>> Narrator: From San Mateo, California, it's theCUBE, covering SnapLogic Innovation Day 2018. Brought to you by SnapLogic. >> Welcome back, Jeff Frick here with theCUBE. We're at the Crossroads, that's 92 and 101 in the Bay Area if you've been through it, you've had time to take a minute and look at all the buildings, 'cause traffic's usually not so great around here. But there's a lot of great software companies that come through here. It's interesting, I always think back to the Siebel Building that went up and now that's Rakuten, who we all know from the Warrior jerseys, the very popular Japanese retailer. But that's not why we're here. We're here to talk to SnapLogic. They're doing a lot of really interesting things, and they have been in data, and now they're doing a lot of interesting things in integration. And we're excited to have a many time CUBE alum. He's Greg Benson, let me get that title right, chief scientist at SnapLogic and of course a professor at University of San Francisco. Greg great to see you. >> Great to see you, Jeff. >> So I think the last time we see you was at Fleet Forward. Interesting open-source project, data, ad moves. The open-source technologies and the technologies available for you guys to use just continue to evolve at a crazy breakneck speed. >> Yeah, it is. Open source in general, as you know, has really revolutionized all of computing, starting with Linux and what that's done for the world. And, you know, in one sense it's a boon, but it introduces a challenge, because how do you choose? And then even when you do choose, do you have the expertise to harness it? You know, the early social companies really leveraged off of Hadoop and Hadoop technology to drive their business and their objectives. And now we've seen a lot of that technology be commercialized and have a lot of service around it. And SnapLogic is doing that as well. We help reduce the complexity and make a lot of this open-source technology available to our customers. >> So, I want to talk about a lot of different things. One of the things is Iris. So Iris is your guys' leverage of machine learning and artificial intelligence to help make integration easier. Did I get that right? >> That's correct, yeah. Iris is the umbrella terms for everything that we do with machine learning and how we use it to enhance the user experience. And one way to think about it is when you're interacting with our product, we've made the SnapLogic designer a web-based UI, drag-and-drop interface to construct these integration pipelines. We connect these things called Snaps. It's like building with Legos to build out these transformations on your data. And when you're doing that, when you're interacting with the designer, we would like to believe that we've made it one of the simplest interfaces to do this type of work, but even with that, there are many times we have to make decisions, like what type of transformation do you do next? How do you configure that transformation if you're talking to an Oracle database? How do you configure it? What's your credentials if you talk to SalesForce? If I'm doing a transformation on data, which fields do I need? What kind of operations do I need to apply to those fields? So as you can imagine, there's lots of situations as you're building out these data integration pipelines to make decisions. And one way to think about Iris is Iris is there to help reduce the complexity, help reduce what kind of decision you have to make at any point in time. So it's contextually aware of what you're doing at that moment in time, based on mining our thousands of existing pipelines and scenarios in which SnapLogic has been used. We leverage that to train models to help make recommendations so that you can speed through whatever task you're trying to do as quickly as possible. >> It's such an important piece of information, because if I'm doing an integration project using the tool, I don't have the experience of the vast thousands and thousands, and actually you're doing now, what, a trillion document moves last month? I just don't have that expertise. You guys have the expertise, and truth be told, as unique as I think I am, and as unique as I think my business processes are, probably, a lot of them are pretty much the same as a lot of other people that are hooking up to SalesForce to Oracle or hooking up Marketta to their CRM. So you guys have really taken advantage of that using the AI and ML to help guide me along, which is probably a pretty high-probability prediction of what my next move's going to be. >> Yeah, absolutely, and you know, back in the day, we used to consider, like, wizards or these sorts of things that would walk you through it. And really that was, it seemed intelligent, but it wasn't really intelligence or machine learning. It was really just hard-coded facts or heuristics that hopefully would be right for certain situations. The difference today is we're using real data, gigabytes of metadata that we can use to train our models. The nice thing about that it's not hard-coded it's adaptive. It's adaptive both for new customers but also for existing customers. We have customers that have hundreds of people that just use SnapLogic to get their business objectives done. And as they're building new pipelines, as they are putting in new expressions, we are learning that for them within their organization. So like their coworkers, the next day, they can come in and then they get the advantages of all the intellectual work that was done to figure something out will be learned and then will be made available through Iris. >> Right. I love this idea of operationalizing machine learning and the augmented intelligence. So how do you apply it? Don't just talk about it, don't give it a name of some dead smart person, but actually apply it to an application where you can start to see the benefit. And that's really what Iris is all about. So what's changed the most in the last year since you launched it? >> You know, one thing I'll say: The most interesting thing that we discovered when we first launched Iris, and I should say one of the first Iris technologies that we introduced was something called the integration assistant. And this was an assistant that would make, make recommendations of the next Snap as you're building out your pipeline, so the next transformation or the next connector, and before we launched it, we did lots of experimentation with different machine learning models. We did different training to get the best accuracy possible. And what we really thought was that this was going to be most useful for the new user, somebody who hasn't really used the product and it turns out, when we looked at our data, and we looked at how it got used, it turns out that yes, new users did use it, but existing or very skilled users were using it just as much if not more, 'cause it turned out that it was so good at making recommendations that it was like a shortcut. Like, even if they knew the product really well, it's still actually a little more work to go through our catalog of 400 plus Snaps and pick something out when if it's just sitting right there and saying, "Hey, the next thing you need to do," you don't even have to think. You just have to click, and it's right there. Then it just speeds up the expert user as well. That was an interesting sort of revelation about machine learning and our application of it. In terms of what's changed over the last year, we've done a number of things. Probably the operationalizing it so that instead of training off of SnapShot, we're now training on a continuous basis so that we get that adaptive learning that I was talking about earlier. The other thing that we have done, and this is kind of getting into the weeds, we were using a decision tree model, which is a type of machine learning algorithm, and we switched to neural nets now, so now we use neural nets to achieve higher accuracy, and also a more adaptive learning experience. The neural net allowed us to bring in sort of like this organizational information so that your recommendations would be more tailored to your specific organization. The other thing we're just on the cusp of releasing is, in the integration assistant, we're working on sort of a, sort of, from beginning-to-end type recommendation, where you were kind of working forward. But what we found is, in talking to people in the field, and our customers who use the product, is there's all kinds of different ways that people interact with a product. They might know know where they want the data to go, and then they might want to work backwards. Or they might know that the most important thing I need this to do is to join some data. So like when you're solving a puzzle with the family, you either work on the edges or you put some clumps in the middle and work to get to. And that puzzle solving metaphor is where we're moving integration assistance so that you can fill in the pieces that you know, and then we help you work in any direction to make the puzzle complete. That's something that we've been adding to. We recently started recommending, based on your context, the most common sources and destinations you might need, but we're also about to introduce this idea of working backwards and then also working from the inside out. >> We just had Gaurav on, and he's talking about the next iteration of the vision is to get to autonomous, to get to where the thing not only can guess what you want to do, has a pretty good idea, but it actually starts to basically do it for you, and I guess it would flag you if there's some strange thing or it needs an assistant, and really almost full autonomy in this integration effort. It's a good vision. >> I'm the one who has to make that vision a reality. The way I like to explain is that customers or users have a concept of what they want to achieve. And that concept is as a thought in their head, and the goal is how to get that concept or thought into something that is machine executable. What's the pathway to achieve that? Or if somebody's using SnapLogic for a lot of their organizational operations or for their data integration, we can start looking at what you're doing and make recommendations about other things you should or might be doing. So it's kind of like this two-way thing where we can give you some suggestions but people also know what they want to do conceptually but how do we make that realizable as something that's executable. So I'm working on a number of research projects that is getting us closer to that vision. And one that I've been very excited about is we're working a lot with NLP, Natural Language Processing, like many companies and other products are investigating. For our use in particular is in a couple of different ways. To be sort of concrete, we've been working on a research project in which, rather than, you know, having to know the name of a Snap. 'Cause right now, you get this thing called a Snap catalog, and like I said, 400 plus Snaps. To go through the whole list, it's pretty long. You can start to type a name, and yeah, it'll limit it, but you still have to know exactly what that Snap is called. What we're doing is we're applying machine learning in order to allow you to either speak or type what the intention is of what you're looking for. I want to parse a CSV file. Now, we have a file reader, and we have a CSV parser, but if you just typed, parse a CSV file, it may not find what you're looking for. But we're trying to take the human description and then connect that with the actual Snaps that you might need to complete your task. That's one thing we're working on. I have two more. The second one is a little bit more ambitious, but we have some preliminary work that demonstrates this idea of actually saying or typing what you want an entire pipeline to do. I might say I want to read data from SalesForce, I want to filter out only records from the last week, and then I want to put those records into Redshift. And if you were to just say or type what I just said, we would give you a pipeline that maybe isn't entirely complete, but working and allows you to evolve it from there. So you didn't have to go through all the steps of finding each individual Snap and connecting them together. So this is still very early on, but we have some exciting results. And then the last thing we're working on with NLP is, in SnapLogic, we have a nice view eye, and it's really good. A lot of the heavy lifting in building these pipelines, though, is in the actual manipulation of the data. And to actually manipulate the data, you need to construct expressions. And expressions in SnapLogic, we have a JavaScript expression language, so you have to write these expressions to do operations, right. One of our next goals is to use natural language to help you describe what you want those expressions to do and then generate those expressions for you. To get at that vision, we have to chisel. We have to break down the barriers on each one of these and then collectively, this will get us closer to that vision of truly autonomous integration. >> What's so cool about it, and again, you say autonomous and I can't help but think autonomous vehicles. We had a great interview, he said, if you have an accident in your car, you learn, the person you had an accident learns a little bit, and maybe the insurance adjuster learns a little bit. But when you have an accident in an autonomous vehicle, everybody learns, the whole system learns. That learning is shared orders of magnitude greater, to greater benefit of the whole. And that's really where you guys are sitting in this cloud situation. You've got all this integration going on with customers, you have all this translation and movement of data. Everybody benefits from the learning that's gained by everybody's participation. That's what is so exciting, and why it's such a great accelerator to how things used to be done before by yourself, in your little company, coding away trying to solve your problems. Very very different kind of paradigm, to leverage all that information of actual use cases, what's actually happening with the platform. So it puts you guys in a pretty good situation. >> I completely agree. Another analogy is, look, we're not going to get rid of programmers anytime soon. However, programming's a complex, human endeavor. However, the Snap pipelines are kind of like programs, and what we're doing in our domain, our space, is trying to achieve automated programming so that, you're right, as you said, learning from the experience of others, learning from the crowd, learning from mistakes and capturing that knowledge in a way that when somebody is presented with a new task, we can either make it very quick for them to achieve that or actually provide them with exactly what they need. So yeah, it's very exciting. >> So we're running out of time. Before I let you go, I wanted to tie it back to your professor job. How do you leverage that? How does that benefit what's going on here at SnapLogic? 'Cause you've obviously been doing that for a long time, it's important to you. Bill Schmarzo, great fan of theCUBE, I deemed him the dean of big data a couple of years ago, he's now starting to teach. So there's a lot of benefits to being involved in academe, so what are you doing there in academe, and how does it tie back to what you're doing here in SnapLogic? >> So yeah, I've been a professor for 20 years at the University of San Francisco. I've long done research in operating systems and distributed systems, parallel computing programming languages, and I had the opportunity to start working with SnapLogic in 2010. And it was this great experience of, okay, I've done all this academic research, I've built systems, I've written research papers, and SnapLogic provided me with an opportunity to actually put a lot of this stuff in practice and work with real-world data. I think a lot of people on both sides of the industry academia fence will tell you that a lot of the real interesting stuff in computer science happens in industry because a lot of what we do with computer science is practical. And so I started off bringing in my expertise in working on innovation and doing research projects, which I continue to do today. And at USF, we happened to have a vehicle already set up. All of our students, both undergraduates and graduates, have to do a capstone senior project or master's project in which we pair up the students with industry sponsors to work on a project. And this is a time in their careers where they don't have a lot of professional experience, but they have a lot of knowledge. And so we bring the students in, and we carve out a project idea. And the students under my mentorship and working with the engineering team work toward whatever project we set up. Those projects have resulted in numerous innovations now that are in the product. The most recent big one is Iris came out of one of these research projects. >> Oh, it did? >> It was a machine learning project about, started around three years ago. We continuously have lots of other projects in the works. On the flip side, my experience with SnapLogic has allowed me to bring sort of this industry experience back to the classroom, both in terms of explaining to students and understanding what their expectations will be when they get out into industry, but also being able to make the examples more real and relevant in the classroom. For me, it's been a great relationship that's benefited both those roles. >> Well, it's such a big and important driver to what goes on in the Bay Area. USF doesn't get enough credit. Clearly Stanford and Cal get a lot, they bring in a lot of smart people every year. They don't leave, they love the weather. It is really a significant driver. Not to mention all the innovation that happens and cool startups that come out. Well, Greg thanks for taking a few minutes out of your busy day to sit down with us. >> Thank you, Jeff. >> All right, he's Greg, I'm Jeff. You're watching theCUBE from SnapLogic in San Mateo, California. Thanks for watching.

>> Narrator: From San Mateo, California, it's theCUBE, covering SnapLogic Innovation Day 2018. Brought to you by SnapLogic. >> Welcome back, Jeff Frick here with theCUBE. We're at the Crossroads, that's 92 and 101 in the Bay Area if you've been through it, you've had time to take a minute and look at all the buildings, 'cause traffic's usually not so great around here. But there's a lot of great software companies that come through here. It's interesting, I always think back to the Siebel Building that went up and now that's Rakuten, who we all know from the Warrior jerseys, the very popular Japanese retailer. But that's not why we're here. We're here to talk to SnapLogic. They're doing a lot of really interesting things, and they have been in data, and now they're doing a lot of interesting things in integration. And we're excited to have a many time Cube alum. He's Greg Benson, let me get that title right, chief scientist at SnapLogic and of course a professor at University of San Francisco. Greg great to see you. >> Great to see you, Jeff. >> So I think the last time we see you was at Fleet Forward. Interesting open-source project, data, ad moves. The open-source technologies and the technologies available for you guys to use just continue to evolve at a crazy breakneck speed. >> Yeah, it is. Open source in general, as you know, has really revolutionized all of computing, starting with Linux and what that's done for the world. And, you know, in one sense it's a boon, but it introduces a challenge, because how do you choose? And then even when you do choose, do you have the expertise to harness it? You know, the early social companies really leveraged off of Hadoop and Hadoop technology to drive their business and their objectives. And now we've seen a lot of that technology be commercialized and have a lot of service around it. And SnapLogic is doing that as well. We help reduce the complexity and make a lot of this open-source technology available to our customers. >> So, I want to talk about a lot of different things. One of the things is Iris. So Iris is your guys' leverage of machine learning and artificial intelligence to help make integration easier. Did I get that right? >> That's correct, yeah. Iris is the umbrella terms for everything that we do with machine learning and how we use it to enhance the user experience. And one way to think about it is when you're interacting with our product, we've made the SnapLogic designer a web-based UI, drag-and-drop interface to construct these integration pipelines. We connect these things called Snaps. It's like building with Legos to build out these transformations on your data. And when you're doing that, when you're interacting with the designer, we would like to believe that we've made it one of the simplest interfaces to do this type of work, but even with that, there are many times we have to make decisions, like what type of transformation do you do next? How do you configure that transformation if you're talking to an Oracle database? How do you configure it? What's your credentials if you talk to SalesForce? If I'm doing a transformation on data, which fields do I need? What kind of operations do I need to apply to those fields? So as you can imagine, there's lots of situations as you're building out these data integration pipelines to make decisions. And one way to think about Iris is Iris is there to help reduce the complexity, help reduce what kind of decision you have to make at any point in time. So it's contextually aware of what you're doing at that moment in time, based on mining our thousands of existing pipelines and scenarios in which SnapLogic has been used. We leverage that to train models to help make recommendations so that you can speed through whatever task you're trying to do as quickly as possible. >> It's such an important piece of information, because if I'm doing an integration project using the tool, I don't have the experience of the vast thousands and thousands, and actually you're doing now, what, a trillion document moves last month? I just don't have that expertise. You guys have the expertise, and truth be told, as unique as I think I am, and as unique as I think my business processes are, probably, a lot of them are pretty much the same as a lot of other people that are hooking up to SalesForce to Oracle or hooking up Marketta to their CRM. So you guys have really taken advantage of that using the AI and ML to help guide me along, which is probably a pretty high-probability prediction of what my next move's going to be. >> Yeah, absolutely, and you know, back in the day, we used to consider, like, wizards or these sorts of things that would walk you through it. And really that was, it seemed intelligent, but it wasn't really intelligence or machine learning. It was really just hard-coded facts or heuristics that hopefully would be right for certain situations. The difference today is we're using real data, gigabytes of metadata that we can use to train our models. The nice thing about that it's not hard-coded it's adaptive. It's adaptive both for new customers but also for existing customers. We have customers that have hundreds of people that just use SnapLogic to get their business objectives done. And as they're building new pipelines, as they are putting in new expressions, we are learning that for them within their organization. So like their coworkers, the next day, they can come in and then they get the advantages of all the intellectual work that was done to figure something out will be learned and then will be made available through Iris. >> Right. I love this idea of operationalizing machine learning and the augmented intelligence. So how do you apply it? Don't just talk about it, don't give it a name of some dead smart person, but actually apply it to an application where you can start to see the benefit. And that's really what Iris is all about. So what's changed the most in the last year since you launched it? >> You know, one thing I'll say: The most interesting thing that we discovered when we first launched Iris, and I should say one of the first Iris technologies that we introduced was something called the integration assistant. And this was an assistant that would make, make recommendations of the next Snap as you're building out your pipeline, so the next transformation or the next connector, and before we launched it, we did lots of experimentation with different machine learning models. We did different training to get the best accuracy possible. And what we really thought was that this was going to be most useful for the new user, somebody who hasn't really used the product and it turns out, when we looked at our data, and we looked at how it got used, it turns out that yes, new users did use it, but existing or very skilled users were using it just as much if not more, 'cause it turned out that it was so good at making recommendations that it was like a shortcut. Like, even if they knew the product really well, it's still actually a little more work to go through our catalog of 400 plus Snaps and pick something out when if it's just sitting right there and saying, "Hey, the next thing you need to do," you don't even have to think. You just have to click, and it's right there. Then it just speeds up the expert user as well. That was an interesting sort of revelation about machine learning and our application of it. In terms of what's changed over the last year, we've done a number of things. Probably the operationalizing it so that instead of training off of SnapShot, we're now training on a continuous basis so that we get that adaptive learning that I was talking about earlier. The other thing that we have done, and this is kind of getting into the weeds, we were using a decision tree model, which is a type of machine learning algorithm, and we switched to neural nets now, so now we use neural nets to achieve higher accuracy, and also a more adaptive learning experience. The neural net allowed us to bring in sort of like this organizational information so that your recommendations would be more tailored to your specific organization. The other thing we're just on the cusp of releasing is, in the integration assistant, we're working on sort of a, sort of, from beginning-to-end type recommendation, where you were kind of working forward. But what we found is, in talking to people in the field, and our customers who use the product, is there's all kinds of different ways that people interact with a product. They might know know where they want the data to go, and then they might want to work backwards. Or they might know that the most important thing I need this to do is to join some data. So like when you're solving a puzzle with the family, you either work on the edges or you put some clumps in the middle and work to get to. And that puzzle solving metaphor is where we're moving integration assistance so that you can fill in the pieces that you know, and then we help you work in any direction to make the puzzle complete. That's something that we've been adding to. We recently started recommending, based on your context, the most common sources and destinations you might need, but we're also about to introduce this idea of working backwards and then also working from the inside out. >> We just had Gaurav on, and he's talking about the next iteration of the vision is to get to autonomous, to get to where the thing not only can guess what you want to do, has a pretty good idea, but it actually starts to basically do it for you, and I guess it would flag you if there's some strange thing or it needs an assistant, and really almost full autonomy in this integration effort. It's a good vision. >> I'm the one who has to make that vision a reality. The way I like to explain is that customers or users have a concept of what they want to achieve. And that concept is as a thought in their head, and the goal is how to get that concept or thought into something that is machine executable. What's the pathway to achieve that? Or if somebody's using SnapLogic for a lot of their organizational operations or for their data integration, we can start looking at what you're doing and make recommendations about other things you should or might be doing. So it's kind of like this two-way thing where we can give you some suggestions but people also know what they want to do conceptually but how do we make that realizable as something that's executable. So I'm working on a number of research projects that is getting us closer to that vision. And one that I've been very excited about is we're working a lot with NLP, Natural Language Processing, like many companies and other products are investigating. For our use in particular is in a couple of different ways. To be sort of concrete, we've been working on a research project in which, rather than, you know, having to know the name of a Snap. 'Cause right now, you get this thing called a Snap catalog, and like I said, 400 plus Snaps. To go through the whole list, it's pretty long. You can start to type a name, and yeah, it'll limit it, but you still have to know exactly what that Snap is called. What we're doing is we're applying machine learning in order to allow you to either speak or type what the intention is of what you're looking for. I want to parse a CSV file. Now, we have a file reader, and we have a CSV parser, but if you just typed, parse a CSV file, it may not find what you're looking for. But we're trying to take the human description and then connect that with the actual Snaps that you might need to complete your task. That's one thing we're working on. I have two more. The second one is a little bit more ambitious, but we have some preliminary work that demonstrates this idea of actually saying or typing what you want an entire pipeline to do. I might say I want to read data from SalesForce, I want to filter out only records from the last week, and then I want to put those records into Redshift. And if you were to just say or type what I just said, we would give you a pipeline that maybe isn't entirely complete, but working and allows you to evolve it from there. So you didn't have to go through all the steps of finding each individual Snap and connecting them together. So this is still very early on, but we have some exciting results. And then the last thing we're working on with NLP is, in SnapLogic, we have a nice view eye, and it's really good. A lot of the heavy lifting in building these pipelines, though, is in the actual manipulation of the data. And to actually manipulate the data, you need to construct expressions. And expressions in SnapLogic, we have a JavaScript expression language, so you have to write these expressions to do operations, right. One of our next goals is to use natural language to help you describe what you want those expressions to do and then generate those expressions for you. To get at that vision, we have to chisel. We have to break down the barriers on each one of these and then collectively, this will get us closer to that vision of truly autonomous integration. >> What's so cool about it, and again, you say autonomous and I can't help but think autonomous vehicles. We had a great interview, he said, if you have an accident in your car, you learn, the person you had an accident learns a little bit, and maybe the insurance adjuster learns a little bit. But when you have an accident in an autonomous vehicle, everybody learns, the whole system learns. That learning is shared orders of magnitude greater, to greater benefit of the whole. And that's really where you guys are sitting in this cloud situation. You've got all this integration going on with customers, you have all this translation and movement of data. Everybody benefits from the learning that's gained by everybody's participation. That's what is so exciting, and why it's such a great accelerator to how things used to be done before by yourself, in your little company, coding away trying to solve your problems. Very very different kind of paradigm, to leverage all that information of actual use cases, what's actually happening with the platform. So it puts you guys in a pretty good situation. >> I completely agree. Another analogy is, look, we're not going to get rid of programmers anytime soon. However, programming's a complex, human endeavor. However, the Snap pipelines are kind of like programs, and what we're doing in our domain, our space, is trying to achieve automated programming so that, you're right, as you said, learning from the experience of others, learning from the crowd, learning from mistakes and capturing that knowledge in a way that when somebody is presented with a new task, we can either make it very quick for them to achieve that or actually provide them with exactly what they need. So yeah, it's very exciting. >> So we're running out of time. Before I let you go, I wanted to tie it back to your professor job. How do you leverage that? How does that benefit what's going on here at SnapLogic? 'Cause you've obviously been doing that for a long time, it's important to you. Bill Schmarzo, great fan of theCUBE, I deemed him the dean of big data a couple of years ago, he's now starting to teach. So there's a lot of benefits to being involved in academe, so what are you doing there in academe, and how does it tie back to what you're doing here in SnapLogic? >> So yeah, I've been a professor for 20 years at the University of San Francisco. I've long done research in operating systems and distributed systems, parallel computing programming languages, and I had the opportunity to start working with SnapLogic in 2010. And it was this great experience of, okay, I've done all this academic research, I've built systems, I've written research papers, and SnapLogic provided me with an opportunity to actually put a lot of this stuff in practice and work with real-world data. I think a lot of people on both sides of the industry academia fence will tell you that a lot of the real interesting stuff in computer science happens in industry because a lot of what we do with computer science is practical. And so I started off bringing in my expertise in working on innovation and doing research projects, which I continue to do today. And at USF, we happened to have a vehicle already set up. All of our students, both undergraduates and graduates, have to do a capstone senior project or master's project in which we pair up the students with industry sponsors to work on a project. And this is a time in their careers where they don't have a lot of professional experience, but they have a lot of knowledge. And so we bring the students in, and we carve out a project idea. And the students under my mentorship and working with the engineering team work toward whatever project we set up. Those projects have resulted in numerous innovations now that are in the product. The most recent big one is Iris came out of one of these research projects. >> Oh, it did? >> It was a machine learning project about, started around three years ago. We continuously have lots of other projects in the works. On the flip side, my experience with SnapLogic has allowed me to bring sort of this industry experience back to the classroom, both in terms of explaining to students and understanding what their expectations will be when they get out into industry, but also being able to make the examples more real and relevant in the classroom. For me, it's been a great relationship that's benefited both those roles. >> Well, it's such a big and important driver to what goes on in the Bay Area. USF doesn't get enough credit. Clearly Stanford and Cal get a lot, they bring in a lot of smart people every year. They don't leave, they love the weather. It is really a significant driver. Not to mention all the innovation that happens and cool startups that come out. Well, Greg thanks for taking a few minutes out of your busy day to sit down with us. >> Thank you, Jeff. >> All right, he's Greg, I'm Jeff. You're watching theCUBE from SnapLogic in San Mateo, California. Thanks for watching.

>> Announcer: Live from San Francisco, it's theCUBE covering Flink Forward brought to you by Data Artisans. >> Hi this is George Gilbert. We are at Flink Forward on the ground in San Francisco. This is the user conference for the Apache Flink Community. It's the second one in the US and this is sponsored by Data Artisans. We have with us Greg Benson, who's Chief Scientist at Snap Logic and also professor of computer science at University of San Francisco. >> Yeah that's great, thanks for havin' me. >> Good to have you. So, Greg, tell us a little bit about how Snap Logic currently sets up its, well how it builds its current technology to connect different applications. And then talk about, a little bit, where you're headed and what you're trying to do. >> Sure, sure, so Snap Logic is a data and app integration Cloud platform. We provide a graphical interface that lets you drag and drop. You can open components that we call Snaps and you kind of put them together like Lego pieces to define relatively sophisticated tasks so that you don't have to write Java code. We use machine learning to help you build out these pipelines quickly so we can anticipate based on your data sources, what you are going to need next, and that lends itself to rapid building of these pipelines. We have a couple of different ways to execute these pipelines. You can think of it as sort of this specification of what the pipeline's supposed to do. We have a proprietary engine that we can execute on single notes, either in the Cloud or behind your firewall in your data center. We also have a mode which can translate these pipelines into Spark code and then execute those pipelines at scale. So, you can do sort of small, low latency processing to sort of larger, batch processing on very large data sets. >> Okay, and so you were telling me before that you're evaluating Flink or doing research with Flink as another option. Tell us what use cases that would address that the first two don't. >> Yeah, good question. I'd love to just back up a little bit. So, because I have this dual role of Chief Scientist and as a professor of Computer Science, I'm able to get graduate students to work on research projects for credit, and then eventually as interns at SnapLogic. A recent project that we've been working on since we started last fall so working on about six or seven months now is investigating Flink as a possible new back end for the SnapLogic platform. So this allows us to you know, to explore and prototype and just sort of figure out if there's going to be a good match between an emerging technology and our platform. So, to go back to your question. What would this address? Well, so, without going into too much of the technical differences between Flink and Spark which I imagine has come up in some of your conversations or it comes up here because they can solve similar use cases our experience with Flink is the code base is easy to work with both from taking our specification of pipelines and then converting them into Flink code that can run. But there's another benefit that we see from Flink and that is, whenever any product, whether it's our product or anybody else's product, that uses something like Spark or Flink as a back end, there's this challenge because you're converting something that your users understand into this target, right, this Spark API code or Flink API code. And the challenge there is if something goes wrong, how do you propagate that back to the users so the user doesn't have to read log files or get into the nuts and bolts of how Spark really works. >> It's almost like you've compiled the code, and now if something doesn't work right, you need to work at the source level. >> That's exactly right, and that's what we don't want our users to do, right? >> Right. >> So one promising thing about Flink is that we're able to integrate the code base in such a way that we have a better understanding of what's happening in the failure conditions that occur. And we're working on ways to propagate those back to the user so they can take actionable steps to remedy those without having to understand the Flink API code iself. >> And what is it, then, about Flink or its API that gives you that feedback about errors or you know, operational status that gives you better visibility than you would get in something else like Spark. >> Yeah, so without getting too too deep on the subject, what we have found is, one thing nice about the Flink code base is the core is written in Scala, but there's a lot of, all the IO and memory handling is written in Java and that's where we need to do our primary interfacing and the building blocks, sort of the core building blocks to get to, for example, something that you build with a dataset API to execution. We have found it easier to follow the transformation steps that Flink takes to end up with the resulting sort of optimized, optimized Flink pipeline. Now by understanding that transformation, like you were saying, the compilation step, by understanding it, then we can work backwards, and understand how, when something happens, how to trace it back to what the user was originally trying to specify. >> The GUI specification. >> Yeah. Right. >> So, help me understand though it sounds like you're the one essentially building a compiler from a graphical specification language down to Spark as the, you know, sort of, pseudo, you know, psuedo compile code, >> Yep. >> Or Flink. And, but if you're the one doing that compilation, I'm still struggling to understand why you would have better reverse engineering capabilities with one. >> It just is a matter of getting visibility into the steps that the underlying frameworks are taking and so, I'm not saying this is impossible to do in Spark, but we have found that we've had, it's been easier for us to get into the transformation steps that Flink is taking. >> Almost like, for someone who's had as much programming as a one semester in night school, like a variable and specter that's already there, >> Yeah, that's a good, there you go, yeah, yeah, yeah. >> Okay, so you don't have to go try and you can't actually add it, and you don't have to then infer it from all this log data. >> Now, I should add, there's another potential Flink. You were asking about use cases and what does Flink address. As you know, Flink is a streaming platform, in addition to being a batch platform, and Flink does streaming differently than how Spark does. Spark takes a microbatch approach. What we're also looking at in my research effort is how to take advantage of Flink's streaming approach to allow the SnapLogic GUI to be used to specify streaming Flink applications. Initially we're just focused on the batch mode but now we're also looking at the potential to convert these graphical pipelines into streaming Flink applications, which would be a great benefit to customers who want-- >> George: Real time integration. >> Want to do what Alibaba and all the other companies are doing but take advantage of it without having to get to the nuts and bolts of the programming. Do it through the GUI. >> Wow, so it's almost like, it's like, Flink, Beam, in terms of obstruction layers, >> Sure. >> And then SnapLogic. >> Greg: Sure, yes. >> Not that you would compile the beam, but the idea that you would have perv and processing and a real-time pipeline. >> Yes. >> Okay. So that's actually interesting, so that would open up a whole new set of capabilities. >> Yeah and, you know, it follows our you know, company's vision in allowing lots of users to do very sophisticated things without being, you know, Hadoop developers or Spark developers, or even Flink developers, we do a lot of the hard work of trying to give you a representation that's easier to work with, right but, also allow you to sort of evolve that and de-bug it and also eventually get the performance out of these systems One of the challenges of course of Spark and Flink is that they have to be tuned, and you have to, and so what we're trying to do is, using some of our machine learning, is eventually gather information that can help us identify how to tune different types of work flows in different environments. And that, if we're able to do that in it's entirety, then we, you know, we take out a lot of the really hard work that goes into making a lot of these streaming applications both scalable and performing. >> Performimg. So this would be, but you would have, to do that, you would probably have to collect well, what's the term? I guess data from the operations of many customers, >> Right. >> Because, as training data, just as the developer alone, you won't really have enough. >> Absolutely, and that's, so that you have to bootstrap that. For our machine learning that we currently use today, we leverage, you know, the thousands of pipelines, the trillions of documents that we now process on a monthly basis, and that allows us to provide good recommendations when you're building pipelines, because we have a lot of information. >> Oh, so you are serving the runtime, these runtime compilations. >> Yes. >> Oh, they're not all hosted on the customer premises. >> Oh, no no no, we do both. So it's interesting, we do both. So you can, you can deploy completely in the cloud, we're a complete SASS provider for you. Most of our customers though, you know, Banks Healthcare, want to run our engine behind their firewalls. Even when we do that though, we still have metadata that we can get introspection, sort of anonymized, but we can get introspection into how things are behaving. >> Okay. That's very interesting. Alright, Greg we're going to have to end it on that note, but uh you know, I guess everyone stay tuned. That sounds like a big step forward in sort of specification of real time pipelines at a graphical level. >> Yeah, well, it's, I hope to be talking to you again soon with more results. >> Looking forward to it. With that, this is George Gilbert, we are at Flink Forward, the user conference for the Apache Flink conference, sorry for the Apache Flink user community, sponsored by Data Artisans, we will be back shortly. (upbeat music)

>> Voiceover: Live from San Francisco it's theCUBE. Covering AWS Summit 2017. Brought to you by Amazon Web Services. (upbeat music) >> Hey welcome back to theCUBE live at the Moscone Center at the Amazon Web Services Summit San Francisco. Very excited to be here, my co-host Jeff Rick. We're now talking to the Chief Scientist and professor at University of San Francisco, Greg Benson of SnapLogic. Greg, welcome to theCUBE, this is your first time here we're excited to have you. >> Thanks for having me. >> Lisa: So talk to us about what SnapLogic is, what do you do, and what did announce recently, today, with Amazon Web Services? >> Greg: Sure, so SnapLogic is a data integration company. We deliver a cloud-native product that allows companies to easily connect their different data sources and cloud applications to enrich their business processes and really make some of their business processes a lot easier. We have a very easy-to-use what we call self-service interface. So previously a lot of the things that people would have to do is hire programmers and do lots of manual programming to achieve some of the same things that they can do with our product. And we have a nice drag-and-drop. We call it digital programming interface to achieve this. And along those lines, I've been working for the last two years on ways to make that experience even easier than it already is. And because we're Cloud-based, because we have access to all of the types of problems that our customers run into, and the solutions that they solve with our product, we can now leverage that, and use it to harness machine-learning. We call this technology Iris, is what we're calling it. And so we've built out this entire meta-data framework that allows us to do data science on all of our meta-data in a very iterative and rapid fashion. And then we look for patterns, we look for historical data that we can learn from. And then what we do is we use that to train machinery and algorithms, in order to improve the customer experience in some way. When they're trying to achieve a task, specifically the first product feature that is based on the Iris technology is called the Integration Assistant. And the Integration Assistant is a very practical tool that is involved in the process of actually building out these pipelines. We call, when you build a pipeline it consists of these things called snaps, right? Snaps encapsulate functionality and then you can connect these snaps together. Now, it's often challenging when you have a problem to figure, OK, it's like a puzzle what snaps do I put together, and when do I put them together? Well, now that we've been doing this for a little while and we have quite a few customers with quite a few pipelines, we have a lot of knowledge about how people have solved those puzzles in the past. So, what we've done with Iris, is we've learned from all of those past solutions and now we give you automatic suggestions on where you might want to head next. And, we're getting pretty good accuracy for what we're predicting. So, we're basically, and this integration system is, a recommendation engine for connecting snaps into your pipelines as they're developing. So it's a real-time assistant. >> Jeff: So if I'm getting this right, it's really the intelligence of the crowd and the fact that you have so many customers that are executing many of the similar, same processes that you use as the basis to start to build the machine-learning to learn the best practices to make suggestions as people are going through this on their own. >> Greg: That's absolutely right. And furthermore, not only can we generalize from all of our customers to help new customers take advantage of this past knowledge, but what we can also do is tailor the suggestions for specific companies. So as you, as a company, as you start to build out more solutions that are specific to your problems, your different integration problems... >> Jeff: Right. >> The algorithms can now be, can learn from those specific things. So we both generalize and then we also make the work that you're doing easier within your company. >> And what's the specific impact? Are there any samples, stories you can share of what is the result of this type of activity? >> Greg: We're just, we're releasing it in May. >> Jeff: Oh OK. >> So it's going to be generally available to customers. >> Couple weeks still. >> Greg: Yeah. So... So... And... So... So we've done internal tests, so we've dove both through sort of the data science, so the experimentation to see, to feed it and get the feedback around how accurately it works. But we've also done user studies and what the user studies, not only did the science show but the user studies show that it can improve the time to completion of these pipelines, as you're building them. >> Lisa: So talk to us a little bit about who your target audience is. We're AWS, as we said. They really started 10 years ago in the start of space and have grown tremendous at getting to enterprise. Who is the target audience for SnapLogic that you're going after to help them really significantly improve their infrastructure get to the cloud, and beyond? >> Greg: So, so, so basically, we work with, largely with IT organizations within enterprises, who are, you know, larger companies are tasked with having sort of a common fabric for connecting, you know, which in an organization is lots of different databases for different purposes, ERP systems, you know, now, increasingly, lots of cloud applications and that's where part of our target is, we work with a lot of companies that still have policies where of course their data must be behind their firewall and maybe even on their premise, so our technology, while we're... we're hosted and run in the cloud, and we get the advantage of the SAS, a SAS platform, we also have the ability to run behind a firewall, and execute these data pipelines in the security domains of the customers themselves. So, they get the advantage of SAS, they get the advantage of things like Iris, and the Integration Assistant, right, because we can leverage all of the knowledge, but they get to adhere to any, you know, any regulatory or security policies that they have. And we don't have to see their data or touch their data. >> Lisa: So helping a customer that was, you know, using a service-oriented architecture or an ETL, modernize their infrastructure? >> Greg: Oh it's completely about modernization. Yeah, I mean, we, you know, our CEO, Gaurav Dhillon has been in the space for a while. He was formerly the CEO of Informatica. And so he has a lot of experience. And when he set out to start SnapLogic he wanted to look, you know, embrace the technologies of the time, right? So we're web-focused, right? We're HTTP and REST and JSON data. And we've centered the core technologies around these modern principles. So that makes us work very well with all the modern applications that you see today. >> Jeff: Look Greg, I want to shift gears a little bit. >> Greg: Yeah. >> You're also a professor. >> Greg: Correct. >> At University of San Francisco and UC Davis. I'd just love to get your perspective from the academic side of the house on what's happening at schools, around this new opportunity with big data, machine-learning, and AI and how that world is kind of changing? And then you are sitting in this great position where you kind of cross-over both... How does that really benefit, you know, to have some of that fresh, young blood, and learning, and then really take that back over, back into the other side of the house? >> Greg: Yeah, so a couple of things. Yeah, professor at University of San Francisco for 19 years. I did my PhD at UC Davis in computer science. And... My background is research in operating systems, parallel and distributed computing, in recent years, big data frameworks, big data processing. And University of San Francisco, itself, we have a, what we call the Senior and Masters Project Programs. Where, we've been doing this for, ever since I've been at USF, where what we do is we partner groups of students with outside sponsors, who are looking for opportunities to explore a research area. Maybe one that they can't allocate, you know, they can't justify allocating funds for, because it's a little bit outside of the main product, right? And so... It's a great win, 'cause our students get experience with a San Francisco, Silicon Valley company, right? So it helps their resume. It enhances their university experience, right? And because, you know, a lot of research happens in academia and computer science but a lot of research is also happening in industry, which is a really fascinating thing, if you look at what has come out of some of the bigger companies around here. And we feel like we're doing the same thing at SnapLogic and at the University of San Francisco. So just to kind of close that loop, students are great because they're not constrained by, maybe, some of us who have been in the industry for a while, about maybe what is possible and what's no so possible. And it's great to have somebody come and look at a problem and say, "You know, I think we could approach this differently." And, in fact, really, the impetus for the Integration Assistant came out of one of these projects where I pitched to our students, and I said "OK, we're going to explore SnapLogic meta-data and we're going to look at ways we can leverage machine-learning in the product on this data." But I left it kind of vague, kind of open. This fantastic student of mine from Thailand, his name is Jump, he kind of, he spent some time looking at the data and he actually said, "You know I'm seeing some patterns here. I'm seeing that, you know, we've got this great repository of these," like I described, "of these solved puzzles. And I think we could use that to train some algorithms." And so we spent, in the project phase, as part of his coursework, he worked on this technology. Then we demoed it at the company. The company said, "Wow, this is great technology. Let's put this into production." And then, there was kind of this transition from sort of this more academic, sort of experimental project into, going with engineers and making it a real feature. >> Lisa: What a great opportunity though, not just for the student to get more real-world applicability, like you're saying, taking it from that very experimental, investigational, academic approach and seeing all of the components within a business, that student probably gets so much more out of just an experiment. But your other point is very valid of having that younger talent that maybe doesn't have a lot of the biases and the pre-conceived notions that those of us that have been in the industry for a while. That's a great pipeline, no pun intended... >> Greg: Sure. >> For SnapLogic, is that something that you helped bring into the company by nature of being a professor? Just sort of a nice by-product? >> Well, so a couple of things there. One is that, like I said, University of San Francisco we were running this project class for a while, and... I got involved, you know, I had been at USF for a long time before I got involved with SnapLogic. I was introduced to Gaurav and there was this opportunity. And initially, right, initially, I was looking to apply some of my research to the technology, their product and their technology. But then it became clear that hey, you know we have this infrastructure in place at the university, they go through the academic training, our students are, it's a very rigorous program, back to your point about what they are exposed to, we have, you know, we're very modern, around big data, machine-learning, and then all of the core computer science that you would expect from a program. And so, yeah, it's been... It's been a great mutually beneficial relationship with SnapLogic and the students. But many other companies also come and pitch projects and those students also do similar types of projects at other companies. I would like to say that I started it at USF but I didn't. It was in existence. But I helped carry it forward. >> Jeff: That's great. >> Lisa: That is fantastic. >> And even before we got started, I mean you said your kind of attitude was to be the iPhone in this space. >> Greg: Of integration, yeah. >> Jeff: So again, taking a very different approach a really modern approach, to the expected behavior of things is very different. And you know, the consumerization of IT in terms of the expected behavior of how we interact with stuff has been such a powerful driver in the development of all these different applications. It's pretty amazing. >> Greg: And I think, you know, just like maybe, now you couldn't imagine most sort-of consumer-facing products not having a mobile application of some sort, increasingly what you're seeing is applications will require machine-learning, right, will require some amount of augmented intelligence. And I would go as far to say that the technology that we're doing at SnapLogic with self-service integration is also going to be a requirement. That, you just can't think of self-service integration without having it powered by a machine-learning framework helping you, right? It almost, like, in a few years we won't imagine it any other way. >> Lisa: And I like the analogy that Jeff, you just brought up, Greg, the being the iPhone of data integration. The simplicity message, something that was very prevalent today at the keynote, about making things simpler, faster, enabling more. And it sounds like that's what you're leveraging computer science to do. So, Greg Benson, Chief Scientist at SnapLogic. Thank you so much for being on theCUBE, you're now CUBE alumni, so that's fantastic. >> Alright. >> Lisa: We appreciate you being here and we appreciate you watching. For my co-host Jeff Rick, I'm Lisa Martin, again we are live from the AWS Summit in San Francisco. Stick around, we'll be right back. (upbeat music)

>>mhm >>Welcome to this cube conversation here in Palo alto California. I'm john Kerry host of the cube. We've got a great awesome conversation with the Ceo and co founder of harness a hot startup jodi Benson who is the co founder and Ceo but also the co founder of unusual ventures which is a really awesome venture capital firm, doing some great work investment but also they have great content over there for entrepreneurs and for people in the community And of course he's also the founder of big labs, his playground. If you're building out new applications also well known for being the founder of Ap dynamics of super successful billion dollar exit as a startup, Salto, Cisco now doing a lot of things and driving harness, solving big problems. So joe t mouthful intro there, you've done a lot. Congratulations on your an amazing entrepreneur career and now your next uh next next opportunities harness among other things. So congratulations. Thank you for coming. >>Thank you john and glad to be here. >>You guys are solving a big problem in software delivery. Obviously software changing the world. You're seeing open source projects increasing in order of magnitude enterprises jumping on open source in general adoption, large scale with cloud software is being delivered faster than ever before and with cloud scale and now edge this huge challenges around how software deployed, managed maintained. You got, we're talking about space to how do you do break fix in space, all these things are happening at a massive scale across the world. You are solving a big problem. So take a minute to explain what harnesses doing, why you guys exist, why you jumping in into this venture. >>Sure. Yeah. You know what harness mission is to simplify supper delivery and make it uh top notch for everyone. Like if you look at like you know the likes of google and facebook and netflix and amazon these companies are mastered the process of software delivery like and your engineers write code and the code is shipped to the end users and they can do it like multiple times a day at their scale and you know at the complexity that they have but most other business in the world they all want to be software companies but it's extremely, extremely hard for them to get there and I saw this firsthand when I was at epidemics as you know as Ceo last there we're about 12 1300 employees in the company and we had about about 3 50 or so engineers in the company For every 10 or 12 engineers, we had one person whose job was to write automation and scripting and tooling for trying to ships off you know uh you know all kind of scripting kind of stuff. We'll write scripts and chef and puppet and sensible and to deploy in aws and whatnot. And you know one day we're doing the math were like you know we have you know about overall about 30 people whose job was to do devops engineering by writing automation etc to deploy somewhere and I would do the math like you know, one engineer cost is 200 k loaded cost at six million a year that you're spending six million a year just writing deployment, scripting, you know, and even with that we were nowhere close to world class like world class is in like what you would think you could ship every day, we chip on demand, you could, you know, you could deploy software, ship software all of that right? And that was the, you know, I looked at that as a problem inside of dynamics and all they have done with customers, I would talk to like large banks, insurance companies and retailers and telcos and I would hear the same challenge like you know, we hear about devops, we go to the all these devops conferences and events and we see the same 10 companies, you know presenting how the home grew some kind of a devops system for software delivery etc. And you know, I mean that was like, you know, we just, we cannot survive with this like and as the world we need to have uh the right kind of platforms for software delivery and simplify this so that everyone could become as good as a google netflix amazon etcetera that stand of our mission at harness that can we take every business in the world, you know and in a few weeks or a few months, can we get them as sophisticated and good in terms of their dueling for software delivery as a google facebook amazon, those kind of companies would be and that's, that's what we're doing. So >>It's a great ambition and by the way it's a bold move and it's needed. I'll tell you, it's interesting. You mentioned some of those commentary about shipping code at that speed Facebook Google. They had that they had they were forced to do that and again they have all that benefit the mainstream enterprise doesn't. But if you even go back 20 years ago, 15 years ago, that's when Amazon was born. You see two and S three is celebrating their 15th birthday. Software. Yeah, hyper scale has had some good moves there. But the average business went from craft, you know, waterfall QA department go back a little bit slower. I won't say slow motion but manageable now with the speed of shipping and the speed of the scale, that's a huge issue. What kind of pressure do you see that putting on the developer, the individual, not just the system because you got the system of development and the devil and the developers themselves. >>I think the developers have have done quite well to this. I feel like, you know, if you look at the software development part of itself, you know the agile development has been happening for quite some time. So developers have learned how to ship things fast and like in a week sprint or a two week sprint or in in kind of faster cycles. They have moved off from the waterfall kind of models like many years ago now. So that's the suffering development side of things then you have the infrastructure side of things which is the like any province in infrastructure fast. Can you get hardware fast? That's the, you know, the cloud has done that well where the challenges the process, the developers are writing code fast enough these days and you have the, you know, the infrastructure itself could be prov isn't and maintained and and and change fast enough but how do you bring it all together and there is the entire process around it. That's not moving fast enough. So that's where the bottom language. So I feel the, you know, and the process is not good. The developer experience becomes really bad bad because developers are waiting for the process to go and you know, they write some code and the code is sitting on the shelf and they are waiting for things. >>Uh they get all pissed off and mad. What's the holdup? Why what's the process? And then security shifting left, wait a minute to go back and rewrite code. This is huge. I want to just get back and just nail it quickly if you don't mind honing in on the value proposition. What is the harness value proposition? What is the pitch, what are you, what are you offering? What are you solving? Can you nail in on that real quick? >>Sure. So what harness is swallowing is simplifying that software delivery by plane, so developer writes code and that code goes goes through a bunch of steps so a bunch of steps which is uh you know you build the code then you you know test the code, you know, then you do integration tests, then you you know go through your security checks, then you go through a compliance checks, then you go through more dusting, then you're deploying a staging environment, then you go one to do a bunch of things on it. Then you start deploying in production environment but in production you will deploy on like a small part of production, verify everything is working well, it's not working well, you'll roll it back, it's working well then you deploy two more things. This entire process could take like weeks for people to do and this is mostly automated, you know in kind of uh uh you know this kind of random scripts here and there etcetera. So we simplify the entire process that you could describe your process in the language, I just described like you know in a very descriptive declarative kind of way like this is the process I want to achieve and hardness will automatically create your pipelines for this. This kind of process and most of these pipelines have a lot of heavy use of intelligence and um L two, it could go from one step to another, like, so many times, like when you say, you know, deploy the guard and and and 1% of my production environment and see everything is working well and if everything is working well, go to the next 10%. But how do you figure out if everything is working well and that's where the intelligence and um El comes in like, you know, what we learn, what is a normal behavior of your application, how does a normal part of the code works like, you know, there, what's the performance behavior, what is a functional behavior? What errors it is? And if everything is good then you go to the next step so that entire cycle harness automatically, uh you know, uh managers and its automated, you know, if you get governance, you get like, you know, high degree of automation, you get a high degree of, you know, security, you get high degree of like, you know, uh uh you know, quality around him. And so it's it's think of like the, the Ci cd has a lot of developers know and know this process is is ci cd on steroids available to you, Right? So you >>sound like you're making it easier on the Ci cd pipeline process, standing it up, detecting it, prototyping it, if you will, for lack of a better description, get get used to the pipeline and then move it out, roll it out and build your own in a way >>that, is that what is that what you're doing? It's like, you know, a lot of these complex ci city pipelines, what people need, you know, it can take them like three months, six months to to put it uh you know, put it together the harness, it's like an hour, an hour, you could put it together, you know, very, very sophisticated uh Ci cd pipeline and the pipeline is, you know, automated is is, you know, it's it's intelligent around like, you know, what is the normal behavior of your of your applications? Uh It's it's just so phenomenally different than how people have done ci cd before that we simplify the process. Automate the process, you know, and make it manageable and very ready to get involved. >>It's funny you mentioned the three weeks weeks it could take to do the csd pipeline. Of course, that doesn't factor in the what happens when you roll it out, people start complaining, playing with it, breaking it, then you gotta go back and do it again. I mean, that's real and that's a real problem, I mean, can you just going to give a taste of the scar tissue that goes on there. What's some of the what are some of the what some of the pain points that you solve? >>Yeah. So, I think the that is that really becomes the core of the pain point, like, you know, people need, like high amount of dependability, easy to change things, you know, it's we call it like the lack of intelligent automation, you know, and the and this heavy amount of developer toil that the developers have to do so much work around around making all of this work like you know it has to be simplified. So that's that's where our value product comes in like you know, it's it's you know uh you can get like a visual builder and like minutes you can build out the entire process which is your job stability at city pipeline or you could also do like a declarative Yamil interface and just like you know in a few lines just right up whatever process you would want and we would review should be shipped with all kind of integrations with every cloud environment, every monitoring system, every system, every kind of testing process, every kind of security scanning so you can just drag and drop and in minutes eur, europe and running, it just creates so much velocity in this entire process. And also this manageability that people have struggled with >>morale to I mean you can imagine the morale developers go up significantly when you start seeing that the developer productivity has always been a big thing but this intelligent automation conversations huge. Some people have it, some people don't, people say they have it, what is how can you, how can the company figure out uh if someone's really got the real deal when it comes to intelligent automation because again, automation is the is key into devops. >>Yeah, I think I I almost started like you know like if you look at the generational evolution of things like the the first generation was uh you know developer writes code and then it will give you will give it to some some mighty at men who will go and deploy the code, run some commands and do things like tradition to was writing scripts that you're right, a lot of scripts that was automation but it was kind of dumb our dimension and that's how we have, you know that that's where the industry is so actually break now even most of it, the third generation is when the automation is you don't write scripts to you know uh to automate things, you tell our system what you want to achieve and it generates automation for you, right? And that's what we call intelligent automation. Where it's all declarative and all the you don't have to maintain a lot of you know scripts etcetera because they are, you know, they can't keep up with it. You know, you have to change the process all the time and if you change the process, it doesn't work, it becomes completely, you know, uh you know, it becomes very fragile to manage it. So that's that's really where intelligent automation comes in, you know, I look at like, you know, if you can have like uh like you look at like a wrestler, you know, making cars the entire assembly line is automated, but it's, but it's if you want to change something in the assembly line, even that process is automated and it's very simple. Right? So it's and that's what gives them so much uh you know, uh you know, uh let's say control and manageability around the manufacturing process. So the software delivery, uh you know, by assembly line, which is the software software by ci cd piper and really should be a more sophisticated and more intelligent as well now. And that's that's an exhibition, >>jodi. You're also pointing out something that we cover a lot on the cube and we've been writing about is how modern software practices are changing, where this team makeup or whatever its speed is key, but also getting data. Everyone who's successful with cloud and cloud scale and now you got the edge opening up and like I said, even space is going to be programmable, Everything's programmable. And the key is to get the data from the use cases right, get something deployed, look at it, get some data and then double down and make it better. That's a modern approach, not build it and then rebuild it and tear it down and rebuild it, which you're kind of leaning into this idea of let's get some delivery going, let's structure it and then feed it more so that the developers can iterate with with, with the pipeline and this is this again, can scale, can you talk about that? Can you comment on your reaction to that? >>Yeah, definitely. That's exactly how we look at it. Like, you know, you uh you want developers to kind of like say they want to do a, you know, automated process to deploy in their communities infrastructure in matter of minutes, you should be able to get started, but now it's like, you know, there's so much data that comes into it. Like, you know that you have monitoring systems systems like ab dynamics and you're like and data dog and you're logging systems your Splunk and elastic and you know, some logic, you have your, you know, different kind of testing systems here, your security scanning, so there's so much data in it. They're like, you know, terabytes and terabytes of data from it. So when you start doing your deployments, we could also come seem all of the data and see like what was the impact of those deployments or court changes in each of these monitoring, dusting, logging gonna systems and you know, what, how the data changes and then now is that based on that we can learn like, you know, what should be your ideal process and what will break in your process and that's that's the how harness platform works. That's the core of that intelligent automation networks, they're expanding it now to bring a few more of the devops use cases into it Also like the one is cloud cost management because when you, when you, you know, uh you know when we started shipping, there's a lot of people would tell us like, you know, you're you're doing a great job helping us managing the quality, which we always were concerned about like when we're deploying things so you know, security, you know, functionality etcetera. But cloud cost is a big challenge as well. You have your paying like tens and tens of millions of dollars to the cloud providers. And when developers do things in an automated way, it could increase without cost suddenly and we don't know what to do how to manage that. So that's the, you know, we we introduced a new model called cloud cost management to as part of the develops software delivery process that every time you're shipping code and we also figure out like, you know, what with impact on on your on your podcast, you know, can we automate the, you know, uh if there is there is too much impact, can we automate the, you know, the roll back around it, you know, can you get and you can you can we stop the delivery process at that point, can we help you troubleshoot and, you know, reduce the cost down? So that's, you know, that's cost becomes another another another dimension to it. Uh you know, then we recently just added uh you know, the next level that's managing feature Flags. And a lot of the time software developers are adding feature flags to like this feature would be given to this consumer and like, you know, and this feature will be given to this consumer until you test it out through uh test kind of thing and like, you know, what is the impact of, you know, uh turning a feature on versus off, you know, we're bringing that into the same ci cd pipeline. So it's kind of an integrated approach to this uh you know, our intelligently automated biplane instead of these uh small point approaches that just very hard to manage. >>I mean the level of data involved the creature flag for instance, the great is an amazing thing because that allows you to do things that used to be extremely difficult to provision. I mean just picking the color of icon, for instance, this kind of blue, I mean I was just, you hear about this, these kinds of things happening at scale and the date is pretty accurate when it comes in. So I think that's an example of the kind of speed and agility that developers want and the question I want to ask you though on that point because this opens up the whole next conversation, you guys have a modern approach and so much traction and you've recently raised big rounds of funding as you go to the market place, your experienced entrepreneur and uh and Ceo you've seen the waves before. What's the big wave that you're on now? What's the big momentum tailwind for harness? Is it the fact that you're creating value for developers or is it the system that you're integrating into with the intelligence to make things smarter and more scalable? What's the or is it all the above? Can you just share what that that story is? >>Yeah, I think it's, it's, it's really, really both of them. But you know, what are our business case when you go to people who tell them like say, if you're you know, 200 developers. uh, you know, we can give you the world's best software delivery tooling at the cost of half to one developer. Right? So like, you know, so which is like 44, 200 person organization at like 200 to 200 to $300,000 a year. They will get the best software delivery tooling better than a Google Facebook Amazon kind of companies very, very quickly. So our, our entire value prop is built on that like a developer experience gets much better. The productivity gets much better. Developers on an average are spending like 20-30% of the time on deployment, delivery-related toil, like unnecessary stuff that we deal with. So it's only 30% more efficiency gain for the developers. Their quality of life gets better that they don't need to worry about like weekends and nights to babysit your deployments and you know, things breaking and troubleshooting things all the time. Right? So that's that's a that's a big big value. But as a business you get much more velocity your innovation velocity is much higher. You know your risk on your, you know your consumers is much lower because your quality of the of of you know how your ship becomes becomes better. So our business case of like you know at the past of like 1-2 develops engineers will get you the best develops uh you know tooling in the world possible. You know it's not a hard business case for us to make, right? That's that's what we we we look at, it becomes pretty pretty obvious for you know as people try our product, you know the business case >>you don't have to really pass the I. Q. Test to figure this one out, okay everyone's happier and you have more options to scale and make more money in new opportunities not just existing business. I mean the feature flagging these new features you can build a new value and take more territory if you're a business or whatever your objective is so clear value. Can you give an example of some recent successes you've had or or traction points that you think is worth notable that people can get their arms around. >>Yeah definitely like you know we are we're helping a lot of uh you know a lot of customers you know doing uh like completely changing their uh their uh their process of software delivery, you know, 11 recent example, uh nationwide insurance, you know, nationwide insurance, you know, moving from their data center kind of approach to public cloud and to communities and to microservices, like a major cloud native re architecture and in a very ambitious aggressive project to do it, you know, in a in a in a short period of time and harness becomes a platform for them to kind of, you know, uh to remove all the bottom leg around the process, the software delivery process. You know, they obviously they still have to do the developer side of things and they have to do the cloud infrastructure side of things, which is they're doing. But the entire process of how you bring together, you know, harness becomes accelerated around it. So a lot of these kind of stories that we when we kind of create this fundamental transformation for our for our for our customers, you know, uh you know, moving to to a public cloud, you know, moving to microservices, moving to communities, you know, re architect things, but they become much faster. Cloud native higher, you know, a true software company and you know, I would say that's that's something we we we we take a they can take a lot of pride in, I think are always our biggest challenge is uh is to is to is to evangelize and and convince the market that this is possible to do with the product, because historically people have got told like, you know, the only way you can do this kind of software delivery processes and tooling is by engineering it on your own. So everyone wants us on the path of writing their own, you know, and and it's very hard for every, every company in the world to become very good in writing your own software delivery, tooling and processes and systems, etcetera. Right? So it's uh and that's it. So, you know, there is still that that education and evangelism needs to be done, that, you know, there is uh there is no point, you're trying to do it on your own, you can get a platform that can do it all for you and you can focus on the your core business of, you know, what you want to innovate on. >>And I think the Devil's movement hasn't been pioneered and you have to hand roll everything and that's the way it was. But now, as the mainstream market picks this up, you're standing on the shoulders of those pioneers, you are one of them. It's awesome to see this modern approach because it's really playing out in real time again, you've done that before, joe t so it's impressive and, you know, you've seen the movie and developed and the earlier versions pre devops. So, so as cloud native comes and start scaling it's going to be for the rest of us. So, great, great that you're providing the platform and the tools and software. I got to ask you if you don't mind because a lot of people are looking at ways for modern approaches to organizing their teams, how would you define the modern devops movement? You look at devops one point. Oh, we got here. Okay, cloud, cloud native, cloud scale, modern applications, pipe lining. Now, we're looking at a whole another level of confluence of uh of integration and speed. How would you define the modern devops movement? >>Yeah, I think that's a that's a very good question. I think that the core of modern devops, what I would call it develops to point to me is developers self service. It was like the first generation of develops was they create this kind of a devoPS team and then the developers will give all the, you know, delivery related stuff that develops team and the devops team starts to become a bottle, like everywhere now, like in the developed steam job is to build a ci pipeline and the city pipeline and the deployment scripts and you know, do like, you know, you want to do a canary deployment, they have to figure it out how to do it, they have to do, like, you know, you are uh you know, all sort of things that the that needs to be done, you create a central develops team and you give it to them and they become like, you know, uh become a big bottleneck, we look at the modern develops or the next generation and develops has to be done around focusing on the developer experience that and making it all self service for the developers. So you have, you have, let's say you are definitely in for a micro service and it's like, you know 57 engineers, you know, modeling a micro service you want like that, they can go and say this is for our micro service, you know, in a matter of minutes or hours, they can engineer the process without having to lean on a central deVOPS team and to do all the work for them and that's you know, by by maybe a modeler or in some kind of mammal interface or something. That's very easy for them, their experience is so easy that they can manage it themselves without the central deVOPS team have to write it all or cut it all and manage it all. But at the same time the center deVOPS teams, job becomes a bar and governance that can they define the guardrails, that they can define the guardrails on like, you know, you have to have this level of security before something goes into production, you have to have this level of quality before something goes into production, you have to have like, you know, uh this, your cost could not be more than this, right? So you define, so in this instance, instead of the center develops team is doing all the work themselves on writing all the stuff they define the guard rails and it becomes a very easy cell service experience of the developers should do things within those, those guard rails. This is what the modern never actually, >>that's awesome and also accelerate more business value And you're nailing it joe t thank you for coming on and great. Uh, the Ceo on the cube ceo and co founder harness harness dot IO. You guys got free trials, free downloads. You got a great, uh, by as you go model also. Um, you're an entrepreneur at heart. Uh, co founder of unusual ventures, Big Labs appdynamics. Now harness. Congratulations. Thanks for coming on. >>Hey, thank you john. >>Okay, this is a cube conversation. I'm john for here in Palo alto California with the cube. Thanks for watching.

>>from around the globe. It's the Cube with digital coverage of AWS Public Sector online brought to you by Amazon Web services. Oven Welcome back to the cubes. Virtual coverage of Amazon Web services. Eight. Of his public sector summit online. We couldn't be there in person, but we're doing remote interviews. I'm John Curry. Your host of the Cube got a great segment from Asia Pacific on the other side of the world from California about social impact, transforming, teaching and learning with cloud technology. Got three great guests. You go. Richard is the CEO and co founder of Guys Tech and Jean Te'o, CEO and founder of Solve Education Founders and CEOs of startups is great. This is squad was the AIPAC regional head. Education, health care, not for profit and research. Ray Ws, he head start big program Vincent. Thanks for coming on, Janine. And you go Thank you for joining. >>Thanks for having us, John. >>We're not there in person. We're doing remote interviews. I'm really glad to have this topic because now more than ever, social change is happening. Um, this next generation eyes building software and applications to solve big problems. And it's not like yesterday's problems there. Today's problems and learning and mentoring and starting companies are all happening virtually digitally and also in person. So the world's changing. So, um, I gotta ask you, Vincent, we'll start with you and Amazon. Honestly, big started builder culture. You got two great founders here. CEO is doing some great stuff. Tell us a little bit what's going on. A pack, >>A lot of >>activity. I mean, reinvent and some it's out. There are really popular. Give us an update on what's happening. >>Thank you. Thank you for the question, John. I think it's extremely exciting, especially in today's context, that we are seeing so much activities, especially in the education technology sector. One of the challenges that we saw from our education technology customers is that they are always looking for help and support in many off the innovation that they're trying to develop the second area off observation that we had waas, that they are always alone with very limited resources, and they usually do not know where to look for in terms, off support and in terms off who they can reach out to. From a community standpoint, that is actually how we started and developed this program called A W s. At START. It is a program specifically for education technology companies that are targeting delivering innovative education solutions for the education sector. And we bring specific benefits to these education technology companies when they join the program. Aws ed start. Yeah, three specific areas. First one is that we support them with technical support, which is really, really key trying to help them navigate in the various ranges off A W S services that allows them to develop innovative services. The second area is leaking them and building a community off like minded education technology founders and linking them also to investors and VCs and lastly, off course, in supporting innovation. We support them with a bit off AWS cop credits promotional credits for them so that they can go on experiment and develop innovations for their customers. >>That's great stuff. And I want to get into that program a little further because I think that's a great example of kind of benefits AWS provides actually free credits or no one is gonna turn away free credits. We'll take the free credits all the time all day long, but really it's about the innovation. Um, Jean, I want to get your thoughts. How would solve education? Born? What problems were you solving? What made you start this company and tell us your story? >>Thank you so much for the question. So, actually, my co founder was invited to speak at an African innovation forum a couple of years back on the topic that he was sharing with. How can Africa skip over the industrialization face and go direct to the knowledge economy? Onda, the discussion went towards in orderto have access to the knowledge economy, unique knowledge. And how do you get knowledge Well through education. So that's when everybody in the conference was a bit stuck right on the advice waas. In order to scale first, we need to figure out a way to not well, you know, engaging the government and schools and teachers, but not depend on them for the successful education initiated. So and that's was what pain walk away from the conference. And when we met in in Jakarta, we started talking about that also. So while I'm Singaporean, I worked in many developing countries on the problem that we're trying to solve this. It might be shocking to you, but UNESCO recently published over 600 million Children and you are not learning on. That is a big number globally right on out of all the SDG per se from U N. Education. And perhaps I'm biased because I'm a computer engineer. But I see that education is the only one that can be solved by transforming bites. But since the other stg is like, you know, poverty or hunger, right, actually require big amount of logistic coordination and so on. So we saw a very, um, interesting trend with mobile phones, particularly smartphones, becoming more and more ubiquitous. And with that, we saw a very, uh, interesting. Fortunately for us to disseminate education through about technology. So we in self education elevate people out of poverty, true, providing education and employment opportunities live urging on tech. And we our vision is to enable people to empower themselves. And what we do is that we do an open platform that provides everyone effected education. >>You could How about your company? What problem you're you saw And how did it all get started? Tell us your vision. >>Thanks, John. Well, look, it all started. We have a joke. One of the co founder, Matthew, had a has a child with severe learning disorder and dyslexia, and he made a joke one day about having another one of them that would support those those kids on Duh. I took the joke seriously, So we're starting sitting down and, you know, trying to figure out how we could make this happen. Um, so it turns out that the dyslexia is the most common learning disorder in the world, with an estimated 10 to 20% off the worldwide population with the disorder between context between 750 million, up to 1.5 billion individual. With that learning disorder on DSO, where we where we sort of try and tackle. The problem is that we've identified that there's two key things for Children with dyslexia. The first one is that knowing that it is dislikes. Yeah, many being assessed. And the second is so what? What do we do about it? And so given or expertise in data science and and I, we clearly saw, unfortunately off, sort of building something that could assess individual Children and adults with dyslexia. The big problem with the assessment is that it's very expensive. We've met parents in the U. S. Specifically who paid up to 6000 U. S. Dollars for for diagnosis within educational psychologist. On the other side, we have parents who wait 12 months before having a spot. Eso What we so clearly is that the observable symptom of dyslexia are reading and everyone has a smartphone and you're smart. Smartphone is actually really good to record your voice. Eso We started collecting order recording from Children and adults who have been diagnosed with dyslexia, and we then trying a model to recognize the likelihood of this lecture by analyzing audio recording. So in theory, it's like diagnosed dyslexic, helping other undiagnosed, dyslexic being being diagnosed. So we have now an algorithm that can take about 10 minutes, which require no priors. Training cost $20. Andi, anyone can use it. Thio assess someone's likelihood off dyslexia. >>You know, this is the kind of thing that really changes the game because you also have learning progressions that air nonlinear and different. You've got YouTube. You got videos, you have knowledge bases, you've got community. Vincent mentioned that Johnny and you mentioned, you know making the bits driver and changing technology. So Jeannine and Hugo, please take a minute to explain, Okay? You got the idea. You're kicking the tires. You're putting it together. Now you gotta actually start writing code >>for us. We know education technology is not you. Right? Um, education games about you. But before we even started, we look at what's available, and we quickly realize that the digital divide is very real. Most technology out there first are not designed for really low and devices and also not designed for people who do not have Internet at hope so way. So with just that assessment, we quickly realized we need toe do something about on board, but something that that that problem is one eyes just one part of the whole puzzle. There's two other very important things. One is advocacy. Can we prove that we can teach through mobile devices, And then the second thing is motivation it again. It's also really obvious, but and people might think that, you know, uh, marginalized communities are super motivated to learn. Well, I wouldn't say that they are not motivated, but just like all of us behavioral changes really hard right. I would love to work out every day, but, you know, I don't really get identity do that. So how do we, um, use technology to and, um, you know, to induce that behavioral change so that date, so that we can help support the motivation to learn. So those are the different things that we >>welcome? >>Yeah. And then the motivated community even more impactful because then once the flywheel gets going and it's powerful, Hugo, your reaction to you know, you got the idea you got, You got the vision you're starting to put. Take one step in front of the other. You got a W s. Take us through the progression, understand the startup. >>Yeah, sure. I mean, what Jane said is very likely Thio what we're trying to do. But for us, there's there's free key things that in order for us to be successful and help as much people as we can, that is free things. The first one is reliability. The second one is accessibility, and the other one is affordability. Eso the reliability means that we have been doing a lot of work in the scientific approach as to how we're going to make this work. And so we have. We have a couple of scientific publications on Do we have to collect data and, you know, sort of published this into I conferences and things like that. So make sure that we have scientific evidence behind us that that support us. And so what that means that we had Thio have a large amount of data >>on and >>put this to work right on the other side. The accessibility and affordability means that, Julian said. You know it needs to be on the cloud because if it's on the cloud, it's accessible for anyone with any device with an Internet connection, which is, you know, covering most of the globe, it's it's a good start on DSO the clock. The cloud obviously allow us to deliver the same experience in the same value to clients and and parent and teacher and allied health professionals around the world. Andi. That's why you know, it's it's been amazing to to be able to use the technology on the AI side as well. Obviously there is ah lot of benefit off being able to leverage the computational power off off the cloud to to make better, argue with them and better training. >>We're gonna come back to both of you on the I question. I think that's super important. Benson. I want to come back to you, though, because in Asia Pacific and that side of the world, um, you still have the old guard, the incumbents around education and learning. But there is great penetration with mobile and broadband. You have great trends as a tailwind for Amazon and these kinds of opportunity with Head Start. What trends are you seeing that are now favoring you? Because with co vid, you know the world is almost kind of like been a line in the sand is before covert and after co vid. There's more demand for learning and education and community now than ever before, not just for education, the geopolitical landscape, everything around the younger generation. There's, um, or channels more data, the more engagement. How >>are you >>looking at this? What's your vision of these trends? Can you share your thoughts on how that's impacting learning and teaching? >>So there are three things that I want to quickly touch on number one. I think government are beginning to recognize that they really need to change the way they approach solving social and economic problems. The pandemic has certainly calls into question that if you do not have a digital strategy, you can't You can find a better time, uh, to now develop and not just developed a digital strategy, but actually to put it in place. And so government are shifting very, very quickly into the cloud and adopting digital strategy and use digital strategy to address some of the key problems that they are facing. And they have to solve them in a very short period of time. Right? We will talk about speed, three agility off the cloud. That's why the cloud is so powerful for government to adult. The second thing is that we saw a lot of schools closed down across the world. UNESCO reported what 1.5 billion students out of schools. So how then do you continue teaching and learning when you don't have physical classroom open? And that's where education, technology companies and, you know, heroes like Janine's Company and others there's so many of them around our ableto come forward and offer their services and help schools go online run classrooms online continue to allow teaching and learning, you know, online and and this has really benefited the overall education system. The third thing that is happening is that I think tertiary education and maybe even catch off education model will have to change. And they recognize that, you know, again, it goes back to the digital strategy that they got to have a clear digital strategy. And the education technology companies like, what? Who we have here today, just the great partners that the education system need to look at to help them solve some of these problems and get toe addressing giving a solution very, very quickly. >>Well, I know you're being kind of polite to the old guard, but I'm not that polite. I'll just say it. There's some old technology out there and Jenny and you go, You're young enough not to know what I t means because you're born in the cloud. So that's good for you. I remember what I t is like. In fact, there's a There's a joke here in the United States that with everyone at home, the teachers have turned into the I T department, meaning they're helping the parents and the kids figure out how to go on mute and how toe configure a network adds just translation. If they're routers, don't work real problems. I mean, this was technology. Schools were operating with low tech zooms out there. You've got video conferencing, you've got all kinds of things. But now there's all that support that's involved. And so what's happening is it's highlighting the real problems of the institutional technology. So, Vincent, I'll start with you. Um, this is a big problem. So cloud solves that one. You guys have pretty much helped. I t do things that they don't want to do any more by automation. This >>is an >>opportunity not necessary. There's a problem today, but it's an opportunity tomorrow. You just quickly talk about how you see the cloud helping all this manual training and learning new tools. >>We are all now living in a cloud empowered economy. Whether we like it or not, we are touching and using services. There are powered by the cloud, and a lot of them are powered by the AWS cloud. But we don't know about it. A lot of people just don't know, right Whether you are watching Netflix, um Well, in the old days you're buying tickets and and booking hotels on Expedia or now you're actually playing games on epic entertainment, you know, playing fortnight and all those kind of games you're already using and a consumer off the cloud. And so one of the big ideas that we have is we really want to educate and create awareness off club computing for every single person. If it can be used for innovation and to bring about benefits to society, that is a common knowledge that everyone needs to happen. So the first big idea is want to make sure that everyone actually is educated on club literacy? The second thing is, for those who have not embarked on a clear cloud strategy, this is the time. Don't wait for for another pandemic toe happen because you wanna be ready. You want to be prepared for the unknown, which is what a lot of people are faced with, and you want to get ahead of the curve and so education training yourself, getting some learning done, and that's really very, very important as the next step to prepare yourself toe face the uncertainty and having programs like AWS EC start actually helps toe empower and catalyzed innovation in the education industry that our two founders have actually demonstrated. So back to you Join. >>Congratulations on the head. Start. We'll get into that real quickly. Uh, head start. But let's first get the born in the cloud generation, Janine. And you go, You guys were competing. You gotta get your APS out there. You gotta get your solutions. You're born in the cloud. You have to go compete with the existing solutions. How >>do you >>view that? What's your strategy? What's your mindset? Janine will start with you. >>So for us, way are very aware that we're solving a problem that has never been solved, right? If not, we wouldn't have so many people who are not learning. So So? So this is a very big problem. And being able to liberate on cloud technology means that we're able to just focus on what we do best. Right? How do we make sure that learning is sufficient and learning is, um, effective? And how do we keep people motivated and all those sorts of great things, um, leveraging on game mechanics, social network and incentives. And then while we do that on the outside way, can just put almost out solved everything to AWS cloud technology to help us not worry about that. And you were absolutely right. The pandemic actually woke up a lot of people and hands organizations like myself. We start to get queries from governments on brother, even big NGOs on, you know, because before cove it, we had to really do our best to convince them until our troops are dry and way, appreciate this opportunity and and also we want to help people realized that in order to buy, adopting either blended approach are a adopting technology means that you can do mass customization off learning as well. And that's what could what we could do to really push learning to the next level. So and there are a few other creative things that we've done with governments, for example, with the government off East Java on top of just using the education platform as it is andare education platform, which is education game Donald Civilization. Um, they have added in a module that teaches Cove it because, you know, there's health care system is really under a lot of strain there, right and adding this component in and the most popular um mitigate in that component is this This'll game called hopes or not? And it teaches people to identify what's fake news and what's real news. And that really went very popular and very well in that region off 25 million people. So tech became not only just boring school subjects, but it can be used to teach many different things. And following that project, we are working with the federal government off Indonesia to talk about anti something and even a very difficult topic, like sex education as well. >>Yeah, and the learning is nonlinear, horizontally scalable, its network graft so you can learn share about news. And this is contextual data is not just learning. It's everything is not like, you know, linear learning. It's a whole nother ballgame, Hugo. Um, your competitive strategy. You're out there now. You got the covert world. How are you competing? How is Amazon helping you? >>Absolutely. John, look, this is an interesting one, because the current competitors that we have, uh, educational psychologist, they're not a tech, So I wouldn't say that we're competing against a competitive per se. I would say that we're competing against the old way of doing things. The challenge for us is to, um, empower people to be comfortable. We've having a machine, you know, analyzing your kids or your recording and telling you if it's likely to be dislikes. Yeah, and in this concept, obviously, is very new. You know, we can see this in other industry with, you know, you have the app that stand Ford created to diagnose skin cancer by taking a photo of your skin. It's being done in different industry. Eso The biggest challenge for us is really about the old way of doing things. What's been really interesting for us is that, you know, education is lifelong, you know, you have a big part in school, but when you're an adult, you learn on Did you know we've been doing some very interesting work with the Justice Department where, you know, we look at inmate and you know, often when people go to jail, they have, you know, some literacy difficulty, and so we've been doing some very interesting working in this field. We're also doing some very interesting work with HR and company who want to understand their staff and put management in place so that every single person in the company are empowered to do their job and and and, you know, achieve success. So, you know, we're not competing against attack. And often when we talk to other ethnic company, we come before you know, we don't provide a learning solution. We provide a assessment solution on e assessment solution. So, really, John, what we're competing against is an old way of doing things. >>And that's exactly why clouds so successful. You change the economics, you're actually a net new benefit. And I think the cloud gives you speed and you're only challenges getting the word out because the economics air just game changing. Right, So that's how Amazon does so well, um, by the way, you could take all our recordings from the Cube, interviews all my interviews and let me know how ideo Okay, so, um, got all the got all the voice recordings from my interview. I'm sure the test will come back challenging. So take a look at that e. I wanna come back to you. But I wanna ask the two founders real quick for the folks watching. Okay on Dhere about Amazon. They know the history. They know the startups that started on Amazon that became unicorns that went public. I mean, just a long list of successes born in the cloud You get big pay when you're successful. Love that business model. But for the folks watching that were in the virtual garages, air in their houses, innovating and building out new ideas. What does Ed start mean for them? How does it work? Would you would recommend it on what are some of the learnings that you have from work with Head Start? >>But our relationship X s start is almost not like client supplier relationship. It's almost like business partners. So they not only help us with protect their providing the technology, but on top of that, they have their system architect to work with my tech team. And they have, you know, open technical hours for us to interact. And on top of that, they do many other things, like building a community where, you know, people like me and Google can meet and also other opportunities, like getting out the word out there. Right. As you know, all of their, uh, startups run on a very thin budget. So how do we not pour millions of dollars into getting out without there is another big benefit as well. So, um definitely very much recommend that start. And I think another big thing is this, right? Uh, what we know now that we have covert and we have demand coming from all over the place, including, like, even a lot of interest, Ally from the government off Gambia, you know? So how do we quickly deploy our technology right there? Or how do we deploy our technology from the the people who are demanding our solution in Nigeria? Right. With technology that is almost frameless. >>Yeah. The great enabling technology ecosystem to support you. And they got the region's too. So the region's do help. I love we call them Cube Region because we're on Amazon. We have our cloud, Hugo, um, and start your observations, experience and learnings from working with aws. >>Absolutely. Look, this is a lot to say, so I'll try and making sure for anyone, but but also for us on me personally, also as an individual and as a founder, it's really been a 365 sort of support. So like Johnny mentioned, there's the community where you can connect with existing entrepreneur you can connect with expert in different industry. You can ask technical expert and and have ah, you know office our every week. Like you said Jenny, with your tech team talking to cloud architect just to unlock any problem that you may have on day and you know, on the business side I would add something which for us has been really useful is the fact that when we when we've approached government being able to say that we have the support off AWS and that we work with them to establish data integrity, making sure everything is properly secured and all that sort of thing has been really helpful in terms off, moving forward with discussion with potential plant and and government as well. So there's also the business aspect side of things where when people see you, there's a perceived value that you know, your your entourage is smart people and and people who are capable of doing great things. So that's been also really >>helpful, you know, that's a great point. The APP SEC review process, as you do deals is a lot easier. When here on AWS. Vincent were a little bit over time with a great, great great panel here. Close us out. Share with us. What's next for you guys? You got a great startup ecosystem. You're doing some great work out there and education as well. Healthcare. Um, how's your world going on? Take a minute, Thio. Explain what's going on in your world, >>John, I'm part of the public sector Team Worldwide in AWS. We have very clear mission statements on by the first is you know, we want to bring about destructive innovation and the AWS Cloud is really the platform where so many off our techs, whether it's a text, healthtech golf text, all those who are developing solutions to help our governments and our education institutions or health care institutions to really be better at what they do, we want to bring about those disruptive innovations to the market as fast as possible. It's just an honor on a privilege for us to be working. And why is that important? It's because it's linked to our second mission, which is to really make the world a better place to really deliver. Heck, the kind of work that Hugo and Janina doing. You know, we cannot do it by ourselves. We need specialists and really people with brilliant ideas and think big vision to be able to carry out what they are doing. And so we're just honored and privileged to be part off their work And in delivering this impact to society, >>the expansion of AWS out in your area has been phenomenal growth. I've been saying to Teresa Carlson, Andy Jassy in the folks that aws for many, many years, that when you move fast with innovation, the public sector and the private partnerships come together. You're starting to see that blending. And you've got some great founders here, uh, making a social impact, transforming, teaching and learning. So congratulations, Janine and Hugo. Thank you for sharing your story on the Cube. Thanks for joining. >>Thank you. Thank >>you, John. >>I'm John Furry with the Cube. Virtual were remote. We're not in person this year because of the pandemic. You're watching a divest Public sector online summit. Thank you for watching

>> live from Washington, D. C. It's the Cube covering a ws public sector summit brought to you by Amazon Web services. >> Welcome back, everyone to the Cubes Live coverage of a ws Public sector summit here in Washington D. C. Our nation's capital. I'm your host, Rebecca Knight co hosting alongside John Farrier wear welcoming Back to the Cuba, Cuba and esteemed Cube veteran Teresa Carlson, vice president Worldwide public Sector A W s. >> Thank you really appreciate always being on the key, But I appreciate you being here and our public sector. Sandy, >> Thank you for having us. So give up. Give us the numbers. How many people are in this room? How many people are here? >> Well, we have now today. Well, for this time that we're here, there's probably about 13,000 people here will expect a couple of 1,000 more. I think by the time it's all said Dan, we'll have about 15,000 at the conference. Of course, you had my keynote today with whole Benson sessions. They're all packed, and tomorrow you'll have Andy, jazzy herewith made ing a fireside chat at 11 o'clock on Wednesday, so I think that room will be overflowing with Andy Kelly as well, Because everybody loves him >> and Andy just coming back from a conference for the Silicon Valley elites on the west coast, where he put a big plug in for public sector, which is awesome. Yes. Now there you guys are kicking some serious butt. Congratulations. >> Thank you. Yeah. Thank you. >> I mean, what's it like for you? You're the leader. You're the chief of the public sector business. You've grown it. It's now cruising altitude that seem so cruising. >> Yeah, it. Well, first of all, this Nana, this would've been possible without Andy Jassy actually kind of believing and the mission of public sector when he hired me in 2010. And you're right, John. We started. You've hurt, covered the story. We started with two people in 2010 at the end of 2010. And now we have thousands of people around the world and, you know, over 35 countries, customers and 100 72 2 countries. And the business is growing at more than 41% every year date of yes, and we're $31,000,000,000. Business with public sector ban important component in that business. So for s here today. It is very meaningful. And the reason it is so meaningful. It is about our customers. And this is This is a testament to that. Our customers left what a TBS provides. And in the public sector business, it is a game changer to their mission way >> We're talking on our insure this morning. Rebecca and I around this new generation of workers, and that's almost like a revolution of red tape. Why's it in the way you gotta do better ways to be management cloud health care you named the vertical isn't a capacity to disrupt, create value. So you have this kind of shift happening. But you guys are also technology leaders. So when when you see things like space, >> Yeah, these were kind >> of tell signs that the CIA adopting the d o d. Look at the big contracts are coming in. People are working it hard. These air tell signs that the growth Israel >> Yeah, grab reaction to that gross Israel and I and I like to talk to my leaders about while we've had phenomenal growth, and that's fantastic. Way really are only getting started because now, in 2018 I really saw our customers doing unbelievable work leads very hard mission. Critical work was that they were meeting from it from it's kind of old environment, moving it on day to be asked, migrating and totally optimizing it. Now what's changing within the intelligence community and D o d is that you know, in 2013 when the icy made this decision made, it started changing even enterprise views of moving to the cloud from a security perspective. But you have that shift has happened. Now you see d o d moving for Jet I, which will be announced hopefully in July or August. Hope hopefully scene. But even without Jed, I. D o. D is making massive mate to cloud. I mean, and by the way, there no blockers now, like a year ago when we talked here, there were still some blockers for them. Today, really pretty much every blocker has been remade so that they can move a lot faster. So even outside of Jed, I we see our d o. D customers moving. You heard Kenny Bow and our debt today on stage, Who's the CEO of the special access program? Talk about what they're doing and why Cloud became an important element of their mission. And I could tell you, Kenny works on some very challenging and difficult mission programs for D. O. D. So that these air kind examples. On the flip side, I met with some CIA's yesterday from the state and local government. Now that has been a super surprising market for me where I'm seeing them. Actually, 2018 was a true change of year for them. Massive workloads in the state Medicaid systems that are moving off of legacy systems on a TVs, justice and public safety systems moving off on TBS. So that's where you're seeing moves. But you know what they shared with me yesterday, and my theme, as you saw today, was removing barriers. But they talked about acquisition barrier still, that states still don't know how to buy cloud, and they were asking for help. Can you help kind of educate and work with their acquisition officials? So it's nice when they're asking us for help in areas that they see their own walkers. >> So what accounts for the fact that these blockers air sort of disappearing as you set up on the main stage this morning? cloud is the new normal, right? Everyone is really adopting this cloud first approach. And what accounts for the fact that these challenges ey're sort of slowly dissipating? Well, there, you know, some of >> the blockers had been very legacy, and I'd like to tell you already that kind of old guard helped create a lot of these models. And most of these models, as an example of acquisition, were created so that governments had to pay at friend. So these models were like, pay me a lot of many a friend and then let's hope I will use them all that technology. So now we come along and say, Actually, no, you don't need to pay us anything up front. You could try it and pay as you use it and then scale that and they're like, Wait, wait a minute. We don't know how to do that model. So part of these things have been created because of all systems that what's changing those systems is that you can't you again if you can't change gravity, and we're at the point where it is the new normal, and you cannot change gravity, and they're seeing security. If you think about security is the number one reason they're moving to the cloud. Once you start having security issues, they on their own start removing blockers because they're like we've got it made faster because we wanted our secure. >> I know you've got a lot of things going on. You got customer visits. Your time's very tight. Appreciate you coming on. But I got to get and I want to talk about check for good programs you launched what happened at the breakfast of the stories. We could go for an hour on that, but I really want to dig into this ground station thing. And one of the coolest thing I saw reinvent when it kind of got launch. This is literally it reminds me the old Christopher Columbus days is the world flat is flat. We'll know the world is round. You have space? Yeah, space and data. It's gonna change the coyote edge to be the world. Right? So this is a game changer. I see this game changer way had your GM on earlier. Brett, what's what's going on with ground? So how is that going to help? Because it's almost provisioning back haul. It's gonna help. Certainly. Rural area st >> Yeah, way ahead of Earth and Space Day yesterday. So we kicked off with that with two amazing speakers. And the reason ground station is so important. By the way, it was a customer of ours in the US intelligence community that told us about six years ago we needed to create this. So you know where I said 95% of our services or customer driven? It was a customer that said, Why doesn't a TVs have a ground station and we really listen to them? Work backwards? And then we launch a ground station. I became general availability in May, and that is really about creating a ubiquitous environment for everyone, for space, for the space and satellite communications. So you can downlink an uplink data. But then the element of utilizing the cloud the process and analyze that data in real time and be ableto have that wherever you are is really I mean, it truly is going to be an opportunity for best commercial enterprises and public sector customers. And you know, John, right now, the pipeline that we have seen already for ground station, even I'm surprised at how Many of our customers and partners are so interested with acid ate a >> government thing about, like traffic lights, bio sensors Now back hauling all that into a global, >> you know, many different way. And now start. If he saw the announced with the Cloud Innovation Center at Cal Poly, we're gonna be doing some research with them on space communications and programs around ground station. Chile is another location You've heard me talk about that has missed tell escapes in the world. And we're gonna be working in Chile doing some work on ground station there in the Middle East. So this is, by the way, global. While the Qena it kind of came. Tosto, >> go to Cal Poly together way. We're gonna go to Chile. >> Chile next. Yeah, chili is great. So you could get two best locations with me. I would love that line here. Next. Exactly 11. Yes. >> Thank you so much for >> back. And make sure we get all those other days. >> Yes, because next time I've got to tell you that tape for good. There's too much not to talk about. So we have to convene again. >> Come to your office in the next couple months of summer. I'll make a trip down. We'll come to >> thank you all for being here. Thank you so much. Thank you. >> Thanks so much, Theresa. I'm Rebecca Knight for John Furrier. Stay tuned. You are watching the Cube.