>> Announcer: Live from New York, it's theCUBE covering BigData New York City 2016, brought to you by headline sponsors Cisco, IBM, Nvidia, and our ecosystem sponsors. Now here are your hosts, Dave Vellante and Peter Burris. >> Welcome back to theCUBE everybody. This is BigData NYC and we are covering wall to wall, we've been here since Monday evening. We we're with Nvidia, Nvidia talking about deep learning, machine learning. Yesterday we had a full slate, we had eight data scientists up on stage yesterday and then we covered the IBM event last night, the rooftop party. Saw David Richards there, hanging out with him, and wall to wall today and tomorrow. Jagane Sundar is here, he is the CTO of WANdisco, great to see you again Jagane. >> Thanks for having me Dave. >> You're welcome. It's been a while since you and I sat down and I know you were on theCUBE recently at Oracle Headquarters, which I was happy to see you there and see the deals that are going on you've got good stuff going on with IBM, good stuff going on with Oracle, the Cloud is eating the world as we sort of predicted and knew but everybody wanted to put their head in the sand but you guys had to accommodate that didn't you. >> We did and if you remember us from a few years ago we were very very interested in the Hadoop space but along the journey we realized that our replication platform is actually much bigger than Hadoop. And the Cloud is just a manifestation of that vision. We had this ability to replicate data, strongly consistent, across wide area networks in different data centers and across storage systems so you can go from HDFS to a Cloud storage system like S3 or Azure Wasabi and we will do it with strong consistency. And that turned out to be a bigger deal than actually providing just replication for the Hadoop platform. So we expanded beyond our initial Hadoop Forex and now we're big in the Cloud. We replicate data to many Cloud providers and customers use us for many use cases like disaster recovery, migration, active/active, Cloud bursting, all of those interesting use cases. >> So any time I get you on theCUBE I like to refresh the 101 for me and for the audience that may not be familiar with it but you say strongly consistent, versus you hear the term eventual consistency, >> Jugane: Correct. >> What's the difference, why is the latter inadequate for the applications that you're serving. >> Right so when people say eventually consistent, what they don't remember is that eventually consistent systems often have different data in the different replicas and once in a while, once every five minutes or 15 minutes, they have to run an anti-entropy process to reconcile the differences and entropy is the total randomness right if you go back to your physics, high school physics. What you're really talking about is having random data and once every 10 minutes making it reconcile and the reconciliation process is very messy, it's like last right winds and the notion of time becomes important, how do you keep time accurate between those. Companies like Google have wonderful infrastructure where they have GPS and atomic clocks and they can do a better job but for the regular enterprise user that's a hard problem so often you get wrong data that's reconciled. So asking the same query you may get different answers and your different replicas. That's a bad sign, you want it consistent enough so you can guarantee results. >> Dave: And you've done this with math, right? >> Exactly, our basis is an algorithm called Paxos, which was invented by a gentleman called Leslie Lamport back in '89 but it took many decades for that algorithm to be widely understood. Our own chief scientists spent over a decade developing those, adding enhancements to make it run over the wide area network. The end result is a strongly consistent system, mathematically proven, that runs over the wide area network and it's completely resistant to failure of all sorts. >> That allows you to sort of create the same type of availability, data consistency as you mentioned Google with the atomic clocks, Spanner I presume, is this fascinating, I mean when the paper came out I was, my eyes were bleeding reading it and but that's the type of capability that you're able to bring to enterprises right? >> That's exactly right, we can bring similar capabilities across diverse networks. You can have regular networking gear, time synchronized by NTP, out in the Cloud, things are running in a virtual machine where time adrift most of the time, people don't realize that VMs are pretty bad at keeping time and all you get up in the Cloud is VMS. Across all those enviroments we can give you strongly consistent replication at the same quality that Google does with their hardware. So that's the value that we bring to the Fortune 500. >> So increasingly enterprises are recognizing that data has an, I don't want to say intrinsic value but data is a source of value in context all by itself. Independent of any hardware, independent of any software. That it's something that needs to be taken care of and you guys have an approach for ensuring that important aspects of it are better taken care of. Not the least of which, is that you can provide an option to a customer who may make a bad technology choice one day to make a better technology choice the next day and not be too worried about dead ending themselves. I'm reminded of the old days when somebody who was negotiating an IBM main frame deal would put an Amdahl coffee cup in front of IBM or put an Oracle coffee cup in front of SAP. Do you find customers metaphorically putting a WANdisco coffee cup in front of those different options and say these guys are ensuring that our data remains ours? >> Customers are a lot more sophisticated now, the scenarios that you pointed out are very very funny but what customers come to us for is the exact same thing, the way they ask it is, I want to move to Cloud X, but I want to make sure that I can also run on Cloud Y and I want to do it seamlessly without any downtime on my on-prem applications that are running. We can give them that. Not only are they building a disaster recovery environment, often they're experimenting with multiple Clouds at the same time and may the better Cloud win. That puts a lot of competition and pressure on the actual Cloud applications they're trying. That's a manifestation in modern Cloud terms of the coffee cup competitor in the face that you just pointed out. Very funny but this how customers are doing it these days. >> So are you using or are they starting to, obviously you are able to replicate with high fidelity with strong fidelity, strong consistency, large volumes of data. Are you starting to see customers, based on that capability actually starting to redesign how they set up their technology plant? >> Absolutely, when customers were talking about hybrid Cloud which was pretty well hyped a year or so ago, they basically had some data on-prem and some other data in the Cloud and they were doing stuff but what we brought to them was the ability to have the same data both on-prem and in the Cloud, maybe you had a weekly analytics job that took a lot of resources. You'd burst that out into the Cloud and run it up there, move the result of that analytics job back on-prem. You'd have it with strong consistency. The result is that true hybrid Cloud is enabled when only when you have the same exact data available in all of your Cloud locations. We're the only company that can provide that so we've got customers that are expanding their Cloud options because of the data consistency we offer. >> And those Cloud options are obviously are increasing >> Jugane: They are. >> But there's also a recognition that it's as we gain more experience with Cloud, that different workloads are better than others as we move up there. Now Oracle with some of their announcements last week may start to push the envelope on that a little bit but as you think about where the need for moving large volumes of data with high, with strong consistency what types of applications do you think people are focusing on? Is it mainly big data or are there other application styles or job types that you think are going to become increasingly important? >> So we've got much more than big data, one of the big sources of leads for us now is our capability to migrate netapp filers up into the Cloud and that has suddenly become very important because an example I'd like to give is a big financial firm that has all of its binaries and applications and user data and netapp filers, the actual data is in HDFS on-prem. They're moving their binaries from the netapp up into the Cloud in a specific Cloud windows equal into the filer and the big data part of it from HDFS up into Cloud object store, we are the only platform that can deal with both in the strong consistent manner that I've talked about and we're a single replication platform so that gives them the ability to make the sort of a migration with very low risk. One of the attributes of our migration is that we do it with no downtime. You don't have to take your online, your on-prem environment offline in order to do the migration so they are doing that so we see a lot of business from that sort of migration efforts where people have data in mass filers, people have data in other non-HDFS storage systems. We're happy to migrate all of those. Our replication platform approach, which we've taken in the last year and a half or so is really paying off in that respect. >> And you couldn't do that with conventional migration techniques because it would take too long, you'd have to freeze the applications? >> A couple of things, one you'd probably have to take the applications offline, second you'd be using tools of periodic synchronization variety such as RSYNC and anybody in the devops or operations whose ever used RSYNC across the wide area network will tell you how bad that experience is. It really is a very bad experience. We've got capability to migrate netapp filer data without imposing a load on the netapp's on-prem so we can do it without pounding the crap out of the netapp's server such that they can't offer service to their existing customers. Very low impact on the network configuration, application configuration. We can go in, start the migration without downtime, maybe it takes two, three days for the data to get up over there because of mavenlink. After that is done, you can start playing with it up in the Cloud. And you can cut over seamlessly so there's so real downtime, that's the capability we've seen. >> But you've also mentioned one data type, binaries, they can't withstand error propagation. >> Jugane: Absolutely. >> And so being able to go to a customer and say you're going to have to move these a couple times over the course of the next n-months or years, as a consequence of the new technology that's now available and we can do so without error propagation is going to have a big impact on how well their IT infrastructure, their IT asset base runs in five years. >> Indeed, indeed. That's very important. Having the ability to actually start the application, having the data in a consistent and true form so you can start, for example, the data base and have it mount the actual data so you can use it up in the Cloud, those are capabilities that are very important to customers. >> So there's another application. If you think about, you tend to be more bulk, the question I'm going to ask is and at what point in time is the low threshold in terms of specific types of data movement. Here's why I'm asking. IOT data is a data source or is a use-case that has often the most stringent physical constraints possible. Time, speed of light, has an implication but also very importantly, this notion of error propagation really matters. If you go from a sensor to a gateway to another gateway to another gateway you will lose bits along the way if you're not very careful. >> Correct. >> And in a nuclear power plant, that doesn't work that way. >> Jugane: Yeah. >> Now we don't have to just look at a nuclear power plant as an example but there's increasingly industrial IOTs starting to dramatically impact not just life and death circumstances but business success or failure. What types of smaller batch use-cases do you guys find yourselves operating in, in places like IOT where this notion of error or air control strong consistency is so critical? >> So one of the most popular applications that use our replication is Spark and Spark Streaming which as you can imagine is a big part of most IOT infrastructure, we can do replication such that you ingest into the closest data center, you go from your server or your car or whatever to the closest data center, you don't have to go multiple hops. We will take care consistency from there on. What that gives you is the ability to say I have 12 data centers with my IOT infrastructure running, one data center goes down, you don't have a downtime at all. It's only the data that was generated inside the data center that's lost. All client machines connecting to that data center will simply connect to another data center, strong replication continues, this gives you the ability to ingest at very large volumes while still maintaining the consistency and IOT is a big deal for us, yes. >> We're out of time but I got a couple of last minute questions if I may. So when you integrate with IBM, Oracle, what kind of technical issues do you encounter, what kind of integration do you have to do, is it lightweight, heavyweight, middleweight? >> It's middleweight I would say. IBM is a great example, they have a deep integration with our product and some of the authentication technology they use was more advanced than what was available in open source at that time. We did a little of work, and they did a little bit of work to make that work, but other than that, it's a pretty straight forward process. The end result is that they have a number of their applications where this is a critical part of their infrastructure. >> Right, and then road map. What can you tell us about, what should we look for in the future, what kind of problems are you going to be solving? >> So we look at our platform as the best replication engine in the world. We're building an SDK, we expect custom plugins for different other applications, we expect more high-speed streaming data such as IOT data, we want to be the choice for replication. As for the plugins themselves, they're getting easier and easier to build so you'll see wide coverage from us. >> Jugane, thanks so much for coming to theCUBE, always a pleasure to have you. >> Thank you for having me. >> You're welcome. Alright keep it right there everybody, we'll be back to wrap. This is theCUBE, we're live from NYC. We'll be right back. (upbeat electronic music)

>> Announcer: theCUBE presents On the Ground. (light electronic music) >> Hello everyone, welcome to this special exclusive On the Ground Cube coverage here at Oracle's Headquarters. I'm John Furier the host of theCUBE, I'm here with my guest, Brad Tewksbury, who's the Senior Director of Business Development for the big data team at Oracle, welcome to On the Ground. >> Thank you, John, good to be here. >> So big day, Brad, you've been in this industry for a long time, you've seen the waves come and go. Certainly at Oracle you've been here for many, many years. >> Yeah. >> Oracle's transforming as as a company and you've been watching it play out. >> Brad: Yeah. >> What is the big thing that's most notable to you that you could illustrate that kind of highlights the Oracle transformation in terms of where it's come from? Obviously the database is the crown jewel, but this big data stuff that you're involved in is really transformative and getting tons of traction. With the Cloud Machine kind of tying in, is this kind of a similar moment for Oracle? Share some thoughts there. >> Yeah I think there's many, if you look at the data management path from going back to client server to where we are today, data has always played a pivotal role, but I would say now every customer is going through this decision making process where they're saying, "Ah-ha data I'm being disrupted by all different companies." Before it was you know, okay I got my data in a database and I do some reporting on it and I can run my business, but it wasn't like I was going to be disrupted by some digital company tomorrow. >> Cause the apps and the databases were kind of tied together. >> They were tied together and things just didn't move as fast as they do today. Now it's in these digital-only companies, they realize that data is their business, right? I think one of the pivotal things that we've been doing some studies with MIT is that 84% of the SMP value of some of these companies comes from companies that have no assets, right? Just data, so like UBER doesn't own any taxis. Airbnb doesn't own any hotels, yet they've got massive valuation, so companies are starting to freak out a little bit and they're starting to say, "Oh my god, I got to leverage my data." So the seminal moment here is saying, "How do I monetize my data?" Before it wasn't this urgency, now there's a sense of like I got to do something with this data, but the predicament they're in is, especially these legacy companies is they've got silos of stuff that's not talking to each other, it's all on different versions and different vendors. >> Well, Oracle's always been in the database business, so you made money by creating software to store data. >> Brad: Right. >> Now it sounds like there's a business model for moving the data around, is that kind of what I'm getting here? So it's not just storing the data software, store the data, it's software to make the data. >> Brad: Yeah. >> Accessible. Yeah, it's three things, I think it's three things. It's ingesting the data, right, from new sources outside of the company, so sensors and social media, right that's one thing. Secondly, it's then managing the data, which we've always done, and then the third thing is analyzing it, so it's that whole continuation and then what's happened here is the management platform is expanded. It's gone from just a relational base to this whole SEQUEL world and this Hadoop world, which we completely support. By no means is this relational a zero-sum game, where it's relational or nothing at all, it's we've expanded the whole data management platform to meet the criteria of whatever the application is and so these are the three data management platforms today, who knows what's going to come tomorrow, we'll support that as well, but the idea is choose the right platform for the application and what's really becoming about is applications, right? And this data management stuff is obviously table stakes, but how do I make my applications dynamic and real-time based on what I have here? >> Four years ago, and CUBE audience will remember, we did theCUBE in Hadoop World, that's called back then before it became Strata Hadoop and O'Reilly and Cloudera Show, but Mike Olson and Ping Lee said, "Oh we have a big data fund," so they thought there was going to be a tsunami of apps, never really happened. Certainly Hadoop didn't become as big as people had thought, but yet Analytics rose up, Analytics became the killer app. >> Brad: Yeah. >> But now we're beyond Analytics. >> Brad: Yeah. >> The use of data for insights, where are the apps coming from now? You had Rocana, here we had Win Disk Scope providing some solutions, where do you guys see the apps coming from? Obviously Oracle has their own set of apps, but outside of Oracle, where are the apps? >> So yeah, it's an interesting phenomena, right? Everyone thought Hadoop is the next great wave and the reality is if you go talk to customers and they're like, "Yeah, I've heard of it, but what do I do with it?" So it's like apps are like what's going to drive this whole stack forward and to that end, the number one thing that people are looking for is 360 view of customers, they all want to know more about customer. I was talking with a customer who represents the equivalent of the Tax Bureau of their county and instead of putting the customer, it's the taxpayer or the customer's at the center and all the different places that you pay taxes, so they want to have one view of you as the taxpayer, so whether you're public entity, private, the number one thing that the apps that people are looking for is show me more about customer. If I'm a bank, a retail, they want to cross-sell that's the number one app. In telcos, they want to know about networking. How do I get this network? I want to understand what's going on here so I can better support my Support Center, but secondary to that we're in this kind of holding pattern. Now what are the next set of apps and so there's a bunch of start-ups here in Silicon Valley that are thinking they have the answer for that and we're partnering with them and opening up a Cloud Marketplace to bring them in and we'll let customers decide who's going to win this. >> Talk about Rocana and their value proposition, they're here talking to us today, what's the deal with Rocana? >> So Rocana is an interesting play, what they have found is that customers, one of the ways they talk about themselves, is they offer a data warehouse to IT. So if I'm the IT guy, I want to go in and have basically a pool of all kinds of log analysis. How's my apps running, do I need to tune the apps? How's the network running, they want a one bucket of how can my operation perform better? So what we've seen from customers is they've come to us and they've said, "okay, what have you got in this new space "of Hadoop that can do that?" Look at log analysis and all kinds of app performances from a Hadoop perspective. They were one of the people, the first persons to answer that, so they're having great success finding out where security breaches are, finding out where network latencies are, better like I said, looking at logs and how things co6uld run better, so that's what they're answering for customers is basically improving IT functions, right, because what's happening is a lot of business people are in charge, right, and they're saying, "I no longer want "to go to IT for everything, I want to be able to just go to basically a data model and do my own analysis of this, "I don't want to have to call IT for everything." So these guys in some way are trying to help that manta. >> Talk about Win Disk Scope, what are they talking about here and how is their relationship with Oracle? They're speaking w6ith us today as well. >> Yeah, so you know, in this big data world what we're seeing a lot of is customers doing a lot of what we call a lab experiment. So they got all this data and they want to do lab experiments, okay great. So then they find this nugget of okay, here's a great data model, we want to do some analysis on this, so let's turn it into a production app. Okay, then what do you do, how do you take it to production? These are the guys that you would call. So they take it into an HA high-availability environment for you and they give you zero data loss, zero down time to do that. One of the things that Oracle's, we're touting is the differentiator in our Cloud is this hybrid approach where you have, you know, you could start out doing test-dev in the Cloud, bring it back on Primm, vice versa, they allow you to do that sync, that link between the Cloud and on Primm. We work today with Cloud Air, we OEM them in our big data appliance, if the customer has Hortonworks, but they also want to work with our stuff, their go-between with that as well. So it's basically they're giving you that production-ready environment that you need in an HA world. >> Brad, thanks for spending some time with us here On the Ground, really appreciate it. >> Yeah. >> I'm John Furier, we're here exclusively On the Ground here at Oracle Headquarters, thanks for watching. (light electronic music)

>> Announcer: theCUBE presents "On the Ground." (light techno music) >> Hello there and welcome to SiliconANGLE's theCUBE, On the Ground, here at Oracle's Headquarters. I'm John Furrier, the host of theCUBE, and I'm here with Neil Mendelson, the Vice President of Product Management for the Big Data Team at Oracle. Welcome to On the Ground, thanks for having us here, at Headquarters. >> Good to be here. >> So big data, obviously a big focus of Oracle OpenWorld, is right around the corner but in general, big data breadth of products from Oracle, has been around for awhile. What's your take on this? Because Oracle is doing very well with this new Cloud storing. My interview with Mark Hurd, 100% of the code has been cloudified. Big data now is a big part of the Cloud dynamic. What are some of the things that you're seeing out in the marketplace around big data, and where does Oracle fit? >> Well, you know, when this whole big data thing started years ago, I mean Hadoop just hit its 10th anniversary, right? Everybody was talking about throwing everything out that they had and there was no reason for SQL anymore and you're just going to throw a bunch of stuff together yourself and put it together and off you go, right? And now I think people have realized that to get the real value out of these new technologies, it's not a question of just the new technologies alone, but how do you integrate those with your existing estates. >> So Oracle obviously is a big database business, you know, I mean Tom Curry, with "Hey the database, take your swim lane", but what's interesting is with Hadoop and some of these other ecosystems, what customers are looking for is to not just use Oracle database but to use whatever they might see as a feature of some use case. >> Neil: Absolutely. >> Hadoop for batch. So you guys have been connecting these systems, so could you just quickly explain for a minute how you guys look at this choice factor from a customer standpoint because there's a role for Hadoop, but Hadoop isn't going to take over the whole world as we see in the ecosystem. What's your role, vis-a-vis the database choice? >> Yeah, so we very much believe when Oracle started, it was all about Database, and it was all about SQL. And we believe now that the new normal is really one that includes both Hadoop, NoSQL, and Relational, right? SQL is of course still a factor, but so are the ability to interface, in via rest interfaces and scripting languages. So for us, it's really a big tent, and we've been taking what we had done previously in Database and really extending that to Data Management over Hadoop and NoSQL. >> We had a great chat at Oracle OpenWorld last year, and you talked about your history at Oracle before you did you run with start-ups. You've seen this movie go on early days with data warehousing, so I got to ask you, big data's not new to Oracle, obviously the database business has been thriving and changing with the Cloud around the corner and certainly here on the doorstep but could you explain Oracle's Database, I mean, big data product offerings? >> Sure. >> What was the first product? Take us through the lineage of where it is, because you guys have products. >> We do. >> And a slew of stuff is coming, I can imagine, I'm sure you can't share much about that but talk about the lineage right now. >> Okay, so we started about three years ago on the Hadoop side by making an appliance made for Hadoop and then in the future, which followed on with Spark. And that appliance has been doing well on the marketplace for a number of years and we've obviously continued to enhance that. We then took what we perfected on premises and we moved that up to the Cloud, so we have a big data cloud service for customers that offer them high-performance access to Hadoop and Spark and without necessarily the need to actually manage security and all the things with it. At OpenWorld, we'll be making a series of announcements, we'll be creating yet another big data Cloud service. This one will be fully managed, fully elastic for customers who only want to take advantage of a Hadoop or Sparks service, as an example, and don't want to deal with the ability to specifically tweak the environment, right? We also announced a little while ago, our family of Cloud Machines, right? So you'll see, a, the first Cloud Machine is one that provides Oracle IaaS and PaaS services and then we'll add to the family. >> John: That's shipping already, though. >> That's shipping already, right? And then we'll add to the family, an Exadata Cloud Machine and a big data Cloud Machine and the Cloud Machines are really kind of a cool concept. They're cool because for a lot of customers from a regulatory point of view or otherwise, they're just not ready for the public Cloud, but everybody wants to take advantage of what the Cloud provides. So how do you do that behind your firewall, right? How do you provide IT as a service? So what Oracle has done essentially, is to package up its Cloud services and able to deliver that to customers behind the firewall and they get the exact same technology that they have on the public Cloud, they build to one architecture and then deploy it wherever they choose. They get the advantages of the Cloud, it's a subscription service, right, but they can deal with but they can adhere to whatever data sovereignty or issues that they might have. >> So let's get to that regulatory dynamic in a second but I just want to back up, so Big Data Appliance, B-D-A you guys call it, Big Data Appliance, that's been out. Big data service... >> Neil: Cloud service started about a year ago. >> Done a year, that's out there. Those laces that connect Appliance that's on-pem with the Cloud. >> Neil: Right. >> And then now you have the cloud machine series of enhancements coming in Oracle Openworld. >> Right, as well as a fully elastic, fully managed cloud service that will add to the mix as well >> Okay, so let's get down, so that's going to bring us fully cloud-enabled. >> Yep. >> Cloud on-premise, >> Both. >> All that kind of dynamic flexibility and an option for cloud configurations and depressuring. Okay, back to the regulatory thing. So what's the big deal about that, because you mentioned that most companies we talk to love the cloud, they love the economics, but there's a lot of fund and fear internally amongst their own team about getting sued, losing data, you know, certain industries that they might have to play, is that a fact and can you explain that for someone and what's important about that. >> Yeah I mean, for some customers it's a real concern, right, and the world is dynamically shifting, I mean, look at what happened a few months ago with you know the Brexit, right, I mean all of a sudden it was OK to have, you know, the data as long as it was in the EU, well the EU is now shifting, so where does the data go, right? So from a regulatory point of view we haven't fully settled in terms of where customer data can be held, exactly how its treated, and you know those things are evolving. So for a number of companies, they want the advantages of the cloud but they don't necessarily want it on the public cloud and that's why we're offering these new cloud machines because they can essentially have their cake and eat it too. >> So interesting, the dynamic then is is that this whole regulatory thing is a moving train. >> Right. >> Relative to the whole global landscape. >> Right. >> Who knows what's going to happen with China and other things, right? >> Right and I think that's what's really terrific is that our history is, of course, were a company that's been around for a while so we started on premises and we moved up to the cloud and our customers are ones that are going to have, kind of, this hybrid kind of a system, right. Other companies started much later and their cloud only and you know while that's great for companies that want the public cloud. What do you do if you're in a regulatory environment that isn't ready to boot public cloud? Now you have to have two architectures, one for on-premises and one for cloud and then how do you deal with a moving landscape where a year from now things that are on premises can move to the cloud and other things that are in the cloud may have to move to back on premises, right? How do you deal with that dynamic going forward and not get stuck. >> So, is it fair to say that Oracle is a big data player in the cloud and on-premise? >> Absolutely, and not just for data management. I think that you know while we started at that core, that's our heritage, we've so much built out our portfolio, we have big data products in the data integration space, in the machine learning space, we have big data products that connect up with our IoT strategy, with data visualization, we've really blossomed as the marketplace has matured bringing additional technology for customers to utilize. >> Okay, so let's get down to the reality and get into the weeds with customer deployments. How do you guys compare vis-a-vis the competition now you got the on-prem with the BDA, Big Data Appliance with the cloud service, cloud machines to create some provisioning, flexibility on whether architectures the customers may choose. >> Yeah. For whatever reason that they would have. >> Okay. How does that compare to the competition? >> On the on-premises side, if we start there, there was a recent Forrester Wave that looked at various Hadoop appliances and we took the number one category or the number one position across all the three categories that they looked at, they looked at the strategy, they looked at the market presence and they looked at the capability of what we offered and we ended up number one in that space. On the cloud side, of course, we're maturing in terms of that offering as well but you know we're really the only company out there that can offer the same architecture both on cloud and on-premises, where you don't necessarily have to go all in on one or the other, and for many companies that's exactly what they're, you know, what they need right. They can't necessarily go all in one way or the other. >> So I got to ask you kind of a, put your Oracle historian tech historian hat on as well as your Oracle executive hat on and talk about some of the technologies that have come and gone over the years and how does that relate to some of the things that are hyped up now? I mean certainly Hadoop, what's supposed to be this new industry, it's going to disrupt the database and Oracle's going to be put out of business and this is how people are going to store stuff, MapReduce. Now people are saying, why even have Hadoop in the cloud when you got object store. So, things come and go, I'm not saying Hadoop is going to come and go but it's good for batch but so, what's your comments on it can you point to industry technology, say okay, that's going to be a feature of something else, that's a real deal? What are some of the things that you look at that you can say... >> So you know we're seeing exactly as you described, a few years ago you go to a conference and it was all about MapReduce. Right now, a seminar in MapReduce, nobody goes, right. Everybody's going to Spark, right, and there's already things that potentially will replace Spark, things like Flink, and we're going to see that continual change and a lot of what we focused on is to be able to provide some level of abstraction between the customers architecture and these moving technology. So, I'll give an example. Our data integration technology, historically that was, you know, you're able to visually describe a set of transformations and then we generated code in SQL or PL/SQL. Now we generate code, not only in SQL and PL/SQL but we generate that same code in Spark. If tomorrow Spark gets replaced with Flink or something else, we simply replace the code generator underneath and all of what the customers built gets preserved and moved into the future. I think a lot of people are now becoming concerned that as they take advantage of open source really really at the very low levels they have the potential to essentially get stuck in a technology which has essentially become obsoleted, right? >> Yeah. >> As any new technology evolves we move from people who just code, right, with all the lower level stuff up to a set of tools and you know we talk to companies now that have huge amounts of now legacy MapReduce code, right, you think only a few years ago... >> It's kinds like cobalt. >> Neil: Yeah. (John laughing) >> Neil: So... >> I's going to be around but not really pervasive. >> Right. So how can you take advantage of these technologies, without necessarily having to get stuck to any one of them. >> So, I'm going to ask you the philosophical question, so Oracle database business has been the star over the years since the founding but even now it seems to me that the role of the database becomes even more important as you connect subsystems, call it, Hadoop, Spark, whatever technology's going to evolve as a feature of an integrated system, if you will, software-based and or engineered system coming together. So that seems to be obvious that you can connect in an open way and give customers choice but that's kind of different from the old Oracle. I have a database everything runs on Oracle, Oracle on Oracle's grade, certainly it runs well but what's the philosophy internally obviously the database team's sitting there it must be like, wow big data is an opportunity for Oracle. >> That's right. Or do they go, no the database business is different. How do you guys talk about that internally and then how do customers take away from that dynamic between the database crown jewel and the opening it up and being more big data driven? >> I think it's ironic because, externally, when you talk to people, they just assume that we're going to be like "Oh my god this is a threat" and we're going to just double down on what we're doing on the database side and we're just going to hunker down and I don't know try to hide, right? But that's exactly the opposite of what we're really been doing internally. We really have embraced these technologies of Hadoop and Spark and NoSQL, and we're essentially seeing data management evolve, that is the new normal. So rather than looking at, not only what we might have said, we did say when we introduced Oracle in the data warehousing market back in '95, We said "Put all your data in the Oracle database." We're not saying that anymore because there are reasons to put data in Hadoop, there are reasons to put data in graph databases, in NoSQL databases, we need to be able to provide those choice while still integrating that data management platform as one integrated entity. >> Would you say then it was fair to say that, from a customer standpoint, by having that open approach gives more faster access to different data types in real time? >> Absolutely. >> John: Then isn't that the core value proposition of big data. >> Yeah, again when the Hadoop new craze first started it was all about unload and put everything in this one store and for a lot of companies today, they still are faced with the this conundrum which says, in order to analyze data, I have to put it all in one place. So that means that you have to move your operational data into one place, you have to move your data warehousing stuff into one place, but then at the same time you mentioned real time. How do you get into the business of moving data from Place A to Place B on a constant basis while still being able to offer real-time access and real-time analytics? The answer is you can't. >> And the value of the data, the data capital, as we've been talking about, McGee bond is an IoT piece of data from a turbine could have really big relevance to the system of record in another database and that has to be exposed and integrated quickly to surface some insight about the quality of that... >> It's the thing that gives you context, right. Today what's going on is that we are getting all access to all these rich data sources and rich data types that we didn't have before, whether that's text information or information coming off sensors and alike, and the relevance of that information is, when we combined it together with the corporate information, the stuff that we have in our existing systems to really reap the true benefit. How do you know, when you get a log file the log file doesn't have anything about the customer in it, the log file just has a, a number associating itself to a customer. You have to tie that together with the customer profile which data which might not exist in Hadoop, maybe it's in a NoSQL store. >> And certainly the Open Source is booming with Oracle. You guys are actively involved in all the different open source ecosystems. >> Sure, we drive a number of open source projects whether it's MySQL or Java or, the list goes on and on. Many people don't think of, you know, they're not even aware that Oracle's behind my MySQL. As an example, right, I mean, I remember talking to my son recently he says, "Do you know anything about MySQL" and I'm like well a little bit. And then as we're talking and were looking through his code, finally I say, "You know this is Ooracle product," He's like no it's not. You know cause... >> It's too cool to be Oracle. >> That's right. That's not a bad thing, right. >> Yeah. I mean the reality of it is, is that you know we've invested a whole lot of time and energy in these technologies and we're really looking to commercialize them to mainstream them, to make them less scary for more people to be able to get value from. Well your son's example's a great illustration of the new Oracle that's out there now this whole new philosophy. Final, give you the last word real quick, for folks watching, what's one thing you'd want to share with them that they may or may not know about Oracle and it's big data strategy? >> Give us a look. Right, I mean I think that when you think of big data and you think of these new technologies, you may not think of Oracle, right. You may think of the new companies that you're more familiar with in the light. The reality of is, is that Oracle has an extraordinarily rich portfolio of technology and services on the cloud as well as like cloud machines. So give us a look, I think you'll be surprised at how open we are, how much of the open source technology we've embedded in our products and how fast were essentially evolving into, what is the new normal. >> Neil thanks so much for spending the with me here On the Ground. I'm john Furrier, you're watching exclusive "On the Ground" coverage here at Oracle Headquarters. Thanks for watching. >> Neil: Thank you.

>> Announcer: theCUBE presents On The Ground. (light techno music) >> Hello, everyone. Welcome to a special, exclusive On the Ground CUBE coverage at Oracle Headquarters. I'm John Furrier, the cohost of theCUBE, and we're here with Joey Echeverria, Platform Technical Lead at Rocana here, talking about big data, cloud. Welcome to this On The Ground. >> Thanks for having me. >> So you guys are a digital native company. What's it like to be a digital native company these days, and what does that mean? >> Yeah, basically if you look across the industry, regardless of if you're in retail or manufacturing, your biggest competitors are the companies that have native digital advantages. What we mean by that is these are companies that you think of as tech companies, right? Amazon's competitive advantage in the retail space is that their entire business is instrumented, everything they do is collected. They collect logs and metrics for everything. They don't view IT as a separate organization, they view it as core to their business. And really what we do at Rocana is build tools to help companies that aren't digital native compete in that landscape, get a leg up, get the same kind of operational insight into their data and their customers, that they don't otherwise have. >> So that's an interesting comment about how IT is fundamental in their business model. In the traditional enterprise, the non-digital if you will, IT's a department. >> Joey: Exactly. >> So big data brings a connection to IT that gives them essentially a new lift, if you will, a new persona inside the company. Talk about that dynamic. >> Yeah, big data really gives you the technical foundation to build the tools and apps on top of those platforms that can compete with these digitally native companies. No longer do you need to go out and hire PhDs from Stanford or Berkeley. You can work with the same technology that they've built, that the open source community has built, and build on top of that, leverage the scalability, leverage the flexibility, and bring all of your data together so that you can start to answer the questions that you need to in order to drive the business forward. >> So do you think IT is more important with big data and some of the cloud technologies or less important? >> I think it starts to dissolve as a stand-alone department but it becomes ingrained in everything that a company does. Your IT department shouldn't just be fixing fax machines or printers, they should really be driving the way that you do your business and think about your business, what data you collect, how you interact with customers. Capturing all of those signals and turning that signal into noise-- Or sorry, filtering out the noise, turning the signal into action so that you can reach your customers and drive the business going forward. >> So IT becomes part of the fabric of the business model, so it's IT everywhere? >> Joey: Exactly, exactly. >> So what are you seeing out there that's disruptive right now, from your standpoint? You guys have a lot of customers that are on the front end of this big wave of data, cloud, and emerging technology. We're seeing certainly great innovations, machine learning, AI, cognitive, Ya know, soon Ford's going to have cars in five years, Uber's going to have self-driving cars in Pittsburgh by this year. I mean, this is a pretty interesting time. What are some of the cool things that you see happening around this dynamic of big-data-meets-IT? >> Yeah, I think one of the biggest things that we see in general is that folks want turnkey solutions. They don't want to have to think about all of the plumbing, they don't want to go out and buy a bunch of servers, rack them themselves, and figure out what's the right bill of materials. They want turnkey, whether that's cloud or physical appliances. And so that's one of the reasons why we work so well with Oracle on their Big Data Appliance. We can turn our application, which helps customers transform their business into being digital native, into a turnkey solution. They don't have to deal with all of the plumbing. They just know that they get a reliable platform that scales the way that they need to, and they're able to deploy these technologies much more rapidly. And we do the same thing with our cloud partners. >> So I got to the tough question. You guys are a start-up, certainly growing really fast, you got a lot of great technical people, but why not just do it yourself? Why partner with Oracle? >> Oh, that's a great question. I mean, Oracle has great reach in the marketplace, they're trusted. We don't want to solve every problem. We really want to partner with other companies, leverage their strengths, they can leverage our strengths and at the end of the day, what we end up building together is a much stronger solution than we could build ourselves. One of the main reasons why we in particular are not, say, a SAS company where we're just hosting everything in the cloud, is we need to go to where the data is and for a lot of these non-digital native companies, that data is still on-prem in their data centers. That being said, we're ready for the transition to the cloud. We have customers running our software in the cloud. We run everything in the cloud internally because, obviously as a small start-up, we don't want to go out and spend a lot of money on physical hardware. So we're really ready for both of those. >> Is this a big trend that you're seeing? 'Cause this is consistent with, some people say, the API economy. People can actually sling APIs together, build connectors, build a core product, but using API as a comprehensive solution is a mix between core and then outsourced, or partnering. Is that a trend that's beyond Rocana? >> Oh, definitely. One of the reasons why we build on top of open source software and open source standards is for that network effect. One of our core tenets is that we don't own the data. You own the data. So we store everything in file formats like Apache Parquet because it has the widest reach, the widest variety of tools that can access it. If there's a use case that you want to perform on our data that our application doesn't solve for you, fire up your Tableau, point it at the exact same data sets and go to town. The data is there for the customer, it's not there for us. >> What's the coolest thing that you're seeing right now in the marketplace, relative to disruption? You've got upcoming start-ups like you guys, Rocana, you got the big companies like Oracle, which are innovating now with opening up and not just being the proprietary database, using an open source. So what are some of the big things you're seeing right now between the dynamics of the big guys and the up-starts? >> Yeah, I think right now the biggest thing is turning data into the central cornerstone of everything that you're doing. No longer can you say, "I'm going to launch this project," without explaining what data are you going to collect, what are the metrics going to look like, how do we know if it's working, how do we know if it's not working. That sort of infusion of data everywhere, and even as you look across broader industry trends, things like IoT. IoT is really just the recognition that every device, every thing needs to have a connection to the network and a connection to the Internet and generate data. And then it's what you do with that data and tools that allow you to make sense of that data that are really going to drive you forward. >> IoT is a great example of your point about IT becoming the fabric because most IoT sensor stuff is not even connected to databases or IT. So now you're seeing this whole renaissance of IT getting into the edge of the network with all this IoT data. I mean, they have to be more diverse in their dealing with the data. >> Exactly, and that's why you need more native analytics. So one of the core parts of our platform is anomaly detection. Across all of your different devices in your data center, you're generating tons of data, tons of data. That data needs to be put into context. What may be a major shift is a problem with one data set isn't a problem with another. And so you have to have that historical context. That's one of the reasons why we also build on these big data platforms, is for things like security use cases. It takes, on average, nine months for you to actually detect that you've been breached. If you don't have the logs from nine months ago, you're not going to be able to find out how they got in, when they got in, so you really need that historical context to put the new data into the proper context and to be able to have the automated analytics that drive you and your analysis forward, rather than forcing you to sort of dumpster dive with just search and guess what's working. >> Dumpster diving into the data swamp, new buzzwords. Yeah, but this is really the big thing. The focus on real time seems to be the hot button, but you need data from a while back to mix in with the real time to get the right insight. Is that kind of the big trend? >> Oh yeah, absolutely. Whenever you talk about machine learning, you want the real time insights from it, but it's only as powerful as the historical data that you have to build those models. And so a big thing that we focus on how to make it easy to build those models, how to do it automatically, how to get away from having 500 different tuna bowls that the customer has to set, and really put it on autopilot. >> Well, making it easy, but also fast. It's got to get in low latency, that's another one. >> Oh absolutely. I mean, we leverage Kafka for just that reason. We're able to bring in millions of events per second into moderate size environments without breaking a sweat. >> Rocana, great stuff. Joey, great to chat with you again, here On The Ground at the Oracle Headquarters. I'm John Furrier, you're watching a special CUBE On The Ground here at Oracle Headquarters. Thanks for watching. (light techno music)