 from Washington DC, it's theCUBE covering. Okay, welcome back everyone. We are here live in Washington DC for a special presentation of Oracle Cloud World, hashtag Cloud World. This is Silicon Angles theCUBE. This is our flagship program. We go out to the events and extract the signal from the noise. I'm John Furrier, my co-host Dave Vellante and our next guest, Sohan Demel, who's the Vice President of Product Strategy and Business Development on the product side at Oracle. Welcome to theCUBE. Thank you, John. So you're in charge of a lot of the products on the software side, exit data, all the good stuff that's been going on. You guys have a great set of products that have great leadership positions. Now with the journey of the cloud end to end, same code base, on-prem, off-prem, now the public, the cloud machine, which is public, hybrid, and private, coming into the data center is pretty compelling. How does that affect exit data and all these other products and how does that all come together? So, John, first of all, cloud at customer is interesting for three primary reasons. First of all, customers won't have requirements around security, data sovereignty, data residency compliance, and they're sometimes hesitant to put their data in the cloud. With cloud at customer, that issue goes away. Secondly, a lot of Oracle customers have interdependent backend systems. These are not isolated environments. They're intricately linked with other systems. So, trying to lift and shift that to the cloud is very complex. And therefore, having it presented at customer, they can preserve those internal integrations across the interdependent systems. So, cloud at customer, you're bringing the cloud to the customer on-premise. On-premises. Because of the complicated backend, because they're running production workloads. They're running production workloads. So, we want to present customers the simplicity of cloud with the payment model, the operating model of cloud in their data center. Okay, so let's talk about Exadata, which is very popular on-premise. How does that fit into the cloud computing model? Because that's a really integral part of a lot of the operation. Obviously, the performance is high, you get kudos for that. But more importantly, the data's on there. All that stuff's going on at Exadata. Now, how does that fit into the cloud? Well, first of all, Exadata has been a tremendous database platform for us. Most of our top customers run Exadata today. It's kind of a global standard for databases. And what we're doing here is we have Exadata in the public cloud today. And we're giving customers that same experience, that same simplicity, that same payment model, the same operating model, the same software, which is completely portable across now at customer and public cloud in their data center. So, I asked Mark Herd a question two months ago when I had my interview with him. I looked up on YouTube, search Mark Herd, Oracle theCUBE, and I asked him, is there going to be a long tail distribution of clouds? And he's like, no, it's all one cloud, but there's different use cases, and it serves a lot of different personas, CMOs, CXOs, developers. But big data is a big part of the value proposition in software, whether it's predictive analytics or other things on the edge of the network on the mobile device. How does the big data cloud, because we're talking about Exadata cloud, you can't shy away from big data. As great as Exadata is for working with structured data sets, the world is fast transforming. There's lots of machine data, social data, web logs, the customers want to mine for that needle in the haystack. They use techniques like Hadoop Spark processing, NoSQL database processing, and really our big data cloud solution addresses that problem squarely with the Hadoop Spark NoSQL capabilities. You know, we do a conference every year with folks at MIT, it's the Chief Data Officer Conference, and they're number one issue with data quality. As you look at big data and you look at cloud, what's happening to data quality? Seems like the problem maybe is getting even more challenging, and how is Oracle helping CDOs or practitioners address that problem? So Dave, as you know, the data, it's garbage in, garbage out, right? So it's all about the quality of the data that you can mine. And as part of that process, the data cleansing, what it's called, data wrangling, enrichment, all of that becomes incredibly important, and we have a separate new cloud service that we have just launched called the Big Data Preparation Cloud Service that takes on that problem squarely, so that customers, before they actually mine that data through something like a Hadoop MapReduce Processing, can actually ingest that data, cleanse it, do all the data wrangling, enrichment, metadata-based enrichment, and actually make sure that high quality data gets in for analytical processing. So that's a service that you guys provide? You provide that with partners? How does that all work? We provide it on the Oracle Public Cloud, and with the Oracle Big Data Cloud Service, that'll be in the AdCustomer model. That particular service will be provided there as well. And there's an appliance associated with Big Data as well, the Big Data Cloud Machine. What is that, and how does that all work? So what we've done, Dave, is we've taken that simplicity of, the cloud simplicity, we mapped it to our Big Data appliance, and we've essentially taken all the integration work, all the secret sauce that we have put. We've taken the Exadata secret sauce, we've ported that to the Big Data platform, and so all of that fast query processing, all of that unified SQL access through Big Data SQL, where with a single SQL query, you can span both the Exadata, the NoSQL, the Hadoop. That's something that's incredibly valuable to our customers, because they don't have to go query and do analytical processing across multiple data stores, put the results together, and then do additional processing on top of that. There's one fast, secure SQL query they can issue across all their data sources. So Han, what is that Big Data secret sauce, and how does that extend and apply into other adjacent appliances, like the Big Data Cloud Machine? So with Exadata, what we did was we took the query processing and we pushed it down to the storage, so that queries can run 10 times, in some cases 100 times faster. And that proprietary technology we have now brought it to the Big Data Cloud platform. I'm guessing I can't just use any storage. So that is why it has to be in the context of other data Big Data appliance and our Big Data Cloud service, because that technology is something that we have that's special to us, that's packaged in our Cloud service. I see, so the portfolio is getting richer and richer and richer, and it's kind of mind boggling. Do you guys think about the different consumers of those various appliances and services? What are the personas that you're seeing emerge as you guys develop this portfolio? Are you developing those for particular roles, or is it sort of more of the traditional IT practitioner? Help us parse through that. Well, Dave, what I tell the traditional IT practitioner is you've got to move up the value chain, okay? The days of the DBA whose primary job was patching systems, backing up systems, I think that day is fast disappearing. Dead end, right? They've got to add value. It's really not about storing the data, it's really about what you can do with the data. It's about delivering value to the business and really trying to make data a capital asset, much like any other capital asset. So you know, for example, with Uber, they have surge pricing. Uber has made their data a capital asset where they can base their pricing off of that supply demand data that's visible in the data capture. Nobody else has that data. And so Uber is uniquely positioned to take advantage of surge pricing. Similarly, I think if you look across different industries, they're going to have a lot of different opportunity to actually, it's not just about storing the data, it's really about what can you do with it? How can it deliver business value? How can you monetize that data? And so that's what we are seeing the data practitioners emerging as. Really, people who can realize the full potential of the data, people who can promote that data to a capital asset status in an organization. It's a really interesting discussion for two reasons. One is there used to be data was a liability, right? And the general counsel wanted to get rid of it. Now it's an asset. And the second is that your point about people trying to create Uber, for example, creating competitive advantage, that's what digitization is all about. It's trying to create competitive advantage, right? That's exactly right. So let's switch shift gears a little bit and talk about the updates to your data management on the public cloud. Can you share some significant updates that you guys have on the public cloud offering database? First of all, in our public cloud offering and also with the cloud of customer offering now, we have a full suite of data management products, everything that spans from departmental and SMB to enterprise to mission critical. So capabilities and price points that span that full spectrum. Also, recently, what we are challenged with, amidst all of this is that data doesn't always live in one place. Some of our customers have data in other clouds. Some of our customers want to integrate their cloud data with on-premises data. So we've just announced the Oracle Golden Gate Cloud Service. The Golden Gate? Oracle Golden Gate is essentially a bi-directional replication service that allows you to send data back and forth. And this enables us to integrate multiple clouds on-premises and cloud and have better integration across the public cloud. So you can wrangle all the data from different sources and different platforms and clouds. It's about bringing that data together. Okay, and the benefits of customers would be what? Just big data analytics, predictive analytics? Well, it's really all of the above because customers need to have connected data. Can we islands, I can have one island of data in cloud A and another island of data in cloud B and yet another island on-premises. We need to have connected data in order to be able to bring that data together, mine it and leverage that asset. So how does that compare to, or is that what you refer to as the big data machine, big data cloud machine? Is there a product that's both of them? Well, the Golden Gate Service is the one that brings all that data together. God, okay, so that's the aggregator, if you will. That's all the processing ingestion. Okay, great. I mean, of course, for customers who have large data sets, petabytes of data, we have a multitude of transfer services where we ship them physically, a storage array, they load up their data and then they ship it FedEx to Oracle and we can load it into the public cloud within Oracle. So they have different formats for customers. If I'm a customer and I say so, and tell me, bottom line, what's under the hood? Why is this all important to me? What is cloud machine, clouded customer, bottom line, what do I get new, what's good, what's all about? What's it all about, bottom lining? Well, customers today, I think, really demand flexibility. They want it quick. They want it to subscribe to a service not have incredible capital expense. They want it to be secure and they want it to be simple. And so if Oracle can take the cloud model, put it on premises, deliver that.