 Live from San Francisco, it's theCUBE, covering AWS Summit 2017, brought to you by Amazon Web Services. Hey, welcome back to theCUBE live at the Moscone Center at the Amazon Web Services Summit San Francisco. Very excited to be here with the co-host Jeff Frick. 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. So talk to us about what SnapLogic is, what do you do, and what did you announce recently today with Amazon Web Services? 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 metadata framework that allows us to do data science on all of our metadata 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 machine learning algorithms in order to improve the customer experience in some way when they're trying to achieve a task. It's 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 out, okay, 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 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 in this integration assistant is a recommendation engine for connecting snaps into your pipelines as you're developing. So it's a real time assistant. 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 a machine learning to learn the best practices to make suggestions as people are going through this on their own. 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, 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 a specific impact? Are there any samples, stories you can share of, what is the result of this type of activity? We're just, we're releasing it in May, so it's going to be generally available to customers. A couple of weeks still. Yeah. So, and so, so we've done internal tests. So we both do sort of the data science of 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 to the algorithm, not only to the science show, but the user studies show that it can improve the time to completion of these pipelines as you're building them. 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 tremendously, 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? So basically, we work largely with the IT organizations within enterprises who are, you know, larger companies are tasked with having sort of a common fabric for connecting, you know, which, you know, in an organization is lots of different, you know, databases for different purposes, ERP systems, you know, now, you know, 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 SaaS 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 SaaS, they get the advantage of things like IRIS in 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, we don't have to see their data or touch their data. So helping a customer that was, you know, using a service-oriented architecture or an ETL modernize their infrastructure? It's completely about modernization. Yeah, I mean, we, you know, our CEO, Grof Dillon has been in this space for a while. He was formerly the CEO of Informatica. And so he had 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 were web focused, right? We're HTTP and REST and JSON data, and we've centered, you know, the core technologies around these modern principles. So that makes us work very well with all the modern applications that you see today. But Greg, I want to shift gears a little bit. You're also a professor at University of San Francisco and you see Davis. I just love to get your perspective from the academic side of the house and 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? 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 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 master's 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 this is, so it's a great win because 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 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, you know, 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 not 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 sort of the impetus for the integration assistant came out of one of these projects where I pitched to our students and I said, okay, we're going to explore SnapLogic metadata and we're going to, 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 suit of mine from Thailand, his name's 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 this, like I described it, these solved puzzles. And, you know, I think we could use that to train some algorithms. And so we spent, you know, in the project phase, as part of his coursework, he worked on this technology. Then we demoed it at the company. Company said, wow, this is great technology. Let's put this in the production. And then there's kind of this transition from sort of this more academic sort of experimental project into going with engineers and making it a real feature. 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 the components within a business, that student probably gets so much more out of just an experiment. But your point, other point is very valid, of having that younger talent that maybe doesn't have a lot of the biases and the preconceived notions that those of us that have been in the industry for a while, that's a great pipeline, no pun intended, for SnapLogic. Is that something that you helped bring into the company by nature of being a professor? Is this sort of a nice byproduct? Well, so a couple of things there. One is that, like I said, University of San Francisco been 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 Gorov 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. We, you know, 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 the core computer science that you would expect from a program. And so, yeah, it's been, it's not, you know, SnapLogic, 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 with other companies. So it's, I would like to say that I started it at USF, but I didn't, it was an existence, but I helped carry it forward. That's great. 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. Right, so, you know, 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. Pretty amazing. And I think, you know, just like maybe, you know, 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 doing self-service integration without having it powered by a machine learning framework helping you, right? And almost like in a few years, we won't imagine it any other way. 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. 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 a CUBE alumni, so that's fantastic. 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.