 Live from the wigwam in Phoenix, Arizona. It's theCUBE, covering Data Platforms 2017. Brought to you by Cubull. Hey, welcome back, everybody. Jeff Frick here with theCUBE, along with George Gilbert. We're at Data Platforms 2017, the historic 99-years-young wigwam resort outside of Phoenix. And we're excited to be joined by our next guest, Alex Sadovsky. He is the director of Data Science for Oracle Data Cloud. Welcome. Thanks, thanks for having me. Absolutely, so I know we got a short time window. You're racing off to your next session. For the people that aren't here, what are you going to be talking about in your session here? So the Oracle Data Cloud, what we do is online advertising. And essentially we have lots and lots of data. Customers come to us and they have some sort of question in mind. They want to say, I want to figure out who's going to buy a minivan in California next month, or who's going to get a hotel in Las Vegas? Who's going to buy Kraft macaroni and cheese? All sorts of different questions. We have all of that data. We have to turn it into actionable insights. Turn it into audiences for them so they can advertise Facebook, Twitter, all over the web. And so what this talk is really focusing on is, how do we take all of this data and use it efficiently? And it's going to talk about the technologies that we've used, specifically Hive, and then moving that technology over to Spark, just so that we can use more data, get quicker processing, and essentially make our clients have a better experience and give them a better product. And do the clients execute the results of this process inside their other Oracle apps, or is it something that they can use with any number of apps? So a lot of the ways that we work, we actually are interface with companies like Facebook and Twitter directly. And so essentially what we're doing is we're partnering with them so that the client, all they really need to do is kind of come to us, either onboard some data through maybe other Oracle applications, or onboard data directly through us, and then push it out. We help push it all the way through the process, all the way into Facebook, et cetera. Yeah, it's just because we were covering Oracle Modern Marketing, which is now Oracle Modern. Customer experience, I'm sure you guys must be tightly integrated with all that. Yeah, and so for Oracle Data Cloud, it's kind of interesting. We're a collaboration of five recently acquired startups. And so it's everything from two to three years ago, all of this coming together. So for us, we're really excited because we're just at the tip of the iceberg of getting into the whole Oracle ecosystem and having that help build up our product even better. So when you say partner with Facebook or Twitter, that would be for brand or direct response advertising that one of your B2B clients has signed up for, or I should say B2B, you're B, the client is B, and the end customer C, so it's a B2B to C. And now, okay, so you help them in a consultative way. You have the data, you have a consultative sales approach. Are you building models for them, or are you telling them, you know, like sort of running a model, sort of which is it? So we run models based upon data. So a customer could come to us with, here are a thousand people that that customer knows bought their product last month, and they say we want to expand our business, we want to advertise to 20 million people who might be similar to those thousand. And so that's where all of our data comes in. We can look at those a thousand people and we can say, hey, did you guys know that most of your customers are millennials? Did you know that most of them tend to live on the West Coast or East Coast populated cities? And we're not really consulting that in the sense of like there's people looking at the data, it's all machine learning. And so computers are looking at all of our data to help get insights from what the customer is bringing. So would it be fair to say then that the, let's say the thousand example that the customer brings in is the training data? Yes. And then you use your data in your databases, your consumer databases to say, to generate essentially scores and say, we're going to send out to these. That's a hundred percent right. They come in with a thousand of their customers. We see how those thousand customers rank up against every single household in the entire United States. I was going to say, we're going to be at Spark Summit in a couple of weeks or a week, whenever it is, I can't keep track of all these shows. So they can't do the whole thing, why Hive to Spark, but in three minutes or less, why Hive to Spark? So number one reason for us and number one reason I think a lot of people are moving to Spark is just speed. Without getting into a lot of technical details, there's just a lot better engine, a lot more flexible engine underneath Spark than kind of traditional Hive. And then machine learning models are, most of the libraries are built in. Yeah, and so Hive doesn't have. Yeah, machine learning is really built into Spark. There's whole projects within Spark built around that. And so for us, we really, Spark considers machine learning kind of a first class citizen. And since that's essentially what our business is, we go 100% into Spark as well. So let me ask you, what is the scope now and potentially in the future for these data-based predictive models where customer comes to you with, you know, essentially some labeled data and then you'll come out with, I guess that's the training data. And then right now you have data in what categories and then what categories would you like to have? So we have data everything from what people are doing on the web, so what they're searching for, what websites they're going for. We have grocery store data, so what people are buying in the grocery store. We have retail data, so what people are buying in the malls. Because a lot of what happens is, you know, even though consumers are spending a lot more time on the web, 80 to 90% of purchases are still made in the store. So we have all of this actual real-world purchase data that we've partnered with different retail partners, including with automotive data too. So that's really like the core of our data. So really what we try to do is have data sets strategically placed all around and that's why the Oracle Data Cloud is made up of so many different startups for really getting expertise from different areas for different data sets to bring that together. Do you need to buy those sources of data or can you license? Data is everything from license to purchased outright to shared revenue sharing with other companies. It's really, there's a huge data market right now. It's kind of the data gold rush and we're trying to get in anywhere we can figure out what's going to help us and what's going to help our customers make better models. What would you like to see in terms of the, if you look at a couple years, where would you like to see your data assets sort of augment all your Oracle application? Yeah, so I think, so augmenting Oracle, really we have so many different data assets that everything from live streaming data of what people are searching for on the web to historically what someone has bought in the last three years. And so as we partner more and more with Oracle, Oracle has different things in healthcare, in retail, in all sorts of B2B applications and our data really can fit almost everywhere. It's really like a data driven sort of product and so we've been partnering with Oracle left and right, many different groups, just trying to figure out where can this data help augment kind of your services. All right, Alex, we got to leave it there. That was a good summary. I know you got to race off to your thing. We'll let you take a breath and get a glass of water. So thanks for using this in your busy day. Thanks so much. All right, he's Alex, he's George. I'm Jeff, you're watching theCUBE from Data Platforms 2017. We'll be right back after this short break. Thanks for watching.