Hello, and welcome to this special presentation. This is theCUBE on Cloud Startups, our special event of Amazon web services startup showcase. I'm John Furrier, host of theCUBE, and excited to be here to talk about the hottest startups around cloud, cloud computing, data, and the future of the enterprise. We've got Rob Harris, Vice President of Solutions Consulting for Stardog, great company. Rob, great to see you. Thanks for coming on. Thank you, John. Thanks. So this is a showcase presentation with AWS startup showcase. You guys are a fast growing startup, knowledge graph. We did a video explaining kind of what we did in the CUBE conversation. Really interesting category, this AWS cloud startups with you guys. Talk about what you guys, take a minute to explain Stardog and what you guys do. Sure. Yeah, here at Stardog, we are really a knowledge graph platform company. So we help build a knowledge graph for our customers tying together the data inside the organization and with data on the cloud, in order for them to be able to find and search and understand the context and relationship of all that data within their own organization. So that's really what we try to facilitate and make successful for our customers. Awesome, what market are you guys targeting? What's the market opportunity? Can you explain the market space that you're building product value in and what's your focus? Sure, yeah, it's pretty exciting. We do a lot from an industry perspective, we target a lot, life sciences or financial, the services and it just tends to be, those are the ones that are most excited in getting started with this, but we certainly have a much broader set of customers in government or in manufacturing. What we really look for is the horizontal type solution where you have a lot of systems that you want to tie together or you want to have that understanding of your data all within context throughout your organization. So anybody struggling with that kind of tying of your data together, whether it's on the cloud or on-prem, that's what we really go after. What's the disruption? Who are you disrupting as you come into the marketplace? Obviously, I love Amazon, it's a hot startup because they got a clean take on something, but somewhat usually is being impacted. Who are you guys disrupting as you come into this market? Yeah, a lot of times we find we're disrupting traditional ETL, right? So centralizing of all your data into one big platform. A lot of people have gone down this path of trying to create these large repositories, data lakes, data warehouses. Yeah, we try to provide additional value on top of them by not forcing you to continue to invest in moving and centralizing all your data together, but connecting it and providing context while leaving and leveraging where it is. Awesome, because a big market opportunity as data warehouses becomes modernized and horizontal control planes and cloud computing as data is the key competitive advantage. Great disruption, great opportunity. So let's talk about the business. StarDog, what do you guys talk about the company or where the headquarters is? What's the, how many employees? What's the business model? How do you guys make money? Yeah, well, headquarters is always a little bit tricky nowadays as we're all so distributed, but officially it is in Arlington, Virginia, although we are all over the globe, mostly in the United States and Europe. Certainly as we look at how do we go to market and what do we do related to that? We have a subscription based model where we help our customers get started, usually small by leveraging a package that they can run either on-prem or in the cloud or directly from the AWS marketplace and letting them connect to the data and then growing out as they grow within their organization, larger, more enterprise-wide type of installations. So that's how we kind of go after it from our company perspective. So your go to market then for the company is it bottoms up organic growth, kind of a freemium get in there or is it kind of mid tier? How do you guys look at that entry? Well, that's a great question. That's exactly right. A lot of times we do start with a freemium type of model. We do have free trials and usability to get started very quickly without having to talk to a salesperson or without having to pay upfront in order to see the value because we want you to be able to understand the value you're gonna get out of our platform right off the bat and get started. Then after you've really tried it out and you see work that apply within your organization, we help make it enterprise-wide. So I have to ask you, I see business model SaaS, I see clouds, great. Are you guys leveraging Amazon web services marketplace at all? We are, we're on the marketplace today with both the free trial as well as the ability through private offers to do whole production instances. So we're really excited about being a part of the marketplace. What we found is that sometimes customers want to run on the cloud, sometimes they want to run on-prem. Wherever they want to run, we want to be sure that we're there. Yeah, Alex, let's pull up that slide on the hybrid architecture for these guys. So I want to bring this up. Since you brought up the business model and you talked about hybrid, this is interesting. This gets into the business model and this is kind of transitions into kind of the technology architecture. Could you walk me through this slide, the knowledge graph and the hybrid cloud? Why is this important for you guys and why is it important for customers? No, this is great. Thank you for pulling this up. What this is really showing is we look toward the future as we really look at how people are deploying knowledge graphs and managing their data. We see that one of the big problems they're trying to address is, what about cloud data that's on the cloud? What about data that's on-prem? Maybe it's in multiple VPCs that you have within the Amazon environment. How do you tie all this together? We all know that moving data around between all of these zones can be expensive and time-consuming and difficult. And so we've come up with an architecture that allows you to run the knowledge graph, an agent of the knowledge graph in each of these zones and they can all talk to each other and coordinate with each other. So they can see data that exists within that zone and pass it on to the other pieces as required or as needed to minimize your kind of in and out fees and to leverage that all that data in one place. I got to ask you, because this comes up a lot in our coverage, data mobility, moving data is expensive. How does that impact you guys and customers? A lot of people have been looking at, hey, you know, the economics of the cloud are phenomenal but at some point, if you got a lot of data, you move compute to the data or you kind of think differently, how do you guys look at that trend? Yeah, that's really our key value prop. As people struggle with this, as people try to figure out, how do I handle this large amount of data without having to generate all this additional cost about moving it around? We really look about how do I push that compute down to the storage layers where the data already exists? And so if you think about our product architecture and I know we have a slide on how our product is really built and how it's pulled together, when you look at our core architecture, we have the graph that represents that connected data, but the exciting part of our architecture, what we do differently than everyone else is by allowing you to keep the data in its existing data silos, whether it's applications or repositories, documents that you already have out there, we allow you to connect to that data where it is, cross zone, whether it's on-prem or on the cloud, and by leveraging the power of Start-on Virtualization Engine, you can connect that data and be able to represent it from one source without having to move it around. But because we also have a persistence layer that's built into our product, you can really determine where's the best home? Is it data that you're going to use a lot and thereby should be really close to where the query engine is, or is it something where you want to federate it out and leverage the compute of that storage layer itself? That flexibility is really why our customers come to us and are excited to use Start-on. That's awesome. Great stuff. Love the slides. Love to look at some pictures that describe the architecture, both as well as the product that I love, how you got the enterprise, high-grade applications, and then you're integrating with other partners. I think that's a really key value. And I think if you're not integrating well in this modern era, you probably won't be surviving much longer. There's pretty much a game changer at this point. Everyone knows that. Question on the technology and product now, keeping it on this theme. What's your secret sauce? Every company's got a secret sauce. What is StarDog's secret sauce? Our secret sauce is really how do we coordinate across all of those applications? So if you can imagine you have Oracle Database or Redshift Repository and you're trying to be able to unify that data in real-time across those applications, there's a lot of thought and needs to go about how to do that efficiently. You don't want to take all the database from both repositories, move them all that data into one place and then figure it out. And so our query planner, how do we coordinate across the multiple applications is really what makes us different and special. On the semantic modeling that you're doing, because I see there's a lot of data there, you got to kind of get an understand context. How do you guys look at reusability, metadata on data? This has become a very key point on not just data warehousing, but it's becoming much more about addressability and discoverability in as fast as possible, low latency with intelligence. This has been a big discussion. How do you guys look at that aspect of the reusability of the data? Yeah, it's one of the exciting parts about starting with a semantic graph and then extending into these capabilities around virtualization and reasoning and inference. By starting with the semantic graph, we allow you to incrementally invest in building out your model and then being able to reuse that model as you go through your implementations. And that's been a big failing as people have looked at the analytical movements recently is so many times people spin up a repository, they answer a particular question and they do an absolutely fine job but then when you have your next question, you have to spin up another repository, build more views, re-ETL the data in and the semantic technology is what allows you to create that common understanding and reuse it over and over and over again. And I think it's time for that to hit mainstream. It's been around a while, it's something that has taken some time to get some adoption around but now that we really have built up awareness around it, we've shown the technology can scale to large volumes. I think it's time to be able to leverage the value that reusability brings. Yeah, one final question on the product and the technology and kind of the architecture is, how do you guys connect the dots going forward as more and more edge nodes become available in the network as that architecture of hybrid that we talked earlier about becomes so complex and so connected. I mean, you're gonna have more connectedness than ever before. It's a very complex network graph theory, right? We're talking about a lot of edges and a lot of traversal. It's billions and billions of edges. I mean, it's complicated. How do you guys see that unfolding and why the star dog remain relevant in that configuration? Yeah, and the simple fact is that people need help, right?  It's a great way to get started, but it's not sufficient in order to really get the value out of that graph that you expect. In the ways we do this twofold, the first bit is really inferencing or reasoning capability, being able to look at the structure of the data, how it's composed and create connections between that data based on logical rules. The second is machine learning, right? Machine learning is how we use things like linear regression algorithms or other types of community detection algorithms in order to build more connections in the data so that you can get really unlocked that value that you're looking for when you're leveraging graph technology. A lot of secret sauce here, a lot of technology graphs, super exciting. Let's get into the final segment around customer traction and what you guys have seen with customers. What are some of the use cases that are popular and what happens if customers aren't going down this road? What are they missing out on? I mean, it's the classic fear of missing out and fear of getting screwed over, right? Are going out of business. I mean, that's motivational at some level, but there are the, do I wait? And people who waited on cloud computing, by the way, were left behind and some never survived. So we're almost in this same dynamic with customers. At some point, you got to put the toe in the water, so to speak, or get going. Take us through some customer examples and use cases where this is working. Yeah, I think both of those areas are great ones to hit on. So when you think about, what are we missing out on? One of our largest customer bases really in pharmaceuticals. And they're using this technology in order to find more connections in the data so that they can really decrease the amount of time for getting a drug to market on the research and development time. They can look more at leveraging the data they've already connected, using related items to be able to accelerate their investments and waiting costs them hundreds of millions if not billions of dollars. So there are certainly ones where being able to adopt this technology early and get value out of it early really pays off, and they're not the only ones. That's only the life sciences space. But there's also the idea, as you said, really about what else am I missing out on? And the data fabric movement, this movement around, how do I lower the cost in my organization about moving data around, creating more ETL jobs, leveraging all these data assets I already have? The data fabric movement is the idea of how do we really automate that? How do we accelerate that? How do we make that an easier process so that it just doesn't cost as much to manage all this data in our organization? And I've observed that more and more we have customers coming to us really interested in this type of use cases that relates to our technology and they are getting ahead of their competitors by really lowering their IT costs in line to focus on these higher value activities. So the bottom line for the customers is what? For you with Star.org, how do they win? What's the reason why they buy and take the freemium and when do they convert over? Take me through the progression of value. When do they see something and why do they increase their consumption? Yeah, the bottom line is you want to try to get more value out of your data at a lower cost and make it easier and faster to do. And so getting started in a single use case, trying out our free version, representing your data and taking a look at what it could look like under a common model, connecting it up with our virtualization services is a great way to really try out the technology and really put your toe in the water to see is this something that would be of value to your organization? As you see that value unlock, as you really understand that you can leverage these data assets with this lower time to value weeks, days in order to unlock a whole repository and connect it to another repository. That's where we love to engage with you and help show you how you can make that successful in a more productive environment. You know, I like about some of the things you're talking about, StarDog has kind of that aspirin aspect but also growth vitamin E as well in terms of the value proposition. A lot of companies are overwhelmed with the data but yet you have this path towards more creation of value through the knowledge graph and reasoning and other value. When does a customer, and this kind of comes back to the customers who are out there potentially watching prospects or future customers, when do they know they need to call you guys up? Is it because they have too many sources? Could you take me through what it looks like in a prospects environment where they would really win with StarDog? What's it look like? What are some of the signs that they need to engage StarDog? Yeah, the two big things that we've seen repeated in our customer base over and over again is if you have a large number of systems out there that aren't connected, that you don't see how all the data can be pulled together between those systems because of different data formats or different languages or different ways that the data is created in those systems. StarDog can certainly help. The second is if you have a large data warehouse or a data lake and you don't see the value being generated out of that because people don't understand where the data is or what context it has with other data within those repositories. Both of those situations are one where we think you get a lot of value out of StarDog and we love to talk to you. So just to understand this. So if you have a lot of systems that either are not connected or connected, whatever, that's great. A lot of sources sitting around, whether it's spreadsheets or Oracle or Redshift. Whatever it is, we loved it. That's right. So you guys wanna ingest as much as possible from sources. That's right, ingest or connect. I mean, that's really the value that we bring is you don't have to pull it all in. You can just map and leverage the data where it lives. We have customers that have petabyte repositories that just map that data into StarDog and we can really facilitate pulling out the value of those systems without you having to move it around again to another repository. Ingest, connect and visual see value. That's right, sounds great. Sounds like a tagline. Great stuff. So just give some examples of who's using it, what big names. Obviously, you guys are hot startup coming out of the Amazon Cloud showcase. Congratulations. What are some names that are working with you guys that can give an indicator of the company that you're keeping right now in terms of resources? Sure, yeah. I mean, our largest customer by far right now the longest customer has been NASA. So they've been a really exciting user of the platform. We've been really excited to see them leverage the platform. Schneider Electric has been a longtime user, Bayer, FINRA in the US, which is a financial services watchdog organization. These are customers that are getting a lot of value out of our platform today and we're excited to work with them. Awesome, Rob. Well, great to see you. Congratulations. Take a minute to just give the plug for the commercial. How do we engage? What's the culture like? You guys hiring? What's the state of the company? Always hiring, yeah. No, it's a great thank you for bringing that up. We're an exciting growing company. As we really reach out more and more to connect more people's data, we find that we're always looking at more resources on building out more connectivity between the individual data sources. So more understanding on that front, as well as more professional services type folks to help people through the process. We've really been trying to minimize the amount of effort that you have to have in order to get started, but we know that people like a helping hand. So we're always looking for people, we're always growing, and we're excited to have the chance to bring this technology out beyond just the semantic group that it has historically been in. You know, you've got a great job, Vice President of Solutions Consulting, essentially you're in a product role, but more like a solution architect meets product, customer facing and also product centering. It's exciting. You're kind of the center of all the action. So what's the coolest thing you've seen from a customer standpoint or an architecture or a deployment or an engagement that you've been involved with that's been kind of like, oh, wow, that's cool. That's game changer. That's something new that we wouldn't have seen a few years ago. Take us through just an example, anecdotal, you don't have to share the company name or you could if you want. That's a great question. There is a company that is working on self-driving cars and being able to leverage the knowledge graph to pull together all of the videos and material they get from the vehicles themselves as well as static information about the sensors. That's been pretty exciting to see. I just recently purchased the Tesla myself. So I'm excited about the whole self-driving car world and to be able to help and participate with these companies is pretty exciting. We just helped one of the large drug manufacturers come to market with one of their drugs earlier than expected and that's a pretty exciting feeling to know that you can really help people by just connecting the data they already have and letting them leverage those resources. That really is something that we're completely very proud of. And the bridge to the future that the customers have to cross with you is also pretty compelling. You got industrial IoT and more and more data. Take a quick minute to describe what that future looks like. Yeah, as we see more and more automation in this process we see a couple of different really exploding areas. The first off, you hit the nail on the head is streaming data, being able to bring in more edge devices being able to really process that data on the fly and be able to help answer questions as these changes in data occur within these sources. That's certainly part of the future. And the other thing that we're really excited about is this more automatic data discovery with an organization. How can we have an agent that goes out and kind of can infer really even what your data is about in the structure of your data without a lot of input for you. And so we've been working a lot with building up these models automatically and letting you have the foundation for integrating your data at just the pushable button. So we're excited about walking alongside our customers in this journey as well. It's a fun area. You talk about reasoning. You got one of the key value propositions you guys have. You talk about AI, you talk about bots and soon it's going to be thinking machines for us. They're going to be doing all the work. I hope they're not too soon, but I am excited about that idea as well. I do think that if you look at organizations today it is fascinating how it's not that the problems are different, but we're trying to automate as much of it as possible so that we can work on the real value clumps of our organizations. And it's not the kind of drudgery work I started as a UVA back in my career on just trying to keep the database up and running. Yeah, you know, nowadays, you know all these autonomous databases and self indexing and self correcting it's just not a task that's needed as much anymore. And we hope we can bring that to the data infrastructure. Automation is a double-edged sword, done right. It's amazing, done wrong. It could cause some damage and inflict some pain and hurt. And so you got to figure it out. Got to have the right data sets. Got to have the right software. And it's a great future. Rob Harris, congratulations for being a AWS startup showcase here on the Cubon Cloud startups with AWS-led partnership. Thank you for coming on and being part of this event. Thank you again. Okay, Rob Harris, Vice President Solutions Consulting at Stardog here for the Cubon Cloud. I'm John Furrier. Thanks for watching.


That's what you're talking about. That's what you're talking about. Now you can read the comments. No function. Play rock, paper, scissors, and get my piece. That's the best part. I know one part. Have you tried the one part? Ooh. I don't think a lot. One part. Can I watch it again? You're looking at the art of art. I don't think so. I don't think so. I don't think so. One, two, three, four. Thank you. Four, four. I wasn't making fun. I was making fun. She's like, don't worry, I won't care. I was like, I'll tell her I don't care. Absolutely. I was like, I'll see you later, but I wouldn't like that. No, you weren't like that. That's more of an evil. I was in. I was out. See? Replay, replay. Yeah, replay. Yeah. I couldn't get myself to do an actual role. Derek, just go. Yeah, I know, I got it. Hey, what's up, you guys? We lost. We lost, guys. It's me right here. Got one more game, you guys. Thumbs up if you're ready. Thumbs up if you're ready. This is the final game. Got blocked a couple of times. That tiredness, get getting in, y'all. We're gonna be in one more game. This one is best two out of three. Let's do it for the win. All those good vibes. All right, you guys, thanks for watching. All right. Thanks for watching, you guys. I will see you guys next time. Yeah.


Okay, I'm going to serve my screen. Can you see the screen now? Yes. Thank you. Okay, so now I'm going to try to resolve the exercise three from yesterday, the one that in which we want to display the list of programs. Okay, so first of all, you have to switch to the branch. And now when you're here, you go to the programs activity, which is that activity, you can find the exercise. So here, you have to do to get the program list. Okay, we're gonna remove it. And now, if we check in our emulator, we can go to the programs and see that now programs were found because we didn't resolve the exercise. So now we are going to resolve it. So first of all, we can call to the SDK and then get the two. So if you remember, this method, if you go to the SDK class, we'll call the digital manager get the two so you're gonna have the two class to start working. So now that we have the two, we can access the module. We want to go to the program module. If you can see that we have a lot of modules here, you can see the already module, the category module, and all the modules that are in the two. So we just want to go to the program module. So we select it.  So we want to go to the program repository now because we want to get the programs.  So if we get paid, and the exercise says that we want to pay them 20 by 20. So we put here 20. And now, and now we have the programs. So if we want to see what happened, we can just relaunch the activity. Then it will run a build. And after that, we should say here, yeah, all the programs, we have here all the programs. So now we're going to start the filtering. We're going to use these two filters here. So after program, we can filter by, well, if you write by, you're going to see all the filters that you have by display name, by organization unit scope, by program type, organization unit list, by color, spray this icon, you can filter it by almost all the properties. So the exercise is telling you that you have to filter by program type. So we're going to go to the program type. And then call your two equals, you can use equals in and also there is some operators here, here. So the equal operator. And then we are going to type with registration, which is what the exercise is telling you to do. Just filter by with registration. We're going to add another filter here by the scope. So you can find the writing. And then we're going to add the scope. You can use the scope, the search, but also the scope that capture is required by the exercise. So now we're going to see if it's working. So we can click here to apply the changes. And we'll restart the activity. So if you wait for a few seconds, you're going to say that the program without registration are going to disappear. So now we are, we have only like three, three different programs. So also here, it's telling you to order the programs by name, A to C. But that's the default. It is by default, the programs are like that. So we are going to do it in the other way just to see the changes. So order by display name. And then we can use the order by direction. We have here the sending and ascending. We're going to use now sending. So opposite that, the exercise is telling you that we are going to do it that way. So again, we'll refresh the activity. And now this one should be the first one and the type around should be the last one. Yeah. So now we have the list of programs in the other direction. If, for example, we want to remove the other filters and add another new filter, we can just, for example, by display name, we're filtering by display name, like, and maybe we can write mal to try to get the malaria programs. So if we filter now, after the synchronization, we'll see that here, only the malaria programs like the malaria focus investigation. And yeah, it's here, malaria focus investigation, malaria case registration, and xx mal because also right here in malaria case. So that's how we sort of resolve the exercise. And well, if you have any question, I'm already happy to try to solve it. Have any questions so far? Okay. If you don't have any question, I think we can move to Victor. You have to make the next exercise. I'm going to stop sharing my screen. All yours. Okay. Okay. This session is about more about listing, in this case, data in the Android app. This is about searching target entity instances. Because in the previous exercise, we saw how to filter and display metadata in the Android app. For metadata, it's always the same. You can filter. It's very generic. Yeah. Once you have done with programs, you can do the same for other things. In the case of data, in some cases, data is quite, has a few things that are different to metadata. And in this case, well, the target entity instance class or the target entity endpoint in the API is something different. It's special. It allows you to filter by other things. I mean, not just properties in the target entity instance class itself, but for example, if the instance has an enrollment or has an attribute of a particular type, or the opening date of the enrollment, things like that. So it's more complex. It's more complex than a regular filter, let's say. In the SDK, this repository, this functionality is both in the target entity instance collection repository, query collection repository. So it allows you to filter by... This is the same as in the Word API. So if you are with a Word API, this will sound from the app to you. For example, you can filter by query, which means search for a value across all the attributes in the TI. Or you can filter by a particular value in a particular attribute. For example, instance whose first name is John, for example. Or you can see that are enrolled in a particular program or with a particular enrollment date. What is different in this repository is that it has the ability to create results online, because we talk always that how the typical workflow in the application is to download the metadata and the data, and then access the local database to solve the results and work with the local data. In this case, the SDK allows you to get the results both locally and remotely, coming from the remote server. And you can combine the results. And this is important because, for example, in large implementations with thousands of TI, you don't want to download all of them in the device. Maybe you only want to download most recent TI's because they are more likely to be used in the device. And then if you want to access another TI, you can search online. So here, in this repository, we have four modes, offline only. It means only store locally, offline first. It shows the local results, local TI, the TI fetch from the database in first place. And then it gets more results from the API online only and online first. And of course, for online only and online first, you need an internet connection, a connection to the server. Well, so this is quite intuitive, but just to show you a diagram about how it works. So we have, in the website, we have the track entity instance query repository, feed the name by attribute, by program, whatever. And we apply the strategy offline first, which means local results in first place, and then it goes to the API. So the flow will be, this will be done by USTK. So USTK will get results from the database in first place. And then the USTK will query the API to get more results, results that are not included in the local database. Okay. The syntax is the same, it's very, very similar to the syntax to access other repository. You have to access the P2 instance, track entity module, track entity instance query, and then filter by whatever you want. And the thing that is different is the method offline only, offline first, online only, online first, that you want to apply. And the case page has this. Okay. So let's do an exercise. So first of all, I would like to show the exercise for you to know where you can check that the result, that your solution is fine. So the first thing you have to do, as usual, is to move to the, to the branch, to the online. This is the exercise five. Sorry for that. Actually, it's four, but we have to switch exercises. So this is the five, a TI search. Check out. Okay. If you learn this grant, so here we have them. Let's get it done up. I'm already logged in with the zero. So if you go to the left, left side menu, the third entry is track entity search. You go there. This is a very, very simple form to query by any activity you want in any program you want by any value. So here we don't have any validation that this attribute belongs to the program. It's just a simple form to check the query input. Oh, yeah. Well, at the front, continue. This branch, the branch exercise five TI search was included yesterday afternoon. So probably you have to update the project. You need to come here, version control and update project because otherwise you are not going to see the branch. Okay. If you go here, you can, for example, select first name, type program and look for John. For example, if I click here, I can click, it's going to crash because the method is not implemented. So let's go to the code. Yeah. Track entity instance search. So here we have this method. Here we have to write the code, very similar to the code that is in the slide. So take a look at that. You have to use this information in the parameters to filter. So you have to filter by this or unit using the standard mode by this, the attributes, by this, by this program, by this attribute, and the attribute should be like this value. Okay. So what you have to do is to write the query and one, once you have the query and you return the dislike data, the pages like data, if you go to the application and you perform any search, you will be able to see the result in the list. So we go back here, exercise five. So the recommendation to test the result is to search by first name, like John, in the program, type program. Because you have there, I can say that you have some results there. And to try different modes, for example, you can try offline only in first play, then offline first, to see the differences or offline only on plain first. Try to do that and give rest in a second. So yes, I think that's all from my side. Okay. I think we can, we can switch to break rooms.