(upbeat music) >> Hello, and welcome to theCUBE's presentation of the a AWS startup showcase, data as code is the theme. This is season two episode two of the ongoing series covering the exciting startups from the AWS ecosystem and we talk about data analytics. I'm your old John Furrier with the cube, and we have Javier De La Torre. who's the founder and chief strategy officer of Carto, which is doing some amazing innovation around geographic information systems or GIS. Javier welcome to the cube for this showcase. >> Thank you. Thank you for having me. >> So, you know, one of the things that you guys are bringing to the table is spatial analytic data that now moves into spatial relations, which is, you know, we know about geofencing. You're seeing more data coming from satellites, ground stations, you name it. Things are coming into the market from a data perspective, that's across the board and geo's one of them GIS systems. This is what you guys are doing in the rise of SQL in particular with spatial. This is a huge new benefit to the world. Can you take a minute to explain what Carto's doing and what spatial SQL is? >> Sure. Yeah. So like you said, like data, obviously we know is growing very fast and as you know now, being leveraged by many organizations in many different ways. There's one part of data, one dimension that is location. We like to say that everything happens somewhere. So therefore everything can be analyzed and understood based on the location. So we like to put an example, if all your neighbors get an alarm in their homes, the likelihood that you will get an alarm increases, right? So that's obvious we are all affected by our surroundings. What is spatial analytics, this type of analytics does is try to uncover those spacial relations so that you can model, you can predict where something is going to happen, or, you know, like, or optimize it, you know, like where else you want it to happen, right? So that's at the core of it. Now, this is something that as an industry has been done for many years, like the GIS or geographic information systems have existed for a long time. But now, and this is what Carto really brings to the table. We're looking at really the marketizing it, so that it's in the hands of any analyst, our vision is that you need to go five years, to a geography school to be able to do this type of spatial analysis. And the way that we want to make that happen is what we call with the rise of a spatial SQL. We add these capabilities around spatial analytics based on the language that is very, very popular for a analysts, which is SQL. So what we do is enables you to do this spatial analysis on top of the well known and well used SQL methods. >> It's interesting the cloud native and the cloud scale wave and now data as code has shown that the old school, the old guard, the old way of doing things, you mentioned data warehousing, okay, as one. BI tools in particular have always been limited. And the scope of the limitation was the environment was different. You have to have domain expertise, rich knowledge of the syntax. Usually it's for an application developer, not for like real time and building it into the CICD pipeline, or just from a workflow standpoint, making it available. The so-called democratization, this is where this connects. And so I got to ask you, what are you most excited about in the innovations at Carto? Can you share some of the things that people might know about or might not know about that's happening at Carto, that takes advantage of this cloud native wave because companies are now on this bandwagon. >> Yeah, no, it is. And cloud native analytics is probably the most disruptive kind of like trend that we've seen over the few years, in our particular space on the spatial it has tremendous effects on the way that we provide our service. So I'd like to kind of highlight four main reasons why cloud analytics, cloud native is super important to us. So the first one is obviously is a scalability, the working with the sizes of data that we work now in terms of location was just not possible or before. So for someone that is performing now analysis on autonomous car, or you're like that has any sensorized GPS on a device and is collecting hundreds of billions of points. If you want to do analysis on that type of data, cloud native allows you to do that in a scalable way, but it also is very cost effective. That is something that you'll see very quickly when your data grows a lot, which is that this computing storage separation, the idea that is store your data at cloud prices, but then use them with these data warehouses that we work in this private, makes for a very, very cost effective solution. But then, you know, there is other two, obviously one of them being SQL and spatial SQL that like means we like to say that SQL is becoming the lingua franca for analytics. So it's used by many products that you can connect through the usage of SQL, but I think like you coming towards why I think it's even more interesting it's like, in the cloud the concept like we all are serving, we are all living in the same infrastructure enables us that we can distribute a spatial data sets to a customer that they can join it on their database on SQL without having to move the data from one another, like in the case of Redshift or Amazon Redshift car connects and you using something called a spectrum, we can connect live to data that is stored on S3. And I think that is going to disrupt a lot the way that we think about data distributions and how cost effective it is. I think, it has a lot of your like potential on it. And in that sense what Carto is providing on top of it in the format of formats like parquet, which is a very popular with big data format. We adding geo parquet, we are specializing this big data technology for doing the spatial analysis. And that to me it is very exciting because it's putting some of the best tools at the hands of doing the space analytics for something that we're not able to do before. So to me, this is one area that I'm very, very excited. >> Well, I want to back up for a second. So you mentioned parquet and the standards around that format. And also you mentioned Redshift, so let me get this right. So you just saying that you can connect into Redshift. So I'm a customer and I have Redshift I'm using, I got my S3, I'm using Redshift for analysis. You're saying you can plug right into Redshift. >> Yes. And this is a very, very, very important part because what Carto does is leverage Redshift computing infrastructure to essentially kind of like do all the analysis. So what we do is we bring a spatial analysis where the data is, where Redshift is versus in the past, what we will do is take the data where the analysis was and that sense, it's at the core of cloud native. >> Okay. This is really where I see the exciting shift where data as code now becomes a reality is that you bring the... It redefines architecture, the script is flipped. The architecture has been redefined. You're making the data move to the environments that needs to move when it has to, if it doesn't have to move you bring compute to it. So you're seeing new kinds of use cases. So I have to ask you on the use cases and examples for Carto AWS customers with spatial analytics, what are some of the examples on how your clients are using cloud native spatial analytics or Carto? >> Yeah. So one, for example, that we've seen a lot, on the AWS ecosystem, obviously because of its suites and its position. We work together with another service in the AWS ecosystem called Amazon Location. So that actually provides you access to maps and SDKs for navigation. So it means that you are like a company that is delivering food or any other goods in the city. We have like hundreds or thousands of drivers around the city moving, doing all these deliveries. And each of these drivers they have an app and they're collecting actively their location, their position, right? So you get all the data and then it gets stored on something like a Redshift data cluster on S3 as well. There's different architectures in there, but now you essentially have like a full log of the activity that is happening on the ground from your business. So what Carto does on top of that data is you connect your data into Carto. And now you can do analysis, for example, for finding out where you user may be placed, another distribution center, you know, for optimizing your delivering routes, or like if you're in the restaurant business where you might want to have a new dark kitchen, right? So all this type of analysis based on, since I know where you're doing your operations, I can post analyze the data and then provide you a different way that you can think about solving your operation. So that's an example of a great use case that we're seeing right now. >> Talk to me about about the traditional BI tools out there, because you mentioned earlier, they lack the specific capabilities. You guys bring that to the table. What about the scalability limitations? Can you talk about where that is? Is there limitations there, obviously, if they don't have the capabilities, you can't scale that's one, but you know, as you start plugging into Redshift, scale and performance matters, what's the issue there? Can you unpack that a little bit real quick? >> Yeah. It goes back to the particulars of the spacial data, location data, like in the use case, like I was describing you very quickly are going to end up with really a lot of your like terabytes, if not petabytes of data very quickly, if you're start aggregating all this data, because it gets created by sensors. So volumes in our world kind of tends to grow a lot now. So when you work with BI tools, there's two things that you have to take in consideration. BI tools are great for seeing things like for example, if all you want to see is where your customers are, a BI tool is great. Seeing, creating a map and seeing your customers. That's totally in the world of BI. But if you want to understand why your customers are there, or where else could they be, you're going to need to perform what we call a spatial analysis. You're going to have to create a spatial model. You're going to have to, and for that BI tools will not give you that that's one side, the other it talks about the volumes that I was describing. Most of these BI tools can handle certain aggregations. Like, for example, if you are reading, if you're connecting your, let's say 10 billion data set to a BI tool, the BI tool will do some aggregations because you cannot display 10,000 rows on a BI tool and that's okay, you get aggregations and that works. But when it comes to a map, you cannot aggregate the data on the map. You actually want to see all the data on the map, and that's what Carto provides you. It allows you to make maps that sees all the data, not just aggregated by county or aggregated by other kind of like area, you see all your data on the map. >> You know, what's interesting is that location based service has been around for a long time. You know, when mobile started even hitting the scene, you saw it get better mashups, Google Maps, all this Google API mashups, things like that. You know, developers are used to it, but they could never get to the promised land on the big data side, because they just didn't have the compute. But now you add in geofencing, geo information, you now have access to this new edge like data, right? So I have to ask you on the mobile side, are you guys working with any 5G or edge providers? Because I can almost imagine that the spatial equation gets more complicated and more data full when you start blowing out edge data, like with 5G, you got more, more things happening at the edge. It's only going to fill in more data points. Can you share that's how that use case is going with mobile, mobile carriers or 5G? >> Yeah, that's totally, yeah. It's totally the case. Well, first, even before, you know, like we are there, we actually helping a lot of telcos on actually planning the 5G deployment. Where do you place your antennas is a very, very important topic when you're like talking about 5G. Because you know, like 5G networks require a lot of density. So it's a lot about like, okay, where do I start deploying my infrastructure to ensure the customers like meet, like have the best service and the places where I want to kind of like go first So like... >> You mean like the RF maps, like understanding how RF propagates. >> Well, that's one signal, but the other is like, imagine that your telco is more interested on, you know, let's say on a certain kind of like consumer profile, like young people that are using the one type of service. Well, we know where these demographics kind of lives. So you might want to start kind of like deploying your 5G in those areas, right. Versus if you go to more commercial and more kind of like residential areas, there might be other demographics. So that's one part around market analysis. Then the second part is once these 5G networks are in place, you're right. I mean, one of the premises that kind of like these news technologies give us is because the network is much smarter. You can have all these edge cases, there's much more location data that can be collected. So what we see now is a rise on the amount of what we call telemetry. That for example, the IOT space can make around location. And that's now enabled because of 5G. So I think 5G is going to be one of those trends that are going to make like more and more data coming into, I mean, more location, data available for analysis. >> So how does that, I mean, this is a great conversation because everyone can realize they're at a stadium and they see multiple bars but they can't get bandwidth. So they got a back haul problem or not enough signal. Everyone knows when they're driving their car, they know they can relate to the consumer side of it. So I get how the spatial data grows. What's the impact to Carto and specifically the cloud, because if you have more data coming in, you need the actionable insight. So I can see the use case, oh, put the antenna here. That's an actionable business decision, more content, more revenue, more happy customers, but where else is the impact to you guys and the spatial piece of it? >> Yeah. Well, I mean like there's many, many factors, right? So one of them, for example, on the telco, one of the things where we realize impact is that it gives the visibility to the operator, for example, around the quality of service. Like, okay, are my customers getting the quality of services where I want? Or like you said, like if there sitting outside a concert the quality of service in one particular area is dropping very fast. So the idea of like being able to now in real time, kind of like detect location issues, like I'm having an issue in this place. That means that then now I can act, I can drive up bandwidth, put more capacity et cetera right. So I think the biggest impact that we are seeing we are going to see on the upcoming years is that like more and more use cases going towards real time. So where, like before it was like, well, now that it has happened, I'm going to analyze it. I'm going to look at, you know, like how I could do better next time towards a more of like an industry where Carto ourselves, we are embedded in more real time type of, you know, like analytics where it's okay, if this happens, then do that, right. So it's going to be more personalized at the level that like in the code environment, it has to be art of a full kind of like pipeline kind of like type of analysis. That's already programmatically prepared to act on real time. >> That's great and it's a good segue. My next question, as more and more companies adopt cloud native analytics, what trends are you seeing out of the key to watch? Obviously you're seeing more developers coming on site, on the scene, open sources growing, what's the big cloud native analytics trends for Carto and geographic information. >> Yeah. So I think you know like the, we were talking before the cloud native now is unstoppable, but one of the things that we are seeing that is still needs to be developed and we are seeing progress is around a standardization, for example, around like data sets that are provided by different providers. What I mean with that is like, you as an organization, you're going to be responsible for your data like that you create on your cloud, right. On S3, or, you know and then you going to have a competing engine, like Redshift and you're going to have all that set up, but then you also going to have to think about like, okay, how do I ingest data from third party providers that are important for my analysis? So for example, Carto provides a lot of demographics, human mobility. we aggregate and clean up and prepare lot of spacial data so that we can then enrich your business. So for us, how we deliver that into your cloud native solution is a very important factor. And we haven't seen yet enough standardization around that. And that's one of the things, what we are pushing, you know, with the concept of geo Parquet of standardizing that body. That's one, then there is another, this is more what I like to say that you know, we are helping companies figure out their own geographies. What we mean by that is like most companies, when they start thinking about like how they interact, on the space, on the location, some of them will work like by zip codes and other by cities, they organize their operations based on a geography in a way, or technically what we call a geographic support system. Well, nowadays, like the most advance companies are defining their geographies in a continuous spectrum in what we call global grid system or spatial indexes that allows them to understand the business, not just as a set of regions, but as a continuous space. And that is now possible because of the technologies that we are introducing around spatial indexes at the cloud native infrastructure. And it provides a great a way to match data with resources and operate at scale. To me those two trends are going to be like very, very important because of the capabilities that cloud native brings to our spatial industry. >> So it changes the operation. So it's data as ops, data as code, is data ops, like infrastructures code means cloud DevOps. So I got to ask you because that's cool. Spatial index is a whole another way to think of it, rather than you go hyper local, super local, you get local zones for AWS and regions. Things are getting down to the granular levels I see that. So I have to ask you, what does data as code mean to you and what does it mean to Carto? Because you're kind of teasing at this new way because it's redefining the operation, the data operations, data engineering. So data as code is real. What does that mean to you? >> No, I think we already seeing it happening to me and to Carto what I will describe data as code is when an organization has moved from doing an analysis after the fact, like where they're like post kind of like analysis in a way to where they're actually kind of like putting analytics on their operational cycle. So then they need to really code it. They need to make these analysis, put them and insert them into the architecture bus, if you want to say of the organization. So if I get a customer, happens to be in this location, I'm going to trigger that and then this is going to do that. Or if this happens, I'm need to open up. And this is where if an organization is going to react in more real time, and we know that organizations need to drive in that direction, the only way that they can make that happen is if they operationalize analytics on their daily operations. And that can only happen with data as code. >> Yeah. And that's interesting. Look at ML ops, AI ops, people talk about that. This is data, so developers meets operations, that's the cloud, data meets code that's operations, that's data business. >> You got it. And add to that, the spacial with Carto and we go it. >> Yeah, because every piece of data now is important. And the spatial's key real quick before we close out, what is the index thing? Explain the benefit real quick of a spatial index. >> Yes. So the spatial index is well everybody can understand how we organize societies politically, right? Our countries, you have like states and then you have like counties and you have all these different kind, what we call administrative boundaries, right? That's a way that we organize information too, right? A spatial index is when you divide the world, not in administrative boundaries, but you actually make a grid. Imagine that you just essentially make a grid of the world. right? And you make that grid so that in every cell you can then split it into, let's say for example, four more cells. So you now have like an organization. You split the world in a grid that you can have multiple resolutions think like Google maps when you see the entire world, but you can zoom in and you end up seeing, you know, like one particular place, so that's one thing. So what a spatial indexes allows you is to technically put, you know like your location, not based coordinate, but actually on one grid place on an index. And we use that then later to correlate, let's say your data with someone else data, as we can use what we call this spatial indexes to do joints very, very fast and we can do a lot of operations with it. So it is a new way to do spatial computing based on this type of indexes, but for more than anything for an organization, what spatial index allows is that you don't need to work on zip codes or in boundaries on artificial boundaries. I mean, your customer doesn't change because he goes from this place to the road, to the other side of the road, this is the same place. It's an arbitrary in location. It's a spatial index break out all of that. You're like you break with your zip codes, you break. And you essentially have a continuous geography, that actually is a much closer look up to the reality. >> It's like the forest and the trees and the bark of the tree. (Javier laughing) You can see everything. >> That's it, you can get a look at everything. >> Javi, great to have you on. In real quick closing give a quick plug for the company, summarize what you do, what you're looking into, how many people you got, when you're hiring, what's the key goals for the company? >> Yeah, sure. So Carto is a company, now we are around 200 people. Our vision is that spatial analytics is something that every organization should do. So we really try to enable organizations with the best data and analysis around spatial. And we do all that cloud native on top of your data warehouse. So what we are really in enabling these organizations is to take that cloud native approach that they're already embracing it also to spatial analysis. >> Javi, founder, chief strategy officer for Carto. Great to have you on data as code, all data's real, all data has impact, operational impact with data is the new big trend. Thanks for coming on and sharing the company story and all your key innovations. Thank you. >> Thanks to you. >> Okay. This is the startup showcase. Data as code, season two episode two of the ongoing series. Every episode will explore new topics and new exciting companies pioneering this next cloud native wave of innovation. I'm John Furrier, your host of theCUBE. Thanks for watching. (upbeat music)

>>Hey everyone. Welcome to this cube conversation featuring Carto I'm Lisa Martin. And today we're excited to be joined by Javier Delatorre, the founder and chief strategy officer at Carto. We're going to be talking about how Carto is bringing cloud native spatial analysis to the cloud with AWS. How do you are great to have you on the program? Talk to us about cartel. What do you guys do? >>Great. So, uh, part two is a location intelligence platform, but we really use some neighboring organizations to work with location data on the AWS cloud. So essentially enabling organizations to analyze what do they, what should they open new stores? Whereas today probably the new internet, in essence, understanding the locations. I mentioned just helping them to figure out where to do things >>From Carter's perspective. Talk to me about why spatial analysis, location data is important. What power does it give to businesses in any industry? >>Right. I mean, we like to say that everything happens somewhere, right? So we understand that, you know, like the physical world is a very important dimension. So understanding where things happens and the relation within space is a pretty fundamental dimension when it comes to another. I like to put examples of, um, before your neighbors, uh, install alarms in their phones, the likelihood that you will get an alarm is also versus quite a lot. So that's eight years old, says that we are influenced by things that happens around us. And if you can model and understand those spacial relations, you can then look to optimize or predict what is going to happen based on where things are happening. And this is something that we've seen a lot, for example, with the pandemic, but now we're seeing, you know, like many organizations utilizing it for yeah. For finding out where they can find new customers, stores, like say, where did they deploy the new infrastructure? Everything that the ANZ has a spatial component. And that's what is spatial analytics and location intelligence allows you to do? >>Give me some examples of spatial data. And the first thing that pops into my mind is GPS. But I know that there's a lot more than that. >>Uh, GPS has been one of the most important types of data for, so since you know, the inability of GPS and, and with mobile and different sensors are staring at it, we've seen an incredible amount of location data coming into place, but you're right. There's many other types of location data that people tend not to be so aware. I'd say any company that is handling customers, you know, they're likely going to have their addresses. So we have the address of the customer. You have a location already, we'll have, we'll call that the process of geocoding. We transform an address that coordinates, right? But you also have the same, you know, with bees, you have the same, uh, with many different sip codes, it's many different ways that you can represent location. And once you identify those, uh, location bits in your data, then you can start thinking about what type of analysis you can do with them. So it is, like I said, like in many, many places, but definitely the, the rise of, uh, GPS and sensors have been very dramatic. Now we see in also like acute stream of location data coming, for example, from satellites, you know, with all these constellations of satellites, capturing daily images on from earlier, that is also giving us a lot of contextual information. But so it is, you know, mobile phones, when you connect to cell towers, there's many different businesses that are now kind of giving us location data. >>So you alluded to that earlier, a lot more businesses are using location data in their strategies. Talk to me about the acceleration that you seen of that in the last couple of years alone. >>Yeah. So I think one thing that we see in, you know, like massively on the industry obviously is these companies are going through the digital transformation. They are applying analytics to bigger and bigger areas of their, of their, of their business, right. And in a way to showcase, to kind of came as while the last time I mentioned that a lot of organizations started to look at, and over the last few years, we've seen that change in a lot. We've seen it within the many more organized spaces. Now making the questions around where things happens, how does actually matter to my business. So this is celebration, you know, has the sensitive men that many more people are now starting to look at, not only seeing things on a map, like, you know, where my customers are, where my warehouses are, my logistics supply chain, where is it located? >>Now, we're starting to see many more organizations looking at questions about how can I predict where something is going to happen, or how can I optimize my business process so that, um, you know, I, I try to reduce the number of kilometers that I have to drive miles. So, um, I guess it's a mix of the need for sustainability optimizing the business process. And the fact that more and more organizations are starting to do much more deep transformation that now location data has become a much more interesting aspect for many more organizations. So I think all these things together has to make in a way that perfect storm. And now we've seen a lot of the men too, um, for companies that want to go will be John seeing things in a map to understanding why things happen in those spaces. And that's, I think that like, again, a multitude of drivers, you know, that is supposed to in this industry. >>Can you talk about some of the key use cases and maybe some of the vertical industries where you've really seen this takeoff in the last couple of years? >>Yes. And I think he's just in a way, one of the most interesting factors of our industry traditional industries have been on the area around security in the public sector was very much on the military and the, in the, in the, uh, intelligence ecosystem. But now we've seen tremendous adoption on industries like retail, right, where they are lying now consolidating what is their, what is their physical presence? Where do they open stores? You know, like, uh, food chains, what do they open restaurants? And it's a much more analytical process now towards making businesses because, and that involves the usage of location intelligence and space analytics. We do touch one, but we still also like tremendous increase in usage on things like telcos telecommunication. Now with all the deployment of 5g networks, fiber optics, most of those operators require a very good understanding of where you should apply your networks, which, which areas you want to go start first tablet, smart CapEx car, like a strategy. >>So that's telco, I would say it's also has been a tremendous increase. Um, the public secretary is obviously very important, you know, especially, you know, with a lot of the, in a way we all got to master or do you know why geography matters? You know, how to understand your location. Um, and the last one that I would say that it's also connected very much with climate change, transportation and logistics are very, very important factor now. So understanding what is the best strategies for last mile delivery, how to organize your warehouses to better meet your needs. Those are the places that now we're seeing really growing really fast. >>So tremendous amount of use cases, a lot of opportunity there for optimization. How have companies traditionally analyze spatial data and why does that need to change? >>Yeah, so, um, I mean, to a certain extent, I would like to say that there's not been, um, that much use of location data. And that I think is one of the most exciting parts that for many organizations, this is the first time that they're looking at location as a, as a need. I mentioned that they need to understand. So there were, there were several organizations doing already a spatial analytics, but right now it's really, we really see in the expansion of our industry and you're not catching up in, in major, uh, major companies. So those are not like more advanced, you know, we'll have used so-called the traditional GIS systems. GIS is a, is a type of software. That's been existing for many years, but it's only the second used by a very small needs of analysts. You have to go almost four years to school, you know, to become a GIS expert and then do GIS analyst. >>This is right now trending dramatically. And I think, you know, Carter's part of that, uh, transition to necessity, making best patient analysis and GIS part of just the generic general analytics. And I think this is one of the most exciting times that we have, because we've seen the demo by station of his face. And it takes now to imagine why there are, so now we've seen, you know, like analysts that, you know, used to be just to know how to make a map. So things are not with a map, you know, where, where something was happening. Now we starting to see them making much more interesting plastics. So I'm like, okay, if it happens here, where else could they be happened? Right. So that's what I, right now, they, the, the, the huge statements, I'd say, I'd say like many organizations is the first time they go into jail. People like me for being very passionate about the possibilities of really improving processes. I mean, this is super, super exciting time. >>I can definitely feel your passion here through zoom, or talk to me a little bit about how cartel and AWS are helping organizations to embrace the democratization of spatial data and really unlock its super powers. >>Yeah. Well, I mean, obviously, you know, that AWS as the leader on the cloud, in a way that has fundamentally changed the way that we think about like analytics, right? So, um, not only the clouds provide us with the scalability, scalability, affordable the scale of anything. So that's one of the things that, you know, has been incredibly, um, transformative in our industry, uh, with AWS. Now we can do analysis at the scale that wasn't possible before. So that's, that's, that's one thing. So for us, you know, what we've embarked with AWS is rethinking how we can do a spatial analytics in the cloud. We're calling it car to cloud native is providing a full cloud native approach towards performing the spatial analytics, traditional GIS. And for us to utilize this game, even as huge amount of scalability, we use services like Retsef the now with their server last capabilities, we like a, an organization have their data already on that data warehouse on breaths test and using Kartra space. >>now they can do a special ethics directly on the warehouse. This is one of the biggest characteristics of cartel made by being the first cloud data platform. Every computing that we do actually gets pushed down to the warehouse. So the customer is already using the computing engine that they're already, they've been using it for many other things they're paying for already. And they give us scalability. Uh, also very cost-effectiveness this storage competed in separation that the rest of service provides. It makes it very competitive from a call like a cost perspective, and then also is very convenient. So it means that you can use just traditional sequel that are many analysts, know how to use it within the tools that they've been using for many. So I think the participation is essential to read safe, and then also with incorporating the Amazon location services. So we can talk to, and it certainly provides a cloud native it's scalable, affordable, efficient, and much more easy to use solution to performance, space analytics that anything that has been done before. >>It's a tremendous amount of opportunity. It sounds like we're just scratching the surface, but really interesting things that cartoon was doing and how you're enabling organizations in every industry to accelerate the use of spatial data. Javier, thank you so much for joining me on the program today. Fascinating information and best of luck to you. >>Thank you very much >>For Javier Delatorre I'm Lisa Martin. You're watching the cubes stay right here for more coverage of the hybrid tech event world.