 Okay, we're back at AWS re-invent 2021. You're watching theCUBE. My name is Dave Vellante. We're here with Jay Theodore, who's the CTO of Enterprise and AI at Esri. And he's joined by Dave Cardella, who's the principal product manager for developer technologies also at Esri. Guys, thanks for coming on. Welcome to theCUBE. Thanks, Dave. Jay, maybe you could give us a little background on Esri. What do you guys do? What are you all about? Sure. Esri's an old timer. We are a 50-year-old software company. We are the pioneers in GIS and the world leader in GIS, the Geographic Information System. We build geospatial infrastructure that's built for the cloud, built for the edge, built for the field also, you can say. So we do mapping and analytics. We help our customers solve very complex challenges by bringing location intelligence into the mix. Our customers sort of like run the world, transform the world, and we sort of like empower them with the technology we have. So that's what we do. The original edge. Now, of course, AWS is coming to you. Yes. Who are your customers? Your main customers, maybe share that. Yeah, we've got over 350,000 customers in, yeah. Scale. Yeah, in the public sector especially, commercial businesses, non-profit organizations, and that really represents tens of millions of users globally. So let's talk a little bit more about how things are changing. As I say, the edge is coming to you. Maybe AI, 50 years ago. Actually 50 years ago was probably a lot of talk about AI. When I came into the business, it was a lot of chatter about it. But now it's real. All this data that we have and the compute power, the cost has come down. So AI is in your title. Yes. Tell us more about that. I think AI has come to age when I went to grad school, AI was still in theory because we didn't have the compute. And of course we didn't have all the data that was collected, right? Now there's a lot of observation data coming in through IoT and many sensors and so on. So what do you do with that? Like human interpretation is pretty challenged, I would say. So that's where AI comes in to augment the intelligence that we have in terms of extracting information. So Geospatial AI specifically, which we focus on, is to try to take location that's embedded with this kind of information and sort of like extract knowledge and information out of them, right? Intelligence out of them. So that's what we focus on, to complement location intelligence with AI, which we call Geospatial AI. So you can observe how things are changing, maybe report on that. And that's got to be a huge thing that we can talk about. So maybe talk about some of the big trends that are driving your business. What are those? Yeah, that's a great question. So I was listening to Sandy Carter's keynote yesterday and she really emphasized the importance of data. And data is crucial to what we do as a technology company. And we curate data globally and we get our data from best of breed sources. And that includes commercial data providers, it includes national mapping agencies and also a community maps program where we get data from our customers, from our global network of distributors and partners. And we take that data, we curate it, we host it and we deliver it back. And so just recently, for example, we're really excited because we released the 2020 Global Land Cover. And so Esri is the first company to release this data at 10 meter resolution for the entire planet. And it's made up of well over 400,000 Earth observations from various satellites. So data is, it's not only a nice to have anymore, it's actually a must have. And so is location when we talk about data, they go hand in hand. 10 meters so I can look at the hole and the roof of my barn. Well, pretty much. Depends on what you're trying to do, right? So I think to talk about it, it's a within context. GIS is all about context, right? It's bringing location into context in your decision-making process. It's sort of like the where, along with the when, what, how and why. That's what GIS brings in. So a lot of problems are challenging because we need to bring these things together. It's sort of like you're tearing various layers of data that you have and then bringing them within context. Very often the context that human minds understand and reflected in the physical world is geographic location, right? So that's what we bring in. And I would say that there's various kinds of data also. Various types of data, formats of data, structured data, unstructured data. Data captured from extraterrestrial, like you can say, satellite imagery, from drones, from IoT. So it's like on the ground, above the ground, under the ground. All these senses are bringing in data, right? So what GIS does is try to map the data to a place on the earth. At very high precision, if you're looking at it locally or at a certain precision, if it's regionally, trying to find patterns, trying to understand what's emerging. And then as you take this and infuse geospatial air into this, you can even predict what is going to happen based on the past. So that's sort of like, you could say, GIS being used for real world problems. Like if you take some examples, COVID, the pandemic is one example. Being able to first discover where it happened, where it's spreading. That's the tracking aspect. And then how you respond to that. And then how you recover. Recovering as humans, as businesses and so on. So we have widespread use of that. The most popular would be the John Hopkins dashboard that everyone's seen. It's got a trillions of hits and so on, right? That's one example. Another example is addressing racial equity by using location information. Similarly, social justice. These are all problems that we face today, right? So GIS is extensively used by our customers to solve such problems. And then of course you have the climate change challenge itself, right? Where you're overlaying all kinds of complex data that we can't comprehend because you have to go back decades and try to bring all that together to compute. So all of this together comes in the form of a geospatial cloud that we have as an offering. So, okay, that's amazing. You're building a super cloud, we call it. So how do you work with AWS? What's the relationship there? Where do developers fit in? Maybe you can talk about that a little bit. Yeah, yeah, that's a great question. So we've got two main integration points with AWS. A lot of our location services that we expose data and capabilities through are built on AWS. So we use storage, we use cloud caching in various, and AWS has various data sets across the world quite heavily. So that's one integration point. The other is a relatively new product that Amazon has released called Amazon Location Service. And so what it does is it brings location and spatial intelligence directly into a developer's AWS dashboard. So the experience that they're already used to, they now get the power of Esri services and location intelligence right at their fingertips. So you're, we started talking about the edge. Your data architecture is very distributed, right? But of course you're bringing it back. So how does that all work? Are you processing locally and then sending some data back? Are you sending all data back? What does that flow look like? I think the key thing is that our customers work with data of all kinds, all formats, all sizes. And some are in real time, some are big data and archive, right? So most recently, just to illustrate that point, this year we released RGS Enterprise on Kubernetes. It's the entire geospatial cloud made available for enterprise customers and that's made available on AWS on EKS. Now when it's available on EKS, that means all these capabilities are microservices, so they can be massively scaled, they're DevOps friendly, and you've got the full mapping and analytics system that's made available for this. And we sort of like built it cloud native from the ground up. And the more important thing that we have now is connectivity redshift. Why is that important? Because a lot of our customers have geospatial data in these cloud data warehouses. Redshift is very important for them. And so you can connect to that, you can discover these massive petabytes of data sets, and then you can set up what we call a query layer. It's basically pushing analytics into redshift and being able to bring out that data for mapping, visualization, for AI workflows and so on. It's pretty amazing and it's pretty exciting at this time. I mean, there's so much data. And then what do you tear it down? It's a glacier of just to save some cost or does it kind of all stay in S3 or is it? So we already worked with S3, we've worked with RDS, we support Amazon, Aurora, our customers are very happy with that. So Redshift is a new offering for us to connect to Redshift. So the way the query layer works is all of your observation data is in Redshift, your other kinds of data, your authoritative data sets could come from various other sources, including in Amazon, Aurora, for example, okay? And then you overlay them and use them. Now, the data in Redshift is usually massive. So when you run the analytic query, we let you cast it as a materialized view or as a snapshot that you can refresh and you can work against that. This is really good because it complements our ability to actually take that data, to put it in a map image which we render server side. It's got very complex cartographic ready symbology and rendering and everything in there. And you get these beautiful rendering of maps that comes out of Redshift data. And you're pushing AI throughout your stack, is that? Yeah, AI is just like infused, right? I mean, it's, I would say, human intelligence augmented for data scientists for everyone, you know, whether you're using it through notebooks or they're using it through applications that we have or developer APIs themselves. So what are some of the big initiatives you're working on near term, midterm? Yeah, so you mentioned what's really driving innovation and it's related to the question that you just asked right now. And I really believe developers drive innovation. They're force multipliers in the solutions that they build. And so that's really the integration point that Esri has with AWS, it's developers. And earlier this year, we released ArcGIS platform which is our platform as a service offering that exposes these powerful location services that Jay just explained as a set of on-demand services that developers can bring in their applications as they want and they can bring in one, they can bring in two or three, whatever they need but they're there when they need them. And also, developers have their client API of choice. So we have our own client APIs that we offer but you're not pigeonholed into that when you're working with ArcGIS platform, developer can bring their own API. Okay, so you call the platform as a service. So are you making your data available as well? Your data, your tooling and then selling that as a service? Our data has always been available as a service I would say. Everything that we do, our GIS tools are accessible as a web service. Is that new? No, that's always been there. That's always been that way. The difference now is everything is built from the ground up to be cloud native. From the ground up to be connected to every data set that's available on AWS, every compute that can be exploited from small to massive in terms of compute and also reaching out to bring all the apps and the developer experiences pushing out to customers. So 50 years ago, you weren't obviously using the cloud. But so you were running everything on-prem, now you're all in the cloud or you're kind of got a mix? What is the data picture for that? We have two major offerings. There's ArcGIS Online where obviously it's offer as a service and it's GIS as a service provided for everyone and that's available everywhere. The other offering we have is ArcGIS Enterprise where some customers run on-premises, some run it in the cloud, especially AWS. Many run it on the edge, some in the field and there's connectivity between this. A lot of our customers are hybrid so they make the best of both. Depending on the kinds of data, the kinds of workflows giving them the choice exactly. And I would say taking Werner's keynote this morning, he talked about what's the next frontier, right? The next frontier could very well be when AWS gets to space and makes compute available there. It's sitting alongside the data that's captured and we've always, like I said, for 50 years worked with satellite imagery or worked with IoT or worked with drone data. It's just getting GIS closer to where the data is. It's the ultimate edge, space. All right, I'll give you guys a quick wrap if you would. Final thoughts? Go ahead, Dave. I really resonate with data and content. We're a technology company, there's no doubt about that. But without good data, not only supplied by ourselves but our customers, Jay mentioned it earlier, our customers bring their own data to our platform and that's really what drives the analytics and the accuracy in the answers to the problems that people are trying to solve. Bring their first party data with your data and then one plus one is good. Yeah, and the key thing about that is not some of the data, it's all of the data that you have. You no more need to be constrained. Yeah, you're not sampling. Yes, exactly. All right, guys, thanks so much. Really interesting story. Congratulations. Thank you for watching. This is Dave Vellante for theCUBE, the leader in global tech coverage. We'll be right back.