple documentation being one, code being the other, but what is the breadth of opportunities that the contributors have to contribute to the project? >>Oh, there's, there's so many. So I actually take care more of outreach efforts in the community. So I helped to organize events and meetups from around the world. And now that we're slowly coming out of the pandemic, I've seen more and more in person meetups. I was just talking to someone from Minneapolis, they're trying to get, get people back together again. They have people in Singapore, in Netherlands from pretty much, you know, all corners of the globe wanting to form not just for the Ansible project, but the local kind of connection with the re people in the region, sometimes in their own language, in their local languages to really work together on the project and just, >>You know, you to create a global Yeah. Network, right? I mean it's like Ansible Global. >>Exactly. >>Create local subnets not to get all networking, >>Right? >>Yeah. >>Yeah. One, one quick thing I want to touch on Theto Fest. I think it's a great opportunity for existing contributors to mentor cause many people like to help bring in new contributors and this is kind of a focal point to be able to focus on that. And then to, to the the other point we, you know, it, it's been, it's been extremely powerful to see as we return these sub communities pop up and, and kind of work with themselves, so on different ways to contribute. So code is kind of the one that gets the most attention. I think documentation I think is a unsung hero, highly important, great way. The logistical component, which is invaluable because it allows us to continue with our adoption and evangelization and things like that. So specifically adoption and evangelize. Evangelization is another place that contributors can join and actually spawn a local meetup and then connect in with the existing community and try to, you know, help increase the network, create a new subject. Yeah. >>Yeah. Network affects huge. And I think the thing that you brought up about reuse is, is part of that whole things get documented properly. The leverage that comes out of that just feeds into the system that flywheel. Absolutely. I mean it's, that's how communities are supposed to work, right? Yep. Yes. >>That's what I was just gonna comment on is the flywheel effect that it's clearly present and very palpable. Thank you so much for joining John, me on the program, talking about the contributors summit, the ways of contribution, the impacts that are being made so far, what Octoberfest is already delivering. And we're, we still have about 10 days or so left in October, so there's still more time for contributors to get involved. We thank you so much for your insights and your time. Thank >>You. Thank you so much for having us. >>Our pleasure. For our guests and John Purer, I'm Lisa Martin. You're watching The Cube Live from Chicago, day two of our coverage of Red Hat Ansible Summit 22. We will see you right n after this short break with our next guest.

(digital music) >> And we're back live in New York. It's theCUBE. It's not SNL, it's better than SNL. Lisa Martin and John Furrier here with about 10,000 to 12,000 folks. (John chuckles) There is a ton of energy here. There's a ton of interest in what's going on. But one of the things that we know that AWS is really well-known for is its massive ecosystem. And one of its ecosystem partners is joining us. Please welcome Domenic Ravita, the VP of Product Marketing from SingleStore. Dominic, great to have you on the program. >> Well, thank you. Glad to be here. >> It's a nice opening, wasn't it? (Lisa and John laughing) >> I love SNL. Who doesn't? >> Right? I know. So some big news came out today. >> Yes. >> Funding. Good number. Talk to us a little bit about that before we dig in to SingleStore and what you guys are doing with AWS. >> Right, yeah. Thank you. We announced this morning our latest round, 116 million. We're really grateful to our customers and our investors and the partners and employees and making SingleStore a success to go on this journey of, really, to fulfill our mission to unify and simplify modern, real time data. >> So talk to us about SingleStore. Give us the value prop, the key differentiators, 'cause obviously customers have choice. Help us understand where you're nailing it. >> SingleStore is all about, what we like to say, the moments that matter. When you have an analytical question about what's happening in the moment, SingleStore is your best way to solve that cost-effectively. So that is for, in the case of Thorn, where they're helping to protect and save children from online trafficking or in the case of True Digital, which early in the pandemic, was a company in Southeast Asia that used anonymized phone pings to identify real time population density changes and movements across Thailand to have a proactive response. So really real time data in the moment can help to save lives quite literally. But also it does things that are just good commercially that gives you an advantage like what we do with Uber to help real time pricing and things like this. >> It's interesting this data intensity happening right now. We were talking earlier on theCUBE with another guest and we said, "Why is it happening now?" The big data has been around since the dupe days. That was hard to work with, then data lakes kicked in. But we seem to be, in the past year, everyone's now aware like, "Wow, I got a lot of data." Is it the pandemic? Now we're seeing customers understand the consequences. So how do you look at that? Because is it just timing, evolution? Are they now getting it or is the technology better? Is machine learning better? What's the forces driving the massive data growth acceleration in terms of implementing and getting stuff out, done? (chuckles) >> We think it's the confluence of a lot of those things you mentioned there. First of all, we just celebrate the 15-year anniversary of the iPhone, so that is like wallpaper now. It's just faded into our daily lives. We don't even think of that as a separate thing. So there's an expectation that we all have instant information and not just for the consumer interactions, for the business interactions. That permeates everything. I think COVID with the pandemic forced everyone, every business to try to move to digital first and so that put pressure on the digital service economy to mature even faster and to be digital first. That is what drives what we call data intensity. And more generally, the economic phenomenon is the data intensive era. It's a continuous competition and game for customers. In every moment in every location, in every dimension, the more data hat you have, the better value prop you can give. And so SingleStore is uniquely positioned to and focused on solving this problem of data intensity by bringing and unifying data together. >> What's the big customer success story? Can you share any examples that highlight that? What are some cool things that are happening that can illustrate this new, I won't say bit that's been flipped, that's been happening for a while, but can you share some cutting edge customer successes? >> It's happening across a lot of industries. So I would say first in financial services, FinTech. FinTech is always at the leading edge of these kind of technology adaptions for speeds and things like that. So we have a customer named IEX Cloud and they're focused on providing real time financial data as an API. So it's a data product, API-first. They're providing a lot of historical information on instruments and that sort of thing, as well as real time trending information. So they have customers like Seeking Alpha, for instance, who are providing real time updates on massive, massive data sets. They looked at lots of different ways to do this and there's the traditional, transactionals, LTP database and then maybe if you want to scale an API like theirs, you might have a separate end-memory cache and then yet another database for analytics. And so we bring all that together and simplify that and the benefit of simplification, but it's also this unification and lower latency. Another example is GE who basically uses us to bring together lots of financial information to provide quicker close to the end-of-month process across many different systems. >> So we think about special purpose databases, you mentioned one of the customers having those. We were in the keynote this morning where AWS is like, "We have the broadest set of special purpose databases," but you're saying the industry can't afford them anymore. Why and would it make SingleStore unique in terms of what you deliver? >> It goes back to this data intensity, in that the new business models that are coming out now are all about giving you this instant context and that's all data-driven and it's digital and it's also analytical. And so the reason that's you can't afford to do this, otherwise, is data's getting so big. Moving that data gets expensive, 'cause in the cloud you pay for every byte you store, every byte you process, every byte you move. So data movement is a cost in dollars and cents. It's a cost in time. It's also a cost in skill sets. So when you have many different specialized data sets or data-based technologies, you need skilled people to manage those. So that's why we think the industry needs to be simplified and then that's why you're seeing this unification trend across the database industry and other parts of the stack happening. With AWS, I mean, they've been a great partner of ours for years since we launched our first cloud database product and their perspective is a little bit different. They're offering choice of the specialty, 'cause many people build this way. But if you're going after real time data, you need to bring it. They also offer a SingleStore as a service on AWS. We offer it that way. It's in the AWS Marketplace. So it's easily consumable that way. >> Access to real time data is no longer a nice-to-have for any company, it's table stakes. We saw that especially in the last 20 months or so with companies that needed to pivot so quickly. What is it about SingleStore that delivers, that you talked about moments that matter? Talk about the access to real time data. How that's a differentiator as well? >> I think businesses need to be where their customers are and in the moments their customers are interacting. So that is the real time business-driver. As far as technology wise, it's not easy to do this. And you think about what makes a database fast? A major way of what makes it fast is how you store the data. And so since 2014, when we first released this, what Gartner called at the time, hybrid transaction/analytical processing or HTAP, where we brought transactional data and analytical data together. Fast forward five years to 2019, we released this innovation called Universal Storage, which does that in a single unified table type. Why that matters is because, I would say, basically cost efficiency and better speed. Again, because you pay for the storage and you pay for the movement. If you're not duplicating that data, moving it across different stores, you're going to have a better experience. >> One of the things you guys pioneered is unifying workloads. You mentioned some of the things you've done. Others are now doing it. Snowflake, Google and others. What does that mean for you guys? I mean, 'cause are they copying you? Are they trying to meet the functionality? >> I think. >> I mean, unification. I mean, people want to just store things and make it, get all the table stakes, check boxes, compliance, security and just keep coding and keep building. >> We think it's actually great 'cause they're validating what we've been seeing in the market for years. And obviously, they see that it's needed by customers. And so we welcome them to the party in terms of bringing these unified workloads together. >> Is it easy or hard? >> It's a difficult thing. We started this in 2014. And we've now have lots of production workloads on this. So we know where all the production edge cases are and that capability is also a building block towards a broader, expansive set of capabilities that we've moved onto that next phase and tomorrow actually we have an event called, The Real Time Data Revolution, excuse me, where we're announcing what's in that new product of ours. >> Is that a physical event or virtual? >> It's a virtual event. >> So we'll get the URL on the show notes, or if you know, just go to the new site. >> Absolutely. SingleStore Real Time Data Revolution, you'll find it. >> Can you tease us with the top three takeaways from Revolution tomorrow? >> So like I said, what makes a database fast? It's the storage and we completed that functionality three years ago with Universal Storage. What we're now doing for this next phase of the evolution is making enterprise features available and Workspaces is one of the foundational capabilities there. What SingleStore Workspaces does is it allows you to have this isolation of compute between your different workloads. So that's often a concern to new users to SingleStore. How can I combine transactions and analytics together? That seems like something that might be not a good thing. Well, there are multiple ways we've been doing that with resource governance, workload management. Workspaces offers another management capability and it's also flexible in that you can scale those workloads independently, or if you have a multi-tenant application, you can segment your application, your customer tenant workloads by each workspace. Another capability we're releasing is called Wasm, which is W-A-S-M, Web Assembly. This is something that's really growing in the open source community and SingleStore's contributing to that open source scene, CF project with WASI and Wasm. Where it's been mentioned mostly in the last few years has been in the browser as a more efficient way to run code in the browser. We're adapting that technology to allow you to run any language of your choice in the database and why that's important, again, it's for data movement. As data gets large in petabyte sizes, you can't move it in and out of Pandas in Python. >> Great innovation. That's real valuable. >> So we call this Code Engine with Wasm and- >> What do you call it? >> Code Engine Powered by Wasm. >> Wow. Wow. And that's open source? >> We contribute to the Wasm open source community. >> But you guys have a service that you- >> Yes. It's our implementation and our database. But Wasm allows you to have code that's portable, so any sort of runtime, which is... At release- >> You move the code, not the data. >> Exactly. >> With the compute. (chuckles) >> That's right, bring the compute to the data is what we say. >> You mentioned a whole bunch of great customer examples, GE, Uber, Thorn, you talked about IEX Cloud. When you're in customer conversations, are you dealing mostly with customers that are looking to you to help replace an existing database that was struggling from a performance perspective? Or are you working with startups who are looking to build a product on SingleStore? Is it both? >> It is a mix of both. I would say among SaaS scale up companies, their API, for instance, is their product or their SaaS application is their product. So quite literally, we're the data engine and the database powering their scale to be able to sign that next big customer or to at least sleep at night to know that it's not going to crash if they sign that next big costumer. So in those cases, we're mainly replacing a lot of databases like MySQL, Postgre, where they're typically starting, but more and more we're finding, it's free to start with SingleStore. You can run it in production for free. And in our developer community, we see a lot of customers running in that way. We have a really interesting community member who has a Minecraft server analytics that he's building based on that SingleStore free tier. In the enterprise, it's different, because there are many incumbent databases there. So it typically is a case where there is a, maybe a new product offering, they're maybe delivering a FinTech API or a new SaaS digital offering, again, to better participate in this digital service economy and they're looking for a better price performance for that real time experience in the app. That's typically the starting point, but there are replacements of traditional incumbent databases as well. >> How has the customer conversation evolved the last couple of years? As we talked about, one of the things we learned in the pandemic was access to real time data and those moments that matter isn't a nice-to-have anymore for businesses. There was that force march to digital. We saw the survivors, we're seeing the thrivers, but want to get your perspective on that. From the customers, how has the conversation evolved or elevated, escalated within an organization as every company has to be a data company? >> It really depends on their business strategy, how they are adapting or how they have adapted to this new digital first orientation and what does that mean for them in the direct interaction with their customers and partners. Often, what it means is they realize that they need to take advantage of using more data in the customer and partner interaction and when they come to those new ideas for new product introductions, they find that it's complicated and expensive to build in the old way. And if you're going to have these real time interactions, interactive applications, APIs, with all this context, you're going to have to find a better, more cost-effective approach to get that to market faster, but also not to have a big sprawling data-based technology infrastructure. We find that in those situations, we're replacing four or five different database technologies. A specialized database for key value, a specialized database for search- >> Because there's no unification before? Is that one of the reasons? >> I think it's an awareness thing. I think technology awareness takes a little bit of time, that there's a new way to do things. I think the old saying about, "Don't pave cow paths when the car..." You could build a straight road and pave it. You don't have to pave along the cow path. I think that's the natural course of technology adaption and so as more- >> And the- pandemic, too, highlighted a lot of the things, like, "Do we really need that?" (chuckles) "Who's going to service that?" >> That's right. >> So it's an awakening moment there where it's like, "Hey, let's look at what's working." >> That's right. >> Double down on it. >> Absolutely. >> What are you excited about new round of funding? We talked about, obviously, probably investments in key growth areas, but what excites you about being part of SingleStore and being a partner of AWS? >> SingleStore is super exciting. I've been in this industry a long time as an engineer and an engineering leader. At the time, we were MemSQL, came into SingleStore. And just that unification and simplification, the systems that I had built as a system engineer and helped architect did the job. They could get the speed and scale you needed to do track and trace kinds of use cases in real time, but it was a big trade off you had to make in terms of the complexity, the skill sets you needed and the cost and just hard to maintain. What excites me most about SingleStore is that it really feels like the iPhone moment for databases because it's not something you asked for, but once your friend has it and shows it to you, why would you have three different devices in your pocket with a flip phone, a calculator? (Lisa and Domenic chuckles) Remember these days? >> Yes. >> And a Blackberry pager. (all chuckling) You just suddenly- >> Or a computer. That's in there. >> That's right. So you just suddenly started using iPhone and that is sort of the moment. It feels like we're at it in the database market where there's a growing awareness and those announcements you mentioned show that others are seeing the same. >> And your point earlier about the iPhone throwing off a lot of data. So now you have data explosions at levels that unprecedented, we've never seen before and the fact that you want to have that iPhone moment, too, as a database. >> Absolutely. >> Great stuff. >> The other part of your question, what excites us about AWS. AWS has been a great partner since the beginning. I mean, when we first released our database, it was the cloud database. It was on AWS by customer demand. That's where our customers were. That's where they were building other applications. And now we have integrations with other native services like AWS Glue and we're in the Marketplace. We've expanded, that said we are a multi-cloud system. We are available in any cloud of your choice and on premise and in hybrid. So we're multi-cloud, hybrid and SaaS distribution. >> Got it. All right. >> Got it. So the event is tomorrow, Revolution. Where can folks go to register? What time does it start? >> 1:00 PM Eastern and- >> 1:00 PM. Eastern. >> Just Google SingleStore Real Time Data Revolution and you'll find it. Love for everyone to join us. >> All right. We look forward to it. Domenic, thank you so much for joining us, talking about SingleStore, the value prop, the differentiators, the validation that's happening in the market and what you guys are doing with AWS. We appreciate it. >> Thanks so much for having me. >> Our pleasure. For Domenic Ravita and John Furrier, I'm Lisa Martin. You're watching theCUBE, live from New York at AWS Summit 22. John and I are going to be back after a short break, so come back. (digital pulsing music)

(upbeat music) >> Hey, guys and girls, welcome back to New York City. Lisa Martin and John Furrier are live with theCUBE at AWS Summit 22, here in The Big Apple. We're excited to be talking about security next. James Arlen joins us, the CISO at Aiven. James, thanks so much for joining us on theCUBE today. >> Absolutely, it's good to be here. >> Tell the audience a little bit about Aiven, what you guys do, what you deliver, and what some of those differentiators are. >> Oh, Aiven. Aiven is a fantastic organization. I'm actually really lucky to work there. It's a database as a service, managed databases, all open source. And we're capital S, serious about open source. So 10 different open source database products delivered as a platform, all managed services, and the game is really about being the most performant, secure, and compliant database as a service on the market, friction free for your developers. You don't need people worrying about how to run databases. You just want to be able to say, here, take care of my data for me. And that's what we do. And that's actually the differentiator. We just take care of it for you. >> Take care of it for you, I like that. >> So they download the open source. They could do it on their own. So all the different projects are out there. >> Yeah, absolutely. >> What do you guys bringing to the table? You said the managed service, can you explain that. >> Yeah, the managed service aspect of it is, really, you could install the software yourself. You can use Postgres or Apache Kafka or any one of the products that we support. Absolutely you can do it yourself. But is that really what you do for a living, or do you develop software, or do you sell a product? So we take and do the hard work of running the systems, running the equipment. We take care of backups, high availability, all the security and compliance things around access and certifications, all of those things that are logging, all of that stuff that's actually difficult to do, well and consistently, that's all we do. >> Talk about the momentum, I see you guys were founded in what? 2016? >> Yes. >> Just in May of '22, raised $210 million in series D funding. >> Yes. >> Talk about the momentum and also from your perspective, all of the massive changes in security. >> It's very interesting to work for a company where you're building more than 100% growth year over year. It's a powers of two thing. Going from one to two, not so scary, two to four, not so scary. 512 to 1024, it's getting scary. (Lisa chuckles) 1024 to 2048, oh crap! I've been with Aiven for just almost two years now, and we are less than 70 when I started, and we're near 500 now. So, explosive growth is very interesting, but it's also that, you're growing within a reasonable burn rate boundary as well. And what that does from a security perspective, is it leaves you in the position that I had. I walked in and I was the first actual CISO. I had a team of four, I now have a team of 40. Because it turns out that like a lot of things in life, as you start unpacking problems, they're kind of fractal. You unpack the problem, you're like oh, well I did deal with that problem, but now I got another problem that I got to deal with. And so there's, it's not turtles all the way down. >> There's a lot of things going on and other authors, survive change. >> And there's fundamental problems that are still not fixed. And yet we treat them like they're fixed. And so we're doing a lot of hard work to make it so that we don't have to do hard work ongoing. >> And that's the value of the managed service. >> Yes. >> Okay, so talk about competition. Obviously, we had ETR on which is Enterprise Research Firm that we trust, we like. And we were looking at the data with the headwinds in the market, looking at the different players like got Amazon has Redshift, Snowflake, and you got Azure Sequence. I think it's called one of those products. The money that's being shifted from on premise data where the old school data warehouse like terra data and whatnot, is going first to Snowflake, then to Azure, then to AWS. Yes, so that points to snowflake being kind of like the bell of the ball if you will, in terms of from a data cloud. >> Absolutely. >> How do you compete with them? What's the pitch 'Cause that seemed to be a knee-jerk reaction from the industry. 'Cause snowflake is hot. They have a good value product. They have a smart team, Databrick is out there too. >> Yeah I mean... >> how do you guys compete against all that. >> So this is that point where you're balancing the value of a specific technology, or a specific technology vendor. And am I going to be stuck with them? So I'm tying my future to their future. With open source, I'm tying my future to the common good right. The internet runs on open source. It doesn't run on anything closed. And so I'm not hitching my wagon to something that I don't control. I'm hitching it to something where, any one of our customers could decide. I'm not getting the value I need from Aiven anymore. I need to go. And we provide you with the tools necessary, to move from our open source managed service to your own. Whether you go on-prem or you run it yourself, on a cloud service provider, move your data to you because it's your data. It's not ours. How can I hold your data? It's like weird extortion ransoming thing. >> Actually speaking, I mean enterprise, it's a big land grab 'cause with cloud you're horizontally scalable. It's a beautiful thing, open source is booming. It's going in Aiven, every day it's just escalating higher and higher. >> Absolutely. >> It is the software business. So open is open. Integration and scale seems to be the competitive advantage. >> Yeah. >> Right. So, how do you guys compete with that? Because now you got open source. How do you offer the same benefits without the lock in, or what's the switching costs? How do you guys maintain that position of not saying the same thing in Snowflake? >> Because all of the biggest data users and consumers tend to give away their data products. LinkedIn gave away their data product. Uber gave away their data product, Facebook gave away their data product. And we now use those as community solutions. So, if the product works for something the scale of LinkedIn, or something the scale of Uber. It will probably work for you too. And scale is just... >> Well Facebook and LinkedIn, they gave away the product to own the data to use against you. >> But it's the product that counts because you need to be able to manipulate data the way they manipulate data, but with yours. >> So low latency needs to work. So horizontally, scalable, fees, machine learning. That's what we're seeing. How do you make that available? Customers want on architecture? What do you recommend? Control plane, data plane, how do you think about that? >> It's interesting. There's architectural reasons to think about it in terms like that. And there's other good architectural reasons to not think about it. There's sort of this dividing line in the cloud, where your cloud service provider, takes over and provides you with the opportunity to say, I don't know. And I don't care >> As long as it's secure >> As long as it's secure absolutely. But there's sort of that water line idea, where if it's below the water line, let somebody else deal. >> What is in the table stakes? 'Cause I like that approach. I think that's a good value proposition. Store it, what boxes have to be checked? Compliance, secure, what are some of the boxes? >> You need to make sure that you've taken care of all of the same basics if you are still running it. Remember you can't absolve yourself of your duty to your customer. You're still on the hook. So, you have to have backups. You have to have access control. You have to understand who's administering it, and how and what they're doing. Good logging, good comprehension there. You have to have anomaly detection, secure operations. You have to have all those compliance check boxes. Especially if you're dealing with regulated data type like PCI data or HIPAA health data or you know what there's other countries besides the United States, there's other kinds of of compliance obligations there. So you have to make sure that you've got all that taken into account. And remember that, like I said, you can't absolve yourself with those things. You can share responsibilities. But you can't walk away from that responsibility. So you still have to make sure that you validate that your vendor knows what they're talking about. >> I wanted to ask you about the cybersecurity skills gap. So I'm kind of giving a little segue here, because you mentioned you've been with Aiven for about two years. >> Almost. >> Almost two years. You've started with a team of four. You've grown at 10X in less than two years. How have you accomplished that, considering we're seeing one of the biggest skills shortages in cyber in history. >> It's amazing, you see this show up in a lot of job Ads, where they ask for 10 years of experience in something that's existed for three years. (John Furrier laughs) And it's like okay, well if I just be logical about this I can hire somebody at less than the skill level that I need today, and bring them up to that skill level. Or I can spend the same amount of time, hoping that I'll find the magical person that has that set of skills that I need. So I can solve the problem of the skills gap by up-skilling the people that I hire. Which is strangely contrary to how this thing works. >> The other thing too, is the market's evolving so fast that, that carry up and pulling someone along, or building and growing your own so to speak is workable. >> It also really helps us with a bunch of sustainability goals. It really helps with anything that has to do with diversity and inclusion, because I can bring forward people who are never given a chance. And say, you know what? You don't have that magical ticket in life, but damn you know what you're talking about? >> It's a classic pedigree. I went to this school, I studied this degree. There's no degree if have to stop a hacker using state of the art malware. (John Furrier laughs) >> Exactly. What I do today as a job, didn't exist when I was in post-secondary at all. >> So when you hire, what do you look for? I mean obviously problem solving. What's your kind of algorithm for hiring? >> Oh, that's a really interesting question. The quickest sort of summary of it is, I'm looking for not a jerk. >> Not a jerk. >> Yeah. >> Okay. >> Because it turns out that the quality that I can't fix in a candidate, is I can't fix whether or not they're a jerk, but I can up-skill them, I can educate them. I can teach them of a part of the world that they've not had any interaction with. But if they're not going to work with the team, if they're going to be, look at me, look at me. If they're going to not have that moment of, I have this great job, and I get to work today. And that's awesome. (Lisa Martin laughs) That's what I'm trying to hire for. >> The essence of this teamwork is fundamental. >> Collaboration. >> Cooperation. >> Curiosity. >> That's the thing yeah, absolutely. >> And everybody? >> Those things, oh absolutely. Those things are really, really hard to interview for. And they're impossible to fix after the fact. So that's where you really want to put the effort. 'Cause I can teach you how to use a computer. I mean it's hard, but it's not that hard. >> Yeah, yeah, yeah. >> Well I love the current state of data management. Good overview, you guys are in the good position. We love open source. Been covering it for, since theCUBE started. It continues to redefine more and more the industry. It is the software industry. Now there's no debate about that. If people want to have that debate, that's kind of waste of time, but there are other ways that are happening. So I have to ask you. As things are going forward with innovation. Okay, if opensource is going to be the software industry. Where's the value? >> That's a fun question wow? >> Is it going to be in the community? Is it the integration? Is it the scale? If you're open and you have low switching costs... >> Yeah so, when you look at Aiven's commitment to open source, a huge part of that is our open source project office, where we contribute back to those core products, whether it's parts of the Apache Foundation, or Postgres, or whatever. We contribute to those, because we have staff who work on those products. They don't work on our stuff. They work on those. And it's like the opposite of a zero sum game. It's more like Nash equilibrium. If you ever watch that movie, "A beautiful mind." That great idea of, you don't have to have winners and losers. You can have everybody loses a little bit but everybody wins a little bit. >> Yeah and that's the open the ethos. >> And that's where it gets tied up. >> Another follow up on that. The other thing I want to get your reaction on is that, now in this modern era of open source, almost all corporations are part of projects. I mean if you're an entrepreneur and you want to get funding it's pretty simple. You start open source project. How many stars you get on GitHub guarantees it's a series C round, pretty much. So open source now has got this new thing going on, where it's not just open source folks who believe in it It's an operating model. What's the dynamic of corporations being part of the system. It used to be, oh what's the balance between corporate and influence, now it's standard. What's your reaction? >> They can do good and they can do harm. And it really comes down to why are you in it? So if you look at the example of open search, which is one of the data products that we operate in the Aiven system. That's a collaboration between Aiven. Hey we're an awesome company, but we're nowhere near the size of AWS. And AWS where we're working together on it. And I just had this conversation with one of the attendees here, where he said, "Well AWS is going to eat your story there. "You're contributing all of this "to the open search platform. "And then AWS is going to go and sell it "and they're going to make more money." And I'm like yep, they are. And I've got staff who work for the organization, who are more fulfilled because they got to deliver something that's used by millions of people. And you think about your jobs. That moment of, (sighs) I did a cool thing today. That's got a lot of value in it. >> And part of something. >> Exactly. >> As a group. >> 100%. >> Exactly. >> And we end up with a product that's used by millions. Some of it we'll capture, because we do a better job running than the AWS does, but everybody ends up winning out of the backend. Again, everybody lost a little, but everybody also won. And that's better than that whole, you have to lose so that I can win. At zero something, that doesn't work. >> I think the silo conversations are coming, what's the balance between siloing something and why that happens. And then what's going to be freely accessible for data. Because the real time information is based upon what you can access. "Hey Siri, what's the weather. "We had a guest on earlier." It says, oh that's a data query. Well, if the weather is, the data weathers stored in a database that's out here and it can't get to the response on the app. Yeah, that's not good, but the data is available. It just didn't get delivered. >> Yeah >> Exactly. >> This is an example of what people are realizing now the consequences of this data, collateral damage or economy value. >> Yeah, and it's understanding how data fits in your environment. And I don't want to get on the accountants too hard, but the accounting organizations, AICPA and ISAE and others, they haven't really done a good job of helping you understand data as an asset, or data as a liability. I hold a lot of customer data. That's a liability to me. It's going to blow up in my face. We don't talk about the income that we get from data, Google. We don't talk about the expense of regenerating that data. We talk about, well what happens if you lose it? I don't know. And we're circling the drain around fiduciary responsibility, and we know how to do this. If you own a manufacturing plant, or if you own a fleet of vehicles you understand the fiduciary duty of managing your asset. But because we can't touch it, we don't do a good job of it. >> How far do you think are people getting into the point where they actually see that asset? Because I think it's out of sight out of mind. Now there's consequences, there's now it's public companies might have to do filings. It's not like sustainability and data. Like, wait a minute, I got to deal with these things. >> It's interesting, we got this great benefit of the move to cloud computing, and the move to utility style computing. But we took away that. I got to walk around and pet my computers. Like oh! This is my good database. I'm very proud of you. Like we're missing that piece now. And when you think about the size of data centers, we become detached from that, you don't really think about, Aiven operates tens of thousands of machines. It would take entire buildings to hold them all. You don't think about it. So how do you recreate that visceral connection to your data? Well, you need to start actually thinking about it. And you need to do some of that tokenization. When was the last time you printed something out, like you get a report and happens to me all the time with security reports. Look at a security report and it's like 150 page PDF. Scroll, scroll, scroll, scroll. Print it out, stump it on the table in front of you. Oh, there's gravitas here. There's something here. Start thinking about those records, count them up, and then try to compare that to something in the real world. My wife is a school teacher, kindergarten to grade three, and tokenizing math is how they teach math to little kids. You want to count something? Here's 10 things, count them. Well, you've got 60,000 customer records, or you have 2 billion data points in your IOT database, tokenize that, what does 2 billion look like? What does $1 million look like in the form of $100 dollars bills on a pallet? >> Wow. >> Right. Tokenize that data, create that visceral connection with it, and then talk about it. >> So when you say tokenized, you mean like token as in decentralization token? >> No, I mean create like a totem or an icon of it. >> Okay, got it. >> A thing you can hold holy. If you're a token company. >> Not token as in Token economics and Crypto. >> If you're a mortgage company, take that customer record for one of your customers, print it out and hold the file. Like in a Manila folder, like it's 1963. Hold that file, and then say yes. And you're explaining to somebody and say yes, and we have 3 million of these. If we printed them all out, it would take up a room this size. >> It shows the scale. >> Right. >> Right. >> Exactly, create that connection back to the human level of interaction with data. How do you interact with a terabyte of data, but you do. >> Right. >> But once she hits upgrade from Google drive. (team laughs) >> What's a terabyte right? We don't hold that anymore. >> Right, right. >> Great conversation. >> Recreate that connection. Talk about data that way. >> The visceral connection with data. >> Follow up after this event. We'd love to dig more and love the approach. Love open source, love what you're doing there. That's a very unique approach. And it's also an alternative to some of the other vast growing plus your valuations are very high too. So you're not like a... You're not too far away from these big valuations. So congratulations. >> Absolutely. >> Yeah excellent, I'm sure there's lots of work to do, lots of strategic work to do with that round of funding. But also lots of opportunity, that it's going to open up, and we know you don't hire jerks. >> I don't >> You have a whole team of non jerks. That's pretty awesome. Especially 40 of 'em. That's impressive James.| >> It is. >> Congratulations to you on what you've accomplished in the course of the team. And thank you for sharing your insights with John and me today, we appreciate it. >> Awesome. >> Thanks very much, it's been great. >> Awesome, for John furrier, I'm Lisa Martin and you're watching theCube, live in New York city at AWS Summit NYC 22, John and I will be right back with our next segment, stick around. (upbeat music)

(gentle music) (upbeat music) (crowd chattering) >> Welcome back to The City That Never Sleeps. Lisa Martin and John Furrier in New York City for AWS Summit '22 with about 10 to 12,000 of our friends. And we've got two more friends joining us here today. We're going to be talking with Haseeb Budhani, one of our alumni, co-founder and CEO of Rafay Systems, and Kevin Coleman, senior manager for Go-to Market for EKS at AWS. Guys, thank you so much for joining us today. >> Thank you very much for having us. Excited to be here. >> Isn't it great to be back at an in-person event with 10, 12,000 people? >> Yes. There are a lot of people here. This is packed. >> A lot of energy here. So, Haseeb, we've got to start with you. Your T-shirt says it all. Don't hate k8s. (Kevin giggles) Talk to us about some of the trends, from a Kubernetes perspective, that you're seeing, and then Kevin will give your follow-up. >> Yeah. >> Yeah, absolutely. So, I think the biggest trend I'm seeing on the enterprise side is that enterprises are forming platform organizations to make Kubernetes a practice across the enterprise. So it used to be that a BU would say, "I need Kubernetes. I have some DevOps engineers, let me just do this myself." And the next one would do the same, and then next one would do the same. And that's not practical, long term, for an enterprise. And this is now becoming a consolidated effort, which is, I think it's great. It speaks to the power of Kubernetes, because it's becoming so important to the enterprise. But that also puts a pressure because what the platform team has to solve for now is they have to find this fine line between automation and governance, right? I mean, the developers, you know, they don't really care about governance. Just give me stuff, I need to compute, I'm going to go. But then the platform organization has to think about, how is this going to play for the enterprise across the board? So that combination of automation and governance is where we are finding, frankly, a lot of success in making enterprise platform team successful. I think, that's a really new thing to me. It's something that's changed in the last six months, I would say, in the industry. I don't know if, Kevin, if you agree with that or not, but that's what I'm seeing. >> Yeah, definitely agree with that. We see a ton of customers in EKS who are building these new platforms using Kubernetes. The term that we hear a lot of customers use is standardization. So they've got various ways that they're deploying applications, whether it's on-prem or in the cloud and region. And they're really trying to standardize the way they deploy applications. And Kubernetes is really that compute substrate that they're standardizing on. >> Kevin, talk about the relationship with Rafay Systems that you have and why you're here together. And two, second part of that question, why is EKS kicking ass so much? (Haseeb and Kevin laughing) All right, go ahead. First one, your relationship. Second one, EKS is doing pretty well. >> Yep, yep, yep. (Lisa laughing) So yeah, we work closely with Rafay, Rafay, excuse me. A lot of joint customer wins with Haseeb and Co, so they're doing great work with EKS customers and, yeah, love the partnership there. In terms of why EKS is doing so well, a number of reasons, I think. Number one, EKS is vanilla, upstream, open-source Kubernetes. So customers want to use that open-source technology, that open-source Kubernetes, and they come to AWS to get it in a managed offering, right? Kubernetes isn't the easiest thing to self-manage. And so customers, you know, back before EKS launched, they were banging down the door at AWS for us to have a managed Kubernetes offering. And, you know, we launched EKS and there's been a ton of customer adoption since then. >> You know, Lisa, when we, theCUBE 12 years, now everyone knows we started in 2010, we used to cover a show called OpenStack. >> I remember that. >> OpenStack Summit. >> What's that now? >> And at the time, at that time, Kubernetes wasn't there. So theCUBE was present at creation. We've been to every KubeCon ever, CNCF then took it over. So we've been watching it from the beginning. >> Right. And it reminds me of the same trend we saw with MapReduce and Hadoop. Very big promise, everyone loved it, but it was hard, very difficult. And Hadoop's case, big data, it ended up becoming a data lake. Now you got Spark, or Snowflake, and Databricks, and Redshift. Here, Kubernetes has not yet been taken over. But, instead, it's being abstracted away and or managed services are emerging. 'Cause general enterprises can't hire enough Kubernetes people. >> Yep. >> They're not that many out there yet. So there's the training issue. But there's been the rise of managed services. >> Yep. >> Can you guys comment on what your thoughts are relative to that trend of hard to use, abstracting away the complexity, and, specifically, the managed services? >> Yeah, absolutely. You want to go? >> Yeah, absolutely. I think, look, it's important to not kid ourselves. It is hard. (Johns laughs) But that doesn't mean it's not practical, right. When Kubernetes is done well, it's a thing of beauty. I mean, we have enough customer to scale, like, you know, it's like a, forget a hockey stick, it's a straight line up, because they just are moving so fast when they have the right platform in place. I think that the mistake that many of us make, and I've made this mistake when we started this company, was trivializing the platform aspect of Kubernetes, right. And a lot of my customers, you know, when they start, they kind of feel like, well, this is not that hard. I can bring this up and running. I just need two people. It'll be fine. And it's hard to hire, but then, I need two, then I need two more, then I need two, it's a lot, right. I think, the one thing I keep telling, like, when I talk to analysts, I say, "Look, somebody needs to write a book that says, 'Yes, it's hard, but, yes, it can be done, and here's how.'" Let's just be open about what it takes to get there, right. And, I mean, you mentioned OpenStack. I think the beauty of Kubernetes is that because it's such an open system, right, even with the managed offering, companies like Rafay can build really productive businesses on top of this Kubernetes platform because it's an open system. I think that is something that was not true with OpenStack. I've spent time with OpenStack also, I remember how it is. >> Well, Amazon had a lot to do with stalling the momentum of OpenStack, but your point about difficulty. Hadoop was always difficult to maintain and hiring against. There were no managed services and no one yet saw that value of big data yet. Here at Kubernetes, people are living a problem called, I'm scaling up. >> Yep. And so it sounds like it's a foundational challenge. The ongoing stuff sounds easier or manageable. >> Once you have the right tooling. >> Is that true? >> Yeah, no, I mean, once you have the right tooling, it's great. I think, look, I mean, you and I have talked about this before, I mean, the thesis behind Rafay is that, you know, there's like 8, 12 things that need to be done right for Kubernetes to work well, right. And my whole thesis was, I don't want my customer to buy 10, 12, 15 products. I want them to buy one platform, right. And I truly believe that, in our market, similar to what vCenter, like what VMware's vCenter did for VMs, I want to do that for Kubernetes, right. And that the reason why I say that is because, see, vCenter is not about hypervisors, right? vCenter is about hypervisor, access, networking, storage, all of the things, like multitenancy, all the things that you need to run an enterprise-grade VM environment. What is that equivalent for the Kubernetes world, right? So what we are doing at Rafay is truly building a vCenter, but for Kubernetes, like a kCenter. I've tried getting the domain. I couldn't get it. (Kevin laughs) >> Well, after the Broadcom view, you don't know what's going to happen. >> Ehh. (John laughs) >> I won't go there! >> Yeah. Yeah, let's not go there today. >> Kevin, EKS, I've heard people say to me, "Love EKS. Just add serverless, that's a home run." There's been a relationship with EKS and some of the other Amazon tools. Can you comment on what you're seeing as the most popular interactions among the services at AWS? >> Yeah, and was your comment there, add serverless? >> Add serverless with AKS at the edge- >> Yeah. >> and things are kind of interesting. >> I mean, so, one of the serverless offerings we have today is actually Fargate. So you can use Fargate, which is our serverless compute offering, or one of our serverless compute offerings with EKS. And so customers love that. Effectively, they get the beauty of EKS and the Kubernetes API but they don't have to manage nodes. So that's, you know, a good amount of adoption with Fargate as well. But then, we also have other ways that they can manage their nodes. We have managed node groups as well, in addition to self-managed nodes also. So there's a variety of options that customers can use from a compute perspective with EKS. And you'll continue to see us evolve the portfolio as well. >> Can you share, Haseeb, can you share a customer example, a joint customer example that you think really articulates the value of what Rafay and AWS are doing together? >> Yeah, absolutely. In fact, we announced a customer very recently on this very show, which is MoneyGram, which is a joint AWS and Rafay customer. Look, we have enough, you know, the thing about these massive customers is that, you know, not everybody's going to give us their logo to use. >> Right. >> But MoneyGram has been a Rafay plus EKS customer for a very, very long time. You know, at this point, I think we've earned their trust, and they've allowed us to, kind of say this publicly. But there's enough of these financial services companies who have, you know, standardized on EKS. So it's EKS first, Rafay second, right. They standardized on EKS. And then they looked around and said, "Who can help me platform EKS across my enterprise?" And we've been very lucky. We have some very large financial services, some very large healthcare companies now, who, A, EKS, B, Rafay. I'm not just saying that because my friend Kevin's here, (Lisa laughs) it's actually true. Look, EKS is a brilliant platform. It scales so well, right. I mean, people try it out, relative to other platforms, and it's just a no-brainer, it just scales. You want to build a big enterprise on the backs of a Kubernetes platform. And I'm not saying that's because I'm biased. Like EKS is really, really good. There's a reason why so many companies are choosing it over many other options in the market. >> You're doing a great job of articulating why the theme (Kevin laughs) of the New York City Summit is scale anything. >> Oh, yeah. >> There you go. >> Oh, yeah. >> I did not even know that but I'm speaking the language, right? >> You are. (John laughs) >> Yeah, absolutely. >> One of the things that we're seeing, also, I want to get your thoughts on, guys, is the app modernization trend, right? >> Yep. >> Because unlike other standards that were hard, that didn't have any benefit downstream 'cause they were too hard to get to, here, Kubernetes is feeding into real app for app developer pressure. They got to get cloud-native apps out. It's fairly new in the mainstream enterprise and a lot of hyperscalers have experience. So I'm going to ask you guys, what is the key thing that you're enabling with Kubernetes in the cloud-native apps? What is the key value? >> Yeah. >> I think, there's a bifurcation happening in the market. One is the Kubernetes Engine market, which is like EKS, AKS, GKE, right. And then there's the, you know, what, back in the day, we used to call operations and management, right. So the OAM layer for Kubernetes is where there's need, right. People are learning, right. Because, as you said before, the skill isn't there, you know, there's not enough talent available to the market. And that's the opportunity we're seeing. Because to solve for the standardization, the governance, and automation that we talked about earlier, you know, you have to solve for, okay, how do I manage my network? How do I manage my service mesh? How do I do chargebacks? What's my, you know, policy around actual Kubernetes policies? What's my blueprinting strategy? How do I do add-on management? How do I do pipelines for updates of add-ons? How do I upgrade my clusters? And we're not done yet, there's a longer list, right? This is a lot, right? >> Yeah. >> And this is what happens, right. It's just a lot. And really, the companies who understand that plethora of problems that need to be solved and build easy-to-use solutions that enterprises can consume with the right governance automation, I think they're going to be very, very successful here. >> Yeah. >> Because this is a train, right? I mean, this is happening whether, it's not us, it's happening, right? Enterprises are going to keep doing this. >> And open-source is a big driver in all of this. >> Absolutely. >> Absolutely. >> And I'll tag onto that. I mean, you talked about platform engineering earlier. Part of the point of building these platforms on top of Kubernetes is giving developers an easier way to get applications into the cloud. So building unique developer experiences that really make it easy for you, as a software developer, to take the code from your laptop, get it out of production as quickly as possible. The question is- >> So is that what you mean, does that tie your point earlier about that vertical, straight-up value once you've set up it, right? >> Yep. >> Because it's taking the burden off the developers for stopping their productivity. >> Absolutely. >> To go check in, is it configured properly? Is the supply chain software going to be there? Who's managing the services? Who's orchestrating the nodes? >> Yep. >> Is that automated, is that where you guys see the value? >> That's a lot of what we see, yeah. In terms of how these companies are building these platforms, is taking all the component pieces that Haseeb was talking about and really putting it into a cohesive whole. And then, you, as a software developer, you don't have to worry about configuring all of those things. You don't have to worry about security policy, governance, how your app is going to be exposed to the internet. >> It sounds like infrastructure is code. >> (laughs) Yeah. >> Come on, like. >> (laughs) Infrastructure's code is a big piece of it, for sure, for sure. >> Yeah, look, infrastructure's code actually- >> Infrastructure's sec is code too, the security. >> Yeah. >> Huge. >> Well, it all goes together. Like, we talk about developer self-service, right? The way we enable developer self-service is by teaching developers, here's a snippet of code that you write and you check it in and your infrastructure will just magically be created. >> Yep. >> But not automatically. It's going to go through a check, like a check through the platform team. These are the workflows that if you get them right, developers don't care, right. All developers want is I want to compute. But then all these 20 things need to happen in the back. That's what, if you nail it, right, I mean, I keep trying to kind of pitch the company, I don't want to do that today. But if you nail that, >> I'll give you a plug at the end. >> you have a good story. >> But I got to, I just have a tangent question 'cause you reminded me. There's two types of developers that have emerged, right. You have the software developer that wants infrastructures code. I just want to write my code, I don't want to stop. I want to build in shift-left for security, shift-right for data. All that's in there. >> Right. >> I'm coding away, I love coding. Then you've got the under-the-hood person. >> Yes. >> I've been to the engines. >> Certainly. >> So that's more of an SRE, data engineer, I'm wiring services together. >> Yeah. >> A lot of people are like, they don't know who they are yet. They're in college or they're transforming from an IT job. They're trying to figure out who they are. So question is, how do you tell a person that's watching, like, who am I? Like, should I be just coding? But I love the tech. Would you guys have any advice there? >> You know, I don't know if I have any guidance in terms of telling people who they are. (all laughing) I mean, I think about it in terms of a spectrum and this is what we hear from customers, is some customers want to shift as much responsibility onto the software teams to manage their infrastructure as well. And then some want to shift it all the way over to the very centralized model. And, you know, we see everything in between as well with our EKS customer base. But, yeah, I'm not sure if I have any direct guidance for people. >> Let's see, any wisdom? >> Aside from experiment. >> If you're coding more, you're a coder. If you like to play with the hardware, >> Yeah. >> or the gears. >> Look, I think it's really important for managers to understand that developers, yes, they have a job, you have to write code, right. But they also want to learn new things. It's only fair, right. >> Oh, yeah. >> So what we see is, developers want to learn. And we enable for them to understand Kubernetes in small pieces, like small steps, right. And that is really, really important because if we completely abstract things away, like Kubernetes, from them, it's not good for them, right. It's good for their careers also, right. It's good for them to learn these things. This is going to be with us for the next 15, 20 years. Everybody should learn it. But I want to learn it because I want to learn, not because this is part of my job, and that's the distinction, right. I don't want this to become my job because I want, I want to write my code. >> Do what you love. If you're more attracted to understanding how automation works, and robotics, or making things scale, you might be under-the-hood. >> Yeah. >> Yeah, look under the hood all day long. But then, in terms of, like, who keeps the lights on for the cluster, for example. >> All right, see- >> That's the job. >> He makes a lot of value. Now you know who you are. Ask these guys. (Lisa laughing) Congratulations on your success on EKS 2. >> Yeah, thank you. >> Quick, give a plug for the company. I know you guys are growing. I want to give you a minute to share to the audience a plug that's going to be, what are you guys doing? You're hiring? How many employees? Funding? Customer new wins? Take a minute to give a plug. >> Absolutely. And look, I come see, John, I think, every show you guys are doing a summit or a KubeCon, I'm here. (John laughing) And every time we come, we talk about new customers. Look, platform teams at enterprises seem to love Rafay because it helps them build that, well, Kubernetes platform that we've talked about on the show today. I think, many large enterprises on the financial service side, healthcare side, digital native side seem to have recognized that running Kubernetes at scale, or even starting with Kubernetes in the early days, getting it right with the right standards, that takes time, that takes effort. And that's where Rafay is a great partner. We provide a great SaaS offering, which you can have up and running very, very quickly. Of course, we love EKS. We work with our friends at AWS. But also works with Azure, we have enough customers in Azure. It also runs in Google. We have enough customers at Google. And it runs on-premises with OpenShift or with EKS A, right, whichever option you want to take. But in terms of that standardization and governance and automation for your developers to move fast, there's no better product in the market right now when it comes to Kubernetes platforms than Rafay. >> Kevin, while we're here, why don't you plug EKS too, come on. >> Yeah, absolutely, why not? (group laughing) So yes, of course. EKS is AWS's managed Kubernetes offering. It's the largest managed Kubernetes service in the world. We help customers who want to adopt Kubernetes and adopt it wherever they want to run Kubernetes, whether it's in region or whether it's on the edge with EKS A or running Kubernetes on Outposts and the evolving portfolio of EKS services as well. We see customers running extremely high-scale Kubernetes clusters, excuse me, and we're here to support them as well. So yeah, that's the managed Kubernetes offering. >> And I'll give the plug for theCUBE, we'll be at KubeCon in Detroit this year. (Lisa laughing) Lisa, look, we're giving a plug to everybody. Come on. >> We're plugging everybody. Well, as we get to plugs, I think, Haseeb, you have a book to write, I think, on Kubernetes. And I think you're wearing the title. >> Well, I do have a book to write, but I'm one of those people who does everything at the very end, so I will never get it right. (group laughing) So if you want to work on it with me, I have some great ideas. >> Ghostwriter. >> Sure! >> But I'm lazy. (Kevin chuckles) >> Ooh. >> So we got to figure something out. >> Somehow I doubt you're lazy. (group laughs) >> No entrepreneur's lazy, I know that. >> Right? >> You're being humble. >> He is. So Haseeb, Kevin, thank you so much for joining John and me today, >> Thank you. >> talking about what you guys are doing at Rafay with EKS, the power, why you shouldn't hate k8s. We appreciate your insights and your time. >> Thank you as well. >> Yeah, thank you very much for having us. >> Our pleasure. >> Thank you. >> We appreciate it. With John Furrier, I'm Lisa Martin. You're watching theCUBE live from New York City at the AWS NYC Summit. John and I will be right back with our next guest, so stick around. (upbeat music) (gentle music)

>>Hello, everyone. Welcome to the cubes coverage here. New York city for AWS Amazon web services summit 2022. I'm John furrier, host of the cube with Dave ante. My co-host. We are breaking it down, getting an update on the ecosystem. As the GDP drops, inflations up gas prices up the enterprise continues to grow. We're seeing exceptional growth. We're here on the ground floor. Live at the Summit's packed house, 10,000 people. Eric Bradley's here. Chief STR at ETR, one of the premier enterprise research firms out there, partners with the cube and powers are breaking analysis that Dave does check that out as the hottest podcast in enterprise. Eric. Great to have you on the cube. Thanks for coming on. >>Thank you so much, John. I really appreciate the collaboration always. >>Yeah. Great stuff. Your data's amazing ETR folks watching check out ETR. They have a unique formula, very accurate. We love it. It's been moving the market. Congratulations. Let's talk about the market right now. This market is booming. Enterprise is the hottest thing, consumers kind of in the toilet. Okay. I said that all right, back out devices and, and, and consumer enterprise is still growing. And by the way, this first downturn, the history of the world where hyperscalers are on full pumping on all cylinders, which means they're still powering the revolution. >>Yeah, it's true. The hyperscalers were basically at this two sun system when Microsoft and an AWS first came around and everything was orbiting around it. And we're starting to see that sun cool off a little bit, but we're talking about a gradient here, right? When we say cool off, we're not talking to shutdown, it's still burning hot. That's for sure. And I can get it to some of the macro data in a minute, if that's all right. Or do you want me to go right? No, go go. Right. Yeah. So right now we just closed our most recent survey and that's macro and vendor specific. We had 1200 people talk to us on the macro side. And what we're seeing here is a cool down in spending. We originally had about 8.5% increase in budgets. That's cool down is 6.5 now, but I'll say with the doom and gloom and the headlines that we're seeing every day, 6.5% growth coming off of what we just did the last couple of years is still pretty fantastic as a backdrop. >>Okay. So you, you started to see John mentioned consumer. We saw that in Snowflake's earnings. For example, we, we certainly saw, you know, Walmart, other retailers, the FA Facebooks of the world where consumption was being dialed down, certain snowflake customers. Not necessarily, they didn't have mentioned any customers, but they were able to say, all right, we're gonna dial down, consumption this quarter, hold on until we saw some of that in snowflake results and other results. But at the same time, the rest of the industry is booming. But your data is showing softness within the fortune 500 for AWS, >>Not only AWS, but fortune 500 across the board. Okay. So going back to that larger macro data, the biggest drop in spending that we captured is fortune 500, which is surprising. But at the same time, these companies have a better purview into the economy. In general, they tend to see things further in advance. And we often remember they spend a lot of money, so they don't need to play catch up. They'll easily more easily be able to pump the brakes a little bit in the fortune 500. But to your point, when we get into the AWS data, the fortune 500 decrease seems to be hitting them a little bit more than it is Azure and GCP. I >>Mean, we're still talking about a huge business, right? >>I mean, they're catching up. I mean, Amazon has been transforming from owning the developer cloud startup cloud decade ago to really putting a dent on the enterprise as being number one cloud. And I still contest that they're number one by a long ways, but Azure kicking ass and catching up. Okay. You seeing people move to Azure, you got Charlie bell over there, Sean, by former Amazonians, Theresa Carlson, people are going over there, there there's lift over at Azure. >>There certainly is. >>Is there kinks in the arm or for AWS? There's >>A couple of kinks, but I think your point is really good. We need to take a second there. If you're talking about true pass or infrastructure is a service true cloud compute. I think AWS still is the powerhouse. And a lot of times the, the data gets a little muddied because Azure is really a hosted platform for applications. And you're not really sure where that line is drawn. And I think that's an important caveat to make, but based on the data, yes, we are seeing some kinks in the armor for AWS. Yes. Explain. So right now, a first of all caveat, 40% net score, which is our proprietary spending metric across the board. So we're not like raising any alarms here. It's still strong that said there are declines and there are declines pretty much across the board. The only spot we're not seeing a decline at all is in container, spend everything else is coming down specifically. We're seeing it come down in data analytics, data warehousing, and M I, which is a little bit of a concern because that, that rate of decline is not the same with Azure. >>Okay. So I gotta ask macro, I see the headwinds on the macro side, you pointed that out. Is there any insight into any underlying conditions that might be there on AWS or just a chronic kind of situational thing >>Right now? It seems situational. Other than that correlation between their big fortune 500, you know, audience and that being our biggest decline. The other aspect of the macro survey is we ask people, if you are planning to decline spend, how do you plan on doing it? And the number two answer is taking a look at our cloud spend and auditing it. So they're kind say, all right, you know, for the last 10 years it's been drunken, sail or spend, I >>Was gonna use that same line, you know, >>Cloud spend, just spend and we'll figure it out later, who cares? And then right now it's time to tighten the belts a little bit, >>But this is part of the allure of cloud at some point. Yeah. You, you could say, I'm gonna, I'm gonna dial it down. I'm gonna rein it in. So that's part of the reason why people go to the cloud. I want to, I wanna focus in on the data side of things and specifically the database. Let, just to give some context if, and correct me if I'm, I'm a little off here, but snowflake, which hot company, you know, on the planet, their net score was up around 80% consistently. It it's dropped down the last, you know, quarter, last survey to 60%. Yeah. So still highly, highly elevated, but that's relative to where Amazon is much larger, but you're saying they're coming down to the 40% level. Is that right? >>Yeah, they are. And I remember, you know, when I first started doing this 10 years ago, AWS at a 70%, you know, net score as well. So what's gonna happen over time is those adoptions are gonna get less and you're gonna see more flattening of spend, which ultimately is going to lower the score because we're looking for expansion rates. We wanna see adoption and increase. And when you see flattening a spend, it starts to contract a little bit. And you're right. Snowflake also was in the stratosphere that cooled off a little bit, but still, you know, very strong and AWS is coming down. I think the reason why it's so concerning is because a it's within the fortune 500 and their rate of decline is more than Azure right >>Now. Well, and, and one of the big trends you're seeing in database is this idea of converging function. In other words, bringing transaction and analytics right together at snowflake summit, they added the capability to handle transaction data, Mongo DB, which is largely mostly transactions added the capability in June to bring in analytic data. You see data bricks going from data engineering and data science now getting into snowflake space and analytics. So you're seeing that convergence Oracle is converging with my SQL heat wave and their core databases, couch base couch base is doing the same. Maria do virtually all these database companies are, are converging their platforms with the exception of AWS. AWS is still the right tool for the right job. So they've got Aurora, they've got RDS, they've got, you know, a dynamo DV, they've got red, they've got, you know, going on and on and on. And so the question everybody's asking is will that change? Will they start to sort of cross those swim lanes? We haven't seen it thus far. How is that affecting the data >>Performance? I mean, that's fantastic analysis. I think that's why we're seeing it because you have to be in the AWS ecosystem and they're really not playing nicely with others in the sandbox right now that now I will say, oh, Amazon's not playing nicely. Well, no, no. Simply to your point though, that there, the other ones are actually bringing in others at consolidating other different vendor types. And they're really not. You know, if you're in AWS, you need to stay within AWS. Now I will say their tools are fantastic. So if you do stay within AWS, they have a tool for every job they're advanced. And they're incredible. I think sometimes the complexity of their tools hurts them a little bit. Cause to your point earlier, AWS started as a developer-centric type of cloud. They have moved on to enterprise cloud and it's a little bit more business oriented, but their still roots are still DevOps friendly. And unless you're truly trained, AWS can be a little scary. >>So a common use case is I'm gonna be using Aurora for my transaction system and then I'm gonna ETL it into Redshift. Right. And, and I, now I have two data stores and I have two different sets of APIs and primitives two different teams of skills. And so that is probably causing some friction and complexity in the customer base that again, the question is, will they begin to expand some of those platforms to minimize some of that friction? >>Well, yeah, this is the question I wanted to ask on that point. So I've heard from people inside Amazon don't count out Redshift, we're making, we're catching up. I think that's my word, but they were kind of saying that right. Cuz Redshift is good, good database, but they're adding a lot more. So you got snowflake success. I think it's a little bit of a jealousy factor going on there within Redshift team, but then you got Azure synapse with the Synap product synapse. Yep. And then you got big query from Google big >>Query. Yep. >>What's the differentiation. What are you seeing for the data for the data warehouse or the data clouds that are out there for the customers? What's the data say, say to us? >>Yeah, unfortunately the data's showing that they're dropping a little bit whose day AWS is dropping a little bit now of their data products, Redshift and RDS are still the two highest of them, but they are starting to decline. Now I think one of the great data points that we have, we just closed the survey is we took a comparison of the legacy data. Now please forgive me for the word legacy. We're gonna anger a few people, but we Gotter data Oracle on-prem, we've got IBM. Some of those more legacy data warehouse type of names. When we look at our art survey takers that have them where their spend is going, that spends going to snowflake first, and then it's going to Google and then it's going to Microsoft Azure and, and AWS is actually declining in there. So when you talk about who's taking that legacy market share, it's not AWS right now. >>So legacy goes to legacy. So Microsoft, >>So, so let's work through in a little context because Redshift really was the first to take, you know, take the database to the cloud. And they did that by doing a one time license deal with par XL, which was an on-prem database. And then they re-engineered it, they did a fantastic job, but it was still engineered for on-prem. Then you along comes snowflake a couple years later and true cloud native, same thing with big query. Yep. True cloud native architecture. So they get a lot of props. Now what, what Amazon did, they took a page outta of the snowflake, for example, separating compute from storage. Now of course what's what, what Amazon did is actually not really completely separating like snowflake did they couldn't because of the architecture, they created a tearing system that you could dial down the compute. So little nuances like that. I understand. But at the end of the day, what we're seeing from snowflake is the gathering of an ecosystem in this true data cloud, bringing in different data types, they got to the public markets, data bricks was not able to get to the public markets. Yeah. And think is, is struggling >>And a 25 billion evaluation. >>Right. And so that's, that's gonna be dialed down, struggling somewhat from a go to market standpoint where snowflake has no troubles from a go to market. They are the masters at go to market. And so now they've got momentum. We talked to Frank sluman at the snowflake. He basically said, I'm not taking the foot off the gas, no way. Yeah. We, few of our large, you know, consumer customers dialed things down, but we're going balls to the >>Wall. Well, if you look at their show before you get in the numbers, you look at the two shows. Snowflake had their summit in person in Vegas. Data bricks has had their show in San Francisco. And if you compare the two shows, it's clear, who's winning snowflake is blew away from a, from a market standpoint. And we were at snowflake, but we weren't at data bricks, but there was really nothing online. I heard from sources that it was like less than 3000 people. So >>Snowflake was 1900 people in 2019, nearly 10,000. Yeah. In 2020, >>It's gonna be fun to sort of track that as a, as an odd caveat to say, okay, let's see what that growth is. Because in fairness, data, bricks, you know, a little bit younger, Snowflake's had a couple more years. So I'd be curious to see where they are. Their, their Lakehouse paradigm is interesting. >>Yeah. And I think it's >>And their product first company, yes. Their go to market might be a little bit weak from our analysis, but that, but they'll figure it out. >>CEO's pretty smart. But I think it's worth pointing out. It's like two different philosophies, right? It is. Snowflake is come into our data cloud. That's their proprietary environment. They're the, they think of the iPhone, right? End to end. We, we guarantee it's all gonna work. And we're in control. Snowflake is like, Hey, open source, no, bring in data bricks. I mean data bricks, open source, bring in this tool that too, now you are seeing snowflake capitulate a little bit. They announce, for instance, Apache iceberg support at their, at the snowflake summit. So they're tipping their cap to open source. But at the end of the day, they're gonna market and sell the fact that it's gonna run better in native snowflake. Whereas data bricks, they're coming at it from much more of an open source, a mantra. So that's gonna, you know, we'll see who look at, you had windows and you had apple, >>You got, they both want, you got Cal and you got Stanford. >>They both >>Consider, I don't think it's actually there yet. I, I find the more interesting dynamic right now is between AWS and snowflake. It's really a fun tit for tat, right? I mean, AWS has the S three and then, you know, snowflake comes right on top of it and announces R two, we're gonna do one letter, one number better than you. They just seem to have this really interesting dynamic. And I, and it is SLT and no one's betting against him. I mean, this guy's fantastic. So, and he hasn't used his war chest yet. He's still sitting on all that money that he raised to your point, that data bricks five, their timing just was a little off >>5 billion in >>Capital when Slootman hasn't used that money yet. So what's he gonna do? What can he do when he turns that on? He finds the right. >>They're making some acquisitions. They did the stream lit acquisitions stream. >>Fantastic >>Problem. With data bricks, their valuation is underwater. Yes. So they're recruiting and their MNAs. Yes. In the toilet, they cannot make the moves because they don't have the currency until they refactor the multiple, let the, this market settle. I I'm, I'm really nervous that they have to over factor the >>Valuation. Having said that to your point, Eric, the lake house architecture is definitely gaining traction. When you talk to practitioners, they're all saying, yeah, we're building data lakes, we're building lake houses. You know, it's a much, much smaller market than the enterprise data warehouse. But nonetheless, when you talk to practitioners that are actually doing things like self serve data, they're building data lakes and you know, snow. I mean, data bricks is right there. And as a clear leader in, in ML and AI and they're ahead of snowflake, right. >>And I was gonna say, that's the thing with data bricks. You know, you're getting that analytics at M I built into it. >>You know, what's ironic is I remember talking to Matt Carroll, who's CEO of auDA like four or five years ago. He came into the office in ma bro. And we were in temporary space and we were talking about how there's this new workload emerging, which combines AWS for cloud infrastructure, snowflake for the simple data warehouse and data bricks for the ML AI, and then all now all of a sudden you see data bricks yeah. And snowflake going at it. I think, you know, to your point about the competition between AWS and snowflake, here's what I think, I think the Redshift team is, you know, doesn't like snowflake, right. But I think the EC two team loves it. Loves it. Exactly. So, so I think snowflake is driving a lot of, >>Yeah. To John's point, there is plenty to go around. And I think I saw just the other day, I saw somebody say less than 40% of true global 2000 organizations believe that they're at real time data analytics right now. They're not really there yet. Yeah. Think about how much runway is left and how many tools you need to get to real time streaming use cases. It's complex. It's not easy. >>It's gonna be a product value market to me, snowflake in data bricks. They're not going away. Right. They're winning architectures. Yeah. In the cloud, what data bricks did would spark and took over the Haddo market. Yeah. To your point. Now that big data, market's got two players, in my opinion, snow flicking data, bricks converging. Well, Redshift is sitting there behind the curtain, their wild card. Yeah. They're wild card, Dave. >>Okay. I'm gonna give one more wild card, which is the edge. Sure. Okay. And that's something that when you talk about real time analytics and AI referencing at the edge, there aren't a lot of database companies in a position to do that. You know, Amazon trying to put outposts out there. I think it runs RDS. I don't think it runs any other database. Right. Snowflake really doesn't have a strong edge strategy when I'm talking the far edge, the tiny edge. >>I think, I think that's gonna be HPE or Dell's gonna own the outpost market. >>I think you're right. I'll come back to that. Couch base is an interesting company to watch with Capella Mongo. DB really doesn't have a far edge strategy at this point, but couch base does. And that's one to watch. They're doing some really interesting things there. And I think >>That, but they have to leapfrog bongo in my >>Opinion. Yeah. But there's a new architecture emerging at the edge and it's gonna take a number of years to develop, but it could eventually from an economic standpoint, seep back into the enterprise arm base, low end, take a look at what couch base is >>Doing. They hired an Amazon guard system. They have to leapfrog though. They need to, they can't incrementally who's they who >>Couch >>Base needs to needs to make a big move in >>Leap frog. Well, think they're trying to, that's what Capella is all about was not only, you know, their version of Atlas bringing to the cloud couch base, but it's also stretching it out to the edge and bringing converged database analytics >>Real quick on the numbers. Any data on CloudFlare, >>I was, I've been sitting here trying to get the word CloudFlare out my mouth the whole time you guys were talking, >>Is this another that's innovated in the ecosystem. So >>Platform, it was really simple for them early on, right? They're gonna get that edge network out there and they're gonna steal share from Akamai. Then they started doing exactly what Akamai did. We're gonna start rolling out some security. Their security is fantastic. Maybe some practitioners are saying a little bit too much, cuz they're not focused on one thing or another, but they are doing extremely well. And now they're out there in the cloud as well. You >>Got S3 compare. They got two, they got an S3 competitor. >>Exactly. So when I'm listening to you guys talk about, you know, a, a couch base I'm like, wow, those two would just be an absolute fantastic, you know, combination between the two of them. You mean >>CloudFlare >>Couch base. Yeah. >>I mean you got S3 alternative, right? You got a Mongo alternative basically in my >>Opinion. And you're going and you got the edge and you got the edge >>Network with security security, interesting dynamic. This brings up the super cloud date. I wanna talk about Supercloud because we're seeing a trend on we're reporting this since last year that basically people don't have to spend the CapEx to be cloud scale. And you're seeing Amazon enable that, but snowflake has become a super cloud. They're on AWS. Now they're on Azure. Why not tan expansion expand the market? Why not get that? And then it'll be on Google next, all these marketplaces. So the emergence of this super cloud, and then the ability to make that across a substrate across multiple clouds is a strategy we're seeing. What do you, what do you think? >>Well, honestly, I'm gonna be really Frank here. The, everything I know about the super cloud I know from this guy. So I've been following his lead on this and I'm looking forward to you guys doing that conference and that summit coming up from a data perspective. I think what you're saying is spot on though, cuz those are the areas we're seeing expansion in without a doubt. >>I think, you know, when you talk about things like super cloud and you talk about things like metaverse, there's, there's a, there, there look every 15 or 20 years or so this industry reinvents itself and a new disruption comes out and you've got the internet, you've got the cloud, you've got an AI and VR layer. You've got, you've got machine intelligence. You've got now gaming. There's a new matrix, emerging, super cloud. Metaverse there's something happening out there here. That's not just your, your father's SAS or is or pass. Well, >>No, it's also the spend too. Right? So if I'm a company like say capital one or Goldman Sachs, my it spend has traditionally been massive every year. Yes. It's basically like tons of CapEx comes the cloud. It's an operating expense. Wait a minute, Amazon has all the CapEx. So I'm not gonna dial down my budget. I want a competitive advantage. So next thing they know they have a super cloud by default because they just pivoted their, it spend into new capabilities that they then can sell to the market in FinTech makes total sense. >>Right? They're building out a digital platform >>That would, that was not possible. Pre-cloud >>No, it wasn't cause you weren't gonna go put all that money into CapEx expenditure to build that out. Not knowing whether or not the market was there, but the scalability, the ability to spend, reduce and be flexible with it really changes that paradigm entire. >>So we're looking at this market now thinking about, okay, it might be Greenfield in every vertical. It might have a power law where you have a head of the long tail. That's a player like a capital one, an insurance. It could be Liberty mutual or mass mutual that has so much it and capital that they're now gonna scale it into a super cloud >>And they have data >>And they have the data tools >>And the tools. And they're gonna bring that to their constituents. Yes, yes. And scale it using >>Cloud. So that means they can then service the entire vertical as a service provider. >>And the industry cloud is becoming bigger and bigger and bigger. I mean, that's really a way that people are delivering to market. So >>Remember in the early days of cloud, all the banks thought they could build their own cloud. Yeah. Yep. Well actually it's come full circle. They're like, we can actually build a cloud on top of the cloud. >>Right. And by the way, they can have a private cloud in their super cloud. Exactly. >>And you know, it's interesting cause we're talking about financial services insurance, all the people we know spend money in our macro survey. Do you know the, the sector that's spending the most right now? It's gonna shock you energy utilities. Oh yeah. I was gonna, the energy utilities industry right now is the one spending the most money I saw largely cuz they're playing ketchup. But also because they don't have these type of things for their consumers, they need the consumer app. They need to be able to do that delivery. They need to be able to do metrics. And they're the they're, they're the one spending right >>Now it's an arms race, but the, the vector shifts to value creation. So >>It's it just goes back to your post when it was a 2012, the trillion dollar baby. Yeah. It's a multi-trillion dollar baby that they, >>The world was going my chassis post on Forbes, headline trillion dollar baby 2012. You know, I should add it's happening. That's >>On the end. Yeah, exactly. >>Trillions of babies, Eric. Great to have you on the key. >>Thank you so much guys. >>Great to bring the data. Thanks for sharing. Check out ETR. If you're into the enterprise, want to know what's going on. They have a unique approach, very accurate in their survey data. They got a great market basket of, of, of, of, of data questions and people and community. Check it out. Thanks for coming on and sharing with. >>Thank you guys. Always enjoy. >>We'll be back with more coverage here in the cube in New York city live at summit 22. I'm John fur with Dave ante. We'll be right back.

(upbeat music) >> Welcome back to theCUBE's continuing coverage of Snowflake Summit '22 live from Caesar's Forum in Las Vegas. I'm Lisa Martin, my cohost Dave Vellante, we've been here the last day and a half unpacking a lot of news, a lot of announcements, talking with customers and partners, and we have another great session coming for you next. We've got a customer and a partner talking tech and data mash. Please welcome Mitesh Shah, VP in market strategy at Elation. >> Great to be here. >> and Ash Naseer great, to have you, senior director of data engineering at Warner Brothers Discovery. Welcome guys. >> Thank you for having me. >> It's great to be back in person and to be able to really get to see and feel and touch this technology, isn't it? >> Yeah, it is. I mean two years or so. Yeah. Great to feel the energy in the conference center. >> Yeah. >> Snowflake was virtual, I think for two years and now it's great to kind of see the excitement firsthand. So it's wonderful. >> Th excitement, but also the boom and the number of customers and partners and people attending. They were saying the first, or the summit in 2019 had about 1900 attendees. And this is around 10,000. So a huge jump in a short time period. Talk a little bit about the Elation-Snowflake partnership and probably some of the acceleration that you guys have been experiencing as a Snowflake partner. >> Yeah. As a snowflake partner. I mean, Snowflake is an investor of us in Elation early last year, and we've been a partner for, for longer than that. And good news. We have been awarded Snowflake partner of the year for data governance, just earlier this week. And that's in fact, our second year in a row for winning that award. So, great news on that front as well. >> Repeat, congratulations. >> Repeat. Absolutely. And we're going to hope to make it a three-peat as well. And we've also been awarded industry competency badges in five different industries, those being financial services, healthcare, retail technology, and Median Telcom. >> Excellent. Okay. Going to right get into it. Data mesh. You guys actually have a data mesh and you've presented at the conference. So, take us back to the beginning. Why did you decide that you needed to implement something like data mesh? What was the impetus? >> Yeah. So when people think of Warner brothers, you always think of like the movie studio, but we're more than that, right? I mean, you think of HBO, you think of TNT, you think of CNN, we have 30 plus brands in our portfolio and each have their own needs. So the idea of a data mesh really helps us because what we can do is we can federate access across the company so that, you know, CNN can work at their own pace. You know, when there's election season, they can ingest their own data and they don't have to, you know, bump up against as an example, HBO, if Game of Thrones is going on. >> So, okay. So the, the impetus was to serve those lines of business better. Actually, given that you've got these different brands, it was probably easier than most companies. Cause if you're, let's say you're a big financial services company, and now you have to decide who owns what. CNN owns its own data products, HBO. Now, do they decide within those different brands, how to distribute even further? Or is it really, how deep have you gone in that decentralization? >> That's a great question. It's a very close partnership, because there are a number of data sets, which are used by all the brands, right? You think about people browsing websites, right? You know, CNN has a website, Warner brothers has a website. So for us to ingest that data for each of the brands to ingest that data separately, that means five different ways of doing things and you know, a big environment, right? So that is where our team comes into play. We ingest a lot of the common data sets, but like I said, any unique data sets, data sets regarding theatrical as an example, you know, Warner brothers does it themselves, you know, for streaming, HBO Max, does it themselves. So we kind of operate in partnership. >> So do you have a centralized data team and also decentralized data teams, right? >> That's right. >> So I love this conversation because that was heresy 10 years ago, five years ago, even, cause that's inefficient. But you've, I presume you've found that it's actually more productive in terms of the business output, explain that dynamic. >> You know, you bring up such a good point. So I, you know, I consider myself as one of the dinosaurs who started like 20 plus years ago in this industry. And back then, we were all taught to think of the data warehouse as like a monolithic thing. And the reason for that is the technology wasn't there. The technology didn't catch up. Now, 20 years later, the technology is way ahead, right? But like, our mindset's still the same because we think of data warehouses and data platforms still as a monolithic thing. But if you really sort of remove that sort of mental barrier, if you will, and if you start thinking about, well, how do I sort of, you know, federate everything and make sure that you let folks who are building, or are closest to the customer or are building their products, let them own that data and have a partnership. The results have been amazing. And if we were only sort of doing it as a centralized team, we would not be able to do a 10th of what we do today. So it's that massive scale in, in our company as well. >> And I should have clarified, when we talk about data mesh are we talking about the implementing in practice, the octagon sort of framework, or is this sort of your own sort of terminology? >> Well, so the interesting part is four years ago, we didn't have- >> It didn't exist. >> Yeah. It didn't exist. And, and so we, our principle was very simple, right? When we started out, we said, we want to make sure that our brands are able to operate independently with some oversight and guidance from our technology teams, right? That's what we set out to do. We did that with Snowflake by design because Snowflake allows us to, you know, separate those, those brands into different accounts. So that was done by design. And then the, the magic, I think, is the Snowflake data sharing where, which allows us to sort of bring data in here once, and then share it with whoever needs it. So think about HBO Max. On HBO Max, You not only have HBO Max content, but content from CNN, from Cartoon Network, from Warner Brothers, right? All the movies, right? So to see how The Batman movie did in theaters and then on streaming, you don't need, you know, Warner brothers doesn't need to ingest the same streaming data. HBO Max does it. HBO Max shares it with Warner brothers, you know, store once, share many times, and everyone works at their own pace. >> So they're building data products. Those data products are discoverable APIs, I presume, or I guess maybe just, I guess the Snowflake cloud, but very importantly, they're governed. And that's correct, where Elation comes in? >> That's precisely where Elation comes in, is where sort of this central flexible foundation for data governance. You know, you mentioned data mesh. I think what's interesting is that it's really an answer to the bottlenecks created by centralized IT, right? There's this notion of decentralizing that the data engineers and making the data domain owners, the people that know the data the best, have them be in control of publishing the data to the data consumers. There are other popular concepts actually happening right now, as we speak, around modern data stack. Around data fabric that are also in many ways underpinned by this notion of decentralization, right? These are concepts that are underpinned by decentralization and as the pendulum swings, sort of between decentralization and centralization, as we go back and forth in the world of IT and data, there are certain constants that need to be centralized over time. And one of those I believe is very much a centralized platform for data governance. And that's certainly, I think where we come in. Would love to hear more about how you use Elation. >> Yeah. So, I mean, elation helps us sort of, as you guys say, sort of, map, the treasure map of the data, right? So for consumers to find where their data is, that's where Elation helps us. It helps us with the data cataloging, you know, storing all the metadata and, you know, users can go in, they can sort of find, you know, the data that they need and they can also find how others are using data. So it's, there's a little bit of a crowdsourcing aspect that Elation helps us to do whereby you know, you can see, okay, my peer in the other group, well, that's how they use this piece of data. So I'm not going to spend hours trying to figure this out. You're going to use the query that they use. So yeah. >> So you have a master catalog, I presume. And then each of the brands has their own sub catalogs, is that correct? >> Well, for the most part, we have that master catalog and then the brands sort of use it, you know, separately themselves. The key here is all that catalog, that catalog isn't maintained by a centralized group as well, right? It's again, maintained by the individual teams and not only in the individual teams, but the folks that are responsible for the data, right? So I talked about the concept of crowdsourcing, whoever sort of puts the data in, has to make sure that they update the catalog and make sure that the definitions are there and everything sort of in line. >> So HBO, CNN, and each have their own, sort of access to their catalog, but they feed into the master catalog. Is that the right way to think about it? >> Yeah. >> Okay. And they have their own virtual data warehouses, right? They have ownership over that? They can spin 'em up, spin 'em down as they see fit? Right? And they're governed. >> They're governed. And what's interesting is it's not just governed, right? Governance is a, is a big word. It's a bit nebulous, but what's really being enabled here is this notion of self-service as well, right? There's two big sort of rockets that need to happen at the same time in any given organization. There's this notion that you want to put trustworthy data in the hands of data consumers, while at the same time mitigating risk. And that's precisely what Elation does. >> So I want to clarify this for the audience. So there's four principles of database. This came after you guys did it. And I wonder how it aligns. Domain ownership, give data, as you were saying to the, to the domain owners who have context, data as product, you guys are building data products, and that creates two problems. How do you give people self-service infrastructure and how do you automate governance? So the first two, great. But then it creates these other problems. Does that align with your philosophy? Where's alignment? What's different? >> Yeah. Data products is exactly where we're going. And that sort of, that domain based design, that's really key as well. In our business, you think about who the customer is, as an example, right? Depending on who you ask, it's going to be, the answer might be different, you know, to the movie business, it's probably going to be the person who watches a movie in a theater. To the streaming business, to HBO Max, it's the streamer, right? To others, someone watching live CNN on their TV, right? There's yet another group. Think about all the franchising we do. So you see Batman action figures and T-shirts, and Warner brothers branded stuff in stores, that's yet another business unit. But at the end of the day, it's not a different person, it's you and me, right? We do all these things. So the domain concept, make sure that you ingest data and you bring data relevant to the context, however, not sort of making it so stringent where it cannot integrate, and then you integrate it at a higher level to create that 360. >> And it's discoverable. So the point is, I don't have to go tap Ash on the shoulder, say, how do I get this data? Is it governed? Do I have access to it? Give me the rules of it. Just, I go grab it, right? And the system computationally automates whether or not I have access to it. And it's, as you say, self-service. >> In this case, exactly right. It enables people to just search for data and know that when they find the data, whether it's trustworthy or not, through trust flags, and the like, it's doing both of those things at the same time. >> How is it an enabler of solving some of the big challenges that the media and entertainment industry is going through? We've seen so much change the last couple of years. The rising consumer expectations aren't going to go back down. They're only going to come up. We want you to serve us up content that's relevant, that's personalized, that makes sense. I'd love to understand from your perspective, Mitesh, from an industry challenges perspective, how does this technology help customers like Warner Brothers Discovery, meet business customers, where they are and reduce the volume on those challenges? >> It's a great question. And as I mentioned earlier, we had five industry competency badges that were awarded to us by Snowflake. And one of those four, Median Telcom. And the reason for that is we're helping media companies understand their audiences better, and ultimately serve up better experiences for their audiences. But we've got Ash right here that can tell us how that's happening in practice. >> Yeah, tell us. >> So I'll share a story. I always like to tell stories, right? Once once upon a time before we had Elation in place, it was like, who you knew was how you got access to the data. So if I knew you and I knew you had access to a certain kind of data and your access to the right kind of data was based on the network you had at the company- >> I had to trust you. >> Yeah. >> I might not want to give up my data. >> That's it. And so that's where Elation sort of helps us democratize it, but, you know, puts the governance and controls, right? There are certain sensitive things as well, such as viewership, such as subscriber accounts, which are very important. So making sure that the right people have access to it, that's the other problem that Elation helps us solve. >> That's precisely part of our integration with Snowflake in particular, being able to define and manage policies within Elation. Saying, you know, certain people should have access to certain rows, doing column level masking. And having those policies actually enforced at the Snowflake data layer is precisely part of our value product. >> And that's automated. >> And all that's automated. Exactly. >> Right. So I don't have to think about it. I don't have to go through the tap on their shoulder. What has been the impact, Ash, on data quality as you've pushed it down into the domains? >> That's a great question. So it has definitely improved, but data quality is a very interesting subject, because back to my example of, you know, when we started doing things, we, you know, the centralized IT team always said, well, it has to be like this, Right? And if it doesn't fit in this, then it's bad quality. Well, sometimes context changes. Businesses change, right? You have to be able to react to it quickly. So making sure that a lot of that quality is managed at the decentralized level, at the place where you have that business context, that ensures you have the most up to date quality. We're talking about media industry changing so quickly. I mean, would we have thought three years ago that people would watch a lot of these major movies on streaming services? But here's the reality, right? You have to react and, you know, having it at that level just helps you react faster. >> So data, if I play that back, data quality is not a static framework. It's flexible based on the business context and the business owners can make those adjustments, cause they own the data. >> That's it. That's exactly it. >> That's awesome. Wow. That's amazing progress that you guys have made. >> In quality, if I could just add, it also just changes depending on where you are in your data pipeline stage, right? Data, quality data observability, this is a very fast evolving space at the moment, and if I look to my left right now, I bet you I can probably see a half-dozen quality observability vendors right now. And so given that and given the fact that Elation still is sort of a central hub to find trustworthy data, we've actually announced an open data quality initiative, allowing for best-of-breed data quality vendors to integrate with the platform. So whoever they are, whatever tool folks want to use, they can use that particular tool of choice. >> And this all runs in the cloud, or is it a hybrid sort of? >> Everything is in the cloud. We're all in the cloud. And you know, again, helps us go faster. >> Let me ask you a question. I could go on forever in this topic. One of the concepts that was put forth is whether it's a Snowflake data warehouse or a data bricks, data lake, or an Oracle data warehouse, they should all be inclusive. They should just be a node on the mesh. Like, wow, that sounds good. But I haven't seen it yet. Right? I'm guessing that Snowflake and Elation enable all the self-serve, all this automated governance, and that including those other items, it's got to be a one-off at this point in time. Do you ever see you expanding that scope or is it better off to just kind of leave it into the, the Snowflake data cloud? >> It's a good question. You know, I feel like where we're at today, especially in terms of sort of technology giving us so many options, I don't think there's a one size fits all. Right? Even though we are very heavily invested in Snowflake and we use Snowflake consistently across the organization, but you could, theoretically, could have an architecture that blends those two, right? Have different types of data platforms like a teradata or an Oracle and sort of bring it all together today. We have the technology, you know, that and all sorts of things that can make sure that you query on different databases. So I don't think the technology is the problem, I think it's the organizational mindset. I think that that's what gets in the way. >> Oh, interesting. So I was going to ask you, will hybrid tables help you solve that problem? And, maybe not, what you're saying, it's the organization that owns the Oracle database saying, Hey, we have our system. It processes, it works, you know, go away. >> Yeah. Well, you know, hybrid tables I think, is a great sort of next step in Snowflake's evolution. I think it's, in my opinion, I, think it's a game changer, but yeah. I mean, they can still exist. You could do hybrid tables right on Snowflake, or you could, you know, you could kind of coexist as well. >> Yeah. But, do you have a thought on this? >> Yeah, I do. I mean, we're always going to live in a time where you've got data distributed in throughout the organization and around the globe. And that could be even if you're all in on Snowflake, you could have data in Snowflake here, you could have data in Snowflake in EMEA and Europe somewhere. It could be anywhere. By the same token you might be using. Every organization is using on-premises systems. They have data, they naturally have data everywhere. And so, you know, this one solution to this is really centralizing, as I mentioned, not just governance, but also metadata about all of the data in your organization so that you can enable people to search and find and discover trustworthy data no matter where it is in your organization. >> Yeah. That's a great point. I mean, if you have the data about the data, then you can, you can treat these independent nodes. That's just that. Right? And maybe there's some advantages of putting it all in the Snowflake cloud, but to your point, organizationally, that's just not feasible. The whole, unfortunately, sorry, Snowflake, all the world's data is not going to go into Snowflake, but they play a key role in accelerating, what I'm hearing, your vision of data mesh. >> Yeah, absolutely. I think going forward in the future, we have to start thinking about data platforms as just one place where you sort of dump all the data. That's where the mesh concept comes in. It is going to be a mesh. It's going to be distributed and organizations have to be okay with that. And they have to embrace the tools. I mean, you know, Facebook developed a tool called Presto many years ago that that helps them solve exactly the same problem. So I think the technology is there. I think the organizational mindset needs to evolve. >> Yeah. Definitely. >> Culture. Culture is one of the hardest things to change. >> Exactly. >> Guys, this was a masterclass in data mesh, I think. Thank you so much for coming on talking. >> We appreciate it. Thank you so much. >> Of course. What Elation is doing with Snowflake and with Warner Brothers Discovery, Keep that content coming. I got a lot of stuff I got to catch up on watching. >> Sounds good. Thank you for having us. >> Thanks guys. >> Thanks, you guys. >> For Dave Vellante, I'm Lisa Martin. You're watching theCUBE live from Snowflake Summit '22. We'll be back after a short break. (upbeat music)

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

(upbeat music) >> Welcome back everyone, theCUBE's three days of wall to wall coverage of Snowflake Summit '22 is coming to an end, but Dave Vellante and I, Lisa Martin are so pleased to have our final guest as none other than the co-founder and president of products at Snowflake, Benoit Dageville. Benoit, thank you so much for joining us on the program. Welcome. >> Thank you. Thank you, thank you. >> So this is day four, 'cause you guys started on Monday. This is Thursday. The amount of people that are still here speaks volumes. We've had close to 10,000 people here. >> Yeah. >> Could you ever have imagined back in the day, 10 years ago that it would come to something like this in such a short period of time? >> Absolutely not. And I always say if I had imagined that I might not have started Snowflake, right. This is somehow scary. I mean and yeah, it's huge. And you can feel the excitement of everyone. It is like mind boggling and the fact that so many people are still there after four days is great. >> Your keynote on Tuesday was fantastic. Your energy was off the charts. It was standing room only. There were overflow rooms. Like we just mentioned, a lot of people are still here. Talk about the evolution of Snowflake, this week's announcements and what it means for the future of the data cloud. >> Yeah, so evolution, I mean, I will start with the evolution. It's true that that's what we have announced. This week is not where we started necessarily. So we started really very quickly with big data combined with data warehouse as one thing. We saw that the world was moving into fragmented siloing data and we thought with Thierry, we are going to combine big data and data warehouse in one system for the cloud with this elasticity and this service simplicity. So simplicity, amazing elasticity, which is this multi workload architecture that I was explaining during the keynotes and really extreme simplicity with the service. Then we realized that there is one other attribute in the cloud, which is unique, which doesn't exist on-premise, which is collaboration. How you can connect different tenets of the platform together. And Google showed that with Google Docs. I always say to me, it was amazing that you could share document and have direct access to document that you didn't produce and you can collaborate on this document. So we wanted to do the same thing for data and this is where we created the data cloud and the marketplace where you can have all these data sets available and really the next evolution I would say is really about applications that are (indistinct) by that data, but are way simpler to use for all the tenets of the data cloud. And this is the way you can share expertise also, including, ML model, everyone talks about ML and the democratization of ML. How are you going to democratize ML? It's not by making necessary training super easy. Such that everyone can train their ML for themselves. It's by having very specialized application where data and ML is at the core, which are shared, through the marketplace and we shall leverage by many tenets of this marketplace that have no necessary knowledge about building this ML models. So that's where, yeah. >> When you and Thierry started the company, I go back to the improbable rise of Kubernetes and there were other more sophisticated container management systems back then, but they chose to focus on simplicity. And you've told me before, that was our main tenet. We are not going to worry about all the complex database stuff. You knew how to do that, but you chose not to. So my question is, did you envision solving those complex problems over time yourselves or through an ecosystem? Was this by design or did you... As you started to get into it, say let's not even try to go there let's partner to go there. >> Yeah, I mean, it's both. It's a combination of both. Snowflake, the simplicity of the platform is really important because if our partners are struggling to put their solution and build solution on top of Snowflake they will not build it. So it's very important that number one, our platform is really easy to use from day one. And that really has to be built inside the platform. You cannot build simplicity on top. You cannot have a complex solution and all of a sudden realize that, oh, this is complex. I need to build another layer on top of it to make it simpler, that will not work. So it had to be built from day one, but you're right. What is going to be Snowflake? I always say in 10 years from now, we just turn 10 years old or we are going to turn 10 years old in few months. Actually a few months, yes. >> Right. >> So for the next 10 years I really believe that most of Snowflake will not be built by Snowflake. And that's the power of the partners and these applications. When you are going to say I'm using Snowflake, actually, probably you are not going to use directly code developed by Snowflake. That code will leverage our platform, but you will use a solution that has been built on top of Snowflake. And this is the way we are going to decouple, the effort of Snowflake and multiply it. >> It's an interesting balance, isn't it? When I think of what you did with Apache Iceberg, if I use Iceberg and I'm not going to get as much functionality, but I may want that openness, but I'm going to get more functionality inside of the data cloud. And I don't know, but if you know the answer to what's going to happen. >> No, that's a super good question. So to explain what we did with Apache Iceberg, and the fact that now it's a native format for us. So everything that you can do with our internal formats, you can do it with Apache Iceberg, including security, defining masking, data masking all the governors that we have, fine grain security aspects, the replications you can define you can use (indistinct) on top of... >> But there's a but, right? But if I do that with native Snowflake tools, I'm going to get an even greater advantage, am I not? >> Yes. So that's what I'm saying. So that's why we embraced Iceberg, because I think we can bring all the benefit of Snowflake to people who have decided to use Iceberg, I mean open formats. Iceberg is a table format. So and why it was important because people had massive investments in open source in Hadoop. And we had a lot of companies saying, we love Snowflake. We want to be a Snowflake customer, but we cannot really migrate all our data. I mean, it will be really costly. And we have a lot of tools that need access, direct access. So this is why we created Iceberg because we can really... I mean, we really think that we can bring the benefit of Snowflake to this data. >> Gives customers optionality. Okay. I use this term super cloud. You don't use the term, but that's okay. And I get a lot of heat for it. But to me, what you're doing is quite a bit different than multicloud because you're creating that abstraction layer. You're bringing value above it. My question to you is, the most of the heat I get is, oh, that's just SaaS. Are you just SaaS? >> No. I mean, no, absolutely not. I mean, you're right we are a super cloud. I mean it's a much better word than saying we are multicloud. Multicloud is often viewed as oh, I have my system and now I can run this system in the different cloud providers. Snowflake is different. We have one single platform for the world, which happens to have some regions are AWS region, some regions are Azure, some regions are GCP, Google and we merge them together. We have this Snowgrid technology that connects all our regions together so that we have really one platform for the world. And that's very important because when you talk about connections of data and expertise applications you want to have global reach, right. It doesn't exist. We are not siloed by region of the world, right? You have a lot of companies which are multinational that have presence everywhere. And you want to have this global reach. The world is not a independent set of regions and countries, right. And that's the realization. So we had to create this global platform for our customers. >> And now you have people building clouds on top of your data cloud, well that to me is the next signal. In your keynote, you talked about seven pillars, all data, all workloads, global architecture, self-managed, programmable, marketplace, governance, which ones are the most important? >> All of them. It's like when you have kids, you don't want to pick and say, this one is my preferred one, so they are really important. All of them, as I said without data, there is no Snowflake, right? So all data is so important that we can reach every data, wherever it is. And Iceberg is a part of that, but all workload is really important because you don't want to put your data in one platform, if you cannot run all your workloads and workloads are much broader than just data warehousing, there is data engineering, data science, ML engineering, (indistinct) all these workloads applications. So that's critical. Programmable is where we are moving, right. We want to be the place where data applications are built. And we think we have a lot of advantages because data application needs to use many workloads at once, right? It's not that that application will do only data warehousing, they need to store their states, they need to use this new workload that we define, which is Unistore. They need to do data engineering because they need to get data, right. They have to save this data. So they need to combine many workload and if they have to stitch this workload, because the platform was not designed as one single product where everything is consistent and works together, that you have to stitch, it's complicated for this application to make it work. So Snowflake is we believe an ideal platform to run these data applications. So all workloads, programmable, obviously, so that you can program. And programmable has two aspects, which is big part of our announcement. Is both data programmability, which is running Python against petabyte, terabytes of data at scale and doing it scale out. So that's what we call data programmability. So both Java, Python and (indistinct), but also running applications like UI. And we had this acquisition of Streamlit. Streamlit now has been fully integrated in Snowflake. We announced that such that not only you can have this data programmability, but you can expose your data through this nice UIs, interactive UI to business users potentially. So it goes all the way there. Global is super important. As we say, we want to be one platform for the world. And of course, as I said, the last pillar, which is somehow critical for us, because we are cloud, we need to have governance. We need to have security of our data. And why it took us so long to do Python is not because it's out to run Python, right? Everyone can run Python it's because we had to secure it. And I talk about it creating this amazing sandboxing technology, such that when you include third party libraries and third party codes, you are guaranteed that this third party code will not reach to infiltrate your data, right. We control the environment that Snowflake provides. >> Can you share us some of the feedback from the customer? You probably had many customer conversations over the last four days. >> Look at that smile. (interviewer laughing) (Lisa laughing) >> Actually not because I was so busy everywhere. Unfortunately, I didn't speak to many customers. Saying that, I had everyone stopping me and talking about what they heard and yeah, there is a huge excitement about all of this. >> What's been the feedback around the theme of the event? The world of data collaboration. Data collaboration is so critical as every company these days must be a data company to compete, to win. What's been from just some of the feedback that you've had customers really embracing data collaboration, what Snowflake is enabling. >> Yeah. I mean, almost every company which is using Snowflake, is collaborating with data. You have heard, the number of stable edges that we have, and there is a real need for that because your data alone... You cannot make sense of your data if it is just alone. It needs to be connected with other data. You haven't not generated. So all data, when you say the first pillar of Snowflake is all data is not only about your data, but is about all the data that's created around you. That puts perspective on your own data. And that's critical and it's so painful to get. I mean, even your data is difficult to have access to your data, but imagine data that you didn't produce. And so yes, so the data collaboration is critical, and then now we expanded it to application and expertise, sharing models, for example, That's going to have a huge impact. >> All data includes now transaction data, right? >> Yes. >> That's a big part of the announcements that you guys made. >> Yeah. So and that's the motivation for that was really, if we want to run application, full application, we announced native applications, which are fully executed and run inside the (indistinct) data cloud, right. They need all the services that application need and in particular managing their states. And so we created Unistore, which is a new workload, which allows you to combine transactional data, which are generated by this application. And at the same time being able to do analytics directly on this data. So we call it Hybrid Table because it has this hybrid aspect. You can do both transactional access to this data and at the same time analytic here without having data pipeline and moving data and transforming it from the transactional system to the analytical system, right. Snowflake is one system. Again, in the spirit of simplifying everything, this is the Snowflake (indistinct). >> I can ask the same question I ask at first, (indistinct) when was the aha moment that you and Thierry had that said, this is not just a better data warehouse, it's actually more than that. You probably didn't call it a data cloud until later on, but did you know that from the beginning or was that something you kind of stumbled into? >> No. So as I said, we founded Snowflake in 2012 and Thierry and I, we locked in my apartment and we were doing the blueprint of Snowflake and trying to find what is the revolution with the cloud for this data warehouse system and analytical system, both big data and data warehouse. And the aha moment was but of course cloud, okay. What is cloud? It's elasticity, it's service and later collaboration. So in the elasticity aspect, when you ask database people, what is elasticity, they will tell you, oh, you have a cluster of nodes. Like if it is Oracle, it would be a (indistinct) cluster. And the elasticities that you can add one node, two node to this cluster without having too much impact on the existing workload, because you need to shuffle data, right. It's hard and doing it online, right, that's elasticity. If you can do that, you are elastic. We thought that that was not very interesting to do that. What is interesting with elasticity is to plug new workloads. You can plug a workload like that and that workload is running without having any impact on other workloads, which are running on the platform. So elasticity for us was having dedicated computer resources to workloads. And these computer resources could start and be part as soon as the workload starts and will shut down when the workload finishes and they will be sized exactly for the demand of that workload. And we thought the aha moment was, okay if we can do that, now we can run a workload with, let's say 10X more computer resources than what you would have used or 100X more. Okay, let's say 100X more because we paralyzed things. Now this workload can run 100X faster, right? That's assuming we do a good job in the scale, which is our IP. And if we can do that, now the computer resources that you have used, you have used them for 100 times less. So you have used 100 times more resources because you have more nodes, but because you go fast, you use them for less time, right? So if you multiply the two it's constant. So you can run and accelerate workload dramatically 10X, 100X for the same price. Even if we are not better in efficiency than competition, just having that was the magic, right? >> You know how Google founders originally had trouble raising money because who needs another search engine? Did you get from original, like when you started going to raise money, Amazon's got a database, so who needs another cloud database? Did you get that early on or was it just obvious Speiser and companies as well. >> Speiser is a little bit on the crazy side and ambitious and so Speiser is Speiser. And of course he had no doubt, but even him was saying Benoit, Thierry, Hadoop, right. Everyone is saying Hadoop is going to be the revolution. And you guys are betting actually against Hadoop because we told Speiser, Hadoop is a bad system, it's going to fail, but at the time everyone was so bullish about Hadoop, everyone was implementing Hadoop that it didn't look like it was going to fail and we were probably wrong. So there was a lot of skepticism about not leveraging Hadoop and not being an Hadoop. Okay, something being on top of Hadoop. That was number one. There was no cloud warehouse at the time we started. Redshift was not started. It was the pioneer somewhere when Snowflake was founded. So creating a data warehouse in the cloud sounded crazy to people. How am I going to move my data over there? And security and what about security, the cloud is not secure. So that was another... >> So you guys predated that Parexel move by... >> Yes. >> Okay, so that's interesting. And I thought when Redshift... I mean, Amazon announced Redshift, I was sure that Mike Speiser will come and say, guys it's too sad, but they beat you guys and they build something and actually it was the reverse. Mike Speiser was super excited and so it was interesting to me. >> Wow, that's amazing. 'Cause John Furrier and I, we were early with theCUBE. when theCUBE started it was like the beginning of Hadoop. And so we brought theCUBE to, I think it was the second Hadoop World and we was rubbing nickels together at the time. And I was so excited bring compute to storage and it made so much sense. But I remember and I won't say who it was, but an early Hadoop committer told me this is going to fail. And I'm like, what? And he started going age basis crap and all this stuff. And I was sad because I was so excited, but it turned out that you had the same (indistinct). >> Because of complexity. Okay, Hadoop failed for two reasons. One is because they decided that, oh, a lot of this database thing, you don't need transaction, you don't need SQL, you don't necessarily, you don't need to go fast. It'll be batch, normal real time interaction with data, no one needs that. >> Cheap storage. >> So a lot of compromise on the very important technology. And at the same time, extreme complexity and complexity for me was, where I was I knew that it was going to fail big time and we bet Snowflake on the failure of Hadoop indeed. >> And there was no cloud early on in Hadoop. >> And there was no cloud too. >> And that was what killed it. That was like... >> You're right. And the model that Hadoop had for data didn't work on block storage. Block storage is not as efficient as HGFS. So that was also another figure. >> Do you ever sit back and think about... So you think about how much money has poured in to separating compute from storage and cloud databases and you started it all. (interviewer laughing) >> Yeah. No, this is... >> Pretty amazing. >> Yeah. >> Right, so that's good. That means that you're onto a good idea, but a lot of people get confused that again, they think that you're a cloud data warehouse and you're not, I mean, you're much more than that. >> Yeah, I hate that. I have to say, because from day one we were not a cloud data warehouse. As I said, it was all about combining the big data, massive amount of unstructured data, petabytes stored as files. Okay, that's very important, store as files where it's very easy to drop data in the system without... Very low cost to combine with data warehouse, full multi statement transaction when people will tell you today, oh, now we are a data warehouse. They don't have multi statement transaction, right. So we had from day one multi statement transaction really efficient SQL. You could run your dashboard. So combining these two worlds was I think the crazy thing, that's the crazy innovation that Snowflake did initially. >> Yeah. >> And I know it's really easy to build data warehouse somewhere, because if you don't think about big data, petabytes, extremely structured data, you remove a lot of complexity. >> This is why Lisa, when you get excited about technology, but you always have to have a, somebody who really deeply understands technology to stink test it, all right so awesome. Thank you for sharing that story. >> Yeah. >> Fantastic. So over 5,900 customers now. I saw over 500 in the Forbes G2K, over almost 10,000 people here this year. If we think back to 2019, there was about what? Less than 2000 people. >> Yeah. >> What do you think is going to happen next year? >> I don't know. I don't like to think about next year. I mean, I always say, Snowflake is so exciting to me because it is like a TV show, right. Where you wait the next season and we have one season every year. So I'm really excited to know what is going to happen next year. And I don't want to project what I think will happen, but all these movements to the Snowflake being the platform for data application. I want to see what people are going to build on our platform. I mean, that's the excitement. >> Season 11 coming up. >> Yes. Season 11. Yes. >> No binge watching here. Benoit, it's been a pleasure to have you on the program. >> Thank you. >> Congratulations on incredible success, the momentum, the energy is contagious. We love it. (Benoit laughing) >> Thank you so much. >> Thank you. >> Bye bye. >> For Benoit Dageville and Dave Vellante, I'm Lisa Martin. You're watching theCUBE's coverage of Snowflake Summit '22. Dave and I will be right back with a wrap. (upbeat music)

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

>>Good morning, everyone. Welcome back to the Cube's coverage of snowflake summit 22 live from Caesar's forum in Las Vegas. Lisa Martin, here with Dave Valante. This is day three of our coverage. We've had an amazing, amazing time. Great conversations talking with snowflake executives, partners, customers. We're gonna be digging into data mesh with data.world. Please welcome John loins, the chief product officer. Great to have you on the program, John, >>Thank you so much for, for having me here. I mean, the summit, like you said, has been incredible, so many great people, so such a good time, really, really nice to be back in person with folks. >>It is fabulous to be back in person. The fact that we're on day four for, for them. And this is the, the solution showcase is as packed as it is at 10 11 in the morning. Yeah. Is saying something >>Yeah. Usually >>Chopping at the bit to hear what they're doing and innovate. >>Absolutely. Usually those last days of conferences, everybody starts getting a little tired, but we're not seeing that at all here, especially >>In Vegas. This is impressive. Talk to the audience a little bit about data.world, what you guys do and talk about the snowflake relationship. >>Absolutely data.world is the only true cloud native enterprise data catalog. We've been an incredible snowflake partner and Snowflake's been an incredible partner to us really since 2018. When we became the first data catalog in the snowflake partner connect experience, you know, snowflake and the data cloud make it so possible. And it's changed so much in terms of being able to, you know, very easily transition data into the cloud to break down those silos and to have a platform that enables folks to be incredibly agile with data from an engineering and infrastructure standpoint, data out world is able to provide a layer of discovery and governance that matches that agility and the ability for a lot of different stakeholders to really participate in the process of data management and data governance. >>So data mesh basically Jamma, Dani lays out the first of all, the, the fault domains of existing data and big data initiatives. And she boils it down to the fact that it's just this monolithic architecture with hyper specialized teams that you have to go through and it just slows everything down and it doesn't scale. They don't have domain context. So she came up with four principles if I may, yep. Domain ownership. So push it out to the businesses. They have the context they should own the data. The second is data as product. We're certainly hearing a lot about that today this week. The third is that. So that makes it sounds good. Push out the, the data great, but it creates two problems. Self-serve infrastructure. Okay. But her premises infrastructure should be an operational detail. And then the fourth is computational governance. So you talked about data CA where do you fit in those four principles? >>You know, honestly, we are able to help teams realize the data mesh architecture. And we know that data mesh is really, it's, it's both a process in a culture change, but then when you want to enact a process in a culture change like this, you also need to select the appropriate tools to match the culture that you're trying to build the process in the architecture that you're trying to build. And the data world data catalog can really help along all four of those axes. When you start thinking first about, let's say like, let's take the first one, you know, data as a product, right? We even like very meta of us from metadata management platform at the end of the day. But very meta of us. When you talk about data as a product, we track adoption and usage of all your data assets within your organization and provide program teams and, you know, offices of the CDO with incredible evented analytics, very detailed that gives them the right audit trail that enables them to direct very scarce data engineering, data architecture resources, to make sure that their data assets are getting adopted and used properly. >>On the, on the domain driven side, we are entirely knowledge graph and open standards based enabling those different domains. We have, you know, incredible joint snowflake customers like Prologis. And we chatted a lot about this in our session here yesterday, where, because of our knowledge graph underpinnings, because of the flexibility of our metadata model, it enables those domains to actually model their assets uniquely from, from group to group, without having to, to relaunch or run different environments. Like you can do that all within one day catalog platform without having to have separate environments for each of those domains, federated governance. Again, the amount of like data exhaust that we create that really enables ambient governance and participatory governance as well. We call it agile data governance, really the adoption of agile and open principles applied to governance to make it more inclusive and transparent. And we provide that in a way that Confederate across those means and make it consistent. >>Okay. So you facilitate across that whole spectrum of, of principles. And so what in the, in the early examples of data mesh that I've studied and actually collaborated with, like with JPMC, who I don't think is who's not using your data catalog, but hello, fresh who may or may not be, but I mean, there, there are numbers and I wanna get to that. But what they've done is they've enabled the domains to spin up their own, whatever data lakes, data, warehouses, data hubs, at least in, in concept, most of 'em are data lakes on AWS, but still in concept, they wanna be inclusive and they've created a master data catalog. And then each domain has its sub catalogue, which feeds into the master and that's how they get consistency and governance and everything else is, is that the right way to think about it? And or do you have a different spin on that? >>Yeah, I, I, you know, I have a slightly different spin on it. I think organizationally it's the right way to think about it. And in absence of a catalog that can truly have multiple federated metadata models, multiple graphs in one platform, I, that is really kind of the, the, the only way to do it, right with data.world. You don't have to do that. You can have one platform, one environment, one instance of data.world that spans all of your domains, enable them to operate independently and then federate across. So >>You just answered my question as to why I should use data.world versus Amazon glue. >>Oh, absolutely. >>And that's a, that's awesome that you've done now. How have you done that? What, what's your secret >>Sauce? The, the secret sauce era is really an all credit to our CTO. One of my closest friends who was a true student of knowledge graph practices and principles, and really felt that the right way to manage metadata and knowledge about the data analytics ecosystem that companies were building was through federated linked data, right? So we use standards and we've built a, a, an open and extensible metadata model that we call costs that really takes the best parts of existing open standards in the semantics space. Things like schema.org, DCA, Dublin core brings them together and models out the most typical enterprise data assets providing you with an ontology that's ready to go. But because of the graph nature of what we do is instantly accessible without having to rebuild environments, without having to do a lot of management against it. It's, it's really quite something. And it's something all of our customers are, are very impressed with and, and, and, and, you know, are getting a lot of leverage out of, >>And, and we have a lot of time today, so we're not gonna shortchange this topic. So one last question, then I'll shut up and let you jump in. This is an open standard. It's not open source. >>No, it's an open built on open standards, built on open standards. We also fundamentally believe in extensibility and openness. We do not want to vertically like lock you into our platform. So everything that we have is API driven API available. Your metadata belongs to you. If you need to export your graph, you know, instantly available in open machine readable formats. That's really, we come from the open data community. That was a lot of the founding of data.world. We, we worked a lot in with the open data community and we, we fundamentally believe in that. And that's enabled a lot of our customers as well to truly take data.world and not have it be a data catalog application, but really an entire metadata management platform and extend it even further into their enterprise to, to really catalog all of their assets, but also to build incredible integrations to things like corporate search, you know, having data assets show up in corporate Wiki search, along with all the, the descriptive metadata that people need has been incredibly powerful and an incredible extension of our platform that I'm so happy to see our customers in. >>So leasing. So it's not exclusive to, to snowflake. It's not exclusive to AWS. You can bring it anywhere. Azure GCP, >>Anytime. Yeah. You know where we are, where we love snowflake, look, we're at the snowflake summit. And we've always had a great relationship with snowflake though, and really leaned in there because we really believe Snowflake's principles, particularly around cloud and being cloud native and the operating advantages that it affords companies that that's really aligned with what we do. And so snowflake was really the first of the cloud data catalogs that we ultimately or say the cloud data warehouses that we integrated with and to see them transition to building really out the data cloud has been awesome. >>Talk about how data world and snowflake enable companies like per lodges to be data companies. These days, every company has to be a data company, but they, they have to be able to do so quickly to be competitive and to, to really win. How do you help them if we like up level the conversation to really impacting the overall business? >>That's a great question, especially right now, everybody knows. And pro is a great example. They're a logistics and supply chain company at the end of the day. And we know how important logistics and supply chain is nowadays and for them and for a lot of our customers. I think one of the advantages of having a data catalog is the ability to build trust, transparency and inclusivity into their data analytics practice by adopting agile principles, by adopting a data mesh, you're able to extend your data analytics practice to a much broader set of stakeholders and to involve them in the process while the work is getting done. One of the greatest things about agile software development, when it became a thing in the early two thousands was how inclusive it was. And that inclusivity led to a much faster ROI on software projects. And we see the same thing happening in data analytics, people, you know, we have amazing data scientists and data analysts coming up with these insights that could be business changing that could make their company significantly more resilient, especially in the face of economic uncertainty. >>But if you have to sit there and argue with your business stakeholders about the validity of the data, about the, the techniques that were used to do the analysis, and it takes you three months to get people to trust what you've done, that opportunity's passed. So how do we shorten those cycles? How do we bring them closer? And that's, that's really a huge benefit that like Prologis has, has, has realized just tightening that cycle time, building trust, building inclusion, and making sure ultimately humans learn by doing, and if you can be inclusive, it, even, it even increases things like that. We all want to, to, to, to help cuz Lord knows the world needs it. Things like data literacy. Yeah. Right. >>So data.world can inform me as to where on the spectrum of data quality, my data set lives. So I can say, okay, this is usable, shareable, you know, exactly of gold standard versus fix this. Right. Okay. Yep. >>Yep. >>That's yeah. Okay. And you could do that with one data catalog, not a bunch of >>Yeah. And trust trust is really a multifaceted and multi multi-angle idea, right? It's not just necessarily data quality or data observability. And we have incredible partnerships in that space, like our partnership with, with Monte Carlo, where we can ingest all their like amazing observability information and display that in a really like a really consumable way in our data catalog. But it also includes things like the lineage who touch it, who is involved in the process of a, can I get a, a, a question answered quickly about this data? What's it been used for previously? And do I understand that it's so multifaceted that you have to be able to really model and present that in a way that's unique to any given organization, even unique within domains within a single organization. >>If you're not, that means to suggest you're a data quality. No, no supplier. Absolutely. But your partner with them and then that you become the, the master catalog. >>That's brilliant. I love it. Exactly. And you're >>You, you just raised your series C 15 million. >>We did. Yeah. So, you know, really lucky to have incredible investors like Goldman Sachs, who, who led our series C it really, I think, communicates the trust that they have in our vision and what we're doing and the impact that we can have on organization's ability to be agile and resilient around data analytics, >>Enabling customers to have that single source of truth is so critical. You talked about trust. That is absolutely. It's no joke. >>Absolutely. >>That is critical. And there's a tremendous amount of business impact, positive business impact that can come from that. What are some of the things that are next for data.world that we're gonna see? >>Oh, you know, I love this. We have such an incredibly innovative team. That's so dedicated to this space and the mission of what we're doing. We're out there trying to fundamentally change how people get data analytics work done together. One of the big reasons I founded the company is I, I really truly believe that data analytics needs to be a team sport. It needs to go from, you know, single player mode to team mode and everything that we've worked on in the last six years has leaned into that. Our architecture being cloud native, we do, we've done over a thousand releases a year that nobody has to manage. You don't have to worry about upgrading your environment. It's a lot of the same story that's made snowflake. So great. We are really excited to have announced in March on our own summit. And we're rolling this suite of features out over the course of the year, a new package of features that we call data.world Eureka, which is a suite of automations and, you know, knowledge driven functionality that really helps you leverage a knowledge graph to make decisions faster and to operationalize your data in, in the data ops way with significantly less effort, >>Big, big impact there. John, thank you so much for joining David, me unpacking what data world is doing. The data mesh, the opportunities that you're giving to customers and every industry. We appreciate your time and congratulations on the news and the funding. >>Ah, thank you. It's been a, a true pleasure. Thank you for having me on and, and I hope, I hope you guys enjoy the rest of, of the day and, and your other guests that you have. Thank you. >>We will. All right. For our guest and Dave ante, I'm Lisa Martin. You're watching the cubes third day of coverage of snowflake summit, 22 live from Vegas, Dave and I will be right back with our next guest. So stick around.

>>Good morning, everyone. Welcome back to the Cube's coverage of snowflake summit live from Caesar's forum in Las Vegas, Lisa Martin, with Dave Valante. Dave, we have had an action packed two days here, talking with loads of folks. There's been about 10,000 attendees here, the momentum, the excitement for snowflake, what they're building, what they're, what they've announced is huge. >>I'll tell you like this is a getaway day and there's still decent amount of buzz going on in the ecosystem here and the exhibit hall. And I was just saying, when you walk around Las Vegas, you'd never know the economy's about the tank with, you know, inflation is on the rise. I mean, Vegas is packed. >>It is packed it a lot of shows going on here. We are excited to welcome Patrick Barch, the senior director of product management at capital one software to the program. Patrick, it's great to have you. >>Thank you. It's great to be here. >>So we all know capital one. I love the commercials. I'm sure you have a, a large say in how fun and creative they are. Talk to us about capital one software. This is a new business software business. It >>Is. And so, you know, from our founding days in 1994, capital one has always recognized the power of data and technology to create differentiated experiences for our customers. But about 10 years ago, we declared that we were gonna reinvent the way that we build and use technology. One of the key steps in that journey was migrating from our owned and operated data centers to the public cloud. But in order to do that, we needed to build a number of products and platforms to help us operate at scale because the market just wasn't quite there yet. And so capital one software, which we announced last week, Woohoo is our first foray into bringing some of those cloud and data management products to market. >>Talk to us about you. Capital one is one of Snowflake's longest running and largest customers. How does snowflake help facilitate that >>A couple different ways? So first snowflake is a, it's a super powerful platform. They've changed the game when it comes to leveraging data. At scale in the cloud, we were an early investor. We were, we were one of their biggest customers. They've been a great partner along the way, helping us adopt the platform. But for us, when we adopted back in 2018 ish, we realized that with all of this power comes a lot of responsibility. And so we needed to make sure that we were putting good governance and good controls around our usage of snowflake from the start. And so, you know, we, we, we needed to build some, some tools to help us optimize our, our usage of snowflake. >>Okay. So you basically said we're going all in the cloud. You guys have made huge investments in, in AWS and obviously snowflake. And then now you're, you're sort of taking what you did internally and exposing it almost like, like Amazon did Amazon retail and then that's how AWS was born. Okay, awesome. What kind of results did you see internally in terms of the primary benefit? If I understand it is cost savings, but also better data management, right? Is that fair? >>So the, the totality of what we've built internally covers both cost savings, data management, data security, adherence to data privacy legislation. The product that we announced here at summit is really focused on cost optimization for snowflake, right? And so with these tools, we've been able to save about 27% on our projected snowflake costs. We've been able to save our teams about 50,000 hours of manual effort by reducing the number of change orders that they have to execute manually through automated infrastructure management. We've reduced our cost per query by about 43%. And so really what these enabled us to do is just get really efficient with how we use the system. You know, one, one of the challenges you might run into with snowflake is, is unexpected costs. And so by leveraging these tools, we've been able to make sure that our costs are predictable and consistent from month to month, which enables us to budget appropriately. >>And, and that's 50,000 hours person hours over what period of time >>Have to get back to you on the exact amounts? I mean, >>Years, months, several years. Weeks. Yeah. Yeah. Okay. So, but we're talking about tens and tens of millions of dollars, right? If you, I mean, just assume a hundred bucks an hour for, for a person just fully loaded. I mean, I'll just do that math. Okay. And 20% percent on snowflake cost. So here's, here's the question? Well, well, first of all, what's the vision, what's the like gimme a five year vision for, for the software group at capital one, >>We wanna bring capital one's data and cloud management expertise to the masses. Okay. We've spoken to a number of companies that are trying to follow in our footsteps. We've, we've heard again and again, that our challenges are their challenges. Our, the path that we walked is the path that they're trying to walk in. So we are super excited about bringing all of our expertise to the market. >>So start with cost savings, but the vision transcends cost savings, absolutely going into security, privacy, data management, >>Absolutely absolutely workflow. And the, the, you know, the industry's in a super interesting place now where it's very fragmented. There is a galaxy of tools out there. You, you look around here, there's hundreds and hundreds of different solutions, but they're point solutions. They're all going after an individual piece of the management puzzle. And what we found was that we needed to create these integrated experiences that were aligned to our team's jobs to be done, not necessarily in terms of, you know, a capability like cataloging or quality or entitlements, you know, in order to efficiently operate at scale, you need to string those things together in a way that lets your team get their job done. >>So my last question on this flow is, I dunno if you're familiar with you guys, maybe familiar with Sarah Wong and Martin CASAA published a piece that got, you know, pretty wide viewing and discussion. They are out out of Andreesen, a 16 Z that the cost of good sold for SaaS companies who are born in the cloud are gonna become so overwhelming that they're gonna repatriate and start managing themselves. And they use Dropbox as an example. Now Dropbox is storage. So it's very specific niche, you know, and I've talked to many, many companies like snowflake about this, and they're like, eh, that ain't happening anytime soon. How do you feel about that? Because if you look at SAS companies that are born in the cloud, their gross margins are, you know, they don't get to 90%, but they're healthy, you know, 75, you know, sometimes 78% even snowflakes, you know, end of decade forecast Scarelli has it. I think it's 78%. And the reason it's not higher is because of the cloud cost. You gotta pay the cloud bills, my belief and I've argued, this is that's okay. I can negotiate cloud bills. I can work with tools like yours over time to keep those down. And the cloud guys are gonna be competing with each other, but, but what do you make of that Patrick >>Cloud costs? Aren't gonna go down. Data is expanding at an exponential rate. The scale of data today is orders of magnitude versus what it was in on-prem systems. And so, you know, I don't think the cloud providers are too worried because data is exploding at such a, a crazy pace. And so it really becomes about using all of those resources as efficiently as possible. And, and in the cloud where compute is fully elastic, it scales infinitely instantly on demand. You know, it's all about getting it's, it's, it's all about making sure that if you're spending more, you're getting more business value. There's not wastage in the system. >>Same question, but different. Do you feel like strategically organizations generally in capital one specifically will, will, will optimize their time on optimizing or spend their, their effort optimizing the cloud costs? Or do you feel like long term you can actually be cheaper to manage yourself? In other words, our, our cloud benefits of not doing all that heavy lifting offset that potential, you know, cost equation. >>I mean, you saved just so much time and effort and headache, not having to manage physical infrastructure. And so like, you know, snowflake, you can write a sequel command to create a database. You can write a sequel command to create a data warehouse. Like the market will not give up that level of simplicity for managing infrastructure. And so I think at the end of the day, you're gonna, you're gonna see a focus on efficiency because what you really want your teams to be focused on your old, your old DBA and data engineering teams is focused on driving customer value, not in the weeds of infrastructure management. >>And that's why I think you guys, this is a great business that you're starting. And I think you, I, frankly, I think you're gonna get a lot of competition, which is a good thing that says you're in a great business and you guys are first >>Talk about the customer experience. You know, we are also as consumers demanding, we wanna be able to transact ASAP. We wanna make sure that, you know, on the swipe fraud detection happens, how does the Slingshot help facilitate and improve the customer experience if I'm transacting or I'm gonna sign up or I'm getting a mortgage. >>So with Slingshot, we enable your company, regardless of what you do at, at capital one, we're, we're a bank to build more personalized experiences for customers in a more cost effective way. And so Enno is our, our intelligent, personal banking assistant with snowflake. We're enable Enno to do way more than we were previously for less than we would've without some of these tools. >>And that's a huge competitive differentiator because we expect as consumers and of whatever it is. We want a personalized experience, right? That's relevant. That's gonna offer us products and services that might build upon what we've already done. >>It's it's kind of table stakes these days. Yes. And so with these tools and with snowflake, we were able to onboard our business teams were able to onboard over 400 new use cases over, over that same time period. And so really what it's enabled us to do is unlock the innovative power of our company and create more of these customer experiences. >>How does the customer visualize those, those cost savings? And, and, and do, do, do you have some tooling, maybe it's in the works to help them predict what kind of cost savings they have based on some modeling that >>You do. And absolutely. So we enable teams to enforce good governance around infrastructure management, up front by building rules and enabling their teams to create warehouses, create databases. And then once that infrastructure is up and running, we give them a whole bunch of dashboards that show transparency and to spend, we enable chargebacks to lines of business in today's consumption, driven business models. It's hard to reconcile at the end of the month, if you spent what you thought you spent and, and data costs have gone from CapEx to OPEX and, but not everybody is an expert. And so we look at usage data, we look at usage history and we come up with recommendations for how you can save money by, you know, tweaking this or tweaking that or better optimizing your, your compute. >>Should we expect you as you expand your opportunity to take your expertise and aim it at AWS more broadly, maybe Redshift more specifically, Google GCP, big query Azure, what, what should we expect there? >>You know, there's, there's a lot of opportunity to help companies optimize costs across other cloud providers as well. This, this concept of elastic compute, isn't just specific to snowflake. That's certainly one path that we could go down. You know, we have a lot of expertise in, in data management as well, and data privacy, data security. And so that's that, that's another path as well that, that we have expertise in. And so, you know, I think it's, it's an exciting time we're in, we're in an exciting place, but it's early days, >>Did you do a working backwards document? Can you share that with us? >>Fortunately >>Not five, five or 10 years down the road, you may decide to do that, right? >>Yeah. Let me, let me check with my PR person to see if I'm allowed to share here. That's >>I mean, I think this is gonna be a huge success and, and I think it it's, it's, it follows a lot of the things that we've learned from AWS. Yeah. And you guys have been all in there and, and, you know, it's funny, right? We laugh about working backwards, customer obsession, two pizza teams. I mean, it really has changed the sort of way that we think about developing software and, and managing infrastructures. I, I think you're gonna have a, a huge business and I, I wish you the best. >>I, I appreciate that. And the, the thing, a lot of that statement is, you know, internal teams are now starting to demand consumer great experiences for the tools that they use. Yeah, for sure. And so one of the things that we did was treat our internal associates. Like they were external customers, we applied design thinking, we applied product management, we built our experience in terms of what are you trying to accomplish? And what's getting in your way, because that's what people have come to expect with all of these consumer experiences, >>Collaboration. That's right. What last question for you? What would you say to peers in your, whatever, same industry, other industries that are really trying to figure out how to get their hands on data to become a data company, what would you advise them? Why should they choose >>Snowflake gives you so many building blocks out of the box to help you create a, a well-managed data ecosystem? You know, the simplicity with which you can create new infrastructure, define policies for that infrastructure onboard new users. I mean, it, it's one of the platforms in internally capital one that has the highest NPS score. And so, you know, if you're looking to adopt a, a data cloud platform, I mean, snowflake is certainly high up on the list of what you should be looking at. >>That's >>Awesome. How do you, do you consider this a SA, is it a consumption or how do you price for this? >>So we, we don't have published pricing at the moment, but it is, it is a SAS product. You know, what we can share is it'll, it'll be a, you know, small fraction of, of your, of your total credit spend with snowflake and, and >>You're thinking a subscription or, or haven't figured that out yet, >>It it'll likely be a, a consumption model based on, you know. Okay. >>So the, so, so say, you know, it's funny SAS, I get it. Software's a service, but it, but because it's consumption, I think it's like modern SAS. If I can say that, you know, it's cloud >>SAS and it, it, you know, it's more important to make sure right now, because we're so early that we're actually providing the right value to customers. We have a pretty generous trial program going on right now where you can try the, the, the software out for free to make sure it, it fits your needs. So, >>Okay. So you're in trial, right. I should have clarified that you're in trial now. And, and so, yeah, of course you haven't figured out exactly how you're gonna price it yet. But >>The, the, the official posture that we're taking is public preview. We've, we've been in private preview for the last six months. We've onboarded a, a couple of customers who are starting to use the product. And so the, the big announcement this week is we're officially in public preview, come on in. >>So you gotta get product market fit. That's right. Before you figure out your pricing and before you, then you, then you're gonna scale. Great. >>What's been the feedback so far >>Overwhelmingly positive. Somebody stopped by the booth and said, oh my God, that's so cool. We've heard a lot of, wow, we need this right now. You know, it's, I had pretty, pretty high expectations coming in, just based on the value that this is created for capital one, but I've, I've been blown away by, by what I've heard from the people who've stopped by our booth. >>Awesome. Patrick, thank you for joining Dave and me on the program, talking about what you're doing with capital one software seems like you're just in early innings, but so much potential to come. We wish you the best of luck with that. And you have to come back and tell us how it's going. Thanks so much. Thanks for having me, our pleasure for Dave ante. I'm Lisa Martin. You're watching the cube our day three coverage of snowflake summit 22 live from Las Vegas continues after a short break.

(bright music) >> Welcome back to the Cube. Our continuing coverage of Snowflake Summit 22, day two, lots of content as I've said, coming at you the last couple of days. Dave and I, Dave Vellante, and Lisa Martin are here with you. We have an exciting guest here next to talk with us about data discovery. Please welcome Shinji Kim, the founder and CEO at Select Star. Welcome to the program. >> Thanks for having me. >> Dave: Great to see you. >> Excited to be here. >> Talk to us about Select Star. What do you guys do? And then we're going to uncrack data discovery. >> Yeah, why'd you start the company? (Shinji laughing) >> Sure. So, Select Star is, on fully automated data discovery platform, that helps any company to be able to find, understand and manage their data. I started this company because after I sold my last company, Concord Systems to Akamai, I started working with a lot of global enterprise companies that manages a lot of IOT devices like automakers or consumer electronics companies. And it became very clear to me that companies are not going to stop anytime soon about collecting more data, more often, and trying to utilize them as much as they can. And cloud providers, and all the new technologies like Snowflake has really helped them to achieve that goal. But the challenges that, I've started noticing, from a lot of these enterprises, is that they now have 100s or 1000s of data sets that they have to manage. And when you are trying to use that data it's almost impossible to find which specific field which specific data sets that you should use out of 1000s and 100s of 1000s of data sets you have. So, that's why I felt like this is the next problem and challenge that I would like to solve. Also because, I have a background of working as a software engineer, data scientist, product manager, in the stages of creating data, transforming data and also querying data and trying to make business decisions on data. Having a right context about the data, is so important, for me to use that data. So, for us, we are trying to solve that challenge around finding and understanding data, and we call that data discovery. >> Wow. That's music to my ears here because I can't tell you how many meetings I've been in, where somebody presents some data and I say, okay, what's the source of that data? What are the assumptions used to derive data? I have different data, you know, and then it becomes this waste of time. My data's better than your data, or everybody has an agenda. You cut through that. >> Yeah, so, data discovery, in a nutshell, we defining as finding, understanding, and managing your data. So, in Select Star, we will automatically bring out, all your, like the schema information. Where does data exist? We will also analyze the SQL query logs as well as activity logs that's generated by any applications and BI tools that are connected on top of your data warehouse, so that any time you're looking at a database any particular database table, column or dashboard, we will tell you, where did this data come from? Where did it originate from? How was this transformed? And which reporting table does this exist? Who's using this data the most inside the company? How are they using it? And which are the dashboards and reports that are built on top of this data set? So you don't have to go out and ask everybody else, "Hey, I'm looking for this type of data. "Has anybody worked with this?" This is actually something that I realize a lot of data analysts and data scientists waste their time on. So yeah, that's really the, what we call fully automated data context that we provide to our customers so that you can truly use all the data that you have in your data warehouse. >> And you do this by understanding the metadata? Or is it some kind of scanning? Or using math or code? >> It's both. So, first of all, we do connect and bring out all the metadata. So, that's all the information under information schema. And then, we also look at all the query history. So all your select SQL queries, all your create queries, create table queries, create view queries. And based on that, we will also match the metadata, where it exists inside those queries and logs. And based on that, we will generate first and foremost, what we would call column level data lineage. Data lineage is all about showing you the flow of data from where it was originated, how it was transformed, and where it exists now. And also, what we call popularity. Who's using what data? How are they using it? And in aggregate, you can also find out, which are the most important data sets in our company? Which are the data sets that can be deprecated because it was like a duplicate of other data sets and nobody's using it anymore? And we like put a, like a popularity score for every single data asset that you have in your company so you can see how that's being used. >> How do your customers take action on the information that you provide them? Do they ultimately automate it? Do they go through a process of sort of the human in the loop? >> Well, we do the automation for them. >> Yeah. >> And we do also provide them with a, really easy to use user interface so that they can add any semantic level data on top. So, that's like tags. Like whether you want to market as, this is a analyst approved table, or do not use table or if you want to put a PII classification of data you can do that on a column. And we will automatically either propagate those annotations throughout the platform. We will also automatically propagate any same matching documentation that you might want to use within the data warehouse. And we will also provide you with, more of a rich text documentation that you can also add on top as a business glossary or like a Wiki that business users can, get a better understanding of data concepts and models as well. >> How do they tag the data? Do they use another tool that does that or? >> No, they can tag it within Select Star. Any table or column has a little icon, tag icon, so you can click on it. Or, we can also give you a view of every database page will have all the tables in one place. You can add a keyword and bulk tag. >> So humans tag. >> Yeah. So humans tag. So in the beginning, humans tag, and then we will automate the propagation of that tag. So if you already tagged, let's say SSN field as a PII, then we will find all the other columns that may use the exact same data, and also tag the same, just as an example. >> Okay so you, once the human puts it in there then you automate the downstream. 'Cause humans sometimes aren't great at classifying and tagging and inconsistencies and I would think that you could use math to improve that. >> And we do have some plans to add more automated tagging system. For example, we are already, we don't necessarily tag them, but we give our customers filters on top of their search results to see, which are the data sets that nobody's using anymore? Which are the data sets that's being created very recently? And you can also filter by who created them or who are the owners. So these are some of the aspects of the data or even like when's the last time was this data updated? So these are the aspects of the operational metadata that we are starting to automate to put more automated annotation, I would say is more coming up towards the end of the year. But in terms of semantic level tagging, like is this data set around customers? Is this data set for marketing, sales, customer support? That is something that we are giving a really easy to use interface for the data team to be able to easily organize them. >> How are you helping organizations? We think of the all the privacy regulations and legislations. How is Select Star a facilitator of data privacy for your clients? Is it part of that play? >> So, I would say, one of the main use cases of data discovery, is data governance. So, starting this company and starting to work with a lot of fortune 500 companies, as well as I would say more like recently IPOed companies that have grown very fast in Silicon valley. Some of those customers have told us that they initially adopted Select Star because they needed a good data catalog and search platform for their data team. But as they are starting to use Select Star and starting to see all these insights about their own data warehouse, they are all kicking off their new data governance projects, because they get to see a really good lay of the land, of how the data is being accessed today. So, this is why we have a very easy to use and also programmatic API so that you can add tags, ownership, and set access control through a Select Star. We are actually just releasing a beta version of our, what we call policy based access control where you can use either role based and attribute based access control so that different roles of the users get to see different versions of a Select star when they log in. And this is just the beginning. Like PII is for example, any column that's already marked as PII. We will always strip out the value before it gets fully processed within Select Star. So even if anybody might stumble upon any sequel queries that other analysts have run, those values won't be available in Select Star at all. >> And you started the company right before the lockdown, right? That must have been crazy. >> Yes, March, 2020 is our, my incorporation of Select Star. It was a very interesting time to start the company. And in a way, I'm glad I did. We had a lot of focus time to really, go heads down, build out the product, and work closely with our customer. And today it's really awesome to get to, provide that support to more customers today. >> And so, what are you doing with Snowflake? >> So Snowflake has been a great partner for us. Lot of customers and Snowflake is really great for this. Basically building single source of truth of your data by connecting all your source of, databases, as well as like your ERP, CRM systems, ad systems, marketing systems, SaaS platform, you can connect them now all to Snowflake, that will all dump all the data inside. So that, allows data team to be able to actually join and crossmatch the customer data across so many different applications. And what we see from a lot of Snowflake customers, hence they end up with many different schemas and tens of thousands of tables. And for them now they are requiring or needing more of a better data discovery tool so that they can use and leverage Snowflake data that they have. So, in that regard so we are a snowflake data governance accelerator partner. And as part of that accelerator program, one of the things that we've integrated with Snowflake is, what we call Snowflake Tag Sync. So if you create any tags in Select Star, and you marked it as a PII, we will also replicate the same tag, to Snowflake. >> Yeah. Okay. >> And so everything is synced in there. And on top of that, a lot of our customers really like using our column level lineage, because we will show how all the data tables within Snowflake is connected to another. And actually last but not least, we actually just released this feature today, called the auto generated ER diagram. ER diagram stands for Entity Relationship Diagram. ERD is like a blueprint of your data model. When your engineers and data architects start creating tables in databases, this is a diagram that they will put together, to show how they are translating business logic into data models in the databases. And that includes, which are the fields for primary keys, foreign keys, and how are different like when you look at Star schema, how different tables are joined together. When all these tables gets migrated into Snowflake, a lot of them actually lose the, the relationships of primary keys and foreign keys. So, many analysts, what we found, is that they are starting to guess, how to join different tables, how to use different data sets together. But because we know how other analysts have actually joined and used the tables in the past, we can give them the guidance and really nice diagram that they can refer to. So that is the ERD diagram that we are releasing today. Available for all customers including our free customers, where you can select any tables, and we will show you the relationship that table has, that you can use right away in your sequel queries. >> And that will facilitate, that simplifies doing more complex joins, yes? Which is an Achilles heel of Snowflake. That's not really what they are about, but they have to rely on the ecosystem to help them do that, which has always been their strategy. The company founded in March 2020, amazing. And then relatively small still, yes? Or is it self-funded? I mean, you've raised a little bit of money, but what's your status? >> Yeah, we raised our seed funding when I first started the company. We've also raised another round of bridge round last year and we plan to raise our another venture round of funding soon. >> Great. Awesome. >> And we're going to be making those investments. What are some of the key parts of the business that you're going to use that funding for? >> There's a lot to build. (Shinji laughing) >> Dave: Yeah. Engineering. >> Obviously more automation features, but having, I would say right now, we have now built a really good foundation of data discovery and that includes fully automated data cataloging for metadata, column level lineage, and also building the usage model like popularity, who's using what, all that type of stuff. So, now we are starting to build really exciting features that leverages these fundamental aspects of data discovery, like auto propagation of tags. We also do auto propagation of documentation. So you write one column description once, and it will get replicated and changed everywhere throughout your data model. We have also other things that we have in store especially more for next year, are, package support for specific use cases like data governance, self-service analytics and cloud cost management. >> Nice, lots of work-- >> Dave: Impressive, I'm blown away. >> And you've accomplished this during a pandemic that's even more impressive. Thank you so much Shinji for coming on, talking to us about Select Star. What you're enabling organizations to do, really derive the context from that data taking a lot of manual work away. We appreciate your insights and your time and wish you the best of luck. >> Well, thanks so much for having me here. This has been great. >> Good. Thanks so much. For Dave Vellante, I'm Lisa Martin. You're watching the Cube's coverage of Snowflake Summit 22, day two. Stick around. Dave has an industry analyst panel common up next. You won't want to miss it. (soft music)

>>Welcome back to Vegas guys, Lisa Martin and Dave Lanta here wrapping up our coverage of day two of snowflake summit. We have given you a lot of content in the last couple of days. We've had a lot of great conversations with snowflake folks with their customers and with partners. And we have an alumni back with us. Please. Welcome back to the queue. Mike Palmer, CEO of Sigma computing. Mike. It's great to see you. >>Thanks for having me. And I guess again >>Exactly. >>It's fantastic me. >>So talk to the audience about Sigma before we get into the snowflake partnership and what you guys are doing from a technical perspective, give us that overview of the vision and some of the differentiators. >>Sure. You know, you've over the last 12 years, companies have benefited from enormous investments and improvements in technology in particular, starting with cloud technologies, obviously going through companies like snowflake, but in terms of the normal user, the one that makes the business decision in the marketing department and the finance team, you know, in the works in the back room of the supply chain, doing inventory very little has changed for those people. And the time had come where the data availability, the ability to organize it, the ability to secure it was all there, but the ability to access it for those people was not. And so what Sigma's all about is taking great technology, finding the skillset they have, which happens to be spreadsheets. There are billion license spreadsheet users in the world and connecting that skillset with all of the power of the cloud. >>And how do you work with snowflake? What are some of the, the what's the joint value proposition? >>How are they as an investor? That's what I wanna know. Ah, >>Quiet, which is the way we like them. No, I'm just kidding. Snowflake is, well, first of all, investment is great, but partnership is even better. Right. You know, and I think snowflake themselves are going through some evolution, but let's start with the basics of technology where this all starts because you know, all of the rest doesn't matter if the product is not great, we work directly on snowflake. And what that means is as an end user, when I, when I sit on that marketing team and I want to understand and, and connect, how did I get a, a customer where I had a pay to add? And they showed up on my website and from my website, they went to a trial. And from there, they touched a piece of syndicated contents. All of that data sits in snowflake and I, as a marketer, understand what it means to me. >>So for the first time, I want to be able to see that data in one place. And I want to understand conversion rates. I want to understand how I can impact those conversion rates. I can make predictions. What that user is doing is going to, to Sigma accessing live data in snowflake, they're able to ask ad hoc questions, questions that were never asked questions, that they don't exist in a filter that were never prepped by a data engineer. So they could truly do something creative and novel in a very independent sort of way. And the connection with Snowflake's live data, the performance, the security and governance that we inherit. These are all facilitators to really expand that access across the enterprise. So at, at a product level, we were built by a team of people, frankly, that also were the original investors in snowflake by two amazing engineers and founders, Rob will and Jason France, they understood how snowflake worked and that shows up in the product for our end customers. >>So, but if I may just to follow up on that, I mean, you could do that without snowflake, but what, it would be harder, more expensive. Describe what you'd have to go through to accomplish that outcome. >>And I think snowflake does a good job of enabling the ecosystem at large. Right. But you know, you always appreciate seeing early access to understand what the architecture's going to look like. You know, some of the things that I will, you know, leaning forward that we've heard here that we're very excited about is snowflake going to attack the TP market, right? The transactional market, one of the transactional database market. I, yeah. Right. You know, one of the things that we see coming, and, and one of the bigger things that we'll be talking about in Sigma is not just that you can do analytics out of snowflake. I think that's something that we do exceptionally well on an ad hoc basis, but we're gonna be the first that allow you to write into snowflake and to do that with good performance. And to do that reliably, we go away from OAP, which is the terminology for data warehousing. >>And we go toward transactional databases. And in that world, understanding snowflake and working collaboratively with them creates again, a much better experience for the end customer. So they, they allow us into those programs, even coming to these conferences, we talk to folks that run the industry teams, trying to up level that message and not just talk database and, and analytics, but talk about inventory management. How do we cut down the gap that exists between POS systems and inventory ordering, right? So that we get fewer stockouts, but also that we don't overorder. So that's another benefit, >>Strong business use cases. >>That's correct. >>And you're enabling those business users to have access to that data. I presume in near real time or near real time, so that they can make decisions that drive marketing forward or finance forward or legal >>Forward. Exactly. We had a customer panel yesterday. An example of that go puff is hopefully most of the viewers are familiar with, as a delivery company. This is a complicated business to run. It's run on the fringes. When we think about how to make money at it, which means that the decisions need to be accurate. They need to be real time. You can't have a batch upload for delivery when they're people are on the street, and then there's an issue. They need to understand the exact order at that time, not in 10 minutes, not from five minutes ago, right. Then they need to understand, do I have inventory in the warehouse when the order comes in? If they don't, what's a replacement product. We had a Mike came in from go puff and walked us through all of the complexity of that and how they're using Sigma to really just shorten those decision cycles and make them more accurate. You know, that's where the business actually benefits and, >>And actually create a viable business model. Cuz you think back to the early, think back to the.com days and you had pets.com, right? They couldn't make any money. Yeah. Without chewy. Okay. They appears to be a viable business model. Right? Part of that is just the efficiencies. And it's sort of a, I dunno if those are customers that they may or may not be, but they should be if they're not >>Chewy is, but okay. You know, and that's another example, but I'll even pivot to the various REI and other retailers. What do they care about cohorts? I'm trying to understand who's buying my product. What can I sell to them next? That, that idea of again, I'm sitting in a department, that's not data engineering, that's not BI now working collaboratively where they can get addend engineer, putting data sets together. They have a BI person that can help in the analytics process. But now it's in a spreadsheet where I understand it as a marketer. So I can think about new hierarchies. I wanna know it by customer, by region, by product type. I wanna see it by all of those things. I want to be able to do that on the fly because then it creates new questions that sort of flow. If you' ever worked in development, we use the word flow constantly, right? And as people that flow is when we have a question, we get an answer that generates a question. We have, we just keep doing that iteratively. That that is where Sigma really shines for them. >>What does a company have to do to really take advantage of, of this? I, if they're kind of starting from a company that's somewhat immature, what are the sort of expectations, maybe even outta scope expectations so they can move faster, accelerate analytics, a lot of the themes that we've heard today, >>What does an immature company is actually even a question in, in and of itself? You know, I think a lot of companies consider themselves to be immature simply because for various constraint reasons, they haven't leveraged the data in the way that they thought possible. Good, >>Good, good definition. Okay. So not, not, >>Not, I use this definition for digital transformation. It very simple. It is. Do you make better decisions, faster McKenzie calls this corporate metabolism, right? Can you speed up the metabolism of, of an enterprise and for me and for the Sigma customer base, there's really not much you have to do once. You've adopted snowflake because for the first time the barriers and the silos that existed in terms of accessing data are gone. So I think the biggest barrier that customers have is curiosity. Because once you have curiosity and you have access, you can start building artifacts and assets and asking questions. Our customers are up and running in the product in hours. And I mean that literally in hours, we are a user in snowflake, that's a direct live connection. They are able to explore tables, raw. They can do joins themselves if they want to. They can obviously work with their data engineering team to, to create data sets. If that's the preferred method. And once they're there and they've ever built a pivot table, they can be working in Sigma. So our customers are getting insights in the first one to two days, you referenced some, those of us are old enough to remember pest.com. Also old enough to remember shelfware that we would buy. We are very good at showing customers that within hours they're getting value from their investment in Sigma. And that, that just creates momentum, right? Oh, >>Tremendous momentum and >>Trust and trust and expansion opportunities for Sigma. Because when you're in one of those departments, someone else says, well, you know, why do you get access to that data? But I don't, how are you doing this? Yeah. So we're, you know, I think that there's a big movement here. People, I often compare data to communication. If you go back a hundred years, our communication was not limited. As it turns out by our desire to communicate, it was limited by the infrastructure. We had the typewriter, a letter and the us postal service and a telephone that was wired. And now we have walk around here. We, everything is, is enabled for us. And we send, you know, hundreds and thousands of messages a day and probably could do more. You will find that is true. And we're seeing it in our product is true of data. If you give people access, they have 10 times as many questions as they thought they had. And that's the change that we're gonna see in business over the next few years, >>Frank Salman's first book, what he was was CEO of snowflake was rise of the data cloud. And he talked about network effects. Basically what he described was Metcalf's law. Again, go back to the.com days, right? And he, Bob Metcalf used the phone system. You know, if there's two people in the phone system, it's not that valuable, right. >>You know, exactly, >>You know, grow it. And that's where the value is. And that's what we're seeing now applied to data. >>And even more than that, I think that's a great analogy. In fact, the direct comparison to what Sigma is doing actually goes one step beyond everything that I've been talking about, which is great at the individual level, but now the finance team and the marketing team can collaborate in the platform. They can see data lineage. In fact, one of our, our big emphasis points here is to eliminate the sweet products. You know, the ones where, you know, you think you're buying something, but you really have a spreadsheet product here and a document product there and a slide product over there. And they, you know, you can do all of that in Sigma. You can write a narrative. You can real time live, edit on numbers. You, you know, if you want to, you could put a picture in it. But you know, at Sigma we present everything out of our product. Every meeting is live data. Every question is answered on the spot. And that's when, you know, you know, to your point about met cap's law. Now everybody's involved in the decision making. They're doing it real time. Your meetings are more productive. You have fewer of them because they're no action items, right. We're answering our questions there and we're, and we're moving forward. >>You know, view were meeting sounds good. Productivity is, is weird now with the, the pandemic. But you know, if you go back to the nineties here am I'm, I'm dating myself again, but that's okay. You know, you, you didn't see much productivity going on when the PC boom started in the eighties, but the nineties, it kicked in and pre pandemic, you know, productivity in the us and Europe anyway has been going down. But I feel like Mike, listen to what you just described. I, how many meetings have we been in where people are arguing about them numbers, what are the assumptions on the numbers wasting so much time? And then nothing gets done and they, then they, they bolt cut that away and you drive in productivity. So I feel like we're on a Renaissance of productivity and a lot of that's gonna be driven by, by data. Yeah. And obviously communications the whole 5g thing. We'll see how that builds out. But data is really the main spring of, I think, a new, new Renaissance in productivity. >>Well, first of all, if you could find an enterprise where you ask the question, would you rather use your data better? And they say, no, like, you know, show me, tell me that I'll short their stock immediately. But I do agree. And I, unfortunately I have a career history in that meeting that you just described where someone doesn't like, what you're showing them. And their first reaction is to say, where'd you get that data? You know, I don't trust it. You know? So they just undermined your entire argument with an invalid way of doing so. Right. When you walk into a meeting with Sigma where'd, where'd you get that data? I was like, that's the live data right now? What question do you want answer >>Lineage, right. Yeah. And you know, it's a Sen's book about, you know, gotta move faster. I mean, this is an example of just cutting through making decisions faster because you're right. Mike and the P the P and L manager in a meeting can, can kill the entire conversation, you know, throw FUD at it. Yeah. You know, protect his or her agenda. >>True. But now to be fair to the person, who's tended to do that. Part of the reason they've done that is that they haven't had access to that data before the meeting and they're getting blindsided. Right. So going back to the collaboration point. Yes. Right. The fact we're coming to this discussion more informed in and of itself takes care of some of that problem. Yeah. >>For sure. And if, and if everybody then agrees, we can move on and now talk about the really important stuff. Yeah. That's good. It >>Seems to me that Sigma is an enabler of that curiosity that you mentioned that that's been lacking. People need to be able to hire for that, but you've got a platform that's going here. You go ask >>Away. That's right in the we're very good. You know, we love being a SaaS platform. There's a lot of telemetry. We can watch what we call our mouse to Dows, you know, which is our monthly average users to our daily average users. We can see what level of user they are, what type of artifacts they build. Are they, you know, someone that creates things from scratch, are they people that tend to increment them, which by the way, is helpful to our customers because we can then advise them, Hey, here's, what's really going on. You might wanna work with this team over here. They could probably be a little better of us using the data, but look at this team over here, you know, they've originated five workbooks in the last, you know, six days they're really on it. There's, there's, you know, that ability to even train for the curiosity that you're referring to is now there, >>Where are your customer conversations? Are they at the lines of business? Are they with the chief data officer? What does that look like these days? >>Great question. So stepping back a bit, what, what is Sigma here to do? And, and our first phase is really to replace spreadsheets, right? And so one of the interesting things about the company is that there isn't a department where a spreadsheet isn't used. So Sigma has an enormous Tam, but also isn't necessarily associated with any particular department or any particular vertical. So when we tend to have conversations, it really depends on, you know, either what kind of investment are you making? A lot of mid-market companies are making best technology investments. They're on a public cloud, they're buying snowflake and they wanna understand what's, what's built to really make this work best over the next number of years. And those are very short sales for us because we, we prove that, you know, in, in minutes to hours, if you're working at a large enterprise and you have three or four other tools, you're asking a different question. >>And often you're asking a question of what I call exploration. We have a product that has dashboards and they've been working for us and we don't wanna replace the dashboard. But when we have a question about the data in the dashboard, we're stuck, how do we get to the raw data? How do we get to the example that we can actually manage? You can't manage a dashboard. You can't manage a trend line, but if you get into the data behind the trend line, you can make decisions to change business process, to change quality, accuracy, to change speed of execution. That is what we're trying to enable. Those conversations happen between the it team who runs technology and the business teams who are responsible for the decisions. So we are, you know, we have a cross departmental sale, but across every department, >>One of the things we're not talking about at this event, which is kind of interesting, cause it's all we've been talking about is the macro supply chain challenges, Ukraine, blah, blah, blah, and the stock market. But, but how are you thinking about that? Macro? The impacts you're seeing, you know, a lot of private companies being, you know, recapped, et cetera, you guys obviously very well funded. Yeah. But how do you think about, I mean, I asked Frank a similar question. He's like, look, it's a marathon. We don't worry about it. We, you know, they made the public market, they get 5 billion in cash. Yeah. Yeah. How are you thinking about it? >>You know, first of all, what's the expression, right? You never, never waste a good, you know, in this case recession, no, we don't have one yet, but the impetus is there, right. People are worried. And when they're worried, they're thinking about their bottom lines, they're thinking about where they're going to get efficiency and their costs. They're already dealing with the supply chain issues of inventory. We all have it in our personal lives. If you've ordered anything in the last six months, you're used to getting it in, you know, days to weeks. And now you're getting in months, you know, we had customers like us foods as a good example, like they're constantly trying to align inventory. They have with transportation that gets that inventory to their end customers, right? And they do that with better data accuracy at the end point, working with us on what we are launching. >>And I mentioned earlier, having more people be able to update that data creates more data, accuracy creates better decisions. We align that then with them and better collaboration with the folks that then coordinate the trucks with Prologis and the panel yesterday, they're the only commercial public company that reports their, their valuations on a quarterly basis. They work with Sigma to trim the amount of time it takes their finance team to produce that data that creates investor confidence that holds up your stock price. So I mean the, the importance of data relative to all the stakeholders in enterprise cannot be overstated. Supply chain is a great example. And yes, it's a marathon because a lot of the technology that drives supply chain is old, but you don't have to rip out those systems to put your data into snowflake, to get better access through Sigma, to enable the people in your environment to make better decisions. And that's the good news. So for me, while I agree, there's a marathon. I think that most of the, I dunno if I could continue this metaphor, but I think we could run quite far down that marathon without an awful lot of energy by just making those couple of changes. >>Awesome. Mike, this has been fantastic. Last question. I, I can tell, I know a lot of growth for Sigma. I can feel it in your energy alone. What are some of the key priorities that you're gonna be focusing on for the rest of the year? >>Our number one priority, our number two priority and number three priority are always build the best product on the market, right? We, we want customers to increase usage. We want them to be delighted. You know, we want them to be RA. Like we have customers at our booth that walk up and it's like, you're building a great company. We love your product. I, if you want to show up happy at work, have customers come up proactively and tell you how your products changed their life. And that is, that is the absolute, most important thing because the real marathon here is that enablement over the long term, right? It is being a great provider to a bunch of great companies under that. We are growing, you know, we've been tripling the company for the fast few years, every year, that takes a lot of hiring. So I would've alongside product is building a great culture with bringing the best people to the company that I guess have my energy level. >>You know, if you could get paid in energy, we would've more than tripled it, you know, but that's always gonna be number two, where we're focused on the segment side, you know, is really the large enterprise customer. At this point, we are doing a great job in the mid-market. We have customer, we have hundreds of customers in our free trial on a constant basis. I think that without wanting to seem over confident or arrogant, I think our technology speaks for itself and the product experience for those users, making a great ROI case to a large enterprise takes effort. It's a different motion. We're, we're very committed to building that motion. We're very committed to building out the partner ecosystem that has been doing that for years. And that is now coming around to the, the snowflake and all of the ecosystem changes around snowflake because they've learned these customers for decades and now have a new opportunity to bring to them. How do we enable them? That is where you're gonna see Sigma going over the next couple of years. >>Wow, fantastic. Good stuff. And a lot of momentum, Mike, thank you so much for joining Dave and me talking about Sigma, the momentum, the flywheel of what you're doing with snowflake and what you're enabling customers to achieve the massive business outcomes. Really cool stuff. >>Thank you. And thank you for continuing to give us a platform to do this and glad to be back in conferences, doing it face to face. It's fantastic. >>It it's the best. Awesome. Mike, thank you for Mike Palmer and Dave ante. I'm Lisa Martin. You've been watching the cube hopefully all day. We've been here since eight o'clock this morning, Pacific time giving you wall the wall coverage of snowflake summit 22 signing off for today. Dave and I will see you right bright and early tomorrow morning. I will take care guys.