(upbeat music) >> Hi everybody, welcome back to London. I'm Dave Vellante and you're watching theCUBE. We go out to the events, we extract the signal from the noise and we've been following AWS generally and the public sector specifically for a number of years now. We've seen the ascendancy of an expansion of public sector. We've covered the career of Teresa Carlson, and we're here in London ahead of AWS Summit London. There's a pre-day here, there's a number of public sector companies, there's a focus on healthcare. Kieron James is here, he's the founder of Wonderful.org. Wonderful.org is a fundraising vehicle, it's a really setup platform essentially for self-service. Kieron, welcome to theCUBE, thanks for coming up. >> Hello. >> So, tell me about Wonderful, why you started this organization? >> Wonderful was kicked off when I got to my 50th birthday, essentially, it's a way to give back. I've been involved in the tech sector for many years and we were sitting on quite a lot of infrastructure. We thought we had some spec paucity as well and we thought what we can do with the resource, human and physical, in terms of giving something back to charities. So, one of the things that looked like a great opportunity was to setup a completely fee-free fundraising platform. And essentially, that's what we kicked off with a brief of concept in 2016. >> So, fee-free meaning I can come in, I can setup my own fundraising vehicle so all the money goes to the recipients. >> A 100%, we have no charges whatsoever to charities, to donors, to fundraisers. And essentially, all the card processing fees as well are covered, and through the generosity of AWS and its NPO program, we've been able to also cover things like hosting as well which has been phenomenal for us, 'cause it really does enable us to give every single penny to charity. >> So, how do you fund your staff? >> The staff currently on our model going forward, if it's one that we continue, if we can continue to support is through secondment. So, we seconded our technical resource from my day job which is essentially running a telecoms business, and those guys are incredibly generous with their time. So, evenings and weekends have been devoted to setting up and maintaining the platform. We've called in favors from people we have networked with over the years. So again, when we moved beyond proof of concept into the current website now, the current build, we were able to get that done with some cost but albeit, a fraction of what we would've paid commercially. And essentially as we move forward, we want the whole concept to Wonderful.org to be something much bigger than just the organization. It's a vehicle for commercial organizations to do good. >> So, lots of in-kind contributions, lots of your time obviously so, when did you start the organization? >> 2016 and essentially, we went through what I describe as a proof of concept. We set three broad milestones, one was the first 100 charities onboard, first 100,000 pounds of a revenue or charity not really revenue but charity donations through the website. And we launched our first Wonderful week where we brought some sports celebrities including Phil Neville, now the manager of the England women's football team. He came on board to do some charity work for us with his family. Once we passed through those three milestones, it was then a case of saying, okay we've achieved all of these now, let's push the button and actually do this properly in inverted commas, and that's when we looked at hosting the thing properly, looked at the commercial build and so on. >> So that those milestones were really the prove the concept. >> Yeah. >> But they're pretty substantial milestone, >> Sure. >> And you hit them pretty fast though. >> We did hit them fast but again to give you some context on that, the first 100,000 through the website probably took us I would guess between 12 and 14 months. In the last 28 days, we've processed about a quarter of a million pounds through the website. So, the growth's been phenomenal and the appetite from the charities is enormous. What's particularly interesting about our sector is that whilst the lot of the events that take place like the London Marathon and so on, are very predictable, we know exactly the date and time that people are gonna be donating. Clearly, you get events that are completely unpredictable. We've gotta be able to respond and be available for donors to give when those kinds of things happen. >> Okay so, this leads me to the conversation about your infrastructure and obviously the Cloud. When you started the organization, you had your own owned premises infrastructure, correct? >> Correct. >> So take us through what that looked like and your decision to move to the Cloud. >> Expensive, disjointed, very complex. So, we were running essentially a full stock on a number of servers that were hosted independently. Co-location was expensive, maintenance was expensive, even things like getting to site were expensive, and if the rare occasions when you do have to do that in a hurry it can be quite time-consuming, particularly as I say given our profile where these guys are really doing it for love not money. So, it became apparent to us, I think learning from some scenarios that we've seen in the real world with other platforms as well when even the predictable events had still created some concerns for some of the charities in terms of availability. So, we've took a long hard look at what we had and said, are we scalable, are we fully available? Probably not, we need to look at this in some detail now. So, that was when we completely re-architected the website and looked at AWS. >> So, it was not only a matter of say scaling up for high demand and unpredictability but you had a fragile infrastructure. >> We did. >> And essentially, (chuckles) you're volunteers trying to keep it together. >> Exactly. >> So that's not a good formula for high availability, right? >> No, absolutely not. >> So, how does that change with the Cloud? >> With the Cloud, I think what we've got now is we've got a really good view of everything. We've got a view of the whole of our infrastructure in one place, so it gives our operations director a lot more peace of mind 'cause he can see all of the resources at his disposal. I think in terms of security, it's far far better for us as well, because we can manage access to various components, available US, depending on who needs access. So, our web developers are currently remote, they're not formally part of the organization. So, we can strict access to things that we don't want 'em to have access and so on and give them full access where it's required. So, I think that's been a lot of peace of mind for the operations director. And just having that confidence in clearly a brand that's got a huge reputation and people feel immensely confident about seeing. So, for us being to put the AWS badge on the website to reinforce to our users, to our donors that we're here, we're solid, we're stable, we're not going anywhere, it's really really important. >> Anyway, you said upfront that Adobe has some skin in the game, they're providing some services, >> They are. >> Some contributions. >> Yeah. >> So, that's gotta be pretty substantial. >> Massive. >> For you guys, yeah. >> Absolutely massive. I mean in all honesty, it's second only to card processing which is a significant cost of doing our business and one which is paid for by our other corporate sponsors. It's our second biggest cost without a doubt or would be if it were a cost but mercifully, AWS has come to the rescue and we're able to do what we're doing now. >> So corporate sponsors, give a little commercial, how does that work? >> Well essentially, our biggest corporate sponsor, our main partner at the moment is The Co Operative Bank and they have underwritten all of our card processing fees for the duration of that partnership. The big caveat with that is that we don't know what they will be and whilst we can provide some forecast based on empirical evidence, worst case scenario, there's another tragedy, people reach for their wallets and give, and suddenly that can go through the roof in the course of a couple of weeks. So, the difficulty in bringing corporate sponsors on for us is just that kind of unpredictability of the sector that we're operating in, but they've been tremendous. >> That's amazing right? >> Yeah. >> 'cause I could say that's a big junk of your cost >> For sure. >> Along with your infrastructure but, I'm fascinated by this organization and just wanna congratulate you on the mission and actually getting it off the ground because we all when we give to a charity, we always ask okay, what are the administrative cost behind this? You go to the website and you look it up and sometimes you just don't feel comfortable, and so what you've done is actually just eliminated that overhead. >> Completely. >> And where do you see this going? I mean you've got like 15 hundred registered charities now. >> Yes, yeah we're up to 15 hundred, again we've had a couple of fairly major events we were endorsed by the Money Saving Expert at number one but how could they not put us at number one. (they both laugh) Would've been very odd if they hadn't, given that we're the only completely fee-free platform. That clearly creates the demand and I think that endorsement was a huge catalyst to the growth. More recently, we've seen other things, BT MyDonate actually pulled out of this sector which has caused a lot of charities to migrate to our platform as well. In terms of where we see it going, we will need to continue to raise money from corporate sponsors to support it. But, there is a real step game in that, we have to manage that growth to meet their expectations as best as we can. But equally, new corporate sponsors coming onboard will want to see that we've got enough eyeballs to make it worth their while getting behind the organization. So, it's that constant game of trying to bring on the next round of funding and getting people through. >> How global do you see this getting? How is it today and in the future? >> Conceptually, there's no reason at all why this shouldn't be a global phenomenon but, we're now very concentrated on the UK, just because of our resource and we do get requests all of the time for international charities, for international fundraisers and so on, but we've gotta be realistic about what we can support. But going back to the point that I made earlier, it really isn't about Wonderful.org, it's about just corporations, fundraisers, charities, donors, we see all of the last three being wonderful all of the time by the nature of what they do, we're just trying to get more corporations to be as wonderful as, sounds terribly sick and fancy, but as AWS has been in supporting what we're doing, it's that sense of what we're trying to achieve here goes beyond one organization. >> Well, and the Cloud allows you to scale potentially to the extent that you can get the resource. There's no reason you can't go global. >> No. >> I'm gonna check it out and see even for a little local charity, can I (he chuckles), >> Absolutely. >> Can I participate, what does that involve? Do you have to have some minimum threshold or? >> No? >> No, anybody can-- >> Anybody, but you need to be a registered UK charity with one of the UK registrars. Beyond that, we go through a little bit of due diligence with the charity, so we will need to see some documentation. So, there's a little manual step in onboarding charities, but for all the right reasons, we wanna be diligent about the people using the platform to give the fundraisers the confidence that they're donating to a charity. So, we don't do any peer-to-peer fundraising, it is literally you'll register as a charity and the fundraisers can support your charity, often led by the fundraisers rather than the charities, interestingly, so the fundraisers will be saying to the charities, why are you not on this platform which gives you everything and you're already on this platform which doesn't. So, there's quite a lot of pressure now coming from the fundraisers to pull the charities in. >> So, there's a lot of word-of-mouth, a lot of peer-to-peer. >> Absolutely. >> Right, you don't really have the funding. >> There is no. >> The budget to go market. >> Not at all. >> Yeah, that's remote. >> Absolutely not. >> Well, hopefully this will help. >> Thank you very much. >> Thanks so much for coming to theCUBE, really appreciate your time. >> Thank you. >> Alright, thank you for watching everybody. This is Dave Vellante, you're watching theCUBE. We'll be back right after this short break from AWS HQ in London, right back. (upbeat music)

(upbeat music) >> Hi everybody, welcome back to London. You're watching The Cube, and we have a special coverage here of the pre-day at AWS headquarters in London. I'm Dave Vellante and The Cube, we go out to the events, we extract the signal from the noise. Alastair Allen is here, the chief technical officer of Healthcare Kainos Software. It's a Belfast based company, publicly traded company. Alastair, welcome to The Cube. Great to see you, thanks for coming out. You were downstairs earlier addressing the audience, we're gonna talk about that. But first of all, tell us about Kainos. >> So Kainos, Belfast based company, formed in the late '80's a spin out of Queens University in Belfast. We've grown to now over 1300 people and we build digital technology to help people work faster, smarter and better. There's two things we do. We provide digital services, bespoke services for public and private sector organizations across the world, and we provide digital platforms for work day customers and also for healthcare organizations. >> So, when you say digital platforms. What exactly do you mean by that? Tell our audience. >> So, our digital platforms in healthcare is something that we can talk about. So platforms to enable both hospitals to digitize their workflow and also regions, so CCG's , STP's within the NHS. To bring information together using a platform and normalizing that data and making it available to clinicians and patients. >> And this is, your flagship product is called Evolve. Correct? >> Correct. >> And you're one of the sort of founders or inventors of Evolve. Tell us more about Evolve. >> So Evolve, originated just over ten years ago, our first customer was Ipswich Hospital and Ipswich had a big problem with paper, with a large medical records library and they asked us to come in and help them digitize that and make it available in an easy to view, accessible format for their clinicians. >> So tell me more about that. So you digitize it, you take all this mounds of paper and what does that do? Other than reduce the amounts of paper. Does it make it searchable? >> Yeah, we index the content, we apply metadata whenever we capture it, trying to make it accessible for clinicians. I think when you digitize paper , the one good thing paper had going for it was you could pick it up and it was tactile. So we've done a lot of work to try and make it mobile, make it accessible, make it searchable and increasingly now with some of the services that AWS provide, we're able to look at taking that even further and getting more information out of that content. >> Add some color to that. So how has the AWS cloud affected your ability to deliver these capabilities to your customers? >> Well I think, the breathe and depth of services that AWS provide, enables us to be able to innovate quickly, to use services like I've mentioned like comprehend medical. That take the heavy lifting, away from us and helps us focus on delivering better applications for our customers. >> So part of what you do, is you architected the software that's running on the cloud. Can you talk a little bit about the architecture? What you guys have built. Presumably the cloud allows you to scale. >> Alastair Allen: Yeah. >> And take advantage of more innovations. But discuss the architecture if you would. >> So, the product that I originally talked about in 2009 and about four years ago in 2015, we decided to re-platform for the cloud. And that was in response to a number of problems that we were seeing in the market. And moved to patient centered care, a drive to try and standardize care away from the variable nature that was there and also to get away from closed silos of information. And we decided at that point to create our platform natively in the cloud and using the services of Amazon web services. So we created a microservices based architecture that runs in multi-candidate cloud native way. With a AWS. That allows us to adopt disciplines like continuous delivery and cultures like DeVops. We've been able to release value quickly and often to our customers. >> So it was a total rewrite of the platform? >> Yes. So we started again from scratch and we developed that using the modern cloud services. And we've used that then for all use cases as well so we've moved beyond just settings within a hospital. And been able to take that beyond the walls of a hospital, out into the community, into primary care, mental health. And delivering solutions like that, across regions within the NHS, to join up information. Where before clinicians would simply not have had access to those. >> In a sense you're migrating your existing install base to the cloud based platform, as I presume it's a SAS based platform. Is that right? >> So, Evolve Integrated Care is a platform it's a SAS based platform. So we run it, we monitor it, we maintain it and we deliver that as a service to our customers. >> And so your existing customers now have an opportunity to migrate and how does that all work? >> Yeah, so we're talking to our existing customers, how they can leverage the cloud based platform and the breathe of different services that it provides. We very much see an opportunity for helping to digitize a hospital. So how do you optimize the flow of patients through a hospital and making sure that clinicians have access to the information. Many of customers have hundreds of applications, information spread across their estate, bringing that together and orchestrating the workflow for particular pathways or particular conditions. >> Plus they have to manage their own infrastructure, I presume. >> Absolutely, and we want to build applications quickly, they want to focus on delivering healthcare. They don't want to focus on managing ten and server rooms within their hospitals. So, our move to the cloud really came about because of our customers telling us that they're struggling to manage this infrastructure. They wanted us to take some of that burden away from them and to help them with some of their security challenges, availability challenges. Quite often their local infrastructure was not very resilient. And by moving to AWS, we were able to use native cloud services to address many of those challenges. >> So you're taking away that heavy lifting for them. AWS takes it away for you. >> Alastair Allen: Yeah. >> In a large regard as well. While your engineers can obviously program the infrastructure. But how have you seen the customers that have moved and taken advantage of this. What has it done for their business specifically? What's the impact? >> So, what I think, it frees up people within their organization to scale up in other areas to do other things. It frees up physical space as well in many cases. It takes away risks and we've all heard of some of the recent security incidents. Wanna Cry was a huge thing in the NHS not so long ago. Coming around from just simple things like not patching servers and work stations. So, by taking on that responsibility we're freeing up those hospital systems to focus on what they do best. >> How do they do that? Do they kind of retrain folks? What's that been like? I presume it wasn't frictionless but it's an opportunity for people to advance their careers. Do you have any visibility on how your customers have handled that? >> To be honest, not a huge amount. It has, I agree, there has been some friction there. It's not always an easy journey, there's a whole mindset change of what people used to do before and the types of activity that they'll do tomorrow. And it's something that our customers are still on a journey on. And so we're quite early on in that process. >> But I would say to folks in the IT community of your expertise's of managing storage arase, there's probably a better future for you if you can move up the stack and learn more about applications , data, machine intelligence. >> Absolutely, higher up the value chain and getting closer to the user, closer to the customer. >> I mean, that's where the difference is. And it's particularly in healthcare right? You try to balance the cost of healthcare, everybody's aware of the rising cost of healthcare with the patient outcomes. And technology is a way to address that problem. Isn't it? >> Absolutely, and I think never before. I think it's just a great time to work in health IT. We've now got access to some fantastic services the rise of artificial intelligence, the machine learning has never before been so available. And really having organizations such as ourselves to really solve those problems that our customers have and introduce those efficiencies and ultimately better patient outcomes. >> So how are you using the data that lives in Evolve, I presume you're looking at applying artificial intelligence and the like, talk about that. But also, how do you ensure security, privacy, etcetera? >> So, a couple things on data, I think one of the things we've done recently is the adoption of the FHIR standard within healthcare and all the data that we aggregate from the various clinical systems, we normalize that down into a single FHIR data profile and that really helps us then have a common data model that our application can use. But that's only the start, that creates the potential then to use that for secondary usage, such as publishing health data analytics and ultimately machine learning. And we're looking at a number of errors in machine learning, I think there are some ethical challenges there to be aware of and we've started with a recent examples of understanding how we can use machine learning to try and get that structured data out of the documents, that's something that we're working on with data with the AWS team at the minute, to leverage a lot of that scanned content that we have and evolve and be able to create the structured outcome. Really to make it easier for clinicians to find information within the medical record. >> So the AWS reinvent last fall, you know Sage Maker was of course buzzing. Is that something that you're looking at? >> It's something, so we haven't used it in Evolve so far but within Kainos we have an AI practice and we have a group of guys that are focused on the AI capability. Evaluating those tools, working with AWS and helping us understand how we can use that technology to solve the problems of our customers. >> Yeah, it's early days. So you talk about helping solve the problems of the customers. Summarize for us the key problems that you see machine intelligence, AI solving. >> I think there's probably different categories of how you could use it. There's the diagnostic sort of use case where you could use AI to help process imagery, to help with the diagnostic process. There's being able to add personalization to whether that be to patients or to clinicians, helping to provide insight into whatever the use case may be and all the use cases similar to that. >> Last words, so you're addressing the pre-day healthcare reform that's going on here at AWS. What's that like, what's going on downstairs, what did you tell the audience? >> Yeah, great day. So we had a group of healthcare professionals across the NHS in Ireland, very interesting group. We spoke this morning, I spoke with our customer Gloucester CC chief and we talked about the shared care record solution that we've delivered into Gloucester. So that's bringing information together for over 600,000 patients across the region and providing information in a single joined up view that was not available before. So great feedback, great interaction, lots of questions afterwards so looking forward to going back down and chatting some more to the group. >> Excellent. Hard to do that without the cloud I would imagine , accommodating all of the 600,000 customers right. >> Not possible. >> Alastair thanks so much for coming to The Cube. >> Thanks, Dave. >> Appreciate having you. Alright, thanks for watching everybody. Keep it right there, we'll be back with our next guest. You're watching The Cube from AWS headquarter in London. We'll be right back. (upbeat music)

(upbeat music) >> Welcome back to London everybody, this is Dave Vellante with The Cube, the leader in tech coverage, and we're here at AWS. We wanted to cover deeper the public sector activity. We've been covering this segment for quite some time, with the public sector summit in DC, went to Bahrain last year, and we wanted to extend that to London. We're doing a special coverage here with a number of public sector folks. Anjenesh Babu is here, he's a network manager at Oxford GLAM. Thanks very much for coming on The Cube, it's good to see you. >> Thank you.], thanks. >> GLAM, I love it. Gardens, libraries and museums, you even get the A in there, which everybody always leaves out. So tell us about Oxford GLAM. >> So we are part of the heritage collection side of the University. And I'm here representing the gardens and museums. In the divisions we've got world renown collections, which has been held for 400 years or more. It comprises of four different museums and the Oxford University Botanic Gardens and Arboretum. So in total, we're looking at five different divisions, spread across probably sixteen different sites, physical sites. And the main focus of the division is to bring out collections to the world, through digital outreach, engagement and being fun, bringing fun into the whole system. Sustainment is big, because we are basically custodians of our collections and it has to be here almost forever, in a sense. And we can only display about 1% of our collections at any one point and we've got about 8.5 million objects. So as you can imagine, the majority of that is in storage. So one way to bring this out to the wider world is to digitize them, curate them and present them, either online or in another form. So that is what we do. >> In your role as the network manager is to makes sure everything connects and works and stays up? Or maybe describe that a little more. >> So, I'm a systems architect and network manager for gardens and museums, so in my role, my primary focus is to bridge the gap between technical and the non-technical functions, within the department. And I also look after network and infrastructure sites, so there's two parts to the role, one is a BAU business as usual function where we keep the networks all going and keep the lights on, basically. The second part is bringing together designs, it's not just solving technical problems, so if I'm looking at a technical problem I step out and almost zoom out to see, what else are we looking at which could be connected, and solve the problem. For example, we could be looking at a web design solution in one part of the project, but it's not relevant just to that project. If you step out and say, we could do this in another part of the program, and we may be operating in silence and we want to breakdown those, that's part of my role as well. >> Okay, so you're technical but you also speak the language of the organization and business. We put it in quotes because you're not a business per say. Okay, so you're digitizing all these artifacts and then making them available 24/7, is that the idea? What are some of the challenges there? >> So the first challenge is only 3% of objects are actually digitized. So we have 1% on display, 3% is actually digitized, it's a huge effort, it's not just scanning or taking photographs, you've got cataloging, accessions and a whole raft of databases that goes behind. And museums historically have got their own separate database collection which is individually held different collection systems, but as public, you don't care, we don't care, we just need to look at the object. You don't want to see, that belongs to the Ashmolean Museum or the picture does. You just want to see, and see what the characteristics are. For that we are bringing together a layer, which integrates different museums, it sort of reflects what we're doing in out SIT. The museums are culturally diverse institutions and we want to keep them that way, because each has got its history, a kind of personality to it. Under the hood, the foundational architecture, systems remain the same, so we can make them modular, expandable and address the same problems. So that's how we are supporting this and making it more sustainable at the same time. >> So you have huge volume, quality is an issue because people want to see beautiful images. You got all this meta data that you're collecting, you have a classification challenge. So how are you architecting this system and what role does the Cloud play in there? >> So, in the first instance we are looking at a lot of collections were on premises in the past. We are moving as a SaaS solution at the first step. A lot of it requires cleansing of data, almost, this is the state of the images we aren't migrating, we sort of stop here let's cleanse it, create new data streams and then bring it to the Cloud. That's one option we are looking at and that is the most important one. But during all this process in the last three years with the GLAM digital program there's been huge amount of changes. To have a static sort of golden image has been really crucial. And to do that if we are going down rate of on premise and trying to build out, scale out infrastructures, it would have a huge cost. The first thing that I looked at was, explore the Cloud options and I was interested in solutions like Snowball and the Storage Gateway. Straightforward, loads up the data and it's on the Cloud, and then I can fill out the infrastructure as much as I want, because we can all rest easy, the main, day one data is in the Cloud, and it's safe, and we can start working on the rest of it. So it's almost like a transition mechanism where we start working on the data before it goes to the Cloud anyway. And I'm also looking at a Cloud clearing house, because there's a lot of data exchanges that are going to come up in the future, vendor to vendor, vendor to us and us to the public. So it sort of presents itself a kind of junction, who is going to fill the junction? I think the obvious answer is here. >> So Snowball or Gateway, basically you either Snowball or Gateway the assets into the Cloud and you decide which one to use based on the size and the cost associated with doing that, is that right? >> Yes, and convenience. I was saying this the other day at another presentation, it's addictive because it's so simple and straight forward to use, and you just go back and say it's taken me three days to transfer 30 terabytes into a Snowball appliance and on the fourth day, it appears in in my packets, so what are we missing? Nothing. Let's do it again next week. So you got the Snowball for 10 days, bring it in transfer, so it's much more straightforward than transferring it over the network, and you got to keep and eye on things. Not that it's not hard, so for example, the first workloads we transferred over to the file gateway, but there's a particular server which had problems getting things across the network, because of out dated OS on it. So we got the Snowball in and in a matter of three days the data was on the Cloud, so to effect every two weeks up on the Snowball, bring it in two weeks, in three days it goes up back on the Cloud. So there's huge, it doesn't cost us any more to keep it there, so the matter of deletions are no longer there. So just keep it on the Cloud shifting using lifecycle policies, and it's straight forward and simple. That's pretty much it. >> Well you understand physics and the fastest way to get from here to there is a truck sometimes, right? >> Well, literally it is one of the most efficient ways I've seen, and continues to be so. >> Yeah, simple in concept and it works. How much are you able to automate the end-to-end, the process that you're describing? >> At this point we have a few proof of concept of different things that we can automate, but largely because a lot of data is held across bespoke systems, so we've got 30 terabytes spread across sixteen hard disks, that's another use case in offices. We've got 22 terabytes, which I've just described, it's on a single server. We have 20 terabytes on another Windows server, so it's quite disparate, it's quite difficult to find common ground to automate it. As we move forward automation is going to come in, because we are looking at common interface like API Gateways and how they define that, and for that we are doing a lot of work with, we have been inspired a lot by the GDS API designs, and we are just calling this off and it works. That is a road we are looking at, but at the moment we don't have much in the way of automation. >> Can you talk a bit more about sustainability, you've mentioned that a couple of times, double click on that, what's the relevance, how are you achieving sustainability? Maybe you could give some examples. >> So in the past sustainability means that you buy a system and you over provision it, so you're looking for 20 terabytes over three years, lets go 50 terabytes. And something that's supposed to be here for three years gets kept going for five, and when it breaks the money comes in. So that was the kind of very brief way of sustaining things. That clearly wasn't enough, so in a way we are looking for sustainability from a new function say, we don't need to look at long-term service contracts we need to look at robust contracts, and having in place mechanisms to make sure that whatever data goes in, comes out as well. So that was the main driver and plus with the Cloud we are looking at the least model. We've got an annual expenditure set aside and that keeps it, sustainability is a lot about internal financial planning and based on skill sets. With the Cloud skill sets are really straightforward to find and we have engaged with quite a few vendors who are partnering with us, and they work with us to deliver work packages, so in a way even though we are getting there with the skills, in terms of training our team we don't need to worry about complex deployments, because we can outsource that in sprints. >> So you have shipped it from a CAPX to an OPX model, is that right? >> Yes >> So what was that like, I mean, was that life changing, was it exhilarating? >> It was exhilarating, it was phenomenally life changing, because it set up a new direction within the university, because we were the first division to go with the public Cloud and set up a contract. Again thanks to the G-Cloud 9 framework, and a brilliant account management team from AWS. So we shifted from the CAPX model to the OPX model with an understanding that all this would be considered as a leased service. In the past you would buy an asset, it depreciates, it's no longer the case, this is a leased model. The data belongs to us and it's straight forward. >> Amazon continues to innovate and you take advantage of those innovations, prices come down. How about performance in the cloud, what are you seeing there relative to your past experiences? >> I wouldn't say it's any different, perhaps slightly better, because the new SDS got the benefit of super fast bandwidth to the internet, so we've got 20 gigs as a whole and we use about 2 gigs at the moment, we had 10 gig. We had to downgrade it because, we didn't use that much. So from a bandwidth perspective that was the main thing. And a performance perspective what goes in the Cloud you frankly find no different, perhaps if anything they are probably better. >> Talk about security for a moment, how early on in the Cloud people were concerned about security, it seems to have attenuated, but security in the Cloud is different, is it not, and so talk about your security journey and what's your impression and share with our audience what you've learned. >> So we've had similar challenges with security, from security I would say there's two pots, one's the contractual security and one is the technical security. The contractual security, if we had spun up our own separate legal agreement with AWS or any other Cloud vendor, it would have taken us ages, but again we went to the digital marketplace, used the G-Cloud 9 framework and it was no brainer. Within a week we had things turned around, and we were actually the first institution to go live with and account with AWS. That is the taken care of. SDS is a third party security assessment template, which we require all our vendors to sign. As soon as we went through that it far exceeds what the SDS requires, and it's just a tick box exercise. And things like data encryption at rest, in transit it actually makes it more secure than what we are running on premise. So in a way technically it's far more secure than what we could ever have achieved that's on premise, and it's all taken care of, straight forward. >> So you've a small fraction of your artifacts today that are digitized. What's the vision, where do you want to take this? >> We're looking at, I'm speaking on behalf of gardens, this is not me, per say, I'm speaking on behalf of my team, basically we are looking at a huge amount of digitization. The collection should be democratized, that's the whole aspect, bringing it out to the people and perhaps making them curators in some form. We may not be the experts for a massive collection from say North America or the Middle East, there are people who are better than us. So we give them the freedom to make sure they can curate it in a secure, scalable manner and that's where the Cloud comes in. And we backend it using authentication that works with us, logs that works with us and roll-back mechanisms that works with us. So that's were we are looking at in the next few years. >> How would you do this without the Cloud? >> Oh. If you're doing it without the Cloud-- >> Could you do it? >> Yes, but we would be wholly and solely dependent on the University network, the University infrastructure and a single point. So when you're looking at the bandwidth it's shared by students using it network out of the university and our collection visitors coming into the university. And the whole thing, the DS infrastructure, everything's inside the university. It's not bad in its present state but we need to look at a global audience, how do you scale it out, how do you balance it? And that's what we're looking at and it would've been almost impossible to meet the goals that we have, and the aspirations, and not to mention the cost. >> Okay so you're going to be at the summit, the Excel Center tomorrow right? What are you looking forward to there for us from a customer standpoint? >> I'm looking at service management, because a lot of our work, we've got a fantastic service desk and a fantastic team. So a lot of that is looking at service management, how to deliver effectively. As you rightly say Amazon is huge on innovation and things keep changing constantly so we need to keep track of how we deliver services, how do we make ourselves more nimble and more agile to deliver the services and add value. If you look at the OS stack, that's my favorite example, so you look at the OS stack you've got seven layers going up from physical then all the way to the application. You can almost read an organization in a similar way, so you got a physical level where you've got cabling and all the way to the people and presentation layer. So right now what we are doing is we are making sure we are focusing on the top level, focusing on the strategies, creating strategies, delivering that, rather than looking out for things that break. Looking out for things that operationally perhaps add value in another place. So that's where we would like to go. >> Anjenesh, thanks so much for coming on The Cube. >> Thank you >> It was a pleasure to have you. All right and thank you for watching, keep right there we'll be back with our next guest right after this short break. You're watching The Cube, from London at Amazon HQ, I call it HQ, we're here. Right back. (upbeat music)

>> Hello, Room. Welcome back to London. You watching the Cube? The leader and tech coverage. My name is Dave Volante. We're here in a special program that we've constructed. It's the day before the eight of US London summit and we wanted to come and talk to some customers, some executives of startups, and really dig into what's going on in the public sector. Chris Heman is here. He's the director of UK and Ireland Public sector for eight of us. Chris, Thanks for coming on the Cube. >> Thanks for vitamins. Christ. >> Yeah. So you guys have a special public sector healthcare pre day that's going on downstairs? What's that all about? >> Yes. So obviously we'LL remain summit tomorrow expecting about twelve thousand people, which is phenomenal today that we could do something with one of our special industries, which is health care. So we've invited a number of customers and executives along for that today to learn more about cloud, how they can get going with the cloud and get, you know, start adopting a pace. So I believe you spoke with the missus about earlier on. So he misses a supplies the n hs, but also people and hs digital and so on her adopting the platform. So that's what today's all about. >> So health care is one of those sectors. It's ripe for disruption. It really hasn't been, you know, disrupted in a big way and digitized and it's starting. But the challenge is, how do you balance the cost of health care? Everybody's sensitized to that with the quality. Yeah, here. And so that's what really the problem. Show yourself. How does he ws in the cloud? Help solve that. >> Yeah, I think across the public sex. Really not just for the healthcare, but, you know, one of the things organizations are trying to do is reduce that large legacy footprint of infrastructure and really deliver against their mission, whether it be patients or citizens or whatever it may be. Ah, good example. In the in the case of the health care is we're working with a partner and I just school Business Services Authority on they have a large call center that was a really, you know, costly experience having traditional call center set up. So they've used our connect platform, our call center platform, and also some voice technologic called Lex. And they're able to reduce they stood up in about three weeks is a phenomenal effort, and they reduce their call volume by forty two percent. So basically getting the computer's towards some of the really easy queries, which, of course, meant that some of the tougher call center queries went to the actual humans and the call center handlers. So you know those sort things, I really think impact the bottom line for the HS and save some cost, but really helping to innovate a swell for for their patients and sis isn't so. >> Let's stay in health care for a second. So any just has, ah, nearly half a billion pound initiative to modernize. So if had they asked me, they didn't ask me. But had they ask me, I say, Well, part of that should be to get rid of the heavy lifting, so moved to the cloud and then really try toe transform your labor force to focus on more value added areas. It's actually helps to solve your problems. Is that essentially, what's happening? >> Understand, so that the contacts into very you know, that the people are now answering fines aren't doing those sort of Monday enquiries were it's just going to take four to six weeks. It's Maur, you know, transferring that. You know that's the computer and letting the humans do the heavy lifting. So I think that's you know, certainly one thing. But I think it's also enabling these organizations to really be closer to their citizens into their patients as well. With free liquor organizations like in the local authority, space, like else prevail. There are also using voice technology with Alexa to enable citizens to answer queries like You know who is my counselor or to update about various things within their sort of council record. And socially public sector organizations love that because they've now got this unique touch point with the sisters and at scale, whereas they would never have been able to do that previously. So that's a really good, you know, close engagement for them. >> So you hear the bromide people say data is the new oil. It's it's the it's the new natural resource. We actually think date is more valuable than oil because you can only use oil in one place. The data you can use many, many places, so data becomes increasingly important. But the problem that most traditional companies have is there, Their data is locked in silos. It's hardened into an application. And so so how are you guys attacking that problem? What do you see? A CZ trends in the customer base in terms of being able tto have sort of, ah, unified data model. And what role does the cloud >> play there? Yeah, I think it's really good questions. So there's a number of things that we're doing. First of all, we're very passionate about public date sets. So we host a number of public day sets like Lanza imagery and these sort of things, you know, fundamentally, we believe data has gravity, so, you know, for overto host and provide this data at scale for researchers and so on. That has tremendous huge benefit. But you're right about public sector organizations, and I silos a good example. Where we've we've worked is with transport for London. Obviously, if you want to get in and around the city of London, typically you go to tear filled look after UK, which runs on a dress, and you'LL say, I want to get from you know, Frank and to Liverpool Street, and that's all kind of running on top of a dress. But the really cool thing is they've opened up all that information so they don't have to develop. Those ups themselves are effectively crowd sourcing the development of those APS. So they've got some four thousand developers now working against all this data. Ah, Delight recently did a study. They reckon it's goingto generate economic benefits of one hundred thirty million pounds per annum just by making this really time data available. So So you're gaining unique business in size. But not only that, you've got organizations like city mapper who can commercialize that data develop, perhaps, and sell those apse on behalf of you know, you took to the community and so on. So you've got double bubble of s on the engagement, but also the public benefit as well. So that's really cool >> now, years ago Ah, in a past life, I had an opportunity when I worked for I d see the research company to run the government business. And when I went around and talked to the heads of military heads, the heads of agencies, there was a common theme. They were trying to close the gap between public sector and commercial. Yeah, and they never quite could get there. The cloud seems to me, Chris, to be changing that. I mean, to me, the CIA deal in twenty thirteen was a seminal moment for just the cloud and need of us specifically. But increasingly, you're seeing innovation. Yeah, it's still very difficult because you get turnover and agencies and administrations and so forth. But what are you seeing in terms of of those trends? Are you seeing public sector organizations leaning in modernizing? And again, what role does the cloud play there? >> Yeah, one hundred cent. I think you're absolutely rise. It is a unifier. In that sense we worked with, you know, we're moving mission systems to the cloud now with our customers. Ah, we worked with Dr Vehicle Stands Agency. So they're responsible for making sure our car's unroadworthy in the UK. They migrated their entire platform, which supports on thirty thousand small businesses. Try the rest in ten weeks. So it's amazing what public sector organizations are able to achieve with the pace of cloud. And a lot of it starts with experimentation. You know, that's the great thing is that you can try something. If it doesn't work, you can turn it off and you haven't lost anything but that that pace of being out to move, even mission systems. So the cloud is happening in public sexual across the board, >> and I mentioned the CIA before they start to be the American sort of parachuting in, and it's obviously a bias that I have. I'm working on my accent. But But But But the CIA was significant because everybody in the early days were so concerned about security that the head of tea in the CIA stood up last year at the D. C. Public sector Summit and said, My worst day of security in the cloud is better, far better than my client server ever. Wass. Yeah. So what about security concerns? Have they abated? They they still there? How is that evolving? >> Well, I think first of always, absolutely right that public sector organizations one hundred percent laser focused on security. But the good news is that we are to you know, its job. Zero for us is absolutely everything that we don't live and breathe by. And I think we've demonstrated that in a number of ways. I mean first of all, just the way in which we operate our physical infrastructure and everything that we do it physical pace, but then above the layer with the kind of the things that are a customer's responsible for. We have something called a shared responsibility model, so the responsibility for kind of everything above the physical infrastructure, but we provide the tools that they just never would've been able to get access to in a in a physical world, you know what our CEO's in public sector organizations do You know every servant you have, you know, just things like that. And they would just be like Now I've got no idea, but with a cloud, you have that visibility. You can see every single thing that's happening in the environment. So you get farm or visibility in control that he ever was ever were able to in a physical world. So I think that's first thing and obviously everything that we do around certification atter stations around. I so certification all the reporting and so on that we do Teo to assure our customers that we do a good job of that level as well. Ministry of Justice actually came out and said you could be more secure in the cloud than on premises and you have to focus on those areas where you're not in the cloud. So I think that was a huge testament by the UK. Come and say, Actually, this is this is secure, and this is fit for purpose, which is which is good. >> One of the things I've observed boat just technology adoption in general. You know, Silicon Valley's unique, obviously, And but, you know, outside of Silicon Valley, maybe technology adoption, you know, twenty years ago occurred more slowly. It seems like cloud adoption is very much consistent across the globe. I wonder if you could talk about that, But then specifically, public sector jobs in the cloud Do you see this Very similar sort of cadence from, you know, us rest of >> world? Yeah, I do. And I think you know, we were doing a fantastic job in the UK, Actually. Really fantastic job. Talked about some of stuff we're doing round. I I am machine learning. You know, some of these things are really leading edge on DH. If you speak to a miss earlier, they're investigating things like Blockchain for their tops of solution. So these sort of things are really pushing the boundary. But Paramount, All of that is this idea that you can experiment to try things. There's no longer there's a kind of is no longer a disparity around. Think something's fundamentally when you when you log into the console, you got access to one hundred sixty five different things and you can get going with you in the UK whether you're in the candor or in North America. So our customers are picking these things up on DH, accelerating a pace, which is which is fantastic trying all different types of things and work lights. >> Okay, if I were to ask Alexa what's gonna happen with Brexit, what would what would you tell me? I think first of >> almost, you know, with the way we think about it is it's just business as usual for us. You know, it's a fairly mundane answer, but fundamentally, you know, organization still need to adapt. This stone is transformed. They still need to evolve, and that's where we're helping and we're leaning in, you know, we're helping them with some of their EU accept programs around tooling and process and things like that. But I still came to adopt cloud a place which is which is also >> so come back to the session that you guys are running downstairs. I saw some of descriptions of it and I think there were three areas of focus. The public payers, the health care providers in the publicly funded research organizations is kind of what you guys are focused on today. So maybe close there and give us a vision for where you see eight of us public sector in the UK and >> I I think this were obviously healthcare's really fast growing vertical for us, which is fantastic upper across the board. Demand has never been greater, which is phenomenal on DH were really pushing the boundaries of what can be achieved. Yeah, we're working with, you know, I talked about some the public sector organizations with working with, you know, partners like he miss, but also small businesses as well as great example. Working with a company called Ad Zuna, which provides job search functionality. They run on a dress and they want a contract for Jobcentre Plus, which part of our department work and pensions. So it's not just the direct engagement we have with our customers. But it's also a ll the partners that we're working with to enable that in tow and functionality, which is which is really good. So we're doing a lot, lots of work in that space. And I could liken see Maura Mohr organizations not just customers in customers, but also partners technology providers coming to talk to us. Ah, and then across the spectrum, in health care, whether it's supplies to the chess or at the NSS himself, an individual trusts and and hospitals and so on, the kind of using our technology. So it's a real broad mixing spectrum of adoption. >> Outstanding, Chris, thanks so much for coming on. The Cube really appreciate it. And they were seeing the growth of a device is a DBS is actually astounding thirty billion dollars run rate company growing at forty plus percent a year. But more importantly, you're starting to see not only region expansion, but you're seeing expansion into specific verticals and ecosystems forming startups. And you guys are doing a great job of attracting these. Thanks very much for coming. Thanks. Thanks. Alright, Keep it right there. Buddy. This is David, Dante and the Cuba right back. Right after this short break. Wait

(electronic music) >> Welcome back to London everybody, this is Dave Vellante with theCUBE, the leader in tech coverage. We're here with a special session in London, we've been following the career of Teresa Carlson around, we asked, "hey, can we come to London to your headquarters there and interview some of the leaders and some of the startups and innovators both in public sector and commercial?" Andy Isherwood is here, he's the managing director of AWS EMEA. Andy, thanks for coming on theCUBE. >> Dave, great to be here, thank you very much for your time. >> So you're about a year in, so that's plenty of time to get acclimated, what are your impressions of AWS and then we'll get into the market? >> Yeah, so it's nearly a year and a half actually, so time definitely goes pretty quickly. So I'd say it's pretty different, I'd say probably a couple of things kind of jump out at me. One is, I think we just have a startup mentality in everything we do. So, y'know, if you think about everything we do kind of works back from the customer and we really feel like a kind of startup at heart. And we always say, y'know, within the organization, we should also make it feel like day one. If we get to day two, y'know, the game's over. So we always try and make day one something that's kind of relevant in what we're doing. I think the second thing is customer obsession. I think we are truly customer obsessed. And you could say that most organizations actually say, y'know, they're customer obsessed. I'd say we're truly customer obsessed in everything we do so if you think about our re:Invent program, if you think about, y'know, London, the summit coming up, what you will notice is that there will be customers everywhere, speaking about their experiences and that's really important. So we start with the customer and we always work back. So super important that we never forget that and if you think about how we develop our services, they start with the customer. We don't go out like a product company would and make great products and sell them. We start with the customer, work back, develop the solutions and then let the customer use them, and we iterate on those developments. So I'd say it's pretty different in those two aspects. I'd say the other thing is, it's just hugely relevant. Every customer I go into, and I've seen hundreds of customers in the last year and a half, were hugely relevant. Y'know, we are at the heart of what people want to do and need to do, which makes it important. >> Yeah, so we've been following the career of Andy Jassy for years and we've learnt about the Working Backwards documents, certainly you guys are raising the bar all the time, is sort of the mantra, and yeah, customer centricity, you said it's different, y'know, we do over a hundred events every year and every company out there talks about, "we're focused on the customer", but what makes AWS different? >> I think it's the fact that we truly listen and work back from the customer. So, y'know, we're not a product company, we don't make products with great R&D people and then take them and sell them. We don't obsess about the competition, y'know, we start with the customer, we go and speak to the customer, I think we listen intently to what they need, and we help them look round corners. We help them think about what they need to do for them to be successful, then we work back and probably 90% of what we do is fundamentally developed from those insights that the customer gives us. That's quite different. That really is a working back methodology. >> We run most of our business on AWS and it's true, so I remember we were in a meeting with Andy Jassy one time and he started asking us how we use the platform and what we like about it and don't like about it, and my business partner, John Furrier, he's kind of our CTO, he starts rattling off a number of things that he wanted to see, and Andy pulls out his pad and he starts writing it down, and he was asking questions back and forth, so I think I've seen that in action. One of the things that we've observed is that the adoption of cloud in EMEA and worldwide is pretty consistent and ubiquitous, there's not like a big gap, y'know, you used to see years later, y'know, Europe would maybe adopt a technology and you're seeing actually in many cases, you certainly see it with mobile, you're seeing greater advancements. GDPR, obviously, is a template for privacy, what are you seeing in Europe in terms of some of the major trends of cloud adoption? >> Yeah, I don't think we're seeing major differences, y'know, people talk a lot about, "well, Europe must be two years behind North America" in terms of adoption. We don't see that, I think it is slightly slower in some countries, but I don't think that's kind of common across the piste. So I'd say that the adoption, and if you think back to some customers that were very early adopters, just from an overall global cloud perspective, companies like Shell, for example, y'know they were really early adopters, and those were European-based companies, you could say they're global companies, absolutely, but a lot of what they did was developed in Europe. So I would say that there are countries that are slower to adopt, sometimes driven by the fact that, y'know, security is an issue, or was an issue, that data sovereignty was a bigger issue for some of these countries. But I think all of those are pretty much passed now, so I think we are very quickly kind of catching up with regards to the North American market. So, yeah. >> You mentioned your sort of startup mentality, you mentioned BP. Is it divisions within a large company like that that are startup-like? Is that what you're seeing in terms of the trends? >> No, I'm seeing three patterns. So I'm seeing a pattern which is, y'know, large organizations that go all-in very quickly, typically, y'know, strong leadership, clear vision, need to move quickly. >> Dave Vellante: We're going cloud? >> Yeah, we're going cloud, and we're going all in and that may be, like an NL would be a great example. So NL's a really good example of a top-down approach, very progressive CIO, very clear-thinking CEO that's driven adoption. So I'd say that's pattern one. For me, pattern two is where large organizations create an entity alongside, so almost a separate business. So probably Openbank is probably a good example, part of Santander. And now that organization has about one and a half million customers, obviously started in Spain, but they built a digital bank, clearly tapping into all of the data and customer sets within Santander, but building an experience which is fundamentally different. >> So a skunkworks that really grew and grew? >> Correct, absolutely, a skunkworks that grew, but grew quickly and now it's becoming y'know, a key part of their business. And then the third area, or the third pattern for me is very much a kind of a bottoms-up-led approach. So this is where the developers basically love the services that we have, they use the services, they typically put them on their credit card or AMEX, and then they'll go and use the services and create real value. That value is then seen and it snowballs. So those are kind of the three patterns. I'd say the only outlier to those three patterns is a startup organization, and as you know we've been hugely successful with startups, from, y'know, Pinterest, to Uber, to Careem, to all of these organizations and those organizations it's really important to influence them early on, to make sure that they are aware, and the developer community and the founders are aware of what we can do and we have a number of programs to really help them do that. And they start to use our services, and as those organizations are successful then our business grows alongside them and they, y'know, typically start to use a lot more of the services. >> One of the defining patterns of three, the bottoms-up and four, the start-ups, is they code infrastructure. And, y'know, sometimes the one, the top-down may not have the skillsets and the disciplines and the structure to do that. What are you seeing in terms of that whole programmable infrastructure, the skillsets, programmers essentially coding the infrastructure? Are you seeing CIOs come in and say, "Okay, we need to re-skill", are they bringing in new staff, kind of like number two, the Openbank example might be, y'know, some rockstars that they wanna sort of assign to the skunkwork. How is the number one category dealing with that in terms of their digital transformation? >> Yeah, so y'know, skills is something that is critically important, having the right skills in the right place at the right time. And if you think about Europe it's a big outsourced market, so a lot of those skills were outsourced typically to a lot of the outsourcing companies, as you'd expect. What you're seeing now is organizations, BP's a good example of this, where they're building the innovation capability back into their organizations to make sure that they can create the offerings and create the user experience and create the business models for the new world. And what we're doing is really trying to make sure that we're enabling those organizations to build the skills. So probably at a number of different levels, kind of, y'know, very basic level, or at a very junior level we're kind of influencing people in schools. So, y'know, we're going to be announcing, or announcing at the summit, Guess IT, which is basically a program to train up year eight students. So you start there, and basically you go all the way through to offering training and certification, we have a very big function associated with that to make sure that we're building the right skills for organizations to be successful, and also then working with partners, so all of those training and certification skills, we are working with the partners like the Cloudreaches of this world, but also the DXCs of this world, the Accentures of this world, the Atoses of this world, really to make sure that they have the right skills and capability, not only around our services but around the movement to cloud which is what these organizations need to do to help them innovate. >> And it sounds like your customers wanna learn how to fish, they see that as IP, in a sense, still work with partners, but help them transfer that knowledge and then, y'know, continue to innovate, raise the bars, as we like to say. >> Yes, yes. >> One of the biggest challenges that we see, we talk to customers all the time, is the data challenge. Particularly companies that have been around for a while, they have a lot of technical debt, the data's locked into these hardened silos, obviously I'm sure you see that as a challenge, maybe can you address that, how you're helping customers deal with that challenge and some of the other things that you see cloud addressing? >> Yeah, so y'know, we're really trying to help customers be successful in doing what they do in the timescale that they're setting themselves, and we're helping them be successful. I think from a data point of view, we have a lot of capability, so just to give you a perspective, so since I've been here that year and a half, we started with 125 services. That number of services has gone to 170-odd services now and the innovation that we have within those services has now reached, I think last year, just over the 1900 level so this is iterations on the product. In addition to that, we are continually building new offerings, so if you think about our database strategy, y'know, it's very much to create databases that customers can use in the right way at the right time to do the right job and that's just not one database, it's a number of different databases tuned for specific needs. So we have 14 databases, for example, which are really geared to make customers use the right database at the right time to achieve the right outcome, and we think that's really important, so that's helping people basically use their data in a different way. Obviously our S3, our core storage offering is critically important and hugely successful. We think that as-is, the bedrock for how people think about their data and then they expand and use data lakes, and then underpinning that is making sure that they've got the right databases to support and use that data effectively. >> At the start of this millennium there was like a few databases, databases was a boring marketplace and now it's exploded, as Inova says, dozens a minute it's actually amazing >> Yep >> how much innovation there is occurring in that space. What's your vision for AWS in EMEA? >> Yeah, so you know the overall Amazon vision is to be the world's most customer-obsessed organization, so y'know, here in EMEA, that holds true, so y'know, we start with the customer, we work back, and we wanna make sure that every single customer's happy with what we're doing. I think the second thing is making sure that we are bringing and enabling customers to be innovative. This is really important to us, and it's really important to the customers that we sell to, y'know, there's many insurgents kind of attacking historic business models, it's really important that we give all of the organizations the ability to use technology, whether they're a small company or a big company. And we call that the democratization of IT, we're making things available that were only available to big companies a while back. Now, we have made those services available to pretty much every single company, whether you're a startup in garage, y'know, to a large global organization. So that's really important that we bring and we continue to democratize IT to make it available for the masses, so that they can go out there and innovate and do what ultimately, customers wanna do, y'know, customers want people to innovate. Customers want a different experience. And it's important that we give organizations the tools and the wherewithal to go and do that. >> Well you've been in the industry long enough, and you've worked at product companies prior to this part of your career, and you know the innovation engine used to be Moore's Law. It used to be how fast can I take advantage of that curve, and that's totally changed now. You see a number of things happening, it's get rid of the heavy lifting, so you can focus on your business, that's what cloud does for you, but it's kind of this combination, the cocktail of data, plus machine intelligence, and then the cloud brings scale, it attracts innovative companies. How do you see, first of all do you buy that sort of new cocktail, and how do you see customers applying that innovation engine? >> Yeah, y'know, to answer the first bit first, we definitely see that cocktail. So y'know, the kind of undifferentiated work that was historically done to kind of build servers and make sure that they ran and all of those things, people don't need to do that now. We do that really really effectively. So they can really focus their time, attention, their money, their efforts, their innovation, on creating new experiences, new products, new offerings, for their customers. And they should also work back from customers themselves and work out what's really required. Every single business model, every single offering, needs to be questioned, by every single organization and I think that's what we do. We give the ability to organizations to really think differently about how they use what we have to do the really important things, the things that differentiate them and the things that ultimately give customers a different experience. And that's why I think we've seen so many very successful companies, y'know, from Airbnb, to Pinterest, to Uber. It's giving people a fundamentally different experience and that's what people want, so y'know, we're here to I think give people the ability to create those different experiences. >> Kind of amazing when you go back and you remember the book Does IT Matter? the Havard Business Review famous... It couldn't have been more wrong, at the same time it couldn't have been more right because it really underscored that IT was broken and that preceded 2006 introduction of EC2 and now technology matters more than ever before, every company's a technology company, y'know, you hear Marc Bennioff talk about software's eating the world, it's so true, and so as companies become technology companies, what's your advice to them? I mean obviously you gotta say, "Let us handle the heavy lifting," but what do they have to do to succeed in their digital transformation in your view? >> Yeah, I think it's about changing the mindset and changing the culture of organizations. So I think you can try and instill new processes and new tools on an organization but fundamentally you've gotta change the culture and I think we have to create and enable cultures to be created that are innovative and that requires, I think, a very different mindset. That requires a mindset which is about, "we don't mind if you fail". Y'know, and we'll applaud failure. We in Amazon have had many failures but it's applauded, and if it's applauded, people try again so they'll dust themselves off and they'll move on. You can see this in Israel which is, y'know, very much a startup nation. You can see people start a business, they might fail. Next day, they start a new one. So I think it's having this culture of innovation that allows people to experiment. Experimentation's good, but it's also prone to failure. But, y'know, out of 10 experiments you're gonna get one that's successful. That one could be the make or break for your organization to move forward, and give customers what they actually need, so, y'know, super important. >> Break things, move fast, right? >> Exactly. >> I love it. All right, what should we expect tomorrow at the London summit? We gotta big crowd coming, it's at the ExCeL Center >> Yeah, I think you'll see us continue to innovate, I think you'll see a lot of people, and I think you'll see a lot of customers talk about their experience and share their experience, y'know, these are learning summits, y'know, they're not kind of show and tell, they're very much about explaining what other customers are doing, how people can use the innovation and you'll see lots of experiences from different customers that people will be able to take away and learn from and go back to their offices and do similar things, but probably in a different way. So, y'know there'll be lots of exciting announcements, as you saw from re:Invent, we continue to innovate at a fair clip, as I said, 1950-odd innovations, y'know, significant releases last year, so not surprisingly you'll see a few of those. >> These summits are like mini re:Invents, aren't they? And as you said, Andy, very customer-focused, customer-centric; a lot of customer content. So, Andy Isherwood, thanks so much for coming on theCUBE, it was really great to have you. >> Great >> All right. >> Thank you >> You're welcome Keep it right there everybody, we'll be back with our next guest right after this short break. This is Dave Vellente, you're watching theCUBE.

>> Hello, everyone. This is a special presentation of the Cube. We're here in London at eight of us, one of eight of US locations in London. My name is Dave Volante and the Q We go, we'd like to go out to the events. We extract a signal from the noise and we've been following the ascendancy of a ws public sector from its early days. If you go back to two thousand thirteen, there was a significant moment in the history of eight of us where it won CIA contract a very large contract. CIA. It was contested by idea. My bm was used to kind of the what sometimes called the old guard the legacy companies used to selling into the government big, big contracts. And here comes this start up essentially eight of us taking away government business with CIA no less huge, huge contract. Well, IBM contested it. Judge Wheeler ruled against IBM for eight of us. And when reading that ruling, it was clear that the eight of US platform was superior to the IBM platform. He laid out the essentially the components of the R F P and the line by line and showed that a ws was the winner and virtually all of the line items. I think there was parody and won the reason why that was so important. It was that there were several factors there. One, It was a major milestone event. No, only Frito. Eight of us. But for cloud in general, if you think about security Ah, CIA, obviously very security conscious. It was the recognition that cloud actually could be more secure than on premises infrastructure. So the government was actually one of the first to kind of realise that and lean into that as a side effect, IBM had to go out and spend two billion dollars on soft layer toe actually compete in the cloud market Plys. So you had all these ripple effects Fast forward today to two thousand nineteen. You have the jet icon to contract a joint enterprise defense initiative. It's a ten billion dollar contract. A ws is in the lead for that contract. Oracle again another old Guard company has contested. And when you look through when when a company contests these bids, a whole lot of public information comes out. What? What the information suggested was that a single cloud the D o d determine that a single cloud was more secure, less complex and more cost effective. And so Oracle contested the the likelihood of an award to a single company because government contracts usually are awarded to multiple vendors. But in this case, because it's so critical tohave the data in one place so that they can serve the field better and responded the field better, the D o. D decided to use a single cloud. So oracles, you know, throwing off all rights of muck into the ring. Ah, basically asking the General Accountability Office to look at it. They did, Ggo said. If we're going to go with the D. O. D s decision, the D. O. D itself did an internal investigation. Now it's narrowed down to two vendors eight of us and Microsoft, and we believe that eight of us is the leading contender. Why is that? It's because eight of us says the most services. It's the most advanced, the highest levels of security and certifications within the government that are necessary to win these types of contracts. Why don't I spend so much time on these things? There's a two milestone events, the CIA contract in two thousand thirteen and what will soon to be the Jet I contract in two thousand nineteen. And what we're seeing is Amazon Web services, a thirty billion dollars run rate company growing at forty plus percent per annum. It's just a massive flywheel effect that we always talk about on the Cube. So we're here in London because we wanted to see how the public sector activities of Amazon are translating into the European markets. So we're here at a special public sector mini summit, if you will. There's a healthcare predate going on. This is ahead of the eight of US London summit, and we're siphoning off a number of the practitioners in and and startups software companies. Eight of US partners in the health care industry, as well as a WS executives particularly focused on the public sector today. So we're doing this sort of. We followed the career of Teresa Carlson for a number of years, seen the ascendancy of a ws public sector. We've covered ah, public sector summit in D. C. We flew to Bahrain last year. John Fairy of my business partner did the Bahrain summit. Bahrain was the first country in the Middle East to declare cloud first. So ah, critical location in the Middle East and you're seeing it now. Europe across a number of industries, obviously n hs than Ethan's. National Health Service is a very prominent in in the UK in a in a big consumer of services all kinds of startups and other software companies trying to sell and helped transform The N H s N hs has ah put forth a half a billion dollars nearly a half a billion dollar pound initiative on modernization. Ah, lot of that modernization is evolving the cloud. So the cube is here. We're trying to peel back the onion, understand what's going on here. Who were the winners? Who was going to get affected? Practitioners of startups, CEOs, nonprofit organizations, NGOs, executives from a ws and across the industry. So we'LL be here. We have three events this week in Ah in London here today at eight of US headquarters in London. Ah, tonight we have an impact investor event and then tomorrow we're at the eight of us Summit in AA in London at the XL Center. So keep it right here. Watch this channel. Check out silicon angle dot com For all the news, check out the cube dot net, which is where we host all these videos. And of course, we could bond downward for all the research. So thank you for watching and keep it right there. And you're watching the Cube this day, Volante.

>> Hello, we are here On the Ground. This is theCUBE's On the Ground program at Centrify's Headquarters. We go to Cricket Liu, chief DNS officer at Infoblox. Been with the company from the beginning. Great to see you again. Wrote the book on DNS. What year was that? That was between DNS, was like, when I was born. >> Yeah, 1992. September 1992 was when it was published. >> Great to see you. We've done some podcasts together over the years. >> Yeah, good to see you too. >> DNS, now obviously global, ICANN's now global, it's part of the U.N., all different governance bodies, but it's certainly still critical infrastructure. >> Yeah, absolutely. >> Critical infrastructure is now the big conversation as the security paradigm has moved from data center to the Cloud, there's no perimeter anymore. >> Yeah. >> How is that changing the DNS game? >> Well, I think that folks are starting to realize how critical DNS is. In October of last year, we had that huge DDoS attack against Dyn, the big DNS hosting provider in New Hampshire and I think that woke a lot of folks up. A lot of folks realized, holy cow, these guys are not too big to fail as they say. Even though they have enormous infrastructure, widely distributed around the globe, they have such a concentrational power that a huge number of really, really popular web properties were inaccessible for quite sometime, so I think that caused a lot of people to look at their own DNS infrastructure and to reevaluate it and say, well maybe I need to do something. >> Interesting about the stack wars that are going on, that attack, as we've lived through and you've been part of it as chief technical officer in many companies. DNS was always that part where it'd be secure but now you have block change, you have new kinds of infrastructure with mobile computing now over 10 years post iPhone. >> Yep, the critical moment. >> How has infrastructure changed, beyond DNS 'cause it still needs to work together? >> Yeah, well, it's funny because we do have all of these new types of devices. We do have new technologies. But a lot of things have remained the same. DNS is still the same. The remarkable thing is that the latest version in my book is 10 years old, actually 11 years old now, so it's older than the iPhone and people still buy it because the underlying theory is still the same. It hasn't changed. It's a testament, really, to the quality of the original design of DNS that it still works for anything and that it's scaled to serve a network as diverse and as large as the internet is today. >> What's your biggest observation, looking back over the past decade with DNS, about the emergence of virtual machines, now Cloud. Again, the game is still the same 'cause DNS is the plumbing and it provides a lot of the key critical infrastructure for the web and now mobile. What's the biggest observations that you've seen over the decade? >> Well I'd say one of the things that's happened over the last several years that's maybe the most important development in DNS is something that we call response policy zones. Up until now, DNS servers have just been sort of blithely complicit when it comes to, for example, malware. Malware wakes up on a device and it assumes that it has DNS available to it and it uses DNS, for example, to find command to control server, maybe a drop server to exfiltrate data to. In the DNS server, even though it's being asked to look up the address record for CommandAndControlServer.Malware.Org, it just happily goes along with it. A few years ago, Paul Vixie, who I've known for a very long time, came up with this idea called response policy zones which is basically to imbue our DNS servers with resolution policy so that you can tell them, hey if you get a query for a domain name that we know is being used maliciously, don't answer it. Don't resolve it like you normally do. Instead, hand back a little white lie like that doesn't exist and moreover, log the fact that somebody looked it up because it's a good indication that they're infected. >> So bringing policy to DNS is really making it more intelligent. >> Yeah, that's right. >> And certainly as networks grow, I was just watching some of my friends setting up the wireless at Burning Man and the whole new change of how Wi-Fi is being deployed and how networks are being constructed is really coming down to some of the basic principles of DNS to route more, be responsive, and this is kind of a new change. >> Yeah, there's a lot going on in changes to the deployment of DNS. It used to be that most big companies ran all their own DNS infrastructure. At this point, I think most large companies don't bother running, for example, what we'd call their external authoritative DNS infrastructure. They give that to a big hosting provider to do, somebody like Dyn or Verisign or Neustar or somebody like that, so that's a big change. >> Cricket, I want to ask you about the CyberConnect Event going on in New York. Infoblox is involved. Security is paramount, so now an industry event. Centrify is the main sponsor. You guys are involved as a vendor, but it's not a vendor event, it's a industry event. It's a broad category. What's your thoughts on this kind of industry event? Usually in events it's been Black Hat or vendor events pushing their wares and selling their stuff but now security is global. What's your take on this event? >> Well, I'm hoping to be able to spend a little bit of time talking to folks who come to the event about DNS and how it can be used as a tool in their security tool chain. The folks who come to us as Infoblox to our events already know about DNS. They're already network administrators or they're responsible for DNS or something like that. My hope is that we can reach a broader audience through CyberConnect and actually talk to folks who maybe haven't considered DNS as a security tool. Who maybe haven't thought about the necessity to bolster their DNS infrastructure. >> One final question since we're on bonus material time. I've got to ask you about the global landscape. I mean, in my early days involved in DNS when I came was from the '98 to the 2000 time frame. International domain names were Unicode. That's not ASCII. So that technically wasn't DNS, but still, they were keywords. They had this global landscape in, say, China, that actually wasn't DNS so there's all these abstraction layers. Has anything actually evolved out of that trend of really bringing an abstraction layer on top of DNS and certainly now with the nation-states with security are issues, China, Russia, et cetera. How does all that play out? >> Well, international domain names have actually taken off in some areas. And basically it's as you say, you have the ability now to use Unicode labels in domain names in certain contexts, for example, if you're using your web browser you can type in a Unicode domain name and then what the web browser does is it translates it into an equivalent ASCII representation and then resolves it using DNS which is the traditional DNS that doesn't actually know about Unicode. There are actually some very interesting security implications to using Unicode. For example, people can register things that have Unicode, we would say, glyphs in them that look exactly like regular ASCII characters. For example, you could register paypal.com where the A's are actually lowercase A's in Cyrillic. It's not the same code point as an ASCII A. So it's visually. >> Great for hackers. >> Oh yeah. Visually indistinguishable from paypal.com in a lot of contexts and people might click on it and go to a page that looks like PayPal's. >> John: So its a phishing dream. >> Yeah, really dangerous potentially and so we're working out some of the implications of that, trying to figure out, within, for example, web browsers, how do we protect the user from things like this? >> And a lot of SSL out there, now you're seeing HTTPS everywhere. Is that now the norm? >> Yeah, actually, within the internet engineering task force, the IETF, after it became obvious that state-sponsored-- >> John: Attacks. >> Eavesdropping. >> You were smiling. >> Was kind of the norm. >> Got to find the right word. >> Yeah, the IETF embarked on an effort called DPRIVE and DPRIVE is basically a bunch of individual tracks to encrypt basically every single part of the DNS channel, especially that between what we call a stub resolver and the recursive DNS server so that if you're a customer here in the United States and a subscriber to an ISP like Comcast or whomever, you can make sure that that first hop between your computer and the ISP is secured. >> We're getting down and dirty under the hood with Cricket Liu on DNS. I got to ask kind of up level to the consumer. One of the things that kind of pisses me off the most when I'm surfing the web is you see the browser doesn't resolve or you go hit someone's website, oh yeah, something.io, these new domain names, top level gTLDs are out there, .media, all these, and companies have firewalls or whatever their equipment is and it doesn't let it through. Because they're trying to protect the perimeter still, must be, I mean, what does that mean when companies aren't letting those URLs then, it is a firewall issue or is it more they're still perimeter based, they're not resolving it, they're afraid of malware? Somethings aren't resolving in? What does that mean? >> Well I think as often as not it's an operational problem. It could be just a misconfiguration on the part of the folks who are hosting the target website's DNS. It could be that. I don't know a lot of folks who-- >> So it's one of their policies or something, it's just kind of locking down. >> Could be that too. Or it could be, for example, that they have a proxy server and they're trying to limit access to the internet by category. Maybe it does categorization and filtering by-- >> Can you work on that? Can you write some code for that? Well thanks, great to see you, thanks for sharing this conversation here On The Ground at Centrify. >> You're welcome. >> And good luck with the CyberConnect Conference. >> Yeah, nice to see you too. >> Alright, I'm John Furrier with On The Ground here on theCUBE at Centfity's headquarters in Silicon Valley. Thanks for watching.

>> Announcer: The Cube presents On the Ground. (techno music) >> Hello and welcome to a special Cube presentation of Oracle On the Ground here at the headquarters in Redwood City. I'm John Furrier, host of the Cube. I'm here with Karen Sigman with Oracle. Great to see you again. Give us an overview what we're doing here with this program. What's the update with Oracle with Big Data and cloud? >> It's pretty exciting, you know. We've been continuing to innovate as you know. We've been expanding our portfolio and the big focus is on choice. So with the Big Data appliance and the Big Data now taking that to the cloud at customer strategy, we're able to now give customers choice of doing their Big Data analytics on premise, in the cloud, or as a cloud service behind their firewall. It's a huge innovation, a whole new change to the way you can do things for our customers. >> So about the operational impact to customers for this, because on premise obviously is key. Cloud economics are right there. But getting something operational has been a big problem for many people with Big Data. How does the Oracle solutions innovate and help solve that problem? >> Well, I think that's the whole value proposition around what we've done with the Big Data plans. What we heard from a lot of our customers is they can build their own, and they can put it all together, but then they have to maintain it. They have to manage it. They have to make sure that everything that they, all the software that they put together, is going to work together. And they're going to be able to keep it up to date. We actually eliminate all the steps. A vast majority of the steps it takes to bring it up online. So we can bring the time to value for doing your data analytics down to days instead of weeks. >> What do you say to customers that have come to you, Oracle customers and potentially new customers, "Hey I have Oracle database" or "I'm considering Oracle database, but I have to use Hadoop. I want to use Hadoop and some of these open source technologies. Can I do that?" >> Of course you can. Oracle is absolutely all about choice. It's the ability for you to be able to use both Oracle and non-Oracle source data in a single appliance and do your analytics against that. That's the value of the Big Data plans. >> What about cloud? The cloud machine? How does that fit into this? >> Yeah, it's all again, it's about, you know, making sure a lot of customers have different ways that they want to do business. They're worried about data sovereignty issues. They can't take it to the cloud, but they want the ability and the agility that goes with a cloud story. The ability to just access a service and run it. What we're doing with the Big Data cloud machine is we're actually allowing customers to take that cloud service and access it just behind their firewalls. So it's the same as if you're logging in to our public cloud. You just log into it in your own data center. >> Karen, so you've been traveling around the globe talking to customers. What are the main things that you're seeing bubbling up from the different conversations from all around the world? Top three things you're hearing from customers? >> I think it's a couple things. One, they don't want to spend their time becoming IT experts anymore. And so the value of an appliance like technology or a cloud service, either one, is exactly what their looking for. Because they want to simplify their IT infrastructure and they want to focus on the things that matter most, which is the applications that drive the business. So that's number one. Number two is they're worried about cost. And so they would like to have cost be specific and more transparent. So they want to make sure that whatever their spending they can actually allocate back to the business units, that's coming from the IT side. So having cloud based models actually helps quite a bit with cost transparency. And I think the third thing is that overall they want to make sure that they can get things done faster. And so the idea of having cloud services gives them that agility that they need. And as I know you only asked for three, but I would say the fourth thing is they're looking for choice. They really want to understand. They want to make sure that if they choose one model, that they're going to be able to flip and they're not going to be locked in. And what Oracle's done with this is given them the capability to either deploy on Oracle, work with Oracle or non-Oracle data, and you can do it on a cloud or you can do it on prim. It's all up to you. >> I sat down with Dave Donatelli for an exclusive interview and then publishing on Forbes and then SiliconANGLE, and he talked about how the traditional infrastructure mainly storage and server vendors, really weren't positioned for success. So I want to ask you, a year now into the Oracle real push and growth of infrastructure products, engineered systems and other things powering all the software and the database and all the good stuff with Big Data cloud. What's changed over the past years? What can you point to has been the big, you know, the needle moving? Was it been performance? Has it been hardware? Number of units? Integration? What's your view on where has the needle moved for the infrastructure engineered systems? >> I think the big thing is that we've really fill out the portfolio. We've made sure that whether you're choosing to do a build your own solution or go for the, you know, fastest time to value with an appliance like technology or you're trying to get the maximal capabilities out of our systems our engineered systems. we've actually built out our portfolio so that you can do all of that on prim or in the cloud or on your premise with a cloud service. We've made sure that whether it's database workloads, application workloads, or analytics workloads, that we have a complete portfolio of solutions that give you that choice. And that's quite different than anything I see in the market. >> Karen, thanks so much for spending time with On the Ground. Appreciate it. >> Thanks. >> I'm John Furrier. We are On the Ground here at Oracle's headquarters. Thanks for watching (techno music)

>> Voiceover: theCUBE presents "On the Ground". (techno music) >> Welcome to a special, exclusive Cube, On The Ground here at Oracle's headquarters. I'm John Furrier, the host of theCube, here with Claude Robinson, Senior Director, Product Management, for conversion infrastructure. Claude, welcome to On The Ground, we are wrapping up a great day of interviews of thought leaders and experts here at Oracle, from Big Data Cloud Machine. All of this is kind of coming together under this converged infrastructure, software in the cloud, big data all kind of connecting in, what's your take on this? How do you wrap this up into a burrito? How do you wrap this thing up and put this together? >> Yeah, so, you know, as everybody said, Big Data is kind of one piece of a bigger puzzle. Customers are looking for a complete solution that they can plug in, and actually just spend time running their business versus going out and building solutions internally. So, you know the old model of going and picking a ton of pieces and vendors of the shelf, spending months putting it together, waiting six to nine months for someone to come back with data that you can analyze to run your business, that doesn't work anymore in today's world. >> We stuff stuff out there, open stack, We see SDN, we see all kinds of stuff in the Cloud, you got Amazon, there is definitely a do it yourself culture out there, certainly on the hardware side, people have been buying servers, buying storage. You guys provide this whole integrated solution, but you don't necessarily have to buy Oracle hardware, but you can run this on that, I mean, so, I mean. Do customers have to buy all of Oracle? What's the ideal configuration for this? >> So basically customers have a choice in terms of what they want to do. Again, the high level thing for the analytics pieces is they basically want to take that data, and make a business decision out of it. And we are giving them the options of doing that where they want, when they want and what platform they want within Oracle. So if they want to do public Cloud, they can. If they want to build their own private Cloud on premise, levering our technology, they can. If they want to go with the old model of a converged system on premise, they can. Again, we are letting them do that, and it's a system that is really easy. It works across all three areas, and it is basically just plug and play. >> One of the things that is interesting about this big data appliance that we were talking about earlier today, one thing I think is attractive about it is that it take the benefits of Oracle, if you are an Oracle customer over at Oracle Database, you know what you're talking about, you know what you have there. It's got value. But you don't have to buy Oracle, to connect to Hadoop. You can use open source if you have Spark or Hadoop, this is an extensionished strategy for Oracle. Give you, essentially, access to a bigger database business, that is free anyway. But the customers get value in that. Is that part of the value proposition that you guys see as well? I mean, because it is not necessarily Orale database, but you are enabling connection to a free software. You guys understand what is going on here? Why is this important? >> So customers today can go out and build their own, but again it is going to take 51 pieces and 100 steps. You know when we were little and we built models, that was great, but when you are running a multi billion dollar business, or a 50 million dollar business, you don't have time to do that. You want something that you can basically take, and plug in. And so, what we have doe is gone out and supported these open source, you know, configurations, that, you know, customers are using to run their business. And we are giving them powerful tools that Oracle builds that plugs into data, that makes that happen. >> So talk about the interviews we had this morning, up and down, so we had a diverse group of people, how would you describe the set of interviews that we had today from an expertise standpoint and subject matter. >> Yeah, so most of these folks are the thought leaders at Oracle and in the industry in Big Data. You saw things that were basically related to hardware. You saw discussions around trends that are happening with the internet of everything, or the internet of things. You saw folks talking about what is happening in terms of what customers want in terms of data labs, or data lakes. So you saw kind of a large representation of expertise within Oracle, speaking to the various pieces of Big Data. >> Converged infrastructure was a term that has been around for a while, but it certainly played out exactly when it first came out. People were like, oh, converged infrastructure, we're are seeing that same kind of converges happening at the data layer, do you guys see that tread connecting with customers, are customers getting it? That concept, we heard things like data lab, data factory. I mean there is almost a converged data infrastructure going on too. >> I think customers get, I think, the old model of converged infrastructure was, hey, lets simplify so of the hardware components. And there was a big piece missing, which was, what are you doing with that hardware? Where is the software pieces? I think the real converged infrastructure that Oracle is bringing to the table and that customers are seeing is the complete package. Its not only the hardware, but the entire software stack as well, because in the old model, if you have a converged infrastructure with hardware, what are you going to do with that? You need software components on top of that, so you are still kind of, you know, building your own with or others off the shelf, you don't have to do that. >> One of the things that people are saying in the marketplace, and, you know, we are also seeing on theCUBE with the Cube coverage, is Oracle is actually thinking about the hardware performance in context to the software of the database, primarily, and then up the stack. What's so important about that notion, of thinking about the performance of the hardware tied to the software, both Oracle and non Oracle? >> You know, I think you could look back at kind of the old mainframe days, if you could basically have a hardware systems and a software working together, and designed to work together, you have the best system, best of breed. I think, you know, because of missing technologies back then, we moved to this kind of open client model. We would kind of have hardware. We would put some third party software on there. You would try to configure it yourself to make it work. Most companies would actually prefer if that system arrived ready to go. So we are kind of, in some way, we are taking the best of both worlds, the best of the optimized system from the past, with the ability of that configuration, that methodology and technology that we have nowadays, and we built a system that basically provides you the best of breed. >> Claude, thanks so much for spending time with us today. I appreciate letting us come in and talk to all the experts here On The Ground here at Oracle Headquarters. >> Thank you, John. >> I'm John Furrier, here On The Ground, for exclusive coverage of Oracle, pre Oracle open world, and also getting all the thought leads around data, data capital, and all data management, on converged infrastructure. I'm John Furrier, you're watching theCUBE. Thanks for watching. (techno music)

>> Announcer: TheCube presents, On the Ground. (upbeat music) Hi everyone, welcome to this special On the Ground, Cube coverage here at Oracle headquarters. I'm John Furrier, the host of theCube, here with Chris Linskey, who's the Vice President, Product Management for Oracle Big Data. Welcome to On the Ground, good to see you. >> Thanks John, nice to meet you. >> So let's talk about big data, and the concepts going on now for analytics. What is going on in your mind around big data, and some of the ideas that with customers are kicking around, because the number one thing we hear is, I got to store the data. Solved, check, database, system of record. But now other databases are popping up. Different types of databases, you got graph databases, you've got unstructured databases. Do I run Oracle for all those? When do I use Oracle? When do I don't use Oracle? So the first question is, what are some of the obstacles that are facing the companies? Is it integration? Is it the choice? What's going on? >> There's a lot. There's a lot of interest in the market around big data. But in terms of companies that are actually using that in kind of a productized fashion to build competitive insight, are less than you would think, because of some of these obstacles. So, we look at it in a few different ways, and we try to tackle the obstacles at Oracle in each of these categories. One of the first big questions to solve, is what you raised. How do I manage the data? I've got a lot of gravity in my data warehouse and in my databases, but now I've got all this new content coming in. It might be social media. It might be log data. Things that you're not sure of the value, so it may not make sense to store in that enterprise data warehouse. That's really where customers are looking at alternate technologies like big data, like Hadoop, to give you both that cost savings, but also to give you specialized access, whether you're doing, like you said, spacial queries, or graph queries. Oracle can give you the right engine for the right job, but what's also important in that data management layer, is doing it in a way that breeds simplicity of ownership. If the cost of ownership is too expensive, no one's going to do that. So we also have an initiative called Big Data SQL, that let's you use that common Oracle database as your front-end, but then queried back to Hadoop, queried back to a spatial or graph engine. You can leave that data there, where it makes the most sense. >> I mean SQL on Hadoop for instance has proven that SQL is the language of most people querying. So, that's out there, so that's done. But it doesn't mean run relational databases all the time, but that's what people are interfacing into other databases. >> Chris: Yeah. >> Is that a pretext to what's really happening? Is that, interfacing to other data sets is really the more important than actually having whole new systems. Because that seems to be ... >> It's a bit of both. The way I look at it is, some companies look at Hadoop as just another data source. I've got some log data, some social data, let me put it in a place that's cost-effective to store. And there using your database as a front-end makes sense. Other customers look at Hadoop and big data more as a data platform, where they want to use that cluster, that compute environment, to do more than just query things and build a chart. And that's where you see some new technologies coming out. In Oracle we call it our data factory. That's around, how can I use all of that compute power to actually do data integration? Right, how can I keep up with that one hour of ETL window I'm given a night to deal with all these new sources? So we see people adopting Hadoop for ... >> That's a tough window, one hour is a tough window. If you're Wall Street, backing up. >> Yeah, some of it's tough. >> Talk about Data Lab. What is this concept that you have been kicking around called Data Lab? >> Exactly. >> What does that mean? >> So I think that's the third pillar. We talked about data management, giving you the right engine. We talked about data factory, giving you that integration capability. But, why go through all that effort, if not to start driving innovation? And that's what we think about as the Data Lab. It's a place where you can experiment with advanced analytics. It's a place where you can experiment with data mashup, and new data combinations. And you do it in a cost-effective way, and a way that breeds this notion of agility. You mentioned the word, system of record before. That's a very great description for the warehouse. You're not going to change your revenue definition, or your customer dimension, in the warehouse. That's what everyone uses. But Hadoop, people look at as a system of innovation. It sits alongside the warehouse. You can put a lot of that same data in there. Often you'll put data that never made it into the warehouse. So you get that big data variety, and then you can use that to come up with new ideas. So that's really the essence of the lab, is bringing in more data sets. Trying more combinations of data. And then also seeing if you can move beyond just descriptive and diagnostic analytics, into predictive. >> So let me just get this right. Factory is all the ingestion, Data Lab is like your, I'd say sandbox, my word. So system of record is the most important data. That's a customer name, a key variable for that, that's in the company's business model. So that's where all the hardcore data is. Social media data might be, hey, I'm geo, piece of geo data, and it's at retail store, says I'm going to buy something, or has local presence. Has my name, which is in the system of record. So, that data is in a different database. Has to go over there and get to the system of record. That's hard. That's actually a hard problem. >> Chris: It is. >> But that's a realistic thing that people want to take is this gestural data pieces, small data, that means something to the system of record, or some engagement data, cross-connected to system of record. Do you guys solve that problem? This is what people want to look for, right? >> We do. What's interesting is, that's an age old problem. We had that with data warehousing. We have it even more now with all the big data sources. And, I think the opportunity here is to decide who should solve that problem. Is it a scarce ETL developer that you have in IT. They have limited cycles. >> That's true. >> Do I have a data scientist? People actually use data scientists to do this sort of data integration work. It's hard to come up with a new predictive model if the data sets don't match up. And, its unfortunate, because that's the PhD guy. And, that's menial labor to a large degree. >> Hard to find PhD's, too. >> It is. I like to call them unicorns. You hear about them, you never really see them. And you definitely don't want the scientist doing that menial labor. The joke we say is that the data scientist has been turned into a data janitor, because of all these tasks that get put on their shoulders. So, we think at Oracle that's an opportunity. With this combination of data management, data factory, and Data Lab on top, you can actually push that work out to your business analyst teams. They can collaborate with IT. They can collaborate with your data scientist if you have them, but the spirit of the Lab is not ... >> So making the analysts and the business folks, make them like data scientists. >> Chris: Exactly. >> As functional as data scientists, without having them being ... >> One of the phrases in the industry is citizen data scientist, and I manage a product called Oracle Big Data Discovery, and that is really our goal. Can we build these very intuitive UI's, that make these analysts produce more output like a data scientist would. >> So what's the architecture to make that happen? Because I think that's right on the money. I think that's a great solution. I think and the example I used is just a small piece of data, but that's a database problem. So by abstracting out to another level with software, you can let people wire their own solutions together. I get that. How do you guys do that from an architecture standpoint? What do you say to customers? How do I do this? What's the playbook? >> It's a good question, because at its core, there's no reason to go about solving this problem, unless it works at the big data scale, right? If you can't analyze petabytes, terabytes of content, you would use a regular BI solution. There's no reason to move over to big data. So, a key aspect of the architecture is scale. But also if you're going to support these analysts, they're not happy if they click on the screen and then they wait five minutes for something to come back. So, interactivity performance is critical too for this user base. Because of that, in products like BDD, and really across a lot of our different initiatives, Apache Spark has become a key piece of our architecture. And that's something you might not expect from Oracle, that we're moving into open source, adopting a lot of those technologies, but we really do see the value of Spark. >> So I asked Neil Mendelson just today the question, where he sees the market going. So I want to ask you a little bit different question, but same question on a different task. What's the next big thing? Because we are on the front end of this really pioneering analytics mindset. >> Chris: Yep. >> Horizontally scalable data sets. Software value propositions, applied to data as currency, if you will. Soon data will be on the balance sheets. Some say, certainly the analysts at Wikibon are saying that, some day it should be an asset class. >> Chris: Data capital is a phrase we use. >> Data capital, love that. And so that is a trend, that could be right around the corner. But that's where it's going. What's the next big thing to get us there? >> I think the first hurdle was just making sense of big data. It took organizations a couple years just to get their head around that, and to build that architecture, so it will scale and people will adopt the system. I think the opportunity now is, at least as we see it in our analytic portfolio is, you've got these users on the system. You've got these Hadoop clusters in place. What can you do with that power? And, we think the big opportunity, especially as we create these data scientists, these citizen data scientists, is machine learning. How can we embed, especially the Spark machine learning libraries, into our products more natively? Such that, you don't have to have the PhD at the outset. You can use that compute power, and you can use the Spark open source libraries, to help bootstrap that process. >> So do you guys solve what I call the data swamp problem? Because, let me explain in more color. Most people are dumping everything in what they call a data lake. And, just store all the data, we'll get to it later. Some of it, mostly it's Hadoop, it's a bunch of batch data. Because they don't know what to do with it yet. So it just sits there. And it gets dirty, and it turns into a swamp. That's what the joke is, data swamp. Ironically we're looking at the lake here at the Oracle headquarters. >> Chris: Pristine, pristine. >> Pristine, the water's flying up through thing, it's beautiful. This is a big problem, because data that's idle, that's not being used in this case, not being intelligently acted upon, can turn into a swamp, is only valuable when needed. Meaning, if something's happening in real time, you go to the data lake, and pull out a piece of data, to your earlier reference, and make it in real time, it's important. So you never know the potential energy of that data, and the value. It could be perfectly useless one minute, extremely valuable the next. Is your value proposition with the big data appliance of analyst tools to connect to those lakes and bring them back? Is that the whole, you guys save the data lake >> There's two pieces >> problem? >> There's two pieces. One is giving you the infrastructure, and for that we have our big data cloud service, our big data appliance. Because, lots of people think big data is just commodity hardware. As you move into analytics and do more in memory, you're going to want that extra capacity. So that's one piece, making sure you've got the horsepower. But then, you need those tools on top. And that's where our big data discovery product focuses. And to your point, what we've done is actually integrate the things that those analysts need when they're in that discovery moment. First thing they need, like you said, I never knew I needed this data set before. It just came up to me. So we give you almost a shopping experience for data. You can go in, type in keywords. I want to look for social media log data. And we actually search into Hadoop, and index all that content. So, it's just like you were on our website. >> So you're kind of keeping the lake moving and clean, because you're indexing it, so you can service data at any given time. >> That's the first piece. The second piece though is again, in your discovery process, you have to recognize this is the first time people will be working with this data. And that's where a lot of these data scientists shine, because they know all the techniques as to how do I interrogate it? What's important? What's not? And that's what we build into our product now. So the analyst can just look at a very visual screen, and it helps them figure out where to focus. Is it worth me spending time? >> It's like almost this bot craze that's going on. You guys are abstracting away the scientist's knowledge into software, and providing almost an interface. >> That's the hope. If you can get a data scientist, trust me, keep them. They're very valuable. >> Catch that unicorn. >> Yes. >> No, it's true though. There's not enough PhD's, or data scientists out there. Soon, there's new curriculum out there, but still. The idea is to scale up, and make the normal person, the citizen be the data scientist. >> And also, it's funny, if you look at the advanced analytic tools, and the data science tools out there, they're very dated. A lot of them were built 15, 20 years ago with that data miner statistician. There's now this new breed of data scientists that they want more compelling interfaces. They expect more. >> Chris, final question. Top three conversations that you have with customers, where they're most challenged. If you had to look at the patterns, applying all the big data techniques in your brain to the three top problems that customers are trying to solve that you guys help. >> Excellent. So the first one I would say by far, and I wish it wasn't the case, but it's, help me justify building out my big data cluster. That's the first one. Lots of companies want to do more with big data, but they're struggling ... >> Just their ROI, or cost, or both. >> The ROI, the cost, really, why should I make that investment? How do I justify it? And I really do think that cloud is going to change that picture dramatically. When I can shift to looking at the CapEx versus OpEx ... >> So you're saying the cloud lowers the bar, in terms of getting value generated, or is it ... >> It does two things. It lowers the financial entry point, and how much you have to justify up front. And it lowers the IT skillset to manage those clusters in the data center. So, two very big problems. >> Great, that's awesome. Second one? >> No that I've solved that. Second one is, okay, well what do I do next? How do I find things? Where should I be looking? And that is where this Data Lab concept is meant to come into play. Some customers will have a perfect use case in mind. That's how they justified the project. They can go and execute that. But a lot of them, again it's this notion of a data lake. I need to pursue a range of experiments. Where do I start? And tools like big data discovery help a lot there. >> So Data Lab is just play with the data, and get a feel for it. >> Yep. And do it in a way that breeds that experimentation. Not just to visualize the data, but change it. Reshape it. Build new models, build new classifications. The last thing I'd say is okay, did I get my ROI, do I have a cluster? Yes. Did I figure out something that looks interesting? Yes. Now I have an idea. What do I do next? It's how do I connect my insights from big data back to the tools that we use every day. >> So this is where the value of the data capital thing you're talking about. The Lab is essentially formulating the key connections for data pipes to connect in. >> Yep. >> Is that kind of the best way to think about it? >> Roughly, yes. Yeah you come up with new ideas, new data products ... >> So you've operationalized it by the third step. >> Yes. And then, how do you do that? In some cases it's, oh, I just push the data, I move the data over to my data warehouse. Which may make sense. But Oracle also has, I think I mentioned it before, Big Data SQL as a product. Which will let you keep that data in Hadoop, keep everything else in your data warehouse, and productization is that easy. So you don't have to worry about moving data. It helps a lot. >> Well that highlights one of the things we always hear all the time, which is skills. >> Chris: Yep. >> And people know SQL. >> Chris: They do. Everyone does. >> Everyone does. Chris, thanks so much for spending the time here On the Ground. Really appreciate chatting with you. This is theCube. Exclusive coverage on the ground at Oracle headquarters. I'm John Furrier, thanks for watching.

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

>> 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)

>> Narrator: The Cube presents On The Ground. (techno music) >> Hello and welcome, I'm Peter Burris With SilconANGLE Media Wikibon, and we're here today doing an on the ground, very important on the ground at Oracle's headquarters. This segment we're talking to Bhagat Nainani who is the group vice president of product development in Oracle's IOT organization. Welcome to The Cube. >> Thank you Peter. >> Now we've got a lot to talk about and because IOT is obviously at the fore front of many people's minds. It's one of the major initiatives happening in business, although a lot business people tell us that when somebody starts throwing IOT concepts at them they're not quite sure exactly what the parameters or what it means. So let's start here. A lot of hype about IOT, what does it mean to Oracle and Oracle's customers. >> Yes so there is definitely a lot of buzz about IOT and it is effecting a lot of industries whether it be manufacturing, transportation, home automation, fleet management, and we expect around 50 billion devices to be connected in the next two to three years and even the devices already connected to the edge and reading over 5 zettabytes of data and very little of that is actually-- >> Peter: Zettabytes. >> Exactly. >> So zettabytes is, megabytes, gigabytes, terabytes. >> Exabytes, then zettabytes. >> Lot of data. >> Lot of data and very little of that is being actually used. And if you look at top any analyst, it's they project somewhere between a one to five trillion dollar market right. But you know numbers aside, there is real business value here. I mean some companies are looking at IOT to improve operational efficiencies. Others want to use IOT to improve the customer experience or create new business models and new revenue streams. So there are clear opportunities here and that's what's affecting a lot of these organizations to the IOT. >> Now as a company tries to do something as complex as introducing a business model, they're going to need a lot of new technology as well as a lot of new good ideas. So what is Oracle's approach to engaging customers in this market place? >> So if many of our customers are going through these digital transformation or industry for all initiatives if you will. And there's some common factors in which in when it comes to IOT. Things like machine safety, productive, productive maintenance. Production reliability, worker productivity. Supply chain optimization. And all of these need extensions to existing applications or new types of applications. So Oracle's approach to IOT is to provide IOT enabled smart applications for things like manufacturing, fleet management, asset monitoring, equipment prognostics things like that. >> But that's much more than Oracle is currently providing right now. >> Exactly. >> So tell us a little bit about how this IOT ecosystem which is very broad, very complex, touches a lot of different parts of business, is embracing Oracle and how Oracle's trying to set up this appropriate partnerships so that customers can in fact get a complete solution. >> Sure, so, if you look at companies embarking on a journey to IOT, we see them go through sort of multiple phases. They start with just connecting their assets. You know so they have assets sitting on the field not connected to the business systems. They start connecting them so that they can get real time visibility for the assets and they can react more quickly to any problems that occur. So now they've reduced the time to react to any issues. That gives them sort of immediate ROI. But soon after they want to move to more of a proactive monitoring. So they're collecting information from all these assets and they want to do predictive analytics, and reduce unplanned down time and predict failures before they actually happen. Once they do that, then they want to transition to using IOT data into their core business processes. Whether it be back office, supply chain processes, ERP processes, or customer facing processes like CRM. Where they start to use IOT data to provide differentiated experiences. And the IOT offerings that we provide essentially help them go through this journey from connected assets all the way to service excellence. >> So when we're talking about connected assets, we're talking about the machinery, as well as the other resources at least that are either handling or running operations but also handling customer engagement. Now this suggests that there is going to be an intimate relationship between the technologies that are collecting all this data, sensing all this data, transmitting all this data, and the systems that are actually responsible for turning these feeds into something that is recognizable by the business as capable of generating a decision. Tell us a little bit about the relationship as you see it between IOT and big data. >> So recently we released an IOT Cloud service and the main difference in our approach to IOT versus many of the other vendors is we look at it from the applications out, as you said from the business out. We want to take the insides from these devices that data coming and make that actionable within your enterprise business processes all right. So the goal of IOT Cloud service is to actually bridge this gap between the operational technology and the IT world. And we do this be providing out of the box applications as well as platform components. I talked about applications like asset monitoring earlier. So there we have a out of the box app that helps you answer questions like how are my assets being used, where they're held. Do they need to be serviced. You look at it monitoring it's about how are my systems doing on the factory floor. Collect data from them constantly so that I can decide which ones to service in the next maintenance window right. Now I'm collecting all this data. This has to be backed by sort of platform components and the platform components fall in sort of this three broad categories right. Connect, analyze, integrate. So the connect part is where you bring the device, on board the devices. And provide bi-directional connectivity to them. So we have this concept called device virtualization which really simplifies how you interact with these devices. And provides a softer representation of those devices in the Cloud so now any application interacting with it doesn't need to know the gateways and the protocols that are used. On the analyze side there are two types of analysis. There is real time analysis which is done on the event stream. And then there's big data analysis that's done where you combine the real time stream along with contextual data sitting in your data lakes or your ERP systems. And then you apply predictive algorithms on top of it. We have a bunch of capabilities here. We provide business user friendly interfaces to model these event crossing functions. And we also provide built in algorithms using our big data services for things like equipment efficiency, remaining useful life, things like that. Right so, big data and IOT are quite related. If you look at the big data techniques like Spark, Hadoop, or some of these services, the type of data they all put it on, data with high velocity, high volume, high variety, IOT data has all the same characteristics of big data right. Now once you've analyzed this data, you also want to integrate it with your back end systems and that's where we provide out of the box connectivity with our SaaS apps as well as our E-business suite and our JD Edwards applications which are commonly used by our enterprise customers. You have the connectivity piece, you have the analytics, and you have the integration. You use these capabilities along with some of our other PaaS services like our business intentions Cloud service or our mobile Cloud source ability or IOT application. >> So you mentioned that these tools are easy to use. You also mentioned the distinction between IT and OT. This combination of IOT and big data analytics is touching a lot of different parts of the business. You have to be able to talk to operational technology people, IT people, you have to be able to talk to developers, you have to be increasingly be able to talk to business people. Historically, this all comes together when developers are engaged to create value out of all these piece parts. Talk a little bit about how Oracle is bringing greater sport to that developer community to bring this all together and turn it into value for a corporation. >> Sure so let's take an example here. Let's take the manufacturing example and then we'll I'll talk about manufacturing and then talk about some of the challenges there and how we enable that. You know we follow it up with community. In manufacturing world when you're doing these IOT kind of solutions, there's a common analysis done called a five M analysis. Man, machine, method, material, measurements. Now if you look at man, method, materials, all of this information is sitting in your ERP system or your databases. Where you have who operated on this system, what training did they receive, what techniques did they use, what raw material was used, who was the supplier. You look at machine and measurements, this is raw data coming from the equipment IOT data and measurements around the tests that were done on the system. You need to combine both of these to create a real predictable analytics solution for manufacturing right. Now today a lot of this has to be done using sophisticated sort of data scientists and you need sophisticated developers who can operate on these various big data components, whether it be Spark, Kafka, Cassandra, all of these. What we are doing at Oracle is trying to provide sort of tools and frame works that abstract away some of that and are targeted towards the citizen developer or the business users. So you don't need to have sophisticated data scientists. Right, we have tools such as big data discovery, big data prep, and other tools such as Apache learning which make it easy to build these kind of models. Now if you are a developer who wants to write all of this from scratch, you will then when you're dealing with different types of structure and unstructured store, you need an abstraction layer that simplifies how you interact with this, how you query it. And so we are providing sequel like interfaces that they're already familiar with. So whether it's a structured store or unstructured store and well, it doesn't matter which native query interface I suppose. You provide a standardized list so that they easily operate on that data. Now even that takes a long time to build an IOT solution so that's where our out of the box applications come in and by providing these out of the box applications for specific use cases around asset monitoring, equipment prognostics, supply chain, we are really trying to reduce the time it takes for you to deploy an IOT solution because these applications already have those built in algorithms. All we are doing is configuring them, providing some parameters, but you don't need to write the algorithm. You take your industrial gateways, connect the devices, and you're ready to go. >> So do you think that there's going to be new applications utilizing some of these new methods or models, or is it going to be just an extension of a lot of the traditional, more operational, financial oriented applications that are already in place. >> It's a combination. So when it comes to things like you know existing maintenance applications, or existing service applications, the interfaces of them used to be you know manual where someone would get a call and they would enter an order into a system or a work order. With IOT those are being extended to have new channels. So for example in our service Cloud, we have added a new channel with IOTs so now the equipment itself reports a problem and when the service technician gets a work order, they already know which part has gone bad. So the whole manual step is taken away. There are other areas where companies are trying to transition to this product as a service model, right. And so those need new ways of monetizing, new types of application for your capture and utilization. There you will need some new application. So it is a combination of the two. >> Now you mentioned earlier the five M model. Man, materials, machines. >> Method. >> Measurement. And method. Just to give you to date myself, the first class on technology I took talked about the four M plus I model. Men, materials, machines, money, and information. So didn't have method. But let's come back to at least what we think at Wikibon, SiliconANGLE, is still the most important piece, men. Or people, the individuals. We're talking about the, we're talking about IOT here, but presumably we're going to also start bringing in those crucial interfaces so that people become a more engaged feature of how these loops are working. Between sensing, and analyzing and printing models, and enacting something in the market place. Tell us a little bit about how Oracle sees the role that people are going to play in these transitions that we're talking about. >> So if you look at the service industry people right. I mean this I give you the example of automatically creating a work order. But with IOT enabled devices, it is transitioning to more of a self service, model or assisted service model where now people have much more information available to them at their fingertips when they are actually looking at problems. Whether it be some part that has failed or a customer has reported an issue, now you can interact with these devices remotely and so now you have significant reduced the time to actually act on any problems and overall improve the customer experience. There is the people part in sort of creating those models and providing sort of information to enrich those models because you know a data scientist can get all the information from the devices and create the models, but you also need the experts who know you know how these systems are supposed to behave. How they were designed, how they behave under certain environment conditions. You take that into account along with the real data that you're getting and that way you can predict how this particular equipment will behave in the field right. >> So Oracle open world is just around the corner. One quick idea. What are you looking for from an Oracle IOT perspective. >> From an Oracle IOT perspective, one of the things we were really looking forward to is the applications that you know we are launching as well as many other applications within Oracle who have now embedded IOT within their offering. So to make those applications smarter and you hear a lot about that at open world. >> And that is one of their key tests of adoption is how fast that happens. Bhagat Nainani thank you very much for being here. Group vice president for IOT product development at Oracle. Again, Peter Burris from The Cube. Thank you very much. >> Baghat: Thank you Peter. (techno 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)

>> Narrator: The Cube presents, On the Ground. (techno music) >> Hi I'm Peter Burris, welcome to, an On the Ground here at Oracle Headquarters, with Silicon Angle media The Cube. Today we're talking to JP Dijcks, who is the master product manager inside, or one of the master product managers, inside Oracle's big data product group, welcome JP. >> Thank you Peter. >> Well, we're going to talk about how developers get access to this plethora, this miasma, this unbelievable complexity of data that's being made possible by IOT, traditional applications, and other sources, how are developers going to get access to this data? >> That's a good question Peter, I still think that one of the key aspects to getting access to that data is SQL, and so that's one of the ways we are driving, try to figure out, can we get the Oracle SQL engine, and all the richness of SQL analytics enabled on all of that data, no matter the what the format is, or no matter where it lives, how can I enable those SQL analytics on that, and then obviously we've all seemed to shift in APIs, and languages, like people don't necessarily always want to speak SQL and write SQL questions, or write SQL queries. So how do we then enable things like R, how do we enable plural, how do we enable Python, all sorts of things like that, how do we do that, and so the thought we had was, can we use SQL as the common meta-data interface? And the common structure around some of this, and enable all of these languages on top of that through the database. So that's kind of the baseline of what we're thinking of, of enabling this to developers and large communities of users. So that's SQL as an access method, do you also envision that SQL will also be a data creation language? As we think about how to envision big data coming together from a modeling perspective. >> So I think from a modeling perspective the meta-data part we certainly look at as a creation or definition language is probably the better word, how do I do structured queries, 'cause that's what SQL stands for, how do I do that on Jason documents, how do I do that on IOT data as you said, how do I get that done, and so we certainly want to create the meta-data, in like a very traditional data base catalog, or if you compare to a Hive Catalog, very much like that. The execution is very different, it uses the mechanisms under the cover that no SQL data bases have, or that Hadoop HDFS offer, and we certainly have no real interest in doing insert into Hadoop, 'cause the transaction mechanisms work very very differently, so its really focused on the meta-data areas and how do I expose that, how do I classify and categorize that data in ways people know and have seen for years. >> So that data manipulation will be handled by native tools, and some of the creations, some of the generation, some of the modeling will be handled now inside SQL, and there are a lot of SQL folks out there that have pretty good afinity for how to work with data. >> That's absolutely correct. >> So that's what it is, now how does it work? Tell us a bit about how this big data SQL is going to work, in a practical world. >> Okay. So we talked about the modeling already. The first step is that we extend the Oracle database and the catalog to understand things like Hive objects or HDFS kind of, where does stuff live. So we expanded and so we found a way to classify the meta-data first and foremost. The real magic is leveraging the Hadoop stack, so you ask a BI question and you want to join data in Oracle transactions, finance information, let's say with IOT data, which you'd reach out to HDFS for, big data SQL runs on the Hadoop notes, so it's local processing of that data, and it works exactly as HDFS and Hadoop work, in other words, I'm going to do processing local, I'm going to ask the name note which blocks am I supposed to read, that'll get run, we generate that query, we put it down to the Hadoop notes. And that's when some of the magic of SQL kicks in, which is really focused on performance, its performance, performance, performance, that's always the problem with federated data, how do I get it to perform across the board. And so what we took was, >> Predictably. >> Predictably, that's an interesting one, predictable performance, 'cause sometimes it works, sometimes it doesn't. So what we did is we took the exadata that was stored on the software, with all the magic as to how do I get a performance out of a file system out of IO, and we put that on the Hadoop notes, and then we push the queries all the way down to that software, and it does filtering, it does predicate pushdown, it leverages features like Parquet and ORC on the HDFS side, and at the end of the day, it kind of takes the IO requests, which is what a SQL query gives, feeds it to the Hadoop notes, runs it locally, and then sends it back to the database. And so we filter out a lot of the gunk we don't need, 'cause you said, oh I only need yesterdays data, or whatever the predicates are, and so that's how we think we can get an architecture ready that allows the global optimization, 'cause we can see the entire ecosystem in its totality, IOT, Oracle, all of it combined, we optimized the queries, push everything down as far as we can, algorithms to data, not data to algorithms, and that's how we're going to run this performance, predictably performance, on all of these pieces of data. >> So we end up with, if I got this right, let me recap, so we've got this notion that for data creation, data modeling, we can now use SQL, understood by a lot of people, doesn't preclude us from using native tools, but at least that's one place where we can see how it all comes together, we continue to use local tools for the actual manipulation elements. >> Absolutely. >> We are now using synergy like structures so we can push algorithm down to the data, so we're moving a small amount of data to a large amount of data, 'cause its cost down and improves predictability, but at the same time we've got meta-data objects that allow us to anticipate with some degree of predictability how this whole thing will run, and how this will come together back at the keynote, got that right? >> Got that right. >> Alright, so, next question is what's the impact of doing it this way? Talk a bit about, if you can, about how its helping folks who run data, who build applications, and who actually who are trying to get business value out of this whole process. >> So if we start with the business value, I think the biggest thing we bring to the table is simplicity, and standardization. If I have to understand how is this object represented in NoSQL, how in HDFS, how did somebody put a Jason file in here, I have to now spend time on literally digging through that, and then does it conform, do I have to modify it, what do I do? So I think the business value comes out of the SQL layer on top of it. It all looks exactly the same. It's well known, it's well understood, its far quicker to get from, I've got a bunch of data, to actually building a VI report, building a dashboard, building KPIs, and integrating that data, there's nothing new to data, its a level of abstraction we put on top of this, whether you use API or in this case we use SQL, 'cause that's the most common analytics language. So that's one part of how it will impact things. The 2nd is, and I think that's where the architecture is completely unique, we keep complete control of the query execution, from the meta-data we just talked about, and that enables us to do global optimization, and we can, and if you think this through a little bit, and go, oh global optimization sounds really cool, what does that mean? I can now actually start pushing processing, I can move data, and its what we've done in the exadata platform for years, data lives on disk, oh, Peter likes to query it very frequently, let's move it up to Flash, let's move it up to in-memory, let's twist the data around. So all the sudden we got control, we understand what gets queried, we understand where data lives, and we can start to optimize, exactly for the usage pattern the customer has, and that's always the performance aspect. And that goes to the old saying of, how can I get data as quickly to a customer when he really needs it, that's what this does, right, how can I optimize this? I've got thousands of people querying certain elements, move them up in the stack and get the performance and all these queries come back in like seconds. Regulatory stuff that needs to go through like five years of data, let's put it in cheap areas, and let's optimize that, and so the impact is cheaper and faster at the end of the day, and all 'cause there's a singular entity almost that governs the data, it governs the queries, it governs the usage patterns, that's what we uniquely bring to the table with this architecture. >> So I want to build on the notion of governance, because actually one of the interesting things you said was the idea that if its all under a common sort of interfaces, then you have greater visibility, where the data is, who owns it, et cetera. If you do this right, one of the biggest challenges that business are having is the global sense of how you govern your data. If you do this right, are you that much closer to having a competent overall data governance? >> I think we were able to set up a big step forward on it, and it sounds very simple, but we now have a central catalog, that actually understands what your data is and where it lives, in kind of like a well-known way, and again it sounds very simple but if you look at silos, that's the biggest problem, you have multiple silos, multiple things are in there, nobody knows really what's in there, so here we start to publish this in like a common structural layer, we have all the technical meta-data, we track who queries what, who does all those things, so that's a tremendous help in governance. The other side of course, because we still use native tools to let's say manipulate some data, or augment or add new data, we now are going to tie in a lot of the meta-data, that comes from say the Hadoop ecosystem, again into this catalog, and while we're probably not there yet just today on the end to end governance everything's kind of out of the box, here we go. >> And probably never will be. >> And we probably never will, you're right, and I think we set a major step forward with just consolidating it, and exposing people to all the data the have, and you can run all the other tools like, crawl my data and check box anything that says SSN, or looks like a social security number, all of those tools are are still relevant. We just have a consolidated view, dramatically improved governance. >> So I'm going to throw you a curve ball. >> Sure. >> Not all data I want to use is inside my business, or is being generated by sensors that I control, how does big data SQL and related technologies play a role in the actual contracting for additional data sources, and sustaining those relationships that are very very fundamental, how data's shared across organizations. Do you see this information being brought in under this umbrella? Do you see Oracle facilitating those types of relationships, introducing standards for data sharing across partnerships becomes even easier? >> I'm not convinced that big data SQL as a technology is going to solve all the problems we see there, I'm absolutely convinced that Oracle is going to work towards that, you see it in so many acquisitions we've done, you see it in the efforts of making data as a service available to people, and to some extent big data SQL will be a foundation layer to make BI queries run smoother across more and more and more pillars of data. If we can integrate database, Hadoop, and NoSQL, there's nothing that says, oh and by the way, storage cloud. >> And we have relatively common physical governance, that I have the same physical governance, and you have the same physical governance, now its easier for us to show how we can introduce governance across our instances. >> Absolutely, and today we focus a lot on HDFS or Hadoop as the next data pillar, storage cloud, ground to cloud, all of those are on the roadmap for big data SQL to catch up with that, and so if you have data as a service, let's declare that cloud for a second, and I have data in my database in my Hadoop cluster, again, all now becomes part of the same ecosystem of data, and it all looks the same to me from a BI query perspective, from an analytics perspective. And then the, how do I get the data sharing standards set up and all that, part of that is driving a lot of it into cloud, and making it all as a service, 'cause again you put a level of abstraction on top of it, that makes it easier to consume, understand where it came from, and capture the meta-data. >> So JP one last question. >> Sure. >> Oracle opens worlds on the horizon, what are you looking for, or what will your customers be looking for as it pertains to this big data SQL and related technologies? >> I think specifically from a big data SQL perspective, is we're going to drive the possible adoption scope much much further, today we work with HDFS an we work with Oracle database, we're going to announce certain things like exadata, Hadoop will be supportive, we hold down super cluster support, we're going to dramatically expand the footprint big data SQL will run on, people who come for big data SQL or analytics sessions you'll see a lot of the roadmap looking far more forward. I already mentioned some things like ground to cloud, how can I run big data SQL when my exadata is on Premis, and then the rest of my HDFS data is in the cloud, we're going to be talking about how we're going to do that, and what do we think the evolution of big data SQL is going to be, I think that's going to be a very fun session to go to. >> JP Dijcks, a master product manager inside the Oracle big data product group, thank you very much for joining us here On the Ground, at Oracle headquarters, this is The Cube.

>> Voiceover: On the Ground, presented by The Cube. Here's your host John Furrier. (techno music) >> Hello, everyone. Welcome to a special On the Ground executive interview with Rob Bearden, the CEO of Hortonworks. I'm John Furrier with The Cube. Rob, welcome to this On the Ground. >> Thank you. >> So I got to ask you, you're five years old this year, your company Hortonworks in June, have Hadoop Summit coming up, what a magical run. You guys went public. Give us a quick update on Hortonworks and what's going on. The five-year birthday, any special plans? >> Well, we're going to actually host the 10-year birthday party of Hadoop, which is you know, started at Yahoo! and open-source community. So everyone's invited. Hopefully you'll be able to make it as well. We've accomplished a lot in the last five years. We've grown to over 1000 employees, over 900 customers. This year is our first full year of being a public company, and the street has us at $265 million dollars in billings. So tremendous progress has happened and we've seen the entire data architecture begin to re-platform around Hadoop now. >> CEOs across the globe are facing profound challenges, data, cloud, mobile, obviously this digital transformation. What are you seeing our there as you talk to your customers? >> Well they view that the digital transformation is a massive opportunity for value creation, for that enterprise. And they realize that they can really shift their business models from being very reactive post-transaction to actually being able to consolidate all of the new paradigm data with the existing transaction data and actually get to a very pro-active model pre-transaction. And so they understand their customer's patterns. They understand the kinds of things that their customers want to buy before they ever engage in the procurement process. And they can make better and more compelling offers at better price points and be able to serve their customers better, and that's really the transformation that's happening and they realize the value of that creation between them and their customer. >> And one of the exciting things about The Cube is we go to all these different industry events and you were speaking last week at an event where data is at the center of the value proposition around digital transformation, and that's really been the key trend that we've been seeing consistently, that buzz word digital transformation. What does that mean to you? Because this is coming up over and over again around this digital platform, digital weathers, digital media or digital engagement. It's all around data. What's your thoughts and what is from your perspective digital transformation? >> Well, it's about being able to derive value from your data and be able to take that value back to your customers under your supply chain, and to be able to create a completely new engagement with how you're managing your interaction with your customers and your supply chain from the data that they're generating and the data that you have about them. >> When you talk to CEOs and people in the business out in the field, how much of this digital transformation do you see as real in terms of progress, real progress? In terms of total transitions, or is it just being talked about now? What's your progress bar meter? How would you peg this trend? >> I would say we're at four and I believe we'll be at six by the end of 2016. And it's one of the biggest movements I've seen since the '90s and ERP, because it's so transformational into the business model by being able to transform the data that we have about our collective entity and our collective customer and collective supply chain, and be able to apply predictive and real-time interactions against that data as events and occurrences are happening, and to be able to quickly offer products and services, and the velocity that that creates to modernization and the value creation back is at a pace that's never been able to happen. And they've really understood the importance of doing that or being disintermediated in their existing spaces. >> You mention ERP, it kind of shows our age, but I'll ask the question. Back in the '90s ERP, CRM, these were processes that were well known, that people automated with technology which was at that time unknown. You got a riser-client server technology, local area networking, TCP IP was emerging, so you got some unknown technology stuff happening, but known processes that were being automated and hence saw that boom. Now you mention today, it's interesting because Peter Burris at Wikibon's thesis says today the processes are unknown and the technology's known, so there's now a new dynamic. It's almost flipped upside-down where this digital transformation is exact opposite. IoT is a great use case where all these unknown things are coming into the enterprise that are value opportunities. Get the technology knows, so now the challenge is how to use technology, to deploy it, and be agile to capture and automate these future and/or real-time unknown processes. Your thoughts on that premise. >> The answers are buried in the data, is the great news, and so the technology as you said is there, and you have these new, unknown processes through Internet of Things, the new paradigm data sets with sensors and clickstream and mobile data. And the good news is they generate the data and we can apply technology to the data through AI and machine learning to really make sure that we understand how to transform the value out of that, out of those data sets. >> So how does IT deal with this? 'Cause going back 30 years IT was a clear line of sight, again, automating those known processes. Now you have unknown opportunities, but you have to be in a position for that. Call that cloud, call that DevOps, call that data driven, whatever the metaphor is. People are being agile, be ready for it. How is that different now and what is the future of data in that paradigm? And how does a customer come to grips and rationalize this notion of I need a clear line of sight of the value, not knowing what the processes is about data. What should they be doing? >> Well, we don't know the processes necessarily, per se, but we do know what the data is telling us because we can bring all that data under management. We can apply the right kind of algorithms, the right kind of tools on it, to give us the outcomes that we want and have the ability to monetize and unlock that value very quickly. >> Hortonworks architecture is kind of designed now at the last Hadoop Summit in Dublin. We heard about the platform. Your architecture's going beyond Hadoop, and it says Hadoop Summit and Hadoop was the key to big data. Going beyond Hadoop means other things. What does that mean for the customer? Because now they're seeing these challenges. How does Hortonworks describe that and what value do you bring to those customers? >> Big data was about data at rest and being able to drive the transformation that it has, being able to consolidate all the transactional platforms into central data architecture. Being able to bring all the new paradigm data sets to the mobile, the clickstream, the IoT data, and bring that together and be able to really transition from being reactive post-transaction to be able to be predictive and interactive pre-transaction. And that's a very, very powerful value proposition and you create a lot of value doing that, but what's really learned through that process is in the digital transformation journey, that actually the further upstream that we can get to engaging with the data, even if we can get to it at the point of origination at the furthest edge, at the point of center, at the actual time of clickstream and we can engage with that data as those events and occurrences are happening and we can process against those events as their happening, it creates higher levels of value. So from the Hortonworks platform we have the ability to manage data at rest with Hadoop, as well as data in motion with the Hortonworks data flow platform. And our view is that we must be able to engage with all the data all the time. And so we bring the platforms to bring data under management from the point of origination all the way through as it's in motion, and to the point it comes at rest and be able to aggregate those interactions through the entire process. >> It's interesting, you mention real-time, and one of the ideas of Hadoop was it was always going to be a data warehouse killer, 'cause it makes a lot of sense. You can store the data. It's unstructured data and you can blend in structured on top of that and build on top of that. Has that happened? And does real-time kind of change that equation? Because there's still a role for a data warehouse. If someone has an investment are they being modernized? Clear that up for me because I just can't kind of rationalize that yet. Data warehouses are old, the older ones, but they're not going away any time soon from what we're hearing. Your thoughts as Hadoop as the data warehouse killer. >> Yeah, well, our strategy from day one has never been to go in and disintermediate any of the existing platforms or any of the existing applications or services. In fact, to the contrary. What we wanted to do and have done from day one is be able to leverage Hadoop as an extension of those data platforms. The DW architecture has limitations to it in terms of how much data pragmatically and economically is really viable to go into the data warehouse. And so our model says let's bring more data under management as an extension to the existing data warehouses and give the existing data warehouses the ability to have a more holistic view of data. Now I think the next generation of evolution is happening right now and the enterprise is saying that's great. We're able to get more value longer from our existing data warehouse and tools investment by bringing more data under management, leveraging a combined architecture of Hadoop and data warehouse. But now they're trying to redefine really what does the data warehouse of the future look like, and it's really about how we make decisions, right? And at what point do we make decisions because in the world of DW today it assumes that data's aggregated post-transaction, right? In the new world of data architecture that's across the IT landscape, it says we want to engage with data from the point it's originated, and we want to be able to process and make decisions as events and as occurrences and as opportunities arise before that transaction potentially ever happens. And so the data warehouse of the future is much different in terms of how and when a decision's made and when that data's processed. And in many cases it's pre-transaction versus post-transaction. >> Well also I would just add, and I want to get your thoughts on this, real-time, 'cause now in the moment at the transaction we now have cloud resources and potentially other resources that could become available. Why even go to the data warehouses? So how has real-time changed the game? 'Cause data in motion kind of implies real-time whether it's IoT or some sort of bank transaction or something else. How has real-time changed the game? >> Well, it's at what point can we engage with the customer, but what it really has established is the data has to be able to be processed whether it be on Prim, in the cloud, or in a hybrid architecture. And we can't be constrained by where the data's processed. We need to be able to take the processing to the data versus having to wait for the data to come to the processing. And I think that's the very powerful part of cloud, the on Prim, and software to find networking, and when you bring all of those platforms together, you get the ability to have a very powerful and elastic processing capability at any point in the life cycle of the data. And we've never been able to put all those pieces together on an economically viable model. >> So I got to ask you, you guys are five years old in June, Hadoop's only 10 years old. Still young, still kind of in the early days, but yet you guys are public company. How are you guys looking at the growth strategy for you guys? 'Cause the trend is for people to go private. You guys went public. You're out in the open. Certainly your competitor Cloud ARIS is private, but people can get that they're kind of behind the curtain. Some say public with a $3 billion dollar graduation, but for the most part you're public. So the question is how are you guys going to sustain the growth? What is the growth strategy? What's your innovation strategy? >> Well if you look at the companies that are going private, those are the companies that are the older platforms, the older technologies, in a very mature market that have not been able to innovate those core platforms and they sort of reached their maturity cycle, and I think going private gives them the ability to do that innovation, maybe change their licensing model, the subscription, and make some of the transformations they need to make. I have no doubt they'll be very successful doing that. Our situation's much different. As the modern IT landscape is re-architecting itself almost across every layer. If you look at what's happening in the networking layer going to SDN. Certainly in our space with data and it's moving away from just transactional siloed environments to central data architectures and next generation data platforms. And being able to go all the way out to the edge and bring data under management through the entire movement cycle. We're in a market that we're able to innovate rapidly. Not only in terms of the architecture of the data platform being able to bring batch, real-time applications together simultaneously on a central data set and consolidate all of the data, but also then be able to move out and do the data in motion and be able to control an entire life cycle. There's a tremendous amount of innovation that's going to happen there, and these are significant growth markets. Both the data in motion and the data at rest market. The data at rest market's a $50 billion dollar marketplace. The data in motion market is a $1 trillion dollar TAM. So when you look at the massive opportunity to create value in these high growth markets, in the ability to innovate and create the next generation data platforms, there's a lot of room for growth and a lot of room for scale. And that's exactly why you should be public when you're going though these large growth markets in a space that's re-platforming, because the CIO wants to understand and have transparent visibility into their platform partners. They want to know how you're doing. Are you executing the plan? Or are you hiding behind a facade of one perception or another. >> Or pivoting or some sort of re-architecture. >> Right, so I think it's very appropriate in a high growth, high innovation market where the IT platforms are going through a re-architecture that you actually are public going through that growth phase. Now it forces discipline around how you operationalize the business and how you run the business, but I think that's very healthy for both the tech and the company. >> Michael Dell told me he wanted to go private mainly because he had to do some work essentially behind the curtain. Didn't want the 90-day shot clock, the demands of Wall Street. Other companies do it because the can't stand alone. They don't have a platform and they're constantly pivoting internally to try to grope and find that groove swing, if you will. You're saying that you guys have your groove swing and as Dave Velanti always says, always get behind a growing total adjustment market or TAM, you saying that. Okay, I buy that. So the TAM's growing. What are you guys doing on the platform side that's enabling your customers to re-platform and take advantage of their current data situation as well as the upcoming IoT boom that's being forecasted? >> Well, the first thing is the genesis of which we started the company around, which is we transformed Hadoop from being a batch architecture, single data set, single application, to being able to actually manage a central data architecture where all data comes under management and be able to drive and evolve from batch to batch interactive and real-time simultaneously over that central data set. And then making sure that it's truly an enterprise viable, enterprise ready platform to manage mission critical workloads at scale. And those are the areas where we're continuing to innovate around security, around data governance, around life cycle management, the operations and the management consoles. But then we want to expand the markets that we operate in and be world class and best tech on planet Earth for that data at rest and our core Hadoop business. But as we then see the opportunities to go out to the edge and from the point of origination truly manage and bring that data under management through its entire life cycle, through the movement process and create value. And so we want to continue to extend the reach of when we have data under management and the value we bring to the data through its entire life cycle. And then what's next is you have that data in its life cycle. You then move into the modern data applications, and if you look at what we've done with cyber security and some of the offerings that we've engaged in the cyber security space, that was our first entry. And that's proven to be a significant game changer for us and our customers both. >> Cyber security certainly a big data problem. Also a cloud opportunity with the horsepower you can get with computing. Give us the update. What are you seeing there from a traction standpoint? What's some of the level of engagements your having with enterprises outside of the NSA and the big government stuff, which I'm sure they're customers don't have to disclose that, but for the most part a normal enterprise are constantly planning as if they are already attacked and they're having different schemes that they're deploying. How are they using your platform for that right now? >> Well, the nature of attacks has changed. And it's evolved from just trying to find the hole in the firewall or where we get into the gateway, to how we find a way through a back door and just hang out in your network and watch for patterns and watch for the ability to aggregate relationships and then pose as a known entity that you can then cascade in. And in the world of cyber security you have to be able to understand those anomalies and be able to detect those anomalies that sit there and watch for their patterns to change. And as you go through a whole life cycle of data management between a cloud on Prim and a hybrid architecture, it opens up many, many opportunities for the bad guys to get in and have very new schemes. And our cyber security models give the ability to really track how those anomalies are attaching, where the patterns are emerging, and to be able to detect that in real-time and we're seeing the major enterprises shift to these new models, and it's become a very big part of our growth. >> So I got to change gears and ask you about open-source. You've been an open-source really from the beginning, I would call first generation commercial. But it was not a tier one citizen at that time. It was an alternative to other privatery platforms, whether you look at the network stack or certainly from software. Now today it's tier one. Still we hear business people kind of like, well, open-source. Why should a business executive care about opens-source now? And what would you say to that person who's watching about the benefits of open-source and some of the new models that could help them. >> Well, open-source in general's going to give a number of things. One, it's going to probably provide the best tech, the most innovation in a space, whether that be at the network layer or whether that be at the middle wear layer, the tools layer or certainly the data layer. And you're going to see more innovation typically happen on those platforms much faster and you've got transparent visibility into it. And it brings an ecosystem with it and I think that's really one of the fundamental issues that someone should be concerned with is what does the ecosystem around my tech look like? An open-source really draws forward a very big ecosystem in terms of innovators of the tech, but also enablers of the tech and adopters of the tech in terms of incremental applications, incremental tool sets. And what it does and the benefit to the end customer is the best tech, the most innovation, and typically operating models that don't generate lock in for 'em, and it gives them optionality to use the tech in the most appropriate architecture in the best economic model without being locked in to a proprietary path that they end up with no optionality. >> So talk about the do-it-yourself mentality. In IT that's always been frowned upon because it's been expensive, time-consuming, yet now with organic open-source and now with cloud, you saw that first generation do-it-yourself, standing up stuff on Amazon, whatnot, is being very viable. It funded shadow IT and a variety of other great things around virtualization, visualization, and so on. Today we're seeing that same pattern swing back to do-it-yourself, is good for organic innovation but causes some complexities. So I want to get your thoughts on this because this seems to be a common thread on our Cube interviews and at Hadoop Summit and at Big Data SV as part of Big Data Week when we were in town. We heard from customers and we heard the following: It's still complex and the total cost of ownership's still too high. That seems to be the common theme for slowing down the rapid acceleration of Hadoop and its ecosystem in general. One, do you agree with that? And two, if so, or what would be than answer to make that go faster? >> Well, I think you're seeing it accelerate. I think you're seeing the complexities dwindle away through both innovation and the tech and the maturing of the tech, as well as just new tool sets and applications that are leveraging it, that take away any complexity that was there. But what I think has been acknowledged is, the value that it creates and that it's worth the do-it-yourself and bringing together the spare techs because the innovation that it brings, the new architectures and the value that it creates as these platforms move into the different use cases that they're enabling. >> So I got to ask you this question. I know you're not going to like it and all the people always say, well John, why does everyone always ask that same question? You guys have a radically different approach than Cloudera. It's the number one question. I get ask them about Cloudera. Cloudera, ask them about Hortonworks. You guys have been battling. They were first. You guys came right fast followers second. With the Yahoo! thing we've been following you guys since day one. Explain the difference between Cloudera, because now a couple things have changed over the past few years. One is, Hadoop wasn't the be all end all for big data. There's been a lot of other things certainly SPARK and some other stuff happening, but yet now enterprises are adopting and coexisting with other stuff. So we've seen Cloudera make some pivots. They certainly got some good technology, but they've had some good right answers and some wrong answers. How've you guys been managing it because you're now public, so we can see all the numbers. We know what the business is doing. But relative to the industry, how are you guys compared to Cloudera? What's the differences? And what are you guys doing differently that makes Hortonworks a better vendor than Cloudera? >> I can't speak to all the Cloudera models and strategies. What I'll tell you is the foundation of our model and strategy is based on. When we founded the company we were as you mentioned, three of four years post Cloudera's founding. We felt like we needed to evolve Hadoop in terms of the architecture, and we didn't want to adopt the batch-oriented architecture. Instead we took the core Hadoop platform and through YARN enabled it to bring a central data architecture together as well as be able to be generating batch interactive in real-time applications, leveraging YARN as the data operating system for Hadoop. And then the real strategy behind that was to open up the data sets, open up the different types of use cases, be able to do it on a central data architecture. But then as other processing engines emerged, whether it be a SPARK as you brought up or some of the other ones that we see coming down the pipe, we can then integrate those engines through YARN onto the central data platform. And we open up the number of opportunities, and that's the core basis. I think that's different than some of the other competitor's technology architecture. >> Looking back now five years, are there moves that you were going to make that others have made, that you look back and say I'm glad we didn't do that given today's landscape? >> What I'm glad we did do is open up to the most use cases and workloads and data sets as possible through YARN, and that's proven to be a very, very, fundamentally differentiation of our model and strategy for anybody in the Hadoop space certainly. And I'm also very happy that we saw the opportunity about a year ago that it needed to be more than just about data at rest on Hadoop, and that actually to truly be the next generation data architecture, that you've got to be able to provide the platforms for data at rest and data in motion and our acquisition of Onyara, to be able to get the NiFi technology so that we're truly capturing the data from the point of origination all the way through the movement cycle until it comes at rest has given us now the ability to do a complete life cycle management for an entire data supply chain. And those decisions have proven to be very, very differentiation between us and any of our other competitors and it's opened up some very, very big markets. More importantly, it's accelerated the time to value that our customers get in the use cases that they're enabling through us. >> How would you talk about the scenario that people are saying about Hadoop not being the end all be all industry? At the same time, 'cause big data, as Aroon Merkey said on the Keblan Dublin. It's bigger than Hadoop now, but Hadoop has become synonymous with big data generally. Where's the leadership coming from in your mind? Because we're certainly not seeing it on the data warehouse side, 'cause those guys still have the old technology, trying to co-exist and re=platform for the future. So question is, is Hortonworks viewing Hadoop as still leading generically as a big data industry or has it become a sidebar of the big data industry? >> Of Hadoop? Hadoop is the platform, and we believe ground zero for big data. But we believe it's bigger than that. It's about all data and being able to manage the entire life cycle of all data, and that starts from the point of origination, until it comes at rest, and be able to continue to drive that entire life cycle. Hadoop certainly is the underpinning of the platform for big data, but it's really got to be about all data. Data at rest, data in motion, and what you'll see is the next leg in this is, the modern data applications that then emerge from that. >> How has the ecosystem in the Hadoop industry, I would agree with by the way the Hadoop players are leading big data in general in terms of innovation. The ecosystem's been a big part of it. You guys have invested in it. Certainly a lot of developers and open-source. How has the ecosystem changed given the current situation from where it was? And where do you see the ecosystem going? With the re-platforming not everyone can have a platform. There's a ton of guys out there that have tools, that are looking for a home, they're trying to figure out the chessboard on what's going on with the ecosystem. What's your thoughts of the current situation and how it will evolve in your view? >> Well, I think one of the strongest statements from day one is whether it's EDW or BI or relational, none of the traditional platform players say the way you solve your big data problem is with my platform. They to a company have a Hadoop platform strategy of some form to bring all of that huge volume of big data under management, and it fits our model very well in that we're not trying to disintermediate, but extend those platforms by leveraging HDP as an extension of their platform. And what that's done is it's created pool markets. It's brought Hadoop into the enterprise with a very specific value proposition in use case, bringing more data under management for that tool, that application, or that platform. And then the enterprises has realized there's other opportunities beyond that. And new use cases and new data sets, we can also gain more leverage from. And that's what's really accelerated-- >> So you see growth in the ecosystem? >> We're actually seeing exponential acceleration of the growth around the ecosystem. Not only in terms of the existing platform and tools and applications for either adopting Hadoop, but now new start-up companies building completely from scratch applications just for the big data sets. >> Let's talk about STARS. We were talking before we sat down about the challenges being an entrepreneur. You mentioned the exponential acceleration of entrepreneurs coming into the ecosystem. That's a safe harbor right now. It seems to be across the board. And a lot of the big platforms have robust, growing ecosystems. What's the current landscape of STARS? I know you're an active investor yourself and you're involved in a lot of different start-up conversations and advisor. What's your view of the current landscape right now? Series A, B, C, growth. Stalling. What needs to be in place for these companies to be successful? What are some of the things that you're seeing? >> You have to be surgically focused right now or on a very particular problem set, maybe even by industry. And understand how to solve the problem and have an absolute correlation to a value proposition and a very well defined and clear model of how you're going to go solve that problem, monetize it, and scale. Or you have to have an incredibly well-financed and deep war chest to go after a platform play that's going after a very large TAM that is enabling a re-platforming at one of the levels and the new IT landscape. >> So laser focus in a stack or vertical, and/or a huge cash from funded benchmark or other VCs, tier one VCs, to have a differentiator. They have to have some sort of enabler. >> To enable a next generation platform and something that's very transformational as a platform that really evolves the IT stack. >> What strategies would you advise entrepreneurs in terms of either white spaces to attack and/or their orientation to this new data layer? Because if this plays out as we were talking about, you're going to have a horizontal data layer where you need eye dropper ability. Need to have data in motion, but data aware. Smart data you integrate into disparate systems. Breaking down the siloed concept. How should an entrepreneur develop or look at that? Is there a certain model you've seen work successfully? Is there a certain open-source group they can jump into? What thoughts would you share? 'Cause this seems to be the toughest nut to crack for entrepreneurs. >> Right now you're seeing a massive shift in the IT data architecture, is one example. You're seeing another massive shift in the network architecture. For example, the SDN, right? You're seeing I think a big shift in the kinds of applications getting away from application functionality to data enabled applications. And I think it's important for the entrepreneur to understand where in the landscape do they really want to position? Where do they bring intellectual capital that can be monetized? Some of the areas that I think you'll see emerge very quickly in the next four, six, eight quarters are the new optimization engines, and so things around AI and machine learning. And now that we have all of the data under management through its entire life cycle, how do I now optimize both where that data's processed, in the cloud or on Prim, or as it's in motion. And there's a massive opportunity through software defined networking to actually come in and now optimize at the purest price point and/or efficiency where that data's managed, where that data's stored, and let it continue to reap the benefits. Just as Amazon's done in retail, if you like this, you should look at that. Just as Yahoo! did, I'll point out with Hadoop, it's advertising models and strategies of being able to put specific content in front of you. Those kinds of opportunities are now available for the processing and storage of data through the entire life cycle across any architectural strategy. >> Are you seeing data from a developer's standpoint being instrumental in their use cases? Meaning as I'm developing on top a data platforms like Hortonworks or others, where there's disparate data, what's their interaction? What's their relationship to the data? How are they using it? What do they need to know? Where's the line in terms of their involvement in the data? >> Well, what we're seeing is very big movement with the developed community that they now want to be able to just let the data tell them where the application service needs to be. Because in the new world of data they understand what the entity relationships are with their customers and the patterns that their customers happening. They now can highly optimize when their customers are about to cross over into from one event to the other, and what that typically means and therefore what the inverted action should be to create the best experience with their customer, to create a higher level of service, to be able to create a better packaged price point at a better margin. They also have the ability to understand it in real-time based on what the data trend is flowing, how well their product's performing. Any obstacles or issues that are happening with their product. So they don't want to have to have application logic that then they run a report on three days, three weeks after some events happened. They now are taking the data and as that data and events are happening in the data and it's telling them what to do and they're able to prescriptively act on whatever event or circumstance unfold from that. >> So they want the data now. They want real-time data embedded in the apps as on the front line developer. >> And they want to optimize what that data is doing as it's unfolding through its natural life cycle. >> Let's talk with your customer base and what their expectations are. What questions should a customer or potential customer ask to their big data vendor as they look at the future? What are the key questions they should ask? >> They should really be comparing what is your architectural strategy, first and foremost. For managing data. And what kinds of data can I manage? What are the limitations in your architecture? What workloads and data sets can't I manage? What are the latency issues that your architecture would create for me? What's your business model that's associated with us engaging together? How much of the life cycle can you enable of my data? How secure are you making my data? What kind of long tail of visibility and chain of custody can I have around the governance? What kind of governance standards are you applying to the data? How much of my governance standards can you help me automate? How easy is it to operate and how intuitive is it? How big is your ecosystem? What's your road map and your strategy? What's next in your application stack? >> So enterprises are looking at simplicity. They're looking for total cost of ownership. How is big data innovation going to solve that problem? Because with IoT, again, a lot of new stuff's happening really, really fast. How do they get their arms around this simplicity question in this total cost of ownership? How should they be thinking about it? >> Well, what the Hadoop platforms have to do and the data in motion platforms have to do is to be able to bring the data under management and bring all of the enterprise services that they have in their existing data platforms, in the areas of security, in the areas of management, in the areas of data governance, so they can truly run mission critical workloads at scale with all the same levels of predictability that they have in isolation, in their existing proprietary platforms. And be able to do it in a way that's very intuitive for their existing platforms to be able to access it, very intuitive for their operations teams to be able to manage it, and very clean and easy for their existing tools and platforms investments to leverage it. >> On the industry landscape right now what are you seeing if a consolidation? Some are saying we're seeing some consolidation. Lot of companies going private. You're seeing people buckle down. It's almost a line. If you weren't born before a certain date for the company, you might have the wrong architecture. Certainly enterprises re-platform, I would agree with that, but as a supplier to customers, you're one of the young guys. You were born in the cloud. You were born in open-source, Hortonworks. Not everyone else is like that, and certainly Oracle's like one of the big guys that keep on doing well. IBM's been around. But they're all changing, as well. And certainly a lot of these growth companies pre-IPO are kind of being sold off. What's your take on the current situation with the bubble, the softening, whatever people calling it. What's your thoughts? >> I think you see some companies who got caught up and if we sort of unpack that to the ones who are going private now, those are the companies that have operated in a very mature market space. They were able to not innovate as much as they would probably have liked to, they're probably locked into a proprietary technology in a non-subscription model of some sort. Maybe a perpetual license model. And those are very different models than the enterprise wants to adopt today and their ability to innovate and grow because the market shrank, forced them to go into very constrained environments. And ultimately, they can be great companies. They have great value propositions, but they need to go through transformations that don't include a 90-day shot clock in the public market. In the markets where there's maybe, I was in the B round or the C round and I was focused on providing a niche offering into one of those mature spaces that's becoming disintermediated or evolve quickly because an open-source company has come into the space or that section of IT stack has morphed into more of a cloud-centric or SAP-centric or an open-source centric environment. They got cut short. Their market's gone away. Their market shrunk. They can't innovate their way out of it. And they then ultimately have to find a different approach, and they may or may not be able to get the financing to do that. We're in a much different position. >> Certainly the down round. We're seeing down rounds from the high valuations. That's the first sign of trouble. >> That's the first sign. I've gotten three calls this week from companies that are liquidating and have two weeks to find a new home. >> Great, we'll look for some furniture for our new growing SiliconANGLE office. >> I think you'll have some good values. >> You personally, looking back over five year now in this journey, what an incredible run you guys have had and fun to watch you guys. What's the biggest thing that surprised you and what's the biggest thing that's happened? If you can talk about those two things 'cause again, a lots happened. The markets changed significantly. You guys went public. You got a big office here. What surprised you and what was the biggest thing that you think was the catalyst of the current trajectory? >> How quickly the market grew. We saw from day one when we started the company that this was a billion dollar opportunity, and that was the bar for starting whatever we did. We were looking for new opportunities. We had to see a billion dollar opportunity. How quickly we have seen the growth and the formation of the market in general. And then how quickly some of the new opportunities have opened up, in particular around streaming, Internet of Things, the new paradigm data sets, and how quickly the enterprises have seen the ability to create a next generation data architecture and the aggressiveness in which their moving to do that with Hadoop. And then how quickly in the last year it swung to also being able to want to bring data in motion under management, as well. >> If you could talk to a customer right here, right now, and they asked you the following question, Rob, look around the corner five years out. Tell me something that someone else can't see that you see, that I should be aware of in my business. And why should I go with Hortonworks? >> It's going to be a table stake requirement to be able to understand from whether it be your customer or your supply chain from the point they begin to engage and the first step towards engaging with your product or your service, what they're trying to accomplish, and to be able to interact with them from that first inception point. It's also going to be table stakes to understand to be able to monitor your product in real-time, and be able to understand how well it's performing, down to the component level so that you can make real-time corrections, improvements, and be able to do that on the fly. The other thing that you're going to see is that it's going to be a table stake requirement to be able to aggregate the data that's happened in that life cycle and give your customer the ability to monetize the data about them. But you as the enterprise will be responsible for creating anonymity, confidentiality and security of the data. But you're going to have to be able to provide the data about your customers and give them the ability to if they choose to monetize the data about them, that the ability to do so. >> So I get that correct, you're basically saying 100% digital. >> Oh, it's by far, within the next five years, absolutely. If you do not have a full digital model, in most industries you'll be disintermediated. >> Final question. What's the big bet that you're making right now at Hortonworks? That you say we're pinning the company on blank, fill in the blank. >> It's not about big data. It's about all data under management. >> Rob, thanks so much for spending the time here On the Ground. Rob Bearden, CEO of Hortonworks here for an executive On the Ground. I'm John for The Cube. Thanks for watching. (techno music)