>>Hey everyone. Welcome back to Miami. Lisa Martin here live with the Cube at IFS Unleashed 2022. We're so excited to be here. We just had a great conversation with Ifss, CEO of Darren Rouse. Now we've got another exciting conversation. F1 is here. You know how much I love f1. Christian Peterson joins us as well, the Chief Product Officer at ifs, and Sean Edwards IT business partner at Aston Martin. F1. Guys, it's great to have you on the program. Thank you for having >>Us. Thank you >>Very much. We were talking about F one. We probably could have an entire conversation just on that, but Christian, I wanna talk with you. It's been three years since the Cube has covered ifs obviously for obvious reasons during that time. So much momentum has happened. IFS cloud was launched about 18 months ago. Give our audience an o, a flavor of IFS, cloud and some of the milestones that you've hit in such a short time period. >>Yeah, I mean IFS cloud is really transformational in many ways. It's transformational for first and foremost for our customers in what enables them to do, but also transformational for us from a technology perspective, how we work and how we do everything. And at the end of the day, it has really surfaced, served around the the, the fact of what we need to do for our customers. And what we saw our customers often do back then, or any company, was they were out looking for EAP solutions or FSM Solutions or EAM Solutions or what have you. And then they were trying to stitch it all together and we, we said like, Hang on a second, these these traditional software s, those are some that I'm guilty. You know, there's some that we actually invented over the years together with analysts. So we invented EER P and we invented CRM and EAM and all these different things. >>But at the end of the day, customers really want a solution to what they are, they are what they're dealing with. And so in these conversations it became very clear that and very repeated conclusions from the conversations that customers wanted something that could manage and help them optimize the use of their assets. Regardless of what industry you're in, assets is such a key component. Either you are using your assets or you're producing assets. Second thing is really get the best use of of your people, your teams and your crew. How do you get the right people on the right job at the same time? How do you assemble the right crew with the right set of skills in the crew? Get them to the right people at the same time. So, and then the final thing is of course customers, you know all the things that you need to do to get customers to answer these ultimate questions, Will you buy from this company again? And they should say yes. That's the ultimate results of moments of service. So that's how we bring it all together and that's what we have been fast at work at. That's what IFS cloud is all about. >>And you, you talked about IFS cloud, being able to to help customers, orchestrate assets, people, customers, Aston Martin being one of those customers. Shawn, you came from ifs so you have kind of the backstory but just give the audience a little bit of, of flavor of your role at Aston Martin and then let's dig into the smart factory. >>Sure. So I previously worked at IFS as a manufacturing consultant. So my bread and butter is production planning in the ERP sector. So we, I Aston Martin didn't have an ERP system pre IFS or a legacy system that wasn't working for them and the team couldn't rely upon it. So what we did was bring IFS in. I was the consultant there and as IFS always preached customer first, well customer first did come and I jumped to support the team. So we've implemented a fully RP solution to manage the production control and the material traceability all the way through from design until delivery to track. And we've mo most recently implemented a warehouse solution at Trackside as well. So we are now tracking our parts going out with the garage. So that's a really exciting time for RFS. In terms of the smart factory, it's not built yet. >>We're we're supposed to move next year. So that's really exciting cause we're quadrupling our footprint. So going from quite a small factory spread out across the North Hampton Share countryside, we're going into one place quadruple in our footprint. And what we're gonna start looking at is using the technology we're implementing there. So enabling 5G to springboard our IFFs implementations going forward with the likes of Internet of things to connect our 15 brand new CMC machines, but also things like R F I D. So that comes with its own challenges on a Formula One car, but it's all about speed of data capture, single point of truth. And IFFs provides that >>And well, Formula One, the first word that comes to mind is speed. >>Absolutely. Second >>Word is crazy. >>We, we are very unique in terms of most customers Christian deals with, they're about speed but also about profit and efficiency. That doesn't matter to us. It is all about time. Time is our currency and if we go quicker in designing and manufacturing, which ifs supports ultimately the cargo quicker. So speed is everything. >>And and if we, if we think of of people, customers and assets at Asset Martin F one, I can't, I can't imagine the quantity of assets that you're building every race weekend and refactoring. >>Absolutely. So a Formula one car that drives out of the garage is made up of 13,000 car parts, most of which, 50% of which we've made in house. So we have to track that all the way through from the smallest metallic component all the way up to the most complex assembly. So orchestrating that and having a single point of truth for people to look at and track is what IFFs has provided us. >>Christian, elaborate on that a little bit in terms of, I mean, what you're facilitating, F1 is such a great example of of speed we talked about, but the fact that you're setting up the car every, every other weekend maybe sometimes back to back weeks, so many massive changes going on. You mentioned 50% of those 13,000 parts you manufacture. Absolutely. Talk about IFS as being a catalyst for that. >>I mean the, it's, it's fascinating with Formula One, but because as a technology geek like me, it's really just any other business on steroids. I mean we talk, we talk about this absolutely high tech, super high tech manufacturing, but even, even before that, the design that goes in with CFDs and how you optimize for different things and loose simulation software for these things goes into manufacturing, goes into wind tunnels and then goes on track. But guess what, when it's on track, it's an asset. It's an asset that streams from how many sensors are on the car, >>I think it's over 10,000 >>Sensors, over 10,000 sensors that streams maybe at 50 hertz or 50 readings. So every lap you just get this mountain of data, which is really iot. So I always say like F one if one did IOT before anybody invented the term. >>Absolutely. >>Yep. You know, F1 did machine learning and AI before anybody thought about it in terms of pattern recognition and things like that with the data. So that's why it's fascinating to work with an organization like that. It's the, it's the sophistication around the technologies and then the pace what they do. It's not that what they do is actually so different. >>It is, it absolutely isn't. We just have to do it really quickly. Really >>Quickly. Right. And the same thing when you talk about parts. I mean I was fascinated of a conversation with, with one of your designers that says that, you know, sometimes we are, we are designing a part and this, the car is now ready for production but the previous version of that part has not even been deployed on the car yet. So that's how quick the innovation comes through and it's, it's, it's fascinating and that's why we like the challenge that Esther Martin gives us because if we can, if we can address that, there's a lot of businesses we can make happy with that as far, >>So Sha I talk a little bit about this is, so we're coming up, there's what four races left in the 2022 season, but this is your busy time because that new car, the 23 car needs to be debuted in what February? So just a few months time? >>Absolutely. So it's a bit cancer intuitive. So our busiest time is now we're ramping up into it. So we co, we go into something called car build which is from December to December to February, which is our end point and there's no move in that point. The car has gotta go around that track in February. So we have got to make those 13,000 components. We've gotta design 'em, we've gotta make 'em and then we've gotta get 'em to the car in February for our moment of service. They said it on stage. Our moment of service as a manufacturing company is that car going around the track and we have to do it 24 times next year and we've gotta start. Well otherwise we're not gonna keep up. >>I'm just gonna ask you what a, what a moment, what's a moment of service in f1 and you're saying basically getting that >>Functional car >>On the track quickly, as quickly as possible and being able to have the technology underpinning that's really abstracting the complexity. >>Absolutely. So I would say our customer ultimately is the driver and the fans they, they need to have a fast car so they can sport it and they ultimately drive it around the track and go get first place and be competitive. So that is our moment of service to our drivers is to deliver that car 24 times next year. >>I imagine they might be a little demanding >>They are and I think it's gonna be exciting with Alonzo coming in, could the driver if we've gotta manage that change and he'll have new things that he wants to try out on a car. So adds another level of complexity to that. >>Well how influential are the drivers in terms some of the, the manufacturing? Like did they, are they give me kind of a a sense of how Alon Fernando Alanzo your team and ifs maybe collaborate, maybe not directly but >>So Alonzo will come in and suggest that he wants cars to work a certain way so he will feed back to the team in terms of we need this car, we need this car part to do this and this car part to do that. So then we're in a cycle when he first gets into the car in that February, we've then gotta turnaround car parts based off his suggestions. So we need to do that again really quickly and that's where IFS feeds in. So we have to have the release and then the manufacturer of the component completely integrated and that's what we achieve with IFFs and >>It needs to be really seamless. >>Absolutely. If, if we don't get it right, that car doesn't go out track so there's no moving deadline. >>Right. That's the probably one of the industries where deadlines do not move. Absolutely. We're so used to things happening in tech where things shift and change and reorgs, but this is one where the dates are set in their firm. >>Absolutely. And we have to do anything we can do to get that car on the track. So yeah, it's just a move. >>Christian, talk about the partnership a little bit from your standpoint in terms of how influential has Aston Martin F1 been in IFS cloud and its first 18 months. I was looking at some stats that you've already gotten 400,000 plus users in just a short time period. How influential are your customers in the direction and even the the next launch 22 R too? >>I mean our customers do everything plain and simple. That's that's what it is. And we have, we have a partnership, I think about every single customer as a partner of ours and we are partnering in taking technology to the next level in terms of, of the outputs and the benefits it can create for our customers. That's what it's all, all about. And I, I always think about these, these three elements I think I mentioned in our state as well. I think the partnership we have is a partnership around innovation. Innovation doesn't not only come from IFS or the technology partner, it comes from discussions, requirements, opportunities, what if like all these things. So innovation comes from everywhere. There's technology driven innovation, there's customer driven innovation, but that's part of the partnership. The second part of the partnership is inspiration. So with innovation you inspire. So when you innovate on something new that inspires new innovation and new thinking and that's again the second part of the partnership. And then the third part is really iterate and execute, right? Because it's great that we can now innovate and we can agree on what we need to do, but now we need to put it into products, put it in technology and put it into actual use. That's when the benefits comes and that's when we can start bringing the bell. >>And I think it's really intrinsically linked. I mean if you look at progress with Formula One teams and their innovation, it's all underpinned by our technology partners and that's why it's so important. The likes of Christian pushes the product and improves it and innovates it because then we can realize the benefits and ultimately save time and go faster. So it's really important that our, our partners and certainly inform one, push the boundaries and find that technology. >>And I think one of the things that we also find very, very important is that we actually understand our customers and can talk the language. So I think that was one of the key things in our engagement, Martin from the beginning is that we had a set of people that really understand Formula One felt it on their bodies and can have the conversation. So when the Formula One teams they say something, then we actually understand what we're talking about. So for instance, when we talk about, you know, track side inventory, well it's not that different from what a field service technician have in his van when he goes service. The only difference is when you see something happening on track, you'll see the parts manager go out to the pit lane with a tablet and say like, oh we need this, we need that, we need this and we need that. And then we'll go back and pick it and put it on the car and the car is service and maintain and off go. Absolutely. >>Yeah that speed always impresses me. >>It's unbelievable. >>Shannon, last question for you. From a smart factory perspective, you said you're moving in next year. What are some of the things that you are excited about that you think are really gonna be transformative but IFS is doing? >>So I think what I'm really excited about once we get in is using the technology they've already put in terms of 5G networks to sort of springboard that into a further IFS implementation. Maybe IFFs cloud in terms of we always struggle to keep the system up to date with, with what's physically happening so that the less data entry and the more automatic sort of data capture, the better it is for the formula on team cuz we improve our our single point of truth. So I'm really excited to look at the internet of things and sort of integrate our CNC machines to sort of feed that information back into ifs. But also the RFID technology I think is gonna be a game changer when we go into the new factory. So really >>Excited. Excellent. Well well done this year. We look forward to seeing Alonso join the team in 23. Fingers >>Crossed. >>Okay. Fingers crossed. Christian, Jeanette, it's been a pleasure to have you on the program. Thank you so much for sharing your insights and how ifs asked Martin are working together, how you really synergistically working together. We appreciate your time. >>Thank you very much for having us. Our >>Thanks for having us. And go Aston >>Woo go Aston, you already here first Lisa Martin, no relation to Aston Martin, but well, I wanna thank Christian Peterson and Shannon Edwards for joining me, talking about IFS and Aston Martin team and what they're doing at Speed and Scale. Stick around my next guest joins me in a minute. >>Thank you.

>>Welcome to this Q conversation. I'm Dave Nicholson. And today we are joined by Andrew ish and Chris Y Moran, both from Gentech. Andrew is the vice president of marketing. Chris John is the, uh, vice president of product engineering, gentlemen, welcome to the cube. >>Welcome David. Thanks for having us. Hey, >>David, thanks for having us on your show. >>Absolutely. Give us just, let's start out by, uh, giving us some background on, on Gentech. How would you describe to a relative coming over and asking you what you do for a living? What Genotech does? >>Well, I'll take a shot at that. I'm the marketing guy, David, but, uh, I think the best way to think of Genotech first and foremost is a software company. We, uh, we do a really good job of bringing together all of that physical security sensor network onto a platform. So people can make sense out of the data that comes from video surveillance, cameras, access control, reads, license plate recognition, cameras, and from a whole host of different sensors that can live out there in the world. Temperature, sensors, microwaves, all sorts of stuff. So we're a company that's really good at making sense of complex data from sensors. That's kind of, I think that's kind of what we >>Do and, and, and we focus specifically on like larger, complex, critical infrastructure type projects, whether they be airports, uh, large enterprise campuses and whatnot. So we're not necessarily your well known consumer type brand. >>So you mentioned physical, you mentioned physical security. Um, what about the intersection between physical security and, and cyber security who are, who are the folks that you work with directly as customers and where do they, where do they sit in that spectrum of cyber versus physical? >>So we predominantly work with physical security professionals and, uh, they typically are responsible for the security of a facility, a campus, a certain area. And we'll talk about security cameras. We'll talk about access control devices with card readers and, and, and locks, uh, intrusion detection, systems, fences, and whatnot. So anything that you would see that physically protects a facility. And, uh, what's actually quite interesting is that, you know, cybersecurity, we, we hear about cybersecurity and depressed all the time, right. And who's been hacked this week is typically like, uh, a headline that we're all like looking at, uh, we're looking for in the news. Um, so we actually do quite a lot of, I would say education work with the physical security professional as it pertains to the importance of cyber security in the physical security system, which in and of itself is an information system. Right. Um, so you don't wanna put a system in place to protect your facility that is full of cybersecurity holes because at that point, you know, your physical security systems becomes, uh, your weakest link in your security chain. Uh, the way I like to say it is, you know, there's no such thing as physical security versus cyber security, it's just security. Uh, really just the concept or a context of what threat vectors does this specific control or mechanism actually protects against >>Those seem to be words to live by, but are, are they aspirational? I mean, do you, do you see gaps today, uh, between the worlds of cyber and physical security? >>I mean, for sure, right? Like we, physical security evolved from a different part of the enterprise, uh, structure then did it or cyber security. So they, they come at things from a different angle. Um, so, you know, for a long time, the two worlds didn't really meet. Uh, but now what we're seeing, I would say in the last 10 years, Christian, about that, there's a huge convergence of cyber security with physical security. It, so information technology with operation technology really coming together quite tightly in the industry. And I think leading companies and sophisticated CISOs are really giving a big pitcher thought to what's going on across the organization, not just in cybersecurity. >>Yeah. I think we've come a long way from CCTV, which stands for closed circuit television, uh, which was typically like literally separated from the rest of the organization, often managed by the facilities, uh, part of any organization. Uh, and now we're seeing more and more organizations where this is converging together, but there's still ways to go, uh, to get this proper convergence in place. But, you know, we're getting there. >>How, how does Gentech approach its addressable market? Is this, is this a direct model? Uh, do you work with partners? What, what does that look like in your world? >>Well, we're a, we're a partner led company Gentech, you know, model on many friends is all about our partners. So we go to market through our integration channel. So we work with really great integrators all around the world. Um, and they bring together our software platform, which is usually forms the nucleus of sort of any O T security network. Uh, they bring that together with all sorts of other things, such as the sensor network, the cabling, all of that. It's a very complex multiplayer world. And also in that, you know, partnership ecosystem and Christian, this is more your world. We have to build deep integrations with all of these companies that build sensors, whether that's access, Bosch, Canon, uh, Hanoi, you know, we're, we're really working with them them. And of course with our storage and server partners >>Like Dell >>Mm-hmm <affirmative>. Yeah. So we have, we have like hundreds of, I would say ecosystem partners, right? Camera manufacturers, uh, access control reader, controller manufacturers, intrusion detection, manufacturers, late LIDAR radar, you know, the list goes on and on and on. And, and basically we bring this all together. The system integrator really is going to pick best of breed based on a specific end customer's I would say requirements and then roll out the system. According >>That's very interesting, you know, at, at Silicon angle on the cube, um, we've initiated coverage of this subject of the question, does hardware still matter? And, and you know, of course we're, we're approaching that primarily from kind of the traditional it, uh, perspective, but you said at the outset, you you're a software company mm-hmm <affirmative>, but clearly correct me if I'm wrong, your software depends upon all of these hardware components and as they improve, I imagine you can do things that maybe you couldn't do before those improvements. The first thing that comes to mind is just camera resolution. Um, you know, sort of default today is 4k, uh, go back five years, 10 years. I imagine that some of the sophisticated things that you can do today weren't possible because the hardware was lagging. Is that, is that a, is that a fair assessment? >>Oh, that's a fair assessment. Just going back 20 years ago. Uh, just VGA resolution on a security camera was like out of this world resolution, uh, even more so if it was like full motion, 30 images per second. So you typically have like, probably even like three 20 by 2 44 images per second, like really lousy resolution, just from a resolution perspective, the, the imagery sensors have, have really increased in terms of what they can provide, but even more so is the horsepower of these devices. Mm-hmm, <affirmative> now it's not uncommon to have, uh, pretty, pretty powerful Silicon in those devices now that can actually run machine learning models and you can actually do computer vision and analytics straight into the device. Uh, as you know, in some of the initial years, you would actually run this on kind of racks of servers in this data center. >>Now you can actually distribute those workloads across on the edge. And what we're seeing is, you know, the power that the edge provides is us as a software company, we have the opportunity to actually bring our workloads where it makes most sense. And in some cases we'll actually also have a ground station kind of in between the sensors and potentially the cloud, uh, because the use case just, uh, calls for it. Uh, just looking from a, from a, from a video security perspective, you know, when you have hundreds or thousands of cameras on an airport, it's just not economical or not even feasible in some cases to bring all that footage to the cloud even more so when 99% of that footage is never watched by anybody. So what's the point. Uh, so you just wanna provide the clips that, that actually do matter to the cloud and for longer term retention, you also want to be able to have sometimes more resilient systems, right? So what happens if the cloud disconnects, you can stop the operations of that airport or stop that operations of that, of that prison, right? It needs to continue to operate and therefore you need higher levels of resiliency. So you do need that hardware. So it's really a question of what it calls for and having the right size type of hardware so that you don't overly complexify the installation, uh, and, and actually get the job done. Are >>You comparing airports to prisons >>Christian? Well, nowadays they're pretty much prepared <laugh>, >>But I mean, this is exactly it, David, but I mean, this payload, especially from the video surveillance, like the, the workload that's going through to the, these ground stations really demands flexible deployment, right? So like we think about it as edge to cloud and, uh, you know, that's, what's really getting us excited because it, it gives so much more flexibility to the, you know, the C I S O and security professionals in places like prisons, airports, also large scale retail and banking, and, uh, other places, >>Universities, the list goes on and on and on, and >>On the flexibility of deployment just becomes so much easier because these are lightweight, you usually word deploying on a Linux box and it can connect seamlessly with like large scale head end storage or directly to, uh, cloud providers. It's, it's really a sophisticated new way of looking at how you architect out these networks. >>You've just given, you've just given a textbook example of why, uh, folks in the it world have been talking about hybrid cloud for, for, for such a long time, and some have scoffed at the idea, but you just, you just present a perfect use case for that combination of leveraging cloud with, uh, on-premises hardware and tracking with hardware advances, um, uh, on, on the subject of camera resolution. I don't know if you've seen this meme, but there's a great one with the, the first deep field image from the, from the, I was gonna say humble, the James web space telescope, uh, in contrast with a security camera F photo, which is really blurry of someone in your driveway <laugh>, uh, which is, which is, uh, sort of funny. The reality though, is I've seen some of these latest generation security cameras, uh, you know, beyond 4k resolution. And it's amazing just, you know, the kind of detail that you can get into, but talk about what what's, what's exciting in your world. What's, what's Gentech doing, you know, over the next, uh, several quarters that's, uh, particularly interesting what's on the leading edge of your, of your world. >>Well, I think right now what's on the leading edges is being driven by our end users. So the, so the, the companies, the governments, the organizations that are implementing our software into these complex IOT networks, they wanna do more with that data, right? It's not just about, you know, monitoring surveillance. It's not just about opening and closing doors or reading license plates, but more and more we're seeing organizations taking this bigger picture view of the data that is generated in their organizations and how they can take value out of existing investments that they've made in sensor networks, uh, and to take greater insight into operations, whether that can be asset utilization, customer service efficiency, it becomes about way more than just, you know, either physical security or cyber security. It becomes really an enterprise shaping O T network. And to us, that is like a massive, massive opportunity, uh, in the, in the industry today. >>Yeah. >>Now you're you're you're oh, go ahead. I'm sorry, Christian, go ahead. Yeah, >>No, it's, it's, it's good. But, you know, going back to a comment that I mentioned earlier about how it was initially siloed and now, you know, we're kind of discovering this diamond in the rough, in terms of all these sensors that are out there, which a lot of organizations didn't even know existed or didn't even know they had. And how can you bring that on kind of across the organizations for non-security related applications? So that's kind of one very interesting kind of, uh, direction that we're, that we've been undergoing for the last few years, and then, you know, security, uh, and physical security for that matter often is kind of the bastard step child. Doesn't get all the budget and, you know, there's lots of opportunities for, to help them increase and improve their operations, uh, as, as Andrew pointed out and really help bringing them into the 21st century. >>Yeah. >>And you're, you're headquartered in Montreal, correct? >>Yes. >>Yeah. So, so the reason, the reason why that's interesting is because, um, and, you know, correct me if I'm, if I'm off base here, but, but you're sort of the bridge between north America and Europe. Uh, and, and, uh, and so you sit at that nexus where, uh, you probably have more of an awareness of, uh, trends in security, which overlap with issues of privacy. Yeah. Where Europe has led in a lot of cases. Um, some of those European like rules are coming to north America. Um, is there anything in your world that is particularly relevant or that concerns you about north America catching up, um, or, or do those worlds of privacy and security not overlap as much as I might think they do? >>Ah, thank you. Any >>Thoughts? >>Absolutely not. No, no. <laugh> joking aside. This is, this is, this is, >>Leave me hanging >><laugh>, uh, this is actually core to our DNA. And, and, and we, we often say out loud how, like Europe has really paved the way for a different way, uh, of, of looking at privacy from a security setting, right. And they're not mutually exclusive. Right. You can have high security all while protecting people's privacy. And it's all of a question of ensuring that, you know, how you kind of, I would say, uh, ethically, uh, use said technology and we can actually put some safeguards in it. So to minimize the likelihood of there being abuse, right? There's, there's something that we do, which we call the privacy protector, which, you know, for all intents and purposes, it's not that complex of an idea. It's, it's really the concept of you have security cameras in a public space or a more sensitive location. And you have your security guards that can actually watch that footage when nothing really happens. >>You, you want to protect people's privacy in these situations. Uh, however, you still want to be able to provide a view to the security guard so they can still make out that, you know, there there's actually people walking around or there's a fight that broke out. And in the likelihood that something did happen, then you can actually view the overall footage. So, and with, with the details that the cameras that you had, you know, the super high mega pixel cameras that you have will provide. So we blur the images of the individuals. We still keep the background. And once you have the proper authorization, and this is based on the governance of the organization, so it can be a four I principle where it could be the chief security officer with the chief privacy officer need to authorize this footage to be kind of UN blurred. And at that point you can UN blur the footage and provide it to law enforcement for the investigation, for example. >>Excellent. I've got Andrew, if you wanted, then I, then I'm. Well, so I, I've a, I have a final question for you. And this comes out of a game that, uh, some friends and I, some friends of mine and I devised over the years, primarily this is played with strangers that you meet on airplanes as you're traveling. But the question you ask is in your career, what you're doing now and over the course of your careers, um, what's the most shocking thing <laugh> that people would learn from what, you know, what do you, what do you find? What's the craziest thing. When you go in to look at these environments that you see that people should maybe address, um, well, go ahead and start with you, Andrew. >>I, >>The most shocking thing you see every day in your world, >>It's very interesting. The most shocking thing I think we've seen in the industry is how willing, uh, some professionals are in our industry to install any kind of device on their networks without actually taking the time to do due diligence on what kind of security risks these devices can have on a network. Because I think a lot of people don't think about a security camera as first and foremost, a computer, and it's a computer with an IP address on a network, and it has a visual sensor, but we always get pulled in by that visual sensor. Right. And it's like, oh, it's a camera. No, it's a computer. And, you know, over the last, I would say eight years in the industry, we've spent a lot of time trying to sensitize the industry to the fact that, you know, you can't just put devices on your, your network without understanding the supply chain, without understanding the motives behind who's put these together and their track record of cybersecurity. So probably the weirdest thing that I've seen in my, um, you know, career in this industry is just the willingness of people not to take time to do due diligence before they hook something up on onto their corporate network where, you know, data can start leaking out, being exfiltrated by those devices and malevolent actors behind them. So gotta ask questions about what you put on your network. >>Christian, did he steal your, did he steal your thunder? Do you have any other, any other thoughts? >>Well, so first of all, there's things I just cannot say on TV. Okay. But you can't OK. >>You can't. Yeah, yeah, yeah. Saying that you're shocked that not everyone speaks French doesn't count. Okay. Let's just get, let's get past that, but, but go, but yeah, go ahead. Any thoughts? >>So, uh, you know, I, I would say something that I I've seen a lot and, and specifically with customers sometimes that were starting to shop for a new system is you'd be surprised by first of all, there's a camera, the likelihood of actually somebody watching it live while you're actually in the field of view of that camera is close to Neil first and foremost, second, there's also a good likelihood that that camera doesn't even record. It actually is not even functional. And, and I would say a lot of organizations often realize that, you know, that camera was not functioning when they actually knew do need to get the footage. And we've seen this with some large incidents, uh, very, uh, bad incidents that happened, uh, whether in the UK or in Boston or whatnot, uh, when they're, when law enforcement is trying to get footage and they realize that a lot of cameras actually weren't recording and, and, and goes back to Andrew's point in terms of the selection process of these devices. >>Yeah. Image resolution is important, like, because you need an, an image that it actually usable so that you can actually do something with it forensically, but you know, these cameras need to be recorded by a reliable system and, and should something happen with the device. And there's always going to be something, you know, power, uh, uh, a bird ate the lens. I don't know what it might be, or squirrel ate the wire. Um, and the camera doesn't work anymore. So you have to replace it. So having a system that provides, you know, you with like health insights in terms of, of, of if it's working or not is, is actually quite important. It needs to be managed like any it environment, right? Yeah. You have all these devices and if one of them goes down, you need to manage it. And most organizations it's fire and forget, I sign a purchase order. I bought my security system, I installed it. It's done. We move on to the next one and seven years later, something bad happens. And like, uhoh, >>It's not a CCTV system. It's a network. Yeah. Life cycle management counts. >>Well, uh, I have to say on that, uh, I'm gonna be doing some research on Canadian birds and squirrels. I, I had no idea, >>Very hungry. >>Andrew, Chris, John, thank you so much. Great conversation, uh, from all of us here at the cube. Thanks for tuning in. Stay tuned. The cube from Silicon angle media, we are your leader in tech coverage.

>>And welcome to this cube conversation here in Palo Alto, California. I'm John furrier, host of the cube. We have a great guest coming in remotely from LA Christian Hernandez developer experienced lead at code fresh code fresh IO. Recently they were on our feature at a startup showcase series, season two episode one cloud data innovations, open source innovations, all good stuff, Christian. Thanks for coming on this cube conversation. >>Thank you. Thank you, John. Thank you for having me on, >>You know, I'm I was really impressed with code fresh. My met with the founders on here on the cube because GI ops AI, everything's something ops devs dev sec ops. You've got AI ops. You've got now GI ops, essentially operationalizing the software future is here and software's eating the world is, was written many years ago, but it's open source is now all. So all things software's open source and that's kind of a done deal. It's only getting better and better. Mainstream companies are contributing. You guys are on this wave of, of this open source tsunami and you got cloud scale. Automation's right there, machine learning, all this stuff is now the next gen of, of, of code, right? So you, your code fresh and your title is developer experience lead. What does that mean right now? What does it mean to be a developer experience lead? Like you make sure people having a good experience. Are you developing you figuring out the product? What does that mean? >>Yeah. That's and it's also part of the, the whole Debre explosion that's happening right now. I believe it's, you know, everyone's always asking, well, what, you know, what is developer advocate? What does that mean developer experience? What does that mean? So, so you, you kind of hit the nail on the head a little bit up there in, in the beginning, is that the, the experience of the developer when using a particular platform, right? Especially the code flash platform. That is my responsibility there at code fresh to enable, to enable end users, to enable partners, to enable, you know, anyone that wants to use the code fresh platform for their C I C D and get ops square flows. So that's, that's really my, my corner of the world is to make sure their experience is great. So that's, it's really what, what I'm here to do >>At food fresh. You know, one of the things I can say of my career, you've been kind of become a historian over time. When I was a developer back in the old days, it was simply you compiled stuff, you did QA on it. You packaged it out. You wanted out the door and you know, that was a workflow right now with the cloud. I was talking with your founders, you got new abstraction layers. Cloud has changed again again, open source. So newer things are coming, right? Like, like, like Kubernetes for instance is a great example that came out of the open source kind of the innovations. But that, and Hadoop, we were mentioning before he came on camera from a storage standpoint, kind of didn't make it because it was just too hard. Right. And it made the developer's job harder. And then it made the developer's requirements to be specialized. >>So you had kind of two problems. You had hard to use a lot of friction and then it required certain expertise when the developers just want to code. Right. So, so you have now the motion of, with GI ops, you guys are in the middle of kinda this idea of frictionless based software delivery with the cloud. So what's different now, can you talk about that specific point because no one wants to be, do hard work and have to redo things. Yeah. Shift left and all that good stuff. What's hard now, what do you guys solve? What's the, what's the friction that you're taking out what's to become frictionless. >>Yeah. Yeah. And you, you, you mentioned a very interesting point about how, you know, things that are coming out almost makes it seem harder nowadays to develop an application. You used to have it to where, you know, kind of a, sort of a waterfall sort of workflow where, you know, you develop your code, you know, you compile it. Right. You know, I guess back in the day, Java was king. I think Java still is, has a, is a large footprint out there where you would just compile it, deploy it. If it works, it works. Alright cool. And you have it and you kind of just move it along in its process. Whereas I think the, the whole idea of, I think Netflix came out with like the, the fail often fail fast release often, you know, the whole Atlassian C I C D thing, agile thing came into play. >>Where now it's, it's a little bit more complex to get your code out there delivered to get your code from one environment to the other environment, especially with the, the Avan of Kubernetes and cloud native architecture, where you can deploy and have this imutable infrastructure where you can just deploy and automate so quickly. So often that there needs to be some sort of new process now into place where to have a new process, like GI ops to where it'll, it it's frictionless, meaning that it's, it, it makes it that process a little easier makes that little, that comp that complex process of deploying onto like a cloud native architecture easier. So that way, as you said before, returning the developers to back to what they care about, mot, the most is just code. I just want to code. >>Yeah. You know, the other thing, cool thing, Christian, I wanna bring up and we'll get into some of the specifics around Argo specifically CD is that the community is responding as a kind of, it takes a village kind of mindset. People are getting into this just saying, Hey, if we can get our act together around some de facto workflows and de facto capabilities, everyone wins. It's a rising tide, floats all boats, kind of concept. CNCF certainly has been a big part of that. Even seen some of the big hyper scales getting behind it. But you guys are part of the founding members of the open get ups working group, Amazon Azure, GitHub, red hat Weaveworks and then a ton of contributors. Okay. So this is kind of cool. This means that there's like people behind this thing. Look, we gotta get here faster. What happened at co con this year? You guys had some news around Argo and you had some news around the hosted solution. Can you take a minute to explain two things, one the open community vibe, and then two, what you guys announced at Coon in Spain. >>Yeah. Yeah. So as far as open get ups, that was, you know, as you said before, code fresh was part of that, that founding committee. Right. Of, of group of people trying to figure out, define what get ups is. Right. We're trying to bring it beyond the, you know, the, the hype word, right beyond just like a marketing term to where we actually define what it actually is, because it is actually something that's out there that people are doing. Right. A lot of people, you know, remember that the, the Chick-fil-A story where it's like, they, they are completely doing, you know, this get ops thing, we're just now wanting, putting definition around it. So that was just amazing to see out at there in, in Cuban. And, but like you said, in QAN, we, you know, we're, we're, we're taking some of that, that acceleration that we see in the community to, and we, we announce our, our hosted get ops offering. >>Right. So hosted get ops is something that our customers have been asking for for a while. Many times when, you know, someone wants to use something like Argo CD, the, in, they install it on their cluster, they get up and running. And, but with, with all that comes like the feed and care of that platform, and, you know, not only just keeping the lights on, but also management security, you know, general maintenance, you know, all the things that, that come along with managing a system. And on top of that comes like the scale aspect of it. Right. And so with scale, so a lot of people go with like a hub and spoke others, go with like a fleet design in, in either case, right. There's, there's a challenge for the feet and care of it. Right. And so with code fresh coast of get ups, we take that management headache away. >>Right? So we, we take the, the, the management of, of Argo CD, the management of, of all of that, and kind of just offer Argo CD as a surface, right. Which offers, you know, allows users to, you know, let us take care of all the, of the get offs, runtime. And so they can concentrate on, you know, their application deployments. Right. And you also get things like Dora metrics, right. Integrated with the platform, you have the ability to integrate multiple CI providers, you know, like get hub actions or whatever, existing Jenkins pipelines. And really that, that code fresh platform becomes like your get ops platform becomes like, you know, your, your central view of the world of, of your, you know, get ups processes. >>Yeah. I mean, that whole single source of truth concept is really kind of needed. I gotta ask you though, with the popularity of the Argo CD on get ups internally, right. That's been clear, right. Kubernetes, the way that's going, it's accelerating fast. People want simple it's scaling, you got automation built in all that good stuff. What was the driver behind the hosted get up solution? Was it customer needs? Was it efficiency all the above? What was specifically and, and why would someone want to have the hosted versus say internal? >>Yeah. So it's, it was really driven by, you know, customer need been something that the customers have been asking for. And it's also been something that, you know, you, you, you have a process of developing an application to, you know, you know, a fleet of clusters in a traditional, you know, I keep saying traditional, get outs practice as if get outs are so old. And, you know, in, you know, when, when, when people first start out, they'll start, you know, installing Argo city on all these clusters and trying to manage that at scale it's, it's, it, it seemed like there was, you know, it it'd be nice if we can just like, be able to consume this as a service. So we don't have to like, worry about, you know, you know, best practices. We don't have to worry about security. We don't just, all of that is taken care of and managed by us at code fresh. So this is like something that, you know, has been asked for and, and something that, you know, we believe will accelerate, you know, developers into actually developing their, their applications. They don't have to worry about managing >>The platform. So just getting this right. Hosted, managed service by you guys on this one, >>Correct? Yes. >>Okay. Got it. All right. So let me, let me get in the Argo real quick, just to kind of just level set for the folks that are, are leaning into this and then kicking the tires. Where are we with Argo? What, why was it so popular? What did it do specifically? Did it just make it easier for developers to manage and monitor Kubernetes, keep 'em updated? What was the specific value behind Argo? Where, where, where did it come from and why is it so popular? >>Yeah, so Argo the Argo project, which is made up of, of a few tools, usually when people say Argo, they meet, they they're talking about Argo CD, but there's also Argo workflows, Argo events, Argo notifications. And, and like I said before, CD with that, and that is something that was developed internally at Intuit. Right? So for those of who don't know, Intuit is the company behind turbo tax. So for those, those of us in the us, we, we know, you know, we know that season all too well, the tax season. And so that was a tool that was developed internally. >>And by the way, Intuit we've done many years. They're very huge cloud adopters. They've been on that train from the day one. They've been, they've been driving a lot of cloud scale too. Sorry >>To interrupt. Yeah. And, and, and yeah, no, and, and, and also, you know, they, they were always open source first, right. So they've always had, you know, they developed something internally. They always had the, the intention of opensourcing it. And so it was really a tool that was born internally, and it was a tool that helped them, you know, get stuff done with Kubernetes. And that's kind of like the tagline they use for, for the Argo project is you need to get stuff done. They wanted their developers to focus less on deploying the application and more right. More than on writing the application itself. And so the, and so the Argo project is a suite of tools essentially that helps deploy onto Kubernetes, you know, using get ups as that, you know, that cornerstone in design, right in the design philosophy, it's so popular because of the ease of use and developer friendliness aspect of it. It's, it's, it's, it's meant to be simple right. In and simple in a, in a good sense of getting up and running, which attracted, you know, developers from, you know, all around the world. You know, other companies like red hat got into it as well. BlackRock also is, is a, is a big contributor, thousands of other independent contributors as well to the Argo project. >>Yeah. Christian, if you bring up a good point and I'm gonna go on a little tangent here, but I wanna get your reaction to something that Dave ante and I, and our cube team has been kind of riffing on lately. You mentioned, you know, Netflix earlier, you mentioned Intuit. There's a kind of a story that's been developing and, and with traction and momentum and trajectory over the past, say 10 years, the companies that went on the cloud, like Netflix into it, snowflake, snowflake, not so much now, but in terms of open source, they're all contributing lift. They're all contributing back to open source, but they're not cloud providers. Right. So you're seeing that kind of first generation, I's a massive contribution to open source. So open source been around for a while, remember the early days, and we'd all participate on projects, but now you have real companies building IP going open source first because they're on a hyperscale cloud, but they're not the cloud themselves. They took advantage of that. So there's kind of this cycle of flywheel of cloud to open source, not from the vendors themselves like Amazon, which services or Azure, but the people who rode their CapEx and built on that scale, feeding into the open source. And then coming back, this is kind of an interesting dynamic. What's your reaction to that? Do you see that? Yeah. Super cloud kind of vibe there. >>Yeah. Yeah. Well, and, and also it, it, I think it's, it's a, it's indicative that, you know, open source is not only, you know, a way to develop, you know, applications, a way to engineer, you know, your project, but also kind of like a strategic advantage in, in, in such a way. Right. You know, you, you see, you see companies like, like, like even like Microsoft has been going into, you know, open source, right. They they've been going to open source first. They made a, a huge pivot to, you know, using open source as, you know, like, like a, like a strategic direction for, for the company. And I think that goes back to, you know, a little bit for my roots, you know, I, I, I always, I always talk about, you know, I always talk about red hat, right. I always talk about, you know, I was, I was, I was in red hat previously and, you know, you know, red hat being, you know, the first billion dollar open source company. >>Right. I, we always joke is like, well, you know, internally, like we know you were a billion dollar company that sold free software. How, you know, how, how does that happen? But it's, it's, it's really, you know, built into the, built into being able to tap into those expert resources. Yeah. You know, people love using software. People love the software they love using, and they wanna improve it. Companies are now just getting out of their way. Yeah. You know, companies now, essentially, it's just like, let's just get out of the way. Let's let people work on, you know, what they wanna work on. They love the software. They wanna improve it. Let's let them, >>It's interesting. A lot of people love the clouds have all this power. If you think about what we are just riffing on and what you just said, the economics and the organic self-governing has always been the open source way where commercial value is enabled. If you play ball, right. Like, oh, red hat, for instance. And now you're seeing the community kind of be that arbiter of the cloud. So, Hey, if everyone can create value on say AWS or Azure, bring it to open source, everyone benefits across all clouds hope eventually. So the choice aspect comes in. So this community angle is huge. And I think it's changing a lot for the better. And I think this is where we're seeing a lot of that growth. And you guys have been the middle level with the Argo project and get ups specifically in that, in that sector. How have you seen that growth? What some dynamics have you seen power dynamics, organic? Is it governed well, whats some of the, the successes, what are some of the challenges? Can you share your thoughts on the community's growth around get ops and Argo project? >>Yeah, yeah. Yeah. So I've been, you know, part of some of these communities, right? Like the, the open, get, get ops community, the Argos community pretty much from the beginning and, and seeing it developed from an idea to, you know, having all these contributors, having, you know, the, the, the buzzword come out of it, you know, the get ups and it be that being the, you know, having it, you know, all over the, you know, social media, all over LinkedIn, all over all, all these, all these different channels, you know, I I've seen things like get ops con, right. So, you know, being part of the, get ops open, get ops community, you know, one of the things we did was we did get ops con it started as a meetup, you know, couple years ago. And now, you know, it was a, you know, we had an actual event at Cuan in Los Angeles. >>You know, we had like, you know, about 50 people there, but then, you know, Cuan in Valencia this past Cuan we had over 200 people, it was a second largest co-located events in, at Cuan. So that just, just seeing that community and, you know, from a personal standpoint, you know, be being part of that, that the, the community being the, the event chair, right. Yeah. Being, being one of the co-chairs was a, was a moment of pride for me being able to stand up there and just seeing a sea of people was like, wow, we just started with a handful of people at a meetup. And now, you know, we're actually having conferences and, and, and speaking of conference, like the Argo community as well, we put in, you know, we put on a virtual only event on Argo con last year. We're gonna do it in person today. You know, this year. >>Do you have a date on that? Do you have a date on that Argo con 22? >>Two? Yeah, yeah, yeah. Argo con September 19th, 2022. So, you know, mark your calendars, it it's, you know, it's a multi-day event, you know, it's, it's part of something else that I've seen in the community where, you know, first we're talk talking about these meetups. Now we're doing multi-day events. We're, you know, in talks of the open, get ups, you know, get ups can also make that a multi-day event. There's just so many talks in so many people that want to be involved in network that, you know, we're saying, well, we're gonna need more days because there's just so many people coming to these events, you know, in, in, you know, seeing these communities grow, not just from like the engineering standpoint, but also from the end user standpoint, but also from the people that are actually doing these things. And, you know, seeing some of these use cases, seeing some of the success, seeing some of the failures, right? Like people love listening to those talks about postmortems, I think are part of my favorite talks as well. So seeing that community grow is, is, you know, on a personal level, it's, it's a point >>It's like CSI for software developers. You want to curious about >>Exactly >>What happened. You know, you know, it's interesting, you mentioned about the, the multiple events at Coon. You know, the vibe that's going on is a very festival vibe, right? You have organic groups coming together. I remember when they had just started doing the day zero programs. Now you have like, almost like multiple stages of content at these events. It feels like, like a Coachella vibe or some sort of like festival vibe, like a lot of things going on and you, and if you pick your kind of area, but you can move around, I find that the kind of the format de Azure I think is going well these days. What do you think about that? >>Yeah, yeah. No, for sure. It's and, and, and I love that that analogy of Coachella, it does feel like, you know, it's, there's something for everyone and you can find what you like, and you'll find a little, you know, a little group, right. A little click of, of, of people that's probably the wrong term to use, but you know, you, you find, you know, you, you know, like-minded people and, you know, passionate about the same thing, right? Like the security guys, they, you know, you see them all clump together, right? Like you see like the, the developer C I CD get ops guys, we all kind of clump together and start talking, you know, about everything that we're doing. And it's, that's, that's, I think that's really something special that coupon, you know, some, you know, it's gotten so big that it's almost impossible to fit everything in a, in a week, because unless there's just so much to do. And there's so much that that interests, you know, someone, but it's >>A code, a code party is what we call it. It's a code party. Yeah. >>It's, it's a code party for sure. For >>Sure. Nerd nerd Fest on, on steroids. Hey, I gotta get, I wanna wrap this up and give you the final word, Christian. Thanks for coming on. Great insight, great conversation. There's a huge, you guys are in the middle of a hot area, obviously large scale data growth. Kubernetes is scaling beautifully and making it easier at managed services. What people want machine learning's kicking in and, and you get automation building in all favoring, the developer and C I CD pipeline and all that good stuff. People want to learn more. Can you take a minute to put the plug in for code fresh on the certification? How do I get involved? Where are you? Is there levels if I want to jump in and get trained and get fluent on code fresh, can you share commentary and, and, and what the status is? >>Yeah, yeah, for sure. So code fresh is offering a free certification, right? For get ups or Argo CD and get ops. The first of it's kind for Argo CD, first of it's kind for get ops is you can actually go get certified with Argo CD and get ops. You know, we there level one is out right now. You can go take that code, fresh.io/certification. It's out there, sign up, you know, you, you don't, you don't need to pay anything, right. It's, it's something it's a, of a free course. You could take level two is coming soon. Right? So level two is coming soon in the next few months, I believe I don't wanna quote a specific day, but soon because I, but soon I, it it's soon, soon as in, as in months. Right? So, you know, we're, we're counting that down where you can not only level one cert level certification, but a level, two more advanced certification for those who have been using Argo for a while, they can still, you know, take that and be, you know, be able to get, you know, another level of certification for that. So also, you know, Argo con will be there. We're, we're part of the programming committee for Argo con, right? This is a community driven event, but, you know, code fresh is a proud diamond sponsor. So we'll be there. >>Where's it located up to us except for eptember 19th multiday or one day >>It's a, it's a multi-day event. So Argo con from 19, 19 20 and 21 in a mountain view. So it'll be in mountain view in the bay area. So for those of you who are local, you can just drive in. Great. >>I'm write that down. I'll plug it. I'll put in the show notes. >>Awesome. Awesome. Yeah. And you will be there so you can talk to me, you can talk to anyone else at code, fresh talking about Argo CD, you know, find, find out more about hosted, get ups code, fresh.io. You know, you can find us in the Argo project, open, get ups community, you know, we're, we're, we're deep in the community for both Argo and get ups. So, you know, you can find us there as well. >>Well, let's do a follow up in when you're in town, so's only a couple months away and getting through the summer, it's already, I can't believe events are back. So it's really great to see face to face in the community. And there was responding. I mean, co con in October, I think that was kind of on the, that was a tough call and then get to see your own in Spain. I couldn't make it. Unfortunately, I had got COVID came down with it, but our team was there. Open sources, booming continues to go. The next level, new power dynamics are developing in a great way. Christian. Thanks for coming on, sharing your insights as the developer experience lead at code fresh. Thanks so much. >>Thank you, John. I appreciate it. >>Okay. This is a cube conversation. I'm John feer, host of the cube. Thanks for watching.

(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)

>>Hey everyone. Welcome back to the Cube's live coverage of snowflake summit 22. We are live at Caesar's forum in Vegas, Lisa Martin, with Dave ante, excited to welcome a VIP fresh from the keynote stage, the SAP, a product at snowflake Christian C Claman Christian. Thank you so much for joining us on the queue today. >>Thank you for having me very exciting. >>And thanks for bringing your energy, loved your keynote. I thought, wow. He is really excited about all of the announcements jam packed. We, and we didn't even get to see the entire keynote talk to us about, and, and for the audience, some of the things going on the product revenue in Q1 fiscal 23, 390 4 million, 85% growth, lot of momentum at snowflake. No doubt. >>So I think that the, the punch line is our innovation is if anything, gaining speed. Uh, we were over the moon excited to share many of these projects with customers and partners, cuz some of these efforts have been going on for multiple years. So, um, lots of interesting announcements across the board from making the existing workloads faster, but also we announced some new workloads getting into cyber security, getting into more transactional workloads with uni store. Um, so we're very excited. >>Well first time being back, this is the fourth summit, but the first time being back since 2019 a tremendous amount has changed for snowflake in that time, the IPO, the massive growth in customers, the massive growth in growth in customers with over 1 million in ARR, you talked about one of the things that clearly did not slow down during the last two years is innovation at snowflake. >>Yeah, that, that, that for, for sure, like, um, even though we, we had a, um, highly in the office culture, we did not miss a beat the moment that we said, Hey, let's all start doing zoom based calls. We, we did. So, uh, I dunno if you saw the, the first five minute minutes of my section in the keynote. Yeah. We, we originally talked about summarizing it and no we're gonna spend 40 minutes here. So we did a one minute clip and whatever gets flashed there. So no, the, the pace of innovation, I think it's second to none and maybe I'll highlight the something that we're very proud of. Snowflake is a single product, a single engine. So if we're making a query performance enhancement, it will help the cyber security workload and the low high concurrency, low latency workload. And eventually we're starting to see some of those enhancements all the way to uni store. So, so we get a lot of leverage out of our investments. What's >>Your favorite announcement? >>That's like picking children. Of course. Um, I think the native applications is the one that looks like, eh, I don't know about it on the surface, but it has the biggest potential to change everything like create an entire ecosystem of solutions for within a company or across companies that I don't know that we know what's possible. >>Well, I I've been saying for a while now that you have this application development stack over here, the database is kind of here and then you have the analytics and data pipeline stack. Those are those separate worlds. We, we talk about bringing data and AI and machine intelligence into applications. The only way that that is actually gonna move forward is if you bring those worlds together is a good example of that happening, um, within a proprietary framework, uh, it's probably gonna happen open source organically and you can sort of roll your own. Is that by design or is it just sort of happening? Well, >>The, the, they bring it all into a single platform obviously by design, cuz there is so much friction today on making all the pieces work together, which database do I use for transactions and how do I move data to my analytics system? And how do I keep system, uh, reference data in sync between the two? So, so it's complicated and our mission was remove all of this friction from, from, from the equation. Uh, the open source versus not the way we think about it is opensourcing open formats or even open APIs it's does it help us deliver the solution that we want for our customer? Does it help us solve their problems? In certain instances, it has done in the past and we've opened source frameworks in, in others. We mentioned at the keynote today, the, the integration of iceberg tables, that's an strong embrace of open technologies, but that does not mean that we want to continue to innovate in our formats. A lot of what you see in the open formats is because snowflake proprietary, uh, innovation. So, uh, we have a very clear philosophy around this. Well >>Like any cloud player, you have to bring open source tools in and make them available for your application developers. But take us through an example of, of uni store and specifically how you're embracing transaction data. What's a customer gonna actually do take us paint a picture >>For us. I I'm gonna give you a very simple use case, but I love it because it, it shows the power of the scenario today. When people are ingesting data into snowflake, you wanna do some book capping associating with those loads. So imagine I have, I dunno, a million files. How many of those files have I loaded? Imagine that one of those loads fail, how do you keep in sync? Whether the data made or not with your bookkeeping today, if you had to do it with a separate transactional database for the bookkeeping and the loading in, in snowflake, it is a lot of complexity for you to know what's where with uni store, you can just say, I'm gonna do the bookkeeping with these new table. It's called hybrid tables. The lows are transactional and all of this is a single transaction. So for, for anyone that has dealt with inconsistencies in database world, this is like a godsend. >>Okay. So my interpretation of that's all about what happens when something goes wrong >><laugh> which is a lot of the, everything about transactions. Yeah. It's what happens when goes wrong and goes wrong. Doesn't mean failures like goes wrong is when you're debiting money from your bank account, not having enough balance that counts as go wrong and the transactions should be aborted. So yes, transactions are all about conflict management and we're simplifying that in a broader set of use cases >>And, and in recovery. So you're, you're in fast recovery. So you're, you're the, the business impact of what you're doing is to sort of simplify that process. Is that the easy way to >>Boil down? Pretty much everything we do is about simplification. Like we, we we've seen organizations are large focusing on wrestling infrastructure as opposed to what are the business problems for a Frank who reference something that, that, that I believe very much in like, which is mission alignment. We are working on helping our customers achieve what they're set out to achieve, not giving them more technology for them to their goal to become, to wrestle the infrastructure. So it's all about ease of use all about simplification removal, friction, >>Just so if I may, so mission alignment, you know, you always hear about technology companies that, you know, provide infrastructure or a service, and then the customer takes that and, and, you know, monetizes it pretty much on their own. What the big change that I'm discerning from these announcements is you're talking about directly monetizing and participating in that monetization as a technology partner, but also the marketplace as well. >>Correct. And I would say in some ways this is not new. This has been happening for the last couple of years with data. Like if you just saw our industry data cloud launches, the financial services cloud, it comes with data providers that help you achieve specific outcomes on a specific industry. Mm-hmm <affirmative> what we're doing now is saying, it's not just data. Maybe it's some business logic, maybe it's some machine learning, maybe it's some user interface. So I think we're just turning the knob on collaboration and it's a continuation of what we've been doing. >>Talk a little bit more about mission alignment. When I heard Frank, Sweetman talk about that this morning. I always love that when I hear cultural alignment with organizations, but as you just said, it's really about enabling our customers to deliver outcomes to their customers as the SVP product. Can you, uh, talk a little bit about how the customers are influencing the product roadmap, the innovations and the speed with which things are coming out at snowflake? >>Yeah, so great question. We have several organizations at snowflake that are organized by vertical by industry. So the, the major sales organization is part of ed that the marketplace business development team is organized like that. We have a separate team that provides top leadership by industry vertical, um, globally. And then even within our solution engineering, there is verticals. So we have a longitudinal view of all the different functions and what do we need to do to achieve a set of use cases in a vertical? And all of those functions are in con constant communication with us on this is where the product is, um, seeing an opportunity or could do better for that vertical. So yeah, I can tell you, and obviously we love when, when there's alignment between those, but that's not always the case. You heard us talk about clean rooms now for some time, clean rooms are applicable to almost any industry, but it's red hot for media and advertising, third party, cookie deprecation, and all of that. So we, we get to, to see that lens, that our innovation is informed by industries. >>So we, we're seeing, obviously the evolution of snowflake we talked about in the keynotes today, you guys talked about 2019 and, you know, pre 2019, even it was to me anyway, your first phase was, Hey, we got a simpler EDW. You know, we're gonna pick that off and put it in the cloud and make it elastic and separate compute from storage, all that kind of cool stuff. And then during the pandemic, it was really IPO, but also the data cloud concept, you sort of laid that vision out. And now you're talking about application development, monetization, what I call the super cloud that layer. Right. Okay. So I, are >>You determin it best? >>Yes. You talk about this, uh, these announcements, how they fit into that larger vision where you're >>Going. Great question. The, the, the notion of the data cloud has not changed one bit. The data cloud thesis is that we want to provide amazing technology for our customers, but also facilitate collaboration and content exchange VR platform. And all that we did today is expand what that content can be. It's not just data or little helper function, it's entire applications, entire experiences. That is the, the summing up the, the, the impact of our announcements today. That, that that's the end of it. So it's still about the data cloud. >>So what is impressive to me is that you guys wouldn't couldn't have a company without the hyperscalers, right? It would be a lot different, right? So you built on top of that and, and now you have your customers building their own super clouds. I call it, I get a lot of grief for that term it's but the, the, the big area of criticism I get is, ah, that's just SAS. And I'm like, no, it's not, no, uh, I, I is everybody public who's announcing stuff. I, I better be careful, but you have customers that are actually building services, taking their data, their tooling, their proprietary information, and putting it on the snowflake data cloud and building their own clouds. Yeah. That's different. Then that's not multi-cloud, which is I can run on a different cloud and it's not, is it sass? If it feels like it's something new from a, from your perspective, is, is it different? >>I, I, I love that you called out that running on all clouds is not what we do right. This days, everyone is multi-cloud, you, you run on a VM or a container, and I multi-cloud check, no, we have a single platform that does multi-region multi-cloud but also cross region cross cloud globally, that that is the essence of what we're doing. So it, it is enabling new capabilities. >>I've I've also said, you know, in many respects, the super cloud hides, the underlying complexity, you think about things like exploiting graviton and a developer. Doesn't need to worry about that. You're gonna worry about that. Uh, but at the same time, they, the, as you get into the develop, the world of application development, some of your developers may want access to some of those cloud primitives. Are you providing both? What's the strategy there? >>Generally not in some areas, we, we, we, I would say bleed through some details that are material, but think of the reality of someone that wants to build a solution, it's really difficult to build an awesome solution in one cloud, Hey, you need to do this. What's the latest instance, and is gravity tank gonna help you or not all of that. Now do it for another one and then do it for another one. And I can tell you it's really difficult because we go through that exercise. Snowflake pouring to a new cloud is somewhere between one and two years of effort and not, not a small number of people because you're looking at security models and storage models. So that's the value that we give to anyone know, wants to build a solution and target customers in all three clouds. I >>Mean, people are still gonna do it themselves, but they're gonna spend a lot more and they're gonna lose their focus on what their real business is. And there'll still be that. I think that D DIY market is enormous for you guys, huge >>Opportunity. And there's also the question on what is the cost of that analysis and that effort. And can we amortize it on behalf of all of our customers? Like we talk about graviton, we have not talked about the many things that we evaluated that were not better price performance for our customers. That evaluation happened. That value was delivered by not moving there. >>And when you do it yourself, the curve looks like, okay, Hey, we can do it ourselves. We can make it pretty Inex. And then, and then the costs are gonna decline, but what really happens, like developing a mobile app, you gotta maintain it. And then if you don't have the scale and you don't have the engineering resources, you're just, the, the costs are gonna continue to go through the roof. I, >>I, I love that you compare it to mobile apps. Like, yeah. I still don't understand why every company that wants to build an app has to build two <laugh>. They got it. Yeah. There is no super cloud for the phone. >>Right. >>That's sort of our, our, our broad vision. Not yet. Not, not the phone, but the super cloud. Yeah, >>Yeah, absolutely. >>You >>Get it. This is, and you look out the ecosystem here. I mean, what a difference that you've been pointing this out, Lisa from, from, from 2019, a lot of buzz, it's all about innovation. You see this at, at thing at the reinvent is like the super bowl obviously. And you see that and it used to be, oh, how is, how is AWS gonna compete with snowflake and separate compute with stores? That's I, I feel like in a large way, that's all gone. It's like, okay, how do we like rise the whole, the whole industry? And that's really where the innovation is. >>We have an amazing partnership with AWS and they benefit from what we do. Yes. There's some competitive elements, but we're changing so many things creating so much opportunity that we're more aligned than not. Yeah. >>Last question for you is continuing on the part AWS partnership front, how does a partner like AWS and other partners, how do they fit into the data cloud narrative that you're talking about to customers? >>I would say that other than the one or two teams that are directly competitive, the rest of their teams are part of in data cloud. Like, uh, our relationship with SageMaker as an example is amazing. And a lot of what we wanna deliver to our customers is choice around machine learning, frameworks and tools. And they're part of the data cloud. We're working with them on how do you push down computation to avoid getting data out, to reinforce governance? So I, I would say that and, and go look at it that they have a hundred and something teams. So if two teams out of hundreds, uh, are, are the competitive element, we are largely aligned. And they're part of data cloud. >>Yeah. I mean, you, your customers consume a lot of compute and storage for, >>For a lot. Yes. >>AWS and, and also, you know, increasingly Azure and, and Google. I mean, it's, um, pretty amazing times, uh, Christian, I want to ask you about, um, couple of terms. Uh, one term that came up a couple of times today in Frank's keynote, he said, I'm not gonna call it a data mesh out kind of out of respect for the purists, which is cool, I thought, but then you had a customer stand up Geico and said, we're building a data. Mesh JPMC is, is speaking at this event, building a data mesh. And I look at things through that prism and say, okay, data mesh is about, you know, decentralization. Some, I I'd be curious as to whether or not you tick that box, but it's about building data products. It's about, uh, uh, self-service infrastructure. And it's about automated computational governance. You are actually tipping a lot of the ticking, a lot of those boxes and, and Mike, I guess the big one is, are, are you building a bigger walled garden? But I, I think you would say, no, it's a, it's a giant distributed network, but, but what, what, what do you say to that? We, >>The latter, the latter, yeah, giant distributed, open cloud and open in the sense that we want anyone to plug in and, and someone can say, well, but I cannot read your file formats. Sure. You can with what we announced today, but it's not about that. Our APIs are open. We have rest APIs. We have JDC ODC, probably most popular interfaces ever. Um, and we want everyone to be part of it. If anything, there's lots of areas that we would not want to go into ourselves cause we want partners and customers to go in there. So, no, we we're looking at a very broad ecosystem. We win based on the value created on top of the platform. Yeah. >>And I makes total sense to me. I mean, I think the imaculate conception of data mesh might be a purely open source version of snowflake. I just don't see that happening anytime soon. And so I, I think you're gonna, you are, I wrote about this creating a defacto standard and >>Exactly, and, and I don't like to get into the terminology that, oh, is the data measure? Not, no go look at the concepts like people used to say, but snowflake is not a data lake. Okay. What is the data lake? It's just a pattern. And if you follow the pattern and you can do it, that's fine. Then there's the, uh, emotional quasi-religious overlay open versus not, I think that's a choice. Not necessarily the concept, >>It's a moving target. I mean, I Unix used to be open. You know, that was the, I agree. Now, the reason why I do think the data mesh conversation is important is because Shaak Dani, when she defined data mesh, she pointed out in my view. Anyway, the problems of getting value outta data is that you go through these hyper specialized teams and they're they're blockers in the organization. And I think you in many respects are attacking that. And it's an organizational issue. >>The, the insights in the pattern are a hundred percent value and aligned with what we do, which is they, you want some amount of centralization, some amount of decentralization living in harmony. Uh, yeah. I have no problem with, with terminology. >>And the governance piece is, is, is massive. Especially it's the, the picture's becoming much more clear. Um, whatever's in the data cloud is a first class citizen, right? And you give all these wonderful benefits. I mean, the interesting thing, what you're doing with Dell and, and pure, I, I asked you that on the analyst call, it's a start. You know, I, I, I mean, >>And I said it briefly in, in, in the keynote this morning, we're publishing a set of standard conformance tests. So any storage system can plug into data cloud. >>Yeah. >>And by the way, it's based on S three APIs, another defect of standard. Like it's not a standard, but everyone is emulating that. And we're plugging >>Into that. Yeah. Nobody's complaining against, against S3 API >>About it is a, oh, it's not a Apache project. We shouldn't, who cares. Everyone has standard horizon net. That's it? >>Well, we've seen the mistakes of the past with this. I mean, look at, look at Hadoop, right? There was this huge battle between, you know, Cloudera and Horton works and map, oh, map bar is proprietary. Oh, Horton works is purely open. Cloudera is open. They're, they're all gone now. I mean, not gone, but they're just, they didn't have it. Right. You know, they, they got unfocused. I go back to Frank's book. They were trying to do too much to, to too many of those, the, the, the zoo animals and you can't fund it all >>To be effective for us. It's very important. I can give you, I don't know, 20 announcements or 50 announcements from the conference, but they're all going a singular goal. And it's, this do not trade off governance of data with the ability to get value out of data. That's everything we do. >>And that's critical for every company in every industry these days that has to be a data company to be, to survive, to be competitive, to be able to extract value from data. If data's currency, how do I leverage a tool like snowflake to be able to extract insights from it that I can act on and create value for my organization, Geico was on stage this morning. Everyone knows Geico and their beloved, um, gecko. Yeah. Is there another customer that you had that you think really articulates the value of the data cloud and to Dave's point how snowflake is becoming that defacto standard data platform? >>Well, we had Goldman Goldman Sachs on stage as well today. And he, he, he, he mentioned it that people think of Goldman as investment banking and all of that, but no, at the heart of what they do, there's a lot of data. And how do they make better decisions? So I think we could run through 20 different examples cuz your premise is the most important. Everything is a data problem. If it is not a data problem, you're not collecting the right data and getting the sense that you could be getting. >>These guys are public, right. >>Adobe. >>Yeah. Right. Adobe's doing it. Yeah. I dunno if the other one is, I don't wanna say, I'll have to ask you off camera, but the other financial firm building a super cloud, right. <laugh> yeah. I call it super cloud. So let be taking advantage of uni store. Yeah. To bring different data types in and monetize it. That's to me, that's the future of data. That's that's been the holy grail, right. >>We, we tried to emphasize that this is, is not a, Hey six, six months ago. We decided to do this. No, this is years in the making mm-hmm <affirmative>, which is why we were so excited to finally share it. Cuz you don't wanna say three years from now, we're gonna have something. No, it was the, now we have it. We have it in preview and it's working at it is as close to the holy grail as it gets. >>Yeah. I mean, look, pressure's on Kristin. Let's face it. Enterprise data warehouse failed to live up to the promises. Uh, certainly the data lakes fail to deliver master data management, all that's a Hadoop, all that stuff. There was a lot of hype around that. And a lot of us got really excited. Me included and then customers spent and they were underwhelmed. Yeah. So you know, you, you, you gotta deliver, you say it, you gotta do it. >>And correct. And then the, the other thing is I would say all of those waves of technology, there was no real better choice. >>Right. They added value. I wouldn't >>Debate that. You have to give it a shot. Like when you've bought 20 different appliances and you have all these silos and someone sells you, Hey, Hadoop will unify it. It sounds good. Just didn't do it. >>Yeah. And no debate that it brought some value for those that were agree. Sophisticated enough to deploy it. And I agree. Yeah. But, but this is a whole different ball game. >>Oh, everything we want to do is democratize and simplify mm-hmm <affirmative> yeah. We could go build something that I don't know. 10 companies in the world could use. That's not the sweet spot. Like how do we advance like the, the state of value generation in the world? That's the scale that we're talking about is go make it easy, accessible for everyone. >>Governed >>Governance and imperative this these days it's law. Yes. So >>Yeah, you have to, but it's not, it's, that's a, that's a ch really difficult challenge to create what I'll call automated or computational governance in a federated manner. That's not trivial. >>And that's our thesis. Everything we're doing is snow park, big announcement today. Python. I I've had people tell me well, but Python should be easy to host the Python run time. Like you can do it. Like I think in a week it took us years. Why? Oh, secure. Oh, details a lot. And <inaudible> mentioned it like securing. That is no easy, uh, feed >>Christian. Thank you so much for joining Dave and me bringing your energy from the keynote stage to the cube, set, breaking down some of the major announcements that have come out today. There's no doubt that the flywheel of innovation at snowflake is alive well and moving quickly, >>Innovation is, uh, at an all time hat snowflake. Thank you for having me. All >>Right. Our pleasure Christian from our guest, Dave ante, Lisa Martin here live in Las Vegas at Caesar's forum covering snowflake summit 22. We right back with our next guest.

>>Welcome everyone to this cube conversation featuring unit Q. I'm your host, Lisa Martin. And we are excited to be joined by Christian Vickle, the founder and CEO of unit Q Christian. Thank you so much for joining me today. >>Thank you so much, Lisa pleasure to be here. >>Let's talk a little bit about unit Q. You guys were founded in 2018, so pretty recent. What is it that unit Q does. And what were some of the gaps in the market that led you to founding the company? >>Yep. So me and my co-founder Nick, we're actually doing our second company now is the unit Q is number two, and our first company was called scout years ago. We were back ES wicks and it was very different from unit Q. It's a social network for meeting people. And it was really during that experience where we saw the impact that quality of the experience quality of the product can have on your growth trajectory and the challenges we faced. How do we test everything before we ship it? And in reality, a modern company will have, let's say, 20 languages supported you support Android, Iowas, web big screen, small screen, you have 20 plus integrations and you have lots of different devices out there that might run your binary a little differently. So who is the ultimate test group of all of these different permutation and that's the end user. >>And we, we saw the, the big gap in the market, sort of the dream platform for us was unit queue. So if, if this would've existed back in the day, we would've been a, a happy purchaser and customer, and it really comes down to how do we, how do we harness the power of user feedback? You know, the end user, that's testing your product every single day in all different configurations. And then they're telling you that, Hey, something didn't work for me. I got double build or the passive recent link didn't work, or I couldn't, you know, when music, when the ad is finished playing on, on my app, the music doesn't resume. So how do we capture those signals into something that the company and different teams can align on? So that's where, you know, unit Q the, the vision here is to build a quality company, to help other companies build higher quality products. >>So really empowering companies to take a data driven approach to product quality. I was looking on your website and noticed that Pandora is one of your customers, but talk to me a little bit about a customer example that you think really articulates the value of what Q unit he was delivering. >>Right? So maybe we should just go back one little step and talk about what is quality. And I think quality is something that is, is a bit subjective. It's something that we live and breathe every day. It's something that can be formed in an instant first impressions. Last it's something that can be built over time that, Hey, I'm using this product and it's just not working for me. Maybe it's missing features. Maybe there are performance related bots. Maybe there is there's even fulfillment related issues. Like we work with Uber and hello, fresh and, and other types of more hybrid type companies in addition to the Pandoras and, and Pinterest and, and Spotify, and these more digital, only products, but the, the end users I'm producing this data, the reporting, what is working and not working out there in many different channels. So they will leave app produce. >>They will write into support. They might engage with a chat support bot. They will post stuff on Reddit on Twitter. They will comment on Facebook ads. So like this data is dispersed everywhere. The end user is not gonna fill out a perfect bug report in a form somewhere that gets filed into gr like they're, they're producing this content everywhere in different languages. So the first value of what we do is to just ingest all of that data. So all the entire surface area of use of feedback, we ingest into a machine and then we clean the data. We normalize it, and then we translate everything into English. And it was actually a surprise to us when we started this company, that there are quite a few companies out there that they're only looking at feedback in English. So what about my Spanish speaking users? What about my French speaking users? >>And when, when, when that is done, like when all of that data is, is need to organized, we extract signals from that around what is impacting the user experience right now. So we break these, all of this data down into something called quality monitors. So quality monitor is basically a topic which can be again, passive reset, link noting, or really anything that that's impacting the end user. And the important part here is that we need to have specific actionable data. For instance, if I tell you, Hey, Lisa music stops playing is a growing trend that our users are reporting. You will tell me, well, what can I do with that? Like what specifically is breaking? So we deploy up to 1500 unique quality monitors per customer. So we can then alert different teams inside of the organization of like, Hey, something broke and you should take a look at it. >>So it's really breaking down data silos within the company. It aligns cross-functional teams to agree on what should be fixed next. Cause there's typically a lot of confusion, you know, marketing, they might say, Hey, we want this fixed engineering. They're like, well, I can't reproduce, or that's not a high priority for us. The support teams might also have stuff that they want to get fixed. And what we've seen is that these teams, they struggle to communicate. So how do we align them around the single source of truth? And I think that's for unit two is early identification of stuff. That's not working in production and it's also aligning the teams so they can quickly triage and say, yes, we gotta fix this right before it snowballs into something. We say, you know, we wanna, we wanna cap catch issues before you go into crisis PR mode, right? So we want to get this, we wanna address it early in the cycle. >>Talk to me about when you're in customer conversations, Christian, the MarTech landscape is competitive. There's nearly 10,000 different solutions out there, and it's growing really quickly quality monitors that you just described is that one of the key things that, that you talk to customers about, that's a differentiator for unit Q. >>Yeah. So I mean, it, it, it comes down to, as you're building your product, right, you, you have, you have a few different options. One is to build new features and we need to build new features and innovate and, and, and that's all great. We also need to make sure that the foundation of the product is working and that we keep improving quality and what, what we see with, with basically every customer that we work with, that, that when quality goes up, it's supercharges the growth machine. So quality goes up, you're gonna see less support tickets. You're gonna see less one star reviews, less one star reviews is of course good for making the store front convert better. You know, I, I want install a 4.5 star app, not a 3.9 star app. We also see that sentiment. So for those who are interested in getting that NPS score up for the next time we measure it, we see that quality is of course a very important piece of that. >>And maybe even more importantly, so sort of inside of the product machine, the different conversion steps, let's say sign up to activate it to coming back in second day, 30 day, 90 day, and so forth. We see a dramatic impact on how quality sort of moves that up and down the retention function, if you will. So it, it really, if you think about a modern company, like the product is sort of the center of the existence of the company, and if the product performs really well, then you can spend more money in marketing because it converts really good. You can hire more engineers, you can hire, you can hire more support people and so forth. So it's, it's really cool to see that when quality improves its supercharges, everything else I think for marketing it's how do you know if you're spending into a broken product or not? >>And I, and I, I feel like marketing has, they have their insights, but it's, it's not deep enough where they can go to engineering and say, Hey, these 10 issues are impacting my MPS score and they're impacting my conversion and I would love for you to fix it. And when you can bring tangible impact, when you can bring real data to, to engineering and product, they move on it cause they also wanna help build the company. And, and so I think that's, that's how we stand out from the more traditional MarTech, because we need to fix the core of, of sort of this growth engine, which is the quality of the product >>Quality of the product. And obviously that's directly related to the customer experience. And we know these days, one of the things I think that's been in short supply the last couple of years is patience. We know when customers are unhappy with the product or service, and you talked about it a minute ago, they're gonna go right to, to Reddit or other sources to complain about that. So being able to, for uniq, to help companies to improve the customer experience, isn't I think table stakes for businesses it's mission critical these days. Yeah, >>It is mission critical. So if you look at the, let's say that we were gonna start a, a music app. Okay. So how do we, how do we compete as a music app? Well, if you, if you were to analyze all different music apps out there, they have more or less the same features app. Like they, the feature differentiation is minimal. And, and if you launch a new cool feature than your competitor will probably copy that pretty quickly as well. So competing with features is really hard. What about content? Well, I'm gonna get the same content on Spotify as apple SD. So competing with content is also really hard. What about price? So it turns out you'll pay 9 99 a month for music, but there's no, there's no 1 99. It's gonna be 9 99. So quality of the experience is one of the like last vectors or areas where you can actually compete. >>And we see consistently that if you' beating your competition on quality, you will do better. Like the best companies out there also have the highest quality experience. So it's, it's been, you know, for us at our last company, measuring quality was something that was very hard. How do we talk about it? And when we started this company, I went out and talked to a bunch of CEOs and product leaders and board members. And I said, how do you talk about quality in a board meeting? And they were, they said, well, we don't, we don't have any metrics. So actually the first thing we did was to define a metrics. We have, we have this thing called this unit Q score, which is on our website as well, where we can base it's like the credit score. So you can see your score between zero and a hundred. >>And if your score is 100, it means that we're finding no quality issues in the public domain. If your score is 90, it means that 10% of the data we look at refers to a quality issue. And the definition of a quality issue is quite simple. It is when the user experience doesn't match the user expectation. There is a gap in between, and we've actually indexed the 5,000 largest apps out there. So we're then looking at all the public review. So on our website, you can go in and, and look up the unit Q score for the 5,000 largest products. And we republish these every night. So it's an operational metric that changes all the time. >>Hugely impactful. Christian, thank you so much for joining me today, talking to the audience about unit Q, how you're turning qualitative feedback into pretty significant product improvements for your customers. We appreciate your insights. >>Thank you, Lisa, have a great day. >>You as well, per Christian Lin, I'm Lisa Martin. You're watching a cube conversation.

>> (bright music) >> Narrator: The Cube, presents Kubecon and Cloudnativecon, year of 2022, brought to you by Red Hat, the cloud native computing foundation and its ecosystem partners. >> Now what we're opening. Welcome to Valencia, Spain in Kubecon Cloudnativecon, Europe, 2022. I'm Keith Townsend, along with my host, Paul Gillin, who is the senior editor for architecture at Silicon angle, Paul. >> Keith you've been asking me questions all these last two days. Let me ask you one. You're a traveling man. You go to a lot of conferences. What's different about this one. >> You know what, we're just talking about that pre-conference, open source conferences are usually pretty intimate. This is big. 7,500 people talking about complex topics, all in one big area. And then it's, I got to say it's overwhelming. It's way more. It's not focused on a single company's product or messaging. It is about a whole ecosystem, very different show. >> And certainly some of the best t-shirts I've ever seen. And our first guest, Jim has one of the better ones. >> I mean a bit cockroach come on, right. >> Jim Walker, principal product evangelist at CockroachDB and Christian Huning, tech director of cloud technologies at Finleap Connect, a financial services company that's based out of Germany, now offering services in four countries now. >> Basically all over Europe. >> Okay. >> But we are in three countries with offices. >> So you're CockroachDB customer and I got to ask the obvious question. Databases are hard and started the company in 2015 CockroachDB, been a customer since 2019, I understand. Why take the risk on a four year old database. I mean that just sounds like a world of risk and trouble. >> So it was in 2018 when we joined the company back then and we did this cloud native transformation, that was our task basically. We had very limited amount of time and we were faced with a legacy infrastructure and we needed something that would run in a cloud native way and just blend in with everything else we had. And the idea was to go all in with Kubernetes. Though early days, a lot of things were alpha beta, and we were running on mySQL back then. >> Yeah. >> On a VM, kind of small setup. And then we were looking for something that we could just deploy in Kubernetes, alongside with everything else. And we had to stack and we had to duplicate it many times. So also to maintain that we wanted to do it all the same like with GitOps and everything and Cockroach delivered that proposition. So that was why we evaluate the risk of relatively early adopting that solution with the proposition of having something that's truly cloud native and really blends in with everything else we do in the same way was something we considered, and then we jumped the leap of faith and >> The fin leap of faith >> The fin leap of faith. Exactly. And we were not dissatisfied. >> So talk to me a little bit about the challenges because when we think of MySQL, MySQL scales to amazing sizes, it is the de facto database for many cloud based architectures. What problems were you running into with MySQL? >> We were running into the problem that we essentially, as a finTech company, we are regulated and we have companies, customers that really value running things like on-prem, private cloud, on-prem is a bit of a bad word, maybe. So it's private cloud, hybrid cloud, private cloud in our own data centers in Frankfurt. And we needed to run it in there. So we wanted to somehow manage that and with, so all of the managed solution were off the table, so we couldn't use them. So we needed something that ran in Kubernetes because we only wanted to maintain Kubernetes. We're a small team, didn't want to use also like full blown VM solution, of sorts. So that was that. And the other thing was, we needed something that was HA distributable somehow. So we also looked into other solutions back at the time, like Vitis, which is also prominent for having a MySQL compliant interface and great solution. We also got into work, but we figured, this is from the scale, and from the sheer amount of maintenance it would need, we couldn't deliver that, we were too small for that. So that's where then Cockroach just fitted in nicely by being able to distribute BHA, be resilient against failure, but also be able to scale out because we had this problem with a single MySQL deployment to not really, as it grew, as the data amounts grew, we had trouble to operatively keep that under control. >> So Jim, every time someone comes to me and says, I have a new database, I think we don't need it, yet another database. >> Right. >> What problem, or how does CockroachDB go about solving the types of problems that Christian had? >> Yeah. I mean, Christian laid out why it exists. I mean, look guys, building a database isn't easy. If it was easy, we'd have a database for every application, but you know, Michael Stonebraker, kind of godfather of all database says it himself, it takes seven, eight years for a database to fully gestate to be something that's like enterprise ready and kind of, be relied upon. We've been billing for about seven, eight years. I mean, I'm thankful for people like Christian to join us early on to help us kind of like troubleshoot and go through some things. We're building a database, it's not easy. You're right. But building a distributor system is also not easy. And so for us, if you look at what's going on in just infrastructure in general, what's happening in Kubernetes, like this whole space is Kubernetes. It's all about automation. How do I automate scale? How do I automate resilience out of the entire equation of what we're actually doing? I don't want to have to think about active passive systems. I don't want to think about sharding a database. Sure you can scale MySQL. You know, how many people it takes to run three or four shards of MySQL database. That's not automation. And I tell you what, this world right now with the advances in data how hard it is to find people who actually understand infrastructure to hire them. This is why this automation is happening, because our systems are more complex. So we started from the very beginning to be something that was very different. This is a cloud native database. This is built with the same exact principles that are in Kubernetes. In fact, like Kubernetes it's kind of a spawn of borg, the back end of Google. We are inspired by Spanner. I mean, this started by three engineers that worked at Google, are frustrated, they didn't have the tools, they had at Google. So they built something that was, outside of Google. And how do we give that kind of Google like infrastructure for everybody. And that's, the advent of Cockroach and kind of why we're doing, what we're doing. >> As your database has matured, you're now beginning a transition or you're in a transition to a serverless version. How are you doing that without disrupting the experience for existing customers? And why go serverless at all? >> Yeah, it's interesting. So, you know, serverless was, it was kind of a an R&D project for us. And when we first started on a path, because I think you know, ultimately what we would love to do for the database is let's not even think about database, Keith. Like, I don't want to think about the database. What we're building too is, we want a SQL API in the cloud. That's it. I don't want to think about scale. I don't want to think about upgrades. I literally like. that stuff should just go away. That's what we need, right. As developers, I don't want to think about isolation levels or like, you know, give me DML and I want to be able to communicate. And for us the realization of that vision is like, if we're going to put a database on the planet for everybody to actually use it, we have to be really, really efficient. And serverless, which I believe really should be infrastructure less because I don't think we should be thinking of just about service. We got to think about, how do I take the context of regions out of this thing? How do I take the context of cloud providers out of what we're talking about? Let's just not think about that. Let's just code against something. Serverless was the answer. Now we've been building for about a year and a half. We launched a serverless version of Cockroach last October and we did it so that everybody in the public could have a free version of a database. And that's what serverless allows us to do. It's all consumption based up to certain limits and then you pay. But I think ultimately, and we spoke a little bit about this at the very beginning. I think as ISVs, people who are building software today the serverless vision gets really interesting because I think what's on the mind of the CTO is, how do I drive down my cost to the cloud provider? And if we can basically, drive down costs through either making things multi-tenant and super efficient, and then optimizing how much compute we use, spinning things down to zero and back up and auto scaling these sort of things in our software. We can start to make changes in the way that people are thinking about spend with the cloud provider. And ultimately we did that, so we could do things for free. >> So, Jim, I think I disagree Christian, I'm sorry, Jim. I think I disagree with you just a little bit. Christian, I think the biggest challenge facing CTOs are people. >> True. >> Getting the people to worry about cost and spend and implementation. So as you hear the concepts of CoachDB moving to a serverless model, and you're a large customer how does that make you think or react to your people side of your resources? >> Well, I can say that from the people side of resources luckily Cockroach is our least problem. So it just kind of, we always said, it's an operator stream because that was the part that just worked for us, so. >> And it's worked as you have scaled it? without you having ... >> Yeah. I mean, we use it in a bit of a, we do not really scale out like the Cockroach, like really large. It's like, more that we use it with the enterprise features of encryption in the stack and our customers then demand. If they do so, we have the Zas offering and we also do like dedicated stacks. So by having a fully cloud native solution on top of Kubernetes, as the foundational layer we can just use that and stamp it out and deploy it. >> How does that translate into services you can provide your customers? Are there services you can provide customers that you couldn't have, if you were running, say, MySQL? >> No, what we do is, we run this, so the SAS offering runs in our hybrid private cloud. And the other thing that we offer is that we run the entire stack at a cloud provider of their choosing. So if they are an AWS, they give us an AWS account, we put it in there. Theoretically, we could then also talk about using the serverless variant, if they like so, but it's not strictly required for us. >> So Christian, talk to me about that provisioning process because if I had a MySQL deployment before I can imagine how putting that into a cloud native type of repeatable CICD pipeline or Ansible script that could be difficult. Talk to me about that. How CockroachDB enables you to create new onboarding experiences for your customers? >> So what we do is, we use helm charts all over the place as probably everybody else. And then each application team has their parts of services, they've packaged them to helm charts, they've wrapped us in a super chart that gets wrapped into the super, super chart for the entire stack. And then at the right place, somewhere in between Cockroach is added, where it's a dependency. And as they just offer a helm chart that's as easy as it gets. And then what the teams do is they have an inner job, that once you deploy all that, it would spin up. And as soon as Cockroach is ready it's just the same reconcile loop as everything. It will then provision users, set up database schema, do all that. And initialize, initial data sets that might be required for a new setup. So with that setup, we can spin up a new cluster and then deploy that stack chart in there. And it takes some time. And then it's done. >> So talk to me about life cycle management. Because when I have one database, I have one schema. When I have a lot of databases I have a lot of different schemas. How do you keep your stack consistent across customers? >> That is basically part of the same story. We have get offs all over the place. So we have this repository, we see the super helm chart versions and we maintain like minus three versions and ensure that we update the customers and keep them up to date. It's part of the contract sometimes, down to the schedule of the customer at times. And Cockroach nicely supports also, these updates with these migrations in the background, the schema migrations in the background. So we use in our case, in that integration SQL alchemy, which is also nicely supported. So there was also part of the story from MySQL to Postgres, was supported by the ORM, these kind of things. So the skill approach together with the ease of helm charts and the background migrations of the schema is a very seamless upgrade operations. Before that we had to have downtime. >> That's right, you could have online schema changes. Upgrading the database uses the same concept of rolling upgrades that you have in Kubernetes. It's just cloud native. It just fits that same context, I think. >> Christian: It became a no-brainer. >> Yeah. >> Yeah. >> Jim, you mentioned the idea of a SQL API in the cloud, that's really interesting. Why does such a thing not exist? >> Because it's really difficult to build. You know, SQL API, what does that mean? Like, okay. What I'm going to, where does that endpoint live? Is there one in California one on the east coast, one in Europe, one in Asia? Okay. And I'm asking that endpoint for data. Where does that data live? Can you control where data lives on the planet? Because ultimately what we're fighting in software today in a lot of these situations is the speed of light. And so how do you intelligently place data on this planet? So that, you know, when you're asking for data, when you're maybe home, it's a different latency than when you're here in Valencia. Does that data follow and move you? These are really, really difficult problems to solve. And I think that we're at that layer of, we're at this moment in time in software engineering, we're solving some really interesting, interesting things cause we are budding against this speed of light problem. And ultimately that's one of the biggest challenges. But underneath, it has to have all this automation like the ease at which we can scale this database like the always on resilient, the way that we can upgrade the entire thing with just rolling upgrades. The cloud native concepts is really what's enabling us to do things at global scale it's automation. >> Let's alk about that speed of light in global scale. There's no better conference for speed of light, for scale, than Kubecon. Any predictions coming out of the show? >> It's less a prediction for me and more of an observation, you guys. Like look at two years ago, when we were here in Barcelona at QCon EU, it was a lot of hype. It's a lot of hype, a lot of people walking around, curious, fascinated, this is reality. The conversations that I'm having with people today, there's a reality. There's people really doing, they're becoming cloud native. And to me, I think what we're going to see over the next two to three years is people start to adopt this kind of distributed mindset. And it permeates not just within infrastructure but it goes up into the stack. We'll start to see much more developers using, Go and these kind of the threaded languages, because I think that distributed mindset, if it starts at the chip all the way to the fingertip of the person clicking and you're distributed everywhere in between. It is extremely powerful. And I think that's what Finleap, I mean, that's exactly what the team is doing. And I think there's a lot of value and a lot of power in that. >> Jim, Christian, thank you so much for coming on the Cube and sharing your story. You know what we're past the hype cycle of Kubernetes, I agree. I was a nonbeliever in Kubernetes two, three years ago. It was mostly hype. We're looking at customers from Microsoft, Finleap and competitors doing amazing things with this platform and cloud native in general. Stay tuned for more coverage of Kubecon from Valencia, Spain. I'm Keith Townsend, along with Paul Gillin and you're watching the Cube, the leader in high tech coverage. (bright music)

>>Hi, welcome to this cube conversation. I'm Lisa Martin and pleased to welcome back. One of our cube alumni, Christian rowdy, the VP of technology strategy at Citrix Christian. Welcome back to the program. >>Thank you, Lisa. And thanks for having me. Great to see you again, and we'll be virtually at this time. >>Great to see you too. It's been a couple of years and quite a few things have changed since we got to sit down at synergy a couple of years together together. Citrix has an exciting new announcement. Let's unpack that. Talk me to me about what you're announcing and what it's going to deliver. >>Sure. You know, as you said, actually, I can't believe it's been a couple of years since we last saw each other. And I think, you know, time's kind of just disappeared within the pandemic. So it actually, as a result of some of those things that we've seen, you know, people get so tired of being stuck in the same place and tired of being on this constant stream of video. And one of the things that we wanted to do was, was actually a vital Citrix launch part, which is kind of our new announcement series that will be delivered via LinkedIn live. But he's really intended to be kind of a short burst approach to providing updates to some of them really important things that we're working on at Citrix. So, uh, hopefully, uh, people would love to say a reason and get some rich information from them. >>And there's going to be a series of three launchpad programs. Now we've seen so much change since the rapid pivot to work from home. Now this worked from anywhere hybrid environment. We've seen the, the massive adoption of cloud and SAS. We've also seen the threat landscape, the attack surface, just expand and expand. Talk to me about why Citrix is doing the launch pad series and then we'll go through each of the three series. >>Yeah, absolutely. So maybe I think just to set a little bit of context, you know, we, we were working on some pretty interesting things, uh, pre pandemic, you know, uh, as a result of the, kind of the, the evolution of Citrix as an organization, but perhaps more importantly, the journey that our customers were on globally, you know, every customer that we had in, in any industry across the world, we're all at various stages of their own digital transformation. And I think what the pandemic has done apart from all the really bad things, actually, if you look at it as a, perhaps one gleaming bit of light in the whole thing was that we've given organizations, whether we realize it or not the opportunity to try this huge remote work experiment. And I think what it has done above anything else has shown that remote work actually works. >>And so as a result of that, what we've seen coming out of the pandemic is that organizations are really going to use that as a springboard. So implement some new strategies, new technologies, and really drive the next generation of that business. So with one eye on that, I think if you were to categorize the three big things that we're looking at from a Citrix perspective, it's really about how to help, we'll continue to help our customers with that accelerated it modernization to really help them understand what it takes to have secure, flexible work in this new post pandemic world. And then also to think about productivity, what does productivity mean in a world of ever more distributed teams? And so the events that we're talking about and specifically the cloud one, we'll focus on some of the new offerings from Citrix, some of the new technologies and talk about the trends that we've seen within our customers. >>So, you know, one of the big things that Citrix has always been very proud of is our market leading position in virtual application and virtual desktop delivery. And even that itself has now begun to emerge into what we call desktop as a service. And there's a ton of new innovations that we've been working on in that space as well. But also if you think about what's happening in cloud, as you talked about, you know, the evolution of applications being from traditional on premises, wills to SAS applications, what we're also seeing is things like the network services that use to support those applications when they looked slightly different, which from a deployment perspective, and now all moving to cloud services, the security that you alluded to in terms of how complicated that is, but how important it is for it, organizations, those services also moving to cloud as the applications begin to look very differently in the future. So extremely excited about the cloud launch. Patino, we're going to talk a lot about those things that we're doing both in the public cloud, in the hybrid cloud. And I think it will resonate well with customers around the world. >>I think it will as well. And you mentioned there are glimmers of hope that we've seen in the last 18 months. And one of the things that this has proved is that work from home can be productive, can be successful. Employees need to be empowered to be able to do that. Let's go ahead and talk through the first, um, program accelerating it monetization. This is Tuesday, September 28th. Let's talk about some of the, of the Citrix innovations that you're going to be announcing. >>Yeah, so I mean, as I mentioned know, we, we, we think about sort of ecstatic. I see modernization in various parts. You know, we tend to start with the classic infrastructure and we've seen over the years that lots of infrastructure, you know, he's leaving the building. And by that we mean the traditional realms of on-premise data centers or co-location facilities, this constant evolution and migration of those services, uh, to, to infrastructure as a service providers from the huge cloud companies that are out there. And we can continue to see that as a, as a huge trend. Of course. Um, one of the things that off the back of that of course is our move from the traditional world of virtual desktops, which was a very on-premise concept into desktop as a service. So really the key around desktop as a service, it's a simplification, some cost optimization and the things that it are looking at in terms of how they can really bring things to the party for their organizations going forward. >>And of course, as we move into that world of everything being delivered as a service know, things like network services, security services, they almost follow. So some of the things that you'll hear about that is really around our application, delivering security and also our move from VDI to DAS. And, you know, you'll hear a lot about what we're doing with the world's leading cloud providers to really add more Citrix value or build on what we've already done with them, but lots, lots more, uh, and really support the, the, the notion of the, every customer is on a journey to cloud one way or the other. And of course, districts will be ready to help at any stage of that journey. >>Every customer is on the journey to cloud. And we've seen that accelerate so much in the last 18 months. Talk to me a little bit about if we, if we think of desktop as a service, as an evolution of VDI, is that what you're saying? >>Yeah. You know, you think about sort of the traditional VDI scenario was that your virtual desktops, where we were using instead of physical desktops, you know, in inside the usual office location, but during the pandemic, you know, we saw so many customers rely on moving to VDI, to cloud, for reasons of scalability and reasons of security, but then also needing to still in many cases, provide access to those sort of traditional physical PCs. And of course, Citrix has had solutions for that for fundamentally many, many years. Um, but what we're also seeing is that organizations are striving for simplicity. You know, the kind of the value of the desktop is being able to deliver it on demand to the end user securely from wherever they are in the world on whatever device they're on. And as we see this sort of establishment of these new working norms, and I'm not a great fan of the phrase, the new normal, I think we have a new now and that now will evolve. You know, they almost daily as we come through the other side of the pandemic. So the real key drivers for us there obviously flexibility, reliability, security, and also cost optimization, which of course is the bread and butter of most conversations we have with CIO and CTO is around the world. >>That's critical. And I'm going to borrow that, um, the new, now, if you don't mind, I'll cite you credit. But I like that. I agree that I hope this is not the new normal, but one of the things that we've seen in the new now on the security front is we've seen this massive increase in ransomware. Everybody went to work from home almost overnight. Suddenly you have millions of devices, IOT devices connecting to corporate networks. Security became the acceleration of security, became a huge challenge for customers in any organization globally. Let's talk about now the second announcement. This is going to be Tuesday, October 5th, empowering a secure distributed workforce. >>Yeah. And I, and I think you you've hit the nail on the head there. I think the one thing that was perhaps completely staggering to everybody was the speed in which organizations were forced to lock their employees out of the physical office locations and by force. I mean, for all the right reasons that are around the health and wellbeing. I mean, if I think back to my earlier career, you know, before I joined Citrix, I was in a large organization and we would, you know, perform these fire drills every so often where we would go through our disaster recovery business continuity plans and really play scenarios out. Like the office in London was unavailable or the office in LA was unavailable, but never once do I remember is doing every office. And every location is offline from tomorrow. And there's no negotiability. If you have a device at home, please use it. >>You know, we can't provide laptops quick enough, especially with the global chip shortage now as well. So whatever device you have, we'll do our best to, to make that secure. And I think there was, uh, an expectation that the employees would sort of play nicely in that scenario. But of course, you know, if you have your home device, you probably don't update it as much as a work device. So it really does require a new set of thinking. And of course, Citrix has been at the forefront of the zero trust evolution. Now the technologies that we have in place do permit remote work and have them for many years. But I think what we're seeing now is a slightly different type of remote work, you know, with different types of, of applications and devices, as you said, different locations, you know, needing to knit all of that together in a sort of a more contextual way so that we can understand, you know, combinations of the end user, their location, the types of applications that they're using the state of their devices, and sort of bring it all that together to really understand, you know, just exactly how much security needs to be applied. >>I think the traditional challenges are still there, you know, too much security and end users will find a way around it because it's not a good user experience. And, you know, perhaps too much user experience without the security leaves, big holes and big problems for organizations. So, yeah, I think this balancing act is really key. And of course, uh, as we go through the launch funnel security, we'll talk about some of the great innovations and solutions that are coming from central. >>You're right with the fact that, uh, you know, this rapid pivot security, the changes, the things that people are saying, the workforce needs to be empowered. You know, we saw this sudden dependence on all these SAS applications to communicate and to collaborate. We also saw with that rapid PennDOT to work from home ransomware, I was doing some research recently, Christian, and that's it, it's up almost 11 X just in the first half of 2021 DDoSs is massively up. People are, are working from home in environments that are just suddenly a bit chaotic. And it's challenging from a security perspective when you have so many distractions to be able to make sure that you're following all the right steps as an employee, um, that you're not clicking on nefarious links and that you're really doing your own due diligence. So having that zero trust and help from folks like Citrix is really key to this new. Now, as you say, >>It is, you know, the unfortunate thing is that wildlife, uh, no end user, or certainly I would hope that no one user would willingly cause a problem from a security perspective. I think just by the very nature of the way that end users thing, can they interact with links in emails or the, uh, you know, interact with attachments in emails? Unfortunately, relying on the human is always going to be the weakest link in the chain. And I think that's why we have to have new approaches to how we address the use of behavior. You know, can we actually, uh, you know, guide people in different ways. There are plenty of technologies that are out there now. And then many, many from Citrix that actually allow us to what we've lovingly said is, is to save the users from themselves. You know, we can't simply rely on every user to be diligent for every single email or every single link that they see. So, you know, being able to actually understand, you know, where the threats are as it relates to the end user and the likely interaction they have, and then being able to combat those threats in the technology at a seamless way is really part of the excited evolution of, of what we're doing with Citrix. And again, lots of great things to come as we go through the security. >>And the third announcement is around worker boosting worker productivity. That's been a challenge that we've all faced in the last 18 months of having, like I said, a minute ago, you know, people that have suddenly kids learning from home spouses, working people competing for bandwidth. Talk to me about some of the things that Citrix is doing to help those workers be more engaged, be plugged in and really be able to get their jobs done from anywhere. >>Yeah, well, you know, I mean, I can give you the benefit of my experience, you know, being, uh, in a, in a home office for, for, for almost 20 months has been completely the antithesis of the opposite of the rest of my career. You know, I've, I've always been very mobile, um, you know, kind of picking up different devices and using them for different things, just purely from a, you know, the perspective of what's most convenient to me. And I think, you know, if you take that and extrapolate it to, to every employee and every organization around the world who has had to invite work into their home, you know, and another soundbite that I use quite often now is that, you know, for the last 20 months, we really haven't been working from home. We've been living at work, you know, and, and, and it's, it's a fact, you know, we've probably done more hours than ever before. >>We've run the risk of burnout more than ever before. And, you know, prior to the pandemic, I know, talked to you and I talked about this very thing, uh, at synergy, you know, w we talked about the notion of needing to focus on employee experience and employee productivity. You know, we saw plenty of examples in customers with huge initiatives around employee experience and employee productivity. You know, CIO is partnering with HR leads and really trying to figure out a map, the employee journey, you know, what is it that they do every day? You know, how can we make their life easier? And perhaps interestingly, how can we reduce some of the mundane overhead, you know, approvals or requests or things that we see in our everyday life, but actually give the employees more time to be valuable and, and do great cognitive work, which is of course, what, what humans do best. >>And so, you know, you remember, we talked about the micro apps back then. We, we we'd completed the acquisition of Sappho, uh, as you and I talked last time when we unveiled micro apps and micro workflows, as a way to really help end users interact within Citrix workspace. So the systems that they use every day, but provide a new way to do that. And just earlier this year, we completed the acquisition and integration of Reich, which was a fantastic addition to the Citrix portfolio. And so we've really begun to think about, you know, how can we actually help employees to do their best work? You know, w w what are the new capabilities that we need within Citrix workspace? What are the new capabilities that we need in Reich? How do we bring all that together with some of the other solutions that we have Citrix Podio is a really interesting suite of productivity applications that we have really aimed at that number one problem, which is how can I get people to be productive, to stay engaged, to lower the burnout and help them do their best work. And I'm really, really excited because there's some fantastic things. So we announced that the work version of the launch pod, which is on October 12th, >>All of those are so critical. You know, I I've always said employee productivity employee is directly related to the customer experience. I've used Wrike myself before, um, for different projects and being able to have productivity tools that allow the employee to engage, to be able to empower them to move projects forward, especially in a time that is still somewhat chaotic is, is critical as is to your point, ensuring that there are the proper tools to facilitate folks so that they get what they need when they need it to help reduce burnout. That's been a big challenge. You're right. That the living at work thing is real, it's persisting, and we're going to be in this hybrid environment for some TBD amount of time longer. So having the ability to be empowered and productive in a secure way, leveraging cloud capabilities is really key. And it's exciting to hear what Citrix launchpad is going to announce over those three days and deliver. >>Yeah. You know, I, I would just say, you know, in, in, in sort of summary where we're, we're really excited about the three areas now, and they really do sort of all come together in some of those challenges that we talked about, you know, specifically around how we can help organizations to address that accelerated it modernization to drive secure, flexible work in the new now, and also to really reach that goal of having extremely productive, distributed teams as we come out the other side of the pandemic. So, you know, lots going on a fantastic time to, to be here and to talk to you and to be at Citrix, of course, with so many, you know, huge customer issues that we, that we have to solve. And we're really excited for the challenge. >>Excellent. And we all are looking forward to that, the Citrix launchpad series, Christian, where can folks go to register for these different programs? >>Yeah, sure. So it's pretty simple. So if we just go to HTTP bit dot Lee, bit dot L Y forward slash Citrix launchpad, and we can sign up through that. >>Excellent. I've already signed up. I'm looking forward to these series, this series, to learn more about what you guys are doing and kind of dig in double click on some of the things that you spoke about Christian. Thank you for joining me today, talking about the launch pad series and letting folks know where they can go to register. >>Thank you. Great to be on the great to see you again. >>Likewise, for Christian Riley, I'm Lisa Martin, you're watching a cube conversation.

(upbeat music) >> Hello everyone, and welcome to this Cube conversation. We're going to dig into some of the more specific and sometimes gory details of managing the nuances of database, database management systems. You know, it's a lot of fun to get it to the daily buzz of cloud and database competition and get a little snarky on Twitter, but there are a lot of mundane issues that you have to address to really do proper database sizing, capacity planning, and you know whether or not database consolidation makes sense. These are not trivial issues. And decades ago they spawned an entire role around the database administrator. They had to do the dirty work of database management so that users and customers would be satisfied. And while automation and cloud are changing that role, at the end of the day, somebody actually has to make the databases work in the cloud and make sure that the business doesn't feel any impact on the transition along the way. So on that note, we have with us Oracle senior director of product management for mission critical databases. He works in Juan Loaiza's group, Chris Craft, and Steve Zivanic whom we know well on the cube says this guy is the Jedi master when it comes to consolidating databases in the cloud. Nobody knows more on the face of the planet Earth. So we're really excited Chris, to have you inside the Cube. Welcome. >> Thanks, thanks Dave. >> That's a very humble thanks. So when it comes to running databases in the cloud can you explain the difference between sizing and capacity planning? Aren't they two sides of the same coin? >> Yeah, you know, they really are. It's like, you know sizing is really part of capacity to planning. It's really, I look at sizing as a one-time effort whereas capacity planning is more your ongoing. You perform sizing initially when the application is deployed. And then, then when you're changing platforms, like going from on-prem to the Cloud you're going to go through a sizing exercise 'cause you're looking at going to a new platform. That's more of a one-time effort, and then ongoing, you're looking at your capacity management over time. So yeah, they are very related so. >> Okay, thank you. So we're going to talk about database consolidation. A lot of people would say, look the cloud makes consolidating databases maybe not irrelevant, but maybe not the best strategy because I got all these different purpose-built databases. Why consolidate databases if they're already going to consolidate it in the cloud in one location? >> Yeah. So, so we're really talking about in in the cloud, you're running virtual machines but consolidation still applies on the virtual machines. So if you have a virtual machine that's dedicated to a database that database is that server, that virtual machine is going to be under utilized over time. So what we're doing with consolidation is running multiple databases within a virtual machine or what it, Oracle virtual cluster. We do everything on clusters. So multiple machines multiple databases within that will drive up the utilization and improve your cost structure. So it's a sizing it's it's absolutely critical on even in the cloud. >> Okay. But, but wouldn't it, I might say to that, wouldn't it be better to have each database have a dedicated VM? I mean, from a performance perspective, it doesn't try to make the database do too much affect performance. >> Yeah. It, so whenever, so we know historically that a database on a dedicated server back in the day that was a physical server, today it's a virtual machine. When you do that, your utilization will be in the range of 15 to 20%. And that's, you know very highly under utilized systems when you do that. So we don't need to isolate things onto dedicated virtual machines for a performance perspective. There are other ways that we can manage that we have resource management built into Oracle and the Oracle database. And then on Exadata we have an integrated IO resource management as well so we can deal with that different ways. >> Okay. So you're basically proposing that you're putting these databases onto a single VM and managing it accordingly. Is there additional details you can provide on that? >> So, you know, we don't put everything into you know, literally one, one VM. You want to have some isolation built in there, but see and take a more pragmatic approach. You know, like every single database in one VM that's the wrong way to go. Each database in a dedicated VM is also the other extreme, also the wrong way to go. So we're kind of right down the middle and be more pragmatic about it, and do some level of consolidation to drive up utilization. >> I remember when I first started following tech I was studying up on, you know kind of how disc drives work and so forth. And there was probably like I can't even remember what it was. It was like probably like 10 megabytes under an actuator. And people were saying, Oh my God, that's so much data. You, you got your blast radius is, is so big. You got to split that up. So it's the same concept, apply with availability. Some would say, there's a problem because you're consolidating all this data and you've got this blast radius that increases. How do you address that? >> And so, you know, redundancy. So we have redundancy at all levels. So if you look at a single, so we're talking about Exadata here, taught in an Exadata machine we can lose up to 24 disc drives out of 30. 30 machines with 36 disc drives, we can use 24 of those. So that'd be 12 per storage cell. You can lose two storage cells as 24 out of 36 drives so we can lose and keep on running. We can also, we also cluster, we also do clustering. So the database servers are clustered together for high availability. So we can take, we can suffer multiple simultaneous failures and keep on running without performance impact either. So it's, so recovery, we handle that in different ways. So it's, look at blast radius from a standpoint, you want some, some isolation for blast radius but we have physical failures is just not something that we're concerned with. >> Why do you deal with taking down a VM? Doesn't that normally mean there's going to be some kind of disruption? >> Oh, so you know, the, so Oracle database, you're talking about real application clusters on on Oracle database, on Exadata. We've got, we have a very fast detection of of failures and then resolution of the failure. So we're looking at a small blip in performance, you know we're looking at a few milliseconds to detect failure and then maybe up around three seconds to actually affect the failover. So the applications that are not getting disconnected, they continue operating in the, in that kind of condition. So that's kind of unique to the Exadata platform. And so, you know, in our cloud, we're running Exadata. We have this built in there. So we're, we're resilient to that type of failure, so. >> And sorry, you mentioned real application clusters. You're saying because you're running real application clusters that's how you're able to become more resilient? >> So yeah, so we have, so Oracle database real application clusters runs on top of a clustered virtual machines on Exadata. We have integration then optimizes the fail over times of that clustering. So it's, it's not the cluster same, it's the optimizations are only built into Exadata. So we have much much faster, much better tighter integration, so much more scalability because of that, that integration that we have. >> Can I run rack in other clouds? Can I put that into Amazon's cloud? >> So, so real application clusters requires two things. It's a, you require shared storage in a fast interconnect, a fast networking interconnecting. And those things just don't exist in the other clouds. We have those built into Exadata in our cloud. And we also, we also allow real application clusters in our relational database, our database cloud service offering as well. But it's, really the highest implementation of that is in Exadata. >> Well, of course I was tongue in cheek joking but this is, this is why, you know, I was listening to Arvind Krishna the other day in IBM Think. And he was saying only 25% of mission critical applications have moved into the cloud. I didn't think it's that high. I mean, but, but what you're doing is basically building a mission critical, you know, cloud or a cloud for mission critical databases. And that's, that's unique. I mean, I would expect other cloud vendors that eventually you know, are going to get there, but you're kind of starting with the hard stuff and working backwards. But, that is what I've always interpreted is unique to Oracle, but how does that affect cost? Isn't that more expensive? >> Actually, no. We're taking services that that start out at a very similar price point. And then we drive. So what we've seen from other customers that are running in like Amazon, for example, we see databases on dedicated virtual machines that run anywhere from 15 to 20% utilization. So what we do is, that low, low utilization, what we do is take that and triple that. So we run, so we run maybe 50% utilization. At that point we still have full redundancy, but we've now made the service one third of the cost. So we're starting at a third, we're starting at a very similar cost. And then we drive it to, you know three times a utilization. This is not crazy numbers. This is, you know, 50% is, is fine and retain the redundancy at that level as well. >> Got it, well so. >> What we've seen is about a third the cost. >> Really? Okay. Well, so, but, what about, like for instance, on AWS, couldn't I run this in a multi availability zone, running RDS or some other cloud database? >> So, so you can run a Multi-AZ environment like in in Amazon, for example, you can run locals. That's what we call local standby. If you do that, you're now instead of being one third, instead of being three times more expensive, you're now six times more expensive. Because that is another copy of the entire platform, the entire instance, the storage, everything on the other availability zone instead of being three times more, it's now six. >> Because you're essentially replicating everything in a brute force mode, right? >> Yeah. It's a data guard standby, local standby in another AZ, or what we call availability domain in our cloud. >> So let's maybe geek out a little bit. So, let's talk more about availability. You know, for years, I mean, I remember going back to reading about this stuff with tandem computers, you know, coincident failures. How are you dealing with those in today's modern world? >> So what we call simultaneous failures is, so we, we deal with that with redundancy in the system. So we have redundancy at all layers in the storage. Like I said earlier, we can take across, you know, two storage cells and each storage cell has a dozen drives. So that's 24 disc drives. That's eight flashcard failures simultaneously. And we keep on running no data loss, no loss of service. That's at the storage layer. We have multiple, multiple redundant networking switches at that, at the networking layer, the internal network. Then we go up into the database server. We then have redundancy across the nodes of a cluster. You have multiple virtual machines that comprise a virtual cluster. So it's at each and every level, we have redundancy. And then we drive the redundancy into the application using what's called application continuity. So the application connections have knowledge of the failure, failure modes of the database. They can follow to the surviving node, and continue operating. >> And you do this with math, you're doing some kind of magic bit slicing, or how do you do that? >> That, so that is that particular thing, application continuity, so technology that's been built into Oracle database since, since 12c, and that it's been around for quite a long time. And it allows the application to follow the rack cluster, any kind of issues with the rack cluster. We can drain connections off. It's very well-proven technology in, you know, prior to to proactive maintenance, we can drain connections over and then it will also handle a failure of a connection as well. And the application following that, yes. >> I learned from my old mainframe days and hanging around with David Floyer. It's always ask, what happens when something goes wrong and it's all about recovery. And you guys have the gold standard there. I mean, we've talked about this a lot. So you got Exadata. That's what is behind your Exadata cloud service, X8M I think you call it, and you've got autonomous database. I'm not great with model numbers, but, but talk about the way you can handle simultaneous failures. I mean, are there like triple redundancies that you've built in? >> Yeah. So everything what we do in our cloud is everything is triple redundancy by default. So we, you can suffer, that way we can suffer two failures and continue operating. So the, the other thing, so recovery, if you look at transaction recovery, when a failure occurs a transaction will flip that session, will flip to the machine that keeps running. It'll reposition all in the work that's in flight, any kind of inflight transactions, any in flight queries that are going on, reposition and continue operating. >> So you've essentially created like the old three site data centers, but you're in a single platform because you're synchronous. But, that same concept in a package. >> It's, you know, it's a lot of times you show a picture of an Exadata. It looks like a single box, but in the box there's some redundancy built in the box. And in fact, in the cloud it's actually across an entire aisle. So it's, we kind of obscure that a little bit, from your provisioning, you know, our database nodes and our storage cells and in the cloud but it's actually across an entire aisle of a dataset. >> Okay, and of course, that's within a synchronous location. Let's talk about disaster recovery, and what you're doing in that area, around Oracle Cloud What are my options there? What's different from other cloud providers we were talking earlier about, AZs, how are you different and what are you doing there? >> Yeah, so we, we talked earlier about the Multi-AZ deployment, what we call it availability domain, AD, so a little different terminology. But we can deploy another, another copy of the database into another availability domain, if you like. It's not often that you lose an entire AZ or AD, it's more, we're protecting from regional failures. So across another region. And that's where we look at, we really look at that as that technology, as a standby, as a data, disaster recovery solution not for HA. HA, we build HA into the machine itself. >> So you're saying, we were talking earlier about AZ, you're saying that's for HA versus DR. Is that, is that what you're contending? >> Yeah, like, you know again, pick on Amazon for a second here. Amazon uses a standby database. What we would normally use for disaster recovery, they're using that for availability. And you're looking at a few minutes of time to flip over to another AZ, whereas within an Exadata frame, we can flip over in milliseconds. We keep continue running. There is no loss of conductivity. And then we use the standby in another region for disaster. That's a true disaster solution. >> As opposed to incurring that penalty of latency, or whatever, to spin up the other resource. >> Right, right. >> Okay, so that's clear how kind of you guys address that, that challenge. Last question, maybe you could give us your take, again folks, coming out of Oracle's mouth, but what's the bottom line cost Delta based on your experience between your service and competitive services? I love these conversations because you're not afraid to talk about the competition, so bring it on. >> I've seen, so we've just based on what we've seen with customers deploying databases in Amazon, versus what, you know we've replaced that within, in our cloud service. We're seeing from just a list price perspective. Now, you know, we discount, I know Amazon discounts, but the only thing I can really speak to is list price perspective. It's about a third the cost. So we're talking about a more powerful platform, runs faster. We get these incredible, we haven't even talked about performance here. Talk about availability, performance where we're getting IO rates, IO latencies in the 19 microsecond range. Now with Exadata, that's going to be 50 times faster than what you get with these traditional cloud vendors. So much, much faster, and a third the cost. >> So talk about discounts, I mean, I know Oracle discounts, Oracle from list price, Oracle provides significant discounts. I'm not as familiar with your cloud pricing but I mean, Amazon's discounts are really in the form of like reserved instances. Is your pricing similar in that regard or different? I mean, if I'm just paying on demand, I'm paying through the nose. I presume it's same with you. If I, but if I buy in bulk getting a discount, is that what you mean by discount? Or is it more similar to the way you've traditionally discounted, you know large customers, the more you spend, the more you you get kind of thing. >> It's a, there's a discount structure. So it's, we don't have the same kind of lock-in like with reserved instance structure, but yeah, it's, there are discounts and that's going to be very customer specific. >> Right. >> So, but I think that the end result we're starting at, a three X differential on the price. >> But the reason I'm asking the question is that the stats you gave me are for list price, right? >> Yeah, yes, yeah. >> Okay, and sure, you're saying that at list price you're, you're less expensive. I, and again, my contention would be just by experiences that your discounts would be more aggressive traditionally in Oracle's traditional business. You know, I've done a lot of Oracle negotiation in my days. And if you're, you know, if you're a big customer you can get good deals. And again, I'm not as familiar with the cloud pricing, but still that's, that's good. If you're doing it on a list price basis, to me, that's a conservative statement if that makes any sense. >> Right, that's where it starts. We know that's where it's starting out. So I, you know, once you get into discounts, it's very customer specific. >> Right. >> We know the starting point is at three X differential. Before you do something in the Multi-AZ would be a six X differential, by the way, so. >> Yeah, okay. All right, Chris. Well, Hey, I appreciate you taking us through this, good stuff, and best of luck, good work. You know, you guys keep, I always say Oracle invest you guys spend a lot of money in RD and, and, you know you're quiet for a while in the cloud and all of a sudden you came out like you invented it. So good job! >> All right. >> All right, thanks. Thanks for coming on. All right. >> Thanks. >> Thank you for watching everybody. This is Dave Vellante for Cube conversations. We'll see you next time. (upbeat music)

>>Okay, We're now going into the technical deep dive. We're gonna geek out here a little bit. Ben Wa Dodgeville is here. He's co founder of Snowflake and president of products. And also joining us is Christian Kleinerman. Who's the senior vice president of products. Gentlemen, welcome. Good to see you. >>Yeah, you that >>get this year, they Thanks for having us. >>Very welcome. So it been well, we've heard a lot this morning about the data cloud, and it's becoming my view anyway, the linchpin of your strategy. I'm interested in what technical decisions you made early on. That that led you to this point and even enabled the data cloud. >>Yes. So? So I would say that that a crowd was built in tow in three phases. Really? The initial phase, as you call it, was it was really about 20 minutes. One regions Teoh, Data Cloud and and that region. What was important is to make that region infinity, infinity scalable, right. And that's our architectural, which we call the beauty cross to share the architectural er so that you can plug in as many were clues in that region as a Z without any limits. The limit is really the underlying prop Provide the, you know, resource is which you know, Cal provide the region as a really no limits. So So that z you know, region architecture, I think, was really the building block of the snowflake. That a cloud. But it really didn't stop there. The second aspect Waas Well, it was really data sharing. How you know munity internets within the region, how to share data between 10 and off that region between different customers on that was also enabled by architectures Because we discover, you know, compute and storage so compute You know clusters can access any storage within the region. Eso that's based off the data cloud and then really faced three Which is critical is the expansion the global expansion how we made you know, our cloud domestic layers so that we could talk You know the snowflake vision on different clouds on DNA Now we are running in three cloud on top of three cloud providers. We started with the ws and US West. We moved to assure and then uh, Google g c p On how this this crowd region way started with one crowd region as I said in the W S U S West, and then we create we created, you know, many you know, different regions. We have 22 regions today, all over the world and all over the different in the cloud providers. And what's more important is that these regions are not isolated. You know, Snowflake is one single, you know, system for the world where we created this global data mesh which connects every region such that not only there's no flex system as a whole can can be aware of for these regions, But customers can replicate data across regions on and, you know, share. There are, you know, across the planet if need be. So So this is one single, you know, really? I call it the World Wide Web. Off data that, that's, you know, is this vision of the data cloud. And it really started with this building block, which is a cloud region. >>Thank you for that. Ben White Christian. You and I have talked about this. I mean, that notion of a stripping away the complexity and that's kind of what the data cloud does. But if you think about data architectures, historically they really had no domain knowledge. They've really been focused on the technology toe ingest and analyze and prepare And then, you know, push data out to the business and you're really flipping that model, allowing the sort of domain leaders to be first class citizens if you will, uh, because they're the ones that creating data value, and they're worrying less about infrastructure. But I wonder, do you feel like customers air ready for that change? >>I I love the observation. They've that, uh, so much energy goes in in in enterprises, in organizations today, just dealing with infrastructure and dealing with pipes and plumbing and things like that and something that was insightful from from Ben Juan and and our founders from from Day one WAAS. This is a managed service. We want our customers to focus on the data, getting the insights, getting the decisions in time, not just managing pipes and plumbing and patches and upgrades, and and the the other piece that it's it's it's an interesting reality is that there is this belief that the cloud is simplifying this, and all of a sudden there's no problem but actually understanding each of the public cloud providers is a large undertaking, right? Each of them have 100 plus services, uh, sending upgrades and updates on a constant basis. And that just distracts from the time that it takes to go and say, Here's my data. Here's my data model. Here's how it make better decisions. So at the heart of everything we do is we wanna abstract the infrastructure. We don't wanna abstract the nuance of each of the cloud providers. And as you said, have companies focus on This is the domain expertise or the knowledge for my industry. Are all companies ready for it? I think it's a It's a mixed bag. We we talk to customers on a regular basis every way, every week, every day, and some of them are full on. They've sort of burned the bridges and, like I'm going to the cloud, I'm going to embrace a new model. Some others. You can see the complete like, uh, shock and all expressions like What do you mean? I don't have all these knobs. 2 to 3 can turn. Uh, but I think the future is very clear on how do we get companies to be more competitive through data? >>Well, Ben Ben. Well, it's interesting that Christian mentioned to manage service and that used to be in a hosting. Guys run around the lab lab coats and plugging things in. And of course, you're looking at this differently. It's high degrees of automation. But, you know, one of those areas is workload management. And I wonder how you think about workload management and how that changes with the data cloud. >>Yeah, this is a great question. Actually, Workload management used to be a nightmare. You know, traditional systems on it was a nightmare for the B s and they had to spend most a lot of their time, you know, just managing workloads. And why is that is because all these workloads are running on the single, you know, system and a single cluster The compete for resources. So managing workload that always explain it as explain Tetris, right? You had the first to know when to run. This work will make sure that too big workers are not overlapping. You know, maybe it really is pushed at night, you know, And And you have this 90 window which is not, you know, efficient. Of course, for you a TL because you have delays because of that. But but you have no choice, right? You have a speaks and more for resource is and you have to get the best out of this speaks resource is. And and for sure you don't want to eat here with her to impact your dash boarding workload or your reports, you know, impact and with data science and and And this became a true nine man because because everyone wants to be that a driven meaning that all the entire company wants to run new workers on on this system. And these systems are completely overwhelmed. So so, well below management was, and I may have before Snowflake and Snowflake made it really >>easy. The >>reason is it's no flag. We leverage the crowds who dedicates, you know, compute resources to each work. It's in the snowflake terminology. It's called a warehouse virtual warehouse, and each workload can run in its own virtual warehouse, and each virtual warehouse has its own dedicated competition resources. It's on, you know, I opened with and you can really control how much resources which workload gas by sizing this warehouses. You know, I just think the compute resources that they can use When the workload, you know, starts to execute automatically. The warehouse, the compute resources are turned off, but turned on by snowflake is for resuming a warehouse and you can dynamically resized this warehouse. It can be done by the system automatically. You know if if the conference see of the workload increases or it can be done manually by the administrator or, you know, just suggesting, you know, uh, compute power. You know, for each workload and and the best off that model is not only it gives you a very fine grain. Control on resource is that this work can get Not only workloads are not competing and not impacting it in any other workload. But because of that model, you can hand as many workload as you want. And that's really critical because, as I said, you know, everyone in the organization wants to use data to make decisions, So you have more and more work roads running. And then the Patriots game, you know, would have been impossible in in a in a centralized one single computer, cross the system On the flip side. Oh, is that you have to have a zone administrator off the system. You have to to justify that. The workload is worth running for your organization, right? It's so easy in literally in seconds, you can stand up a new warehouse and and start to run your your crazy on that new compute cluster. And of course, you have to justify if the cost of that because there is a cost, right, snowflake charges by seconds off compute So that cost, you know, is it's justified and you have toe. You know, it's so easy now to hire new workflow than you do new things with snowflake that that that you have to to see, you know, and and look at the trade off the cost off course and managing costs. >>So, Christian been while I use the term nightmare, I'm thinking about previous days of workload management. I mean, I talked to a lot of customers that are trying to reduce the elapsed time of going from data insights, and their nightmare is they've got this complicated data lifecycle. Andi, I'm wondering how you guys think about that. That notion of compressing elapsed time toe data value from raw data to insights. >>Yeah, so? So we we obsess or we we think a lot about this time to insight from the moment that an event happens toe the point that it shows up in a dashboard or a report or some decision or action happens based on it. There are three parts that we think on. How do we reduce that life cycle? The first one which ties to our previous conversation is related toe. Where is their muscle memory on processes or ways of doing things that don't actually make us much sense? My favorite example is you say you ask any any organization. Do you run pipelines and ingestion and transformation at two and three in the morning? And the answer is, Oh yeah, we do that. And if you go in and say, Why do you do that? The answer is typically, well, that's when the resource is are available Back to Ben Wallace. Tetris, right? That's that's when it was possible. But then you ask, Would you really want to run it two and three in the morning? If if you could do it sooner, we could do it. Mawr in time, riel time with when the event happened. So first part of it is back to removing the constraints of the infrastructures. How about running transformations and their ingestion when the business best needs it? When it's the lowest time to inside the lowest latency, not one of technology lets you do it. So that's the the the easy one out the door. The second one is instead of just fully optimizing a process, where can you remove steps of the process? This is where all of our data sharing and the snowflake data marketplace come into place. How about if you need to go in and just data from a SAS application vendor or maybe from a commercial data provider and imagine the dream off? You wouldn't have to be running constant iterations and FTP s and cracking C S V files and things like that. What if it's always available in your environment, always up to date, And that, in our mind, is a lot more revolutionary, which is not? Let's take away a process of ingesting and copying data and optimize it. How about not copying in the first place? So that's back to number two on, then back to number three is is what we do day in and day out on making sure our platform delivers the best performance. Make it faster. The combination of those three things has led many of our customers, and and And you'll see it through many of the customer testimonials today that they get insights and decisions and actions way faster, in part by removing steps, in part by doing away with all habits and in part because we deliver exceptional performance. >>Thank you, Christian. Now, Ben Wa is you know, we're big proponents of this idea of the main driven design and data architecture. Er, you know, for example, customers building entire applications and what I like all data products or data services on their data platform. I wonder if you could talk about the types of applications and services that you're seeing >>built >>on top of snowflake. >>Yeah, and And I have to say that this is a critical aspect of snowflake is to create this platform and and really help application to be built on top of this platform. And the more application we have, the better the platform will be. It is like, you know, the the analogies with your iPhone. If your iPhone that no applications, you know it would be useless. It's it's an empty platforms. So So we are really encouraging. You know, applications to be belong to the top of snowflake and from there one actually many applications and many off our customers are building applications on snowflake. We estimated that's about 30% are running already applications on top off our platform. And the reason is is off course because it's it's so easy to get compute resources. There is no limit in scale in our viability, their ability. So all these characteristics are critical for for an application on DWI deliver that you know from day One Now we have improved, you know, our increased the scope off the platform by adding, you know, Java in competition and Snow Park, which which was announced today. That's also you know, it is an enabler. Eso in terms off type of application. It's really, you know, all over and and what I like actually needs to be surprised, right? I don't know what well being on top of snowflake and how it will be the world, but with that are sharing. Also, we are opening the door to a new type of applications which are deliver of the other marketplace. Uh, where, You know, one can get this application died inside the platform, right? The platform is distributing this application, and today there was a presentation on a Christian T notes about, >>you >>know, 20 finds, which, you know, is this machine learning, you know, which is providing toe. You know, any users off snowflake off the application and and machine learning, you know, to find, you know, and apply model on on your data and enrich your data. So data enrichment, I think, will be a huge aspect of snowflake and data enrichment with machine learning would be a big, you know, use case for these applications. Also, how to get there are, you know, inside the platform. You know, a lot of applications led him to do that. Eso machine learning. Uh, that engineering enrichments away. These are application that we run on the platform. >>Great. Hey, we just got a minute or so left in. Earlier today, we ran a video. We saw that you guys announced the startup competition, >>which >>is awesome. Ben, while you're a judge in this competition, what can you tell us about this >>Yeah, >>e you know, for me, we are still a startup. I didn't you know yet, you know, realize that we're not anymore. Startup. I really, you know, you really feel about you know, l things, you know, a new startups, you know, on that. That's very important for Snowflake. We have. We were started yesterday, and we want to have new startups. So So the ends, the idea of this program, the other aspect off that program is also toe help, you know, started to build on top of snowflake and to enrich. You know, this this pain, you know, rich ecosystem that snowflake is or the data cloud off that a cloud is And we want to, you know, add and boost. You know that that excitement for the platform, so So the ants, you know, it's a win win. It's a win, you know, for for new startups. And it's a win, ofcourse for us. Because it will make the platform even better. >>Yeah, And startups, or where innovation happens. So registrations open. I've heard, uh, several, uh, startups have have signed up. You goto snowflake dot com slash startup challenge, and you can learn mawr. That's exciting program. An initiative. So thank you for doing that on behalf of of startups out there and thanks. Ben Wa and Christian. Yeah, I really appreciate you guys coming on Great conversation. >>Thanks for David. >>You're welcome. And when we talk, Thio go to market >>pros. They >>always tell us that one of the key tenets is to stay close to the customer. Well, we want to find out how data helps us. To do that in our next segment. Brings in to chief revenue officers to give us their perspective on how data is helping their customers transform. Business is digitally. Let's watch.

(upbeat music) >> Hi everyone, thank you for joining us at the Data Cloud Summit. The last couple of months have been an exciting time at Snowflake. And yet, what's even more compelling to all of us at Snowflake is what's ahead. Today I have the opportunity to share new product developments that will extend the reach and impact of our Data Cloud and improve the experience of Snowflake users. Our product strategy is focused on four major areas. First, Data Cloud content. In the Data Cloud silos are eliminated and our vision is to bring the world's data within reach of every organization. You'll hear about new data sets and data services available in our data marketplace and see how previous barriers to sourcing and unifying data are eliminated. Second, extensible data pipelines. As you gain frictionless access to a broader set of data through the Data Cloud, Snowflakes platform brings additional capabilities and extensibility to your data pipelines, simplifying data ingestion, and transformation. Third, data governance. The Data Cloud eliminates silos and breaks down barriers and in a world where data collaboration is the norm, the importance of data governance is ratified and elevated. We'll share new advancements to support how the world's most demanding organizations mobilize your data while maintaining high standards of compliance and governance. Finally, our fourth area focuses on platform performance and capabilities. We remain laser focused on continuing to lead with the most performant and capable data platform. We have some exciting news to share about the core engine of Snowflake. As always, we love showing you Snowflake in action, and we prepared some demos for you. Also, we'll keep coming back to the fact that one of the characteristics of Snowflake that we're proud as staff is that we offer a single platform from which you can operate all of your data workloads, across clouds and across regions, which workloads you may ask, specifically, data warehousing, data lake, data science, data engineering, data applications, and data sharing. Snowflake makes it possible to mobilize all your data in service of your business without the cost, complexity and overhead of managing multiple systems, tools and vendors. Let's dive in. As you heard from Frank, the Data Cloud offers a unique capability to connect organizations and create collaboration and innovation across industries fueled by data. The Snowflake data marketplace is the gateway to the Data Cloud, providing visibility for organizations to browse and discover data that can help them make better decisions. For data providers on the marketplace, there is a new opportunity to reach new customers, create new revenue streams, and radically decrease the effort and time to data delivery. Our marketplace dramatically reduces the friction of sharing and collaborating with data opening up new possibilities to all participants in the Data Cloud. We introduced the Snowflake data marketplace in 2019. And it is now home to over 100 data providers, with half of them having joined the marketplace in the last four months. Since our most recent product announcements in June, we have continued broadening the availability of the data marketplace, across regions and across clouds. Our data marketplace provides the opportunity for data providers to reach consumers across cloud and regional boundaries. A critical aspect of the Data Cloud is that we envisioned organizations collaborating not just in terms of data, but also data powered applications and services. Think of instances where a provider doesn't want to open access to the entirety of a data set, but wants to provide access to business logic that has access and leverages such data set. That is what we call data services. And we want Snowflake to be the platform of choice for developing discovering and consuming such rich building blocks. To see How the data marketplace comes to live, and in particular one of these data services, let's jump into a demo. For all of our demos today, we're going to put ourselves in the shoes of a fictional global insurance company. We've called it Insureco. Insurance is a data intensive and highly regulated industry. Having the right access control and insight from data is core to every insurance company's success. I'm going to turn it over to Prasanna to show how the Snowflake data marketplace can solve a data discoverability and access problem. >> Let's look at how Insureco can leverage data and data services from the Snowflake data marketplace and use it in conjunction with its own data in the Data Cloud to do three things, better detect fraudulent claims, arm its agents with the right information, and benchmark business health against competition. Let's start with detecting fraudulent claims. I'm an analyst in the Claims Department. I have auto claims data in my account. I can see there are 2000 auto claims, many of these submitted by auto body shops. I need to determine if they are valid and legitimate. In particular, could some of these be insurance fraud? By going to the Snowflake data marketplace where numerous data providers and data service providers can list their offerings, I find the quantifying data service. It uses a combination of external data sources and predictive risk typology models to inform the risk level of an organization. Quantifying external sources include sanctions and blacklists, negative news, social media, and real time search engine results. That's a wealth of data and models built on that data which we don't have internally. So I'd like to use Quantifind to determine a fraud risk score for each auto body shop that has submitted a claim. First, the Snowflake data marketplace made it really easy for me to discover a data service like this. Without the data marketplace, finding such a service would be a lengthy ad hoc process of doing web searches and asking around. Second, once I find Quantifind, I can use Quantifind service against my own data in three simple steps using data sharing. I create a table with the names and addresses of auto body shops that have submitted claims. I then share the table with Quantifind to start the risk assessment. Quantifind does the risk scoring and shares the data back with me. Quantifind uses external functions which we introduced in June to get results from their risk prediction models. Without Snowflake data sharing, we would have had to contact Quantifind to understand what format they wanted the data in, then extract this data into a file, FTP the file to Quantifind, wait for the results, then ingest the results back into our systems for them to be usable. Or I would have had to write code to call Quantifinds API. All of that would have taken days. In contrast, with data sharing, I can set this up in minutes. What's more, now that I have set this up, as new claims are added in the future, they will automatically leverage Quantifind's data service. I view the scores returned by Quantifind and see the two entities in my claims data have a high score for insurance fraud risk. I open up the link returned by Quantifind to read more, and find that this organization has been involved in an insurance crime ring. Looks like that is a claim that we won't be approving. Using the Quantifind data service through the Snowflake data marketplace gives me access to a risk scoring capability that we don't have in house without having to call custom APIs. For a provider like Quantifind this drives new leads and monetization opportunities. Now that I have identified potentially fraudulent claims, let's move on to the second part. I would like to share this fraud risk information with the agents who sold the corresponding policies. To do this, I need two things. First, I need to find the agents who sold these policies. Then I need to share with these agents the fraud risk information that we got from Quantifind. But I want to share it such that each agent only sees the fraud risk information corresponding to claims for policies that they wrote. To find agents who sold these policies, I need to look up our Salesforce data. I can find this easily within Insureco's internal data exchange. I see there's a listing with Salesforce data. Our sales Ops team has published this listing so I know it's our officially blessed data set, and I can immediately access it from my Snowflake account without copying any data or having to set up ETL. I can now join Salesforce data with my claims to identify the agents for the policies that were flagged to have fraudulent claims. I also have the Snowflake account information for each agent. Next, I create a secure view that joins on an entitlements table, such that each agent can only see the rows corresponding to policies that they have sold. I then share this directly with the agents. This share contains the secure view that I created with the names of the auto body shops, and the fraud risk identified by Quantifind. Finally, let's move on to the third and last part. Now that I have detected potentially fraudulent claims, I'm going to move on to building a dashboard that our executives have been asking for. They want to see how Insureco compares against other auto insurance companies on key metrics, like total claims paid out for the auto insurance line of business nationwide. I go to the Snowflake data marketplace and find SNL U.S. Insurance Statutory Data from SNP. This data is included with Insureco's existing subscription with SMP so when I request access to it, SMP can immediately share this data with me through Snowflake data sharing. I create a virtual database from the share, and I'm ready to query this data, no ETL needed. And since this is a virtual database, pointing to the original data in SNP Snowflake account, I have access to the latest data as it arrives in SNPs account. I see that the SNL U.S. Insurance Statutory Data from SNP has data on assets, premiums earned and claims paid out by each us insurance company in 2019. This data is broken up by line of business and geography and in many cases goes beyond the data that would be available from public financial filings. This is exactly the data I need. I identify a subset of comparable insurance companies whose net total assets are within 20% of Insureco's, and whose lines of business are similar to ours. I can now create a Snow site dashboard that compares Insureco against similar insurance companies on key metrics, like net earned premiums, and net claims paid out in 2019 for auto insurance. I can see that while we are below median our net earned premiums, we are doing better than our competition on total claims paid out in 2019, which could be a reflection of our improved claims handling and fraud detection. That's a good insight that I can share with our executives. In summary, the Data Cloud enabled me to do three key things. First, seamlessly fine data and data services that I need to do my job, be it an external data service like Quantifind and external data set from SNP or internal data from Insureco's data exchange. Second, get immediate live access to this data. And third, control and manage collaboration around this data. With Snowflake, I can mobilize data and data services across my business ecosystem in just minutes. >> Thank you Prasanna. Now I want to turn our focus to extensible data pipelines. We believe there are two different and important ways of making Snowflakes platform highly extensible. First, by enabling teams to leverage services or business logic that live outside of Snowflake interacting with data within Snowflake. We do this through a feature called external functions, a mechanism to conveniently bring data to where the computation is. We announced this feature for calling regional endpoints via AWS gateway in June, and it's currently available in public preview. We are also now in public preview supporting Azure API management and will soon support Google API gateway and AWS private endpoints. The second extensibility mechanism does the converse. It brings the computation to Snowflake to run closer to the data. We will do this by enabling the creation of functions and procedures in SQL, Java, Scala or Python ultimately providing choice based on the programming language preference for you or your organization. You will see Java, Scala and Python available through private and public previews in the future. The possibilities enabled by these extensibility features are broad and powerful. However, our commitment to being a great platform for data engineers, data scientists and developers goes far beyond programming language. Today, I am delighted to announce Snowpark a family of libraries that will bring a new experience to programming data in Snowflake. Snowpark enables you to write code directly against Snowflake in a way that is deeply integrated into the languages I mentioned earlier, using familiar concepts like DataFrames. But the most important aspect of Snowpark is that it has been designed and optimized to leverage the Snowflake engine with its main characteristics and benefits, performance, reliability, and scalability with near zero maintenance. Think of the power of a declarative SQL statements available through a well known API in Scala, Java or Python, all these against data governed in your core data platform. We believe Snowpark will be transformative for data programmability. I'd like to introduce Sri to showcase how our fictitious insurance company Insureco will be able to take advantage of the Snowpark API for data science workloads. >> Thanks Christian, hi, everyone? I'm Sri Chintala, a product manager at Snowflake focused on extensible data pipelines. And today, I'm very excited to show you a preview of Snowpark. In our first demo, we saw how Insureco could identify potentially fraudulent claims. Now, for all the valid claims InsureCo wants to ensure they're providing excellent customer service. To do that, they put in place a system to transcribe all of their customer calls, so they can look for patterns. A simple thing they'd like to do is detect the sentiment of each call so they can tell which calls were good and which were problematic. They can then better train their claim agents for challenging calls. Let's take a quick look at the work they've done so far. InsureCo's data science team use Snowflakes external functions to quickly and easily train a machine learning model in H2O AI. Snowflake has direct integrations with H2O and many other data science providers giving Insureco the flexibility to use a wide variety of data science libraries frameworks or tools to train their model. Now that the team has a custom trained sentiment model tailored to their specific claims data, let's see how a data engineer at Insureco can use Snowpark to build a data pipeline that scores customer call logs using the model hosted right inside of Snowflake. As you can see, we have the transcribed call logs stored in the customer call logs table inside Snowflake. Now, as a data engineer trained in Scala, and used to working with systems like Spark and Pandas, I want to use familiar programming concepts to build my pipeline. Snowpark solves for this by letting me use popular programming languages like Java or Scala. It also provides familiar concepts in APIs, such as the DataFrame abstraction, optimized to leverage and run natively on the Snowflake engine. So here I am in my ID, where I've written a simple scalar program using the Snowpark libraries. The first step in using the Snowpark API is establishing a session with Snowflake. I use the session builder object and specify the required details to connect. Now, I can create a DataFrame for the data in the transcripts column of the customer call logs table. As you can see, the Snowpark API provides native language constructs for data manipulation. Here, I use the Select method provided by the API to specify the column names to return rather than writing select transcripts as a string. By using the native language constructs provided by the API, I benefit from features like IntelliSense and type checking. Here you can see some of the other common methods that the DataFrame class offers like filters like join and others. Next, I define a get sentiment user defined function that will return a sentiment score for an input string by using our pre trained H2O model. From the UDF, we call the score method that initializes and runs the sentiment model. I've built this helper into a Java file, which along with the model object and license are added as dependencies that Snowpark will send to Snowflake for execution. As a developer, this is all programming that I'm familiar with. We can now call our get sentiment function on the transcripts column of the DataFrame and right back the results of the score transcripts to a new target table. Let's run this code and switch over to Snowflake to see the score data and also all the work that Snowpark has done for us on the back end. If I do a select star from scored logs, we can see the sentiment score of each call right alongside the transcript. With Snowpark all the logic in my program is pushed down into Snowflake. I can see in the query history that Snowpark has created a temporary Java function to host the pre trained H20 model, and that the model is running right in my Snowflake warehouse. Snowpark has allowed us to do something completely new in Snowflake. Let's recap what we saw. With Snowpark, Insureco was able to use their preferred programming language, Scala and use the familiar DataFrame constructs to score data using a machine learning model. With support for Java UDFs, they were able to run a train model natively within Snowflake. And finally, we saw how Snowpark executed computationally intensive data science workloads right within Snowflake. This simplifies Insureco's data pipeline architecture, as it reduces the number of additional systems they have to manage. We hope that extensibility with Scala, Java and Snowpark will enable our users to work with Snowflake in their preferred way while keeping the architecture simple. We are very excited to see how you use Snowpark to extend your data pipelines. Thank you for watching and with that back to you, Christian. >> Thank you Sri. You saw how Sri could utilize Snowpark to efficiently perform advanced sentiment analysis. But of course, if this use case was important to your business, you don't want to fully automate this pipeline and analysis. Imagine being able to do all of the following in Snowflake, your pipeline could start far upstream of what you saw in the demo. By storing your actual customer care call recordings in Snowflake, you may notice that this is new for Snowflake. We'll come back to the idea of storing unstructured data in Snowflake at the end of my talk today. Once you have the data in Snowflake, you can use our streams and past capabilities to call an external function to transcribe these files. To simplify this flow even further, we plan to introduce a serverless execution model for tasks where Snowflake can automatically size and manage resources for you. After this step, you can use the same serverless task to execute sentiment scoring of your transcript as shown in the demo with incremental processing as each transcript is created. Finally, you can surface the sentiment score either via snow side, or through any tool you use to share insights throughout your organization. In this example, you see data being transformed from a raw asset into a higher level of information that can drive business action, all fully automated all in Snowflake. Turning back to Insureco, you know how important data governance is for any major enterprise but particularly for one in this industry. Insurance companies manage highly sensitive data about their customers, and have some of the strictest requirements for storing and tracking such data, as well as managing and governing it. At Snowflake, we think about governance as the ability to know your data, manage your data and collaborate with confidence. As you saw in our first demo, the Data Cloud enables seamless collaboration, control and access to data via the Snowflake data marketplace. And companies may set up their own data exchanges to create similar collaboration and control across their ecosystems. In future releases, we expect to deliver enhancements that create more visibility into who has access to what data and provide usage information of that data. Today, we are announcing a new capability to help Snowflake users better know and organize your data. This is our new tagging framework. Tagging in Snowflake will allow user defined metadata to be attached to a variety of objects. We built a broad and robust framework with powerful implications. Think of the ability to annotate warehouses with cost center information for tracking or think of annotating tables and columns with sensitivity classifications. Our tagging capability will enable the creation of companies specific business annotations for objects in Snowflakes platform. Another key aspect of data governance in Snowflake is our policy based framework where you specify what you want to be true about your data, and Snowflake enforces those policies. We announced one such policy earlier this year, our dynamic data masking capability, which is now available in public preview. Today, we are announcing a great complimentary a policy to achieve row level security to see how role level security can enhance InsureCo's ability to govern and secure data. I'll hand it over to Artin for a demo. >> Hello, I'm Martin Avanes, Director of Product Management for Snowflake. As Christian has already mentioned, the rise of the Data Cloud greatly accelerates the ability to access and share diverse data leading to greater data collaboration across teams and organizations. Controlling data access with ease and ensuring compliance at the same time is top of mind for users. Today, I'm thrilled to announce our new row access policies that will allow users to define various rules for accessing data in the Data Cloud. Let's check back in with Insureco to see some of these in action and highlight how those work with other existing policies one can define in Snowflake. Because Insureco is a multinational company, it has to take extra measures to ensure data across geographic boundaries is protected to meet a wide range of compliance requirements. The Insureco team has been asked to segment what data sales team members have access to based on where they are regionally. In order to make this possible, they will use Snowflakes row access policies to implement row level security. We are going to apply policies for three Insureco's sales team members with different roles. Alice, an executive must be able to view sales data from both North America and Europe. Alex in North America sales manager will be limited to access sales data from North America only. And Jordan, a Europe sales manager will be limited to access sales data from Europe only. As a first step, the security administrator needs to create a lookup table that will be used to determine which data is accessible based on each role. As you can see, the lookup table has the row and their associated region, both of which will be used to apply policies that we will now create. Row access policies are implemented using standard SQL syntax to make it easy for administrators to create policies like the one our administrators looking to implement. And similar to masking policies, row access policies are leveraging our flexible and expressive policy language. In this demo, our admin users to create a row access policy that uses the row and region of a user to determine what row level data they have access to when queries are executed. When users queries are executed against the table protected by such a row access policy, Snowflakes query engine will dynamically generate and apply the corresponding predicate to filter out rows the user is not supposed to see. With the policy now created, let's log in as our Sales Users and see if it worked. Recall that as a sales executive, Alice should have the ability to see all rows from North America and Europe. Sure enough, when she runs her query, she can see all rows so we know the policy is working for her. You may also have noticed that some columns are showing masked data. That's because our administrator's also using our previously announced data masking capabilities to protect these data attributes for everyone in sales. When we look at our other users, we should notice that the same columns are also masked for them. As you see, you can easily combine masking and row access policies on the same data sets. Now let's look at Alex, our North American sales manager. Alex runs to st Korea's Alice, row access policies leverage the lookup table to dynamically generate the corresponding predicates for this query. The result is we see that only the data for North America is visible. Notice too that the same columns are still masked. Finally, let's try Jordan, our European sales manager. Jordan runs the query and the result is only the data for Europe with the same columns also masked. And you reintroduced masking policies, today you saw row access policies in action. And similar to our masking policies, row access policies in Snowflake will be accepted Hands of capability integrated seamlessly across all of Snowflake everywhere you expect it to work it does. If you're accessing data stored in external tables, semi structured JSON data, or building data pipelines via streams or plan to leverage Snowflakes data sharing functionality, you will be able to implement complex row access policies for all these diverse use cases and workloads within Snowflake. And with Snowflakes unique replication feature, you can instantly apply these new policies consistently to all of your Snowflake accounts, ensuring governance across regions and even across different clouds. In the future, we plan to demonstrate how to combine our new tagging capabilities with Snowflakes policies, allowing advanced audit and enforcing those policies with ease. And with that, let's pass it back over to Christian. >> Thank you Artin. We look forward to making this new tagging and row level security capabilities available in private preview in the coming months. One last note on the broad area of data governance. A big aspect of the Data Cloud is the mobilization of data to be used across organizations. At the same time, privacy is an important consideration to ensure the protection of sensitive, personal or potentially identifying information. We're working on a set of product capabilities to simplify compliance with privacy related regulatory requirements, and simplify the process of collaborating with data while preserving privacy. Earlier this year, Snowflake acquired a company called Crypto Numerix to accelerate our efforts on this front, including the identification and anonymization of sensitive data. We look forward to sharing more details in the future. We've just shown you three demos of new and exciting ways to use Snowflake. However, I want to also remind you that our commitment to the core platform has never been greater. As you move workloads on to Snowflake, we know you expect exceptional price performance and continued delivery of new capabilities that benefit every workload. On price performance, we continue to drive performance improvements throughout the platform. Let me give you an example comparing an identical set of customers submitted queries that ran both in August of 2019, and August of 2020. If I look at the set of queries that took more than one second to compile 72% of those improved by at least 50%. When we make these improvements, execution time goes down. And by implication, the required compute time is also reduced. Based on our pricing model to charge for what you use, performance improvements not only deliver faster insights, but also translate into cost savings for you. In addition, we have two new major announcements on performance to share today. First, we announced our search optimization service during our June event. This service currently in public preview can be enabled on a table by table basis, and is able to dramatically accelerate lookup queries on any column, particularly those not used as clustering columns. We initially support equality comparisons only, and today we're announcing expanded support for searches in values, such as pattern matching within strings. This will unlock a number of additional use cases such as analytics on logs data for performance or security purposes. This expanded support is currently being validated by a few customers in private preview, and will be broadly available in the future. Second, I'd like to introduce a new service that will be in private preview in a future release. The query acceleration service. This new feature will automatically identify and scale out parts of a query that could benefit from additional resources and parallelization. This means that you will be able to realize dramatic improvements in performance. This is especially impactful for data science and other scan intensive workloads. Using this feature is pretty simple. You define a maximum amount of additional resources that can be recruited by a warehouse for acceleration, and the service decides when it would be beneficial to use them. Given enough resources, a query over a massive data set can see orders of magnitude performance improvement compared to the same query without acceleration enabled. In our own usage of Snowflake, we saw a common query go 15 times faster without changing the warehouse size. All of these performance enhancements are extremely exciting, and you will see continued improvements in the future. We love to innovate and continuously raise the bar on what's possible. More important, we love seeing our customers adopt and benefit from our new capabilities. In June, we announced a number of previews, and we continue to roll those features out and see tremendous adoption, even before reaching general availability. Two have those announcements were the introduction of our geospatial support and policies for dynamic data masking. Both of these features are currently in use by hundreds of customers. The number of tables using our new geography data type recently crossed the hundred thousand mark, and the number of columns with masking policies also recently crossed the same hundred thousand mark. This momentum and level of adoption since our announcements in June is phenomenal. I have one last announcement to highlight today. In 2014, Snowflake transformed the world of data management and analytics by providing a single platform with first class support for both structured and semi structured data. Today, we are announcing that Snowflake will be adding support for unstructured data on that same platform. Think of the abilities of Snowflake used to store access and share files. As an example, would you like to leverage the power of SQL to reason through a set of image files. We have a few customers as early adopters and we'll provide additional details in the future. With this, you will be able to leverage Snowflake to mobilize all your data in the Data Cloud. Our customers rely on Snowflake as the data platform for every part of their business. However, the vision and potential of Snowflake is actually much bigger than the four walls of any organization. Snowflake has created a Data Cloud a data connected network with a vision where any Snowflake customer can leverage and mobilize the world's data. Whether it's data sets, or data services from traditional data providers for SaaS vendors, our marketplace creates opportunities for you and raises the bar in terms of what is possible. As examples, you can unify data across your supply chain to accelerate your time and quality to market. You can build entirely new revenue streams, or collaborate with a consortium on data for good. The possibilities are endless. Every company has the opportunity to gain richer insights, build greater products and deliver better services by reaching beyond the data that he owns. Our vision is to enable every company to leverage the world's data through seamless and governing access. Snowflake is your window into this data network into this broader opportunity. Welcome to the Data Cloud. (upbeat music)

>> Hello everyone, we're here at the Snowflake Data Cloud Summit. This is the Tech Titans panel. We're going to explore some of the trends that are shaping new data capabilities and specifically how organizations are transforming their companies, with data and insights. And with me are three amazing guest panelists. Christian Kleinerman is the senior vice president of product at Snowflake. He's joined by Mark Nelson, who's the EVP of product development at Salesforce/Tableau and Mai-Lan Thompson Bukovec, who's the vice president of Block and Object Storage at Amazon web services. Folks, thanks so much for coming on the program. Great to see you all. >> Thanks for having us. >> Nice to see you. >> Glad to be here. >> Excellent, so here in this session, you know, we have the confluence of the data cloud. We have simple and cost effective storage repositories and the visualization of data. These are three ingredients that are really critical for quickly analyzing and turning data into insights and telling stories with data. So, Christian, let me start with you. Of course, this is all enabled by the Cloud and Snowflake. You're extending that to this data cloud. One of the things that we can do today with data that we say weren't able to do maybe five years ago. >> Yeah, certainly I think there is lots of things that we can integrate specific actions but if you were to zoom out and look at the big picture, our ability to reason through data to inform our choices to date with data is bigger than ever before. There are still many companies that have to decide to sample data or to throw away older data, or they don't have the right data from external companies to put their decisions and actions in context. Now we have the technology and the platforms to bring all that data together, tear down silos and look a 360 of a customer or entire action. So I think it's reasoning through data that has increased the capability of organizations dramatically in the last few years. >> So Mai-Lan, when I was a young pup, at IDC, I started the storage program there, many, many moons ago. And so I always pay attention to what's going on in storage, back of my mind. And S3 people forget, sometimes, that was actually the very first cloud product announced by AWS, which really ushered in the cloud era. And that was 2006, it fundamentally changed the way we think about storing data. I wonder if you can explain how S3 specifically in an object storage generally, you know, with get put really transformed storage from a blocker to an enabler of some of these new workloads that we're seeing. >> Absolutely, I think it has been transformational for many companies in every industry. And the reason for that is because in S3, you can consolidate all the different data sets that today are scattered around so many companies, different data centers. And so if you about it, S3 gives the ability to put unstructured data which are video recordings and images. It puts semi structured data which is the CSV file, which every company has lots of. And that has also support for structured data types like parquet files, which drive a lot of the business decisions that every company has to make today. And so if you think about S3, which launched on Pi day in March of 2006, S3 started off as an object store, but it has evolved into so much more than that, where companies all over the world, and every industry are taking those different data sets, they're putting it in S3, they're growing their data and then they're growing the value that they capture on top of that data. And that is the separation we see that snowflake talks about and many of the pioneers across different industries talk about, which is a separation of the growth of storage and the growth of your computer applications. And what's happening is that when you have a place to put your data like S3, which is secure by default and has the availability and the durability and the operational profile you know, and can trust, then the innovation of the application developers really take over, and you know, one example of that is where we have a customer in the financial sector and they started to use S3 to put their customer care recordings. And they were just using it for storage because that obviously dataset grows very quickly. And then somebody in their fraud department got the idea of doing machine learning on top of those customer care recordings. And when they did that they found really interesting data that they could then feed into their fraud detection models. And so you get this kind of alchemy of innovation that happens when you take the datasets of today and yesterday and tomorrow you put them all in one place which is the history and the innovation of your application, developers just takes over and builds, not just what you need today but what you need in the future as well. >> Thank you for that. Mark, I want to bring you into this panel. It's great to have you here. So thank you. I mean, Tableau has been a game changer for organizations. I remember my first, Tableau conference, passionate customers and really bringing cloud-like agility and simplicity to visualization just totally changed the way people thought about data and met with massive data volumes and simplified access. And now we're seeing new workloads that are developing on top of data and Snowflake data and the cloud. Can you talk about how your customers are really telling stories and bringing to life those stories with data on top of things like S3, which Mai-Lan was just talking about? >> Yeah, for sure. Building on what Christian and Mai-Lan have already said our mission at Tableau has always been help people see and understand data. And you look at the amazing advances that are happening in storage and data processing. And now, the data that you can see and play with is so amazing, right? Like at this point in time, it's really nothing short of a new microscope or a new telescope that really lets you understand patterns. They were always there in the world, but you literally couldn't see them because of the limitations of the amount of data that you could bring into the picture, because of the amount of processing power and the amount of sharing of data that you could bring into the picture. And now like you said, these three things are coming together and this amazing ability to see and tell stories with your data combined with the fact that you've got so much more data at your fingertips, the fact that you can now process that data, look at that data share that data in ways that was never possible. Again, I'll go back to that analogy. It feels like the invention of a new microscope, a new telescope a new way to look at the world and tell stories and get to insights that were just, were never possible before. >> So thank you for that, and then Christian I want to come back to this notion of the data cloud and, you know, it's a very powerful concept and of course it's good marketing, but I wonder if you could add some additional color for the audience. I mean, what more can you tell us about the data cloud, how you're seeing it evolving and maybe building on some of the things that Mark was just talking about just in terms of, you know, bringing this vision into reality? >> Certainly, yeah. Data cloud for sure, is bigger and more concrete than just the marketing value of it. The big insight behind our vision for the data cloud is that just the technology, a capability, just a cloud data platform is not what gets organizations to be able to be a data driven, to be able to make great use of data or be highly capable in terms of data ability. The other element beyond technology is the access and availability of data to put their own data in context or enrich based on the knowledge or data from other third parties. So the data cloud, the way to think about it is, is a combination of both technology, which for Snowflake is our Cloud Data platform in all the workloads, the ability to do data warehousing and queries and speeds and feeds fit in there and data engineering, et cetera. But it's also, how do we make it easier for our customers to have access to the data that they need or they could benefit to improve the decisions for their own organizations. Think of the analogy of a set top box. I can give you a great technically set top box but if there's no content on the other side, it makes it difficult for you to get value out of it. That's how we should all be thinking about it, the data cloud, it's technology, but it's also seamless access to data. >> And Mai-Lan, can you give us a sense of the scope and what kind of scale are you seeing with Snowflake on AWS? >> Well, Snowflake has always driven as Christian as a very high transaction rate to S3. And in fact, when Christian and I were talking just yesterday, we were talking about some of the things that have really been remarkable about the long partnership that we've had over the years. And so I'll give you an example of how that evolution has really worked. So as you know, S3 has, is, you know, the first AWS services that is launched and we have customers who have petabytes, hundreds of petabytes and exabytes of storage on history. And so from the ground up S3 has been built for scale. And so when we have customers, like Snowflake that have very high transaction rates for requests, for S3 storage, we put our customer hat on and we ask customers like Snowflake, how do you think about performance? Not just what performance do you need but how do you think about performance? And you know, when Christian and his team were working through the demands of making requests to their S3 data, they were talking about some pretty high spikes over time and just a lot of volume. And so when we built improvements, into our performance over time, we put that hat on for work, you know, Snowflake was telling us what they needed. And then we built our performance model not around a bucket or an account. We built it around a request rate per prefix, because that's what Snowflake and other customers told us they needed. And so when you think about how we scale our performance, we scale it based on a prefix and not a bucket in our account, which other cloud providers do. We do it in this unique way because 90% of our customer roadmap across AWS comes from customer requests. And then that's what Snowflake and other customers were saying is that, "Hey, I think about my performance based on a prefix and of an object and not some, you know, arbitrary semantic of how I happened to organize my buckets." I think the other thing I would also throw out there for skill is, as you might imagine, S3 is a very large distributed system. And again, if I go back to how we architected for our performance improvements, we architected in such a way that a customer like Snowflake, could come in and they could take advantage of horizontally scaling. They can do parallel data retrievals and puts in gets for your data. And when they do that they can get tens of thousands of requests per second because they're taking advantage of the scale of S3. And so, you know, when we think about scale it's not just scale which is the growth of your storage, which every customer needs. IDC says that digital data is growing at 40% year over year. So every customer needs a place to put all of those storage sets that are growing. But the way we also have worked together for many years is this, how can we think about how Snowflake and other customers are driving these patterns of access on top of the data, not just the last history of the storage, but the access and then how can we architect often very uniquely as I talked about with our request rate in such a way that they can achieve what they need to do not just today, but in the future. >> I don't know, three companies here that don't often take their customer hats off. Mark, I wonder if we could come to you, you know, during the Data Cloud Summit, we've been exploring this notion that innovation in technology is really evolved from point products you know, the next generation of server or software tool to platforms that made infrastructure simpler or called functions and now it's evolving into leveraging ecosystems. You know, the power of many versus the resources of one. So my question is, you know, how are you all collaborating and creating innovations that your customers can leverage? >> Yeah, for sure, so certainly, you know Tableau and Snowflake, you know, kind of where were dropped at natural partners from the beginning, right? Like putting that visualization engine on top of Snowflake to, you know, combine that processing power and data and the ability to visualize it was obvious. As you talk about the larger ecosystem now of course, Tableau is part of Salesforce. And so there's a much more interesting story now to be told across the three companies, one in two and a half maybe as we talk about Tableau and Salesforce combined together of really having this full circle of Salesforce you know, with this amazing set of business apps that so much value for customers and getting the data that comes out of their Salesforce applications, putting it into Snowflake so that you can combine that, share that, you process it combine it with data, not just for across Salesforce, but from your other apps in a way that you want. And then put Tableau on top of it. Now you're talking about this amazing platform ecosystem of data, you know, coming from your most valuable business applications in the world with the most, you know, sales opportunity objects, marketing, service, all of that information flowing into this flexible data platform and then this amazing visualization platform on top of it. And there's really no end of the things that our customers can do with that combination >> Christian we're out of time, but I wonder if you could bring us home and I want to end with, you know let's say, you know, people, some people here maybe they don't, maybe they're still struggling with the cumbersome nature of let's say their on-prem data, warehouses. You know, the kids just unplugged them because they rely on them for certain things like reporting but let's say they to raise the bar on their data and analytics, what would you advise for a next step for them? >> Yeah I think the first part or first step to take is around embrace the cloud and the promise on the abilities of cloud technology. There's many studies where relative to peers, companies that are embracing data are coming out ahead and outperforming their peers. And with traditional technology on-prem technology, you ended up with a proliferation of silos and copies of data. And a lot of energy went into managing those on-prem systems and making copies and data governance and security and cloud technology and the type of platform that the Snowflake has brought to market enables organizations to focus on the data, the data model, the data insights, and not necessarily on managing the infrastructure. So I think that will be the first recommendation from our end. Embrace cloud, get onto a modern cloud data platform, make sure that you're spending your time on data, not managing infrastructure and seeing what the infrastructure lets you do. >> It makes a lot of sense, guys. Thanks, thanks so much. We'll have to end it there and thank you everybody for watching. Keep it right there. We'll be back, with the next segment, right after this short break.

>> Hello and welcome to the session featuring CarMax, driving efficiency and innovation with Ansible. I'm your host Christian Emery. I've been at CarMax for over 18 years in several roles ranging from operations to engineering. And in my current role, I'm responsible for CarMax's private cloud and continuous integration, continuous delivery pipelines. Now, my journey with automation started many years ago when I was a Unix and a Linux admin. Day after day, there was always that routine of manual tasks and processes like backups and routine maintenance. Each tasks had a lot of value to the business, but also required consistency, reliability and completion, and demanded quality for system stability. However, it was really boring to carry out the same thing every day. And personally I had a hunger to do more, bring greater value to the business, and need to realize greater satisfaction through my contributions in my career. And this is where automation came into my life. But before we jump into the presentation, I do want to share a little bit about CarMax. For those who may not know, CarMax has been a unique force in the used car industry since 1993. Through innovation and integrity, we've revolutionized the way people buy and sell used cars. We pride ourselves on the experience we provide our customers and our associates to make it possible. And by changing the way we assist our customers, we've also changed the journey of our associates, providing careers in exciting collaborative work environments. In today's presentation, I'm going to cover the early chapters of the CarMax Ansible story. Topics discussed will highlight business need, why we selected Ansible, rapid adoption and our results. And throughout the presentation, I'm also going to share a lot of thoughts and lessons learned to help you with your automation journey. And while listening to the story, I'd like to challenge you to think about your own business needs, technology challenges, and how your team organizes or organization improves approaches automation. Now in our first year, I was challenged to achieve 5,000 hours in efficiency using Ansible. That was a really intimidating number. But we met the challenge and exceeded it. And since then, we've continued to expand our automation through incremental improvements in everyday work to tackling larger operational challenges like regular changes to the environment, routine upgrades and improved infrastructure delivery. Additionally, we expanded automation adoption across multiple teams. We increased our user and contributor base by over five times. And some of that growth was through organic cross team collaboration. However, the greatest growth we had seen was through hackathons, innovation days where we're able to actively collaborate with other teams using Ansible to solve a business problem. And across all those users, we crossed over 15,000 hours of efficiency gain. And I use that term efficiency gained as a measurement to show not only just labor savings, but also tell the story behind other work we accomplished. And keep in mind, this is work that we wouldn't have been able to achieve without automation. And through that user base and hours of efficiency realized, we implemented over 150,000 successful changes. So how do we get there? Earlier I told you about my personal interest in automation and how I've carried that into my current role. And as a leader, I challenge my team to standardize processes and automate as much as possible. We started initially with really repetitive tasks, much like a game of whack-a-mole, but more importantly, through our experimentation, we quickly found we could get better and more consistent results. We soon applied the same approach to our automation for even greater success. But before Ansible, we started to run into issues where team members were taking a more siloed approach to the work. And in an early retrospective, we came to realize that there is a need for a bigger picture mindset. And from that point on, we agreed to standards to increase quality in our code. However, we still occasionally ran into quality issues. Some of these challenges were from homegrown technology, lack of integration and general infrastructure. Now, this is all compounded by the fact that we were using different scripting and programming languages, and not everyone on the team was familiar with Python when compared to say Bash or PowerShell. And while our homegrown solutions made a difference, we thought there could be better ways to meet that demand from the business to do more, better and faster. But like most things in technology, there's always a different tool and approach to get something done. However, some of these other tools required agents on servers making a deployment, a major effort on its own. And additionally, the learning curve was steeper for systems admins and engineers that don't have as much development experience. But this is where Ansible came into the picture. It was easy to use with human readable code. It was an agentless solution allowing us to get started without as much ramp up time. We also liked the fact that it was built on an open standard and a growing user community with an increasing engagement base from partner in vendor integrators. Even better, it had an API we could use to integrate our other platforms as needed. Most recently with the introduction of Ansible collections, we can use community content with greater focus on our automation while worrying less about building new tools. Now, once we select an Ansible as our automation platform, we took a three part approach to implementation and building a foundation for its use. And as I discuss each of these areas, I just like you to consider how to best prepare your teams or organizations for using Ansible. And while planning the transformation, be sure to identify any sort of constraints, roadblocks, and how you plan to measure those results. People, arguably people are the most important part of the equation. You can have all the processes and ways to measure return, but at the end of the day, you need your teams to make that work happen. Start by asking yourself, how well does the team handle change? Are there resource challenges with aligning people and work? Do the people have the right level of knowledge? Do they need training? And how do you start with one team to quickly begin or expand automation? Processes, documentation, standards. Processes are those great ingredients for success in any technology organization. How well are your existing processes documented? Are there any sort of defined standards methods to approaching work? What about your environments? How well does your organization handle executing processes or changes? And lastly, technology. We always need to show results for our investments and technology can help us show that math. Does your organization use metrics and measurements to track progress and results? How do you define or measure success for a project? How should return on investment be measured or quantified? Like I mentioned before, I can't stress it enough, your people, your teams are the most important part of implementing Ansible. They'll be responsible for implementing and developing, maintaining the platform as well as following standards to execute that transformation. And to be successful, they need to have tools, environments, and knowledge. But one of the great things about Ansible is its comparatively easy learning curve. Ansible playbooks are written in a human readable markup language. And I found that most systems admins and engineers are able to pick up Ansible relatively quickly. And for our adoption, some folks were able to pick it up and begin development, while others were a little bit more comfortable and confident with just a little bit of training. Now, Ansible also democratizes technology, freeing up admins and engineers from traditional OS defined silos. Additionally, Ansible playbooks can be consumed by teams without explicit knowledge of the systems or the underlying technology. That's only if a playbook is well written and returns consistent results each time. For us, we first used Ansible to improve our delivery and reduce repeatable manual tasks. Then we turned our attention to shifting left self-service and we're now focused on enabling developers by getting out of the way. These improvements afforded our teams more time to deliver new capabilities to the business. But another benefit to that is teams were able to devote more time to learning and experimenting. When teams first started automating, there's always that impulse or need to go after that biggest win. I would always caution folks to start simple, find small wins to build that experience. These incremental gains are going to feel small, but they quickly add up over time. And as you're going to see, the work should always be done in those smaller increments to return value faster while allowing the ability to quickly make corrections or change course all together. Now, another huge benefit of using that smaller code increment is reuse. These smaller building blocks can and will be used time and time again, reducing future development efforts. And as we quickly learned, one of the best places to start with automation are documented processes. Each step in a process is already documented, it's a huge opportunity to convert it to code and step through those manual processes. And at CarMax, one of the first places we started out was our server checklist process. The process was really thorough, had over a hundred steps to validate systems, make sure they have the right configuration security and specs for each build. And while that process really gave us good consistent results, it was time consuming. It was also prone to human error. But once we automated each of those steps in validations, we were able to turn our focus to the next bottleneck in the process to speed up delivery. And this is why it's always important to strive for quality through consistent predictable results. Automation is just another tool to help make that vision a reality. And when working with teams, it's also important to understand development best practices, keep it simple, and always use version control with code. Better yet, if you're from an ops background, I'd say partner with your development teams to help with this part of the journey. And lastly, when it comes to integrations between platforms and systems, use a modular design, be flexible because technology changes, and over time, so are your integrations. And when it comes to Ansible or just automation in general, there's always that need for efficiency, consistency, reliability, and flexible integrations. And to make this become a reality, you really need to take both a low tech and a high tech approach. If you recall earlier, I mentioned starting with documented processes. That low tech road involves using process mapping value stream analysis tools where you lay out processes end to end to determine the amount of time it takes to execute a process. These processes can be mapped out using whiteboard, sticky notes or by software tools. And from there, more importantly, you can visualize the process bottlenecks and the areas of improvement should be pretty visible. So for CarMax, what we did was we mapped out our infrastructure delivery. We found it was a huge opportunity. But it was also an area we were more comfortable automating given our deep knowledge of the process. So years ago, when we started the process, our time to deliver virtual environments was about two days. Fast forward to now, we can consistently deliver the same infrastructure in just minutes. And in turn, we reuse portions of that process and code for OS refreshes, virtual machine rehydration, system recovery and hypervisor upgrades, just to name a few. And by freeing up team members to do more knowledge work and spend less time on operations, we're able to pivot more resources on the team to align with the business on strategic initiatives. Team members also had more time to do training, research and development for new capabilities, and other areas for future innovation. Now, Ansible gave us a tool where we need to think more like a DevOps organization. And admittedly, a lot of what I've talked about so far has been very operation centric, but systems engineers were all of a sudden writing a testing code, building tools, delivering infrastructure via code, pipelines and API integrations. And as a result, we instantly had to build and strengthen the collaborative relationship between traditional development and operations teams, we had to break down those silos. But the developers appreciate it because they can focus on developing code and not necessarily worry about environments being ready in time or configured correctly. Conversely, operations teams can be focused more on improvements, new capabilities, and spending less time on firefighting. But regardless of the outcomes, you need data to tell that story. And these data elements can start with the hard numbers from reduced cycle times when we were mapping out processes, you can use delivery and SLA metrics. Those were some easy go to numbers. But also consider how you tell that efficiency story. And remember, ROI isn't always about money or the time savings. So as an example, metrics we used included the number of teams using the platform, active contributors, workflows, processes run, and efficiency gain calculations. And as we evolve our journey, the metrics may change along with that story that we need to tell. So to recap, at CarMax, we put people first and you should too. Think about the resources and knowledge your teams are going to need to be successful. And like I said earlier, remember to start small, reuse code as much as possible. This is going to help teams realize faster return on their efforts and start that snowball effect where gains quickly compound over time. Have a vision and decide on targeted outcomes for your team or organization. Then build ROI metrics to help tell that story. But a big part of innovation is experimenting and learning from mistakes. So take a chance, try something new. And in closing, I'd like to thank you for your time. I sincerely hope our results and lessons learned will help you on your automation journey wherever it takes you.

>>LA from Las Vegas. It's the cube covering AWS reinvent 2019, brought to you by Amazon web services and along with its ecosystem partners. >>Oh, welcome back. Inside the sands, we continue our coverage here. Live coverage on the cube of AWS. Reinvent 2019. We're in day three at has been wall to wall, a lot of fun here. Tuesday, Wednesday now Thursday. Dave Volante. I'm John Walls and we're joined by Christian Rahman who was the founder and CEO of for Christian. Good morning to you. Good morning. Thanks for having afternoon. If you're watching on the, uh, on the East coast right now. Um, let's talk about sleep a little bit. I know you're all about data, um, but let's go ahead and introduce the company to those at home who might not be familiar with what your, your poor focus was. The primary focus. Absolutely. So athlete is a managed ETL as a service company. ETL is extract, transform, and load basically about getting data from different data sources, like different applications and databases into a place where it can be analyzed. >>Typically a data warehouse or a data Lake. So let's talk about the big picture then. I mean, because this has been all about data, right? I mean, accessing data, coming from the edge, coming from multiple sources, IOT, all of this, right? You had this proliferation of data and applications that come with that. Um, what are you seeing that big picture wise in terms of what people are doing with their data, how they're trying to access their data, how to turn to drive more value from it and how you serve all those masters, if you will. So there are a few trends that we see these days. One is a, you know, an obvious one that data warehouses are moving to the cloud, right? So, you know, uh, companies used to have, uh, data warehouses on premises and now they're in the cloud. They're, uh, cheaper and um, um, and more scalable, right? With services like a Redshift and snowflake in particular on AWS. Um, and then, uh, another trend is that companies have a lot more applications than they used to. You know, in the, um, in the old days you would have maybe a few data ware, sorry, databases, uh, on premises that you would integrate into your data warehouses. Nowadays you have companies have hundreds or even thousands of applications, um, that effectively become data silos, right? Where, um, uh, analysts are seeing value in that data and they want to want to have access to it. >>So, I mean, ETL is obviously not going away. I mean, it's been here forever and it'll, it'll be here forever. The challenge with ETL has always been it's cumbersome and it's expensive. It's, and now we have this new cloud era. Um, how are you guys changing ETL? >>Yeah. ETL is something that everybody would like to see go away. Everybody would just like, not to do it, but I just want to get access to their data and it should be very unfortunate for you. Right. Well, so we started, uh, we started athlete because we saw that ETL is not going away. In fact, with all the, uh, all these applications and all these needs that analysts have, it's actually becoming a bigger problem than it used to be. Um, and so, uh, what we wanted to do is basically take, take some of that pain out, right? So that companies can get to analyzing their data faster and with less engineering effort. >>Yeah. I mean, you hear this, you know, the typical story is that data scientists spend 80% of their time wrangling data and it's, and it's true in any situation. So, um, are you trying to simplify, uh, or Cloudify ETL? And if so, how are you doing that? >>So with, uh, with the growth in the number of data analysts and the number of data analytics projects that companies wants to take on the, the traditional model of having a few engineers that know how to basically make the data available for analysts, that that model is essentially now broken. And so, uh, just like you want to democratize, uh, BI and democratize analytics, you essentially have to democratize ETL as well, right? Basically that process of making the data ready for analysis. And, uh, and that is really what we're doing at athlete. We're, we're opening up ETL to a much broader audience. >>So I'm interested in how I, so I'm in pain. It's expensive. It's time consuming. Help me Christian, how, how can you help me, sir? >>So, so first of all, we're, we're, um, uh, at least specifically we're a hundred percent AWS, so we're deeply focused on, uh, Redshift data warehouses and S3 and good data lakes. Uh, and you know, there's tremendous amount of innovation. Um, those two sort of sets of technologies now, um, Redshift made a bunch of very cool announcements era at AWS reinvent this year. Um, and so what we do is we take the, uh, the infrastructure piece out, you know, so you can deploy athlete as a hosted service, uh, where we manage all the infrastructure for you or you can deploy it within your VPC. Um, again, you know, in a much, much simplified way, uh, compared to a traditional ETL technologies. Um, and then, you know, beyond that taking, uh, building pipelines, you know, building data pipelines used to be something that would take engineers six months to 18 months, something like that. But, um, but now what we, what we see is companies using athlete, they're able to do it much faster often, um, often an hours or days. >>A couple of questions there. So it's exclusively red shift, is that right? Or other analytic databases and make is >>a hundred percent AWS we're deeply focused on, on integrating well with, with AWS technologies and services. So, um, so on the data warehousing side, we support Redshift and snowflake. >>Okay, great. So I was going to ask you if snowflake was part of that. So, well you saw red shift kind of, I sort of tongue in cheek joke. They took a page out of snowflake separating compute and storage that's going to make customers very happen so they get happy. So they can scale that independently. But there's a big trend going on. I wonder if you can address it in your, you were pointing out before that there's more data sources now because of the cloud. We were just having that conversation and you're seeing the data exchange, more data sources, things like Redshift and snowflake, uh, machine intelligence, other tools like Databricks coming in at the Sage maker, a Sage maker studios, making it simpler. So it's just going to keep going faster and faster and faster, which creates opportunities for you guys. So are you seeing that trend? It's almost like a new wave of compute and workload coming into the cloud? >>Yeah, it's, it's super interesting. Companies can now access, um, a lot more data, more varied data, bigger volumes of data that they could before and um, and they want faster access to it, both in terms of the time that it takes to, you know, to, to bite zero, right? Like the time, the time that it takes to get to the first, uh, first analysis. Um, and also, um, and also in terms of the, the, the data flow itself, right? They, they not want, um, up to the second or up to the millisecond, um, uh, essentially fresh data, uh, in their dashboards and for interactive analysis. And what about the analytics side of this then when we were talking about, you know, warehousing but, but also having access to it and doing something with it. Um, what's that evolution looking like now in this new world? So lots of, um, lots of new interesting technologies there to, um, um, you know, on the, on the BI side and, um, and our focus is on, on integrating really well with the warehouses and lakes so that those, those BI tools can plug in and, and, um, um, and, and, you know, um, get access to the data straight away. Okay. >>So architecturally, why are you, uh, how are you solving the problem? Why are you able to simplify? I'm presuming it's all built in the cloud. That's been, that's kind of an obvious one. Uh, but I wonder if you could talk about that a little bit because oftentimes when we talk to companies that have started born in the cloud, John furrier has been using this notion of, you know, cloud native. Well, the meme that we've started is you take out the T it cloud native and it's cloud naive. So you're cloud native. Now what happens oftentimes with cloud native guys is much simpler, faster, lower cost, agile, you know, cloud mentality. But maybe some, sometimes it's not as functional as a company that's been around for 40 years. So you have to build that up. What's the state of ETL, you know, in your situation. Can you maybe describe that a little bit? How is it that the architecture is different and how address functionality? >>Yeah, I mean, um, so a couple of things there. Uh, um, you, you mentioned Redshift earlier and how they now announce the separation of storage and compute. I think the same is true for e-tail, right? We can, we can build on, um, on these great services that AWS develops like S three and, and, uh, a database migration service and easy to, um, elastic MapReduce, right? We can, we can take advantage of all these, all these cloud primitives and um, um, and, and so the, the infrastructure becomes operationally, uh, easier that way. Um, and, and less expensive and all, all those good things. >>You know, I wonder, Christian, if I can ask you something, given you where you live in a complicated world, I mean, data's complicated and it's getting more complicated. We heard Andy Jassy on Tuesday really give a message to the, to the enterprise. It wasn't really so much about the startups as it previously been at, at AWS reinvent. I mean, certainly talking to developers, but he, he was messaging CEOs. He had two or three CEOs on stage. But what we're describing here with, with red shift, and I threw in Databricks age maker, uh, elastic MapReduce, uh, your tooling. Uh, we just had a company on that. Does governance and, and builders have to kind of cobble these things together? Do you see an opportunity to actually create solutions for the enterprise or is that antithetical to the AWS cloud model? What, what are your thoughts? >>Oh, absolutely know them. Um, uh, these cloud services are, are fantastic primitives, but um, but enterprises clearly have a lot of, and we, we're seeing a lot of that, right? We started out in venture Bactec and, and, and got, um, a lot of, a lot of venture backed tech companies up and running quickly. But now that we're sort of moving up market and, and uh, and into the enterprise, we're seeing that they have a requirements that go way beyond, uh, beyond what, what venture tech, uh, needs. Right. And in terms of security, governance, you know, in, in ETL specifically, right? That that manifests itself in terms of, uh, not allowing data to flow out of, of the, the company's virtual private cloud for example. That's something that's very important in enterprise, a much less important than in, uh, in, in venture-backed tech. Um, data lineage. Right? That's another one. Understanding how data, uh, makes it from, you know, all those sources into the warehouse. What happens along the way. Right. And, and regulated industries in particular, that's very important. >>Yeah. I mean, I, you know, AWS is mindset is we got engineers, we're going to throw engineers at the problem and solve it. Many enterprises look at it differently. We'll pay money to save time, you know, cause we don't have the time. We don't have the resource, I feel like I, I'd like to see sort of a increasing solutions focus. Maybe it's the big SIS that provide that. Now are you guys in the marketplace today? We are. Yup. That's awesome. So how's that? How's that going? >>Yeah. Um, you mean AWS market? Yes. Yes. Uh, yeah, it's, it's um, um, that's definitely one, one channel that, uh, where there's a lot of, a lot of promise I think both. Um, for, for for enterprise companies. Yeah. >>Cause I mean, you've got to work it obviously it doesn't, just the money just doesn't start rolling in you gotta you gotta market yourselves. >>But that's definitely simplifies that, um, that model. Right? So delivering, delivering solutions to the enterprise for sure. So what's down the road for you then, uh, from, from ETL leaps perspectives here or at leaps perspectives. Um, you've talked about the complexities and what's occurred and you're not going away. ETL is here to say problems are getting bigger. What do you see the next year, 12, 18, 24 months as far as where you want to focus on? What do you think your customers are going to need you to focus on? So the big challenge, right is that, um, um, bigger and bigger companies now are realizing that there is a ton of value in their data, in all these applications, right? But in order to, in order to get value out of it, um, you have to put, uh, engineering effort today into building and maintaining these data pipelines. >>And so, uh, so yeah, so our focus is on reducing that, reducing those engineering requirements. Um, right. So that both in terms of infrastructure, pipeline, operation, pipeline setup, uh, and, and those kinds of things. So where, uh, we believe that a lot of that that's traditionally been done with specialized engineering can be done with great software. So that's, that's what we're focused on building. I love the, you know, the company tagged the perfect data pipeline. I think of like the perfect summer, the guy catching a big wave out in Maui or someplace. Good luck on catching that perfect data pipeline you guys are doing. You're solving a real problem regulations. Yeah. Good to meet you. That cause more. We are alive at AWS reinvent 2019 and you are watching the cube.

>> Announcer: Live from Boston, Massachusetts. It's theCUBE, covering IFS World Conference 2019. Brought to you by IFS. >> We're back at IFS World 2019 from the Hynes Convention Center in Boston. I'm Dave Volonte, with my co-host, Paul Gillen. You're watching theCUBE, the leader in live tech coverage. We go out to the events, we extract the signal from the noise, get the best guest, Christian Peterson is here. He's the chief product officer at IFS. Christian, great to see you. >> All right, thank you very much. Happy to be here. >> Your first IFS World Conference, so ... >> It is mine ... >> Mine too, so ... >> Yeah, I'm happy to be here. It's just like getting an injection of customer input and feedback in a very short amount of time So, that's uh, that's awesome. I really love it. >> Yeah, these events are great to connect with customers its one to many conversations. But, give us a sense of your background and why you were attracted to IFS. Why did you join? >> Well from a background perspective, I've always been in the effects of business and technology and uh, you know my passion has always been what we can actually do with technology for businesses to innovate, to differentiate, to do new things to automate things. Really, really a strong believer in the promise of software. Because that's what software is all about. Um, so, um, I have a past with Starbucks, I've started ELP companies, I've been with Microsoft. Uh, for fifteen, sixteen years. Um, have been with SAP for a number of years. So I joined, I joined IFS last year, um, really because of the transformation and the uh, the journey I just was on and the passion that IFS has always had for the customers. And the outcomes we've created for customers. It's just a perfect environment to, to uh to realize the dream of providing value to customers, outcomes for customers, and leveraging technology in the process. >> Yeah, so see you're a challenger, hashtag for the challenger. A hashtag is started. >> Really, really I mean you were at the giant uh, SAP and going to a smaller, not much smaller, but a smaller company, What were they doing that you thought that excited you so much? >> Well the exciting thing again is the focus on the customer and the close proximity to customers in everything I.. >> Wouldn't SAP, sorry to interrupt, wouldn't SAP be the same thing though? >> Let me just, let me put it this way, I went to IFS because I (intelligible) really, really brilliantly. So, is that a, is that a nice way of saying it. (laughter) >> (laughing) Okay. >> So were here for your keynote today you sort of laid out a roadmap, a little vision uh, talked a little bit about digital transformation. But, I wanted to talk about, the, you made a big big emphasis on your API platform. Open API's, embracing that, uh its been somewhat a criticism of you guys in the past. And so, maybe it's a response to that or a response to customers, but why the platform, why, to explain it, its importance and how it fits into your roadmap going forward. >> Well the API enablement is important for many different perspectives. First of all, we use API's ourselves. To create user experiences and drive a lot of the innovation where they are merging technology and so forth. That's one aspect of it. So just for our own, our own level of innovation and the pace at which we can innovate with, going forward on the API platform, is, is, is is dramatic. The second area is really again back to the digital transformation that customers are really driving out there um, a lot of that involves, um, really most companies becoming software companies themselves. So now we have a lot of our customers that actually have developers, they're writing software they're driving new offerings to their customers. And to get value out of these offerings for their customers They really need to get access to a a lot of the capabilites that lives inside of the IFS models. They need to get access to data, to get access to processes because, on of the keys in digital transformation regardless in what shape or form it comes is, you need data, you need massive amounts of data. And you need data from within your firewall you need data from third party, and you need structure data all structure data. And participating in that world is absolutely essential that you have that open API philosophy where you expose yourself and your own data and API's. But, also so we can turn the other way and we can consume data and API's from others so we can create similar scenarios. So it's really about being apart of the ecosystem of, uh, of technologies and solutions that customers rely on. And that's why we joined also, the open API foundation. >> You also demonstrated this morning, uh Orena, your new customer experience platform. Talk about what that is and why it's important. >> Well, so it's, it's important of course again because we, um, um, we have this generational shift in people that are coming into the workforce that expect and want to work differently. And, um, if you think about how people actually work, to do and get things done today, or think about ourselves. Now, we're no spring chickens anymore, right, we've been around... >> Speak for yourself. >> We've seen DOS, we've seen DOS systems. >> Yeah my hand went up in the 3.1 question. >> When the three point, did you put the mouse on the screen as well? (laughing) I've literally seen that. So we've been through that, but the people we are getting into the workforce now they have a different mentality. They are not thinking about what they do. Like, we are thinking about, "how does the system work?" "Where do I click? Where do I go next?" The intuition that people now apply to the system when they start working with them, the systems just have to reflect that intuition. It has to be intuitive, it has to be immersive as well. And the immersive part is really based on what the users see, what they do. The contextual information, the contextual intelligence they get in the context of what they do should want them to do more. Because they can, so they get dragged in and the new type of users, they just have that natural intuition, because that's how you browse the web. You go to one place on the web, go to the next thing, You get inspired by this, you go there. And there's no reason why the systems that you get your work done, why they shouldn't be the exact same thing. Orena is a huge step in that direction, together with our mobile enablement on multiple form factors and devices. >> So you, you mentioned you know saw everybody's becoming a software company, every company is becoming, you've been in the software business for awhile you work for a software company now. You're talking about Orena, you're talking about API integration, I showed you our software. My point is, software is hard. (laughs) There's a talent war for employees, we talked about that off camera. Um, so, as you see these companies digitally transforming, becoming software companies, Mark Endrese's, "software is eating the world", Mark Beneoff, "Everybody is becoming a software company", How are they doing? And what role can you play, IFS, in terms of helping them become a software company. Because it's, it's so damn difficult. >> Yeah, I think that the role of being a software company I think the absolute differentiation they want to create through software and differentiate the offerings or other things that they really want to do, We can't really help them there, because they're differentiated. Like if you're differentiated, you can't find something standard and use for that. But we can enable it and um, as we're looking at it, a lot of the emerging technologies that we can enable them with to achieve it, that's a number of things we can do. And, we are introducing a notion of an application, of application services here, where we really, enable these emerging technologies in the context of what we do. So, while you hear about technologies or augmented realities, mixed realities, artificial intelligence and robotics and IOT and artificial intelligence, all the stuff that you have, we take that and put into context of the focus industries that we focus on and the solution categories that we focus on. So EAP, enterprise asset management, service management. And in that way our customers can focus on what they actually need to do with it, versus focus on the, on the technologies. >> And the API platform allows those customers to, whatever the build to integrate to their ERP system if in fact... >> That's correct, that's correct. And as I mentioned, we also use API's not only on the front end of what we provide and expose all we have, but we also consume on the back end. So the way we actually consume the application services and drag them in and embed them is through API, these application services. >> I understand you're working on an entirely new architecture that you will be debuting in the spring of 2020. How is that going to change the game? >> We don't really think about it as a new architecture. We think about it as a natural evolution that includes some of these things. Uh, so for instance, the introducing, uh the introduction of the application services layer that I mentioned, is more a new layer in our architecture that we introduced. So we don't think about it as a new architecture, we're just evolving what we have. And because of that evolution, that is something that our entire product portfolio will benefit from. Um, and, I already mentioned today how we are aligning the product portfolio from an experience perspective. We are bringing the arena experience through our FSM product to our um, PSO product, to our customer engagement product and so forth. So we are aligning that front end experience on the same design patterns, so forth, because you know, a good experience is a good user experience. >> You talk about Orena bot and this, this gentleman here, who's given us this talk, just through out a gardner status. That, that by, I don't know, by whatever year 2023, uh, more money will be spent on bots than mobile integration. Which is, you know, quite a prediction. Your thoughts. >> Well, I, you know, there's, there's always all kinds of interesting predictions. I think actually, um, I actually think, um, there, amount of money may go down but I think the number of bots will go up dramatically. And, I think we will actually get to a situation where, bots will be creating bots. (laughs) Right? So, That's when you get, when we talk about intelligent and autonomous systems, I really believe it. Because there is no reason why we should not begin to see autonomy in software. >> Dave: Right. >> Um, we see it, uh, I use the example this morning, that we put our lives in the hands of technology everyday, when you go in your car and you use adaptor to cruise to control, you're trusting technology. Like, when you are driving your Tesla. I mean there was an example in San Francisco, uh, I think, uh, in December last year, where the police had been following a driver for 17 miles. And the car wouldn't stop because it was driving itself, and the driver was sleeping. So, they had to, they had to, you know, call up Tesla and say like how can we manipulate this technology so the car actually stops, so the police gradually got the car to stop. And, uh, you know, finally the guy woke up and uh, he'd probably had one too many. But he claimed he wasn't driving, so they shouldn't charge him, but, they did. (laughter) >> Of course, yes. Well bots are getting better, but I still, I still often know when I'm talking to a bot, but it's getting better, wouldn't you say? >> Christian: Yeah, it's getting reallly good. >> Paul: I know, last year I was completely fooled by a fundraising bot. But, I got a phone call from a bot that I spoke to for ninety seconds before realizing it was a bot. (laughter) So it's, its getting pretty good. As you look at, at the technology that excites you, about what you're bringing with your product, you talked a lot this morning about different kinds of technology and how you want to be a leader. What technologies excite you the most about the markets you are serving? >> I tell you what excites me the most is to work through the different levels of, of, uh, digital transformation that I talked about. I'm excited about the reflection between businesses and technology. I'm excited about the reflections between people and experiences, and I'm excited about the reflections between automation and efficiency. We have a lot of technology at our hands, That can help us achieve these different things. But, at the end of the day, it's the outcomes that matter. The technologies are exciting and you know, I can get super geeky about a lot of different technologies. But if it doesn't relate to any, any, not technical vision of product, but any business vision you have on what you actually want to do with it as a business, then I think it becomes dangerous. But, of course we have our geek sessions, where we geek out on all these different things. But, we try to separate that from when we actually, uh, you know, designing and building things directly into the product. But we need the geek sessions to get inspired. And understand what is available, so we can put it in the context of what our customers need today and also what they'll be needing in the future. >> Since you have some decent observation space and digital transformation, I want to ask a question. Uh, uh, our partner ETR, they have a data platform. And I was down in New York last week just talking to them and, one of the theories is, is so spending is starting to slow down a little bit overall on the macro. One of the theories is that digital transformation in the last two years, there's been a lot of experimentation. So a lot of try and, you know, everything. And now they're going into the production with, with what they, what they feel will delivery business value. And two things are happening is their premise. One is, they're narrowing down the focus on new technologies and make, making bets for all the disruptive technologies. The other is, a lot of the legacy stuff, they are pulling out. Saying, "okay, we're moving on." Um, are you seeing that, are you seeing this sort of... That, the bell weathers anyway going heavy now into production with digital transformation. What are you seeing? >> I think its a progression. >> Dave: Uh huh. >> I think it's scenario based. I don't see, I don't see companies making like, an all out bet from one day to another. >> Dave: Just mixed. >> It's mixed and I think you need to take a cautious approach because, you know, you don't, you... When you're in the technology world, you don't always get it right in the first go, we certainly don't get it right, the first time all the time, right? So, often times its important to get something out there. Learn from it, innovate, fail fast sometimes. Um, the worst thing you can do is not acknowledge when you have mad a mistake, And I think that is a risk that some companies also, bear with digital transformation is... If you need to adjust what you, what you thought was the right thing to do, make the adjustment as quickly as possible. >> Dave: You talked in your keynote about tailoring solutions and I want to understand your philosophy. How dogmatic are you, uh, uh, about, uh, not making customizations versus allowing your customers to make those, those tailored? And, and how do you manage that from a, you know cloud and SaaS delivery, evergreen, I think you call it stand point? >> Christian: We, we, absolutely believe that customers should have solutions that match exactly what they need and so forth. We also heard from stage today that, a good philosophy, I really subscribe to that philosophy, that if you're doing things that, you know, is not really differentiating you as a company or something just use a standard process. Why do something custom if it doesn't mean anything. Then you can adjust your processes to that. But if you have things that really differentiate you as a company, you obviously want to have the technology that supports that. And since that is differentiated, you're not likely to have a standard package file. So in that process, what we need to enable is, we need to enable these scenarios where you can extend, uh, we call it extend on the inside, extend on the outside, but you can achieve what you want but, do it in a way where, you do it in a declarative way. Not by creating or modifying code. So instead we want to make sure that our, the code that we have, that is part of the standard product, can actually interpret declarative code. And that means when we have upgrades and all that stuff, we upgrade the core but the declarative code that the customer has that is, specific to them, remains there and stays there. >> Dave: And that's why the API platform is critical. >> Paul: Right. >> You said no product will be announced or shipped without API enablement, period the end. >> That's correct, We can not because, we can not create a use of front end to anything that doesn't, that isn't API enabled. So, it's very simple. >> Paul: That's a modern architecture. I am curious about you said that one of the reasons that you're at IFS is because it's so customer focused. What is it that this company does differently from companies you've worked at in the past, that exemplifies that customer focus? >> Christian: I think it goes deep um, not only into the culture but also how we actually have people in, all the way in to the individual development teams. Um, I've been in other software companies and the development teams you have developers, you have QA's, you have, you know...testers, you have, you know... Programming just to write the specifications, so forth. We actually have industry solution specialists embedded into the development teams. So, we are, we are, probably our own, you know, worst critic um, and of course then working hand and hand with customers in their processes is essential. But again, if we don't provide the out...if we don't provide the value and the output from what we create for our customers, then it's worth nothing. And that's really the philosophy. If we do not provide value, technology means nothing. >> Dave: So the intersection of domain expertise and software development. Uh Chris, the last question is sort of, what do you hope to get out of this event? Things that you hope to, to take away, or learn or convey to your customers? >> Well I always, I always, look to get feedback. I'm a sucker for feedback and input and learning. Uh, so first of all, I can't wait to walk the expo floor here and really see what all our partners are bringing to the table of innovation. Because they're doing amazing things, so I always enjoy spending a few hours on the, on the expo floor. In the process, get to meet a lot of people, uh and then during the sessions if we can or I'll always end any presentation with an email address. Any, anybody, any customer, any partner will always be able to email me, uh directly, and I, you know... Sometimes a little hard to keep up, but I will respond to every single request. >> Dave: Feedback is a gift. Christian, thanks so much for coming on theCUBE, it was great to see ya. >> Thank you. >> Alright, thank you very much. >> Alright, thank you for watching everybody. Keep it right there, we'll be back with our next guest. We're at IFS World, Boston. You're watching theCUBE. (upbeat music)