>>Hello everyone and welcome back to Las Vegas. I've got my jazz hands because I am very jazzed to be here at AWS Reinvent Live from the show floor all week. My name is Savannah Peterson, joined with the infamous John Farer. John, how you feeling >>After feeling great? Love? What's going on here? The vibe is a cloud, cloud native. Lot of security conversation, data, stuff we love Cloud Native, >>M I >>A L, I mean big news. Security, security, data lake. I mean, who would've thought Amazon have a security data lake? You know, e k s, I mean >>You might have with that tweet you had out >>Inside outside the containers. Reminds me, it feels like coan here. >>It honestly, and there's a lot of overlap and it's interesting that you mention CubeCon because we talked to the next company when we were in Detroit just a couple weeks ago. Teleport E is the CEO and founder F Welcome to the show. How you doing? >>I'm doing well. Thank you for having me today. >>We feel very lucky to have you. We hosted Drew who works on the product marketing side of Teleport. Yeah, we got to talk caddies and golf last time on the show. We'll talk about some of your hobbies a little bit later, but just in case someone's tuning in, unfamiliar with Teleport, you're all about identity. Give us a little bit of a pitch, >>Little bit of our pitch. Teleport is the first identity native infrastructure access platform. It's used by engineers and it's used by machines. So notice that I used very specific choice of words first identity native, what does it mean? Identity native? It consists of three things and we're writing a book about those, but I'll let you know. Stay >>Tuned on that front. >>Exactly, yes, but I can talk about 'em today. So the first component of identity, native access is moving away from secrets towards true identity. The secrets, I mean things like passwords, private keys, browser cookies, session tokens, API keys, all of these things is secrets and they make you vulnerable. The point is, as you scale, it's absolutely impossible to protect all of the seekers because they keep growing and multiplying. So the probability of you getting hacked over time is high. So you need to get rid of secrets altogether that that's the first thing that we do. We use something called True Identity. It's a combination of your biometrics as well as identity of your machines. That's tpms, HSMs, Ubikes and so on, so forth. >>Go >>Ahead. The second component is Zero Trust. Like Teleport is built to not trust the network. So every resource inside of your data center automatically gets configured as if there is no perimeter it, it's as safe as it was on the public network. So that's the second thing. Don't trust the network. And the third one is that we keep access policy in one place. So Kubernetes clusters, databases on stage, rdp, all of these protocols, the access policy will be in one place. That's identity. Okay, >>So I'm, I'm a hacker. Pretend I'm a hacker. >>Easy. That sounds, >>That sounds really good to me. Yeah, I'm supposed to tell 'em you're hacker. Okay. I can go to one place and hack that. >>I get this question a lot. The thing is, you want centralization when it comes to security, think about your house being your AWS account. Okay? Everything inside your furniture, your valuable, like you'll watch collection, like that's your data, that's your servers, paper clusters, so and so forth. Right Now I have a choice and your house is in a really bad neighborhood. Okay, that's the bad internet. Do you wanna have 20 different doors or do you want to have one? But like amazing one, extremely secure, very modern. So it's very easy for you to actually maintain it and enforce policy. So the answer is, oh, you probably need to have >>One. And so you're designing security identity from a perspective of what's best for the security posture. Exactly. Sounds like, okay, so now that's not against the conventional wisdom of the perimeter's dead, the cloud's everywhere. So in a way kind of brings perimeter concepts into the posture because you know, the old model of the firewall, the moat >>It Yeah. Just doesn't scale. >>It doesn't scale. You guys bring the different solution. How do you fit into the new perimeters dead cloud paradigm? >>So the, the way it works that if you are, if you are using Teleport to access your infrastructure, let's just use for example, like a server access perspective. Like that machine that you're accessing doesn't listen on a network if it runs in Teleport. So instead Teleport creates this trusted outbound tunnels to the proxy. So essentially you are managing devices using out going connection. It's kind of like how your phone runs. Yeah. Like your phone is actually ultimate, it's like a teleport like, like I It's >>Like teleporting into your environment. >>Yeah, well play >>Journal. But >>Think about actually like one example of an amazing company that's true Zero trust that we're all familiar with would be Apple. Because every time you get a new iOS on your phone, the how is it different from Apple running massive software deployment into enormous cloud with billions of servers sprinkle all over the world without perimeter. How is it possible That's exactly the kind of technology that Teleports >>Gives you. I'm glad you clarified. I really wanted to get that out on the table. Cuz Savannah, this is, this is the paradigm shift around what an environment is Exactly. Did the Apple example, so, okay, tell 'em about customer traction. Are people like getting it right away? Are their teams ready? Are they go, oh my god this is >>Great. Pretty much you see we kinda lucky like in a, in a, like in this business and I'm walking around looking at all these successful startups, like every single one of them has a story about launching the right thing at just the right like moment. Like in technology, like the window to launch something is extremely short. Like months. I'm literally talking months. So we built Teleport started to work on it in like 2015. It was internal project, I believe it or not, also a famous example. It's really popular like internal project, put it on GitHub and it sat there relatively unnoticed for a while and then it just like took off around 2000 >>Because people start to feel the pain. They needed it. Exactly, >>Exactly. >>Yeah. The timing. Well and And what a great way to figure out when the timing is right? When you do something like that, put it on GitHub. Yeah. >>People >>Tell you what's up >>Yeah's Like a basketball player who can just like be suspended in the air over the hoop for like half the game and then finally his score and wins >>The game. Or video gamer who's lagged, everyone else is lagging and they got the latency thing. Exactly. Thing air. Okay. Talk about the engineering side. Cause I, I like this at co con, you mentioned it at the opening of this segment that you guys are for engineers, not it >>Business people. That's right. >>Explain that. Interesting. This is super important. Explain why and why that's resonating. >>So there is this ongoing shift on more and more responsibilities going to engineers. Like remember back in the day before we even had clouds, we had people actually racking servers, sticking cables into them, cutting their fingers, like trying to get 'em in. So those were not engineers, they were different teams. Yeah. But then you had system administrators who would maintain these machines for you. Now all of these things are done with code. And when these things are done with code and with APIs, that shifts to engineers. That is what Teleport does with policy. So if you want to have a set of rules that govern who or what and when under what circumstances can access what data like on Kubernetes, on databases, on, on servers wouldn't be nice to use code for it. So then you could use like a version control and you can keep track of changes. That's what teleport enables. Traditionally it preferred more kind of clicky graphical things like clicking buttons. And so it's just a different world, different way of doing it. So essentially if you want security as code, that's what Teleport provides and naturally this language resonates with this persona. >>Love that. Security is coding. It's >>A great term. Yeah. Love it. I wanna, I wanna, >>Okay. We coined it, someone else uses it on the show. >>We borrow it >>To use credit. When did you, when did you coin that? Just now? >>No, >>I think I coined it before >>You wanted it to be a scoop. I love that. >>I wish I had this story when I, I was like a, like a poor little 14 year old kid was dreaming about security code but >>Well Dave Ante will testify that I coined data as code before anyone else but it got 10 years ago. You >>Didn't hear it this morning. Jimmy actually brought it back up. Aws, you're about startups and he's >>Whoever came up with lisp programming language that had this concept that data and code are exact same thing, >>Right? We could debate nerd lexicon all day on the cube. In fact, that could even be a segment first >>Of we do. First of all, the fact that Lisp came up on the cube is actually a milestone because Lisp is a very popular language for object-oriented >>Grandfather of everything. >>Yes, yes, grandfather. Good, good. Good catch there. Yeah, well done. >>All right. I'm gonna bring us back. I wanna ask you a question >>Talking about nerd this LIS is really >>No, I think it's great. You know how nerdy we can get here though. I mean we can just hang out in the weeds the whole time. All right. I wanna ask you a question that I asked Drew when we were in Detroit just because I think for some folks and especially the audience, they may not have as distinctive a definition as y'all do. How do you define identity? >>Oh, that's a great question. So identity as a term was, it was always used for security purposes. But most people probably use identity in the context of single signon sso. Meaning that if your company uses identity for access, which instead of having each application have an account for you, like a data entry with your first name, last name emails and your role. Yeah. You instead have a central database, let's say Okta or something like that. Yep. And then you, you use that to access everything that's kind of identity based access because there is a single source of identity. What we say is that we, that needs to be extended because it it no longer enough because that identity can be stolen. So if someone gets access to your Okta account using your credentials, then they can become you. So in order for identity to be attached to you and become your true identity, you have to rely on physical world objects. That's biometrics your facial fingerprint, like your facial print, your fingerprints as well as biometric of your machine. Like your laptops have PPM modules on it. They're absolutely unique. They cannot be cloned stolen. So that is your identity as well. So if you combine whatever is in Octa with the biker chip in this laptop and with your finger that collectively is your true identity, which cannot be stolen. So it's can't be hacked. >>And someone can take my finger like they did in the movies. >>So they would have to do that. And they would also have to They'd >>Steal your match. Exactly, exactly. Yeah. And they'd have to have your eyes >>And they have to, and you have >>Whatever the figure that far, they meant what >>They want. So that is what Drew identity is from telecom and >>Biometric. I mean it's, we're so there right now it's, it's really not an issue. It's only getting faster and better to >>Market. There is one important thing I said earlier that I want to go back to that I said that teleport is not just for engineers, it's also for machines. Cuz machines they also need the identity. So when we talk about access silos and that there are many different doors into your apartment, there are many different ways to access your data. So on the infrastructure side, machines are doing more and more. So we are offloading more and more tasks to them. That's a really good, what do machines use to access each other? Biome? They use API keys, they use private keys, they use basically passwords. Yeah. Like they're secrets and we already know that that's bad, right? Yeah. So how do you extend biometrics to machines? So this is why AWS offers cloud HSM service. HSM is secure hardware security module. That's a unique private key for the machine that is not accessible by anyone. And Teleport uses that to give identities to machines. Does do >>Customers have to enable that themselves or they have that part of a Amazon, the that >>Special. So it's available on aws. It's available actually in good old, like old bare metal machines that have HSMs on them on the motherboard. And it's optional by the way Teleport can work even if you don't have that capability. But the point is that we tried, you >>Have a biometric equivalent for the machines with >>Take advantage of it. Yeah. It's a hardware thing that you have to have and we all have it. Amazon sells it. AWS sells it to us. Yeah. And Teleport allows you to leverage that to enhance security of the infrastructure. >>So that classic hardware software play on that we're always talking about here on the cube. It's all, it's all important. I think this is really fascinating though. So I had an on the way to the show, I just enrolled in Clear and I had used a different email. I enrolled for the second time and my eyes wouldn't let me have two accounts. And this was the first time I had tried to sort of hack my own digital identity. And the girl, I think she was humoring me that was, was kindly helping me, the clear employee. But I think she could tell I was trying to mess with it and I wanted to see what would happen. I wanted to see if I could have two different accounts linked to my biometric data and I couldn't it, it picked it up right away. >>That's your true >>Identity. Yeah, my true identity. So, and forgive me cuz this is kind of just a personal question. It might be a little bit finger finger to the wind, but how, just how much more secure if you could, if you could give us a, a rating or a percentage or a a number. How much more secure is leveraging biometric data for identity than the secrets we've been using historically? >>Look, I could, I played this game with you and I can answer like infinitely more secure, right? Like but you know how security works that it all depends on implementation. So let's say you, you can deploy teleport, you can put us on your infrastructure, but if you're running, let's say like a compromised old copy of WordPress that has vulnerability, you're gonna get a hack through that angle. But >>Happens happens to my personal website all the time. You just touched Yeah, >>But the fact is that we, I I don't see how your credentials will be stolen in this system simply because your TPM on your laptop and your fingerprint, they cannot be downloaded. They like a lot of people actually ask us a slightly different question. It's almost the opposite of it. Like how can I trust you with my biometrics? When I use my fingerprint? That's my information. I don't want the company I work at to get my fingerprint people. I think it's a legit question to ask. >>Yeah. And it's >>What you, the answer to that question is your fingerprint doesn't really leave your laptop teleport doesn't see your fingerprint. What happens is when your fingerprint gets validated, it's it's your laptop is matching what's on the tpm. Basically Apple does it and then Apple simply tells teleport, yep that's F or whoever. And that's what we are really using. So when you are using this form authentication, you're not sharing your biometric with the company you work at. >>It's a machine to human confirmation first and >>Then it's it. It's basically you and the laptop agreeing that my fingerprint matches your TPM and if your laptop agrees, it's basically hardware does validation. So, and teleport simply gets that signal. >>So Ed, my final question for you is here at the show coupon, great conversations there for your company. What's your conversations here like at reinvent? Are you meeting with Amazon people, customers? What are some of the conversations? Because this is a much broader, I mean it's still technical. Yep. But you know, a lot of business kind of discussions, architectural refactoring of organizations. What are some of the things that you're talking about here with Telepo? What are, >>So I will mention maybe two trends I observed. The first one is not even security related. It's basically how like as a cloud becomes more mature, people now actually at different organizations develop their own internal ways of doing cloud properly. And they're not the same. Because when cloud was earlier, like there were this like best practices that everyone was trying to follow and there was like, there was just a maybe lack of expertise in the world and and now finding that different organizations just do things completely different. Like one, like for example, yeah, like some companies love having handful, ideally just one enormous Kubernetes cluster with a bunch of applications on it. And the other companies, they create Kubernetes clusters for different workloads and it's just like all over the map and both of them are believed that they're doing it properly. >>Great example of bringing in, that's Kubernetes with the complexity. And >>That's kind of one trend I'm noticing. And the second one is security related. Is that everyone is struggling with the access silos is that ideally every organization is dreaming about a day, but they have like one place which is which with great user experience that simply spells out this is what policy is to access this particular data. And it gets a automatically enforced by every single cloud provider, but every single application, but every single protocol, but every single resource. But we don't have that unfortunately Teleport is slowly becoming that, of course. Excuse me for plugging >>TelePro. No, no worries. >>But it is this ongoing theme that everyone is can't wait to have that single source of truth for accessing their data. >>The second person to say single source of truth on this stage in the last 24 >>Hours or nerds will love that. I >>Know I feel well, but it's all, it all comes back to that. I keep using this tab analogy, but we all want everything in one place. We don't wanna, we don't wanna have to be going all over the place and to look for >>Both. Because if it's and everything else places, it means that different teams are responsible for it. Yeah. So it becomes this kind of internal information silo as well. So you not even, >>And the risks and liabilities there, depending on who's overseeing everything. That's awesome. Right? So we have a new challenge on the cube specific to this show thing of this as your 30 minute or 30 minute that would be bold. 32nd sizzle reel, Instagram highlight. What is your hot take? Most important thing, biggest theme of the show this year. >>This year. Okay, so here's my thing. Like I want cloud to become something I want it to be. And every time I come here and I'm like, are we closer? Are we closer? So here's what I want. I want all cloud providers collectively to kind of merge. So then when we use them, it feels like we are programming one giant machine. Kind of like in the matrix, right? The movie. So like I want cloud to feel like a computer, like to have this almost intimate experience you have with your laptop. Like you can like, like do this and the laptop like performs the instructions. So, and it feels to me that we are getting closer. So like walking around here and seeing how everything works now, like on the single signon on from a security perspective, there is so that consolidation is finally happening. So it's >>The software mainframe we used to call it back in 2010. >>Yeah, yeah. Just kind of planetary scale thing. Yes. It's not the Zuckerberg that who's building metaverse, it's people here at reinvent. >>Unlimited resource for developers. Just call in. Yeah, yeah. Give me some resource, spin me up some, some compute. >>I would like alter that slightly. I would just basically go and do this and you shouldn't even worry about how it gets done. Just put instructions into this planetary mainframe and mainframe will go and figure this out. Okay. >>We gotta take blue or blue or red pill. I >>Know. I was just gonna say y'all, we are this, this, this, this segment is lit. >>We got made tricks. We got brilliant. We didn't get super cloud in here but we, we can weave that in. We got >>List. We just said it. So >>We got lisp. Oh great con, great conversation. Cloud native. >>Outstanding conversation. And thank you so much for being here. We love having teleport on the show. Obviously we hope to see you back again soon and and Drew as well. And thank all of you for tuning in this afternoon. Live from Las Vegas, Nevada, where we are hanging out at AWS Reinvent with John Furrier. I'm Savannah Peterson. This is the Cube. We are the source for high tech coverage.

>>Good afternoon everyone, and welcome back to Cuan where my cohost John Farer and I are broadcasting live, along with Lisa Martin from Cuan Detroit, Michigan. We are joined this afternoon by two very interesting gentlemen who also happen to be legends on the cube. John, how long have you known the next few? They've, >>They've made their mark on the cube with Jerry Chen from Greylock was one of our most attended cube guests. He's a VC partner at Greylock and an investor and this company that just launched their new cloud observability platform should be a great segment. >>Well, I'm excited. I are. Are you excited? Should I string this out just a little bit longer? No, I won't. I won't do that to you. Please welcome Martin and Jeff from Chronosphere Martin. Jeff, thank you so much for being >>Here. Thank you for having us. Thank you. >>I noticed right away that you have raised a mammoth series C. Yeah. 200 million if I'm not mistaken. >>That is correct. >>Where's the company at? >>Yeah, so we raised that series C a year ago. In fact, we were just talking about it a year ago at Cub Con. Since then, at the time we're about 80 employees or so. Since then, we've tripled the headcount, so we're over 200 people. Casual, triple casual, triple of the headcount. Yeah. Luckily it was the support of business, which is also tripled in the last year. So we're very lucky from that perspective as well. And a couple of other things we're pretty proud of last year. We've had a hundred percent customer retention, which is always a great thing to have as a SaaS platform there. >>Real metric if you've had a hundred percent. I'm >>Kidding. It's a good metric to, to put out there if you had a hundred percent. I would say for sure. It's an A for sure and exactly welcome to meet >>Anyone else who's had a hundred percent >>Customer attention here at coupon this week and 90% of our customers are using more of the service and, and you know, therefore paying more for the service as well. So those are great science for us and I think it shows that we're clearly doing something right on the product side. I would say. And >>Last and last time you're on the cube. We're talking about about the right data. Not so much a lot of data, if I remember correctly. Yeah, a hundred percent. And that was a unique approach. Yeah, it's a data world on relative observability. And you guys just launched a new release of your platform, cloud native platform. What's new in the platform? Can you share an update on what you guys release? >>Yeah, well we did and, and you, you bring up a great point. You know, like it's not just in observably but overall data is exploding. Alright, so three things there. It's like, hey, can your platform even handle the explosion of data? Can it control it over time and make sure that as your business grows, the data doesn't continue explode at the same time. And then for the end users, can they make sense of all this data? Cuz what's the point of having it if the end users can't make sense of it? So actually our product announcement this time is a pretty big refresh of, of a lot of features in our, in our platform. And it actually tackles all three of these particular components. And I'll let Jeff, our head of product, Doug, >>You, you run product, you get the keys to the kingdom, I do product roadmap. People saying, Hey this, take this out. You're under a lot of pressure. What makes the platform platform a great observability product? >>So the keystone of what we do that's different is helping you control the data, right? As we're talking about there's an infinite amount of data. These systems are getting more and more and more complicated. A lot of what we do is help you understand the utility of the telemetry so that you can optimize for keeping and storing and paying for the data that's actually helpful as opposed to the stuff that isn't. >>What's the benefit now with observability, with all the noise out in the marketplace, there's been a shift over the past couple years. Cloud native at scale, you're seeing a lot more automation, almost a set to support the growth for more application development. We had a Docker CEO on earlier today, he said there are more applications being deployed in the past year than in the history of open source. So more and more apps are being deployed, more data's being generated. What's the key to observability right now that's gonna separate the winners from the losers? >>Yeah, I think, you know, not only are there more applications being deployed, but there are smaller and small applications being deployed mostly on containers these days more than if they, hence this conference gets larger and larger every year. Right? So, you know, I think the key is a can your system handle this data explosion is, is the first thing. Not only can it handle the data explosion, but you know, APM solutions have been around for a very long time and those were really introspecting into an application. Whereas these days what's more important is, well how is your application interfacing with every other application in your distributed architecture there, right? So the use case is slightly different there. And then to what Jeff was saying is like once the data is there, not only making use of what is actually useful to you, but then having the end user make sense of it. >>Because we, we, we always think about the technology changes. We forget that the end users are different now we used to have IT operations team operating everything and the developers would write the application, just throw it over the wall. These days the developers have to actually operate this thing in production. So the end users of these systems are very different as well. And you can imagine these are folks, your average developer as maybe not operated things for many years in production before. So they need to, that they need to pick up a new skill set, they need to use new tooling in order to, to do that. So yeah, it's, it's, >>And you got the developer persona, you got a developer that's building products for builders and developers that are building products to be consumed. So they're not, they're not really infrastructure builders, they're just app developers. >>Exactly. Exactly. That's right. And that's what a lot of the new functionality that we're introducing here at the show is all about is helping developers who build software by day and are on call by night, actually get in context. There's so much data chances of when that, when one of those pages goes off and your number comes up, that the problem happens to be in the part of the system that you know a lot about are pretty low, chances are you're gonna get bothered about something else. So we've built a feature, we call it collections that's about putting you in the right context and connecting you into the piece of the system where the problem is to orient you and to get you started. So instead of waiting through, through hundreds of millions of things, you're waiting through the stuff that's in the immediate neighborhood of where the >>Problem is. Yeah. To your point about data, you can't let it go unchecked. That's right. You gotta gotta understand that. And we were talking about containers again with, again with docker, you know, nuance point, but oh, scan your container. But not everyone's scanning the containers security nightmare, right? I mean, >>Well I think one of the things that I, I loved in reading the notes in preparation for you coming up is you've actually created cloud native observability with the goal of eliminating engineering burnout. And what you're talking about there is actually the cognitive burden of when things happen. Yeah, for sure. We we're, you know, we're not just designing for when everything goes right, You need to be prepared for when everything goes wrong and that poor lonely individual in the middle of the night has, it's >>A tough job. >>Has to navigate that >>And, and observability is just one thing you gotta mean like security is another thing. So, so many more things have been piled on top of the developer in addition to actually creating the application. Right? It is. There is a lot. And you know, observably is one of those key things you need to do your job. So as much as, as much as we can make that easier, that's a better bit. Like there are so many things being piled on right now. >>That's the holy grail right there. Because they don't want to be doing exactly >>The work. Exactly. They're not observability experts. >>Exactly. And automating that in. So where do you guys weigh in on the automation wave? Everything's automation. Yeah. Is that kind of a hand waving or what's going on? What's the reality? What's actually happening? >>Yeah, I think automation I think is key. You hear a lot of ai ml ops there. I, I don't know if I really believe in that or having a machine self heal itself or anything like that. But I think automation is key because there are a lot of repeatable tasks in a lot of what you're doing. So once you detect that something goes wrong, generally if you've seen it before, you know what the fix is. So I think automation plays a key on the sense that once it's detected again the second time, the third time, okay, I know what I did the previous time, let, let's make sure we can do that again. So automation I think is key. I think it helps a lot with the burnout. I dunno if I'd go as far as the >>Same burnout's a big deal. >>Well there's an example again in the, in the stuff we're releasing this week, a new feature we call query accelerator. That's a form of automation. Problem is you got all this data, mountain of data, put you in the right context so you're at least in the right neighborhood, but now you need to query it. You gotta get the data to actually inform the specific problem you're trying to solve. And the burden on the developer in that situation is really high. You have to know what you're looking for and you have to know how to efficiently ask for it. So you're not waiting for a long time and >>We >>Built a feature, you tell us what you want, we will figure out how to get it for you efficiently. That's the kind of automation that we're focused on. That's actually a good service. How can we, it >>Sounds >>Blissful. How can we accelerate and optimize what you were gonna do anyway, rather than trying to read your mind or predict the future. >>Yes, >>Savannah, some community forward. Yeah, I, I'm, so I'm curious, you, you clearly lead with a lot of empathy, both of you and, and putting your, well you probably have experience with this as well, but putting your mind or putting yourself in the mind to the developer are, what's that like for you from a product development standpoint? Are you doing a lot of community engagement? Are you talking to developers to try and anticipate what they're gonna be needing next in terms of, of your offering? Or how has that work >>For you? Oh, for sure. So, so I run product, I have a lot of product managers who work for me. Somebody that I used to work with, she was accusing me, but what she called, she called me an anthropologist of a product manager. I >>Get these kind of you, the very good design school vibes from you both of you, which >>Is, and the reason why she said the way you do this, you go and you live with them in order to figure out what a day in their life is really like, what the job is really like, what's easy, what's hard. And that's what we try to aim at and try to optimize for. So that's very much the way that we do all of >>Our work. And that's really also highlights the fact that we're in a market that requires acute realtime data from the customer. Cause it's, and it's all new data. Well >>Yeah, it's all changing. The tools change every day. I mean if we're not watching how, and >>So to your point, you need it in real time as well. The whole point of moving to cloud native is you have a reliable product or service there. And like if you need to wait a few minutes to even know that something's wrong, like you've already lost at that point, you've already lost a ton of customers, potentially. You've already lost a ton of business. You know, to your point about the, the community earlier, one other thing we're trying to do is also give back to the community a little bit. So actually two days ago we just announced the open source of a tool that we've been using in our product for a very long time. But of course our product is, is a paid product, right? But actually open source a part of that tool thus that the broader community can benefit as well. And that tool which, which tool is that? It's, it's called Prom lens. And it's actually the Prometheus project is the open sourced metrics project that everybody uses. So this is a query builder that helps developers understand how to create queries in a much more efficient way. We've had in our product for a long time, but we're like, let's give that back to the community so that the broader community of developers out there can have a much easier time creating these queries as well. What's >>Been the feedback? >>We only now it's two days ago so I'm not, I'm not exactly sure. I imagine >>It's great. They're probably playing with it right now. >>Exactly. Exactly. Exactly. For sure. I imagine. Great. >>Yeah, you guys mentioned burnout before and we heard this a lot now you mentioned in terms of data we've been hearing and reporting about Insta security world, which is also data specific observability ties right into security. Yep. How does a company figure out, first of all, burnout's a big problem. It's more and more data coming. It's like, it's like doesn't stop and the breaches are coming too. How does a company know when they need that their observability strategy is broken? Is there sig signs of you know, burnout? Is there signs of breaches? I mean, what are some of the tell signs that if I'm a CSO I go, you know what, maybe I should check out promisee. When do, when do you guys match in and go we're a perfect fit to solve that problem? >>Yeah, I, I would say, you know, because we're focused on the observability side, less so on the security side, some of those signals are like how many incidents do you have? How many outages do you have? What's the occurrence of these things and how long does it take to recover from from from these particular incidents? How >>Upsetting are we finding customers? >>Upsetting are >>Customer. Exactly. >>And and one trend was seeing >>Not churn happening. Exactly. >>And one trend we're seeing in the industry is that 68% of companies are saying that they're having more incidents over time. Right. And if you have more incidents, you can imagine more engineers are being paid, are being woken up and they're being put under more stress. And one thing you said that very interesting is, you know, I think generally in the observability world, you ideally actually don't want to figure out the problem when it goes wrong. Ideally what you want to do these days is figure out how do I remediate this and get the business back to a running state as quickly as I can. And then when the business isn't burning, let me go and figure out what the underlying root cause is. So the strategy there is changed as well from the APM days where like I don't want to figure out the problem in real time. I wanna make sure my business and my service is running as it should be. And then separately from that, once it is then I wanna go >>Under understand that assume it's gonna happen, be ready to close that isolate >>The >>Fire. Exactly. Exactly. And, and you know, you can imagine, you know the whole movement towards C I C D, like generally when you don't touch a system, nothing goes wrong. You deploy change, first thing you do is not figure out why you change break thing. Get that back like underplay that change roll that change back, get your business back to a estate and then take the time where you're not under pressure, you're not gonna be burnt out to figure out what was it about my change that that broke everything. So, yeah. Got >>It. >>Well it's not surprising that you've added some new exciting customers to the roster. We have. We have. You want to tell the audience who they might >>Be? Yes. It's been a few big names in the last year we're pretty excited about. One is Snapchat, I think everybody knows, knows that application And one is Robin Hood. So you know, you can imagine very large, I'll say tech forward companies that have completed their migrations to, to cloud native or a wallet on their way to Cloudnative and, and we like helping those customers for sure. We also like helping a lot of startups out there cause they start off in the cloud native world. Like if you're gonna build a business today, you're gonna use Kubernetes from day one. Right? But we're actually interestingly seeing more and more of is traditional enterprises who are just early, pretty early on in their cloudnative migration then now starting to adopt cloud native at scale and now they're running to the same problems. As well >>Said, the Gartner data last year was something like 85% of companies had not made that transformation. Right. So, and that, I mean that's looking at larger scale companies, obviously >>A hundred, you're >>Right on the pulse. They >>Have finished it, but a lot of them are starting it now. So we're seeing pilot >>Projects, testing and cadence. And I imagine it's a bit of a different pace when you're working with some of those transforming companies versus those startups that are, are just getting rolling. I >>Love and you know, you have a lot of legacy use case you have to, like, if you're a startup, you can imagine there's no baggage, there's no legacy. You're just starting brand new, right? If you're a large enterprise, you have to really think about, okay, well how do I get my active business moved over? But yeah. >>Yeah. And how do you guys see the whole cloud native scale moving with the hyper scales? Like aws? You've got a lot of multi-cloud conversation. We call it super cloud in our narrative, but there's now this new, we're gonna get some of common services being identified. We're seeing a, we're seeing a lot more people recognize and with Kubernetes that hey, you know what, you could get some common services maybe across clouds with SOS doing storage. We got Min iOS doing some storage. Yeah. Cloud flare, I mean starting to see a lot more non-hyper scale systems. >>Yeah, I mean I, and I think that's the pattern there and I think it, it's, especially for enterprise at the top end, right? You see a, a lot of companies are trying to de-risk by saying, Hey, I, I don't want to bet maybe on one cloud provider, I sort of need to hedge my bets a little bit. And Kubernetes is a great tool to go do that. You can imagine some of these other tools you mentioned is a great way to do that. Observability is another great way to do that. Or the cloud providers have their observability or monitoring tooling, but it's really optimized just for that cloud provider, just for those services there. So if you're really trying to run either your custom applications or a multi-cloud approach, you really can't use one cloud providers solution to go solve that problem. Do you >>Guys see yourselves with that unifying >>Layer? We, we, we are a little bit as that lay because it's agnostic to each of the cloud providers. And the other thing is we actually like to understand where our customers run and then try to run their observability stack on a different cloud provider. Cuz we use the cloud ourselves. We're not running our own data centers of course, but it's an interesting thing where everybody has a common dependency on the cloud provider. So when us e one ofs hate to call them out, but when us E one ofs goes down, imagine half the internet goes down, right? And that's the time that you actually need observability. Right? Seriously. And every other tooling there. So we try to find out where do you run and then we try to actually run you elsewhere. But yeah, >>I like that. And nobody wants to see the ugly bits anyway. Exactly. And we all know who when we're all using someone when everything >>Exactly. Exactly, exactly. >>People off the internet. So it's very, I, I really love that. Martin, Jeff, thank you so much for being here with us. Thank you. What's next? What, how do people find out, how do they get one of the jobs since three Xing your >>Employee growth? We're hiring a lot. I think the best thing is to go check out our website chronosphere.io. You'll find out a lot about our, our, our careers, our job openings, the culture we're trying to build here. Find out a lot about the product as well. If you do have an observability problem, like that's the best place to go to find out about that as well. Right. >>Fantastic. Well if you want to join a quarter billion, a quarter of a billion dollar rocket ship over here and certainly a unicorn, get in touch with Martin and Jeff. John, thank you so much for joining me for this very special edition and thank all of you for tuning in to the Cube live here from Motor City. My name's Savannah Peterson and we'll see you in a little bit. >>Robert Herbeck. People obviously know you from Shark Tanks, but the Herbeck group has been really laser focused on cyber security. So I actually helped to bring my.

(upbeat jingle) >> The history of Exadata in the platform is really unique. And from my vantage point, it started earlier this century as a skunkworks inside of Oracle called Project Sage back when grid computing was the next big thing. Oracle saw that betting on standard hardware would put it on an industry curve that would rapidly evolve. Last April, for example, Oracle announced the availability of Exadata X9M in OCI, Oracle Cloud Infrastructure. One thing that hasn't been as well publicized is that Exadata on OCI is using AMD's EPYC processors in the database service. EPYC is not Eastern Pacific Yacht Club for all you sailing buffs, rather it stands for Extreme Performance Yield Computing, the enterprise grade version of AMD's Zen architecture which has been a linchpin of AMD's success in terms of penetrating enterprise markets. And to focus on the innovations that AMD and Oracle are bringing to market, we have with us today, Juan Loaiza, who's executive vice president of mission critical technologies at Oracle, and Mark Papermaster, who's the CTO and EVP of technology and engineering at AMD. Juan, welcome back to the show. Mark, great to have you on The Cube in your first appearance, thanks for coming on. Juan, let's start with you. You've been on The Cube a number of times, as I said, and you've talked about how Exadata is a top platform for Oracle database. We've covered that extensively. What's different and unique from your point of view about Exadata Cloud Infrastructure X9M on OCI? >> So as you know, Exadata, it's designed top down to be the best possible platform for database. It has a lot of unique capabilities, like we make extensive use of RDMA, smart storage. We take advantage of everything we can in the leading hardware platforms. X9M is our next generation platform and it does exactly that. We're always wanting to be, to get all the best that we can from the available hardware that our partners like AMD produce. And so that's what X9M in it is, it's faster, more capacity, lower latency, more iOS, pushing the limits of the hardware technology. So we don't want to be the limit, the software database software should not be the limit, it should be the actual physical limits of the hardware. That that's what X9M's all about. >> Why, Juan, AMD chips in X9M? >> We're introducing AMD chips. We think they provide outstanding performance, both for OTP and for analytic workloads. And it's really that simple, we just think the performance is outstanding in the product. >> Mark, your career is quite amazing. I could riff on history for hours but let's focus on the Oracle relationship. Mark, what are the relevant capabilities and key specs of the AMD chips that are used in Exadata X9M on Oracle's cloud? >> Well, thanks. It's really the basis of the great partnership that we have with Oracle on Exadata X9M and that is that the AMD technology uses our third generation of Zen processors. Zen was architected to really bring high performance back to X86, a very strong roadmap that we've executed on schedule to our commitments. And this third generation does all of that, it uses a seven nanometer CPU that is a core that was designed to really bring throughput, bring really high efficiency to computing and just deliver raw capabilities. And so for Exadata X9M, it's really leveraging all of that. It's really a balanced processor and it's implemented in a way to really optimize high performance. That is our whole focus of AMD. It's where we've reset the company focus on years ago. And again, great to see the super smart database team at Oracle really partner with us, understand those capabilities and it's been just great to partner with them to enable Oracle to really leverage the capabilities of the Zen processor. >> Yeah. It's been a pretty amazing 10 or 11 years for both companies. But Mark, how specifically are you working with Oracle at the engineering and product level and what does that mean for your joint customers in terms of what they can expect from the collaboration? >> Well, here's where the collaboration really comes to play. You think about a processor and I'll say, when Juan's team first looked at it, there's general benchmarks and the benchmarks are impressive but they're general benchmarks. And they showed the base processing capability but the partnership comes to bear when it means optimizing for the workloads that Exadata X9M is really delivering to the end customers. And that's where we dive down and as we learn from the Oracle team, we learn to understand where bottlenecks could be, where is there tuning that we could in fact really boost the performance above that baseline that you get in the generic benchmarks. And that's what the teams have done, so for instance, you look at optimizing latency to our DMA, you look at optimizing throughput on oil TP and database processing. When you go through the workloads and you take the traces and you break it down and you find the areas that are bottlenecking and then you can adjust, we have thousands of parameters that can be adjusted for a given workload. And that's the beauty of the partnership. So we have the expertise on the CPU engineering, Oracle Exadata team knows innately what the customers need to get the most out of their platform. And when the teams came together, we actually achieved anywhere from 20% to 50% gains on specific workloads, it is really exciting to see. >> Mark, last question for you is how do you see this relationship evolving in the future? Can you share a little roadmap for the audience? >> You bet. First off, given the deep partnership that we've had on Exadata X9M, it's really allowed us to inform our future design. So in our current third generation, EPYC is that is really what we call our epic server offerings. And it's a 7,003 third gen and Exadara X9M. So what about fourth gen? Well, fourth gen is well underway, ready for the future, but it incorporates learning that we've done in partnership with Oracle. It's going to have even more through capabilities, it's going to have expanded memory capabilities because there's a CXL connect express link that'll expand even more memory opportunities. And I could go on. So that's the beauty of a deep partnership as it enables us to really take that learning going forward. It pays forward and we're very excited to fold all of that into our future generations and provide even a better capabilities to Juan and his team moving forward. >> Yeah, you guys have been obviously very forthcoming. You have to be with Zen and EPYC. Juan, anything you'd like to add as closing comments? >> Yeah. I would say that in the processor market there's been a real acceleration in innovation in the last few years, there was a big move 10, 15 years ago when multicore processors came out. And then we were on that for a while and then things started stagnating, but in the last two or three years, AMD has been leading this, there's been a dramatic acceleration in innovation so it's very exciting to be part of this and customers are getting a big benefit from this. >> All right. Hey, thanks for coming back on The Cube today. Really appreciate your time. >> Thanks. Glad to be here. >> All right and thank you for watching this exclusive Cube conversation. This is Dave Vellante from The Cube and we'll see you next time. (upbeat jingle)

hybrid work is the new reality according to the most recent survey data from enterprise technology research cios expect that 65 of their employees will work either as fully remote or in a hybrid model splitting time between remote and in office remote of course can be anywhere it could be home it could be at the beach overseas literally anywhere there's internet so it's no surprise that these same technology executives cite security as their number one priority well ahead of other critical technology initiatives including collaboration software cloud computing and analytics which round out the top four in the etr survey now as we've reported securing endpoints was important prior to the pandemic but the explosion in the past two plus years of remote work and corollary device usage has made the problem even more acute and let's face it managing sprawling i.t assets has always been a pain patch management for example has been a nagging concern for practitioners and with ransomware attacks on the rise it's critical that security teams harden it assets throughout their life cycle staying current and constantly staying on top of vulnerabilities within the threat surface welcome to this special program on the cube enable and secure the everywhere workplace brought to you by ivanti in this program we highlight key partnerships between avanti and its ecosystem to address critical problems faced by technology and security teams in our first segment we explore a collaboration between avanti and sentinel one where the two companies are teaming to simplify patch management my name is dave vellante and i'll be your host today and with me are nayaki nayar who's the president and chief product officer at avanti and nick warner president and security of the security group at sentinel one welcome naki and nick and hackie good to have you back in the cube great to see you guys thank you thank you dave uh really good to be back on cube uh i'm a veteran of cube so thank you for having us and um look forward to a great discussion today yeah you better thanks okay hey good nick nick good to have you on as well what do we need to know about this partnership please so uh if you look at uh we are super excited about this partnership nick thank you for joining us on this session today um when you look at ivanti ivanti has been a leader in two big segments uh we are a leader in unified endpoint management with the acquisition of mobileye now we have a holistic end-to-end management of all devices be it windows linux mac ios you name it right so we have that seamless single pane of glass to manage all devices but in addition to that we are also a leader in risk-based patch management um dave that's what we are very excited about this partnership with the with central one where now we can combine the strength we have in the risk-based patch management with central one's xdr platform and truly help address what i call the need of the hour with our customers for them to be able to detect uh vulnerabilities and being able to remediate them proactively remediate them right so that's what we are super excited about this partnership and nick would love to hand it over to you to talk about uh the partnership and the journey ahead of us thanks and you know from center one's perspective we see autonomous vulnerability assessment and remediation as really necessary given the evolution uh in the sophistication the volume and the ferocity of threats out there and what's really key is being able to remediate risks and machine speed and also identify vulnerability exposure in real time and you know if you look traditionally at uh vulnerability scanning and patch management they've really always been two separate things and when things are separate they take time between the two coordination communication what we're looking to do with our singularity xdr platform is holistically deliver one unified solution that can identify threats identify vulnerabilities and automatically and autonomously leverage patch management to much better protect our customers so maybe maybe that's why patch management is such a challenge for many organizations because as you described nick it's sort of a siloed from security and those worlds are coming together but maybe you guys could address the specific problems that you're trying to solve with this collaboration yeah so if you look at uh just in a holistic level uh dave today cyber crime is at catastrophic heights right and this is not just a cio or a cso issue this is a board issue every organization every enterprise is addressing this at the board level and when you double click on it one of the challenges that we have heard from our customers over and over again is the complexity and the manual processes that are in place for remediation or patching all their operating systems their applications their third party apps and that is where it's very very time consuming very complex very cumbersome and the question is how do we help them automate it right how do we help them remove those manual processes and autonomously intermediate right so which is where this partnership between ivanti and central one helps organizations to bring this autonomous nature to bring those proactive predictive capabilities to detect an issue prioritize that issue based on risk-based prioritization is what we call it and autonomously remediate that issue right so that's where uh this partnership really really uh helps our customers address the the top concerns they have in cyber crime or cyber security got it so prioritization automation nick maybe you could address what are the keys i mean you got to map vulnerabilities to software updates how do you make sure that your the patches there's not a big lag between your patch and and the known vulnerabilities and you've got this diverse set of you know i.t portfolio assets how do you manage all that it's a great question and i and i think really the number one uh issue around this topic is that security teams and it teams are facing a really daunting task of identifying all the time every day all the vulnerabilities in their ecosystem and the biggest problem with this is how do they get context and priority and i think what people have come to realize through the years of dealing with with patch management uh and vulnerability scanning is that patching without the context of what the possible impact or priority of that risk is really comes down to busy work and i think what's so important in a totally interconnected world with attacks happening at machine speed is being able to take that precious asset that we call time and make sure you properly prioritize that how we're doing it from sentinel one singularity xdr perspective is by leveraging autonomous threat information and being able to layer that against vulnerability information to properly view through that lens the highest priority threats and vulnerabilities that you need to patch and then using our single agent technology be able to autonomously remediate and patch those vulnerabilities whether or not it's on a mac a pc server a cloud workload and the beauty of our solution is it gives you proper clarity so you can see the impact of vulnerabilities each and every day in your environment and know that you're doing the right thing in the right order got it okay so the context gives you the risks profile allows you to prioritize and then of course you can you know remediate what else should we know about this this joint solution uh in terms of you know what it is how i engage any other detail on how it addresses the the problem specifically yeah so it's all about race against the time um uh dave when it's how we help our customers uh detect the vulnerability prioritize it and remediate it the attackers are able to weaponize those vulnerabilities and and have an attack right so it's really it's how we help our customers be a lot more proactive and predictive address those vulnerabilities versus um before the attackers really get access to it right so that's where our joint solution in fact i always say whatever edr with this edr or mdr or xdr the r portion of that r is very one he comes in our neurons for patch management or what we call neurons but risk based patch management combined with um central ones xdr is where we truly uh bring the combined solutions to to to life right so the r is where ivanti really plays a big part in uh in the joint solution yeah absolutely the response i mean people i think all agree you're going to get infiltrated that's how you respond to it you know the thing about this topic is when you make a business case a lot of times you'll go to the cfo and say hey if we don't do this we're going to be in big trouble and so it's this fear factor and i get that it's super important but but are there other measurements of success that that you you can share in other words how are customers going to determine the value of this joint solution so it's a mean time to repair let me go nick and then i'm sure you have your uh metrics and how you're measuring the success it's about how we can detect an issue and repair that issue it's reducing that mean time to repair as much as possible and making it as real-time as possible for our customers right that's where the true outcome through success and the metric that customers can track measure and continuously improve on nick you want to add to that for sure yeah you know you make some great great points niaki and what what i would add is um what sentinel one singularity platform is known for is automated and autonomous detection prevention and response and remediation across threats and if you look traditionally at patch management or vulnerability assessment they're typically deployed and run in point-of-time solutions what i mean by that is that they're scans and re-scans the way that advanced edr solutions and xdr solutions such as single one singularity platform work is we're constantly recording everything that's happening on all of your systems in real time and so what we do is literally eliminate the window of opportunity between a patch being uh needed a vulnerability being discovered and you knowing that you have that need for that vulnerability to be patched in your environment you don't have to wait for that 12 or 24-hour window to scan for vulnerabilities you will immediately know it in your network you'll also know the security implications of that vulnerability so you know when and how to prioritize and then furthermore you can take autonomous hatching measures against that so at the end of the day the name of the game in security is time and it's about reducing that window of opportunity for the adversaries for the threat actors and this is a epic leap forward in in doing that for our customers and that capability nick is a function of your powerful agent or is it architecture where's that come from that's a great question it's it's a combination of a couple of things the first is our agent technology which performs constant monitoring on every system every behavior every process running on all your systems live and in real time so this is not a batch process that that kicks up once a day this is always running in the background so the moment a new application is installed the moment a new application version is deployed we know about it we record it instantaneously so if you think about that and layer against getting best in class vulnerability information from a partner like avanti and then also being able to deploy patch management against that you can start to see how you're applying that in real time in your environment and the last thing i i'd like to add is because we're watching everything and then layering it against thread intel and context using our proprietary machine learning technology that that idea of being able to prioritize and escalate is critical because if you talk to security providers there's a couple different uh challenges that they're facing and i would say the top two are alert fatigue and then also human human power limitations and so no security team has enough people on their team and no security teams have an absence of alerts and so the fact that we can prioritize alerts surface the ones that are the most important give context to that and also save them precious hours of their personnel's time by being able to do this autonomously and automatically we're really killing two birds with one stone that's great there's the business case right there you just laid out some other things that we can measure right it all comes back to the data doesn't it we got to go but i'll give you the last word yeah i mean we are super excited about this partnership uh like nick said uh we believe in how we can help our customers discover all the assets we have they have um manage those assets but a big chunk of it is how we help them secure it right secure uh their devices the applications the data that's on those devices the end points and being able to provide an experience a service experience at the end of the day so that end users don't have to worry about securing you don't have to think about security it should be embedded it should be autonomous and it should be contactually personalized right so uh that's the journey we are on and uh thank you nick for this great partnership and look forward to a great journey ahead of us thank you yeah thanks to both of you nick appreciate it okay keep it right there after this quick break we're gonna be back to look at how ivanti is working with other partners to simplify and harden the anywhere workplace you're watching the cube your leader in enterprise and emerging tech coverage [Music] you

[Music] the history of exadata in the platform is really unique and from my vantage point it started earlier this century as a skunk works inside of oracle called project sage back when grid computing was the next big thing oracle saw that betting on standard hardware would put it on an industry curve that would rapidly evolve and i remember the oracle hp database machine which was announced at oracle open world almost 15 years ago and then exadata kept evolving after the sun acquisition it became a platform that had tightly integrated hardware and software and today exadata it keeps evolving almost like a chameleon to address more workloads and reach new performance levels last april for example oracle announced the availability of exadata x9m in oci oracle cloud infrastructure and introduced the ability to run the autonomous database service or the exa data database service you know oracle often talks about they call it stock exchange performance level kind of no description needed and sort of related capabilities the company as we know is fond of putting out benchmarks and comparisons with previous generations of product and sometimes competitive products that underscore the progress that's being made with exadata such as 87 percent more iops with metrics for latency measured in microseconds mics instead of milliseconds and many other numbers that are industry-leading and compelling especially for mission-critical workloads one thing that hasn't been as well publicized is that exadata on oci is using amd's epyc processors in the database service epyc is not eastern pacific yacht club for all your sailing buffs rather it stands for extreme performance yield computing the enterprise grade version of amd's zen architecture which has been a linchpin of amd's success in terms of penetrating enterprise markets and to focus on the innovations that amd and oracle are bringing to market we have with us today juan loyza who's executive vice president of mission critical technologies at oracle and mark papermaster who's the cto and evp of technology and engineering at amd juan welcome back to the show mark great to have you on thecube and your first appearance thanks for coming on yep happy to be here thank you all right juan let's start with you you've been on thecube a number of times as i said and you've talked about how exadata is a top platform for oracle database we've covered that extensively what's different and unique from your point of view about exadata cloud infrastructure x9m on oci yeah so as you know exadata it's designed top down to be the best possible platform for database uh it has a lot of unique capabilities like we make extensive use of rdma smart storage we take advantage of you know everything we can in the leading uh hardware platforms and x9m is our next generation platform and it does exactly that we're always wanting to be to get all the best that we can from the available hardware that our partners like amd produce and so that's what x9 in it is it's faster more capacity lower latency more ios pushing the limits of the hardware technology so we don't want to be the limit the software the database software should not be the limit it should be uh the actual physical limits of the hardware and that that's what x9m is all about why won amd chips in x9m uh yeah so we're we're uh introducing uh amd chips we think they provide outstanding performance uh both for oltp and for analytic workloads and it's really that simple we just think that performance is outstanding in the product yeah mark your career is quite amazing i've been around long enough to remember the transition to cmos from emitter coupled logic in the mainframe era back when you were at ibm that was an epic technology call at the time i was of course steeped as an analyst at idc in the pc era and like like many witnessed the tectonic shift that apple's ipod and iphone caused and the timing of you joining amd is quite important in my view because it coincided with the year that pc volumes peaked and marked the beginning of what i call a stagflation period for x86 i could riff on history for hours but let's focus on the oracle relationship mark what are the relevant capabilities and key specs of the amd chips that are used in exadata x9m on oracle's cloud well thanks and and uh it's really uh the basis of i think the great partnership that we have with oracle on exadata x9m and that is that the amd technology uses our third generation of zen processors zen was you know architected to really bring high performance you know back to x86 a very very strong road map that we've executed you know on schedule to our commitments and this third generation does all of that it uses a seven nanometer cpu that is a you know core that was designed to really bring uh throughput uh bring you know really high uh efficiency uh to computing uh and just deliver raw capabilities and so uh for uh exadata x9m uh it's really leveraging all of that it's it's a uh implemented in up to 64 cores per socket it's got uh you know really anywhere from 128 to 168 pcie gen 4 io connectivity so you can you can really attach uh you know all of the uh the necessary uh infrastructure and and uh storage uh that's needed uh for exadata performance and also memory you have to feed the beast for those analytics and for the oltp that juan was talking about and so it does have eight lanes of memory for high performance ddr4 so it's really as a balanced processor and it's implemented in a way to really optimize uh high performance that that is our whole focus of uh amd it's where we've you know reset the company focus on years ago and uh again uh you know great to see uh you know the the super smart uh you know database team at oracle really a partner with us understand those capabilities and it's been just great to partner with them to uh you know to you know enable oracle to really leverage the capabilities of the zen processor yeah it's been a pretty amazing 10 or 11 years for both companies but mark how specifically are you working with oracle at the engineering and product level you know and what does that mean for your joint customers in terms of what they can expect from the collaboration well here's where the collaboration really comes to play you think about a processor and you know i'll say you know when one's team first looked at it there's general benchmarks and the benchmarks are impressive but they're general benchmarks and you know and they showed you know the i'll say the you know the base processing capability but the partnership comes to bear uh when it when it means optimizing for the workloads that exadata x9m is really delivering to the end customers and that's where we dive down and and as we uh learn from the oracle team we learned to understand where bottlenecks could be uh where is there tuning that we could in fact in fact really boost the performance above i'll say that baseline that you get in the generic benchmarks and that's what the teams have done so for instance you look at you know optimizing latency to rdma you look at just throughput optimizing throughput on otp and database processing when you go through the workloads and you take the traces and you break it down and you find the areas that are bottlenecking and then you can adjust we have you know thousands of parameters that can be adjusted for a given workload and that's again that's the beauty of the partnership so we have the expertise on the cpu engineering uh you know oracle exudated team knows innately what the customers need to get the most out of their platform and when the teams came together we actually achieved anywhere from 20 percent to 50 gains on specific workloads it's really exciting to see so okay so so i want to follow up on that is that different from the competition how are you driving customer value you mentioned some you know some some percentage improvements are you measuring primarily with with latency how do you look at that well uh you know we are differentiated with the uh in the number of factors we bring a higher core density we bring the highest core density certainly in x86 and and moreover what we've led the industry is how to scale those cores we have a very high performance fabric that connects those together so as as a customer needs more cores again we scale anywhere from 8 to 64 cores but what the trick is uh that is you add more cores you want the scale the scale to be as close to linear as possible and so that's a differentiation we have and we enable that again with that balanced computer of cpu io and memory that we design but the key is you know we pride ourselves at amd of being able to partner in a very deep fashion with our customers we listen very well i think that's uh what we've had the opportunity uh to do with uh juan and his team we appreciate that and and that is how we got the kind of performance benefits that i described earlier it's working together almost like one team and in bringing that best possible capability to the end customers great thank you for that one i want to come back to you can both the exadata database service and the autonomous database service can they take advantage of exadata cloud x9m capabilities that are in that platform yeah absolutely um you know autonomous is basically our self-driving version of the oracle database but fundamentally it is the same uh database course so both of them will take advantage of the tremendous performance that we're getting now you know when when mark takes about 64 cores that's for chip we have two chips you know it's a two socket server so it's 128 128-way processor and then from our point of view there's two threads so from the database point there's 200 it's a 256-way processor and so there's a lot of raw performance there and we've done a lot of work with the amd team to make sure that we deliver that to our customers for all the different kinds of workload including otp analytics but also including for our autonomous database so yes absolutely allah takes advantage of it now juan you know i can't let you go without asking about the competition i've written extensively about the big four hyperscale clouds specifically aws azure google and alibaba and i know that don't hate me sometimes it angers some of my friends at oracle ibm too that i don't include you in that list but but i see oracle specifically is different and really the cloud for the most demanding applications and and top performance databases and not the commodity cloud which of course that angers all my friends at those four companies so i'm ticking everybody off so how does exadata cloud infrastructure x9m compare to the likes of aws azure google and other database cloud services in terms of oltp and analytics value performance cost however you want to frame it yeah so our architecture is fundamentally different uh we've architected our database for the scale out environment so for example we've moved intelligence in the storage uh we've put uh remote direct memory access we put persistent memory into our product so we've done a lot of architectural changes that they haven't and you're starting to see a little bit of that like if you look at some of the things that amazon and google are doing they're starting to realize that hey if you're gonna achieve good results you really need to push some database uh processing into the storage so so they're taking baby steps toward that you know you know roughly 15 years after we we've had a product and again at some point they're gonna realize you really need rdma you really need you know more uh direct access to those capabilities so so they're slowly getting there but you know we're well ahead and what you know the way this is delivered is you know better availability better performance lower latency higher iops so and this is why our customers love our product and you know if you if you look at the global fortune 100 over 90 percent of them are running exit data today and even in the in our cloud uh you know over 60 of the global 100 are running exadata in the oracle cloud because of all the differentiated uh benefits that they get uh from the product uh so yeah we're we're well ahead in the in the database space mark last question for you is how do you see this relationship evolving in the future can you share a little road map for the audience you bet well first off you know given the deep partnership that we've had on exudate x9m uh it it's really allowed us to inform our future design so uh in our current uh third generation epic epyc is uh that is really uh what we call our epic server offerings and it's a 7003 third gen in and exudate x9m so what about fourth gen well fourth gen is well underway uh you know it and uh and uh you know ready to you know for the for the future but it incorporates learning uh that we've done in partnership with with oracle uh it's gonna have even more through capabilities it's gonna have expanded memory capabilities because there's a cxl connect express link that'll expand even more memory opportunities and i could go on so you know that's the beauty of a deep partnership as it enables us to really take that learning going forward it pays forward and we're very excited to to fold all of that into our future generations and provide even a better capabilities to one and his team moving forward yeah you guys have been obviously very forthcoming you have to be with with with zen and epic juan anything you'd like to add as closing comments yeah i would say that in the processor market there's been a real acceleration in innovation in the last few years um there was you know a big move 10 15 years ago when multi-core processors came out and then you know we were on that for a while and then things started staggering but in the last two or three years and amd has been leading this um there's been a dramatic uh acceleration in innovation in this space so it's very exciting to be part of this and and customers are getting a big benefit from this all right chance hey thanks for coming back in the cube today really appreciate your time thanks glad to be here all right thank you for watching this exclusive cube conversation this is dave vellante from thecube and we'll see you next time [Music]

>>Good morning, everyone. Welcome to the cubes day two coverage of VMware Explorer, 2022 live from San Francisco. Lisa Martin here with Dave Nicholson. We're excited to kick off day two of great conversations with VMware partners, customers it's ecosystem. We've got a V an alumni back with us Kumer on Siva corporate VP of business development from AMD joins us. Great to have you on the program in person. Great >>To be here. Yes. In person. Indeed. Welcome. >>So the great thing yesterday, a lot of announcements and B had an announcement with VMware, which we will unpack that, but there's about 7,000 to 10,000 people here. People are excited, ready to be back, ready to be hearing from this community, which is so nice. Yesterday am B announced. It is optimizing AMD PON distributed services card to run on VMware. Bsphere eight B for eight was announced yesterday. Tell us a little bit about that. Yeah, >>No, absolutely. The Ben Sando smart neck DPU. What it allows you to do is it, it provides a whole bunch of capabilities, including offloads, including encryption DEC description. We can even do functions like compression, but with, with the combination of VMware project Monterey and, and Ben Sando, we we're able to do is even do some of the vSphere, actual offloads integration of the hypervisor into the DPU card. It's, it's pretty interesting and pretty powerful technology. We're we're pretty excited about it. I think this, this, this could, you know, potentially, you know, bring some of the cloud value into, in terms of manageability, in terms of being able to take care of bare metal servers and also, you know, better secure infrastructure, you know, cloudlike techniques into the, into the mainstream on-premises enterprise. >>Okay. Talk a little bit about the DPU data processing unit. They talked about it on stage yesterday, but help me understand that versus the CPU GPU. >>Yeah. So it's, it's, it's a different, it's a different point, right? So normally you'd, you'd have the CPU you'd have we call it dumb networking card. Right. And I say dumb, but it's, it's, you know, it's just designed to go process packets, you know, put and put them onto PCI and have the, the CPU do all of the, kind of the, the packet processing, the, the virtual switching, all of those functions inside the CPU. What the DPU allows you to do is, is actually offload a bunch of those functions directly onto the, onto the deep view card. So it has a combination of these special purpose processors that are programmable with the language called P four, which is one, one of the key things that pan Sando brings. Here's a, it's a, it's a real easy to program, easy to use, you know, kind of set so that not some of, some of our larger enterprise customers can actually go in and, you know, do some custom coding depending on what their network infrastructure looks like. But you can do things like the V switch in, in the, in the DPU, not having to all have that done on the CPU. So you freeze up some of the CPU course, make sure, make sure infrastructure run more efficiently, but probably even more importantly, it provides you with more, with greater security, greater separation between the, between the networking side and the, the CPU side. >>So, so that's, that's a key point because a lot of us remember the era of the tonic TCP, I P offload engine, Nick, this isn't simply offloading CPU cycles. This is actually providing a sort of isolation. So that the network that's right, is the network has intelligence that is separate from the server. Is that, is that absolutely key? Is that absolutely >>Key? Yeah. That's, that's a good way of looking at it. Yeah. And that's, that's, I mean, if you look at some of the, the, the techniques used in the cloud, the, you know, this, this, this in fact brings some of those technologies into, into the enterprise, right. So where you are wanting to have that level of separation and management, you're able to now utilize the DPU card. So that's, that's a really big, big, big part of the value proposition, the manageability manageability, not just offload, but you know, kind of a better network for enterprise. Right. >>Right. >>Can you expand on that value proposition? If I'm a customer what's in this for me, how does this help power my multi-cloud organization? >>Yeah. >>So I think we have some, we actually have a number of these in real customer use cases today. And so, you know, folks will use, for example, the compression and the, sorry, the compression and decompression, that's, that's definitely an application in the storage side, but also on the, just on the, just, just as a, as a DPU card in the mainstream general purpose, general purpose server server infrastructure fleet, they're able to use the encryption and decryption to make sure that their, their, their infrastructure is, is kind of safe, you know, from point to point within the network. So every, every connected, every connection there is actually encrypted and that, that, you know, managing those policies and orchestrating all of that, that's done to the DPU card. >>So, so what you're saying is if you have DPU involved, then the server itself and the CPUs become completely irrelevant. And basically it's just a box of sheet metal at that point. That's, that's a good way of looking at that. That's my segue talking about the value proposition of the actual AMD. >>No, absolutely. No, no. I think, I think, I think the, the, the CPUs are always going to be central in this and look. And so, so I think, I think having, having the, the DPU is extremely powerful and, and it does allow you to have better infrastructure, but the key to having better infrastructure is to have the best CPU. Well, tell >>Us, tell >>Us that's what, tell us us about that. So, so I, you know, this is, this is where a lot of the, the great value proposition between VMware and AMD come together. So VMware really allows enterprises to take advantage of these high core count, really modern, you know, CPU, our, our, our, our epic, especially our Milan, our 7,003 product line. So to be able to take advantage of 64 course, you know, VMware is critical for that. And, and so what they, what they've been able to do is, you know, know, for example, if you have workloads running on legacy, you know, like five year old servers, you're able to take a whole bunch of those servers and consolidate down, down into a single node, right. And the power that VMware gives you is the manageability, the reliability brings all of that factors and allows you to take advantage of, of the, the, the latest, latest generation CPUs. >>You know, we've actually done some TCO modeling where we can show, even if you have fully depreciated hardware, like, so it's like five years old plus, right. And so, you know, the actual cost, you know, it's already been written off, but the cost just the cost of running it in terms of the power and the administration, you know, the OPEX costs that, that are associated with it are greater than the cost of acquiring a new set of, you know, a smaller set of AMD servers. Yeah. And, and being able to consolidate those workloads, run VMware, to provide you with that great, great user experience, especially with vSphere 8.0 and the, and the hooks that VMware have built in for AMD AMD processors, you actually see really, really good. It's just a great user experience. It's also a more efficient, you know, it's just better for the planet. And it's also better on the pocketbook, which is, which is, which is a really cool thing these days, cuz our value in TCO translates directly into a value in terms of sustainability. Right. And so, you know, from, from energy consumption, from, you know, just, just the cost of having that there, it's just a whole lot better >>Talk about on the sustainability front, how AMD is helping its customers achieve their sustainability goals. And are you seeing more and more customers coming to you saying, we wanna understand what AMD is doing for sustainability because it's important for us to work with vendors who have a core focus on it. >>Yeah, absolutely. You know, I think, look, I'll be perfectly honest when we first designed our CPU, we're just trying to build the biggest baddest thing that, you know, that, that comes out in terms of having the, the, the best, the, the number, the, the largest number of cores and the best TCO for our customers, but what it's actually turned out that TCO involves energy consumption. Right. And, and it involves, you know, the whole process of bringing down a whole bunch of nodes, whole bunch of servers. For example, we have one calculation where we showed 27, you know, like I think like five year old servers can be consolidated down into five AMD servers that, that ratio you can see already, you know, huge gains in terms of sustainability. Now you asked about the sustainability conversation. This I'd say not a week goes by where I'm not having a conversation with, with a, a, a CTO or CIO who is, you know, who's got that as part of their corporate, you know, is part of their corporate brand. And they want to find out how to make their, their infrastructure, their data center, more green. Right. And so that's, that's where we come in. Yeah. And it's interesting because at least in the us money is also green. So when you talk about the cost of power, especially in places like California, that's right. There's, there's a, there's a natural incentive to >>Drive in that direction. >>Let's talk about security. You know, the, the, the threat landscape has changed so dramatically in the last couple of years, ransomware is a household word. Yes. Ransomware attacks happened like one every 11 seconds, older technology, a little bit more vulnerable to internal threats, external threats. How is AMD helping customers address the security fund, which is the board level conversation >>That that's, that's, that's a, that's a great, great question. Look, I look at security as being, you know, it's a layered thing, right? I mean, if you talk to any security experts, security, doesn't, you know, there's not one component and we are an ingredient within the, the greater, you know, the greater scheme of things. A few things. One is we have partnered very closely with the VMware. They have enabled our SUV technology, secure encrypted virtualization technology into, into the vSphere. So such that all of the memory transactions. So you have, you have security, you know, at, you know, security, when you store store on disks, you have security over the network and you also have security in the compute. And when you go out to memory, that's what this SUV technology gives you. It gives you that, that security going, going in your, in your actual virtual machine as it's running. And so the, the, we take security extremely seriously. I mean, one of the things, every generation that you see from, from AMD and, and, you know, you have seen us hit our cadence. We do upgrade all of the security features and we address all of the sort of known threats that are out there. And obviously this threats, you know, kind of coming at us all the time, but our CPUs just get better and better from, from a, a security stance. >>So shifting gears for a minute, obviously we know the pending impossible acquisition, the announced acquisition of VMware by Broadcom, AMD's got a relationship with Broadcom independently, right? No, of course. What is, how's that relationship? >>Oh, it's a great relationship. I mean, we, we, you know, they, they have certified their, their, their niche products, their HPA products, which are utilized in, you know, for, for storage systems, sand systems, those, those type of architectures, the hardcore storage architectures. We, we work with them very closely. So they, they, they've been a great partner with us for years. >>And you've got, I know, you know, we are, we're talking about current generation available on the shelf, Milan based architecture, is that right? That's right. Yeah. But if I understand correctly, maybe sometime this year, you're, you're gonna be that's right. Rolling out the, rolling out the new stuff. >>Yeah, absolutely. So later this year, we've already, you know, we already talked about this publicly. We have a 96 core gen platform up to 96 cores gen platform. So we're just, we're just taking that TCO value just to the next level, increasing performance DDR, five CXL with, with memory expansion capability. Very, very neat leading edge technology. So that that's gonna be available. >>Is that NextGen P C I E, or has that shift already been made? It's >>Been it's NextGen. P C I E P C E gen five. Okay. So we'll have, we'll have that capability. That'll be, that'll be out by the end of this year. >>Okay. So those components you talk about. Yeah. You know, you talk about the, the Broadcom VMware universe, those components that are going into those new slots are also factors in performance and >>Yeah, absolutely. You need the balance, right? You, you need to have networking storage and the CPU. We're very cognizant of how to make sure that these cores are fed appropriately. Okay. Cuz if you've just put out a lot of cores, you don't have enough memory, you don't have enough iOS. That's, that's the key to, to, to, you know, our approach to, to enabling performance in the enterprise, make sure that the systems are balanced. So you get the experience that you've had with, let's say your, you know, your 12 core, your 16 core, you can have that same experience in the 96 core in a node or 96 core socket. So maybe a 192 cores total, right? So you can have that same experience in, in a tune node in a much denser, you know, package server today or, or using Melan technology, you know, 128 cores, super, super good performance. You know, its super good experience it's, it's designed to scale. Right. And especially with VMware as, as our infrastructure, it works >>Great. I'm gonna, Lisa, Lisa's got a question to ask. I know, but bear with me one bear >>With me. Yes, sir. >>We've actually initiated coverage of this question of, you know, just hardware matter right anymore. Does it matter anymore? Yeah. So I put to you the question, do you think hardware still matters? >>Oh, I think, I think it's gonna matter even more and more going forward. I mean just, but it's all cloud who cares just in this conversation today. Right? >>Who cares? It's all cloud. Yeah. >>So, so, so definitely their workloads moving to the cloud and we love our cloud partners don't get me wrong. Right. But there are, you know, just, I've had so many conversations at this show this week about customers who cannot move to the cloud because of regulatory reasons. Yeah. You know, the other thing that you don't realize too, that's new to me is that people have depreciated their data centers. So the cost for them to just go put in new AMD servers is actually very low compared to the cost of having to go buy, buy public cloud service. They still want to go buy public cloud services and that, by the way, we have great, great, great AMD instances on, on AWS, on Google, on Azure, Oracle, like all of our major, all of the major cloud providers, support AMD and have, have great, you know, TCO instances that they've, they've put out there with good performance. Yeah. >>What >>Are some of the key use cases that customers are coming to AMD for? And, and what have you seen change in the last couple of years with respect to every customer needing to become a data company needing to really be data driven? >>No, that's, that's also great question. So, you know, I used to get this question a lot. >>She only asks great questions. Yeah. Yeah. I go down and like all around in the weeds and get excited about the bits and the bites she asks. >>But no, I think, look, I think the, you know, a few years ago and I, I think I, I used to get this question all the time. What workloads run best on AMD? My answer today is unequivocally all the workloads. Okay. Cuz we have processors that run, you know, run at the highest performance per thread per per core that you can get. And then we have processors that have the highest throughput and, and sometimes they're one in the same, right. And Ilan 64 configured the right way using using VMware vSphere, you can actually get extremely good per core performance and extremely good throughput performance. It works well across, just as you said, like a database to data management, all of those kinds of capabilities, DevOps, you know, E R P like there's just been a whole slew slew of applications use cases. We have design wins in, in major customers, in every single industry in every, and these, these are big, you know, the big guys, right? >>And they're, they're, they're using AMD they're successfully moving over their workloads without, without issue. For the most part. In some cases, customers tell us they just, they just move the workload on, turn it on. It runs great. Right. And, and they're, they're fully happy with it. You know, there are other cases where, where we've actually gotten involved and we figured out, you know, there's this configuration of that configuration, but it's typically not a, not a huge lift to move to AMD. And that's that I think is a, is a key, it's a key point. And we're working together with almost all of the major ISV partners. Right. And so just to make sure that, that, that they have run tested certified, I think we have over 250 world record benchmarks, you know, running in all sorts of, you know, like Oracle database, SAP business suite, all of those, those types of applications run, run extremely well on AMD. >>Is there a particular customer story that you think really articulates the value of running on AMD in terms of enabling bus, big business outcome, safer a financial services organization or healthcare organization? Yeah. >>I mean we, yeah, there's certainly been, I mean, across the board. So in, in healthcare we've seen customers actually do the, the server consolidation very effectively and then, you know, take advantage of the, the lower cost of operation because in some cases they're, they're trying to run servers on each floor of a hospital. For example, we've had use cases where customers have been able to do that because of the density that we provide and to be able to, to actually, you know, take, take their compute more even to the edge than, than actually have it in the, in those use cases in, in a centralized matter. The another, another interesting case FSI in financial services, we have customers that use us for general purpose. It, we have customers that use this for kind of the, the high performance we call it grid computing. So, you know, you have guys that, you know, do all this trading during the day, they collect tons and tons of data, and then they use our computers to, or our CPUs to just crunch to that data overnight. >>And it's just like this big, super computer that just crunches it's, it's pretty incredible. They're the, the, the density of the CPUs, the value that we bring really shines, but in, in their general purpose fleet as well. Right? So they're able to use VMware, a lot of VMware customers in that space. We love our, we love our VMware customers and they're able to, to, to utilize this, they use use us with HCI. So hyperconverge infrastructure with V VSAN and that's that that's, that's worked works extremely well. And, and, and our, our enterprise customers are extremely happy with that. >>Talk about, as we wrap things up here, what's next for AMD, especially AMD with VMwares VMware undergoes its potential change. >>Yeah. So there there's a lot that we have going on. I mean, I gotta say VMware is one of the, let's say premier companies in terms of, you know, being innovative and being, being able to drive new, new, interesting pieces of technology and, and they're very experimentive right. So they, we have, we have a ton of things going with them, but certainly, you know, driving pin Sando is, is very, it is very, very important to us. Yeah. I think that the whole, we're just in the, the cusp, I believe of, you know, server consolidation becoming a big thing for us. So driving that together with VMware and, you know, into some of these enterprises where we can show, you know, save the earth while we, you know, in terms of reducing power, reducing and, and saving money in terms of TCO, but also being able to enable new capabilities. >>You know, the other part of it too, is this new infrastructure enables new workloads. So things like machine learning, you know, more data analytics, more sophisticated processing, you know, that, that is all enabled by this new infrastructure. So we, we were excited. We think that we're on the precipice of, you know, going a lot of industries moving forward to, to having, you know, the next level of it. It's no longer about just payroll or, or, or enterprise business management. It's about, you know, how do you make your, you know, your, your knowledge workers more productive, right. And how do you give them more capabilities? And that, that is really, what's exciting for us. >>Awesome Cooper. And thank you so much for joining Dave and me on the program today, talking about what AMD, what you're doing to supercharge customers, your partnership with VMware and what is exciting. What's on the, the forefront, the frontier, we appreciate your time and your insights. >>Great. Thank you very much for having me. >>Thank you for our guest and Dave Nicholson. I'm Lisa Martin. You're watching the cube live from VMware Explorer, 22 from San Francisco, but don't go anywhere, Dave and I will be right back with our next guest.

(electronic music) >> Welcome back to our studios in Palo Alto, California. My name is Dave Vellante, I'm here with John Furrier, who is taking a quick break. You know, in one of the early examples that we used of so called super cloud was Snowflake. We called it a super data cloud. We had, really, a lot of fun with that. And we've started to evolve our thinking. Years ago, we said that data was going to form in the cloud around industries and ecosystems. And Benoit Dogeville is a many time guest of theCube. He's the co-founder and president of products at Snowflake. Benoit, thanks for spending some time with us, at Supercloud 22, good to see you. >> Thank you, thank you, Dave. >> So, you know, like I said, we've had some fun with this meme. But it really is, we heard on the previous panel, everybody's using Snowflake as an example. Somebody how builds on top of hyper scale infrastructure. You're not building your own data centers. And, so, are you building a super data cloud? >> We don't call it exactly that way. We don't like the super word, it's a bit dismissive. >> That's our term. >> About our friends, cloud provider friends. But we call it a data cloud. And the vision, really, for the data cloud is, indeed, it's a cloud which overlays the hyper scaler cloud. But there is a big difference, right? There are several ways to do this super cloud, as you name them. The way we picked is to create one single system, and that's very important, right? There are several ways, right. You can instantiate your solution in every region of the cloud and, you know, potentially that region could be AWS, that region could be GCP. So, you are, indeed, a multi-cloud solution. But Snowflake, we did it differently. We are really creating cloud regions, which are superimposed on top of the cloud provider region, infrastructure region. So, we are building our regions. But where it's very different is that each region of Snowflake is not one instantiation of our service. Our service is global, by nature. We can move data from one region to the other. When you land in Snowflake, you land into one region. But you can grow from there and you can, you know, exist in multiple cloud at the same time. And that's very important, right? It's not different instantiation of a system, it's one single instantiation which covers many cloud regions and many cloud provider. >> So, we used Snowflake as an example. And we're trying to understand what the salient aspects are of your data cloud, what we call super cloud. In fact, you've used the word instantiate. Kit Colbert, just earlier today, laid out, he said, there's sort of three levels. You can run it on one cloud and communicate with the other cloud, you can instantiate on the clouds, or you can have the same service running 24/7 across clouds, that's the hardest example. >> Yeah. >> The most mature. You just described, essentially, doing that. How do you enable that? What are the technical enablers? >> Yeah, so, as I said, first we start by building, you know, Snowflake regions, we have today 30 regions that span the world, so it's a world wide system, with many regions. But all these regions are connected together. They are meshed together with our technology, we name it Snow Grid, and that makes it hard because, you know, Azure region can talk to a WS region, or GCP regions, and as a user for our cloud, you don't see, really, these regional differences, that regions are in different potentially cloud. When you use Snowflake, you can exist, your presence as an organization can be in several regions, several clouds, if you want, geographic, both geographic and cloud provider. >> So, I can share data irrespective of the cloud. And I'm in the Snowflake data cloud, is that correct? I can do that today? >> Exactly, and that's very critical, right? What we wanted is to remove data silos. And when you insociate a system in one single region, and that system is locked in that region, you cannot communicate with other parts of the world, you are locking data in one region. Right, and we didn't want to do that. We wanted data to be distributed the way customer wants it to be distributed across the world. And potentially sharing data at world scales. >> Does that mean if I'm in one region and I want to run a query, if I'm in AWS in one region, and I want to run a query on data that happens to be in an Azure cloud, I can actually execute that? >> So, yes and no. The way we do it is very expensive to do that. Because, generally, if you want to join data which are in different region and different cloud, it's going to be very expensive because you need to move data every time you join it. So, the way we do it is that you replicate the subset of data that you want to access from one region from other region. So, you can create this data mesh, but data is replicated to make it very cheap and very performing too. >> And is the Snow Grid, does that have the metadata intelligence to actually? >> Yes, yes. >> Can you describe that a little? >> Yeah, Snow Grid is both a way to exchange metadata. So, each region of Snowflake knows about all the other regions of Snowflake. Every time we create a new region, the metadata is distributed over our data cloud, not only region knows all the region, but knows every organization that exists in our cloud, where this organization is, where data can be replicated by this organization. And then, of course, it's also used as a way to exchange data, right? So, you can exchange data by scale of data size. And I was just receiving an email from one of our customers who moved more than four petabytes of data, cross region, cross cloud providers in, you know, few days. And it's a lot of data, so it takes some time to move. But they were able to do that online, completely online, and switch over to the other region, which is very important also. >> So, one of the hardest parts about super cloud that I'm still trying to struggling through is the security model. Because you've got the cloud as your sort of first line of defense. And now we've got multiple clouds, with multiple first lines of defense, I've got a shared responsibility model across those clouds, I've got different tools in each of those clouds. Do you take care of that? Where do you pick up from the cloud providers? Do you abstract that security layer? Do you bring in partners? It's a very complicated. >> No, this is a great question. Security has always been the most important aspect of Snowflake sense day one, right? This is the question that every customer of ours has. You know, how can you guarantee the security of my data? And, so, we secure data really tightly in region. We have several layers of security. It starts by creating every data at rest. And that's very important. A lot of customers are not doing that, right? You hear of these attacks, for example, on cloud, where someone left their buckets. And then, you know, you can access the data because it's a non-encrypted. So, we are encrypting everything at rest. We are encrypting everything in transit. So, a region is very secure. Now, you know, from one region, you never access data from another region in Snowflake. That's why, also, we replicate data. Now the replication of that data across region, or the metadata, for that matter, is really our least secure, so Snow Grid ensures that everything is encrypted, everything is, we have multiple encryption keys, and it's stored in hardware secure modules, so, we bit Snow Grid such that it's secure and it allows very secure movement of data. >> Okay, so, I know we kind of, getting into the technology here a lot today, but because super cloud is the future, we actually have to have an architectural foundation on which to build. So, you mentioned a bucket, like an S3 bucket. Okay, that's storage, but you also, for instance, taking advantage of new semi-conductor technology. Like Graviton, as an example, that drives efficiency. You guys talk about how you pass that on to your customers. Even if it means less revenue for you, so, awesome, we love that, you'll make it up in volume. And, so. >> Exactly. >> How do you deal with the lowest common denominator problem? I was talking to somebody the other day and this individual brought up what I thought was a really good point. What if we, let's say, AWS, have the best, silicon. And we can run the fastest and the least expensive, and the lowest power. But another cloud provider hasn't caught up yet. How do you deal with that delta? Do you just take the best of and try to respect that? >> No, it's a great question. I mean, of course, our software is extracting all the cloud providers infrastructure so that when you run in one region, let's say AWS, or Azure, it doesn't make any difference, as far as the applications are concerned. And this abstraction, of course, is a lot of work. I mean, really, a lot of work. Because it needs to be secure, it needs to be performance, and every cloud, and it has to expose APIs which are uniform. And, you know, cloud providers, even though they have potentially the same concept, let's say block storage, APIs are completely different. The way these systems are secure, it's completely different. There errors that you can get. And the retry mechanism is very different from one cloud to the other. The performance is also different. We discovered that when we starting to port our software. And we had to completely rethink how to leverage block storage in that cloud versus that cloud, because just off performance too. And, so, we had, for example, to stripe data. So, all this work is work that you don't need as an application because our vision, really, is that application, which are running in our data cloud, can be abstracted for this difference. And we provide all the services, all the workload that this application need. Whether it's transactional access to data, analytical access to data, managing logs, managing metrics, all of this is abstracted too, so that they are not tied to one particular service of one cloud. And distributing this application across many region, many cloud, is very seamless. >> So, Snowflake has built, your team has built a true abstraction layer across those clouds that's available today? It's actually shipping? >> Yes, and we are still developing it. You know, transactional, Unistore, as we call it, was announced last summit. So, they are still, you know, work in progress. >> You're not done yet. >> But that's the vision, right? And that's important, because we talk about the infrastructure, right. You mention a lot about storage and compute. But it's not only that, right. When you think about application, they need to use the transactional database. They need to use an analytical system. They need to use machine learning. So, you need to provide, also, all these services which are consistent across all the cloud providers. >> So, let's talk developers. Because, you know, you think Snowpark, you guys announced a big application development push at the Snowflake summit recently. And we have said that a criterion of super cloud is a super paz layer, people wince when I say that, but okay, we're just going to go with it. But the point is, it's a purpose built application development layer, specific to your particular agenda, that supports your vision. >> Yes. >> Have you essentially built a purpose built paz layer? Or do you just take them off the shelf, standard paz, and cobble it together? >> No, we build it a custom build. Because, as you said, what exist in one cloud might not exist in another cloud provider, right. So, we have to build in this, all these components that a multi-application need. And that goes to machine learning, as I said, transactional analytical system, and the entire thing. So that it can run in isolation physically. >> And the objective is the developer experience will be identical across those clouds? >> Yes, the developers doesn't need to worry about cloud provider. And, actually, our system will have, we didn't talk about it, but a marketplace that we have, which allows, actually, to deliver. >> We're getting there. >> Yeah, okay. (both laughing) I won't divert. >> No, no, let's go there, because the other aspect of super cloud that we've talked about is the ecosystem. You have to enable an ecosystem to add incremental value, it's not the power of many versus the capabilities of one. So, talk about the challenges of doing that. Not just the business challenges but, again, I'm interested in the technical and architectural challenges. >> Yeah, yeah, so, it's really about, I mean, the way we enable our ecosystem and our partners to create value on top of our data cloud, is via the marketplace. Where you can put shared data on the marketplace. Provide listing on this marketplace, which are data sets. But it goes way beyond data. It's all the way to application. So, you can think of it as the iPhone. A little bit more, all right. Your iPhone is great. Not so much because the hardware is great, or because of the iOS, but because of all the applications that you have. And all these applications are not necessarily developed by Apple, basically. So, we are, it's the same model with our marketplace. We foresee an environment where providers and partners are going to build these applications. We call it native application. And we are going to help them distribute these applications across cloud, everywhere in the world, potentially. And they don't need to worry about that. They don't need to worry about how these applications are going to be instantiated. We are going to help them to monetize these applications. So, that unlocks, you know, really, all the partner ecosystem that you have seen, you know, with something like the iPhone, right? It has created so many new companies that have developed these applications. >> Your detractors have criticized you for being a walled garden. I've actually used that term. I used terms like defacto standard, which are maybe less sensitive to you, but, nonetheless, we've seen defacto standards actually deliver value. I've talked to Frank Slootman about this, and he said, Dave, we deliver value, that's what we're all about. At the same time, he even said to me, and I want your thoughts on this, is, look, we have to embrace open source where it makes sense. You guys announced Apache Iceberg. So, what are your thoughts on that? Is that to enable a developer ecosystem? Why did you do Iceberg? >> Yeah, Iceberg is very important. So, just to give some context, Iceberg is an open table format. >> Right. >> Which was first developed by Netflix. And Netflix put it open source in the Apache community. So, we embraced that open source standard because it's widely used by many companies. And, also, many companies have really invested a lot of effort in building big data, Hadoop Solutions, or DataX Solution, and they want to use Snowflake. And they couldn't really use Snowflake, because all their data were in open format. So, we are embracing Iceberg to help these companies move through the cloud. But why we have been reluctant with direct access to data, direct access to data is a little bit of a problem for us. And the reason is when you direct access to data, now you have direct access to storage. Now you have to understand, for example, the specificity of one cloud versus the other. So, as soon as you start to have direct access to data, you lose your cloud data sync layer. You don't access data with API. When you have direct access to data, it's very hard to sync your data. Because you need to grant access, direct access to tools which are not protected. And you see a lot of hacking of data because of that. So, direct access to data is not serving well our customers, and that's why we have been reluctant to do that. Because it is not cloud diagnostic. You have to code that, you need a lot of intelligence, why APIs access, so we want open APIs. That's, I guess, the way we embrace openness, is by open API versus you access, directly, data. >> iPhone. >> Yeah, yeah, iPhone, APIs, you know. We define a set of APIs because APIs, you know, the implementation of the APIs can change, can improve. You can improve compression of data, for example. If you open direct access to data now, you cannot evolve. >> My point is, you made a promise, from governed, security, data sharing ecosystem. It works the same way, so that's the path that you've chosen. Benoit Dogeville, thank you so much for coming on theCube and participating in Supercloud 22, really appreciate that. >> Thank you, Dave. It was a great pleasure. >> All right, keep it right there, we'll be right back with our next segment, right after this short break. (electronic music)

>> Announcer: From theCUBE studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> The rapid pace of cloud adoption has changed the way organizations approach cybersecurity. Specifically, the cloud is increasingly becoming the first line of cyber defense. As such, along with communicating to the board and creating a security aware culture, the chief information security officer must ensure that the shared responsibility model is being applied properly. Meanwhile, the DevSecOps team has emerged as the critical link between strategy and execution, while audit becomes the free safety, if you will, in the equation, i.e., the last line of defense. Hello, and welcome to this week's, we keep on CUBE Insights, powered by ETR. In this "Breaking Analysis", we'll share the latest data on hyperscale, IaaS, and PaaS market performance, along with some fresh ETR survey data. And we'll share some highlights and the puts and takes from the recent AWS re:Inforce event in Boston. But first, the macro. It's earning season, and that's what many people want to talk about, including us. As we reported last week, the macro spending picture is very mixed and weird. Think back to a week ago when SNAP reported. A player like SNAP misses and the Nasdaq drops 300 points. Meanwhile, Intel, the great semiconductor hope for America misses by a mile, cuts its revenue outlook by 15% for the year, and the Nasdaq was up nearly 250 points just ahead of the close, go figure. Earnings reports from Meta, Google, Microsoft, ServiceNow, and some others underscored cautious outlooks, especially those exposed to the advertising revenue sector. But at the same time, Apple, Microsoft, and Google, were, let's say less bad than expected. And that brought a sigh of relief. And then there's Amazon, which beat on revenue, it beat on cloud revenue, and it gave positive guidance. The Nasdaq has seen this month best month since the isolation economy, which "Breaking Analysis" contributor, Chip Symington, attributes to what he calls an oversold rally. But there are many unknowns that remain. How bad will inflation be? Will the fed really stop tightening after September? The Senate just approved a big spending bill along with corporate tax hikes, which generally don't favor the economy. And on Monday, August 1st, the market will likely realize that we are in the summer quarter, and there's some work to be done. Which is why it's not surprising that investors sold the Nasdaq at the close today on Friday. Are people ready to call the bottom? Hmm, some maybe, but there's still lots of uncertainty. However, the cloud continues its march, despite some very slight deceleration in growth rates from the two leaders. Here's an update of our big four IaaS quarterly revenue data. The big four hyperscalers will account for $165 billion in revenue this year, slightly lower than what we had last quarter. We expect AWS to surpass 83 billion this year in revenue. Azure will be more than 2/3rds the size of AWS, a milestone from Microsoft. Both AWS and Azure came in slightly below our expectations, but still very solid growth at 33% and 46% respectively. GCP, Google Cloud Platform is the big concern. By our estimates GCP's growth rate decelerated from 47% in Q1, and was 38% this past quarter. The company is struggling to keep up with the two giants. Remember, both GCP and Azure, they play a shell game and hide the ball on their IaaS numbers, so we have to use a survey data and other means of estimating. But this is how we see the market shaping up in 2022. Now, before we leave the overall cloud discussion, here's some ETR data that shows the net score or spending momentum granularity for each of the hyperscalers. These bars show the breakdown for each company, with net score on the right and in parenthesis, net score from last quarter. lime green is new adoptions, forest green is spending up 6% or more, the gray is flat, pink is spending at 6% down or worse, and the bright red is replacement or churn. Subtract the reds from the greens and you get net score. One note is this is for each company's overall portfolio. So it's not just cloud. So it's a bit of a mixed bag, but there are a couple points worth noting. First, anything above 40% or 40, here as shown in the chart, is considered elevated. AWS, as you can see, is well above that 40% mark, as is Microsoft. And if you isolate Microsoft's Azure, only Azure, it jumps above AWS's momentum. Google is just barely hanging on to that 40 line, and Alibaba is well below, with both Google and Alibaba showing much higher replacements, that bright red. But here's the key point. AWS and Azure have virtually no churn, no replacements in that bright red. And all four companies are experiencing single-digit numbers in terms of decreased spending within customer accounts. People may be moving some workloads back on-prem selectively, but repatriation is definitely not a trend to bet the house on, in our view. Okay, let's get to the main subject of this "Breaking Analysis". TheCube was at AWS re:Inforce in Boston this week, and we have some observations to share. First, we had keynotes from Steven Schmidt who used to be the chief information security officer at Amazon on Web Services, now he's the CSO, the chief security officer of Amazon. Overall, he dropped the I in his title. CJ Moses is the CISO for AWS. Kurt Kufeld of AWS also spoke, as did Lena Smart, who's the MongoDB CISO, and she keynoted and also came on theCUBE. We'll go back to her in a moment. The key point Schmidt made, one of them anyway, was that Amazon sees more data points in a day than most organizations see in a lifetime. Actually, it adds up to quadrillions over a fairly short period of time, I think, it was within a month. That's quadrillion, it's 15 zeros, by the way. Now, there was drill down focus on data protection and privacy, governance, risk, and compliance, GRC, identity, big, big topic, both within AWS and the ecosystem, network security, and threat detection. Those are the five really highlighted areas. Re:Inforce is really about bringing a lot of best practice guidance to security practitioners, like how to get the most out of AWS tooling. Schmidt had a very strong statement saying, he said, "I can assure you with a 100% certainty that single controls and binary states will absolutely positively fail." Hence, the importance of course, of layered security. We heard a little bit of chat about getting ready for the future and skating to the security puck where quantum computing threatens to hack all of the existing cryptographic algorithms, and how AWS is trying to get in front of all that, and a new set of algorithms came out, AWS is testing. And, you know, we'll talk about that maybe in the future, but that's a ways off. And by its prominent presence, the ecosystem was there enforced, to talk about their role and filling the gaps and picking up where AWS leaves off. We heard a little bit about ransomware defense, but surprisingly, at least in the keynotes, no discussion about air gaps, which we've talked about in previous "Breaking Analysis", is a key factor. We heard a lot about services to help with threat detection and container security and DevOps, et cetera, but there really wasn't a lot of specific talk about how AWS is simplifying the life of the CISO. Now, maybe it's inherently assumed as AWS did a good job stressing that security is job number one, very credible and believable in that front. But you have to wonder if the world is getting simpler or more complex with cloud. And, you know, you might say, "Well, Dave, come on, of course it's better with cloud." But look, attacks are up, the threat surface is expanding, and new exfiltration records are being set every day. I think the hard truth is, the cloud is driving businesses forward and accelerating digital, and those businesses are now exposed more than ever. And that's why security has become such an important topic to boards and throughout the entire organization. Now, the other epiphany that we had at re:Inforce is that there are new layers and a new trust framework emerging in cyber. Roles are shifting, and as a direct result of the cloud, things are changing within organizations. And this first hit me in a conversation with long-time cyber practitioner and Wikibon colleague from our early Wikibon days, and friend, Mike Versace. And I spent two days testing the premise that Michael and I talked about. And here's an attempt to put that conversation into a graphic. The cloud is now the first line of defense. AWS specifically, but hyperscalers generally provide the services, the talent, the best practices, and automation tools to secure infrastructure and their physical data centers. And they're really good at it. The security inside of hyperscaler clouds is best of breed, it's world class. And that first line of defense does take some of the responsibility off of CISOs, but they have to understand and apply the shared responsibility model, where the cloud provider leaves it to the customer, of course, to make sure that the infrastructure they're deploying is properly configured. So in addition to creating a cyber aware culture and communicating up to the board, the CISO has to ensure compliance with and adherence to the model. That includes attracting and retaining the talent necessary to succeed. Now, on the subject of building a security culture, listen to this clip on one of the techniques that Lena Smart, remember, she's the CISO of MongoDB, one of the techniques she uses to foster awareness and build security cultures in her organization. Play the clip >> Having the Security Champion program, so that's just, it's like one of my babies. That and helping underrepresented groups in MongoDB kind of get on in the tech world are both really important to me. And so the Security Champion program is purely purely voluntary. We have over 100 members. And these are people, there's no bar to join, you don't have to be technical. If you're an executive assistant who wants to learn more about security, like my assistant does, you're more than welcome. Up to, we actually, people grade themselves when they join us. We give them a little tick box, like five is, I walk on security water, one is I can spell security, but I'd like to learn more. Mixing those groups together has been game-changing for us. >> Now, the next layer is really where it gets interesting. DevSecOps, you know, we hear about it all the time, shifting left. It implies designing security into the code at the dev level. Shift left and shield right is the kind of buzz phrase. But it's getting more and more complicated. So there are layers within the development cycle, i.e., securing the container. So the app code can't be threatened by backdoors or weaknesses in the containers. Then, securing the runtime to make sure the code is maintained and compliant. Then, the DevOps platform so that change management doesn't create gaps and exposures, and screw things up. And this is just for the application security side of the equation. What about the network and implementing zero trust principles, and securing endpoints, and machine to machine, and human to app communication? So there's a lot of burden being placed on the DevOps team, and they have to partner with the SecOps team to succeed. Those guys are not security experts. And finally, there's audit, which is the last line of defense or what I called at the open, the free safety, for you football fans. They have to do more than just tick the box for the board. That doesn't cut it anymore. They really have to know their stuff and make sure that what they sign off on is real. And then you throw ESG into the mix is becoming more important, making sure the supply chain is green and also secure. So you can see, while much of this stuff has been around for a long, long time, the cloud is accelerating innovation in the pace of delivery. And so much is changing as a result. Now, next, I want to share a graphic that we shared last week, but a little different twist. It's an XY graphic with net score or spending velocity in the vertical axis and overlap or presence in the dataset on the horizontal. With that magic 40% red line as shown. Okay, I won't dig into the data and draw conclusions 'cause we did that last week, but two points I want to make. First, look at Microsoft in the upper-right hand corner. They are big in security and they're attracting a lot of dollars in the space. We've reported on this for a while. They're a five-star security company. And every time, from a spending standpoint in ETR data, that little methodology we use, every time I've run this chart, I've wondered, where the heck is AWS? Why aren't they showing up there? If security is so important to AWS, which it is, and its customers, why aren't they spending money with Amazon on security? And I asked this very question to Merrit Baer, who resides in the office of the CISO at AWS. Listen to her answer. >> It doesn't mean don't spend on security. There is a lot of goodness that we have to offer in ESS, external security services. But I think one of the unique parts of AWS is that we don't believe that security is something you should buy, it's something that you get from us. It's something that we do for you a lot of the time. I mean, this is the definition of the shared responsibility model, right? >> Now, maybe that's good messaging to the market. Merritt, you know, didn't say it outright, but essentially, Microsoft they charge for security. At AWS, it comes with the package. But it does answer my question. And, of course, the fact is that AWS can subsidize all this with egress charges. Now, on the flip side of that, (chuckles) you got Microsoft, you know, they're both, they're competing now. We can take CrowdStrike for instance. Microsoft and CrowdStrike, they compete with each other head to head. So it's an interesting dynamic within the ecosystem. Okay, but I want to turn to a powerful example of how AWS designs in security. And that is the idea of confidential computing. Of course, AWS is not the only one, but we're coming off of re:Inforce, and I really want to dig into something that David Floyer and I have talked about in previous episodes. And we had an opportunity to sit down with Arvind Raghu and J.D. Bean, two security experts from AWS, to talk about this subject. And let's share what we learned and why we think it matters. First, what is confidential computing? That's what this slide is designed to convey. To AWS, they would describe it this way. It's the use of special hardware and the associated firmware that protects customer code and data from any unauthorized access while the data is in use, i.e., while it's being processed. That's oftentimes a security gap. And there are two dimensions here. One is protecting the data and the code from operators on the cloud provider, i.e, in this case, AWS, and protecting the data and code from the customers themselves. In other words, from admin level users are possible malicious actors on the customer side where the code and data is being processed. And there are three capabilities that enable this. First, the AWS Nitro System, which is the foundation for virtualization. The second is Nitro Enclaves, which isolate environments, and then third, the Nitro Trusted Platform Module, TPM, which enables cryptographic assurances of the integrity of the Nitro instances. Now, we've talked about Nitro in the past, and we think it's a revolutionary innovation, so let's dig into that a bit. This is an AWS slide that was shared about how they protect and isolate data and code. On the left-hand side is a classical view of a virtualized architecture. You have a single host or a single server, and those white boxes represent processes on the main board, X86, or could be Intel, or AMD, or alternative architectures. And you have the hypervisor at the bottom which translates instructions to the CPU, allowing direct execution from a virtual machine into the CPU. But notice, you also have blocks for networking, and storage, and security. And the hypervisor emulates or translates IOS between the physical resources and the virtual machines. And it creates some overhead. Now, companies like VMware have done a great job, and others, of stripping out some of that overhead, but there's still an overhead there. That's why people still like to run on bare metal. Now, and while it's not shown in the graphic, there's an operating system in there somewhere, which is privileged, so it's got access to these resources, and it provides the services to the VMs. Now, on the right-hand side, you have the Nitro system. And you can see immediately the differences between the left and right, because the networking, the storage, and the security, the management, et cetera, they've been separated from the hypervisor and that main board, which has the Intel, AMD, throw in Graviton and Trainium, you know, whatever XPUs are in use in the cloud. And you can see that orange Nitro hypervisor. That is a purpose-built lightweight component for this system. And all the other functions are separated in isolated domains. So very strong isolation between the cloud software and the physical hardware running workloads, i.e., those white boxes on the main board. Now, this will run at practically bare metal speeds, and there are other benefits as well. One of the biggest is security. As we've previously reported, this came out of AWS's acquisition of Annapurna Labs, which we've estimated was picked up for a measly $350 million, which is a drop in the bucket for AWS to get such a strategic asset. And there are three enablers on this side. One is the Nitro cards, which are accelerators to offload that wasted work that's done in traditional architectures by typically the X86. We've estimated 25% to 30% of core capacity and cycles is wasted on those offloads. The second is the Nitro security chip, which is embedded and extends the root of trust to the main board hardware. And finally, the Nitro hypervisor, which allocates memory and CPU resources. So the Nitro cards communicate directly with the VMs without the hypervisors getting in the way, and they're not in the path. And all that data is encrypted while it's in motion, and of course, encryption at rest has been around for a while. We asked AWS, is this an, we presumed it was an Arm-based architecture. We wanted to confirm that. Or is it some other type of maybe hybrid using X86 and Arm? They told us the following, and quote, "The SoC, system on chips, for these hardware components are purpose-built and custom designed in-house by Amazon and Annapurna Labs. The same group responsible for other silicon innovations such as Graviton, Inferentia, Trainium, and AQUA. Now, the Nitro cards are Arm-based and do not use any X86 or X86/64 bit CPUs. Okay, so it confirms what we thought. So you may say, "Why should we even care about all this technical mumbo jumbo, Dave?" Well, a year ago, David Floyer and I published this piece explaining why Nitro and Graviton are secret weapons of Amazon that have been a decade in the making, and why everybody needs some type of Nitro to compete in the future. This is enabled, this Nitro innovations and the custom silicon enabled by the Annapurna acquisition. And AWS has the volume economics to make custom silicon. Not everybody can do it. And it's leveraging the Arm ecosystem, the standard software, and the fabrication volume, the manufacturing volume to revolutionize enterprise computing. Nitro, with the alternative processor, architectures like Graviton and others, enables AWS to be on a performance, cost, and power consumption curve that blows away anything we've ever seen from Intel. And Intel's disastrous earnings results that we saw this past week are a symptom of this mega trend that we've been talking about for years. In the same way that Intel and X86 destroyed the market for RISC chips, thanks to PC volumes, Arm is blowing away X86 with volume economics that cannot be matched by Intel. Thanks to, of course, to mobile and edge. Our prediction is that these innovations and the Arm ecosystem are migrating and will migrate further into enterprise computing, which is Intel's stronghold. Now, that stronghold is getting eaten away by the likes of AMD, Nvidia, and of course, Arm in the form of Graviton and other Arm-based alternatives. Apple, Tesla, Amazon, Google, Microsoft, Alibaba, and others are all designing custom silicon, and doing so much faster than Intel can go from design to tape out, roughly cutting that time in half. And the premise of this piece is that every company needs a Nitro to enable alternatives to the X86 in order to support emergent workloads that are data rich and AI-based, and to compete from an economic standpoint. So while at re:Inforce, we heard that the impetus for Nitro was security. Of course, the Arm ecosystem, and its ascendancy has enabled, in our view, AWS to create a platform that will set the enterprise computing market this decade and beyond. Okay, that's it for today. Thanks to Alex Morrison, who is on production. And he does the podcast. And Ken Schiffman, our newest member of our Boston Studio team is also on production. Kristen Martin and Cheryl Knight help spread the word on social media and in the community. And Rob Hof is our editor in chief over at SiliconANGLE. He does some great, great work for us. Remember, all these episodes are available as podcast. Wherever you listen, just search "Breaking Analysis" podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me directly at David.Vellante@siliconangle.com or DM me @dvellante, comment on my LinkedIn post. And please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching. Be well, and we'll see you next time on "Breaking Analysis." (upbeat theme music)

(upbeat music) >> Hello, everyone. Welcome to the theCUBE's presentation of the AWS Startup Showcase. The theme, this showcase is MarTech, the emerging cloud scale customer experiences. Season two of episode three, the ongoing series covering the startups, the hot startups, talking about analytics, data, all things MarTech. I'm your host, John Furrier, here joined by Christian Wiklund, founder and CEO of unitQ here, talk about harnessing the power of user feedback to empower marketing. Thanks for joining us today. >> Thank you so much, John. Happy to be here. >> In these new shifts in the market, when you got cloud scale, open source software is completely changing the software business. We know that. There's no longer a software category. It's cloud, integration, data. That's the new normal. That's the new category, right? So as companies are building their products, and want to do a good job, it used to be, you send out surveys, you try to get the product market fit. And if you were smart, you got it right the third, fourth, 10th time. If you were lucky, like some companies, you get it right the first time. But the holy grail is to get it right the first time. And now, this new data acquisition opportunities that you guys in the middle of that can tap customers or prospects or end users to get data before things are shipped, or built, or to iterate on products. This is the customer feedback loop or data, voice of the customer journey. It's a gold mine. And it's you guys, it's your secret weapon. Take us through what this is about now. I mean, it's not just surveys. What's different? >> So yeah, if we go back to why are we building unitQ? Which is we want to build a quality company. Which is basically, how do we enable other companies to build higher quality experiences by tapping into all of the existing data assets? And the one we are in particularly excited about is user feedback. So me and my co-founder, Nik, and we're doing now the second company together. We spent 14 years. So we're like an old married couple. We accept each other, and we don't fight anymore, which is great. We did a consumer company called Skout, which was sold five years ago. And Skout was kind of early in the whole mobile first. I guess, we were actually mobile first company. And when we launched this one, we immediately had the entire world as our marketplace, right? Like any modern company. We launch a product, we have support for many languages. It's multiple platforms. We have Android, iOS, web, big screens, small screens, and that brings some complexities as it relates to staying on top of the quality of the experience because how do I test everything? >> John: Yeah. >> Pre-production. How do I make sure that our Polish Android users are having a good day? And we found at Skout, personally, like I could discover million dollar bugs by just drinking coffee and reading feedback. And we're like, "Well, there's got to be a better way to actually harness the end user feedback. That they are leaving in so many different places." So, you know what, what unitQ does is that we basically aggregate all different sources of user feedback, which can be app store reviews, Reddit posts, Tweets, comments on your Facebook ads. It can be better Business Bureau Reports. We don't like to get to many of those, of course. But really, anything on the public domain that mentions or refers to your product, we want to ingest that data in this machine, and then all the private sources. So you probably have a support system deployed, a Zendesk, or an Intercom. You might have a chatbot like an Ada, or and so forth. And your end user is going to leave a lot of feedback there as well. So we take all of these channels, plug it into the machine, and then we're able to take this qualitative data. Which and I actually think like, when an end user leaves a piece of feedback, it's an act of love. They took time out of the day, and they're going to tell you, "Hey, this is not working for me," or, "Hey, this is working for me," and they're giving you feedback. But how do we package these very messy, multi-channel, multiple languages, all over the place data? How can we distill it into something that's quantifiable? Because I want to be able to monitor these different signals. So I want to turn user feedback into time series. 'Cause with time series, I can now treat this the same way as Datadog treats machine logs. I want to be able to see anomalies, and I want to know when something breaks. So what we do here is that we break down your data in something called quality monitors, which is basically machine learning models that can aggregate the same type of feedback data in this very fine grained and discrete buckets. And we deploy up to a thousand of these quality monitors per product. And so we can get down to the root cause. Let's say, passive reset link is not working. And it's in that root cause, the granularity that we see that companies take action on the data. And I think historically, there has been like the workflow between marketing and support, and engineering and product has been a bit broken. They've been siloed from a data perspective. They've been siloed from a workflow perspective, where support will get a bunch of tickets around some issue in production. And they're trained to copy and paste some examples, and throw it over the wall, file a Jira ticket, and then they don't know what happens. So what we see with the platform we built is that these teams are able to rally around the single source of troop or like, yes, passive recent link seems to have broken. This is not a user error. It's not a fix later, or I can't reproduce. We're looking at the data, and yes, something broke. We need to fix it. >> I mean, the data silos a huge issue. Different channels, omnichannel. Now, there's more and more channels that people are talking in. So that's huge. I want to get to that. But also, you said that it's a labor of love to leave a comment or a feedback. But also, I remember from my early days, breaking into the business at IBM and Hewlett-Packard, where I worked. People who complain are the most loyal customers, if you service them. So it's complaints. >> Christian: Yeah. >> It's leaving feedback. And then, there's also reading between the lines with app errors or potentially what's going on under the covers that people may not be complaining about, but they're leaving maybe gesture data or some sort of digital trail. >> Yeah. >> So this is the confluence of the multitude of data sources. And then you got the siloed locations. >> Siloed locations. >> It's complicated problem. >> It's very complicated. And when you think about, so I started, I came to Bay Area in 2005. My dream was to be a quant analyst on Wall Street, and I ended up in QA at VMware. So I started at VMware in Palo Alto, and didn't have a driver's license. I had to bike around, which was super exciting. And we were shipping box software, right? This was literally a box with a DVD that's been burned, and if that DVD had bugs in it, guess what it'll be very costly to then have to ship out, and everything. So I love the VMware example because the test cycles were long and brutal. It was like a six month deal to get through all these different cases, and they couldn't be any bugs. But then as the industry moved into the cloud, CI/CD, ship at will. And if you look at the modern company, you'll have at least 20 plus integrations into your product. Analytics, add that's the case, authentication, that's the case, and so forth. And these integrations, they morph, and they break. And you have connectivity issues. Is your product working as well on Caltrain, when you're driving up and down, versus wifi? You have language specific bugs that happen. Android is also quite a fragmented market. The binary may not perform as well on that device, or is that device. So how do we make sure that we test everything before we ship? The answer is, we can't. There's no company today that can test everything before the ship. In particular, in consumer. And the epiphany we had at our last company, Skout, was that, "Hey, wait a minute. The end user, they're testing every configuration." They're sitting on the latest device, the oldest device. They're sitting on Japanese language, on Swedish language. >> John: Yeah. >> They are in different code paths because our product executed differently, depending on if you were a paid user, or a freemium user, or if you were certain demographical data. There's so many ways that you would have to test. And PagerDuty actually had a study they came out with recently, where they said 51% of all end user impacting issues are discovered first by the end user, when they serve with a bunch of customers. And again, like the cool part is, they will tell you what's not working. So now, how do we tap into that? >> Yeah. >> So what I'd like to say is, "Hey, your end user is like your ultimate test group, and unitQ is the layer that converts them into your extended test team." Now, the signals they're producing, it's making it through to the different teams in the organization. >> I think that's the script that you guys are flipping. If I could just interject. Because to me, when I hear you talking, I hear, "Okay, you're letting the customers be an input into the product development process." And there's many different pipelines of that development. And that could be whether you're iterating, or geography, releases, all kinds of different pipelines to get to the market. But in the old days, it was like just customer satisfaction. Complain in a call center. >> Christian: Yeah. >> Or I'm complaining, how do I get support? Nothing made itself into the product improvement, except for slow moving, waterfall-based processes. And then, maybe six months later, a small tweak could be improved. >> Yes. >> Here, you're taking direct input from collective intelligence. Okay. >> Is that have input and on timing is very important here, right? So how do you know if the product is working as it should in all these different flavors and configurations right now? How do you know if it's working well? And how do you know if you're improving or not improving over time? And I think the industry, what can we look at, as far as when it relates to quality? So I can look at star ratings, right? So what's the star rating in the app store? Well, star ratings, that's an average over time. So that's something that you may have a lot of issues in production today, and you're going to get dinged on star ratings over the next few months. And then, it brings down the score. NPS is another one, where we're not going to run NPS surveys every day. We're going to run it once a quarter, maybe once a month, if we're really, really aggressive. That's also a snapshot in time. And we need to have the finger on the pulse of product quality today. I need to know if this release is good or not good. I need to know if anything broke. And I think that real time aspect, what we see as stuff sort of bubbles up the stack, and not into production, we see up to a 50% reduction in time to fix these end user impacting issues. And I think, we also need to appreciate when someone takes time out of the day to write an app review, or email support, or write that Reddit post, it's pretty serious. It's not going to be like, "Oh, I don't like the shade of blue on this button." It's going to be something like, "I got double billed," or "Hey, someone took over my account," or, "I can't reset my password anymore. The CAPTCHA, I'm solving it, but I can't get through to the next phase." And we see a lot of these trajectory impacting bugs and quality issues in these work, these flows in the product that you're not testing every day. So if you work at Snapchat, your employees probably going to use Snapchat every day. Are they going to sign up every day? No. Are they going to do passive reset every day? No. And these things are very hard to instrument, lower in the stack. >> Yeah, I think this is, and again, back to these big problems. It's smoke before fire, and you're essentially seeing it early with your process. Can you give an example of how this new focus or new mindset of user feedback data can help customers increase their experience? Can you give some examples, 'cause folks watching and be like, "Okay, I love this value. Sell me on this idea, I'm sold. Okay, I want to tap into my prospects, and my customers, my end users to help me improve my product." 'Cause again, we can measure everything now with data. >> Yeah. We can measure everything. we can even measure quality these days. So when we started this company, I went out to talk to a bunch of friends, who are entrepreneurs, and VCs, and board members, and I asked them this very simple question. So in your board meetings, or on all hands, how do you talk about quality of the product? Do you have a metric? And everyone said, no. Okay. So are you data driven company? Yes, we're very data driven. >> John: Yeah. Go data driven. >> But you're not really sure if quality, how do you compare against competition? Are you doing as good as them, worse, better? Are you improving over time, and how do you measure it? And they're like, "Well, it's kind of like a blind spot of the company." And then you ask, "Well, do you think quality of experience is important?" And they say, "Yeah." "Well, why?" "Well, top of fund and growth. Higher quality products going to spread faster organically, we're going to make better store ratings. We're going to have the storefronts going to look better." And of course, more importantly, they said the different conversion cycles in the product box itself. That if you have bugs and friction, or an interface that's hard to use, then the inputs, the signups, it's not going to convert as well. So you're going to get dinged on retention, engagement, conversion to paid, and so forth. And that's what we've seen with the companies we work with. It is that poor quality acts as a filter function for the entire business, if you're a product led company. So if you think about product led company, where the product is really the centerpiece. And if it performs really, really well, then it allows you to hire more engineers, you can spend more on marketing. Everything is fed by this product at them in the middle, and then quality can make that thing perform worse or better. And we developed a metric actually called the unitQ Score. So if you go to our website, unitq.com, we have indexed the 5,000 largest apps in the world. And we're able to then, on a daily basis, update the score. Because the score is not something you do once a month or once a quarter. It's something that changes continuously. So now, you can get a score between zero and 100. If you get the score 100, that means that our AI doesn't find any quality issues reported in that data set. And if your score is 90, that means that 10% will be a quality issue. So now you can do a lot of fun stuff. You can start benchmarking against competition. So you can see, "Well, I'm Spotify. How do I rank against Deezer, or SoundCloud, or others in my space?" And what we've seen is that as the score goes up, we see this real big impact on KPI, such as conversion, organic growth, retention, ultimately, revenue, right? And so that was very satisfying for us, when we launched it. quality actually still really, really matters. >> Yeah. >> And I think we all agree at test, but how do we make a science out of it? And that's so what we've done. And when we were very lucky early on to get some incredible brands that we work with. So Pinterest is a big customer of ours. We have Spotify. We just signed new bank, Chime. So like we even signed BetterHelp recently, and the world's largest Bible app. So when you look at the types of businesses that we work with, it's truly a universal, very broad field, where if you have a digital exhaust or feedback, I can guarantee you, there are insights in there that are being neglected. >> John: So Chris, I got to. >> So these manual workflows. Yeah, please go ahead. >> I got to ask you, because this is a really great example of this new shift, right? The new shift of leveraging data, flipping the script. Everything's flipping the script here, right? >> Yeah. >> So you're talking about, what the value proposition is? "Hey, board example's a good one. How do you measure quality? There's no KPI for that." So it's almost category creating in its own way. In that, this net new things, it's okay to be new, it's just new. So the question is, if I'm a customer, I buy it. I can see my product teams engaging with this. I can see how it can changes my marketing, and customer experience teams. How do I operationalize this? Okay. So what do I do? So do I reorganize my marketing team? So take me through the impact to the customer that you're seeing. What are they resonating towards? Obviously, getting that data is key, and that's holy gray, we all know that. But what do I got to do to change my environment? What's my operationalization piece of it? >> Yeah, and that's one of the coolest parts I think, and that is, let's start with your user base. We're not going to ask your users to ask your users to do something differently. They're already producing this data every day. They are tweeting about it. They're putting in app produce. They're emailing support. They're engaging with your support chatbot. They're already doing it. And every day that you're not leveraging that data, the data that was produced today is less valuable tomorrow. And in 30 days, I would argue, it's probably useless. >> John: Unless it's same guy commenting. >> Yeah. (Christian and John laughing) The first, we need to make everyone understand. Well, yeah, the data is there, and we don't need to do anything differently with the end user. And then, what we do is we ask the customer to tell us, "Where should we listen in the public domain? So do you want the Reddit post, the Trustpilot? What channels should we listen to?" And then, our machine basically starts ingesting that data. So we have integration with all these different sites. And then, to get access to private data, it'll be, if you're on Zendesk, you have to issue a Zendesk token, right? So you don't need any engineering hours, except your IT person will have to grant us access to the data source. And then, when we go live. We basically build up this taxonomy with the customers. So we don't we don't want to try and impose our view of the world, of how do you describe the product with these buckets, these quality monitors? So we work with the company to then build out this taxonomy. So it's almost like a bespoke solution that we can bootstrap with previous work we've done, where you don't have these very, very fine buckets of where stuff could go wrong. And then what we do is there are different ways to hook this into the workflow. So one is just to use our products. It's a SaaS product as anything else. So you log in, and you can then get this overview of how is quality trending in different markets, on different platforms, different languages, and what is impacting them? What is driving this unitQ Score that's not good enough? And all of these different signals, we can then hook into Jira for instance. We have a Jira integration. We have a PagerDuty integration. We can wake up engineers if certain things break. We also tag tickets in your support system, which is actually quite cool. Where, let's say, you have 200 people, who wrote into support, saying, "I got double billed on Android." It turns out, there are some bugs that double billed them. Well, now we can tag all of these users in Zendesk, and then the support team can then reach out to that segment of users and say, "Hey, we heard that you had this bug with double billing. We're so sorry. We're working on it." And then when we push fix, we can then email the same group again, and maybe give them a little gift card or something, for the thank you. So you can have, even big companies can have that small company experience. So, so it's groups that use us, like at Pinterest, we have 800 accounts. So it's really through marketing has vested interest because they want to know what is impacting the end user. Because brand and product, the lines are basically gone, right? >> John: Yeah. >> So if the product is not working, then my spend into this machine is going to be less efficient. The reputation of our company is going to be worse. And the challenge for marketers before unitQ was, how do I engage with engineering and product? I'm dealing with anecdotal data, and my own experience of like, "Hey, I've never seen these type of complaints before. I think something is going on." >> John: Yeah. >> And then engineering will be like, "Ah, you know, well, I have 5,000 bugs in Jira. Why does this one matter? When did it start? Is this a growing issue?" >> John: You have to replicate the problem, right? >> Replicate it then. >> And then it goes on and on and on. >> And a lot of times, reproducing bugs, it's really hard because it works on my device. Because you don't sit on that device that it happened on. >> Yup. >> So now, when marketing can come with indisputable data, and say, "Hey, something broke here." And we see the same with support. Product engineering, of course, for them, we talk about, "Hey, listen, you you've invested a lot in observability of your stack, haven't you?" "Yeah, yeah, yeah." "So you have a Datadog in the bottom?" "Absolutely." "And you have an APP D on the client?" "Absolutely." "Well, what about the last mile? How the product manifests itself? Shouldn't you monitor that as well using machines?" They're like, "Yeah, that'd be really cool." (John laughs) And we see this. There's no way to instrument everything, lowering the stack to capture these bugs that leak out. So it resonates really well there. And even for the engineers who's going to fix it. >> Yeah. >> I call it like empathy data. >> Yup. >> Where I get assigned a bug to fix. Well, now, I can read all the feedback. I can actually see, and I can see the feedback coming in. >> Yeah. >> Oh, there's users out there, suffering from this bug. And then when I fix it and I deploy the fix, and I see the trend go down to zero, and then I can celebrate it. So that whole feedback loop is (indistinct). >> And that's real time. It's usually missed too. This is the power of user feedback. You guys got a great product, unitQ. Great to have you on. Founder and CEO, Christian Wiklund. Thanks for coming on and sharing, and showcase. >> Thank you, John. For the last 30 seconds, the minute we have left, put a plug in for the company. What are you guys looking for? Give a quick pitch for the company, real quick, for the folks out there. Looking for more people, funding status, number of employees. Give a quick plug. >> Yes. So we raised our A Round from Google, and then we raised our B from Excel that we closed late last year. So we're not raising money. We are hiring across go-to-markets, engineering. And we love to work with people, who are passionate about quality and data. We're always, of course, looking for customers, who are interested in upping their game. And hey, listen, competing with features is really hard because you can copy features very quickly. Competing with content. Content is commodity. You're going to get the same movies more or less on all these different providers. And competing on price, we're not willing to do. You're going to pay 10 bucks a month for music. So how do you compete today? And if your competitor has a better fine tuned piano than your competitor will have better efficiencies, and they're going to retain customers and users better. And you don't want to lose on quality because it is actually a deterministic and fixable problem. So yeah, come talk to us if you want to up the game there. >> Great stuff. The iteration lean startup model, some say took craft out of building the product. But this is now bringing the craftsmanship into the product cycle, when you can get that data from customers and users. >> Yeah. >> Who are going to be happy that you fixed it, that you're listening. >> Yeah. >> And that the product got better. So it's a flywheel of loyalty, quality, brand, all off you can figure it out. It's the holy grail. >> I think it is. It's a gold mine. And every day you're not leveraging this assets, your use of feedback that's there, is a missed opportunity. >> Christian, thanks so much for coming on. Congratulations to you and your startup. You guys back together. The band is back together, up into the right, doing well. >> Yeah. We we'll check in with you later. Thanks for coming on this showcase. Appreciate it. >> Thank you, John. Appreciate it very much. >> Okay. AWS Startup Showcase. This is season two, episode three, the ongoing series. This one's about MarTech, cloud experiences are scaling. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>>mhm. >>Good morning. Live from Las Vegas. That snowflake Summit 22. Lisa Martin With Day Volonte David's Great. We have three wall to wall days of coverage at Snowflake Summit 22 this year. >>Yeah, it's all about data and bringing data to applications. And we've got some big announcements coming this week. Super exciting >>collaboration around data. We are excited to welcome our first two guests before the keynote. We have seen Nielsen in S V. P of data and Analytics Service offering head at emphasis. And Chris Dignan alumni is back with us to chief revenue officer at stuff like guys. Great to have you on the programme. Thanks for having us. Thank you very much. So he'll tell us what's going on with emphasis and snowflake and the partnership. Give us all that good stuff. >>Yeah, No, I think with the convergence of, uh, data digital and computing economy, um, you know that convergence is creating so much possibilities for for customers, uh, snowflake and emphases working together to help our customers realise the vision and these possibilities that are getting driven. We share a very strategic partnership where we are thinking ahead for our customers in terms of what, uh, we can do together in order to build solutions in order to bring out the expertise that is needed for such transformations and also influencing the thinking, Um, and the and the point of view in the market together so that, you know there is there is cohesive approach to doing this transformation and getting to those business outcomes. So it's a It's a partnership that's very successful and its strategic for for our customers, and we continue to invest for the market. >>Got some great customer. Some of my favourite CVS, Nike, William Sanoma. Gotta love that one. Chris talked to us about the snowflake data cloud. What makes it so unique and compelling in the market? >>Well, I think our customers, really they are going through digital transformation today, and they're moving from on premise to the cloud and historically speaking, there just hasn't been the right tool set to help them do that. I think snowflake brings to the table an opportunity for them to take all of their data and take it and and allow it to go from one cloud to the other so they can sit on a W s it can sit on Azure can sit on G, C, P and I can move around from cloud to cloud, and they can do analytics on top of that. >>So data has been traditionally really hard. And we saw that in the big data movement. But we learned a lot. Uh, and AI has been, you know, challenging. So what are you seeing with with customers? What are they struggling with? And how are you guys helping them? >>Yeah. So if you look at the customer journey, they have invested in a number of technologies in the past and are now at a juncture where they need to transform that landscape. They have the challenges of legacy debt that they need to, you know, get rid of or transform. They have the challenges of really bringing, you know, a cohesive understanding within the enterprise as to what these possibilities are for their business. Given the strategy that they are pursuing, um, business and I t cycles are not necessarily aligned. Um, you have the challenge of very fragmented data landscape that they have created over a period of time. How do you, you know, put all these together and work with a specific outcome in mind so that you're not doing transformation for the purpose of transformation. But to be able to actually drive new business models, new data driven products and services ability for you to collaborate with your partners and create unique competitive advantage in the market. And how do you bring those purposes together with the transformation that that's really happening? And and that's where you know our our customers, um, you know, grapple with the challenges of bringing it together. So, >>Chris, how do you see? Because it was talking about, uh, legacy that I think technical debt. Um, you kind of started out making the data warehouse easier. Then this data cloud thing comes out. You're like, Oh, that's an interesting vision and all of a sudden it's way more than vision. You get this huge ecosystem you're extending, we're gonna hear the announcements this morning. We won't. We won't spill the beans, but but really expanding the data cloud. So it's hard to keep up with with where you're at. So I think modernisation, right? So how do you think about modernisation? How are your customers thinking about it? And what's the scope of Snowflake. >>Well, you know, I think historically, you asked about AI and Ml and, you know, in the A I world historically, they've lacked data, and I think because we're the data cloud, we're bringing data, you know, and making it available and democratising it for everybody. And then, you know, partners like emphasis are actually helping us bring, you know, applications and new business models to to the table to our customers and their innovating on top of the data that we already have in the Snowflake Data Club. >>Chris, can you talk about some of the verticals where you guys are successful with emphasis that the three that I mentioned are retailers, But I know that finance, healthcare and life sciences are are huge for smooth, like talk to me, give us a perspective of the verticals that are coming to you. Guys saying help us out with transport. >>You know, I'll give you just an example. So So in the in the retail space, for example, Kraft Heinz is a is a joint customer of ours. And, you know, they've been all in on on snowflakes, Data Cloud and one of our big customers as well it is is Albertsons, and Albertans realises, Oh my gosh, I have all this information around the consumer in in the grocery stores and Kraft Heinz. They want access to that, and they actually can make supply chain decisions a lot faster if they have access to it. So with snowflakes data sharing, we can actually allow them to share data. Albertans share data directly with Kraft, Heinz and Kraft. Heinz can actually make supply chain decisions in real time so that these are some of the stuff that emphasis and stuff like help our customers self. >>So traditionally, the data pipeline goes through some very highly specialised individuals, whether the data engineer, the data scientists and data analyst. So that example that you just gave our organisation you mentioned before democratisation. So democratisation needs to be as a businessperson, I actually can get access to the data. So in that example that you gave between Kraft, Heinz and and and Albertson, is it the the highly hyper specialised teams sharing that data? Or is it actually extending into the line of business focus? >>That's so that's the interesting part for us is I think, snowflake, we just recently reorganise my sales team this year into verticals, and the reason we did that is customers no longer want to talk to us about speeds and feeds of how fast my database goes. They want to actually talk about business outcomes. How do I solve for demand forecasting? How do I supply fix my supply chain issues? Those are things. Those are the. That's how we're aligning with emphasis. So well is they've been doing this for a long time, Can only we haven't. And so we need their help on getting us to the next level of of the sales motion and talking to our customers on solving these business challenges in >>terms of that next level. So no question for you. Where are the customer conversations happening? At what level? I mean, we've seen such dramatic changes in the market in the last couple of years. Now we're dealing with inflation rising interest rates. Ukraine. Are you seeing the conversations in terms of building data platforms rising up the C suite? As every company recognises, we're going to be a data company. We're not gonna be a business. >>Absolutely. And I think all the macroeconomic forces that you talked about that's working on the enterprises globally is actually leading them to think about how to future proof their business models. Right? And there are tonnes of learning that they've hired in the last two or three years and digitising in embracing more digital models. The conversation with the customers have really pivoted towards business outcome. It is a C suite conversation. It is no longer just an incremental change for the for the companies they recognise. That data has been touted as a strategic asset for a long time, but I think it's taking a purpose and a meaning as to what it does for for the customers, the conversations are around industry verticals. You know, what are the specific challenges and opportunities that the the enterprises have, uh, and how you realise those and these cuts across multiple different layers. You know, we're talking about how your democratised data, which in our point of view, is absolute, must in terms of putting a foundation that doesn't take super specialised people to be able to run every operation and every bit of data that you process we have invested in building autonomous data and a state that can process data as it comes in without any manual intervention and take it all the way to consumption but also investing in those industry solutions. Along with snowflake, we launched the healthcare and life Sciences solution. We launched the only channel for retail and CPG. And these are great examples of how Snowflake Foundation enables democratisation on one side but also help solve business problems. In fact, with Snowflake, we have a very, uh, special partnership because our point of view on data economy is about how you connect with the network partners externally, and snowflake brings native capabilities. On this, we leverage that to Dr Exchanges for our customers and one of the services company in the recycling business. Uh, we're actually building and in exchange, which will allow the data points from multiple different sources and partners to come together. So they have a better understanding of their customers, their operations, the field operations and things >>like building a data ecosystem. Yes. Alright, They they Is it a two sided market place where you guys are observers and providing the the technology and the process, you know, guidance. What's your role in that? >>Yeah. So, um, we were seeing their revolution coming? Uh, two stages. Maybe even more. Um, customers are comfortable building an ecosystem. That's kind of private for them. Which means that they know who they are sharing data with. They know what the data is getting used for. And how do you really put governance on this? So that on one side you can trust it on the other side. There is a good use of that data, Uh, and not, uh, you know, compromise on their quality or privacy and some of the other regulations. But we do see this opening up to the two sided market places as well. Uh, some of the industry's lend themselves extremely well for that kind of play. We have seen that happening in trading area. We've seen that happen. And, uh, you know, the credit checks and things like that which are usually open for, you know, those kind of ecosystem. But the conversations and the and the programmes are really leading towards towards that in the market. >>You know, Lisa, one of things I wrote about this weekend is I was decided to come to stuff like summit and and see one of the, you know, thesis I have is that we're going to move not just beyond analytics, including analytics, but also building data products that can be monetised and and I'm hoping we're going to see some of that here. Are you seeing that Christian in the customer? It's It's >>a great question, David. So So we have You know, I just thought of it as as he was talking about. We have a customer who's a very large customer of ours who's in the financial services space, and they handle roughly 40% of the credit card transactions that happen in the US and they're coming to us and saying they want to go from zero in data business today to a $2 billion business over the next five years, and they're leaning on us to help them do that. And one of the things that's exciting for me is they're coming to us not saying Hey, how do you do it? You know, they're saying, Hey, we want to build a consumption model on top of snowflake and we want to use you as the delivery mechanism and the billing mechanism to help us actually monetise that data. So yes, the answer is. You know, I I used to sell to, you know, chief Data Officers and and see IOS. Now I'm talking to VPs of sales and I'm talking to chief operating officers and I'm talking to CEOs about how do we actually create a new revenue stream? And that's just I mean, it's exhilarating to have those conversations. That's >>data products. They don't have to worry about the infrastructure that comes from the cloud. They don't have to worry about the governance, as Senior was saying, Just put >>it in stuff like Just >>put stuff like that. So I call it The super cloud is kind of a, you know, a funny little tongue in cheek. But it's happening. It's this layer. It's not just multiple clouds. You see a lot of your critical competitors adjacent competitors saying, Hey, we're now running in in Google or we're running in Azure. We've been running on AWS. This is different. This is different, isn't it? It's a cloud that floats above the The infrastructure of the hyper scale is, and that's that's a new era. I think >>it's a new error. I think they're you know, I think the hyper scholars want to, you know, keep us as a as a data warehouse and and we're not. The customers are not letting them so So I think that's you know where emphasis kind of saw the light early on. And they were our innovation partner of the year, uh, this past year and they're helping us in our customers innovate, >>but you're uniquely qualified to do that where? I don't think it's the hyper scholars agenda. At least I never say never with the hyper scale is, but yeah, they have focused on providing infrastructure. And, yeah, they have databases and other tools. But that that cross cloud that continuum to your point, talking to VPs of sales and how do you generate revenue? That maybe, is a conversation that they have, but not explicitly as to how to actually do it in a data >>cloud. That's right. I mean, those and those are the Those are the fun conversations because you're you're saying, Hey, we can actually create a new revenue stream. And how can we actually help you solve our joint customers problems? So, yes, it is. Well, >>that's competitive differentiation for businesses. I mean, this is, as I mentioned Every company has to be a data company. If they're not, they're probably not going to be around much longer. They've got to be able to to leverage a data platform like snowflake, to find insights, be able to act on them and create value new services, new products to stay competitive, to stay ahead of the competition. That's no longer nice to have >>100%. I mean, I think they're they're all scared. I mean, you know, like if you look in the financial services space, they look at some of the fintech, as you know, the giant £800 gorillas look at the small fintech has huge threats to the business, and they're coming to us and say, How can we innovate our business now? And they're looking at us as the the innovator, and they're looking at emphasis to help them do that. So I think these are These are incredible times. >>So the narrative on Wall Street, of course, this past earnings season was consumption and who has best visibility and and they they were able to snowflake had a couple of large customers dial down consumption, some consumer facing. Here's the thing. If you're selling a data product for more than it costs you to make. If you dial down consumption in the future, you're gonna dial down revenue. So that's it's going to become less and less discretionary over time. And that, to me, is the next error. That's really exciting. >>The key, The key there is understanding the unit of measure. I think that's the number. One question that we get from customers is what is the unit of measure that we care about, that we want to monetise because to your point, it costs you more to make the product. You're not going to sell it right? And so I think that those are the things that the energy that we're spending with customers today is advising them, jointly advising them on how to actually monetise the specific, you know, unit of measure that they care >>about because when they get the Amazon bill or the snowflake bill, the CFO starts knocking the door. The answer has to be well, look at all the revenue that we generated and all the operating profit and the free cash flow that we drove, and then it's like, Oh, I get it. Keep doing it well, if I'm >>if I'm going on sales calls with the VP of sales and his their sales team, fantastic, right generated helping them generate revenue, right? That's a great conversation >>dynamic. And I think the adoption is really driven through the value, uh, that they can drive in their ecosystem. Their products are similar to products and services that these companies sell. And if you're embedding data inside Syria into your products services, that makes you that much more competitive in the market and drive value for your stakeholders. And that's essentially the future business model that we're talking about. On one side, the other one is the agility. Things aren't remaining constant, they are constantly changing, and we talked about some of those forces earlier. All of this is changing. The landscape is changing the the needs in the economy and things like that, and how you adapt to those kind of models in the future and pivoted on data capabilities that lets you identify new opportunities and and create new value. >>Speaking of creating new value last question guys, before we wrap, what's the go to market approach here between the two companies working customers go to get engaged. I imagine both sides. >>Yeah. I mean, the way that partnership looks good to me is is sell with co selling. So So I think, you know, we look at developing joint solutions with emphasis. They've done a wonderful job of leading into our partnership. So, you know, Sue Neill and I have a regular cadence where we talked every quarter, and our sales teams and our partner teams are are all leaning in and co selling. I don't know if you >>have Absolutely, um, you know, we we proactively identify, you know, the opportunities for our customers. And we work together at all levels within, you know, between the two companies to be able to bring a cohesive solution and a proposition for the customers. Really help them understand how to, you know, what is it that they can, um, get to and how you get that journey actually executed. And it's a partnership that works very seamlessly through that entire process, not just upstream when we're selling, but also downstream and we're executing. And we've had tremendous success together and look forward to more. >>Congratulations on that success, guys. Thank you so much for coming on talking about new possibilities with data and AI and sharing some of the impact that the technologies are making. We appreciate your insights. >>Thank you. Thank >>you. Thank you So much >>for our guests and a Volonte. I'm Lisa Martin. You're watching the Cube live in Las Vegas from Snowflake Summit 22 back after the keynote with more breaking news. Mhm, mhm.

(upbeat music) >> Hello and welcome to this Cube Conversation. I'm here in Palo Alto, California. I'm John Furrier host of theCUBE, and I'm here with mparticle. With Chee Chew, Chief Product Officer. Thanks for joining us today. Thanks for coming on. >> Thank you. It's great to be here. >> So mparticle's doing some pretty amazing things around managing customer data end to end as a data platform. A lot of integrations. You guys are state of the art cloud scale for this new kind of use case of using the data for customer value in real time. A lot of good stuff going on. So I really want to dig into this whole prospect. So what is the company about first? Take a minute to explain what is mparticle for the folks watching? >> Yeah, absolutely. Well, if you think about the world today where it's like cloud computing and businesses are getting a lot of data from customers as consumers go online. And they have these cloud services that are collecting all this data about the customer. How do you get it organized? How do you have all that data that's in different departments, reconcile them and like give it to your departments. So they can really personalize the experiences. We've all had these experiences where, you know, like we're this loyal customer of a brand, we shop there a lot. And then we go over to like the customer service and they act like they have no idea who we are. Our job is to help businesses really understand the customer and be able to treat them in a personal way. To do the very best for every experience. >> Well, Chee you're in a really big spot there with the company, Chief Product... You got the keys to the kingdom over there. You're overseeing all the action. You got a platform, a bunch of solutions you're enabling. Customer data has been around for a long time. We hear big systems in the past, oh got to leverage the customer data. But why is the customer data more important now than ever as developers and cloud scale are emerging in. Why is customer data becoming more and more valuable to organizations? >> No. Well, customer data has been around for like decades and decades. The amount of customer data being generated online has just accelerated. It's been exponential. There's been more data collected in the past four years than the past 40 years. And like businesses are just starting to realize, how much of a goldmine that could be for them. If they could really harness it. And especially in today's world where treating it properly, respecting people's privacy, really doing well by the customer, earning the right to use that data is ever so important. The combination that brings the need for solutions like mparticle. >> Talk about some of the enablement that you guys offer your customers. You got a platform, you got a lot of moving parts in there. A lot of key components, a lot of integrations. With all the best platforms to connect to. We're in an API economy. So trust is huge. You got to have the data governance. Everything's got to work together. It's a really hard problem. How do you guys enable value there? What is the key product value that you guys are enabling? >> Yeah, it is a hard problem. And with the data being so important to businesses and treating it well and collecting it from all the different aspects, there are many places where we... Our customers really value the services we bring. As you mentioned, we have a large set of integrations. We can get data in from pretty much any system that you have. Even if you built it yourself, we have ways of enabling you to collect that data from all around the company. Then we reconcile them. So we create one single view of the customer. We adhere to all the privacy regulations around the world to make sure that you're compliant with not only laws but with the trust with your consumers. We clean that data and then we distribute it to all the systems where you really want to create personalized experiences. So the collection, the reconciliation, the cleaning, the conformance, and then the distribution. Those are all key events that we do to bring value to customers. >> It's funny in all these major shifts, you're seeing all the same things. You got to be a media company. You got to be a data company. Got to be a video company. Got to be a cloud company. So in the digital transformation, you know with machine learning and AI really at the center of the application value now, you can measure everything in a company. So, smart leadership saying, hey, if we can measure everything, don't we want to know what's going on with respect to our customer. The journey they call it. So, you know, there's the industry taglines of customer best in class experiences, capturing the moments that matter. Describe how you do that. Because moments that matter to me feel like something that's real time or something that's super important, that's contextualized. You got to get that context with that journey. How do you guys do that? This is something I'm intrigued about. >> Yeah, absolutely. And you know, I... This hearken backs to my experience when I was at Amazon doing retail and we really focus on personalization and the notion of when you go to one page or one screen on your mobile device and then you go to the very next page. That very next page has to be personalized with the things that you did on... Just seconds ago on the previous one. That idea of being at the interaction speed, keeping up with the customers. That's what, we've... What we provide for our brands. It's not enough to just collect the data, churn on it, do a bunch of like calculations and then tomorrow figure out what to do. Tomorrow figure out how to personalize it. It has to be in interaction time with our customers. >> John: It's interesting too. You'll have experience in big companies, hyperscalers with large, you know, media business and data. Bringing that to normal companies, enterprises, and mid-market, they have to then stand up their own staff. They have to operationalize this in a large data strategy that maximizes the value. How do brands do this effectively? Can you share best practice of what's the best way to stand up and operationalize the team, the developers, the strategy. >> Chee: Yeah and this is a great question. And right now with the world... The way the world and the industry is developing, businesses don't all do it the same way. Like at Amazon, we built our own. Now we had several hundred engineers in my team who are collecting the data, analyzing it, and really cleaning it. Not every company can afford a couple hundred engineers just to do this... Solve this one problem. Which is why I'm super excited about what we're doing at mparticle, where we're trying to make that available to every company in the world. Whether you're a huge brand, like an NBC, or you're a smaller, medium size startup. Like you have a lot of data and we can help make it accessible for you. Now, many companies do start and build it from scratch and the problems early on, seem very tractable. But then as new laws come out, as the platform changes, as Apple and iOS change the rules on what you can collect and what data you can't collect. That puts you on this treadmill of always like reinvesting and reinvesting in the data collection. And not as much at innovating on your business. And then many companies turn around and decide, oh I understand why you want a company like an mparticle, providing that service. >> It's interesting. You guys do a lot of that... The key value proposition that we hear a lot for successful companies. You take care of that the heavy differentiate... Undifferentiated heavy lifting. So the customer can focus on the value. This seems to be the theme of of the data problem that companies want to solve. There's a lot of grunt work that has to get done. A lot of, you know, get down and dirty and work on stuff. If you can just automate it, make it go faster, then you can apply more creative processes and tools onto getting more growth or more value out of the use case. Can you... Is that something that's happening here? >> Oh yeah, absolutely. You know, the dirty secret that if you talk to any like machine learning scientist data engineer, what they'll tell you is it seems like the world is sexy when you talk to new like computer science students about like building models. But when they go to industry they spend like 80 or 90% of their time cleaning data, getting access to data, like getting the right permissions. And they spend like 10 to 20% of the time actually building models and doing the really interesting things that you want your data science to do. That's a really expensive way of getting to your models. And that's why you're right. Services like, mparticle, like our core business is to take that grunt work and that... Things that might be less exciting and bespoke to your business. Like that's the stuff that we get excited about. And we want to provide the best op... Best in breed experience for our customers. >> Yeah. There's no doubt, every company will have to have this really complex, hard to solve platform problem. You either buy or build it. I mean, you're not... Not everyone's Amazon, right? So not everyone can do that. So you got to have the integrations, you got to have the personalizations, you got to have the data quality and you got to have the data governance in there too. You can't forget the fact that you'd be dealing with potentially trusted parties that don't work for you. Right? So this is a huge connection point that I want to just quickly get into. Quickly, APIs connects companies but now also connects data. How do you view that? How should customers think about the connection points when they start to share customer data with other companies? >> Yeah, you're totally right in that. Not only is it important for you to do this in terms of saving your time in engineering and all the amount of work you have, but the risk is super high. If you treat customers data incorrectly, you can break trust with your consumers. It takes a long time to build that trust and just a moment to lose it. And so it is more than just engineering time savings but it is also a risk to the business. Now... Then you go to down to like, how do you do it? Why APIs? The reason for us, our push on really the API platform is to give power to developers. Within your company, you may have some innovation that you want, some way you want to really differentiate yourself from the rest of the field. If we provided only standard UI. Standard ways of doing it, then our customers would all behave and have the same capabilities as every other customer. But by us providing APIs it allows our customers to really innovate and make the platform bend to their will. To support the unique ideas that they have. So that's our approach of why we really focus on the customer data infrastructure. >> John: Yeah, it's a great opportunity Chee, I really appreciate your time. Real final question for you, as folks look at this opportunity to have a data platform and mparticle, one that you have. They're going to probably ask you the question of, hey I got developers too. I'm hiring more and more cloud native developers. We're API first, obviously we're cloud native. We love that direction. We're distributed computing. All that great stuff at the edge. I got machine learning. But I really want to integrate, I want to control the experience. I want to be agile and fast. Can you help us? What's your answer to that question? >> Absolutely. If you look at the things that your engines are doing, and you ask them how much of what they're doing is similar to what you expect from other similar companies and how much is really unique to your business. You'll probably find that a minority of the work is really unique to that business. And the majority are things that are common problems that other companies struggle with. Our job is to help take that away. So you can really focus on what's unique, bespoke, and innovative for you. >> John: Follow up to that real quick, as you're the Chief Product Officer. Talk to the folks out there who are watching, who may not know what goes on in a product organization. You're making all kinds of trade offs. You got a product roadmap, you've got the 20 mile stare. You have a North Star. What should they know about mparticle, about the product that they... That's important for them to either pay attention to or they may not know about. >> You know, my... When I think about mparticle, it's not just a product but it's the whole offering. And what you want to know about mparticle is we really work hard to empower our customers, whether it's through the API platforms. So that you have the full flexibility to do whatever you want or through our customer service and our support teams. We are... Have a great reputation with our customers about really focusing on and unblocking them, enabling whatever the heart desires. >> John: Yeah and building on top of it. Sounds great. Chee, thanks for coming on. Appreciate the update on mparticle. Thanks for your time. Great to see you. >> Absolutely. Thank you for your time. >> Okay. This is theCUBE conversation. I'm John Furrier, host of theCUBE. Thanks for watching. (upbeat music)

>> Narrator: The cube presents, the Kubecon and Cloudnativecon Europe, 2022 brought to you by Red Hat, the cloud native computing foundation and its ecosystem partners. >> Welcome to Valencia, Spain in Kubecon and Cloudnativecon Europe, 2022. I'm your host Keith Townsend. It's been a amazing day, three days of coverage 7,500 people, 170 sponsors, a good mix of end user organizations, vendors, just people with open source at large. I've loved the conversations. We're not going to stop that coverage just because this is the last session of the conference. Colin Murphy, senior software engineer, Adobe, >> Adobe. >> Oh, wow. This is going to be fun. And then Liam Randall, the chair of CNCF Cloud Native WebAssembly Day. >> That's correct. >> And CNCF & CEO of Cosmonic. >> That's right. >> All right. First off, let's talk about the show. How has this been different than other, if at all of other Kubecons? >> Well, first I think we all have to do a tremendous round of applause, not only for the vendors, but the CNC staff and all the attendees for coming out. And you have to say, Kubecon is back. The online experiences have been awesome but this was the first one, where Hallwaycon was in full effect. And you had the opportunity to sit down and meet with so many intelligent and inspiring peers and really have a chance to learn about all the exciting innovations that have happened over the last year. >> Colin. >> Yeah, it's been my most enjoyable Kubecon I've ever been to. And I've been to a bunch of them over the last few years. Just the quality of people. The problems that we're solving right now, everywhere from this newer stuff that we're talking about today with WebAssembly but then all these big enterprises trying to getting involved in Kubernetes >> Colin, to your point about the problems that we're solving, in many ways the pandemic has dramatically accelerated the pace of innovation, especially inside the CNCF, which is by far the most critical repository of open source projects that enterprises, governments and individuals rely on around the world, in order to deliver new experiences and to have coped and scaled out within the pandemic over the last few years. >> Yeah, I'm getting this feel, this vibe of the overall show that feels like we're on the cuff for something. There's other shows throughout the year, that's more vendor focused that talk about cloud native. But I think this is going to be the industry conference where we're just getting together and talking about it and it's going to probably be, in the next couple of years, the biggest conference of the year, that's just my personal opinion. >> I actually really strongly agree with you. And I think that the reason for that is the diversity that we get from the open source focus of Kubecon Kubecon has started where the industry really started which was in shared community projects. And I was the executive at Capital One that led the donation of cloud custodian into the CNCF. And I've started and put many projects here. And one of the reasons that you do that is so that you can build real scalable communities, Vendors that oftentimes even have competing interest but it gives us a place where we can truly collaborate where we can set aside our personal agendas and our company's agendas. And we can focus on the problems at hand. And how do we really raise the bar for technology for everybody. >> Now you two are representing a project that, you know as we look at kind of, how the web has evolved the past few decades, there's standards, there's things that we know that work, there's things that we know that don't work and we're beyond cloud native, we're kind of resistant to change. Funny enough. >> That's right. >> So WebAssembly, talk to me about what problem is WebAssembly solving that need solving? >> I think it's fitting that here on the last day of Kubecon, we're starting with the newest standard for the web and for background, there's only four languages that make up what we think of as the modern web. There's JavaScript, there's HTML, there's CSS, and now there's a new idea that's WebAssembly. And it's maybe not a new idea but it's certainly a new standard, that's got massive adoption and acceleration. WebAssembly is best thought of as almost like a portable little virtual machine. And like a lot of great ideas like JavaScript, it was originally designed to bring new experiences to browsers everywhere. And as organizations looked at the portability and security value props that come from this tiny little virtual machine, it's made a wonderful addition to backend servers and as a platform for portability to bring solutions all the way out to the edge. >> So what are some of the business cases for WebAssembly? Like what problem, what business problem are we solving? >> So it, you know, we would not have been able to bring Photoshop to the web without WASM. >> Wow. >> And just to be clear, I had nothing to do with that effort. So I want to make sure everybody understands, but if you have a lot of C++ or C code and you want to bring that experience to the web browser which is a great cost savings, cause it's running on the client's machines, really low latency, high performance experiences in the browser, WASM, really the only way to go. >> So I'm getting hints of fruit berry, Java. >> Liam: Yeah, absolutely. >> Colin: Definitely. >> You know, the look, WebAssembly sounds similar to promises you've heard before, right ones, run anywhere. The difference is, is that WebAssembly is not driven by any one particular vendor. So there's no one vendor that's trying to bring a plug in to every single device. WebAssembly was a recognition, much like Kubecon, the point that we started with around the diversity of thought ideas and representation of shared interest, of how do we have a platform that's polyglot? Many people can bring languages to it, and solutions that we can share and then build from there. And it is unlocking some of the most amazing and innovative experiences, both on the web backend servers and all the way to the edge. Because WebAssembly is a tiny little virtual machine that runs everywhere. Adobe's leadership is absolutely incredible with the things that they're doing with WebAssembly. They did this awesome blog post with the Google Chrome team that talked about other performance improvements that were brought into Chrome and other browsers, in order to enable that kind of experience. >> So I get the general concept of WebAssembly and it's one of those things that I have to ask the question, and I appreciate that Adobe uses it but without the community, I mean, I've dedicated some of my team's resources over the years to some really cool projects and products that just died on the buying cause there was no community around. >> Yeah. >> Who else uses WebAssembly? >> Yeah, I think so. We actually, inside the CNCF now, have an entire day devoted just to WebAssembly and as the co-chair of the CNCF Cloud Native WebAssembly Day, we really focus on bringing those case studies to the forefront. So some of the more interesting talks that we had here and at some of the precursor weekend conferences were from BMW, for example, they talked about how they were excited about not only WebAssembly, but a framework that they use on WebAssembly called WASM cloud, that lets them a flexibly scale machine learning models from their own edge, in their own vehicles through to their developer's workstations and even take that data onto their regular cloud Kubernetes and scale analysis and analytics. They invested and they just released a machine learning framework for one of the many great WebAssembly projects called WASM cloud, which is a CNCF project, a member project here in the CNCF. >> So how does that fit in overall landscape? >> So think of WebAssembly, like you think of HTML. It's a technology that gives you a lot of concept and to accelerate your journey on those technologies, people create frameworks. For example, if you were going to write a UI, you would not very likely start with an empty document you'd start with a react or view. And in a similar vein, if you were going to start a new microservice or backend application, project for WebAssembly, you might use WASM cloud or you might use ATMO or you might use a Spin. Those are three different types of projects. They all have their own different value props and their own different opinions that they bring to them. But the point is is that this is a quickly evolving space and it's going to dramatically change the type of experiences that we bring, not only to web browsers but to servers and edges everywhere. >> So Colin, you mentioned C+ >> Colin: Yeah. >> And other coding. Well , talk to me about the ramp up. >> Oh, well, so, yeah, so, C++ there was a lot of work done in scripting, at Adobe. Taking our C++ code and bringing it into the browser. A lot of new instructions, Cimdi, that were brought to make a really powerful experience, but what's new now is the server side aspect of things. So, just what kind of, what Liam was talking about. Now we can run this stuff in the data center. It's not just for people's browsers anymore. And then we can also bring it out to the edge too, which is a new space that we can take advantage of really almost only through WebAssembly and some JavaScript. >> So wait, let me get this kind of under hook. Before, if I wanted a rich experience, I have to run a heavy VDI instance on the back end so that I'm basically getting remote desktop calls from a light thin client back to my backend server, that's heavy. >> That is heavy. >> WebAssembly is alternative to that? >> Yes, absolutely. Think of WebAssembly as a tiny little CPU that is a shim, that we can take the places that don't even traditionally have a concept of a processor. So inside the browser, for example, traditionally cloud native development on the backend has been dominated by things like Docker and Docker is a wonderful technology and Container is a wonderful technology that really drove the last 10 years of cloud native with the great lift and shift, if you will. Take our existing applications, package them up in this virtual desktop and then deliver them. But to deliver the next 10 years of experiences, we need solutions that let us have portability first and a security model that's portable across the entire landscape. So this isn't just browsers and servers on the back end, WebAssembly creates an a layer of equality from truly edge to edge. It's can transcend different CPUs, different operating systems. So where containers have this lower bound off you need to be running Linux and you need to be in a place where you're going to bring Kubernetes. WebAssembly is so small and portable, it transcends that lower bound. It can go to places like iOS. It can go to places like web browsers. It can even go to teeny tiny CPUs that don't even traditionally have a full on operating systems inside them. >> Colin: Right, places where you can't run Docker. >> So as I think about that, and I'm a developer and I'm running my back end and I'm running whatever web stack that I want, how does this work? Like, how do I get started with it? >> Well, there's some great stuff Liam already mentioned with WASM cloud and Frmion Spin. Microsoft is heavily involved now on providing cloud products that can take advantage of WebAssembly. So we've got a lot of languages, new languages coming in.net and Ruby, Rust is a big one, TinyGo, really just a lot of places to get involved. A lot of places to get started. >> At the highest level Finton Ryan, when he was at Gartner, he's a really well known analyst. He wrote something profound a few years ago. He said, WebAssembly is the one technology, You don't need a strategy to adopt. >> Mm. >> Because frankly you're already using it because there's so many wonderful experiences and products that are out there, like what Adobe's doing. This virtual CPU is not just a platform to run on cloud native and to build applications towards the edge. You can embed this virtual CPU inside of applications. So cases where you would want to allow your users to customize an application or to extend functionality. Give you an example, Shopify is a big believer in WebAssembly because while their platform covers, two standard deviations or 80% of the use cases, they have a wonderful marketplace of extensions that folks can use in order to customize the checkout process or apply specialized discounts or integrate into a partner ecosystem. So when you think about the requirements for those scenarios, they line up to the same requirements that we have in browsers and servers. I want real security. I want portability. I want reuseability. And ultimately I want to save money and go faster. So organizations everywhere should take a few minutes and do a heads up and think about one, where WebAssembly is already in their environment, inside of places like Envoy and Istio, some of the most popular projects in the cloud native ecosystem, outside of Kubernetes. And they should perhaps consider studying, how WebAssembly can help them to transform the experiences that they're delivering for their customers. This may be the last day of Kubecon, but this is certainly not the last time we're going to be talking about WebAssembly, I'll tell you that. >> So, last question, we've talked a lot about how to get started. How about day two, when I'm thinking about performance troubleshooting and ensuring clients have a great experience what's day two operation like? >> That's a really good question. So there's, I know that each language kind of brings their own tool chain and their, and you know we saw some great stuff on, on WASM day. You can look it up around the .net experience for debugging, They really tried to make it as seamless and the same as it was for native code. So, yeah, I think that's a great question. I mean, right now it's still trying to figure out server side, It's still, as Liam said, a shifting landscape. But we've got some great stuff out here already >> You know, I'd make an even bigger call than that. When I think about the last 20 years as computing has evolved, we've continued to move through these epics of tech that were dominated by a key abstraction. Think about the rise of virtualization with VMware and the transition to the cloud. The rise of containerization, we virtualized to OS. The rise of Kubernetes and CNCF itself, where we virtualize cloud APIs. I firmly believe that WebAssembly represents the next epic of tech. So I think that day two WebAssembly continues to become one of the dominant themes, not only across cloud native but across the entire technical computing landscape. And it represents a fundamentally gigantic opportunity for organizations such as Adobe, that are always market leading and at the cutting edge of tech, to bring new experiences to their customers and for vendors to bring new platforms and tools to companies that want to execute on that opportunity. >> Colin Murphy, Liam Randall, I want to thank you for joining the Cube at Kubecon Cloudnativecon 2022. I'm now having a JavaScript based app that I want to re-look at, and maybe re-platforming that to WebAssembly. It's some lot of good stuff there. We want to thank you for tuning in to our coverage of Kubecon Cloudnativecon. And we want to thank the organization for hosting us, here from Valencia, Spain. I'm Keith Townsend, and you're watching the Cube, the leader in high tech coverage. (bright music)

>> Narrator: theCUBE presents KubeCon and CloudNativeCon Europe 2022, brought to you by Red Hat, the Cloud Native Computing Foundation and its ecosystem partners. >> Welcome to Valencia, Spain, KubeCon, CloudNativeCon Europe 2022. I'm Keith Townsend, your host. And we're continuing the conversations around ecosystem cloud native, 7,500 people here, 170 plus show for sponsors. It is for open source conference, I think the destination. I might even premise that this may be, this may eventually roll to the biggest tech conference in the industry, maybe outside of AWS re:Invent. My next guest is Nick van Wiggeren. >> Wiggeren. >> VP engineering of PlanetScale. Nick, I'm going to start off the conversation right off the bat PlanetScale cloud native database, why do we need another database? >> Well, why don't you need another database? I mean, are you happy with yours? Is anyone happy with theirs? >> That's a good question. I don't think anyone is quite happy with, I don't know, I've never seen a excited database user, except for guys with really (murmurs) guys with great beards. >> Yeah. >> Keith: Or guys with gray hair maybe. >> Yeah. Outside of the dungeon I think... >> Keith: Right. >> No one is really is happy with their database, and that's what we're here to change. We're not just building the database, we're actually building the whole kind of start to finish experience, so that people can get more done. >> So what do you mean by getting more done? Because MySQL has been the underpinnings of like massive cloud database deployments. >> 100% >> It has been the de-facto standard. >> Nick: Yep. >> For cloud databases. >> Nick: Yep. >> What is PlanetScale doing in enabling us to do that I can't do with something like a MySQL or a SQL server? >> Great question. So we are MySQL compatible. So under the hood it's a lot of the MySQL you know and love. But on top of that we've layered workflows, we've layered scalability, we've layered serverless. So that you can get all of the the parts of the MySQL, that dependability, the thing that people have used for 20, 30 years, right? People don't even know a world before MySQL. But then you also get this ability to make schema changes faster. So you can kind of do your work quicker get to the business objectives faster. You can scale farther. So when you get to your MySQL and you say, well, can we handle adding this one feature on top? Can we handle the user growth we've got? You don't have to worry about that either. So it's kind of the best of both worlds. We've got one foot in history and we've got one foot in the new kind of cloud native database world. We want to give everyone the best of both. >> So when I think of serverless because that's the buzzy world. >> Yeah. >> But when I think of serverless I think about developers being able to write code. >> Yep. >> Deploy the code, not worry about VM sizes. >> Yep. >> Amount of disk space. >> Yep. >> CPU, et cetera. But we're talking about databases. >> Yep. >> I got to describe what type of disk I want to use. I got to describe the performance levels. >> Yep. >> I got all the descriptive stuff that I have to do about infrastructures. Databases are not... >> Yep. >> Keith: Serverless. >> Yep. >> They're the furthest thing from it. >> So despite what the name may say, I can guarantee you PlanetScale, your PlanetScale database does run on at least one server, usually more than one. But the idea is exactly what you said. So especially when you're starting off, when you're first beginning your, let's say database journey. That's a word I use a lot. The furthest thing from your mind is, how many CPUs do I need? How many disk iOS do I need? How much memory do I need? What we want you to be able to do is get started on focusing on shipping your code, right? The same way that Lambda, the same way that Kubernetes, and all of these other cloud native technologies just help people get done what they want to get done. PlanetScale is the same way, you want a database, you sign up, you click two buttons, you've got a database. We'll handle scaling the disk as you grow, we'll handle giving you more resources. And when you get to a spot where you're really starting to think about, my database has got hundreds of gigabytes or petabytes, terabytes, that's when we'll start to talk to you a little bit more about, hey, you know it really does run on a server, we ain't got to help you with the capacity planning, but there's no reason people should have to do that up front. I mean, that stinks. When you want to use a database you want to use a database. You don't want to use, 747 with 27 different knobs. You just want to get going. >> So, also when I think of serverless and cloud native, I think of stateless. >> Yep. >> Now there's stateless with databases, help me reconcile like, when you say it's cloud native. >> Nick: Yep. >> How is it cloud native when I think of cloud native as stateless? >> Yeah. So it's cloud native because it exists where you want it in the cloud, right? No matter where you've deployed your application on your own cloud, on a public cloud, or something like that, our job is to meet you and match the same level of velocity and the same level of change that you've got on your kind of cloud native setup. So there's a lot of state, right? We are your state and that's a big responsibility. And so what we want to do is, we want to let you experiment with the rest of the stateless workloads, and be right there next to you so that you can kind of get done what you need to get done. >> All right. So this concept of clicking two buttons... >> Nick: Yeah. >> And deploying, it's a database. >> Nick: Yep. >> It has to run somewhere. So let's say that I'm in AWS. >> Nick: Yep. >> And I have AWS VPC. What does it look like from a developer's perspective to consume the service? >> Yeah. So we've got a couple of different offerings, and AWS is a great example. So at the very kind of the most basic database unit you click, you get an endpoint, a host name, a password, and the username. You feed that right into your application and it's TLS secure and stuff like that, goes right into the database no problem. As you grow larger and larger, we can use things like AWS PrivateLink and stuff like that, to actually start to integrate more with your AWS environment, all the way over to what we call PlanetScale Managed. Which is where we actually deploy your data plan in your AWS account. So you give us some permissions and we kind of create a sub-account and stuff like that. And we can actually start sending pods, and hold clusters and stuff like that into your AWS account, give you a PrivateLink, so that everything looks like it's kind of wrapped up in your ownership but you still get the same kind of PlanetScale cloud experience, cloud native experience. >> So how do I make calls to the database? I mean, do I have to install a new... >> Nick: Great question. >> Like agent, or do some weird SQL configuration on my end? Or like what's the experience? >> Nope, we just need MySQL. Same way you'd go, install MySQL if you're on a Mac or app store to install MySQL on analytics PC, you just username, password, database name, and stuff like that, you feed that into your app and it just works. >> All right. So databases are typically security. >> Nick: Yep. >> When my security person. >> Nick: Yep. >> Sees a new database. >> Nick: Yep. >> Oh, they get excited. They're like, oh my job... >> Nick: I bet they do. >> My job just got real easy. I can find like eight or nine different findings. >> Right. >> How do you help me with compliance? >> Yeah. >> And answering these tough security questions from security? >> Great question. So security's at the core of what we do, right? We've got security people ourselves. We do the same thing for all the new vendors that we onboard. So we invest a lot. For example, the only way you can connect to a PlanetScale database even if you're using PrivateLink, even if you're not touching the public internet at all, is over TLS secured endpoint, right? From the very first day, the very first beta that we had we knew not a single byte goes over the internet that's not encrypted. It's encrypted at rest, we have audit logging, we do a ton internally as well to make sure that, what's happening to your database is something you can find out. The favorite thing that I think though is all your schema changes are tracked on PlanetScale, because we provide an entire workflow for your schema changes. We actually have like a GitHub Polar Request style thing, your security folks can actually look and say, what changes were made to the database day in and day out. They can go back and there's a full history of that log. So you actually have, I think better security than a lot of other databases where you've got to build all these tools and stuff like that, it's all built into PlanetScale. >> So, we started out the conversation with two clicks but I'm a developer. >> Nick: Yeah. >> And I'm developing a service at scale. >> Yep. >> I want to have a SaaS offering. How do I automate the deployment of the database and the management of the database across multiple customers? >> Yeah, so everything is API driven. We've got an API that you can use supervision databases to make schema changes, to make whatever changes you want to that database. We have an API that powers our website, the same API that customers can use to kind of automate any part of the workflow that they want. There's actually someone who did talk earlier using, I think, wwww.crossplane.io, or they can use Kubernetes custom resource definitions to provision PlanetScale databases completely automatically. So you can even do it as part of your standard deployment workflow. Just create a PlanetScale database, create a password, inject it in your app, all automatically. >> So Nick, as I'm thinking about scale. >> Yep. >> I'm thinking about multiple customers. >> Nick: Yep. >> I have a successful product. >> Nick: Yep. >> And now these customers are coming to me with different requirements. One customer wants to upgrade once every 1/4, another one, it's like, you know what? Just bring it on. Like bring the schema changes on. >> Yep. >> I want the latest features, et cetera. >> Nick: Right. >> How do I manage that with PlanetScale? When I'm thinking about MySQL it's a little, that can be a little difficult. >> Nick: Yeah. >> But how does PlanetScale help me solve that problem? >> Yeah. So, again I think it's that same workflow engine that we've built. So every database has its own kind of deploy queue, its own migration system. So you can automate all these processes and say, on this database, I want to change this schema this way, on this database I'm going to hold off. You can use our API to drive a view into like, well, what's the schema on this database? What's schema on this database? What version am I running on this database? And you can actually bring all that in. And if you were really successful you'd have this single plane of glass where you can see what's the status of all my databases and how are they doing, all powered by kind of the PlanetScale API. >> So we can't talk about databases without talking about backup. >> Nick: Yep. >> And recovery. >> Yep. >> How do I back this thing up and make sure that I can fall back? If someone deleted a table. >> Nick: Yep. >> It happens all the time in production. >> Nick: Yeah, 100%. >> How do I recover from it? >> So there's two pieces to this, and I'm going to talk about two different ways that we can help you solve this problem. One of them is, every PlanetScale database comes with backups built in and we test them fairly often, right? We use these backups. We actually give you a free daily backup on every database 'cause it's important to us as well. We want to be able to restore from backup, we want to be able to do failovers and stuff like that, all that is handled automatically. The other thing though is this feature that we launched in March called the PlanetScale Rewind. And what Rewind is, is actually a schema migration undo button. So let's say, you're a developer you're dropping a table or a column, you mean to drop this, but you drop the other one on accident, or you thought this column was unused but it wasn't. You know when you do something wrong, you cause an incident and you get that sick feeling in your stomach. >> Oh, I'm sorry. I've pulled a drive that was written not ready file and it was horrible. >> Exactly. And you kind of start to go, oh man, what am I going to do next? Everyone watching this right now is probably squirming in their seat a bit, you know the feeling. >> Yeah, I know the feeling >> Well, PlanetScale gives you an undo button. So you can click, undo migration, for 30 minutes after you do the migration and we'll revert your schema with all the data in it back to what your database looked like before you did that migration. Drop a column on accident, drop a table on accident, click the Rewind button, there's all the data there. And, the new rights that you've taken while that's happened are there as well. So it's not just a restore to a point in time backup. It's actually that we've replicated your rights sent them to both the old and the new schema, and we can get you right back to where you started, downtime solved. >> Both: So. >> Nick: Go ahead. >> DBAs are DBAs, whether they've become now reformed DBAs that are cloud architects, but they're DBAs. So there's a couple of things that they're going to want to know, one, how do I get my zero back up in my hands? >> Yeah. >> I want my, it's MySQL data. >> Nick: Yeah. >> I want my MySQL backup. >> Yeah. So you can just take backups off the database yourself the same way that you're doing today, right? MySQL dump, MySQL backup, and all those kinds of things. If you don't trust PlanetScale, and look, I'm all about backups, right? You want them in two different data centers on different mediums, you can just add on your own backup tools that you have right now and also use that. I'd like you to trust that PlanetScale has the backups as well. But if you want to keep doing that and run your own system, we're totally cool with that as well. In fact, I'd go as far as to say, I recommend it. You never have too many backups. >> So in a moment we're going to run Kube clock. So get your... >> Okay, all right. >> You know, stand tall. >> All right. >> I'll get ready. I'm going to... >> Nick: I'm tall, I'm tall. >> We're both tall. The last question before Kube clock. >> Nick: Yeah. >> It is, let's talk a little nerve knobs. >> Nick: Okay. >> The reform DBA. >> Nick: Yeah. >> They want, they're like, oh, this query ran a little bit slow. I know I can squeeze a little bit more out of that. >> Nick: Yeah. >> Who do they talk to? >> Yeah. So that's a great question. So we provide you some insights on the product itself, right? So you can take a look and see how are my queries performing and stuff like that. Our goal, our job is to surface to you all the metrics that you need to make that decision. 'Cause at the end of the day, a reform DBA or not it is still a skill to analyze the performance of a MySQL query, run and explain, kind of figure all that out. We can't do all of that for you. So we want to give you the information you need either knowledge or you know, stuff to learn whatever it is because some of it does have to come back to, what's my schema? What's my query? And how can I optimize it? I'm missing an index and stuff like that. >> All right. So, you're early adopter of the Kube clock. >> Okay. >> I have to, people say they're ready. >> Nick: Ooh, okay. >> All the time people say they're ready. >> Nick: Woo. >> But I'm not quite sure that they're ready. >> Nick: Well, now I'm nervous. >> So are you ready? >> Do I have any other choice? >> No, you don't. >> Nick: Then I am. >> But are you ready? >> Sure, let's go. >> All right. Start the Kube clock. (upbeat music) >> Nick: All right, what do you want me to do? >> Go. >> All right. >> You said you were ready. >> I'm ready, all right, I'm ready. All right. >> Okay, I'll reset. I'll give you, I'll give, see people say they're ready. >> All right. You're right. You're right. >> Start the Kube clock, go. >> Okay. Are you happy with how your database works? Are you happy with the velocity? Are you happy with what your engineers and what your teams can do with their database? >> Follow the dream not the... Well, follow the green... >> You got to be. >> Not the dream. >> You got to be able to deliver. At the end of the day you got to deliver what the business wants. It's not about performance. >> You got to crawl before you go. You got to crawl, you got to crawl. >> It's not just about is my query fast, it's not just about is my query right, it's about, are my customers getting what they want? >> You're here, you deserve a seat at the table. >> And that's what PlanetScale provides, right? PlanetScale... >> Keith: Ten more seconds. >> PlanetScale is a tool for getting done what you need to get done as a business. That's what we're here for. Ultimately, we want to be the best database for developing software. >> Keith: Two, one. >> That's it. End it there. >> Nick, you took a shot, I'm buying it. Great job. You know, this is fun. Our jobs are complex. >> Yep. >> Databases are hard. >> Yep. >> It is the, where your organization keeps the most valuable assets that you have. >> Nick: A 100%. >> And we are having these tough conversations. >> Nick: Yep. >> Here in Valencia, you're talking to the leader in tech coverage. From Valencia, Spain, I'm Keith Townsend, and you're watching theCUBE, the leader in high tech coverage. (upbeat music)

(upbeat music) >> Hello, and welcome to theCUBE's presentation of the AWS Startup Showcase: Data as Code. This is Season 2, Episode 2 of the ongoing SaaS covering exciting startups from the AWS ecosystem and we're going to talk about the future of enterprise data analytics. I'm your host, John Furrier and today we're joined by Gian Merlino CTO and co-founder of Imply.io. Welcome to theCUBE. >> Hey, thanks for having me. >> Building analytics apps with Apache Druid and Imply is what the focus of this talk is and your company being showcased today. So thanks for coming on. You guys have been in the streaming data large scale for many, many years of pioneer going back. This past decade has been the key focus. Druid's unique position in that market has been key, you guys been empowering it. Take a minute to explain what you guys are doing over there at Imply. >> Yeah, for sure. So I guess to talk about Imply, I'll talk about Druid first. Imply is a open source based company and Apache Druid is the open source project that the Imply product's built around. So what Druid's all about is it's a database to power analytical applications. And there's a couple things I want to talk about there. The first off is, is why do we need that? And the second is why are we good at, and I'll just a little flavor of both. So why do we need database to power analytical apps? It's the same reason we need databases to power transactional apps. I mean, the requirements of these applications are different analytical applications, apps where you have tons of data coming in, you have lots of different people wanting to interact with that data, see what's happening both real time and historical. The requirements of that kind of application have sort of given rise to a new kind of database that Druid is one example of. There's others, of course, out there in both the open source and non open source world. And what makes Druid really good at it is, people often say what is Druid's big secret? How is it so good? Why is it so fast? And I never know what to say to that. I always sort of go to, well it's just getting all the little details right. It's a lot of pieces that individually need to be engineered, you build up software in layers, you build up a database in layers, just like any other piece of software. And to have really high performance and to do really well at a specific purpose, you kind of have to get each layer right and have each layer have as little overhead as possible. And so just a lot of kind of nitty gritty engineering work. >> What's interesting about the trends over the past 10 years in particular, maybe you can go back 10, 15 years is state of the art database was, stream a bunch of data put it into a pile, index it, interrogate it, get some reports, pretty basic stuff and then all of a sudden now you have with cloud, thousands of databases out there, potentially hundreds of databases living in the wild. So now data with Kafka and Kinesis, these kinds of technologies streaming data's happening in real time so you don't have time to put it in a pile or index it. You want real time analytics. And so perhaps whether they're mobile app, Instagrams of the world, this is now what people want in the enterprise. You guys are the heart of this. Can you talk about that dynamic of getting data quickly at scale? >> So our thinking is that actually both things matter. Realtime data matters but also historical context matters. And the best way to get historical context out of data is to put it in a pile, index it, so to speak, and then the best way to get realtime context to what's happening right now is to be able to operate on these streams. And so one of the things that we do in Druid, I wish I had more time to talk about it but one of the things that we do in Druid is we kind of integrate this real time processing and this historical processing. So we actually have a system that we call the historical system that does what you're saying, take all this data, put in a pile, index it for all your historical data. And we have a system that we call the realtime system that is pulling data in from things like Kafka, Kinesis, getting data pushed into it as the case may be. And this system is responsible for all the data that's recent, maybe the last hour or two of data will be handled by this system and then the older stuff handled by historical system. And our query layer blends these two together seamlessly so a user never needs to think about whether they're querying realtime data or historical data. It's presented as a blended view. >> It's interesting and you know a lot of the people just say, Hey, I don't really have the expertise, and now they're trying to learn it so their default was throw into a data lake. So that brings back that historical. So the rise of the data lake, you're seeing Databricks and others out there doing very well with the data lakes. How do you guys fit into that 'cause that makes it a lot of sense too cause that looks like historical information? >> So data lakes are great technology. We love that kind of stuff. I would say that a really popular pattern, with Druid there's actually two very popular patterns. One is, I would say streaming forward. So stream focus where you connect up to something like Kafka and you load data to stream and then we will actually take that data, we'll store all the historical data that came from the stream and instead of blend those two together. And another other pattern that's also very common is the data lake pattern. So you have a data lake and then you're sort of mirroring that data from the data lake into Druid. This is really common when you have a data lake that you want to be able to build an application on top of, you want to say I have this data in the data lake, I have my table, I want to build an application that has hundreds of people using it, that has really fast response time, that is always online. And so when I mirror that data into Druid and then build my app on top of that. >> Gian take me through the progression of the maturity cycle here. As you look back even a few years, the pioneers and the hardcore streaming data using data analytics at scale that you guys are doing with Druid was really a few percentage of the population doing that. And then as the hyperscale became mainstream, it's now in the enterprise, how stable is it? What's the current state of the art relative to the stability and adoption of the techniques that you guys are seeing? >> I think what we're seeing right now at this stage in the game, and this is something that we kind of see at the commercial side of Imply, what we're seeing at this stage of the game is that these kinds of realization that you actually can get a lot of value out of data by building interactive apps around it and by allowing people to kind of slice and dice it and play with it and just kind of getting out there to everybody, that there is a lot of value here and that it is actually very feasible to do with current technology. So I've been working on this problem, just in my own career for the past decade, 10 years ago where we were is even the most high tech of tech companies were like, well, I could sort of see the value. It seems like it might be difficult. And we're kind of getting from there to the high tech companies realizing that it is valuable and it is very doable. And I think that was something there was a tipping point that I saw a few years ago when these Druid and database like really started to blow up. And I think now we're seeing that beyond sort of the high tech companies, which is great to see. >> And a lot of people see the value of the data and they see the application as data as code means the application developers really want to have that functionality. Can you share the roadmap for the next 12 months for you guys on what's coming next? What's coming around the corner? >> Yeah, for sure. I mentioned during the Apache open source community, different products we're one member of that community, very prominent one but one member so I'll talk a bit about what we're doing for the Druid project as part of our effort to make Druid better and take it to the next level. And then I'll talk about some of the stuff we're doing on the, I guess, the Druid sort of commercial side. So on the Druid side, stuff that we're doing to make Druid better, take it to the next level, the big thing is something that we really started writing about a few weeks ago, the multi-stage query engine that we're working on, a new multi-stage query engine. If you're interested, the full details are on blog on our website and also on GitHub on Apache Druid GitHub, but short version is Druid's. We're sort of extending Druid's Query engine to support more and varied kinds of queries with a focus on sort of reporting queries, more complex queries. Druid's core query engine has classically been extremely good at doing rapid fire queries very quickly, so think thousands of queries per second where each query is maybe something that involves a filter in a group eye like a relatively straightforward query but we're just doing thousands of them constantly. Historically folks have not reached for technologies like Druid is, really complex and a thousand line sequel queries, complex supporting needs. Although people really do need to do both interactive stuff and complex stuff on the same dataset and so that's why we're building out these capabilities in Druid. And then on the implied commercial side, the big effort for this year is Polaris which is our cloud based Druid offering. >> Talk about the relationship between Druid and Imply? Share with the folks out there how that works. >> So Druid is, like I mentioned before, it's Apache Druid so it's a community based project. It's not a project that is owned by Imply, some open source projects are sort of owned or sponsored by a particular organization. Druid is not, Druid is an independent project. Imply is the biggest contributor to Druid. So the imply engineering team is contributing tons of stuff constantly and we're really putting a lot of the work in to improve Druid although it is a community effort. >> You guys are launching a new SaaS service on AWS. Can you tell me about what that's happening there, what it's all about? >> Yeah, so we actually launched that a couple weeks ago. It's called Polaris. It's very cool. So historically there's been two ways, you can either get started with Apache Druid, it's open source, you install it yourself, or you can get started with Imply Enterprise which is our enterprise offering. And these are the two ways you can get started historically. One of the issues of getting started with Apache Druid is that it is a very complicated distributed database. It's simple enough to run on a single server but once you want to scale things out, once you get all these things set up, you may want someone to take some of that operational burden off your hands. And on the Imply Enterprise side, it says right there in the name, it's enterprise product. It's something that may take a little bit of time to get started with. It's not something you can just roll up with a credit card and sign up for. So Polaris is really about of having a cloud product that's sort of designed to be really easy to get started with, really self-service that kind of stuff. So kind of providing a really nice getting started experience that does take that maintenance burden and operational burden away from you but is also sort of as easy to get started with as something that's database would be. >> So a more developer friendly than from an onboarding standpoint, classic. >> Exactly. Much more developer friendly is what we're going for with that product. >> So take me through the state of the art data as code in your mind 'cause infrastructure is code, DevOps has been awesome, that's cloud scale, we've seen that. Data as Code is a term we coined but means data's in the developer process. How do you see data being integrated into the workflow for developers in the future? >> Great question. I mean all kinds of ways. Part of the reason that, I kind of alluded to this earlier, building analytical applications, building applications based on data and based on letting people do analysis, how valuable it is and I guess to develop in that context there's kind of two big ways that we sort of see these things getting pushed out. One is developers building apps for other people to use. So think like, I want to build something like Google analytics, I want to build something that clicks my web traffic and then lets the marketing team slice and dice through it and make decisions about how well the marketing's doing. You can build something like that with databases like Druid and products like what we're having in Imply. I guess the other way is things that are actually helping developers do their own job. So kind of like use your own product or use it for yourself. And in this world, you kind of have things like... So going beyond what I think my favorite use case, I'll just talk about one. My favorite use case is so I'm really into performance, I spend the last 10 years of my life working on high performance database so obviously I'm into this kind of stuff. I love when people use our product to help make their own products faster. So this concept of performance monitoring and performance management for applications. One thing that I've seen some of our customers do and some of our users do that I really love is when you kind of take that performance data of your own app, as far as it can possibly go take it to the next level. I think the basic level of using performance data is I collect performance data from my application deployed out there in the world and I can just use it for monitoring. I can say, okay my response times are getting high in this region, maybe there's something wrong with that region. One of the very original use cases for Druid was that Netflix doing performance analysis, performance analysis more exciting than monitoring because you're not just understanding that there's a performance, is good or bad in whatever region sort of getting very fine grain. You're saying in this region, on this server rack for these devices, I'm seeing a degradation or I'm seeing a increase. You can see things like Apple just rolled out a new version of iOS and on that new version of iOS, my app is performing worse than the older version. And even though not many devices are on that new version yet I can kind of see that because I have the ability to get really deep in the data and then I can start slicing nice that more. I can say for those new iOS people, is it all iOS devices? Is it just the iPhone? Is it just the iPad? And that kind of stuff is just one example but it's an example that I really like. >> It's kind of like the data about the data was always good to have context, you're like data analytics for data analytics to see how it's working at scale. This is interesting because now you're bringing up the classic finding the needle in the haystack of needles, so to speak where you have so much data out there like edge cases, edge computing, for instance, you have devices sending data off. There's so much data coming in, the scale is a big issue. This is kind of where you guys seem to be a nice fit for, large scale data ingestion, large scaled data management, large scale data insights kind of all rolled in to one. Is that kind of-? >> Yeah, for sure. One of the things that we knew we had to do with Druid was we were building it for the sort of internet age and so we knew it had to scale well. So the original use case for Druid, the very first one that we ended up building for, the reason we build in the first place is because that original use case had massive scale and we struggled finding something, we were literally trying to do what we see people doing now which is we're trying to build an app on a massive data set and we're struggling to do it. And so we knew it had to scale to massive data sets. And so that's a little flavor of kind know how that works is, like I was mentioning earlier this, this realtime system and historical system, the realtime system is scalable, it's scalable out if you're reading from Kafka, we scale out just like any other Kafka consumer. And then the historical system is all based on what we call segments which are these files that has a few million rows per file. And a cluster is really big, might have thousands of servers, millions of segments, but it's a design that is kind of, it's a design that does scale to these multi-trillion road tables. >> It's interesting, you go back when you probably started, you had Twitter, Netflix, Facebook, I mean a handful of companies that were at the scale. Now, the trend is you're on this wave where those hyperscalers and, or these unique huge scale app companies are now mainstream enterprise. So as you guys roll out the enterprise version of building analytics and applications, which Druid and Imply, they got to going to get religion on this. And I think it's not hard because it's distributed computing which they're used to. So how is that enterprise transition going because I can imagine people would want it and are just kicking the tires or learning and then trying to put it into action. How are you seeing the adoption of the enterprise piece of it? >> The thing that's driving the interest is for sure doing more and more stuff on the internet because anything that happens on the internet whether it's apps or web based, there's more and more happening there and anything that is connected to the internet, anything that's serving customers on the internet, it's going to generate an absolute mountain of data. And the only question is not if you're going to have that much data, you do if you're doing anything on the internet, the only question is what are you going to do with it? So that's I think what drives the interest, is people want to try to get value out of this. And then what drives the actual adoption is I think, I don't want to necessarily talk about specific folks but within every industry I would say there's people that are leaders, there's organizations that are leaders, teams that are leaders, what drives a lot of interest is seeing someone in your own industry that has adopted new technology and has gotten a lot of value out of it. So a big part of what we do at Imply is that identify those leaders, work with them and then you can talk about how it's helped them in their business. And then also I guess the classic enterprise thing, what they're looking for is a sense of stability, a sense of supportability, a sense of robustness and this is something that comes with maturity. I think that the super high tech companies are comfortable using some open source software that's rolled off the presses a few months ago; he big enterprises are looking for something that has corporate backing, they're looking for something that's been around for a while and I think that Druid technologies like it are breaching that little maturity right now. >> It's interesting that supply chain has come up in the software side. That conversation is a lot now, you're hearing about open source being great, but in the cloud scale, you can get the data in there to identify opportunities and also potentially vulnerabilities is big discussion. Question for you on the cloud native side, how do you see cloud native, cloud scale with services like serverless Lambda, edge merging, it's easier to get into the cloud scale. How do you see the enterprise being hardened out with Druid and Imply? >> I think the cloud stuff is great, we love using it to build all of our own stuff, our product is of course built on other cloud technologies and I think these technologies built on each other, you sort of have like I mentioned earlier, all software is built in layers and cloud architecture is the same thing. What we see ourselves as doing is we're building the next layer of that stack. So we're building the analytics database layer. You saw when people first started doing these in public cloud, the very first two services that came out you can get a virtual machine and you can store some data and you can retrieve that data but there's no real analytics on it, there's just kind of storage and retrieval. And then as time goes on higher and higher levels get built out delivering more and more value and then the levels mature as they go up. And so the the bottom of layers are incredibly mature, the top most layers are cutting edge and there's a kind of a maturity gradient between those two. And so what we're doing is we're building out one of those layers. >> Awesome extraction layers, faster performance, great stuff. Final question for you, Gian, what's your vision for the future? How do you Imply and Druid it going? What's it look like five years from now? >> I think that for sure it seems like that there's two big trends that are happening in the world and it's going to sound a little bit self serving for me to say it but I believe what we're doing here says, I'm here 'cause I believe it, I believe in open source and I believe in cloud stuff. That's why I'm really excited that what we're doing is we're building a great cloud product based on a great open source project. I think that's the kind of company that I would want to buy from if I wasn't at this company and I was just building something, I would want to buy a great cloud product that's backed by a great open source project. So I think the kind of the way I see the industry going, the way I see us going and I think would be a great place to end up just kind of as an engineering world, as an industry is a lot of these really great open source projects doing things like what Kubernetes doing containers, we're doing with analytics et cetera. And then really first class really well done cloud versions of each one of them and so you can kind of choose, do you want to get down and dirty with the open source or do you want to choose just kind of have the abstraction of the cloud. >> That's awesome. Cloud scale, cloud flexibility, community getting down and dirty open source, the best of both worlds. Great solution. Goin, thanks for coming on and thanks for sharing here in the Showcase. Thanks for coming on theCUBE. >> Thank you too. >> Okay, this is theCUBE Showcase Season 2, Episode 2. I'm John Furrier, your host. Data as Code is the theme of this episode. Thanks for watching. (upbeat music)

(upbeat music) >> Welcome, everyone, to theCUBE's presentation of the AWS Startup Showcase. And this episode two of season two is called "Data as Code," the ongoing series covering exciting new startups in the AWS ecosystem. I'm John Furrier, your host of theCUBE. Today, we're excited to be joined by Jon Dahl, who is the co-founder and CEO of MUX, a hot new startup building cloud video for developers, video with data. John, great to see you. We did an interview on theCube Conversation. Went into big detail of the awesomeness of your company and the trend that you're on. Welcome back. >> Thank you, glad to be here. >> So, video is everywhere, and video for pivot to video, you hear all these kind of terms in the industry, but now more than ever, video is everywhere and people are building with it, and it's becoming part of the developer experience in applications. So people have to stand up video into their code fast, and data is code, video is data. So you guys are specializing this. Take us through that dynamic. >> Yeah, so video clearly is a growing part of how people are building applications. We see a lot of trends of categories that did not involve video in the past making a major move towards video. I think what Peloton did five years ago to the world of fitness, that was not really a big category. Now video fitness is a huge thing. Video in education, video in business settings, video in a lot of places. I think Marc Andreessen famously said, "Software is eating the world" as a pretty, pretty good indicator of what the internet is actually doing to the economy. I think there's a lot of ways in which video right now is eating software. So categories that we're not video first are becoming video first. And that's what we help with. >> It's not obvious to like most software developers when they think about video, video industries, it's industry shows around video, NAB, others. People know, the video folks know what's going on in video, but when you start to bring it mainstream, it becomes an expectation in the apps. And it's not that easy, it's almost a provision video is hard for a developer 'cause you got to know the full, I guess, stack of video. That's like low level and then kind of just basic high level, just play something. So, in between, this is a media stack kind of dynamic. Can you talk about how hard it is to build video for developers? How is it going to become easier? >> Yeah, I mean, I've lived this story for too long, maybe 13 years now, when I first build my first video stack. And, you know, I'll sometimes say, I think it's kind of a miracle every time a video plays on the internet because the internet is not a medium designed for video. It's been hijacked by video, video is 70% of internet traffic today in an unreliable, sort of untrusted network space, which is totally different than how television used to work or cable or things like that. So yeah, so video is hard because there's so many problems from top to bottom that need to be solved to make video work. So you have to worry about video compression encoding, which is a complicated topic in itself. You have to worry about delivering video around the world at scale, delivering it at low cost, at low latency, with good performance, you have to worry about devices and how every device, Android, iOS, web, TVs, every device handles video differently and so there's a lot of work there. And at the end of the day, these are kind of unofficial standards that everyone's using. So one of the miracles is like, if you want to watch a video, somehow you have to get like Apple and Google to agree on things, which is not always easy. And so there's just so many layers of complexity that are behind it. I think one way to think about it is, if you want to put an image online, you just put an image online. And if you want to put video online, you build complex software, and that's the exact problem that MUX was started to help solve. >> It's interesting you guys have almost creating a whole new category around video infrastructure. And as you look at, you mentioned stack, video stack. I'm looking at a market where the notion of a media stack is developing, and you're seeing these verticals having similar dynamics with cloud. And if you go back to the early days of cloud computing, what was the developer experience or entrepreneurial experience, you had to actually do a lot of stuff before you even do anything, provision a server. And this has all kind of been covered in great detail in the glory of Agile and whatnot. It was expensive, and you had that actually engineer before you could even stand up any code. Now you got video that same thing's happening. So the developers have two choices, go do a bunch of stuff complex, building their own infrastructure, which is like building a data center, or lean in on MUX and say, "Hey, thank you for doing all that years of experience building out the stacks to take that hard part away," but using APIs that they have. This is a developer focused problem that you guys are solving. >> Yeah, that's right. my last company was a company called Zencoder, that was an API to video encoding. So it was kind of an API to a small part of what MUX does today, just one of those problems. And I think the thing that we got right at Zencoder, that we're doing again here at MUX, was building four developers first. So our number one persona is a software developer. Not necessarily a video expert, just we think any developer should be able to build with video. It shouldn't be like, yeah, got to go be a specialist to use this technology, because it should become just of the internet. Video should just be something that any developer can work with. So yeah, so we build for developers first, which means we spend a lot of time thinking about API design, we spend a lot of time thinking about documentation, transparent pricing, the right features, great support and all those kind of things that tend to be characteristics of good developer companies. >> Tell me about the pipe lining of the products. I'm a developer, I work for a company, my boss is putting pressure on me. We need video, we have all this library, it's all stacking up. We hired some people, they left. Where's the video, we've stored it somewhere. I mean, it's a nightmare, right? So I'm like, okay, I'm cloud native, I got an API. I need to get my product to market fast, 'cause that is what Agile developers want. So how do you describe that acceleration for time to market? You mentioned you guys are API first, video first. How do these customers get their product into the market as fast as possible? >> Yeah, well, I mean the first thing we do is we put what we think is probably on average, three to four months of hard engineering work behind a single API call. So if you want to build a video platform, we tell our customers like, "Hey, you can do that." You probably need a team, you probably need video experts on your team so hire them or train them. And then it takes several months just to kind of to get video flowing. One API call at MUX gives you on-demand video or live video that works at scale, works around the world with good performance, good reliability, a rich feature set. So maybe just a couple specific examples, we worked with Robin Hood a few years ago to bring video into their newsfeed, which was hugely successful for them. And they went from talking to us for the first time to a big launch in, I think it was three months, but the actual code time there was like really short. I want to say they had like a proof of concept up and running in a couple days, and then the full launch in three months. Another customer of ours, Bandcamp, I think switched from a legacy provider to MUX in two weeks in band. So one of the big advantages of going a little bit higher in the abstraction layer than just building it yourself is that time to market. >> Talk about this notion of video pipeline 'cause I know I've heard people I talk about, "Hey, I just want to get my product out there. I don't want to get stuck in the weeds on video pipeline." What does that mean for folks that aren't understanding the nuances of video? >> Yeah, I mean, it's all the steps that it takes to publish video. So from ingesting the video, if it's live video from making sure that you have secure, reliable ingest of that live feed potentially around the world to the transcoding, which is we talked a little bit about, but it is a, you know, on its own is a massively complicated problem. And doing that, well, doing that well is hard. Part of the reason it's hard is you really have to know where you're publishing too. And you might want to transcode video differently for different devices, for different types of content. You know, the pipeline typically would also include all of the workflow items you want to do with the video. You want to thumbnail a video, you want clip, create clips of the video, maybe you want to restream the video to Facebook or Twitter or a social platform. You want to archive the video, you want it to be available for downloads after an event. If it's just a, if it's a VOD upload, if it's not live in the first place. You have all those things and you might want to do simulated live with the video. You might want to actually record something and then play it back as a live stream. So, the pipeline Ty typically refers to everything from the ingest of the video to the time that the bits are delivered to a device. >> You know, I hear a lot of people talking about video these days, whether it's events, training, just want peer to peer experience, video is powerful, but customers want to own their own platform, right? They want to have the infrastructure as a service. They kind of want platform as a service, this is cloud talk now, but they want to have their own capability to build it out. This allows them to get what they want. And so you see this, like, is it SaaS? Is it platform? People want customization? So kind of the general purpose video solution does it really exist or doesn't? I mean, 'cause this is the question. Can I just buy software and work or is it going to be customized always? How do you see that? Because this becomes a huge discussion point. Is it a SaaS product or someone's going to make a SaaS product? >> Yeah, so I think one of the most important elements of designing any software, but especially when you get into infrastructure is choosing an abstraction level. So if you think of computing, you can go all the way down to building a data center, you can go all the way down to getting a colo and racking a server like maybe some of us used to do, who are older than others. And that's one way to run a server. On the other extreme, you have just think of the early days of cloud competing, you had app engine, which was a really fantastic, really incredible product. It was one push deploy of, I think Python code, if I remember correctly, and everything just worked. But right in the middle of those, you had EC2, which was, EC2 is basically an API to a server. And it turns out that that abstraction level, not Colo, not the full app engine kind of platform, but the API to virtual server was the right abstraction level for maybe the last 15 years. Maybe now some of the higher level application platforms are doing really well, maybe the needs will shift. But I think that's a little bit of how we think about video. What developers want is an API to video. They don't want an API to the building blocks of video, an API to transcoding, to video storage, to edge caching. They want an API to video. On the other extreme, they don't want a big application that's a drop in white label video in a box like a Shopify kind of thing. Shopify is great, but developers don't want to build on top of Shopify. In the payments world developers want Stripe. And that abstraction level of the API to the actual thing you're getting tends to be the abstraction level that developers want to build on. And the reason for that is, it's the most productive layer to build on. You get maximum flexibility and also maximum velocity when you have that API directly to a function like video. So, we like to tell our customers like you, you own your video when you build on top of MUX, you have full control over everything, how it's stored, when it's stored, where it goes, how it's published, we handle all of the hard technology and we give our customers all of the flexibility in terms of designing their products. >> I want to get back some use case, but you brought that up I might as well just jump to my next point. I'd like you to come back and circle back on some references 'cause I know you have some. You said building on infrastructure that you own, this is a fundamental cloud concept. You mentioned API to a server for the nerds out there that know that that's cool, but the people who aren't super nerdy, that means you're basically got an interface into a server behind the scenes. You're doing the same for video. So, that is a big thing around building services. So what wide range of services can we expect beyond MUX? If I'm going to have an API to video, what could I do possibly? >> What sort of experience could you build? >> Yes, I got a team of developers saying I'm all in API to video, I don't want to do all that transit got straight there, I want to build experiences, video experiences on my app. >> Yeah, I mean, I think, one way to think about it is that, what's the range of key use cases that people do with video? We tend to think about six at MUX, one is kind of the places where the content is, the prop. So one of the things that use video is you can create great video. Think of online courses or fitness or entertainment or news or things like that. That's kind of the first thing everyone thinks of, when you think video, you think Netflix, and that's great. But we see a lot of really interesting uses of video in the world of social media. So customers of ours like Visco, which is an incredible photo sharing application, really for photographers who really care about the craft. And they were able to bring video in and bring that same kind of Visco experience to video using MUX. We think about B2B tools, videos. When you think about it, all video is, is a high bandwidth way of communicating. And so customers are as like HubSpot use video for the marketing platform, for business collaboration, you'll see a lot of growth of video in terms of helping businesses engage their customers or engage with their employees. We see live events obviously have been a massive category over the last few years. You know, we were all forced into a world where we had to do live events two years ago, but I think now we're reemerging into a world where the online part of a conference will be just as important as the in-person component of a conference. So that's another big use case we see. >> Well, full disclosure, if you're watching this live right now, it's being powered by MUX. So shout out, we use MUX on theCUBE platform that you're experiencing in this. Actually in real time, 'cause this is one application, there's many more. So video as code, is data as code is the theme, that's going to bring up the data ops. Video also is code because (laughs) it's just like you said, it's just communicating, but it gets converted to data. So data ops, video ops could be its own new category. What's your reaction to that? >> Yeah, I mean, I think, I have a couple thoughts on that. The first thought is, video is a way that, because the way that companies interact with customers or users, it's really important to have good monitoring and analytics of your video. And so the first product we ever built was actually a product called MUX video, sorry, MUX data, which is the best way to monitor a video platform at scale. So we work with a lot of the big broadcasters, we work with like CBS and Fox Sports and Discovery. We work with big tech companies like Reddit and Vimeo to help them monitor their video. And you just get a huge amount of insight when you look at robust analytics about video delivery that you can use to optimize performance, to make sure that streaming works well globally, especially in hard to reach places or on every device. That's we actually build a MUX data platform first because when we started MUX, we spent time with some of our friends at companies like YouTube and Netflix, and got to know how they use data to power their video platforms. And they do really sophisticated things with data to ensure that their streams well, and we wanted to build the product that would help everyone else do that. So, that's one use. I think the other obvious use is just really understanding what people are doing with their video, who's watching what, what's engaging, those kind of things. >> Yeah, data is definitely there. You guys mentioned some great brands that are working with you guys, and they're doing it because of the developer experience. And I'd like you to explain, if you don't mind, in your words, why is the MUX developer experience so good? What are some of the results you're seeing from your customers? What are they saying to you? Obviously when you win, you get good feedback. What are some of the things that they're saying and what specific develop experiences do they like the best? >> Yeah, I mean, I think that the most gratifying thing about being a startup founder is when your customers like what you're doing. And so we get a lot of this, but it's always, we always pay attention to what customers say. But yeah, people, the number one thing developers say when they think about MUX is that the developer experience is great. I think when they say that, what they mean is two things, first is it's easy to work with, which helps them move faster, software velocity is so important. Every company in the world is investing and wants to move quickly and to build quickly. And so if you can help a team speed up, that's massively valuable. The second thing I think when people like our developer experience is, you know, in a lot of ways that think that we get out of the way and we let them do what they want to do. So well, designed APIs are a key part of that, coming back to abstraction, making sure that you're not forcing customers into decisions that they actually want to make themselves. Like, if our video player only had one design, that that would not be, that would not work for most developers, 'cause developers want to bring their own design and style and workflow and feel to their video. And so, yeah, so I think the way we do that is just think comprehensively about how APIs are designed, think about the workflows that users are trying to accomplish with video, and make sure that we have the right APIs, make sure they're the right information, we have the right webhooks, we have the right SDKs, all of those things in place so that they can build what they want. >> We were just having a conversation on theCUBE, Dave Vellante and I, and our team, and I'd love to get you a reaction to this. And it's more and more, a riff real quick. We're seeing a trend where video as code, data as code, media stack, where you're starting to see the emergence of the media developer, where the application of media looks a lot like kind of software developer, where the app, media as an app. It could be a chat, it could be a peer to peer video, it could be part of an event platform, but with all the recent advances, in UX designers, coders, the front end looks like an emergence of these creators that are essentially media developers for all intent and purpose, they're coding media. What's your reaction to that? How do you see that evolving? >> I think the. >> Or do you agree with it? >> It's okay. >> Yeah, yeah. >> Well, I think a couple things. I think one thing, I think this goes along through saying, but maybe it's disagreement, is that we don't think you should have to be an expert at video or at media to create and produce or create and publish good video, good audio, good images, those kind of things. And so, you know, I think if you look at software overall, I think of 10 years ago, the kind of DevOps movement, where there was kind of a movement away from specialization in software where the same software developer could build and deploy the same software developer maybe could do front end and back end. And we want to bring that to video as well. So you don't have to be a specialist to do it. On the other hand, I do think that investments and tooling, all the way from video creation, which is not our world, but there's a lot of amazing companies out there that are making it easier to produce video, to shoot video, to edit, a lot of interesting innovations there all the way to what we do, which is helping people stream and publish video and video experiences. You know, I think another way about it is, that tool set and companies doing that let anyone be a media developer, which I think is important. >> It's like DevOps turning into low-code, no-code, eventually it's just composability almost like just, you know, "Hey Siri, give me some video." That kind of thing. Final question for you why I got you here, at the end of the day, the decision between a lot of people's build versus buy, "I got to get a developer. Why not just roll my own?" You mentioned data center, "I want to build a data center." So why MUX versus do it yourself? >> Yeah, I mean, part of the reason we started this company is we have a pretty, pretty strong opinion on this. When you think about it, when we started MUX five years ago, six years ago, if you were a developer and you wanted to accept credit cards, if you wanted to bring payment processing into your application, you didn't go build a payment gateway. You just probably used Stripe. And if you wanted to send text messages, you didn't build your own SMS gateway, you probably used Twilio. But if you were a developer and you wanted to stream video, you built your own video gateway, you built your own video application, which was really complex. Like we talked about, you know, probably three, four months of work to get something basic up and running, probably not live video that's probably only on demand video at that point. And you get no benefit by doing it yourself. You're no better than anyone else because you rolled your own video stack. What you get is risk that you might not do a good job, maybe you do worse than your competitors, and you also get distraction where you've just taken, you take 10 engineers and 10 sprints and you apply it to a problem that doesn't actually really give you differentiated value to your users. So we started MUX so that people would not have to do that. It's fine if you want to build your own video platform, once you get to a certain scale, if you can afford a dozen engineers for a VOD platform and you have some really massively differentiated use case, you know, maybe, live is, I don't know, I don't have the rule of thumb, live videos maybe five times harder than on demand video to work with. But you know, in general, like there's such a shortage of software engineers today and software engineers have, frankly, are in such high demand. Like you see what happens in the marketplace and the hiring markets, how competitive it is. You need to use your software team where they're maximally effective, and where they're maximally effective is building differentiation into your products for your customers. And video is just not that, like very few companies actually differentiate on their video technology. So we want to be that team for everyone else. We're 200 people building the absolute best video infrastructure as APIs for developers and making that available to everyone else. >> John, great to have you on with the showcase, love the company, love what you guys do. Video as code, data as code, great stuff. Final plug for the company, for the developers out there and prospects watching for MUX, why should they go to MUX? What are you guys up to? What's the big benefit? >> I mean, first, just check us out. Try try our APIs, read our docs, talk to our support team. We put a lot of work into making our platform the best, you know, as you dig deeper, I think you'd be looking at the performance around, the global performance of what we do, looking at our analytics stack and the insight you get into video streaming. We have an emerging open source video player that's really exciting, and I think is going to be the direction that open source players go for the next decade. And then, you know, we're a quickly growing team. We're 60 people at the beginning of last year. You know, we're one 50 at the beginning of this year, and we're going to a add, we're going to grow really quickly again this year. And this whole team is dedicated to building the best video structure for developers. >> Great job, Jon. Thank you so much for spending the time sharing the story of MUX here on the show, Amazon Startup Showcase season two, episode two, thanks so much. >> Thank you, John. >> Okay, I'm John Furrier, your host of theCUBE. This is season two, episode two, the ongoing series cover the most exciting startups from the AWS Cloud Ecosystem. Talking data analytics here, video cloud, video as a service, video infrastructure, video APIs, hottest thing going on right now, and you're watching it live here on theCUBE. Thanks for watching. (upbeat music)