>> Announcer: theCUBE presents On the Ground. (ambient techno music) >> Hello, everyone. Welcome to a special theCUBE presentation of On the Ground here at Oracle's corporate headquarters. I'm John Furrier, the host of theCUBE, I'm here with Chai Pydimukkala, senior director of product management with Oracle. Welcome to On the Ground, appreciate you coming on. >> Thank you very much. >> So, talk about the data integration strategy and plans for Oracle, and what are some of the products that make that up? >> Oracle data integration, we've been around for more than 15 years. We've been helping our customers to move data from various systems, sources, and targets. Our products consist of a real-time data integration product, which is used for continuous availability of real-time replication, which is Oracle Golden Gate. It's our marquee product, it's been around for two decades. We also have a ETL product called Oracle Data Integrator, which is a product that actually takes the data, and then, it transforms the data in the source and the target itself. It's not like the older technologies, where you pull the data out of the system and process it in a middle tier. Instead of that, we actually leverage the power of the source of the target. And that's where we started. We have a data quality suite and a complete data governance foundation. We have about 12,000 customers, you know, talk about the largest banks, largest telcos in the world. Each and every one of them use our product, so that completes our data integration product portfolio. >> So, what is this new data integration cloud suite we've been hearing about because that's interesting, ties into that? Does that relate and how does that play? >> Absolutely, so what we have done is one of the things that we have been focused as Oracle is, we have had so much traction in the cloud space, so we have seen that when customers are moving their database systems or applications or platforms into the cloud, one of the key challenges remains is how do you get that data from on-premise to cloud, or cloud to on-premise. That's where data integration comes into play, and what we have done is we have taken the existing technologies that we have, like our Golden Gate, like Oracle Data Integrator, and data governance foundation, and we are making it as a part of a solution stack that gets available, that gets provisioned in cloud, so that any customer can come in and get these products, Oracle cloud integration stack, data integration stack, and then, they can start doing moving data from on-premise to cloud, or cloud to on-premise, or pure cloud use cases. And the stack that we are envisioning is we are not only looking at our traditional products that we have, like Golden Gate, which is a replication product, and ODI, Oracle Data Integrator, but we are also introducing couple of new products. One is Dataflow machine learning, which I'll talk about it in detail, and then, we also have a data-wrangling product called Big Data Preparation Cloud Service, which is already launched and available today, where people are going to look at data and start doing semantic extraction of the data. That's the biggest announcement is our customers will be able to come to us, and instead of focused on the real-time use case or a batch use case, they'll be able to get a solution stack, a platform, that they can use for data integration, be it real-time or be it batch or be it application integration or database integration. >> What's this Oracle Dataflow ML, machine learning thing about, Chai, because that's also kind of a new thing that's coming up? >> You know, I think one of the things that we have done at Oracle is we have been in the forefront of innovation, so a lot times we do solve enterprise level machine critical use cases, but one of the things internally that we have done is we have been embracing, constantly embracing, real-time and open source technologies, big data technologies, and cloud technologies. One thing that we observed in the marketplace is the traditional ETL is like driving a car using your rear-view mirror. You're not actually analyzing the data as it's coming in, you're actually have moved the data, transformed the data, and looking at the data, and started making decisions. Instead of doing that, what we think is we have built a new platform where we can analyze data as it's flowing through. So, let's say your transactions are coming in. You want to detect any fraud on your transactions, banking transactions, what we can do is now we can feed the data, capture the data using Golden Gate and feed it into this engine called Dataflow Machine Learning engine, and then, we'll be able to do a lot of fraud analytics in real-time on it. The whole paradigm of the batch ETL versus real-time ETL is evolving right now, and what we are introducing is a platform that's completely built on an OpenStack Spark-based platform. We are leveraging natural language processing and machine learning, so that as the data comes in, be it your transactional data, be it any other seeming data, we can actually look at the data and give you more insights in real-time so that either you can create alerts or events, or you can detect fraud, or you can actually get more insights and do transformation on the data and make it available to your business. >> How much does open source play into this? You mentioned that. A lot of people always ask me that, so I had to ask you. >> One of the things that we have consistently have managed to do is not to reinvent the same thing again and again. For example, when we actually talked about, envisioned about Dataflow machine learning, the technology itself, we had one thing in mind that we did not want to introduce another engine. If you look at the traditional ETL companies that are going obsolete right now, they're introducing their own engine where they feed the data into this engine. But what we think is the future is that this open source community is so rich, and there are so many people are working on it, we need to leverage those contributions. For example, our Oracle Data Integrator never had an engine, so we followed the same principle, and even in Dataflow, we don't have an engine, we use the Spark libraries, we use the machine learning capability, we use the algorithms from natural language processing, excuse me, and then, we actually combine all this information and we can process them natively on a Hadoop platform, which is the open source platform. And then, lo and behold, you can get more insights into your-- >> You're not restricting customers. You let them do whatever they want with the data if it's connected in, say, a big data appliance, and, or cloud suite. >> Yes. >> So, you kind of give them the choice. >> Yes, so, one thing that we have done very consciously at Oracle is, we acknowledge Oracle database as the number one database in the world. We have more than 50% of the enterprise customers, Fortune 500 customers, actually almost all of the Fortune 500 customers use us, right? But the point is we also realize that there are all these other heterogeneous sources where people have been using to store data. The polyglot architecture where people store graphs in a graph database or NoSQL key value pairs in a NoSQL type of database is valid, and we understand the use cases. So, all the product capabilities-- >> They're not mutually exclusive. A database now can be put where the data makes sense. >> Exactly. >> But you guys just still be the systems of record. >> Yes. >> 'Cause you're the CRM, the ERP, you have all these data systems that are powering business. >> Absolutely, so. >> Why would you restrict data coming in, right? >> Exactly, so one of the things that companies want to do and customers want to do is they want to be able to take the mission-critical transaction data that they have, and they want to be able to combine it with the social media data or the interaction data that they're getting, or the weblogs data, and they want to be able to correlate the information and get more insights. If you look at it like, you know, if you look at customer experience, if you want to really know your customers, what they are doing, you want to get the CRM data, which is their mission-critical data, but you also want to combine it with the social networking data, what do they like, what are they interacting with, what are they clicking on the website, so that you can combine both. We have been a heterogeneous platform, we have customers, we have got a customer who actually uses us only for non-Oracle systems, which is absolutely fine with us. We are in the business of data integration. We do it very well with Oracle technologies, but we can also support other technologies. >> I mean, you guys don't ask customers to be Oracle database everywhere, but in the key areas you do. The question I have to ask you is the one I get all the time from customers and people out in the field, practitioners, and I'm going to paraphrase kind of the pattern question. Oracle, you guys are amazing on the database side, but I want to just integrate other data sources, and I don't want to have to buy Oracle. That's what I'm looking for. What are you doing, Oracle, to make your database smarter? Because their, the customer's view is, okay, I've got Oracle database, you know. Can I get out of that swim lane and expand the intelligence of the Oracle database to a Hadoop, to a Spark, to another environment? >> We have done a lot of-- >> How do you address that? >> We have done a lot of innovation in terms of database, I just think data management in general. First of all, on the data integration side, we have had customers, the largest cell phone company in the world, moves data from an Oracle database to a Kafka-based queue to do further analysis. The largest electric car manufacturing company is actually trying to optimize their assembly lines in real-time so that they don't lose money if their assembly line goes down. We have done a lot of innovation where, and a lot of these customers are using big data type of technologies to get additional insight, so we don't stop them from taking data out from Oracle database or putting data back into Oracle database. Not only that, what we have introduced is. >> You're encouraging people to move data fast around to and from Oracle. Why not, right? >> Exactly, because if you want to get more insights, you want to combine all kinds of data, your interaction data, your NoSQL data, your weblog data. We are saying that bring it in, you can use a big data platform. We have an offering called Big Data Appliance cloud, Big Data Appliance, and we are offering it as a cloud service, too, where you can actually take an Oracle database, and you can take a big data system, and we can connect it, and we have connected it with NoSQL, with Big SQL adapters, so that you can issue SQL, and it can operate on both these sets of data. >> Operationally, that's a really easy way for a customer, rather than deploying a separate system, training assist admin. >> Exactly. >> Cost of ownership is probably going through the roof. >> Absolutely >> Do you see that as a key enabler? >> Absolutely, absolutely, and I think we are in the business of data integration. We treat all data sources and targets equally, and we'll try and support because when people are, when customers are making this journey to the cloud, it's important that we treat everybody equally. >> The old joke that we have, Dave Vellante and I on theCUBE, we say if customers wake up from a coma from 10 years ago and they're in today's world, and the data warehouse is all different, what do you say to that person? Well, welcome back to the real world, but I mean, that's the kind of awakening that these enterprises are having, where a lot of people haven't made the investment, but now are under a lot of pressure to modernize. They know Oracle database, they've had some great relationships, but now all of a sudden the world has changed. What do you say to those folks, what is the most compelling thing that's changed over the past five to 10 years, that's happening now that didn't happen then? >> I think the two big pivots that we have had in the industry are the big data pivot, where people are looking at multiple data management systems and the big data pivot, and then, the cloud pivot because cloud is very important, and we have seen our customers, we have been helping our customers to move entire data center into the cloud, in Oracle public cloud infrastructure, where they are saying I don't want, I want to reduce my total cost of ownership, improve productivity, I want to get all these tools that are already available out there, and I don't want to install this software on my system. Data warehouse as an analytical store will still exist, but what's happening is the transition where you move this data, transform the data, where you transform the data, and where you create operational data stores is changing, and that's where we come in and we say, if you have a big data system, you can create your operational data store over there, transform all the data over there and send it to your warehousing system. We are not, you know, we, because data warehousing again it's post-analysis. It's not real-time analysis as the data is flowing in, so I think, and then the cloud, you know, all we have made sure that for our customers, all the platforms that are available today, we have both infrastructure as a service platforms, SaaS-based service, and we also have data as a service, we are making sure that all these innovation platforms that we have created, including data integration, are available to our cloud customers. Anybody who wants to go to the cloud, and they want to get away from these other, older mainframe systems, they can come in and use our data integration technology, use our database, use our big data appliance cloud service, and just pivot to the cloud immediately, and don't have to wait. >> So, speed to the cloud, speed to a modern architecture. If I hear you correctly, you're saying that Oracle's philosophy and strategy is to have the best modern data management system given the customer's best choice. >> Absolutely. >> Would that be a fair statement? >> Absolutely. And to add to that-- >> Of course, buying some Oracle database, but using open source if they want to. >> Absolutely. >> Where the tool makes sense. >> Because one of the things that we have done on our cloud is we not only offer our platforms, we also offer big data platforms. If you want Kafka as a service, it's going to be available. Spark as a service, it's available. We have embraced Docker. A lot of these things are available. >> How 'about the competition, where do they stand compared to Oracle? >> You know what, can I say, I spent 10 years at a competitor, and then, I made the change, I joined Oracle three years ago, and that competitor is not even a public company anymore. On the data integration space, we have dominated, we have grown. We have got about 12,00 customers and it's growing. We are adding new logos everyday. >> John: And what's the difference, why is that, why are you guys competitive? >> Because the three things that we are focused on is no engine, so we did not invest in an engine for our transformation, so we don't pull in the data and transform it in our engine, that's one. Second is real-time. We are focused on real-time because we know that the future is people will want to analyze this data in real-time, so our real-time platform, which is Golden Gate platform, is world-class and it's the number one platform. And the last one is we make this, everything, we make it easily available in the cloud and for big data platforms. So, you don't have to change anything, it's fairly simple. >> Chai, thanks for spending some time with me on the ground here at your headquarters. >> Thank you very much. >> I'm John Furrier here, exclusive coverage of Oracle here On the Ground with theCUBE. I'm John Furrier, thanks for watching. (light electronic music)