 Live, from Las Vegas, it's theCUBE. Covering Informatica World 2019. Brought to you by Informatica. Welcome back everyone to theCUBE's live coverage of Informatica World 2019 here in Las Vegas. I'm your host, Rebecca Knight, along with my co-host, John Furrier. We are joined by Ariel Kelman. He is the VP Worldwide Marketing at AWS. Thank you so much for coming on theCUBE. Thanks for having me on today. So let's start out just at 10,000 feet and talk a little bit about what you're seeing as sort of the major cloud and AI trends and what your customers are telling you. Yeah, so I mean, clearly machine learning and AI is really at the forefront of a lot of discussions in enterprise IT and there's massive interest but it's still really early. And one of the things that we're seeing companies really focus on now is just getting all their data ready to do the machine learning training, as opposed to also in addition, I mean, training up all their people to be able to use these new skills. But we're seeing tons of interest. It's still very early, but one of the reasons here at Informatica World is that getting all the data imported and ready is it's almost doubled or tripled in importance as it was when people were just trying to do analytics. Now they're doing machine learning as well. We're seeing huge interest in that. I want to get into some of the cloud trends with your business, but first what's the relationship with Informatica? I don't know, we see them certainly at re-invent. Why are you here? Was there an announcement? What's the big story? We've been working together for a long time. It's very complimentary products of number of areas. I think the relationship really started to deepen when we released Redshift in 2013 and having so many customers that wanted to get data into the cloud to do data warehousing, we're already using Informatica and to help get the data loaded and cleansed. And so really they're one of the great partners that's fueling moving data to the cloud and helping our customers be more successful with Redshift. You know, one of the things I really admire about you guys is that you're very customer-centric. We've been following Amazon as you know, since actually the second re-invent, Q's been there every time and just watching the growth, you know, cloud certainly has been a power source for innovation. SaaS companies that are born in the cloud have exponentially scaled faster than most enterprises because they use data. And so data has been a heart of all the successful SaaS businesses. That's why startups gravitated to the cloud right away. But now that you guys got enterprise adoption, you guys have been customer-centric. And as you listen to customers, what are you guys hearing from that? Because the data on premises, you got more compliance, you got more regulation, you got, I mean, news today, oh, more privacy. And then now you got region countries with different laws. So the complexity around even just regulatory, never mind tech complexity. How are you guys helping customers when they say, hey, you know what, I want to get to the cloud, love Amazon, love the cloud, but I got to clean up my on-prem house. Yeah, I would say like a lot, if you look at a lot of the professional services work that we do, a lot of it is around getting the company prepared and organized with all their data before they move it to the cloud, segmenting it, understanding the different security and regulatory requirements, coming up with a plan of what they need, what data they're going to maybe abstract up before they load it. And there's a lot of work there. And you know, we've been focused on trying to help customers. I mean, if they're a partner helping migrate to the cloud, is that the other data? Yeah, there's technology pieces, companies like Informatica, helping to extract and transform and load the data and all the data governance policies. But then also for a lot of our systems, integrator partners, Cognizant, Accenture, Deloitte, they're very involved in these projects. There's a lot of work that goes on that a lot of people don't talk about just before you can even start doing the machine learning. And a lot of that's getting your data ready. So what are some of the best practices that have emerged in working with companies that, as you said, there's a lot of pre-work that needs to be done and they need to be very thoughtful about sort of getting their data sorted. Well, I think the number one thing that I see and I recommend is to actually first take a step back from the data and to focus on what are the business requirements of what questions are you trying to answer, let's say with machine learning or with data science advanced analytics. And then back out the data from that. We see a lot of companies, sometimes we'll have it be a data science driven project. Okay, here's all the data that we have, let's put it in one place, when you may not be spending time proportionate to the value of the data. And so that's one of the key things that we see and to come up with a strong plan around what business questions you're trying to answer. On the growth of Amazon, you guys certainly have had great record numbers, growth, even the double digit kind of growth you're seeing on top of your baseline's been phenomenal. Clearly number one in the cloud. Enterprise has been a big focus. I noticed that on the NHL, your logo's on the iAster and the playoffs, you got the stat cast. You guys are creating a lot of awareness, you see a lot of billboards everywhere, a lot of TV ads. That's, is that part of the strategy is to get you guys more brand awareness? What's the- We're trying, you know, it's part of our overall brand awareness strategy. What we're trying to do is to help, we're trying to communicate to the world how our customers are being successful using our technology, specifically machine learning and AI. It's one of these things where so many companies want to do it, but they say, well, what, what am I supposed to use it for? And so, you know, if they dumb down what marketing is at AWS, it's inspiring people about what they can run in the cloud with AWS, what use cases they should consider us for, and then we spend a lot of energy giving them the technical education enablement so they can be successful using our products. At the end of the day, we make money when our customers are successful using our products. One of the hottest products has been SageMaker. I've seen that grow of AI has gone mainstream. That's a great tailwind for you guys because it kind of encapsulates or kind of doesn't have to get all nerdy about cloud and, you know, infrastructure and SaaS. AI kind of speaks to many people. It's one of the hottest curriculums and topics in the world. And with SageMaker, we're trying to address a problem that we see in most of our customers where the everyday developer is not, does not have expertise in machine learning. They want to learn it. And so we think that anything we can do to make it easier for every developer to ramp up on machine learning the better. So that's why we came up with SageMaker as a platform to really make all three stages of machine learning easier. Getting your data prepared for training, training and optimized models, and then running inference to make the predictions and incorporate that into people's applications. One of the themes that's really emerging in this conversation is the need to make sure developers are ready and that your people are skilled up and know what they need to know. How is AWS thinking about the skills gap and what are you doing to remedy it? Yeah, I mean, a couple of things. I mean, we're really, like a lot of things we do. We say, what are all the ways we can attack the problem? And let's try and help. So we have free training that we've been creating online. We've been partnering with the large online training firms like Udacity and Coursera. We have an ML solutions lab that help companies prototype we have a pretty significant professional services team and then we're working with all of our systems integrators partners to build up their machine learning practices into a new area for a lot of them and we've been pushing them to add more people so they can help our customers. Talk about the conferences. You have reinvented the core conference. We've been the cube there. We just also covered London, Amazon's web services summit. At 22,000 registered, 14,000 showed up. Get a huge global reach now. How do you keep up with this? We're trying to help our customers keep up with all the technology. I mean, really we have about maybe 25 or so of these summits around the world usually around two days, several thousand people, free conferences and what we're trying to do is they're free. The summits are free and it's like we introduced so much new technology, new services, deeper functionality within our existing services and our customers are very hungry to learn the latest best practices and how they can use these. And so we're trying to be in all of the major areas to come in and provide deep educational content to help our customers. And reinvents come around the corner. Any themes there early on, numbers wise. Last year you had, again, record numbers. I mean, at some point. Yeah, we had over 50,000 people. We're going to have even more and we've been expanding to more and more locations around Las Vegas. And we're going to keep growing. There's a lot of demand. I mean, we want to be able to provide a reinvent experience for as many people as we want to attend. What's the biggest skill set for folks graduating this month? My daughters graduate from Cal Berkeley and a lot of others are graduating high school. Everyone wants to either jump into some sort of data related field. Doesn't have to be computer science, hard core, no numbers are up. What's your view of skill sets that are needed right now that weren't in curriculum or what pieces of curriculum should people be learning to be successful if machine learning continues to grow from helping videos surface to collecting customer data, machine learning's going to be feeding the AI applications and SaaS businesses. Yeah, I mean, look, you just forget about machine learning, you go to a higher level. There's not enough good developers. I mean, we're in a world now where any enterprise that is going to be successful is going to have their own software developers. They're going to be writing their own software. That's not how the world was 15 years ago. But if you're a large corporation and you're outsourcing your technology, you're going to get disrupted by someone else who does believe in custom software and developers. So the demand for really good software engineers, I mean, we deal with all the time for hiring, it is always going to outstrip supply. And so for young people, I encourage them to start coding and to not be over-reliant on the university curriculums, which don't always keep pace with the latest trends. And you guys got a ton of material online too, go to your site. Okay, on the next question around, as someone figures out, okay, enterprise versus pure SaaS, you guys have proven with the cloud that startups can grow very fast and the list goes on. Airbnb, Pinterest, Zoom communications, disrupting existing big, mature markets by having access to the data. So how do you talk about customers when you say, hey, you know, I want to be like a SaaS company, like a consumer company, leverage data, but I've got a lot of stuff on premise. So how do I not make that data constrained? How do you guys talk about that conversation? Because that seems to be the top conversation here. It's not to say be consumer, it's consumer-like. Leveraging data, because if data's not into AI, there's no, AI doesn't work, right? So it can't be constrained by anything. Well, you know, you talk to a lot of companies and first they don't even know what they don't know in terms of what is the data and where is it and what are the pieces that are important? And so, you know, we encourage people to do a good amount of strategy work before they even start to move bits up to the cloud. And of course then we have a lot of ways we can help them from our snowball machines that they can plug in all the way to our snowmobile, which is a semi-truck that you can drive up to your data center and offload very large amounts of data and drive it over to our data centers. One of the things that is trending, we had Ali from Databricks talking, he's obviously believes a lot of the same philosophy as you guys do, data in the cloud. And one of his arguments was, is that there's a lot of data sets in these marketplaces now where you can really leverage other people's data. And we see that on cybersecurity where people are starting to share data. And cloud is a better model for that than trying to ship drives around. And there's a time for Snowball, I get that, and Snowmobile, the big trucks for large ingestion into the cloud. But to enterprise, this is a new phenomenon. No one really shared a lot in the old days. This is a new dynamic. Talk about that. I mean, sharing, selling, monetizing data. If there's something that is important, there will be a market for it. And I think we're seeing that just the hunger, everything from enterprises to startups that want more data, whether it's for machine learning to train their models, or just to run analytics and compare against their data sets. So I think the commercial opportunity is pretty large. I think you're right on that. I think that's a great insight. I mean, no one ever thought about data as a service. Remember, data sets standpoint, because data sets feed machine learning. All right, so what's new? Errol, give the plug on what's going on with AWS. What's new? What's on your plate? What's notable? I mean, I love the NHL, couldn't resist that plug for you, but being a hockey fan. But what's new in your world? You know, we're in early planning stages on our re-invent conference. Our engineers are hard at work on a lot of new technology that we're going to have ready to announce at our re-invent show. You know, also my team's been doing a lot of work with the sports organizations. We've had some interesting machine learning work with Major League Baseball. They rolled out this year a new machine learning model to do stolen base predictions. So you can see on some of the broadcasts as the runner goes past first base, we'll have a ticker that will show what the probability is that they'll be successful stealing second base if they choose to run. Trying to make a little more entertaining all those scenes we've seen in the past of the pitcher throwing the ball back to first, trying to use AI machine learning to give a little bit more insight into what's going on. That's the stat cast, part of that stat cast. And you get anything new coming around that besides that new data? Yeah, I think Major League Baseball is hard at work on some new models that I think will be announced fairly soon. All right, to wrap up Informatica real quick, announcement here, news coming out here. How are you guys working with Informatica in the field? Is there any, can you share more about the relationship? Yeah, I think we'll see we're going to have an announcement a little bit later today. I mean, it's around the subject we've been talking about. Making it easier for customers to be successful moving their data to the cloud so they could start to benefit from the agility, the speed, and the cost savings of data analytics and machine learning in the cloud. And so when you're working with customers, I mean, because this is the thing about Amazon, it is a famously innovative cutting edge company. And when we talk about the hunger that you described of these customers, isn't it just they want to be around Amazon and kind of rub shoulders with this really creative thinking four steps ahead kind of company? I mean, how do you let your innovation rub off on these customers? I mean, there's a couple ways. We do, one of the things we've done recently is these innovation workshops. We have this thing, we talk about a lot of this working backwards process where we force the engineers to write a press release before we'll green light the product because we feel like if you can't clearly articulate the customer benefit, then we probably shouldn't start investing, right? And so that's one of the processes that we use to help us innovate better and more effectively. And so we've been, we walk customers through this. We have them come, you know, there's an international company that I was part of one of the efforts we did in Palo Alto last year where we had a bunch of their leadership team out for two days of workshops where we worked a bunch of ideas through our process. And so we do some of that, but the other areas we try and capture areas where we think that we've innovated in some interesting way into a service that then customers can use. Like Amazon Connect, I think is a good example of it. This is our contact center call routing technology. And you know, one of the things Amazon's consumer business is known for is having great customer support, customer service. And they spent a lot of time and energy making sure that calls get routed and tells you the right people that you don't sit on hold forever. And so we figure we're probably not the only company that could benefit from that. Kind of like with AWS when we figure out how to run infrastructure securely in high performance availability. And so we turn that into a service and it's become a very successful service for us. A lot of companies have similar contact center problems. As a customer, I can attest to being on hold a lot. Ariel, thank you so much for coming on theCUBE. It's been great talking to you. I appreciate it, thank you. I'm Rebecca Knight for John Furrier. You are watching theCUBE. Stay tuned.