>> Is and who we are today as as a country, as a universe. >> Narrator: Congratulations Reggie Jackson, (inspirational music) you are a CUBE alumni. (upbeat music) >> Announcer: Live from Las Vegas it's theCUBE covering Splunk.Conf19. Brought to you by Splunk. >> Okay, welcome back everyone it's theCUBE's live coverage here in Las Vegas for Splunk.Conf19. I am John Furrier host of theCUBE. It's the 10th Anniversary of Splunk's .Conf user conference. Our 7th year covering it. It's been quite a ride, what a wave. Splunk keeps getting stronger and better, adding more features, and has really become a powerhouse from a third party security standpoint. We got a C-SO in theCUBE on theCUBE today. Chief Information Security, John Frushour Deputy Chief (mumbles) New York-Presbyterian The Award Winner from the Data to Everywhere Award winner, welcome by theCube. >> Thank you, thank you. >> So first of all, what is the award that you won? I missed the keynotes, I was working on a story this morning. >> Frushour: Sure, sure. >> What's the award? >> Yeah, the Data Everything award is really celebrating using Splunk kind of outside its traditional use case, you know I'm a security professional. We use Splunk. We're a Splunk Enterprise Security customer. That's kind of our daily duty. That's our primary use case for Splunk, but you know, New York Presbyterian developed the system to track narcotic diversion. We call it our medication analytics platform and we're using Splunk to track opioid diversion, slash narcotic diversions, same term, across our enterprise. So, looking for improper prescription usage, over prescription, under prescription, prescribing for deceased patients, prescribing for patients that you've never seen before, superman problems like taking one pill out of the drawer every time for the last thirty times to build up a stash. You know, not resupplying a cabinet when you should have thirty pills and you only see fifteen. What happened there? Everything's data. It's data everything. And so we use this data to try to solve this problem. >> So that's (mumbles) that's great usage we'll find the drugs, I'm going to work hard for it. But that's just an insider threat kind of concept. >> Frushour: Absolutely. >> As a C-SO, you know, security's obviously paramount. What's changed the most? 'Cause look at, I mean, just looking at Splunk over the past seven years, log files, now you got cloud native tracing, all the KPI's, >> Frushour: Sure. >> You now have massive volumes of data coming in. You got core business operations with IOT things all instrumental. >> Sure, sure. >> As a security offer, that's a pretty big surface area. >> Yeah. >> How do you look at that? What's your philosophy on that? >> You know, a lot of what we do, and my boss, the C-SO (mumbles) we look at is endpoint protection and really driving down to that smaller element of what we complete and control. I mean, ten, fifteen years ago information security was all about perimeter control, so you've got firewalls, defense and depth models. I have a firewall, I have a proxy, I have an endpoint solution, I have an AV, I have some type of data redaction capability, data masking, data labeling capability, and I think we've seen.. I don't think security's changed. I hear a lot of people say, "Oh, well, information security's so much different nowadays." No, you know, I'm a military guy. I don't think anything's changed, I think the target changed. And I think the target moved from the perimeter to the endpoint. And so we're very focused on user behavior. We're very focused on endpoint agents and what people are doing on their individual machines that could cause a risk. We're entitling and providing privilege to end users today that twenty years ago we would've never granted. You know, there was a few people with the keys to the kingdom, and inside the castle keep. Nowadays everybody's got an admin account and everybody's got some level of privilege. And it's the endpoint, it's the individual that we're most focused on, making sure that they're safe and they can operate effectively in hospitals. >> Interviewer: What are some of the tactical things that have changed? Obviously, the endpoint obviously shifted, so some tactics have to change probably again. Operationally, you still got to solve the same problem: attacks, insider threats, etc. >> Frushour: Yeah. >> What are the tactics? What new tactics have emerged that are critical to you guys? >> Yeah, that's a tough question, I mean has really anything changed? Is the game really the game? Is the con really the same con? You look at, you know, titans of security and think about guys like Kevin Mitnick that pioneered, you know, social engineering and this sort of stuff, and really... It's really just convincing a human to do something that they shouldn't do, right? >> Interviewer: Yeah. >> I mean you can read all these books about phone freaking and going in and convincing the administrative assistant that you're just late for meeting and you need to get in through that special door to get in that special room, and bingo. Then you're in a Telco closet, and you know, you've got access. Nowadays, you don't have to walk into that same administrative assistant's desk and convince 'em that you're just late for the meeting. You can send a phishing email. So the tactics, I think, have changed to be more personal and more direct. The phishing emails, the spear phishing emails, I mean, we're a large healthcare institution. We get hit with those types of target attacks every day. They come via mobile device, They come via the phishing emails. Look at the Google Play store. Just, I think, in the last month has had two apps that have had some type of backdoor or malicious content in them that got through the app store and got onto people's phones. We had to pull that off people's phones, which wasn't pretty. >> Interviewer: Yeah. >> But I think it's the same game. It's the same kind to convince humans to do stuff that they're not supposed to do. But the delivery mechanism, the tactical delivery's changed. >> Interviewer: How is Splunk involved? Cause I've always been a big fan of Splunk. People who know me know that I've pretty much been a fan boy. The way they handle large amounts of data, log files, (mumbles) >> Frushour: Sure. >> and then expand out into other areas. People love to use Splunk to bring in their data, and to bring it into, I hate to use the word data leg but I mean, Just getting... >> Yeah >> the control of the data. How is data used now in your world? Because you got a lot of things going on. You got healthcare, IOT, people. >> Frushour: Sure, sure. >> I mean lives are on the line. >> Frushour: Lives are on the line, yeah. >> And there's things you got to be aware of and data's key. What is your approach? >> Well first I'm going to shamelessly plug a quote I heard from (mumbles) this week, who leads the security practice. She said that data is the oxygen of AI, and I just, I love that quote. I think that's just a fantastic line. Data's the oxygen of AI. I wish I'd come up with it myself, but now I owe her a royalty fee. I think you could probably extend that and say data is the lifeline of Splunk. So, if you think about a use case like our medication analytics platform, we're bringing in data sources from our time clock system, our multi-factor authentication system, our remote access desktop system. Logs from our electronic medical records system, Logs from the cabinets that hold the narcotics that every time you open the door, you know, a log then is created. So, we're bringing in kind of everything that you would need to see. Aside from doing something with actual video cameras and tracking people in some augmented reality matrix whatever, we've got all the data sources to really pin down all the data that we need to pin down, "Okay, Nurse Sally, you know, you opened that cabinet on that day on your shift after you authenticated and pulled out this much Oxy and distributed it to this patient." I mean, we have a full picture and chain of everything. >> Full supply chain of everything. >> We can see everything that happens and with every new data source that's out there, the beauty of Splunk is you just add it to Splunk. I mean, the Splunk handles structured and unstructured data. Splunk handles cis log fees and JSON fees, and there's, I mean there's just, it doesn't matter You can just add that stream to Splunk, enrich those events that were reported today. We have another solution which we call the privacy platform. Really built for our privacy team. And in that scenario, kind of the same data sets. We're looking at time cards, we're looking at authentication, we're looking at access and you visited this website via this proxy on this day, but the information from the EMR is very critical because we're watching for people that open patient records when they're not supposed to. We're the number five hospital in the country. We're the number one hospital in the state of New York. We have a large (mumbles) of very important people that are our patients and people want to see those records. And so the privacy platform is designed to get audit trails for looking at all that stuff and saying, "Hey, Nurse Sally, we just saw that you looked at patient Billy's record. That's not good. Let's investigate." We have about thirty use cases for privacy. >> Interviewer: So it's not in context of what she's doing, that's where the data come in? >> That's where the data come in, I mean, it's advanced. Nurse Sally opens up the EMR and looks at patient Billy's record, maybe patient Billy wasn't on the chart, or patient Billy is a VIP, or patient Billy is, for whatever reason, not supposed to be on that docket for that nurse, on that schedule for that nurse, we're going to get an alarm. The privacy team's going to go, "Oh, well, were they supposed to look at that record?" I'm just giving you, kind of, like two or three uses cases, but there's about thirty of them. >> Yeah, sure, I mean, celebrities whether it's Donald Trump who probably went there at some point. Everyone wants to get his taxes and records to just general patient care. >> Just general patient care. Yeah, exactly, and the privacy of our patients is paramount. I mean, especially in this digital age where, like we talked about earlier, everyone's going after making a human do something silly, right? We want to ensure that our humans, our nurses, our best in class patient care professionals are not doing something with your record that they're not supposed to. >> Interviewer: Well John, I want to hear your thoughts on this story I did a couple weeks ago called the Industrial IOT Apocalypse: Now or Later? And the provocative story was simply trying to raise awareness that malware and spear phishing is just tactics for that. Endpoint is critical, obviously. >> Sure. >> You pointed that out, everyone kind of knows that . >> Sure. >> But until someone dies, until there's a catastrophe where you can take over physical equipment, whether it's a self-driving bus, >> Frushour: Yeah. >> Or go into a hospital and not just do ransom ware, >> Frushour: Absolutely. >> Actually using industrial equipment to kill people. >> Sure. >> Interviewer: To cause a lot of harm. >> Right. >> This is an industrial, kind of the hacking kind of mindset. There's a lot of conversations going on, not enough mainstream conversations, but some of the top people are talking about this. This is kind of a concern. What's your view on this? Is it something that needs to be talked about more of? Is it just BS? Should it be... Is there any signal there that's worth talking about around protecting the physical things that are attached to them? >> Oh, absolutely, I mean this is a huge, huge area of interest for us. Medical device security at New York Presbyterian, we have anywhere from about eighty to ninety thousand endpoints across the enterprise. Every ICU room in our organization has about seven to ten connected devices in the ICU room. From infusion pumps to intubation machines to heart rate monitors and SPO2 monitors, all this stuff. >> Interviewer: All IP and connected. >> All connected, right. The policy or the medium in which they're connected changes. Some are ZP and Bluetooth and hard line and WiFi, and we've got all these different protocols that they use to connect. We buy biomedical devices at volume, right? And biomedical devices have a long path towards FDA certification, so a lot of the time they're designed years before they're fielded. And when they're fielded, they come out and the device manufacturer says, "Alright, we've got this new widget. It's going to, you know, save lives, it's a great widget. It uses this protocol called TLS 1.0." And as a security professional I'm sitting there going, "Really?" Like, I'm not buying that but that's kind of the only game, that's the only widget that I can buy because that's the only widget that does that particular function and, you know, it was made. So, this is a huge problem for us is endpoint device security, ensuring there's no vulnerabilities, ensuring we're not increasing our risk profile by adding these devices to our network and endangering our patients. So it's a huge area. >> And also compatible to what you guys are thinking. Like I could imagine, like, why would you want a multi-threaded processor on a light bulb? >> Frushour: Yeah. >> I mean, scope it down, turn it on, turn it off. >> Frushour: Scope it down for its intended purpose, yeah, I mean, FDA certification is all about if the device performs its intended function. But, so we've, you know, we really leaned forward, our CSO has really leaned forward with initiatives like the S bomb. He's working closely with the FDA to develop kind of a set of baseline standards. Ports and protocols, software and services. It uses these libraries, It talks to these servers in this country. And then we have this portfolio that a security professional would say, "Okay, I accept that risk. That's okay, I'll put that on my network moving on." But this is absolutely a huge area of concern for us, and as we get more connected we are very, very leaning forward on telehealth and delivering a great patient experience from a mobile device, a phone, a tablet. That type of delivery mechanism spawns all kinds of privacy concerns, and inter-operability concerns with protocol. >> What's protected. >> Exactly. >> That's good, I love to follow up with you on that. Something we can double down on. But while we're here this morning I want to get back to data. >> Frushour: Sure. >> Thank you, by the way, for sharing that insight. Something I think's really important, industrial IOT protection. Diverse data is really feeds a lot of great machine learning. You're only as good as your next blind spot, right? And when you're doing pattern recognition by using data. >> Frushour: Absolutely. >> So data is data, right? You know, telecraft, other data. Mixing data could actually be a good thing. >> Frushour: Sure, sure. >> Most professionals would agree to that. How do you look at diverse data? Because in healthcare there's two schools of thought. There's the old, HIPAA. "We don't share anything." That client privacy, you mentioned that, to full sharing to get the maximum out of the AI or machine learning. >> Sure. >> How are you guys looking at that data, diverse data, the sharing? Cause in security sharing's good too, right? >> Sure, sure, sure. >> What's your thoughts on sharing data? >> I mean sharing data across our institutions, which we have great relationships with, in New York is very fluid at New York Presbyterian. We're a large healthcare conglomerate with a lot of disparate hospitals that came as a result of partnership and acquisition. They don't all use the same electronic health record system. I think right now we have seven in play and we're converging down to one. But that's a lot of data sharing that we have to focus on between seven different HR's. A patient could move from one institution to the next for a specialty procedure, and you got to make sure that their data goes with them. >> Yeah. >> So I think we're pretty, we're pretty decent at sharing the data when it needs to be shared. It's the other part of your question about artificial intelligence, really I go back to like dedication analytics. A large part of the medication analytics platform that we designed does a lot of anomaly detections, anomaly detection on diversion. So if we see that, let's say you're, you know, a physician and you do knee surgeries. I'm just making this up. I am not a clinician, so we're going to hear a lot of stupidity here, but bare with me. So you do knee surgeries, and you do knee surgeries once a day, every day, Monday through Friday, right? And after that knee surgery, which you do every day in cyclical form, you prescribe two thousand milligrams of Vicodin. That's your standard. And doctors, you know, they're humans. Humans are built on patterns. That's your pattern. Two thousand milligrams. That's worked for you; that's what you prescribe. But all of the sudden on Saturday, a day that you've never done a knee surgery in your life for the last twenty years, you all of a sudden perform a very invasive knee surgery procedure that apparently had a lot of complications because the duration of the procedure was way outside the bounds of all the other procedures. And if you're kind of a math geek right now you're probably thinking, "I see where he's going with this." >> Interviewer: Yeah. >> Because you just become an anomaly. And then maybe you prescribe ten thousand milligrams of Vicodin on that day. A procedure outside of your schedule with a prescription history that we've never seen before, that's the beauty of funneling this data into Splunk's ML Toolkit. And then visualizing that. I love the 3D visualization, right? Because anybody can see like, "Okay, all this stuff, the school of phish here is safe, but these I've got to focus on." >> Interviewer: Yeah. >> Right? And so we put that into the ML Toolkit and then we can see, "Okay, Dr. X.." We have ten thousand, a little over ten thousand physicians across New York Presbyterian. Doctor X right over here, that does not look like a normal prescriptive scenario as the rest of their baseline. And we can tweak this and we can change precision and we can change accuracy. We can move all this stuff around and say, "Well, let's just look on medical record number, Let's just focus on procedure type, Let's focus on campus location. What did they prescribe from a different campus?" That's anomalous. So that is huge for us, using the ML Toolkit to look at those anomalies and then drive the privacy team, the risk teams, the pharmacy analytics teams to say, "Oh, I need to go investigate." >> So, that's a lot of heavy lifting for ya? Let you guys look at data that you need to look at. >> Absolutely. >> Give ya a (mumbles). Final question, Splunk, in general, you're happy with these guys? Obviously, they do a big part of your data. What should people know about Splunk 2019, this year? And are you happy with them? >> Oh, I mean Splunk has been a great partner to New York Presbyterian. We've done so much incredible development work with them, and really, what I like to talk about is Splunk for healthcare. You know, we've created, we saw some really important problems in our space, in this article. But, we're looking, we're leaning really far forward into things like risk based analysis, peri-op services. We've got a microbial stewardship program, that we're looking at developing into Splunk, so we can watch that. That's a huge, I wouldn't say as big of a crisis as the opioid epidemic, but an equally important crisis to medical professionals across this country. And, these are all solvable problems, this is just data. Right? These are just events that happen in different systems. If we can get that into Splunk, we can cease the archaic practice of looking at spreadsheets, and look up tables and people spending days to find one thing to investigate. Splunk's been a great partner to us. The tool it has been fantastic in helping us in our journey to provide best in-class patient care. >> Well, congratulations, John Frushour, Deputy Chief Information Security Officer, New York Presbyterian. Thanks for that insight. >> You're welcome. >> Great (mumbles) healthcare and your challenge and your opportunity. >> Congratulations for the award winner Data to Everything award winner, got to get that slogan. Get used to that, it's two everything. Getting things done, he's a doer. I'm John Furrier, here on theCube doing the Cube action all day for three days. We're on day two, we'll be back with more coverage, after this short break. (upbeat music)

>> From the ARIA Resort in Las Vegas, it's theCube. Covering AWS Marketplace. Brought to you by Amazon Web Services. >> Hey, welcome back everybody. Jeff Frick here with theCube. We are at AWS Reinvent 2018. We got to get a number, I don't know how many people are here, but Vegas is packed. I think it's in six different venues tonight. We're at the ARIA at the hub with the AWS Marketplace & Service Catalog Experience, kicking everything off. We're excited to be joined by cube alumni. Last we saw him, I think it was in San Francisco Summit 2017. Carl Krupitzer, the CEO of ThingLogix. Carl, great to see you. >> Thank you it's great to be here. >> So I think you were saying before we turned the cameras on, you came early days. This whole piece here was not even as big a the room we're in. >> Right well we were part of the service launch for IoT, and that was just a few years ago, and it's exponentially bigger. Yeah. Just the expo, this is not even the expo floor right? And this is bigger than what we had originally. So excited to see it grow. >> So IoT keeps growing, growing, growing. That's all we hear about. In Industrial IoT, we did the Industrial IoT launch with GE back in better days. For them, huge opportunity. Really seeing a lot of momentum. What are some of the observations you're seeing actually out in marketplace? >> You know it's interesting. When we first started with the IoT service offering for AWS, there was a lot of proof of concepts going on, a lot of people kind of hacking their way through understanding what IoT is and how it could impact their business. And I think we've gotten to the point now where we're seeing more production roll-outs with very considerate business drivers behind it. >> Right. I think it's funny you're talking about doing some research for this, and you guys are really specific. I love it. It's not Greenfield projects you know? Have specific design objectives, have specific KPIs, have specific kind of ideas about what the functionality you want before you just kind of jump into IoT space with two feet. >> Right. Yeah we strongly discourage companies from just jumping in with both feet just because right? It's an expensive undertaking IoT, and it has the potential to really change your business for the better if you do it well. >> So where are you seeing the most uptake? Or maybe that surprises you the most in these early days? Kind of industry wise? >> We see a lot of creative use cases starting to come up. Kind of that secondary use of data, and one of the things that we've-- we kind of describe our customers having a life cycle of IoT right? They come in to solve a specific problem with us, which is usually a scalability, or a go to market issue. And then very quickly, they kind of get to the art of the possible. What can we do next? And we see a lot of companies really getting creative with the way they do things. From charging with-- using our FID tags in sub-Saharan Africa for water to solar power and things like that. It's interesting to see companies that didn't exist a few years ago, and couldn't have existed a few years ago, really kind of getting a lot of traction now. >> Right. It's funny we did an interview with Zebra Sports a few years ago actually now. And they're the one that's old RFID technology that put the pads in the shoulder pads for all the NFL players. They're on the refs, they're in the balls. It is such a cool way to apply on old technology to a new application and then really open up this completely different kind of consumer experience in watching sports. When you've got all this additional data about how fast are they running and what's their acceleration. And I think they had one example where they showed a guy in an interception. They had the little line tracker. Before he'd gotten all the way back in, it was a pick six. It's unbelievable now with this data. >> Our Middle Eastern group is actually doing a pilot right now for camel racing. So we're doing telemetry attached to the camels that are running around the tracks. We're getting speed and heart rate and those sorts of things. So it's everywhere right? >> I love it. Camel racing. So we're here at the AWS Marketplace Experience. So tell us a little bit about how's it working with AWS. How's the the marketplace fit within your entire kind of go to market strategy? >> Well so for us, the marketplace is really key to our go to market strategy right? I mean we're a small company and we-- our sales team is really kind of focused on helping customers solve problems and the marketplace really offers us the ability to not have to deal with a lot of the infrastructure things of servicing a customer right? They can go there, they can self sign up, they can implement the platform, our technology platform on their own and then billing is taken off of our plate. So it's not something that we have to have a bunch of resources dedicated to. >> Is there still a big services component though, that you still have to come in to help them as you say kind of define nice projects and good KPI's and kind of good places to start? Or do they often times on the marketplace purchase just go off to the races on their own? >> So it's a combination. If companies are looking to solve a specific problem with an IoT platform like Foundry, it's definitely a self implementable thing and it's becoming more and more self implementable. Foundry really deploys into a customers account using Cloud formation, and Cloud formation templates allow us to kind of create these customized solutions that can then be deployed. So it's-- we're getting a combination of both. >> Yeah, and I would imagine it's taken you into all kinds of markets that you just don't-- you just don't have the manpower to cover when you have a distribution partner at EWS. >> Yeah it's made things a lot faster for us to be able to spin up vertical solutions or specific offerings for a particular large customer. Marketplace can take care of all of the infrastructure on that. >> Alright so what are you looking for here at Reinvent 2018? You've been coming to these things for awhile. I know Andy's tweeting out, his keynote is ready to have the chicken wing contest I think, last night at midnight. Too late for me, I didn't make it. (laughs) >> For us I mean, some of the more exciting things that are out there are the emergence of server-less right? You see server-less, all of those AWS services really taking off. >> Right. >> But there's also the Sumarian, the ARVR's really kind of exploding. So for us it's really about, this is a great place for us to see the direction that AWS is heading and then make sure that our offering, and our technology is layered on top of that appropriately. >> And what are you hearing from your customers about Edge? All the talk about Edge and there's some fudd I think going about how does Edge work with Cloud and to me it's like two completely separate technology applications, but then you know what you're trying to accomplish. As kind of the buzzwords, Edge gets beyond the buzz and actually starts to be implemented, what do you kind of seeing and how's that working together with some of the services that Amazon's got? >> I mean Edge architecture's are an important component to a solution. Especially solutions that require real time data processing and decision making at the shop floor or whatever you have. AWS has taken very big strides toward creating service offerings and products down at the Edge that interface well with the Cloud. So for us, our perspective on it is that the Edge is really a reflection of the business logic and the processes and things that we define and build for a customer. Because ultimately those Edge processes have to feed the enterprise processes, which is what we really focus on right? How do we get machine data into enterprise systems? So Edge technology for us is definitely a consideration and when we build our select technology solutions, we look at Edge as a component in that architecture and we try to meet the needs of the customers specific use case when it comes to Edge. >> Right. Yeah it's not killing the Cloud. Who said that? - Right. >> So silly. >> Yeah it can't kill it. >> It's not slowing down this thing. >> Right. Alright Carl well thanks for taking a few minutes and have great Reinvent. >> Yeah thank you. - [Jeff] Hydrate. >> Thanks for your time. Definitely. - They say hydrate. Alright he's Carl, I'm Jeff. You're watching theCube. We're at AWS Marketplace inservice catalog experience. We're at the Aria in the quads. Stop on by. Thanks for watching we'll see you next time.

>> Announcer: You are CUBE Alumni. Live from Munich, Germany, it's theCUBE. Covering, DataWorks Summit Europe 2017. Brought to you by Hortonworks. >> Hello, everyone, welcome back to live coverage at DataWorks 2017, I'm John Furrier with my cohost, Dave Vellante. Two days of coverage here in Munich, Germany, covering Hortonworks and Yahoo, presenting Hadoop Summit, now called DataWorks 2017. Our next guest is Carlo Vaiti, who's the HPE chief technology strategist, EMEA Digital Solutions, Europe, Middle East, and Africa. Welcome to theCUBE. >> Thank you, John. >> So we were just chatting before we came on, of your historic background at IBM, Oracle, and now HPE, and now back into the saddle there. >> Don't forget Sun Microsystems. >> Sun Microsystems, sorry, Sun, yeah. I mean, great, great run. >> It was a long run. >> You've seen the computer revolution happen. I worked at HP for nine years, from '88 to '97. Again, Dave was a premier analyst during that run of client-server. We've seen the computer revolution happen. Now we're seeing the digital revolution where the iPhone is now 10 years old, Cloud is booming, data's at the center of the value proposition, so a completely new disruptive capability. >> Carlo: Sure, yes. >> So what are you doing as the CTO, chief technologist for HPE, how are you guys bringing this story together? 'Cause there's so much going on at HPE. You got the services spit, you got the software split, and HP's focusing on the new style of IT, as Meg Whitman calls it. >> So, yeah. My role in EMEA is actually all about having basically a visionary kind of strategy role for what's going to be HP in the future, in terms of IT. And one of the things that we are looking at is, is specifically to have, we split our strategy in three different aspects, so three transformation areas. The first one which we usually talk is what I call hybrid IT, right, which is basically making services around either On-Premise or on Cloud for our customer base. The second one is actually power the Intelligent Edge, so is actually looking after our collaboration and when we acquire Aruba components. And the third one, which is in the middle, and that's why I'm here at the DataWorks Summit, is actually the data-analytics aspects. And we have a couple of solution in there. One is the Enterprise great Hadoop, which is part of this. This is actually how we generalize all the figure and the strategy for HP. >> It's interesting, Dave and I were talking yesterday, being in Europe, it's obviously a different sideshow, it's smaller than the DataWorks or Hadoop Summit in North America in San Jose, but there's a ton of Internet of things, IoT or IIoT, 'cause here in Germany, obviously, a lot of industrial nations, but in Europe in general, a lot of smart cities initiatives, a lot of mobility, a ton of Internet of things opportunity, more than in the US. >> Absolutely. >> Can you comment on how you guys are tackling the IoT? Because it's an Intelligent Edge, certainly, but it's also data, it's in your wheelhouse. >> Yes, sure. So I'm actually working, it's a good question, because I'm actually working a couple of projects in Eastern Europe, where it's all about Industrial IoT Analytics, IIoTA. That's the new terminology we use. So what we do is actually, we analyze from a business perspective, what are the business pain points, in an oil and gas company for example. And we understand for example, what kind of things that they need and must have. And what I'm saying here is, one of the aspects for example, is the drilling opportunity. So how much oil you can extract from a specific rig in the middle of the North Sea, for example. This is one of the key question, because the customer want to understand, in the future, how much oil they can extract. The other one is for example, the upstream business. So doing on the retail side and having, say, when my customer is stopping in a gas station, I want go in the shop, immediately giving, I dunno, my daughter, a kind of campaign for the Barbie, because they like the Barbie. So IoT, Industrial IoT help us in actually making a much better customer experience, and that's the case of the upstream business, but is also helping us in actually much faster business outcomes. And that's what the customer wants, right? 'Cause, and was talking with your colleague before, I'm talking to the business guy. I'm not talking to the IT anymore in these kind of place, and that's how IoT allow us a chance to change the conversation at the industry level. >> These are first-time conversations too. You're getting at the kinds of business conversations that weren't possible five years ago. >> Carlo: Yes, sure. >> I mean and 10 years ago, they would have seemed fantasy. Now they're reality. >> The role of analytics in my opinion, is becoming extremely key, and I said this morning, for me my best center is that the detail, is the stone foundation of the digital economy. I continue to repeat this terminology, because it's actually where everything is starting from. So what I mean is, let's take a look at the analytic aspect. So if I'm able to analyze the data close to the shop floor, okay, close to the shop manufacturing floor, if I'm able to analyze my data on the rig, in the oil and gas industry, if I'm able to analyze doing preprocessing analytics, with Kafka, Druid, these kind of open-source software, where close to the Intelligent Edge, then my customers going to be happy, because I give them very fast response, and the decision-maker can get to decision in a faster time. Today, it takes a long time to take these type of decision. So that's why we want to move into the power Intelligent Edge. >> So you're saying, data's foundational, but if you get to the Intelligent Edge, it's dynamic. So you have a dynamic reactive, realtime time series, or presences of data, but you need the foundational pre-data. >> Perfect. >> Is that kind of what you're getting at? >> Yes, that's the first step. Preprocessing analytics is what we do. In the next generation of, we think is going to be Industrial IoT Analytics, we're going to actually put massive amount of compute close to the shop manufacturing floor. We call internally or actually externally, convergent planned infrastructure. And that's the key point, right? >> John: Convergent plan? >> Convergent planned infrastructure, CPI. If you look at in Google, you will find. It's a solution we bring in the market a few months ago. We announce it in December last year. >> Yeah, Antonio's smart. He also had a converged systems as well. One of the first ones. >> Yeah, so that's converge compute at the edge basically. >> Correct, converge compute-- >> Very powerful. >> Very powerful, and we run analytics on the edge. That's the key point. >> Which we love, because that means you don't have to send everything back to the Cloud because it's too expensive, it's going to take too long, it's not going to work. >> Carlo: The bandwidth on the network is much less. >> There's no way that's going to be successful, unless you go to the edge and-- >> It takes time. >> With a cost. >> Now the other thing is, of course, you've got the Aruba asset, to be able to, I always say, joke, connect the windmill. But, Carlo, can we go back to the IoTA example? >> Carlo: Correct, yeah. >> I want to help, help our audience understand, sort of, the new HP, post these spin merges. So perviously you would say, okay, we have Vertica. You still have partnership, or you still own Vertica, but after September 1st-- >> Absolutely, absolutely. It's part of the columnar side-- >> Right, yes, absolutely, but, so. But the new strategy is to be more of a platform for a variety of technology. So how for instance would you solve, or did you solve, that problem that you described? What did you actually deliver? >> So again, as I said, we're, especially in the Industrial IoT, we are an ecosystem, okay? So we're one element of the ecosystem solution. For the oil and gas specifically, we're working with other system integrator. We're working with oil and the industry gas expertise, like DXC company, right, the company that we just split a few days ago, and we're working with them. They're providing the industry expertise. We are a infrastructure provided around that, and the services around that for the infrastructure element. But for the industry expertise, we try to have a kind of little bit of knowledge, to start the conversation with the customer. But again, my role in the strategy is actually to be a ecosystem digital integrator. That's the new terminology we like to bring in the market, because we really believe that's the way HP role is going to be. And the relevance of HP is totally depending if we are going to be successful in these type of things. >> Okay, now a couple other things you talked about in your keynote. I'm just going to list them, and then we can go wherever we want. There was Data Link 3.0, Storage Disaggregation, which is kind of interesting, 'cause it's been a problem. Hadoop as a service, Realtime Everywhere, and then Analytics at the Edge, which we kind of just talked about. Let's pick one. Let's start with Data Link 3.0. What is that? John doesn't like the term data link. He likes data ocean. >> I like data ocean. >> Is Data Link 3.0 becoming an ocean? >> It's becoming an ocean. So, Data Link 3.0 for us is actually following what is going to be the future for HDFS 3.0. So we have three elements. The erasure coding feature, which is coming on HDFS. The second element is around having HDFS data tier, multi-data tier. So we're going to have faster SSD drives. We're going to have big memory nodes. We're going to have GPU nodes. And the reason why I say disaggregation is because some of the workload will be only compute, and some of the workload will be only storage, okay? So we're going to bring, and the customer require this, because it's getting more data, and they need to have for example, YARN application running on compute nodes, and the same level, they want to have storage compute block, sorry, storage components, running on the storage model, like HBase for example, like HDFS 3.0 with the multi-tier option. So that's why the data disaggregation, or disaggregation between compute and storage, is the key point. We call this asymmetric, right? Hadoop is becoming asymmetric. That's what it mean. >> And the problem you're solving there, is when I add a node to a cluster, I don't have to add compute and storage together, I can disaggregate and choose whatever I need, >> Everyone that we did. >> based on the workload. >> They are all multitenancy kind of workload, and they are independent and they scale out. Of course, it's much more complex, but we have actually proved that this is the way to go, because that's what the customer is demanding. >> So, 3.0 is actually functional. It's erasure coding, you said. There's a data tier. You've got different memory levels. >> And I forgot to mention, the containerization of the application. Having dockerized the application for example. Using mesosphere for example, right? So having the containerization of the application is what all of that means, because what we do in Hadoop, we actually build the different clusters, they need to talk to each other, and change data in a faster way. And a solution like, a product like SQL Manager, from Hortonworks, is actually helping us to get this connection between the cluster faster and faster. And that's what the customer wants. >> And then Hadoop as a service, is that an on-premise solution, is that a hybrid solution, is it a Cloud solution, all three? >> I can offer all of them. Hadoop is a service could be run on-premise, could be run on a public Cloud, could be run on Azure, or could be mix of them, partially on-premise, and partially on public. >> And what are you seeing with regard to customer adoption of Cloud, and specifically around Hadoop and big data? >> I think the way I see that option is all the customer want to start very small. The maturity is actually better from a technology standpoint. If you're asking me the same question maybe a year ago, I would say, it's difficult. Now I think they've got the point. Every large customer, they want to build this big data ocean, note the delay, ocean, whatever you want to call it. >> John: Love that. (laughs) >> All right. They want to build this data ocean, and the point I want to make is, they want to start small, but they want to think very high. Very big, right, from their perspective. And the way they approach us is, we have a kind of methodology. We establish the maturity assessment. We do a kind of capability maturity assessment, where we find that if the customer is actually a pioneer, or is actually a very traditional one, so it's very slow-going. Once we determine where is the stage of the customer is, we propose some specific proof of concept. And in three months usually, we're putting this in place. >> You also talked about realtime everywhere. We in our research, we talk about the, historically, you had batchy of interactive, and now you have what we call continuous, or realtime streaming workloads. How prevalent is that? Where do you see it going in the future? >> So I think is another train for the future, as I mentioned this morning in my presentation. So and Spark is actually doing the open-source memory engine process, is actually the core of this stuff. We see 60 to 70 time faster analytics, compared to not to use Spark. So many customer implemented Spark because of this. The requirement are that the customer needs an immediate response time, okay, for a specific decision-making that they have to do, in order to improve their business, in order to improve their life. But this require a different architecture. >> I have a question, 'cause you, you've lived in the United States, you're obviously global, and spent a lot of time in Europe as well, and a lot of times, people want to discuss the differences between, let's make it specific here, the European continent and North America, and from a sophistication standpoint, same, we can agree on that, but there are still differences. Maybe, more greater privacy concerns. The whole thing with the Cloud and the NSA in the United States, created some concerns. What do you see as the differences today between North America and Europe? >> From my perspective, I think we are much more for example take IoT, Industrial IoT. I think in Europe we are much more advanced. I think in the manufacturing and the automotive space, the connected car kind of things, autonomous driving, this is something that we know already how to manage, how to do it. I mean, Tesla in the US is a good example that what I'm saying is not true, but if I look at for example, large German manufacturing car, they always implemented these type of things already today. >> Dave: For years, yeah. >> That's the difference, right? I think the second step is about the faster analytic approach. So what I mentioned before. The Power the Intelligent Edge, in my opinion at the moment, is much more advanced in the US compared to Europe. But I think Europe is starting to run back, and going on the same route. Because we believe that putting compute capacity on the edge is what actually the customer wants. But that's the two big differences I see. >> The other two big external factors that we like to look at, are Brexit and Trump. So (laughs) how 'about Brexit? Now that it's starting to sort of actually become, begin the process, how should we think about it? Is it overblown? It is critical? What's your take? >> Well, I think it's too early to say. UK just split a few days ago, right, officially. It's going to take another 18 months before it's going to be completed. From a commercial standpoint, we don't see any difference so far. We're actually working the same way. For me it's too early to say if there's going to be any implication on that. >> And we don't know about Trump. We don't have to talk about it, but the, but I saw some data recently that's, European sentiment, business sentiment is trending stronger than the US, which is different than it's been for the last many years. What do you see in terms of just sentiment, business conditions in Europe? Do you see a pick up? >> It's getting better, it is getting better. I mean, if I look at the major countries, the P&L is going positive, 1.5%. So I think from that perspective, we are getting better. Of course we are still suffering from the Chinese, and Japanese market sometimes. Especially in some of the big large deals. The inclusion of the Japanese market, I feel it, and the Chinese market, I feel that. But I think the economy is going to be okay, so it's going to be good. >> Carlo, I want to thank you for coming on and sharing your insight, final question for you. You're new to HPE, okay. We have a lot of history, obviously I was, spent a long part of my career there, early in my career. Dave and I have covered the transformation of HP for many, many years, with theCUBE certainly. What attracted you to HP and what would you say is going on at HP from your standpoint, that people should know about? >> So I think the number one thing is that for us the word is going to be hybrid. It means that some of the services that you can implement, either on-premise or on Cloud, could be done very well by the new Pointnext organization. I'm not part of Pointnext. I'm in the EG, Enterprise Group division. But I am fan for Pointnext because I believe this is the future of our company, is on the services side, that's where it's going. >> I would just point out, Dave and I, our commentary on the spin merge has been, create these highly cohesive entities, very focused. Antonio now running EG, big fans, of where it's actually an efficient business model. >> Carlo: Absolutely. >> And Chris Hsu is running the Micro Focus, CUBE Alumni. >> Carlo: It's a very efficient model, yes. >> Well, congratulations and thanks for coming on and sharing your insights here in Europe. And certainly it is an IoT world, IIoT. I love the analytics story, foundational services. It's going to be great, open source powering it, and this is theCUBE, opening up our content, and sharing that with you. I'm John Furrier, Dave Vellante. Stay with us for more great coverage, here from Munich after the short break.