>> Announcer: From New York, it's the Cube. Covering Big Data New York City 2016. Brought to you by Headline Sponsors, Cisco, IBM, NVIDIA, and our ecosystem sponsors. Now, here are your hosts, Dave Vellante and George Gilbert. >> Welcome back to New York City everybody, this is the Cube, the worldwide leader in live tech coverage, and we've been going wall to wall since Monday here at Strata plus Hadoop World, Big Data NYC is our show within the show. Omer Trajman is here, he's the CEO of Rocana, Cube alum, good to see you again. >> Yeah you too, it's good to be here again. >> What's the deal with the shirt, it says, 'your boss is useless', what are you talking about? >> So, if I wasn't on mic'd up, I'd get up and show you, ~but you can see in the faint print that it's not talking about how your boss is useless, right, it's talking about how you make better use of data and what your boss' expectations are. The point we're trying to get across is that context matters. If you're looking at a small fraction of the information then you're not going to get the full picture, you're not going understand what's actually going on. You have to look at everything, you have no choice today. >> So Rocana has some ambitious plans to enter this market, generally referred to as IT operations, if I can call it that, why does the world need another play on IT operations? >> In IT operations? If you look at the current state of IT operations in general, and specifically people think of this largely versus monitoring, is I've got a bunch of systems, I can't keep track of everything, so I'm going to pick and choose what I pay attention to. I'm going to look at data selectively, I'm only going to keep it for as long as I can afford to keep it, and I'm not going to pay attention to the stuff that's outside that hasn't caused problems, yet. The problem is, the yet, right? You all have seen the Delta outages, the Southwest issues, the Neiman Marcus website, right? There's plenty of examples of where someone just wasn't looking at information, no one was paying attention to it or collecting it and they got blindsided. And in today's pace of business where everything is digital, everyone's interacting with the machines directly, everything's got to be up all the time. Or at least you have to know that something's gone askew and fix it quickly. And so our take is, what we call total operational visibility. You got to pay attention to everything all the time and that's easier said than done. >> Well, because that requires you got to pay attention to all the data, although this reminds me of IP meta in 2010, said, "Sampling is dead", alright? Do you agree he's right? >> Trajman: I agree. And so it's much more than that, of course right, sampling is dead, you want to look at all the details all the time, you want to look at it from all sources. You want to keep enough histories so if you're the CIO of a retailer, if your CEO says, "Are we ready for Cyber Monday, can you take a look at last year's lead up and this years", and the CEO's going to look back at them and say, "I have seven days of data (chuckles), "what are you talking about, last year". You have to keep it for as long as you need to, to address business issues. But collecting the data, that's step one, right? I think that's where people struggle today, but they don't realize that you can't just collect it all and give someone a search box, or say, "go build your charts". Companies don't have data scientists to throw at these problems. You actually have to have the analytics built in. Things that are purpose built for data center and IT operations, the machine learning models, the built in cubes, the built in views, visualizations that just work out of the box, and show you billions of events a day, the way you need to look at that information. That's prebuilt, that comes out of the box, that's also a key differentiator. >> Would it be fair to say that Hadoop has historically has been this repository for all sorts of data, and but it was a tool set, and that Splunk was the anti-Hadoop, sort of out of the box. It was an application that had some... It collected certain types of data and it had views out of the box for that data. Sounds like you're trying to take the best of each world where you have the full extensibility and visibility that you can collect with all your data in Hadoop but you've pre built all the analytic infrastructure that you need to see your operations in context. >> I think when you look at Hadoop and Splunk and your concert of Rocana's the best of both worlds, is very apt. It's a prepackaged application, it just installs. You don't have to go in under the covers and stitch everything together. It has the power of scalability that Hadoop has, it has the openness, right, 'cause you can still get at the data and do what you need with it, but you get an application that's creating value, day one. >> Okay, so maybe take us... Peel back the onion one layer, if you can go back to last year's Cyber Monday and you've got out of the box functionality, tell us how you make sense out of the data for each organization, so that the context is meaningful for them. >> Yeah, absolutely. What's interesting is that it's not a one time task, right? Every time you're trying to solve a slightly different problem, or move the business in different direction, you want to look at data differently. So we think of this more as a toolkit that helps you navigate where to find the root cause or isolate where a particular problem is, or where you need to invest, or grow the business. In the Cyber Monday example, right what you want to look at is, let me take a zoom out view, I just want to see trends over time, the months leading up or the weeks leading up to Cyber Monday. Let's look at it this year. Let's look at it last year. Let's stack on the graph everything from the edge caching, to the application, to my proxy servers to my host servers through to my network, gimmie the broad view of everything, and just show me the trend lines and show me how those trend lines are deviating. Where is there unexpected patterns and behavior, and then I'm going to zoom in on those. And what's causing those, is there a new disconfiguration, did someone deploy a new network infrastructure, what has caused some change? Or is it just... It's all good, people are making more money, more people are coming to the website it's actually a capacity issue, we just need to add more servers. So you get the step back, show me everything without a query, and then drag and drop, zoom in to isolate where are there particular issues that I need to pay attention to. >> Vellante: And this is infrastructure? >> Trajman: It's infrastructure all the way through application... >> Correct? It is? So you can do application performance management, as well? >> We don't natively do the instrumentation there's a whole domain which is, bytecode instrumentation, we partner with companies that provide APM functionality, take that feed and incorporate it. Similar to a partner with companies that do wire level deep packet inspection. >> Vellante: I was going to say... >> Yeah, take that feed and incorporate it. Some stuff we do out of the box. NetFlow, things like IPFIX, STATSD, Syslog, log4j, right? There's kind of a lot of stuff that everyone needs standard interfaces that we do out of the box. And there's also pre-configured, content oriented parsers and visualizations for an OpenStack or for Cloud Foundry or for a Blue Coat System. There's certain things that we see everywhere that we can just handle out of the box, and then there's things that are very specific to each customer. >> A lot of talk about machine learning, deep learning, AI, at this event, how do you leverage that? >> How do we fit in? It's interesting 'cause we talk about the power delivers in the product but part of it is that it's transparent. Our users, who are actually on the console day to day or trying to use Rocana to solve problems, they're not data scientists. They don't understand the difference between analytic queries and full text search. They understand understand machine learning models. >> They're IT people, is that correct? >> They're IT folks, whose job it is to keep the lights on, right? And so, they expect the software to just do all of that. We employ the data scientists, we deliver the machine learning models. The software dynamically builds models continuously for everything it's looking at and then shows it in a manner that someone can just look at it and make sense of it. >> So it might be fair to say, maybe replay this, and if it's coming out right, most people, and even the focus of IBM's big roll out this week is, people have got their data links populated and they're just now beginning to experiment with the advanced analytics. You've got an application where it's already got the advanced analytics baked into such an extent that the operator doesn't really care or need to know about it. >> So here's the caveat, people have their data links populated with the data they know they need to look at. And that's largely line of business driven, which is a great area to apply big data machine learning, analytics, that's where the data scientists are employed. That's why what IBM is saying makes sense. When you get to the underlying infrastructure that runs it day to day, the data lakes are not populated. >> Interviewer: Oh, okay. >> They're data puddles. They do not have the content of information, the wealth of information, and so, instead of saying, "hey, let's populate them, "and then let's try to think about "how to analyze them, and then let's try to think about "how get insights from them, and then let's try to think "about, and then and then", how about we just have a product that does it all for you? That just shows you what to do. >> I don't want to pollute my data lake with that information, do I? >> What you want is, you want to take the business feeds that have been analyzed and you want to overlay them, so you want to send those over to probably a much larger lake, which is all the machine data underneath it. Because what you end up with especially as people move towards more elastic environments, or the hybrid cloud environments, in those environments, if a disk fails or machine fails it may not matter. Unless you can see the topline revenue have an impact, maybe it's fine to just leave the dead machine there and isolate it. How IT operates in those environments requires knowledge of the business in order to become more efficient. >> You want to link the infrastructure to the value. >> Trajman: Exactly. >> You're taking feeds essentially, from the business data and that's informing prioritization. >> That's exactly right. So take as an example, Point of Sale systems. All the Point of Sale systems today, they're just PCs, they're computers, right? I have to monitor them and the infrastructure to make sure it's up and running. As a side effect, I also know the transactions. As an IT person, I not only know that a system is up, I know that it's generating the same amount of revenue, or a different amount of revenue than it did last week, or that another system is doing. So I can both isolate a problem as an IT person, right, as an operator, but I can also go to the business and say, "Hey nothing's wrong with the system, we're not making as much money as we were, why is that", and let's have a conversation about that. So it brings IT into a conversation with the business that they've never been able to have before, using the data they've always had. They've always had access to. >> Omer, We were talking a little before about how many more companies are starting to move big parts of their workloads into public cloud. But the notion of hybrid cloud, having a hybrid cloud strategy is still a bit of a squishy term. >> Trajman: Yeah. (laughs) >> Help us fill in, for perhaps, those customers who are trying to figure out how to do it, where you add value and make that possible. >> Well, what's happening is the world's actually getting more complex with cloud, it's another place that I can use to cost effectively balance my workloads. We do see more people moving towards public cloud or setting up private cloud. We don't see anyone whole scale, saying "I'm shutting down everything", and "I'm going to send everything to Amazon" or "I'm going to send everything to Microsoft". Even in the public cloud, it's a multi cloud strategy. And so what you've done is, you've expanded the number of data centers. Maybe I add, a half dozen data centers, now I've got a half dozen more in each of these cloud providers. It actually exacerbates the need for being able to do multi-tier monitoring. Let me monitor at full fidelity, full scale, everything that's happening in each piece of my infrastructure, aggregate the key parts of that, forward them onto something central so I can see everything that's going on in one place, but also be able to dive into the details. And that hybrid model keeps you from clogging up the pipes, it keeps you from information overload, but now you need it more than ever. >> To what extent does that actually allow you, not just to monitor, but to re-mediate? >> The sooner you notice that there's an issue, the sooner you can address that issue. The sooner you see how that issue impacts other systems, the more likely you are to identify the common root cause. An example is a customer that we worked with prior to Rocana, had spent an entire weekend isolating an issue, it was a ticket that had gotten escalated, they found the root cause, it was a core system, and they looked at it and said, "Well if that core system was actually "the root cause, these other four systems "should have also had issues". They went back into the ticketing system, sure enough, there were tickets that just didn't get escalated. Had they seen all of those issues at the same time, had they been able to quickly spin the cube view of everything, they would have found it significantly faster. They would have drawn that commonality and seen the relationships much more quickly. It requires having all the data in the same place. >> Part of the actionable information is to help triage the tickets in a sense, of that's the connection to remediation. >> Trajman: Context is everything. >> Okay. >> So how's it going? Rocana's kind of a heavy lift. (Trajman laughs) You're going after some pretty entrenched businesses that have been used to doing things a certain way. How's business? How you guys doing? >> Business is, it's amazing, I mean, the need is so severe. We had a prospective customer we were talking to, who's just starting to think about this digital transformation initiative and what they needed from an operational visibility perspective. We connected them with an existing customer that had rolled out a system and, the new prospect looked at the existing customer, called us up and said, "That," (laughs) "that's what we want, right there". Everyone's got centralized log analytics, total operational visibility, people are recognizing these are necessary to support where the business has to go and businesses are now realizing they have to digitize everything. They have to have the same kind of experience that Amazon and Google and Facebook and everyone else has. Consumers have come to expect it. This is what is required from IT in order to support it, and so we're actually getting... You say it's a heavy lift, we're getting pulled by the market. I don't think we've had a conversation where someone hasn't said, "I need that", that's what we're going through today that is my number one pang. >> That's good. Heavy lifts are good if you've got the stomach for it. >> Trajman: That's what I do. >> If you got a tailwind, that's fantastic. It sounds like things are going well. Omer, congratulations on the success we really appreciate you sharing it with our Cube audience. >> Thank you very much, thanks for having me. >> You're welcome. Keep it right there everybody. We'll be back with our next guest, this is the Cube, we're live, day four from NYC. Be right back.

>> 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)

(upbeat electronic music) >> Hello, everyone. Welcome to the Cube Studios in Palo Alto, California. I'm John Furrier, the co-host of the Cube and co-founder of SiliconANGLE Media Inc. I'm here with George Gilbert for a Wikibon conversation on the state of the big data. George Gilbert is the analyst at Wikibon covering big data. George, great to see you. Looking good. (laughing) >> Good to see you, John. >> So George, you're obviously covering big data. Everyone knows you. You always ask the tough questions, you're always drilling down, going under the hood, and really inspecting all the trends, and also looking at the technology. What are you working on these days as the big data analyst? What's the hot thing that you're covering? >> OK, so, what's really interesting is we've got this emerging class of applications. The name that we've used so far is modern operational analytic applications. Operational in the sense that they help drive business operations, but analytical in the sense that the analytics either inform or drive transactions, or anticipate and inform interactions with people. That's the core of this class of apps. And then there are some sort of big challenges that customers are having in trying to build, and deploy, and operate these things. That's what I want to go through. >> George, you know, this is a great piece. I can't wait to (mumbling) some of these questions and ask you some pointed questions. But I would agree with you that to me, the number one thing I see customers either fumbling with or accelerating value with is how to operationalize some of the data in a way that they've never done it before. So you start to see disciplines come together. You're starting to see people with a notion of digital business being something that's not a department, it's not a marketing department. Data is everywhere, it's horizontally scalable, and the smart executives are really looking at new operational tactics to do that. With that, let me kick off the first question to you. People are trying to balance the cloud, On Premise, and The Edge, OK. And that's classic, you're seeing that now. I've got a data center, I have to go to the cloud, a hybrid cloud. And now the edge of the network. We were just taking about Block Chain today, there's this huge problem. They've got the balance that, but they've got to balance it versus leveraging specialized services. How do you respond to that? What is your reaction? What is your presentation? >> OK, so let's turn it into something really concrete that everyone can relate to, and then I'll generalize it. The concrete version is for a number of years, everyone associated Hadoop with big data. And Hadoop, you tried to stand up on a cluster on your own premises, for the most part. It was on had EMR, but sort of the big company activity outside, even including the big tech companies was stand up a Hadoop cluster as a pilot and start building a data lake. Then see what you could do with sort of huge amounts of data that you couldn't normally sort of collect and analyze. The operational challenges of standing up that sort of cluster was rather overwhelming, and I'll explain that later, so sort of park that thought. Because of that complexity, more and more customers, all but the most sophisticated, are saying we need a cloud strategy for that. But once you start taking Hadoop into the cloud, the components of this big data analytic system, you have tons more alternatives. So whereas in Cloudera's version of Hadoop you had Impala as your MPP sequel database. On Amazon, you've got Amazon Redshift, you've got Snowflake, you've got dozens up MPP sequel databases. And so the whole playing field shifts. And not only that, Amazon has instrumented their, in that particular case, their application, to be more of a more managed service, so there's a whole lot less for admins to do. And you take that on sort of, if you look at the slides, you take every step in that pipeline. And when you put it on a different cloud, it's got different competitors. And even if you take the same step in a pipeline, let's say Spark on HDFS to do your ETL, and your analysis, and your shaping of data, and even some of the machine learning, you put that on Azure and on Amazon, it's actually on different storage foundation. So even if you're using the same component, it's different. There's a lot of complexity and a lot of trade off that you got to make. >> Is that a problem for customers? >> Yes, because all of a sudden, they have to evaluate what those trade offs are. They have to evaluate the trade off between specialization. Do I use the best to breed thing on one platform. And if I do, it's not compatible with what I might be running on prem. >> That'll slow a lot of things down. I can tell you right now, people want to have the same code base on all environments, and then just have the same seamless operational role. OK, that's a great point, George. Thanks for sharing that. The second point here is harmonizing and simplifying management across hybrid clouds. Again, back to your point. You set that up beautifully. Great example, open source innovation hits a roadblock. And the roadblock is incompatible components in multiple clouds. That's a problem. It's a management nightmare. How do harmonization about hybrid cloud work? >> You couldn't have asked it better. Let me put it up in terms of an X Y chart where on the x-axis, you have the components of an analytic pipeline. Ingest, process, analyze, predict, serve. But then on the y-axis, this is for an admin, not a developer. These are just some of the tasks they have to worry about. Data governance, performance monitoring, scheduling and orchestration, availability and recovery, that whole list. Now, if you have a different product for each step in that pipeline, and each product has a different way of handling all those admin tasks, you're basically taking all the unique activities on the y-axis, multiplying it by all the unique products on the x-axis, and you have overwhelming complexity, even if these are managed services on the cloud. Here now you've got several trade offs. Do I use the specialized products that you would call best to breed? Do I try and do end to end integration so I get simplification across the pipeline? Or do I use products that I had on-prem, like you were saying, so that I have seamless compatibility? Or do I use the cloud vendors? That's a tough trade off. There's another similar one for developers. Again, on the y-axis, for all the things that a developer would have to deal with, not all of them, just a sample. The data model and the data itself, how to address it, the programing model, the persistence. So on that y-axis, you multiply all those different things you have to master for each product. And then on the x-axis, all the different products and the pipeline. And you have that same trade off, again. >> Complexity is off the charts. >> Right. And you can trade end to end integration to simplify the complexity, but we don't really have products that are fully fleshed out and mature that stretch from one end of the pipeline to the other, so that's a challenge. Alright. Let's talk about another way of looking at management. This was looking at the administrators and the developers. Now, we're getting better and better software for monitoring performance and operations, and trying to diagnose root cause when something goes wrong and then remediate it. There's two real approaches. One is you go really deep, but on a narrow part of your application and infrastructure landscape. And that narrow part might be, you know, your analytic pipeline, your big data. The broad approach is to get end to end visibility across Edge with your IOT devices, across on-prem, perhaps even across multiple clouds. That's the breadth approach, end to end visibility. Now, there's a trade off here too as in all technology choices. When you go deep, you have bounded visibility, but that bounded visibility allows you to understand exactly what is in that set of services, how they fit together, how they work. Because the vendor, knowing that they're only giving you management of your big data pipeline, they can train their models, their machine learning models, so that whenever something goes wrong, they know exactly what caused it and they can filter out all the false positives, the scattered errors that can confuse administrators. Whereas if you want breadth, you want to see end to end your entire landscape so that you can do capacity planning and see if there was an error way upstream, something might be triggered way downstream or a bunch of things downstream. So the best way to understand this is how much knowledge do you have of all the pieces work together, and how much knowledge you have of all the pieces, the software pieces fit together. >> This is actually an interesting point. So if I kind of connect the dots for you here is the bounded root cause analysis that we see a lot of machine learning, that's where the automation is. >> George: Yeah. >> The unbounded, the breadth, that's where the data volume is. But they can work together, that's what you're saying. >> Yes. And actually, I hadn't even got to that, so thanks for taking it out. >> John: Did I jump ahead on that one? (laughing) >> No, no, you teed it out. (laughing) Because ultimately-- >> Well a lot of people want to know where it's going to be automated away. All the undifferentiated labored and scale can be automated. >> Well, when you talk about them working together. So for the deep depth first, there's a small company called Unravel Data that sort of modeled eight million jobs or workloads of big data workloads from high tech companies, so they know how all that fits together and they can tell you when something goes wrong exactly what goes wrong and how to remediate it. So take something like Rocana or Splunk, they look end to end. The interesting thing that you brought up is at some point, that end to end product is going to be like a data warehouse and the depth products are going to sit on top of it. So you'll have all the contextual data of your end to end landscape, but you'll have the deep knowledge of how things work and what goes wrong sitting on it. >> So just before we jump to the machine learning question which I want to ask you, what you're saying is the industry is evolving to almost looking like a data warehouse model, but in a completely different way. >> Yeah. Think of it as, another cue. (laughing) >> John: That's what I do, George. I help you out with the cues. (laughing) No, but I mean the data warehouse, everyone knows what that was. A huge industry, created a lot of value, but then the world got rocked by unstructured data. And then their bounded, if you will, view has got democratized. So creative destruction happened which is another word for new entrants came in and incumbents got rattled. But now it's kind of going back to what looks like a data warheouse, but it's completely distributed around. >> Yes. And I was going to do one of my movie references, but-- >> No, don't do it. Save us the judge. >> If you look at this starting in the upper right, that's the data lake where you're collecting all the data and it's for search, it's exploratory. As you get more structure, you get to the descriptive place where you can build dashboards to monitor what's going on. And you get really deep, that's when you have the machine learning. >> Well, the machine learning is hitting the low hanging fruit, and that's where I want to get to next to move it along. Sourcing machine learning capability, let's discuss that. >> OK, alright. Just to set contacts before we get there, notice that when you do end to end visibility, you're really seeing across a broad landscape. And when I'm showing my public cloud big data, that would be depth first just for that component. But you would do breadth first, you could do like a Rocana or a Splunk that then sees across everything. The point I wanted to make was when you said we're reverting back to data warehouses and revisiting that dream again, the management applications started out as saying we know how to look inside machine data and tell you what's going on with your landscape. It turns out that machine data and business operations data, your application data, are really becoming one and the same. So what used to be a transaction, there was one transaction. And that, when you summarized them, that went into the data warehouse. Then we had with systems of engagement, you had about 100 interaction events that you tracked or sort of stored for everything business transaction. And then when we went out to the big data world, it's so resource intensive that we actually had 1,000 to 10,000 infrastructure events for every business transaction. So that's why the data volumes have grown so much and why we had to go back first to data lake, and then curate it to the warehouse. >> Classic innovation story, great. Machine learning. Sourcing machine learning capabilities 'cause that's where the rubber starts hitting the road. You're starting to see clear skies when it comes to where machine learning is starting fit in. Sourcing machine learning capabilities. >> You know, even though we sort of didn't really rehearse this, you're helping cue me on perfectly. Let me make the assertion that with machine learning, we have the same shortage of really trained data scientists that we had when we were trying to stand up Hadoop clusters and do big data analytics. We did not have enough administrators because these were open source components built from essentially different projects, and putting them all together required a huge amount of skills. Data science requires, really, knowledge of algorithms that even really sophisticated programmers will tell you, "Jeez, now I need a PhD "to really understand how this stuff works." So the shortage, that means we're not going to get a lot of hand-built machine learning applications for a while. >> John: In a lot of libraries out there right now, you see TensorFlow from Google. Big traction with that application. >> George: But for PhDs, for PhDs. My contention is-- >> John: Well developers too, you could argue developers, but I'm just putting it out there. >> George: I will get to that, actually. A slide just on that. Let me do this one first because my contention is the first big application, widespread application of machine learning, is going to be the depth first management because it comes with a model built in of how all the big data workloads, services, and infrastructure fit together and work together. And if you look at how the machine learning model operates, when it knows something goes wrong, let's say an analytic job takes 17 hours and then just falls over and crashes, the model can actually look at the data layout and say we have way too much on one node, and it can change the settings and change the layout or the data because it knows how all the stuff works. The point about this is the vendor. In this particular example, Unravel Data, they built into their model an understanding of how to keep a big data workload running as opposed to telling the customer, "You have to program it." So that fits into the question you were just asking which is where do you get this talent. When you were talking about like TensorFlow, and Cafe, and Torch, and MXnet, those are all like assembly language. Yes, those are the most powerful places you could go to program machine learning. But the number of people is inversely proportional to the power of those. >> John: Yeah, those are like really unique specialty people. High, you know, the top guys. >> George: Lab coats, rocket scientists. >> John: Well yeah, just high end tier one coders, tier one brains coding away, AI gurus. This is not your working developer. >> George: But if you go up two levels. So go up one level is Amazon machine learning, Spark machine learning. Go up another level, and I'm using Amazon as an example here. Amazon has a vision service called Recognition. They have a speech generation service, Natural Language. Those are developer ready. And when I say developer ready, I mean developer just uses an API, you know, passes in the data that comes out. He doesn't have to know how the model works. >> John: It's kind of like what DevOps was for cloud at the end of the day. This slide is completely accurate in my opinion. And we're at the early days and you're starting to see the platforms develop. It's the classic abstraction layer. Whoever can extract away the complexity as AI and machine learning grows is going to be the winning platform, no doubt about it. Amazon is showing some good moves there. >> George: And you know how they abstracted away. In traditional programming, it was just building higher and higher APIs, more accessible. In machine learning, you can't do that. You have to actually train the models which means you need data. So if you look at the big cloud vendors right now. So Google, Microsoft, Amazon, and IBM. Most of them, the first three, they have a lot of data from their B to C businesses. So you know, people talking to Echo, people talking to Google Assistant or Siri. That's where they get enough of their speech. >> John: So data equals power? >> George: Yes. >> By having data, you have the ingredients. And the more data that you have, the more data that you know about, the more data that has information around it, the more effective it can be to train machine learning algorithms. >> Yes. >> And the benefit comes back to the people who have the data. >> Yes. And so even though your capabilities get narrower, 'cause you could do anything on TensorFlow. >> John: Well, that's why Facebook is getting killed right now just to kind of change tangents. They have all this data and people are very unhappy, they just released that the Russians were targeting anti-semitic advertising, they enabled that. So it's hard to be a data platform and still provide user utility. This is what's going on. Whoever has the data has the power. It was a Frankenstein moment for Facebook. So there's that out there for everyone. How do companies do the right thing? >> And there's also the issue of customer intellectual property protection. As consumers, we're like you can take our voice, you can take all our speech to Siri or to Echo or whatever and get better at recognizing speech because we've given up control of that 'cause we want those services for free. >> Whoever can shift the data value to the users. >> George: To the developers. >> Or to the developers, or communities, better said, will win. >> OK. >> In my opinion, that's my opinion. >> For the most part, Amazon, Microsoft, and Google have similar data assets. For the most part, so far. IBM has something different which is they work closely with their industry customers and they build progressively. They're working with Mercedes, they're working with BMW. They'll work on the connected car, you know, the autonomous car, and they build out those models slowly. >> So George, this slide is really really interesting and I think this should be a roadmap for all customers to look at to try to peg where they are in the machine learning journey. But then the question comes in. They do the blocking and tackling, they have the foundational low level stuff done, they're building the models, they're understanding the mission, they have the right organizational mindset and personnel. Now, they want to orchestrate it and implement it into action. That's the final question. How do you orchestrate the distributed machine learning feedback and the data coherency? How do you get this thing scaling? How do these machines and the training happen so you have the breadth, and then you could bring the machine learning up the curve into the dashboard? >> OK. We've saved the best for last. It's not easy. When I show the chevrons, that's the analytic data pipeline. And imagine in the serve and predict at the very end, let's take an IOT app, a very sophisticated one. which would be an autonomous car. And it doesn't actually have to be an autonomous one, you could just be collected a lot of information off the car to do a better job insuring it, the insurance company. But the key then is you're collecting data on a fleet of cars, right? You're collecting data off each one, but you're also collecting then the fleet. And that, in the cloud, is where you keep improving your model of how the car works. You run simulations to figure out not just how to design better ones in the future, but how to tune and optimize the ones that are on the road now. That's number three. And then in four, you push that feedback back out to the cars on the road. And you have to manage, and this is tricky, you have to make sure that the models that you trained in step three are coherent, or the same, when you take out the fleet data and then you put the model for a particular instance of a car back out on the highway. >> George, this is a great example, and I think this slide really represents the modern analytical operational role in digital business. You can't look further than Tesla, this is essentially Tesla, and now all cars as a great example 'cause it's complex, it's an internet (mumbling) device, it's on the edge of the network, it's mobility, it's using 5G. It encapsulates everything that you are presenting, so I think this is example, is a great one, of the modern operational analytic applications that supports digital business. Thanks for joining this Wikibon conversaion. >> Thank you, John. >> George Gilbert, the analyst at Wikibon covering big data and the modern operational analytical system supporting digital business. It's data driven. The people with the data can train the machines that have the power. That's the mandate, that's the action item. I'm John Furrier with George Gilbert. Thanks for watching. (upbeat electronic music)

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