from our studios in the heart of Silicon Valley Palo Alto California this is a cute conversation hi and welcome to the cube Studios for another cube conversation where we go in-depth with thought leaders driving innovation across the tech industry I'm your host Peter Burris AI is on the forefront of every board in every enterprise on a global basis as well as machine learning deep learning and other advanced technologies that are intended to turn data into business action that differentiates the business leads to more revenue leads to more profitability but the challenge is is that all of these new use cases are not able to be addressed with the traditional ways that we've set up the workflows that we've set up to address them so as a consequence we're going to need greater opera's the operationalization of how we translate business problems into ml and related technology solutions big challenge we've got a great guest today to talk about it non-division diof is a distinguished technologist and lead data scientists at HPE in the blue data team nonde welcome to the cube thank you happy to be here so ananda let's start with this notion of a need for an architected approach to how we think about matching AI ml technology to operations so that we get more certain results better outcomes more understanding of where we're going and how the technology is working within the business absolutely yeah ai and doing AI in an enterprise is not new there have been enterprise-grade tools in the space before but most of them have a very prescribed way of doing things sometimes you use custom sequel to use that particular tool or the way you present data to that tool requires some level of pre-processing which makes you copy the data into the tool so you have already data fidelity maybe at risk and you have a data duplication happening and then the scale right when you talk about doing AI at the scale that is required now considering data is so big and there is a variety of data sets for the scale it can probably be done but there is a huge cost associated with that and you may still not meet the variety of use cases that you want to actually work on so the problem now is to make sure that you empower your users who are working in the space and augment them with the right set of technologies and the ability to bring data in a timely manner for them to work on these solutions so it sounds as though what we're trying to do is simplify the process of taking great ideas and turn it into great outcomes but you mentioned users I think it's got to start with or let me ask you if we have to start here that we've always thought about how is going to center in the data science or the data scientist as these solutions have start to become more popularized if diffused across the industry a lot more people are engaging are all roles being served as well as you need to be absolutely I think that's the biggest challenge right in the past you know when we talk about very prescribed solutions end to end was happening within those tools so the different user persona were probably part of that particular solution and also the way these models came into production which is really making it available for a consumer is read coding or redeveloping this in technologies that were production friendly which is you're rewriting that and sequel you're recording that and C so there is a lot of details that are lost in translation and the third big problem was really having visibility or having a say from a developer's point of view or a data scientist point of view in how these things are performing in production that how do you actually take it back take that feedback back into deciding you know is this model still good or how do you retrain so when you look at this lifecycle holistically this is an iterative process it is no longer you know workflow where you hand things off this is not a water flow methodology anymore this is a very very continuous and iterative process especially in the New Age data science the tools that are developing where you build the model that developer decides what the run time is and the run times are capable of serving those models as is you don't have to recode you don't have to lose things during translation so with this back to your question of how do you serve two different roles now all those personas and all those roles have to be part of the same project and they have to be part of the same experiment they're just serving different parts of the lifecycle and now you've whatever tooling you provide or whatever architecture technologies you provide have to look at it holistically there has to be continuous development there has to be collaboration there has to be central repositories that actually cater to those needs so each so the architected approach needs to be able to serve each of the roles but in a way that is collaborative and is ultimately put in service to the outcome and driving the use of the technology forward well that leads to another question should it should the should this architected approach be tied to one or another set of algorithms or one or another set of implementation infrastructure or does it have to be able to serve a wide array of Technology types yeah great question right this is a living ecosystem we can no longer build for you know you plant something for the next two years or the next three years technologies are coming every day and the reason is because the types of use cases are evolving and what you need to solve that use case is completely different when you look at two different use cases so whatever standards you come up with you know the consistency has to be across how a user is on-boarded into the system a consistency has to be about data access about security about how does one provision these environments but as far as what tool is used or how is that tool being applied to a specific problem there's a lot of variability in there and it has to cater your architecture has to make sure that this variability is addressed and it is growing so HPE spends a lot of time with customers and you're learning from your customer successes and how you turn that into tooling that leads to this type of operator operationalization but give us some visibility into some of those successes that really stand out for you that have been essential to how HP has participated in this journey to create better tools for better AI and m/l absolutely you know traditionally with blue data HPE now you know we've been exposed to a lot of big data processing technologies where the current landscape the data is different data is not always at rest data is not structured you know data is coming it could be a stream of data it could be a picture and in the use cases like we talked about you know it could be image recognition or a voice recognition where the type of data is very different right so back to how we've learnt from our customers like in my role I talked to you know tens of customers on a daily or weekly basis and each one of them are at a different level of maturity in their life cycle and these are some very established customers but you know the various groups that are adopting this new age technologies even within an organization there is a lot of variability so whatever we offered them we have to help support all of that particular user groups there are some who are coming from the classic or language background there are some that are coming from Python background some are doing things in Scala someone doing things in SPARC and there are some commercial tools that they're using like h2o driverless AI or data iku so what we have to look at is in this life cycle we have to make sure that all these communities are represented and/or addressed and if they build a model in a specific technology how do we consume that how do we take it in then how do we deploy that from an end to point of view it doesn't matter where a model gets built it does matter how end-users access it it doesn't matter how security is applied to it it does matter how scaling is applied to it so really there is a lot of consistency is required in the operationalization and also in how you onboard those different tools how do you make sure that consistency or methodology or standard practices are applied in this entire lifecycle and also monitoring that's a huge aspect right when you have deployed a model and it's in production monitoring means two different things to people where is it even available you know when you go to a website when you click on something is a website available very similarly when you go to an endpoint or you're scoring against a model is that model available do you have enough resources can it scale depending on how much requests come in that's one aspect of monitoring and the second aspect is really how was the model performing you know is that what is the accuracy what is the drift when is it time to retrain so you no longer have the luxury to look at these things in isolation right so it we want to make sure that all these things can be addressed in a manner knowing that this iteration sometimes can be a month sometimes it can be a day sometimes it's probably a few hours and that is why it can no longer be an isolated and even infrastructure point of view some of these workloads may need things like GPU and you may need it for a very short amount of time let how do you make sure that you give what is needed for that duration that is required and take it back and assign it to something else because these are very valuable resources so I want to build on if I may on that notion of onboarding the tools we're talking about use cases that enterprises are using today to create business value we're talking about HPE as an example delivering tooling that operationalize is how that's done today but the reality is we're gonna see the state of the art still evolve pretty dramatically over the next few years how is HPE going about ensuring that your approach and the approach you working with your customers does not get balkanized does not get you know sclerotic that it's capable of evolving and changing as folks learn new approaches to doing things absolutely you know it this has to start with having an open architecture you know you have to there has to be standards without which enterprises can't run but at the same time those standards shouldn't be so constricting that it doesn't allow you to expand into newer use cases right so what HP EML ops offers is really making sure that you can do what you do today in a best-practice manner or in the most efficient manner bringing time to value you know making sure that there is you know instant provisioning or access to beta or making sure that you don't duplicate data compute storage separation containerization you know these are some of the standard best practice technologies that are out there making sure that you adopt those and what these sets users for is to make sure that they can evolve with the later use cases you can never have you know you can never have things you know frozen in time you just want to make sure that you can evolve and this is what it sets them up for and you evolve with different use cases and different tools as they come along nada thanks very much has been a very it's been a great conversation we appreciate you being on the cube thank you Peter so my guest has been non Division I of the distinguished technologists and lead data scientists at HPE blue data and for all of you thanks for joining us again for another cube conversation on Peter burst see you next time you [Music]

live from Orlando Florida it's the cube covered conf 18 got to you by Splunk welcome back to our land Oh everybody I'm Dave Volante with my co-hosts two minima and you're watching the cube the leader in live tech coverage we're brought here by Splunk toises Splunk off 18 hashtag spunk conf 18 Susan st. Leger is here she's the president of worldwide field operations at Splunk Susan thanks for coming on the cube thanks so much for having me today so you're welcome so we've been reporting actually this is our seventh year we've been watching the evolution of Splunk going from sort of hardcore IT OPSEC ops now really evolving in doing some of the things that when everybody talked about big data back in the day and spunk really didn't they talked about doing all these things that actually they're using Splunk for now so it's really interesting to see that this has been a big tailwind for you guys but anyway big week for you guys how do you feel I feel incredible we had you know we've it announced more innovations today just today then we have probably in the last three years combined we have another big set of innovations to announce tomorrow and you know just as an indicator of that I think you heard Tim today our CTO say on stage we to date have 282 patents and we are one of the world leaders in terms of the number of patents that we have and we have 500 pending right so if you think about 282 since the inception of the company and 500 pending it's a pretty exciting time for spunk people talk about that flywheel we were talking stew and I were talking earlier about some of the financial metrics and you know you have a lot of a large deal seven-figure deals which which you guys pointed out on your call let's see that's the outcome of having happy customers it's not like you turn to engineer that you just serving customers and that's what what they do I talk about how Splunk next is really bringing you into new areas yeah so spike next is so exciting there's really three three major pillars if you will design principles to spunk next one is to help our customers access data wherever it lives another one is to get actionable outcomes from the data and the third one is to allow unleash the power spunk to more users so there really the three pillars and if you think about maybe how we got there we have all of these people within IT and security that are the experts on Splunk the swing ninjas ful and their being they see the power of spunk and how it can help all these other departments and so they're being pulled in to help those other departments and they're basically saying Splunk help us help our business partners make it easier to get there to help them unleash the power spunk for them so they don't necessarily need us for all of their needs and so that's really what's what next is all about it's about making it again access data easier actionable outcomes and then more users and so we're really excited about it so talk about those new users I mean obviously the ITA ops they're your peeps so are they sort of advocating to you into the line of business or are you probably being dragged into the line of business what's that dynamic like yeah it's definitely we're customer success first and we're listening to our customers and they're asking us to take them that should go there with them right there being pulled that they know that what we what we say with our customers what are what our deepest customers understand about us is everybody needs funk it's just not everyone knows it yet and I said they're teaching their business why they need it and so it's really a powerful thing and so we're partnering with them to say how do we help them create business applications more which you'll see tomorrow in our announcements to help their business users you know one of the things that strikes us if we were talking it was the DevOps gentleman when you look at the companies that are successful with so-called digital transformation they have data at the core and they have sort of I guess I don't want to say a single data model but it's not a data model of stovepipes and that's what he described and essentially if I understand the power of Splunk just in talking to some of your customers it's really that singular data model that everybody can collaborate on with get advice from each other across the organization so not this sort of stovepipe model it seems like a fundamental linchpin of digital transformation even though you guys haven't been using that overusing that term thank you sort of a sign of smug you didn't use the big data term when big data was all hot now you use it same thing with digital transformation you're a fundamental it would seem to me to a lot of companies digital transformation that's exactly if you think about we started nineteen security but the reason for that is they were the first ones to truly do digital transformation right those are just the two the two organizations that started but exactly the way that they did it now all the other business units are trying to do it and that same exact platform that same exact platform that we use there's no reason we can't use it for those other areas those other functions but but if we want to go there faster we have to make it easier to use spunk and that's what you're seeing with spunk next you know I look at my career the last couple of decades we've been talking about oh well there's going to we're gonna leverage data and there's go where we want to be predictive on the models but that the latest wave of kind of AI ml and deep learning what I heard what you're talking about and in the Splunk next maybe you could talk a little bit about why it's real now and why we're actually going to be able to do more with our data to be able to extract the value out of it and really enable businesses sure so I think machine learning is that is at the heart of it and you know we we actually do two things from a machine learning perspective number one is within each of our market groups so IT security IT operations we have data scientists that work to build models within our applications so we build our own models and then we're hugely transparent with our customers about what those models are so they can tweak them if they like but we pre build those so that they have them in each of those applications so that's number one and and that's part of the actionable outcomes right ml helps drive actionable outcomes so much faster the second aspect is the ML TK right which is we give the our customers in ml TK so they can you know build their own algorithms and leverage everything all of the models that are out there as well so I think that two-fold approach really helps us accelerate the insights that we give to our customers Susan how are you evolving your go-to-market model as you think about Splunk next and just think about more line of business interactions so what are you doing on the go-to-market side yeah so the go to market when you think about reaching all of those other verticals if you will right it's very much going to be about the ecosystem all right so it's it's going to be about the solution provider ecosystem about the ISV ecosystem about the big the si is both boutique and the global s is to help us really Drive Splunk into all the verticals and meet their needs and so that will be one of the big things that you see we will obviously still have our horizontal focus across IT and security but we are really understanding what are the use cases within financial services what are the use cases within healthcare that can be repeated thousands of times and if you saw some of the announcements today in particular the data stream processor which allows you to act on data in motion with millisecond response that now puts you as close to real-time as anything we've ever seen in the data landscape and that's going to open up just a series of use cases that nobody ever thought of using spoil for so I wonder what you're hearing from customers when they talk about how do they manage that that pace of change out there I really like I walked around the show floor stuff I've been hearing lots people talking about you know containers and we had one of the your customers talking about how kubernetes fits into what they're doing seems like it really is a sweet spot for spunk that you can deal with all of these different types of information and it makes it even more important for customers to come to you yeah as you heard from Doug today in our keynote our CEO and the keynote it is a messy world right and part of the message just because it's a digital explosion and it's not going to get any slower it's just going to continue to get faster and I know you met with some of our customers earlier today and if'n carnival if you think about the landscape of NIF right I mean their mission is to protect the arsenal of nuclear weapons for the country right to make them more efficient to make them safer and if you think about all of it they not only have traditional IT operations and security they have to worry about but they have this landscape of lasers and all these sensors everywhere and that and when you look at that that's the messy data landscape and I think that's where Splunk is so uniquely positioned because of our approach you can operate on data in motion or at rest and because there is no structuring upfront I would I want to come back to what you said about real-time because that you know I oh I've said this now for a couple years but never used to use the term when Big Data was at its the peak of what does a gardener call it the hype cycle you guys didn't use that term and and so when you think about the use cases and in the Big Data world you've been hearing about real time forever now you're talking about it enterprise data warehouse you know cheaper EDW is fraud detection better analytics for the line of business obviously security and IT ops these are some of the use cases that we used to hear about in Big Data you're doing like all these now and sort of your platform can be used in all of these sort of traditional Big Data use cases am i understanding that problem 100% understanding it properly you know Splunk has again really evolved and if you think about again some of the announcements today think about date of fabric search right rather than saying you have to put everything into one instance or everything into one place right we're saying we will let you operate across your entire landscape and do your searches at scale and you know spunk was already the fastest at searching across your global enterprise to start with and when we were two to three times faster than anybody who compete it with us and now we improve that today by fourteen hundred percent I don't I don't even know where like you just look at again it ties back to the innovations and what's being done in our developer community within our engineering and team in those traditional use cases that I talked about in big data it was it was kind of an open source mess really complex zookeeper is the big joke right and always you know hive and pig and you know HBase and blah blah blah and we're practitioners of a lot of that stuff that's it's very complex essentially you've got a platform that now can be used the same platform that you're using in your traditional base that you're bringing to the line of business correct okay right it's the same exact platform we are definitely putting the power of Splunk in in the users hand so by doing things like mobile use on mobile and AR today and again I wish I could talk about what's coming tomorrow but let's just say our business users are going to be pretty blown away by what they're going to see tomorrow in our announcements yeah so I mean I'm presuming these are these are modern it's modern software micro services API base so if I want to bring in those open source tool tools I can in fact what you'll actually see when you understand more about the architecture is we're actually leveraging a lot of open-source and what we do so you know capabilities a spark and flink and but what we're doing is we're masking the complex the complexity of those from the user so instead of you having to do your own spark environment your own flink environment and you know having to figure out Kafka on your own and how you subscribe to what we're giving you all that we're we're masking all that for you and giving you the power of leveraging those tools so this becomes increasingly important my opinion especially as you start bringing in things like AI and machine learning and deep learning because that's going to be adopted both within a platform like use as yours but outside as well so you have to be able to bring in innovations from others but at the same time to simplify it and reduce that complexity you've got to infuse AI into your own platform and that's exactly what you're doing it's exactly what we're doing it's in our platform it's in our applications and then we provide the toolkit the SDK if you will so users can take it to another level all right so you've got 16,000 customers today if I understand the vision of SPARC next you're looking to get an order of magnitude more customers that you of it as addressable market talk to us about the changes that need to happen in the field is it just you're hitting an inflection point you've got those you know evangelists out there and I you know I see the capes and the fezzes all over the show so how is your field get ready to reach that broader audience yeah I think that's a great question again once again it will I'll tell you what we're doing internally but it's also about the ecosystem right in order to go broader it has to be about this this Splunk ecosystem and on the technology side we're opening the aperture right it's micro services it's ap eyes it's cloud there's there's so much available for that ecosystem and then from a go-to-market perspective it's really about understanding where the use cases are that can be repeated thousands of times right that the the the big problems that each of those verticals are trying to solve as opposed to the one corner use case that you know you could you could solve for one customer and that was actually one of the things we found is when we did analysis we used to do case studies on Big Data number one use case that always came back was custom because nothing was repeatable and that's how we were seeing you know a little bit more industry specific issues I was at soft ignite last week and you know Microsoft is going deep on verticals to get specific as to you know for IOT and AI how they can get specific in those environments I agreed I think again one of the things that so unique about Splunk platform is because it is the same platform that's at the underlying aspect that serves all of those use cases we have the ability in my opinion to do it in a way that's far less custom than anybody else and so we've seen the ecosystem evolve as well again six seven years ago it was kind of a tiny technology ecosystem and last year in DC we saw it really starting to expand now you walk around here you see you know some big booths from some of the SI partners that's critical because that's global scale deep deep industry expertise but also board level relationships absolutely that's another part of the the go-to markets Splunk becomes more strategic this is a massive Tam expansion that where we are potentially that we're witnessing with Splunk how do you see those conversations changing are you personally involved in more of those boardroom discussions definitely personally involved in your spot on to say that that's what's happening and I think a perfect example is you talk to Carnival today right we didn't typically have a lot of CEOs at the Splunk conference right now we have CEOs coming to the spunk conference right because it is at that level of strategic to our customers and so when you think about Carnival and yes they're using it for the traditional IT ops and security use cases but they're also using it for their customer experience and who would ever think you know ten years ago or even five years ago of Splunk as a customer experience platform but really what's at the heart of customer experience it's data so speaking of the CEO of Carnival Arnold Donald it's kind of an interesting name and and so he he stood up in the States today talking about diversity doubling down on diversity as an african-american you know you frankly in our industry you don't see a lot of african-americans CEOs you don't see a ton of women CEOs you don't see the son of women with with president in their title so he he made a really kind of interesting statement where he said something to the effect of forty years ago when I started in the business I didn't work with a lot of people like me and I thought that was a very powerful statement and he also said essentially look at if we're diverse we're gonna beat you every time your thoughts as an executive and in tech and a woman in tech so first of all i 100% agree with him and i can actually go back to my start i was a computer scientist at NSA so i didn't see a lot of people who looked like me and so from that perspective I know exactly where he's coming from and I am I'll tell you at Splunk we have a huge investment in diversity and not because it's a checkbox but because we believe in exactly what he says it's a competitive edge when you get people who think differently because you came from a different background because you're a different ethnicity because you were educated differently whatever it is whether it's gender whether it's ethnicity whether it's just a different approach to thinking all differentiation puts a different lens and and that way you don't get stove you don't have stovepipe thinking and I what I love about our culture at spunk is that we we call it a high growth mindset and if you're not intellectually curious and you don't want to think beyond the boundaries then it's probably not a good fit for you and a big part of that is having a diverse environment we do a lot of spunk to drive that we actually posted our gender diversity statistics last year because we believe if you don't measure it you're never going to improve it and it was a big step right to say we want to publish it we want to hold herself accountable and we've done a really nice job of moving it a little over 1% in one year which for our population is pretty big but we're doing really unique things like we have all job descriptions are now analyzed there's actually a scientific analysis that can be done to make sure that the job description does not bias whether men are women whether men alone or whether it's you know gender neutral so that that's exciting obviously we have a big women in technology program and we have a high potential focus on our top women as well what's interesting about your story Susan and we spent a lot of time on the cube talking about diversity generally in women in tech specifically we support a lot of WI t and we always talk him frequently we're talking about women and engineering roles or computer science roles and how they they oftentimes even when they graduate with that degree they don't come into tech and what strikes me about your path is your technical and yet now you've become this business executive so and I would imagine that having that background that technical background only helped in terms of especially in this industry so there are paths beyond just the technical role one hundred percent it first of all it's a huge advantage I believe it's the core reason why I am where I am today because I have the technical aptitude and while I enjoyed the business side of it as much and I love the sales side and the marketing side and all of the above the truth of the matter is at my core I think it's that intellectual curiosity that came out of my technical background that kept me going and really made me very I took risks right and if you look at my career it's much more of a jungle gym than a ladder and the way you know I always give advice to young people who generally it's young women who ask but oh sometimes it's the young men as well which is like how did you get to where you are how do I plan that how do I get and the truth of the matter is you can't if you try and plan it it's probably not going to work out the exactly the way you plan and so my advice is to make sure that you every time you're going to make a move your ask yourself what am I going to learn Who am I going to learn from and what is it going to add to my experience that I can materially you know say is going to help me on a path to where I ultimately want to be but I think if you try and figure it out and plan a perfect ladder I also think that when you try and do a ladder you don't have what I call pivots which is looking at things from different lenses right so me having been on the engineering side on the sales side on the services side of things it gives me a different lens and understanding the entire experience of our customers as well as the internals of an organization and I think that people who pivot generally are people who are intellectually curious and have intellectual capacity to learn new things and that's what I look for when I hire people I love that you took a nonlinear progression to the path that you're in now and it's speaking of you know the the technical I think if you're in this business you better like tech or what are you doing in this business but the more you understand technology the more you can connect the dots between how technology is impacting business and then how it can be applied in new ways so well congratulations on your careers you got a long way to go and thanks so much for coming on the queue so much David I really appreciate it thank you okay keep it right - everybody stew and I'll be back with our next guest we're live from Splunk Don Capcom 18 you're watching the cube [Music]

hey welcome back here ready Jeff Rick here with the key we're at the chief data scientist USA conference in downtown San Francisco and we're really excited to have a representative from IBM Sam Lightstone distinguished engineer from IBM join us Sam great to se you thank you very much pleasure to be here absolutely so we cover a ton of IBM events we're at world of Watson world lots of developer conference big the big event in New York earlier this year around strata so you know we're big fans of all the things that IBM is doing and in Rob Thomas and the SPARC group so I could go on and on but we won't go there we'll talk about what you were talking about earlier today and kind of let the cat out of the bag which is always exciting breaking news or breaking Bay there I don't know exactly how we would describe it but you talked about something new IBM data confluence yeah you could share this what's that all about yeah so it's a it's a whole new idea a whole new paradigm that were that we were incubating right now inside of IBM and it's not yet available but we're hoping to start trials in January ish timeframe but it comes from a realization that so much data is about to come upon us from distributed data sources you know everybody's got not only your cell phone but increasingly data is coming from Internet of Things you're gonna have data coming from your car data come from your glasses some smart meters on your house and it's deluge of data and the way that people like to do data science on this data today is they pull this data from these devices and put it into a central repository which is which is a perfectly legitimate strategy but it means that you're creating copies of the data and there's a certain complexity of dragging that data through the internet into some central repository so the idea that we had with data confluence is to leave the data where it is and create and allow the data all these different data sources if you can imagine cars you can imagine cell phones or smart meters on buildings allow them to find one another and collaborate on data science problems like a computational mesh so that we can bring hundreds thousands millions of microprocessors to bear on the data where it lives without moving it around and our theory is not only is that simpler for everyone because the data doesn't have to move around but we can actually bring more computation to bear because every one of those data sources has compute and has persistence and you can multiply the the opportunities right and you took a chance you ran a live demo which is you know always risky business at any anything but but there was a really interesting because concepts that you highlighted kind of organically forming adapting constellation right of these of these sources and the example you use they were solar panels but for them to do this kind of automatically if you will as opposed to someone going in and scripting and building the structure because tomorrow as you demonstrated in your demo you might want to add more or add more so exactly that dynamic functions are pretty pretty interesting yeah and it's a very powerful concept and a very necessary concept and the reason it's so necessary is these devices could be anywhere right and you could have most your devices in New York but a few of them in the Yukon or Alaska or something and you don't want them to all be equally connected right so it's important to be sensitive to create this network that is sort of geospatially aware and connectivity aware not not just sort of hard-coded you know so that so one aspect of that is to be sensitive to network latency and topology that's one reason why it has to be automatic the other reason has to be automatic is if you really want this to scale to thousands of devices you can't have some programmer trying to figure out who connects to what right it's just too hard right so making it really adaptive and automatic is super important another thing that's really important for the Internet of Things is depending on the on the circumstance but if you can imagine cell phones for example you can have a network of thousands millions of phones but at any point in time somebody some of those funds are gonna be turned off so the network has to be adaptive to the possibility that devices go offline right are there intentionally like a phone perhaps unintentionally because they break you know if you have a device on a smart meter it may simply break and then that particular device is offline for a period of time right so the network has to be resilient to that and that's part of what we've been building in particular using technology that we incubated in our UK labs in Hursley so it's it's been a great collaboration across IBM this is not just you know one you know one set of people in one lab but actually a corporate collaboration and really our goal is to make this as you say automatic but I would I would say beyond automatic to make it resilient right there's got to be resilient and fault tolerant because the complexities that we could be dealing with are just too large for human being to deal with right and clearly and distributed right that's the big thing guys we're leveraging IBM bluemix cloud you know all this stuff doesn't happen with with cloud capabilities and the demo you did here you were here the data center was concerned San Jose and the actual data elements were in in Toronto so just you know Amazon and Microsoft and Google are always you know get talked about a lot it within the cloud space but really iBM is making major players and it if not in that top three certainly right there in the fourth position as a leader in cloud and then what this cloud enables and then really kind of with the whole cognitive push you know that's a priority for Ginni and the team to really bring more intelligence he's exactly right and what data confluence you know what we're hoping not only to tap in to data science on distributed systems for IOT and also for enterprise use cases as well but really to take it to the next level of hybrid cloud because these data sources could be in the cloud and they could be on-premises they could be anywhere in the world and you can mix and match and that's really a very powerful capability for our customers many companies now struggling as their data is now part cloud and part on-premises right and in the compute as well right you could deal shift exactly compute from the edge to the cloud you know a dynamic fashion based on what the kind of optimal solution is or as you said sometimes over the edges off lined and you can't do it there it's exactly right so kind of a cool story you said this came out of a out of something called blue unicorn what is blue you know fantastic so blue unicorn was an initiative that a few of us got together on inside of IBM you probably know some of these folks Rob Thomas so I think you've interviewed gears from Karachay Leah and myself and the three of us got together and we said you know we want to find a more effective way to tap in to the creative juices of our staff we got some of the greatest minds in the world working at IBM we hire brilliant people PhDs masters of the top schools all over the world and all too often we hire these people and we tell them what they should be working on that wouldn't it be better if we could find a repeatable process for them to come to us and say here's the next big innovation that IBM ssin should have and blue unicorn came out of that desire to tap into and and nurture this creative passion of of our staff and was really designed almost like an internal VC initiative so people would would come to us with proposals and we've got those proposals we start out with hundreds and feted it down to dozens that down to just a small few that we would fund from the ones that we funded you know that would go through periodic reviews until eventually we ended up with a very small set that are still being incubated and and did a confluence happen to have been one of those projects awesome so it's different than kind of the 10% thing this is actually almost like an internal you you put your proposal together you pitch it whereas if it was an internal VC you get funded and then you go do that with your team right one thing I would say is one of the you know as we're setting up we're trying to find ways to make it work make it efficient one of the best filtering factors that we came up with is that people had to show us running code before it was funded right right and that was amazing because that meant people had to work nights and weekends they had to have that level of passion and commitment for their idea to get to that level of vetting and that was incredible that that definitely filtered the people who were super passionate about what they were doing and the people just said yeah I'd like to tinker and that was tremendous okay and then you're here at the show melting a small show tight group kind of multi industry any good takeaway surprises from the last couple days here at the chief data science USA show you know it's been an amazing conference actually and some great speakers some great insights I think one of the most useful insights for me was was I was curious to hear from this audience what is the duration of data that is important to them do they need to see data from the last hour the last month the last year the last 10 years and of course it does vary from problem to problem but many people said you know for the work that I do I need about three months to build a model and then once I have a model I'm really looking at the last two to four weeks of data to gain data science insight and that was a very important point for me especially as we continue on our work on analytics data science and IBM it's very important for us to understand the range of data that that people are using shorter than you seem sure yeah it's shorter because I know certainly in the data warehousing space that I've been working a lot of my career in people do data analytics on you know six months a year or three years right so this is this is it definitely is somewhat of a shift and it tells us something about our society that things are moving faster and the idea that's older than six months is is usually not as interesting anymore yeah really shows kind of the dynamic real-time nature it's not this is analyzing just the old stuff is interesting but not nearly as interesting as being on top of where's the spark stream somebody's other thing is funny Beth Comstock kicked off the GU minds and machines event a couple days ago she said we even walk faster in cities they've done so everything is continuing to speed up right all right so you're from now you're back here what are we gonna be talking about Wow okay well you know we just launched a few months or a few weeks ago actually the the Watson Data Platform a huge event for us and it really is for us the foundation the data foundation of all the cognitive computing that we're that IBM is coming out with it's gonna bring together data science and data storage and collaboration across you know amongst analysts and data scientists together all all one platform for all your data needs I'm hoping that a year from now I'm going to speak to you about how data confluence is a core part of that of that platform and we're gonna be raeng analytics on millions of devices all over the world all right Sam well thanks for taking a few minutes I know you gotta go catch an airplane for stopping by and sharing your insight thank you all right Sam lights on I'm Jeff Creek you're watching the cube thanks for watching

>> Announcer: Live from San Francisco it's The Cube covering Oracle OpenWorld 2016 brought to you by Oracle. Now here's your host, John Furrier and Peter Burris. >> Hey welcome back everyone. We are live in San Francisco at Oracle OpenWorld 2016. This is SiliconANGLE, the key of our flagship program. We go out to the events, extract a signal from the noise. I'm John Furrier, Co-CEO of SiliconANGLE with Peter Burris, head of Research at SiliconANGLE as well as the General Manager of Wikibon Research, our next guest is Siddhartha Agarwal, Vice-President of Product Management and Strategy of Oracle Cloud Platform. Welcome back to the Cube, good to see you. >> Yes, hi John. Great to be here. >> So I've seen a lot of great stuff. The core messaging from the corporate headquarters Cloud Cloud Cloud, but there's so much stuff going on in Oracle on all the applications. We've had many great conversations around the different, kind of, how the price are all fitting into the cloud model. But Peter and I were talking yesterday in our wrap-up about, we're the developers. >> Siddhartha: Yeah. >> Now and someone made a joke, oh they're at JavaOne, which is great. A lot of them are at JavaOne, but there's a huge developer opportunity within the Oracle core ecosystem because Cloud is very developer friendly. Devops, agile, cloud-native environments really cater to, really, software developers. >> Yeah, absolutely and that's a big focus area for us because we want to get developers excited about the ability to build the next generation of applications on the Oracle Cloud. Cloud-native applications, microservices-based applications and having that environment be open with choice of programming languages, open in terms of choice of which databases they want, not just Oracle database. NoSQL, MySQL, other databases and then choice of the computeship that you're using. Containers, bare metal, virtual environments and an open standard. So it's giving a very open, modern easy platform for developers so that they'll build on our platform. >> You know, one of the things that we always talk about at events is when we talk to companies really trying to win the hearts and minds of developers. You always hear, we're going to win the developers. They're like an object, like you don't really win developers. Developers are very fickle but very loyal if you can align with what they're trying to do. >> Siddartha: Yeah. >> And they'll reject hardcore tactics of selling and lock-in so that's a concern. It's a psychology of the developers. They want cool but they want relevance and they want to align with their goals. How do you see that 'cause I think Oracle is a great ecosystem for a developer. How do you manage that psychology 'cause Oracle has traditionally been an enterprise software company, so software's great but... Amazon has a good lead on the developers right now. You know, look at the end of the day you have to get developers realizing that they can build excellent, fun creative applications to create differentiation for their organizations, right, and do it fast with cool technologies. So we're giving them, for example, not just the ability to build with Java EE but now they can build in Java SE with Tomcat, they can build with Node, they can build with PHP and soon they'll be able to do it with Ruby and Daikon. And we're giving that in a container-based platform where they don't necessarily have to manage the container. They get automatic scalability, they get back up batching, all of that stuff taken care of for them. Also, you know, being able to build rich, mobile applications, that's really important for them. So how they can build mobile applications using Ionic, Angular, whatever JavaScript framework they want, but on the back end they have to be able to connect these mobile apps to the enterprise. They have to get location-based inside and to where the person is who's using the mobile app. They need to be able to get inside and tell how the mobile app's been used, and you've heard Larry talk about the Chatbot platform, right? How do you engage with customers in a different way through Facebook Messenger? So those are some of the new technologies that we're making very easily available and then at the end of the day we're giving them choice of databases so it's not just Oracle database that you get up and running in the Cloud and it's provision managed, automated for you. But now you can ask for NoSQL databases. You can have Cassandra, MongoDB run on our IaaS and MySQL. We just announced MySQL enterprise edition available as a service in the Public Cloud. >> Yeah one of the things that developers love, you know, being an ex-developer myself in the old days, is, and we've talked to them... They're very loyal but they're very pragmatic and they're engineers, basically they're software engineers. They love tools, great tools that work, they want support, but they want distribution of their product that they create, they're creators, so distribution ultimately means modernization but developers don't harp too much on money-making although they'd want to make money. They don't want to be abandoned on those three areas. They don't want to be disloyal. They want to be loyal, they want support and they want to have distribution. What does Oracle bring to the table to address those three things? >> Yeah, they're a few ways in which we're thinking of helping developers with distributions. For example, one is, developers are building applications that they exposing their APIs and they want to be able to monetize those APIs because they are exposing business process and a logic from their organization as APIs so we're giving them the ability to have portals where they can expose their APIs and monetize the APIs. The other thing is we've also got the Oracle Cloud Marketplace where developers can put their stuff on Oracle Cloud Marketplace so others can be leveraging that content and they're getting paid for that. >> How does that work? Do they plug it into the pass layer? How does the marketplace fit in if I'm a developer? >> Sure, the marketplace is a catalog, right, and you can put your stuff on the catalog. Then when you want to drag and drop something, you drop it onto Oracle PaaS or onto Oracle IaaS. So you're taking the application that you've built and then you got it to have something that-- >> John: So composing a solution on the fly of your customer? >> Well, yeah exactly, just pulling a pre-composed solution that a developer had built and being able to drop it onto the Oracle PaaS and IaaS platform. >> So the developer gets a customer and they get paid for that through the catalog? >> Yes, yes, yes and it's also better for customers, right? They're getting all sorts of capability pre-built for them, available for them, ready for them. >> So one of the things that's come up, and we've heard it, it was really amplified too much but we saw it and it got some play. In developer communities, the messaging on the containers and microservers as you mentioned earlier. Huge deal right now. They love that ability to have the containerization. We even heard containers driving down into the IaaS area, so with the network virtualization stuff going on, so how is that going to help developers? What confidence will you share to developers that you guys are backing the container standards-- >> Siddhartha: Absolutely. >> Driving that, participating in that. >> Well I think there are a couple of things. First of all, containers are not that easy in terms of when you have to orchestrate under the containers, you have to register these containers. Today the technology is for containers to be managed, the orchestration technology which is things like Swarm, Kubernetes, MISO, et cetera. They're changing very rapidly and then in order to use these technologies, you have to have a scheduler and things like that. So there's a stack of three or four, relatively recent technologies, changing at a relatively fast pace and that creates a very unstable stack for someone who create production level stuff for them, right? The docker container that they built actually run from this slightly shaky stack. >> Like Kubernetes or what not. >> Yeah yeah and so what we've done is we're saying, look, we're giving you container as a service so if you've already created docker containers, you can now bring those containers as is to the Oracle Public Cloud. You can take this application, these 20 containers and then from that point on we've taken care of putting the containers out, scaling the containers up, registering the containers, managing the containers for you, so you're just being able to use that environment as a developer. And if you want to use the PaaS, that's that IaaS. If you want to use the PaaS, then the PhP node, JavaSE capability that I told you was also containerized. You're just not exposed to docker there. Actually, I know he's got a question, but I want to just point out Juan Loaiza, who was on Monday, he pointed out the JSON aspect of the database was I thought was pretty compelling. From a developer's standpoing, JSON's very really popular with managing APIs. So having that in the database is really kind of a good thing so people should check out that interview. >> Very quickly, one of the historical norm for developers is you start with a data model and then you take various types of tools and you build code that operates against that development for that basic data model. And Oracle obviously has, that's a big part of what your business has historically been. As you move forward, as we start looking at big data and the enormous investment that businesses are making in trying to understand how to utilize that technology, it's not going as well as a lot folks might've thought it would in part because the developer community hasn't fully engaged how to generate value out of those basic stacks of technology. How is Oracle, who has obviously a leadership position in database and is now re-committing itself to some of these new big data technologies, how're you going to differentially, or do you anticipate differentially presenting that to developers so they can do more with big data-like technologies? >> They're a few things that we've done, wonderful question. First of all, just creating the Hadoop cluster, managing the Hadoop cluster, scaling out the Hadoop cluster requires a lot of effort. So we're giving you big data as a service where you don't have to worry about that underlying infrastructure. The next problem is how do you get data into the data lake, and the data has been generated at tremendous volume. You think about internet of things, you think about devices, et cetera. They're generating data at tremendous volume. We're giving you the ability to actually be able to use a streaming, Kafka, Sparc-based serviced to be able to bring data in or to use Oracle data intergration to be able to stream data in from, let's say, something happening on the Oracle database into your big data hub. So it's giving you very easy ways to get your data into the data hub and being able to do that with HDFS, with Hive, whichever target system you want to use. Then on top of that data, the next challenge is what do you visualize, right? I mean, you've got all this data together but a very small percentage is actually giving you insight. So how do you look at this and find that needle in the haystack? So for that we've given you the ability to do analytics with the BI Cloud service to get inside into the data where we're actually doing machine learning. And we're getting inside from the data and presenting those data sets to the most relevant to the most insightful by giving you some smart insights upfront and by giving you visualizations. So for example, you search for, in all these forms, what are the users says as they entered in the data. The best way to present that is by a tag cloud. So giving you visualization that makes sense, so you can do rich discovery and get rich insight from BI Cloud service and the data visualization cloud service. Lastly, if you have, let's say, five years of data on an air conditioner and the product manager's trying to get inside into that data saying, hey what should I fix so that that doesn't happen next time around. We're giving you the big data discovery cloud service where you don't have to set up that data lab, you don't have to set up the models, et cetera. You could just say replicate two billing rows, we'll replicate it in the cloud for you within our data store and you can start getting insight from it. >> So how are developers going to start using these tools 'cause it's clear that data scientists can use it, it's clear that people that have more of analytic's background can use it. How're developers going to start grabbing a lot of these capabilities, especially with machine learning and AI and some of the other things on the horizon? And how do you guys anticipate you're going to present this stuff to a developer community so that they can, again, start creating more value for the business? Is that something that's on the horizon? >> You know it's here, it's not on the horizon, it's here. We're helping developers, for example, build a microservice that wants to get data from a treadmill that one of the customers is running on, right? We're trying to get data from one of the customers on the treadmills. Well the developer now creates a microservice where the data from the treadmill has been ingested into a data lake. We've made it very easy for them to ingest into the data lake and then that microservice will be able to very easily access the data, expose only the portion of the data that's interesting. For example, the developer wants to create a very rich mobile app that presents the customer running with all the insight into the average daily calorie burn and what they're doing, et cetera. Now they can take that data, do analytics on it and very easily be able to present it in the mobile platform without having to work through all the plumbing of the data lake, of the ingestion, of the visualization, of the mobile piece, of the integration of the backend system. All of that is being provided so developers can really plug and play and have fun. >> Yeah, they want that fun. Building is the fun part, they want to have fun-- >> They want relevance, great tools and not have to worry about the infrastructure. >> John: They want distribution. They want their work to be showcased. >> Peter: That's what I mean about relevance, that's really about relevance. >> They want to work on the cool stuff and again-- >> And be relevant. >> Developers are starting to have what I call the nightclub effect. Coding is so much fun now, there's new stuff that comes out. They want to hack with the new codes. They want to play with some that fit the form factor with either a device or whatnot. >> Yeah and one other thing that we've done is, we've made the... All developers today are doing containers delivery because they need to release code really fast, right. It's no longer about months, it's about days or hours that they have to release. So we're giving a complete continuous delivery framework where people can leverage Git for their code depository, they can use Maven for continuous integration, they can use Puppet and Chef for stripping. The can manage the backlog of their task. They can do code reviews, et cetera, all done in the cloud for them. >> So lifestyles, hospitality. Taking care of developers, that's what you got to do. >> Exactly, that's a great analogy. You know all these things, they have to have these tools that they put together and what we're doing is we're saying, you don't have to worry about putting together those tools, just use them. But if you have some, you can plug in. >> Well we think, Wikibon and SiliconeANGLE, believe that there's going to be a tsunami of enterprise developers with the consumerization of IT, now meaning the Cloud, that you're going to see enterprise development, just a boom in development. You're going to see a lot more activity. Now I know it's different in development by it's not just pure Cloud need, it's some Legacy, but it's going to be a boom so we think you guys are very set up for that. Certainly with the products, so my final question for you Siddhartha is, what's your plans? I mean, sounds great. What're you going to do about it? Is there a venture happening? How're you guys going to develop this opportunity? What're you guys going to do? >> So the product sets are already there but we're evolving those products sets to a significant pace. So first of all, you can go to cloud.oracle.com/tryit and try these cloud services and build the applications on it, that's there. We've got a portal called developer.oracle.com where you can get resources on, for example, I'm a JavaScript developer. What's everything that Oracle's doing to help JavaScript developers? I'm a MySQL developer. what's everyone doing to help with that? So they've got that. Then starting at the beginning of next year, we're going to roll out a set of workshops that happen in many cities around the world where we go work with developers, hands on, and getting them inside an experience of how to build these rich, cloud-native, microservices-based applications. So those are some of the things and then our advocacy program. We already have the ACE Program, the ACE Directive Program. Working with that program to really make it a very vibrant, energetic ecosystem that is helping, building a sort of sample codes and building expert knowledge around how the Oracle environment can be used to build really cool microservices-based, cloud-native-- >> So you're investing, you're investing. >> Siddhartha: Oh absolutely. >> Any big events, you're just more little events, any big events, any developer events you guys going to do? >> So we'll be doing these workshops and we'll be sponsoring a bunch non-Oracle developer events and then we'll be launching a big developer event of our own. >> Great, so final question. What's in it for the developer? If I'm a developer, what's in it for me? Hey I love Oracle, thanks for spending the money and investing in this. What's in it for me? Why, why should I give you a look? >> Because you can do it faster with higher quality. So that microservices application that I was talking about, if you went to any other cloud and tried to build that microservices-based application that got data from the treadmill into a data lake using IoT and the analytics integration with backend applications, it would've taken you a lot longer. You can get going in the language of your choice using the database of your choice, using standards of your choice and have no lock-in. You can take your data out, you can take your code out whenever you want. So do it faster with openness. >> Siddhartha, thanks for sharing that developer update. We were talking about it yesterday. Our prayers were answered. (laughing) You came on The Cube. We were like, where is the developer action? I mean we see that JavaOne, we love Java, certainly JavaScript is awesome and a lot of good stuff going on. Thanks for sharing and congratulations on the investments and to continuing bringing developer goodness out there. >> Thank you, John. >> This The Cube, we're sharing that data with you and we're going to bring more signal from the noise here after this short break. You're watching The Cube. (electronic beat)

>> Narrator: Live, from San Francisco. It's the CUBE. Covering Oracle Open World 2016. Brought to you by Oracle. Now, here's your host: John Furrier and Peter Burris. (Music) (Background Noise) >> Okay, welcome back everyone. We are here, live at Oracle OpenWorld 2016. This is SiliconANGLE Media, it's The CUBE. Our flagship program, we go out to the events and extract the signal from the noise. I'm John Furrier, the co-CEO of SiliconANGLE, with Peter Burris, head of research for SiliconANGLE Media, as well as General Manager of Wikibon Research. Our next guest, I'm excited to have him back because he's a product guy and we love to go deep into the products. CUBE alumni, Juan Loaiza Senior Vice President of Database Technologies, veteran of Oracle, welcome back to The CUBE. Great to see you. >> Thanks, great to be here. >> Love talking to the product guys on the development side because we get to go deep into the road map. And we're going to try to get as much information out of you as possible. But you'll do your best to hold back, like you did last year. Only kidding. >> I know. (laughter) >> Okay no. >> You must have me confused with somebody else. (laughter) >> Maybe that was Larry Ellison, well he hasn't been on yet. Larry, we'll get you on. >> He's not so good at holding back either. (laughter) >> That's why we don't let him on. That's why they won't let him on, I think. That's, Larry would be too comfortable in The CUBE. No, in all seriousness, joking aside, the hottest areas right now is in your wheel house. Engineered systems, which is going to be a real enabler for Oracle on the performance side. And as you make your own chips, ZF SPARC and Exodeum All this other cool stuff is going to go faster, faster, faster, lower cost, higher performance. The database... >> Better security. Better availability. >> Security, I mean. Amazing stuff. But the database is where the crown jewel is for Oracle, always has been. Before you put Web Logic on it, make it sticky. But now you've got the cloud. The cloud is a environment for great opportunity for the database, business and other databases, some Oracle, some not Oracle. What's going on with the database and the Cloud? Can you take a minute to explain the current situation? >> Yeah, so that's a big question. (laughter) What's going on? So what do you want to start with database or do you want to start out with the Cloud? >> John: Let's start with the database. What's going on with the database? And what does that mean for customers as it moves to the cloud? >> Yeah. So, database we're in the process of releasing our next big database. We don't release databases very often. It only really happens every few years. It's a very big deal. So, what we're trying to do with our next generation database is modernize the whole infrastructure, adjust to a lot of the big transformations that are happening in the marketplace. So among those are things like big data. Where do we go with big data? So, with our new generation database we're making big database and database work seamlessly together. So we have something called big data SOQL, where we can query data regardless of whether it's in Hadoop, NoSQL, Oracle. It's completely transparent. So customers no longer have these silos of information. Another big thing in database is datatype search engine. So new generation wanted JSON, it's called JSON, which is a new data format, so it's used in javascripts. So web developers develop in javascript. They represent data in JSON. And then they say, hey. I don't want to take my JSON data and convert it to relational data. It's a big pain. >> John: True. So, one of the things we've done in our new generation 12-step database was say, hey. Take that JSON, we'll put that directly into the database. We'll allow it to be queried. We'll make it highly available >> John: Without a schema. Without any kind of a schema, >> Nothing. >> just throw it in there unstructured. >> Juan: Just throw it in there. That's right. So we've made it very simple for new-age developers to use JSON with databases. That's another really big thing that's happening. >> So tell us what, just let's double down on that for a second. JSON has been a big trend in API based systems, lot of abilities in JSON endpoints. For user experience, whether it's mobile or web, very prevalent now. Pretty much standard. >> Juan: Yes. >> How does that get rendered itself from a customer's perspective? Are you saying that Oracle will just onboard it into the database itself? Or is it a separate product? Or is it, I mean... >> - [Juan] Directly into the data. So we have native JSON directly into the data. We've essentially added JSON as a datatype. We've added the sequel, we have SOQL extensions. You can access JSON like an index... >> John: So, I can run in single queries on JSON? >> You can, exactly right. You can very simply run SOQL queries on JSON. >> And what's the impact to the customer? >> Juan: And all the stuff that comes with that. >> John: And what does that solve? What problem does that solve? >> It solves two problems. One is, people like that datatype. So new-age developers, they're writing in javascript. They have JSON and they just want to use it. So they don't have to convert it. >> John: Which by the way, everyone's running in javascripts. >> Right, that's right. That's the big programming language. And the other thing is unstructured data. So, data that's not structured initially, that every piece of data has its own structure. So it's a representation for saying, that dynamic, unstructured representation that's very standard in the industry. A little bit like XML used to be before. JSON is kind of the new XML, the new-age XML. >> John: Yeah, that's true. How about the data lay concept? Because Hadoop as a market, just didn't make it, right? I mean, it's out, Hadoop is out there >> Juan: Yes. SPARC is certainly relevant because you have, you know, that kind of use space and memory and faster processing. But the real power is that that a batch oriented data set. As things like Hadoop and SPARC evolve, how does that relate to Oracle's product road map? >> Juan: Yeah, so we have our own Hadoop big data plans, where we run a cloudera-based Hadoop product and what we're trying to do is make those work seamlessly with existing databases. So there's certain kinds of workload and applications that hadoop is really good for. You know, kind of a frivolous example is if you want to find cats in pictures, you're not going to do that with an Oracle database. So you know, here's a billion pictures. Find all the pictures that contain cats. Not a good application for Oracle, right? On the other hand, if you're running analytic queries against relational data that's perfect for Oracle. So we see that these technologies can coexist. So there's certain kinds of applications that are really good for that dual kind of work. Or that certain kind of applications are really good for relational. And what we need to do is make sure that these things run seamlessly. >> John: What's the glue between those two layers? >> Peter: Well that's just it, there's even more applications where they're going to want to use both. >> That's right. That's right. We can't, >> So, what's the glue? >> Eventually everyone goes to both, right? >> Peter: Yeah, so what's the glue? What is that glue? >> Well, there's a number of glues that we built. Which is, one is called big data SOQL. It lets you query seamlessly across them. We also have connectors that let you move data seamlessly between them. So, those are kind of the main glues between them. >> So one of the things that we've observed is that, to John's point, there's been a lot more downloads of hadoop than we've seen go into production. It's become a very, very complex ecosystem and it's got some limitations, batch-oriented, et cetera. The challenge that businesses have is that they try to run pilots around hadoop, because they find themselves piloting the hardware, hadoop, the clusters, all the way up to the use case. And a lot of times they end up failing. How does something like the big data pliers facilitate piloting? Because it looks like it should reduce the complexity of the infrastructure and give people the opportunity to spend more time on the use case. >> I mean, you've got it exactly right. Which is, you know, there's some people that are hobbyists. Right, like there's people that want to build their own log cabins. They want to cut their own trees, kind of build their own planks and put together their log cabin. And that's kind of how hadoop started. It was kind of a hobbyist model, right? And hadoop has kind of moved to the next level. Now, it's people that want to get stuff done. And it's like, I don't want to chop trees. You know, I want to be living in a, just give me a house. >> John: Well actually, I wouldn't say hobbyist. I mean Yahoo had a need, they needed log cabins. >> Right. >> So they built one. You know, but it was a use case. The web scaler guys needed an unstructured... >> Right. >> It has to be scalable. >> But a lot of people are very much, kind of thinking build your own. So now a lot of people want a solution. They're like, you know, I don't want to be building this. So that's where big data plans come in. Because it's a complete solution. It includes the hardware, it's been pre-tubed, pre-optimized. It includes the cloudera software. It includes all our connectors and it includes support for the whole thing. Because that's the other part. You know when you put together your own house, who are you going to call when it leaks? Right? You're on your own when it leaks. If Oracle puts it together, we can support the entire staff when you have any kind of issue, any kind of problem. And that's the kind of stuff enterprises want. It's not a hobby anymore once it becomes an enterprise >> Peter: So given that we're in a big data universe right now, where we've got use case that are proliferating very fast and we have limited experience about them. But the technologies underlying that we're deploying to build those use cases are also proliferating very fast. Is it going to be possible for the open source model that presumes downloads, try buy, not sales people, not a lot of learning, not a lot of hand-holding to make it possible to fix that whole thing or make it all come together? Or is a company like Oracle going to have to step in and take some responsibility for guiding how the market evolves? >> Yes, so open source and Oracle can work together. I mean, we have Lennox distributions. We own MYSOQL. So Oracle and Open Source is... >> Peter: You're not at odds. >> That's right. We, in fact, are one of the major Open Source companies in the world. But you know, like I said, real businesses are in it as a hobby. They want a solution. They're looking at this as a tool. And a lot of times they want somebody that can support it, that can physically assure that it's going to work for them. And they have someone they can call. It's not just hey, I'm going to post a message on a message board and hope that somebody responds. Right? I mean when you have, you know, airplanes in the air. when you have, you know, dollars flying across the network. You need a solution. You need somebody you can call and you can guarantee is going to solve the problem. And also that can ensure that the technology moves in the right direction, takes into account what users want. And that, you know, a certain level of quality and assurance is built into it. >> Peter: So let's build on that. When you look at the future of database, what do you see? >> Juan: Well, there's a lot of different, so database is in a very big change. There's some big changes happening in the database world right now. More than probably ever before. One that we've been kind of talking about is sort of this big data hadoop. Another thing is JSON. Another area is in-memory is a very big change that is happening in databases. The whole moving into in-memory, into these different kinds of formats. Along with that, Oracle is pioneering moving database algorithms directly into the chips. The chip technology, to make it run dramatically faster, to make it more available, make it more secure. That's another big thing. Building multitenancy directly into the database, that's another big area that Oracle is pioneering. Instead of having it, kind of cloudify the database directly, negatively inside the database. Another big area that we've been working on is putting native sharding of databases directly into the database. >> How about data protection? >> Well that's in the multitenancy, right? Take me through the multitenancy a little bit. How does multitenancy inside the database going to work? >> Well, okay. So that's what we call our multitenant database. It's a little bit like VM. So, Vms say, hey it looks like I have a physical machine. But in fact I have a fraction of a machine. It looks like, it looks to me like a physical machine. In fact, it's a virtual machine. >> Peter: Okay. >> We're doing the same kind of thing with the database. So it looks like I have a physical database to the application. But in fact, you're sharing a database among many users. So what is the advantage of that? The advantage of that is we don't have one database. Or thousands of databases anymore. So many of our customers have deployed thousands of databases. It becomes a huge maintenance headache to have thousands of databases. Especially in today's security world where you have to constantly patch and update these things. You can't just kind of leave them alone anymore. So if you have a small number of physical databases and lots of virtual databases it completely saves costs. It's more agile. Opex lower. Capex lower. That's the new world of multitenant cloud data. >> John: Also it's brand new with appliances. And I want to get your thoughts on last year the big range that I liked was this zero data loss >> Recovery plan, yes >> ZDLRA. >> Juan: That's right. You got it right. >> What's the, I mean very fascinating, basically zero data loss. >> Peter: It's cool technology. >> Juan: Yes. So what is, is that still on the, out there? What's going on with that? >> Zero data loss and recovery parts is our fastest growing appliance right now. >> John: It is? >> Yes. Easily. It's been very well received by the market. We have some of the biggest banks now, running it. Financial institutions, retailers. Why? Because its a very simple value proposition. Which is, hey I want to protect my data in a way that it's constantly protected that I don't lose any data. In a way that is scalable. In a way that offloads my production database. It's a very simple... >> That's a grace saving situation, right? So like the people that have these security breaches, is this where that fits? Where's the use case for ZDLRA? >> ZDLRA is not security, it's about availability. >> John: Okay, so if someone basically shuts the data center down. >> Right. If that database becomes corrupted... >> John: In one region. >> If there's some natural disaster. If there's a bomb. If there's a whatever. Is my data protected? Will I lose anything? Nobody can afford to lose data anymore. In the old days, when you did a backup, you did a nightly backup and then if something happened, then you'd restore it. Well guess what? That doesn't work anymore. We're too dependent. So, nobody wants to lose their airline records. Nobody wants to lose their bank records. Nobody wants to lose their retail records. We can't afford to lose data anymore. We need a solution that's zero data loss. >> I'm surprised aren't, there's not more fanfare at the show about that. I was really impressed last year I'm glad to hear it's doing well. Containers. Database containers. >> Juan: Yes. This is something that we talked about a little bit last time. >> Juan: That's the same as multitenants. >> Okay. That's multitenancy. >> Juan: It's different terminology for that. >> okay, now cloud based databases. Now we get to the cloud. Where does all this go to the cloud. >> Okay, so you know traditionally customers deployed on premeses. what we're doing now is we're taking the Oracle database that we've developed the last 40 years. It's the most sophisticated database in the world. And we're moving it onto the cloud. So what does the customer get? They get, they can provision it instantly. So you go onto our website and say I want a database. Here's the size. Here's the number of CPUs I want. Boom. They get it. They pay monthly instead of paying upfront. They don't pay for the licenses. They just pay us a monthly fee. And then Oracle operates the whole thing. It's like, I don't want manage it. I just want to use it. So that's the benefit of the cloud. I go somewhere. I need a database. I get it right away. I don't have to mess with it. And I pay monthly. >> John: So the Oracle, on your Oracle cloud you would then deploy all those other goodness, ZDLRA, all the other technology >> Juan: All that stuff, yes. (crosstalk) behind the curtain, so to speak. >> Juan: So we have a range of offerings in our cloud. So we have a regular database service. We have an enterprise service. And then we have a high end service, an exit data cloud service, right? >> That runs our exit data. Super fast, super available. And then we have something called exit data express, which is the lowest cost cloud database in the world. So we have kind of three things, depending on what the customer wants. They want a smaller database for really low cost. They want a super mission critical, high performance database or they kind of want something in the middle. So we span the whole range. And, by the way, our high end is higher than anybody else. Our low end is lower cost than anybody else. So we span a bigger range than anyone else. >> You know Juan, next year we need to get an hour with you. >> Juan: Yes. >> To cover all the... >> Juan: It's a lot of topics. >> No. You're a great guest. And you have a lot of experience and a lot of, and we appreciate the insight. I'll give you the final word, I want to get one more answer out of you because you're awesome. You're sharing great insight. For the folks watching, what's the one thing or one or two, three things they should know about Oracle, Cloud, the technology, the database? The things going on at Oracle that they may not be hearing about it could be the best selling things. Something that's not on the main stream press reporting. >> Well, you know our Oracle cloud is pretty simple. I mean, the main thing to understand is that it's 100% compatible with databases on premises. So it's very easy to move workloads back and forth. That's the main thing. And the other thing is, we are, we use the exact same infrastructure. So we've been developing, for example, our exit data product, which is kind of the precursor to cloud. It's a very specialized database system run on premises. And now we're running that in the cloud. So again, the customer can get the exact same thing. And our latest offering is cloud at customer. So we take those same cloud attributes and we can put them >> John: It's the cloud machine, right? >> inside the customer database. >> Juan: Yeah, so we have a cloud machine, an exelated cloud machine, and a big data cloud machine. >> John: So customers get all the choices of Oracle. >> That's right. So the customer has full choice, they can move to the cloud if and when they want at the speed they want. They can move back and forth. They can do disaster recovery in the cloud. They can do backup in the cloud. They can do development in the cloud. So all these range of offerings, all these range of choices are now the customers. >> So true or false? Larry Ellison is the master at the long game? >> Juan: Larry thinks long term, absolutely. >> John: Of course, true. >> Yes, absolutely. He's brilliant and he's shown it over and over again. >> I agree, big fan. Yesterday's key note, Larry could've done better. But he was too busy getting all those announcements out that he was mailing in at the end. It was so many announcements. >> Juan: It's hard these days because Oracle, there's so much happening at Oracle. There's so much happening at Oracle. Juan, Thanks so much for spending your valuable time with us at the CUBE, we really appreciate it. This is SiliconANGLE Media's The CUBE. We go out to the events I'm John Furrier, Juan Loaiza Senior Vice President Juan Laoiza, Senior Vice President of Database Platform Services. Live in San Francisco. We'll be right back. (Music)