>> Announcer: From Berlin, Germany, it's the CUBE covering Dataworks Summit Europe 2018. Brought to you by Hortonworks. >> Welcome to the CUBE, we're here at Dataworks Summit 2018 in Berlin. I'm James Kobielus. I am the lead analyst for big data analytics on the Wikibon team of SiliconANGLE Media On the CUBE, we extract the signal from the noise and here at Dataworks Summit, the signal is big data analytics and increasingly the imperative for many enterprises is compliance with GDPR, the General Data Protection Regulation comes in five weeks, May 25th. There's more things going on so what I'm going to be doing today for the next 20 minutes or so is from Hortonworks I have Abhas Ricky who is the director of strategy and innovation. He helps customers, and he'll explain what he does, but at a high level, he helps customers to identify the value of investments in big data, analytics, big data platforms in their business. And Abhas, how do you justify the value of compliance with GDPR. I guess, the value would be avoid penalties for noncompliance, right? Can you do it as an upside as well? Is there an upside in terms of if you make an investment, and you probably will need to make an investment to comply, Can you turn this around as a strategic asset, possibly? Yeah, so I'll take a step back first. >> James: Like a big data catalog and so forth. >> Yeah, so if you look at the value part which you said, it's interesting that you mentioned it. So there's a study which was done by McKinsey which said that only 15% of executives can understand what is the value of a digital initiative, let alone big data initiative. >> James: Yeah. >> Similarly, Gardner says that if you look at the various portraits and if you look at various issues, the fundamental thing which executives struggle with identifying the value which they will get. So that is where I pitch in. That is where I come in and do a data perspective. Now if you look at GDPR specifically, one of the things that we believe, and I've done multiple blogs around that and webinars, GDPR should be treated at a business opportunity because of the fact that -- >> James: Any opportunity? Business opportunity. It shouldn't necessarily be seen as a compliance burden on costs or your balance sheets because of the fact, it is the one single opportunity which allows you to clean up your data supply chain. It allows you to look at your data assets with a holistic view, and if you create a transparent data supply chain, and your IT systems talk to each other. So some of the provisions, as you know, in addition to right to content, right to portability, etc. It is also privacy by design which says that you have to be proactive in defining your IT systems and architecture. It's not necessarily reactive. But guess what? If you're able to do that, you will see the benefits in other use cases like single view of customer or fraud or anti-money laundering because at the end of the day, all GDPR is allowing you to say is that where do you store your data, what's the lineage, what's the provenance? Can you identify what the personally identifiable information is for any particular customer? And can you use that to your effect as you go forward? So it's a great opportunity because to be able to comply with the provisions, you've got to take steps before that which is essentially streamlining your data operations which obviously will have a domino effect on the efficiency of other use cases. So I believe it's a business opportunity. >> Right, now part of that opportunity in terms of getting your arms around what data you have, when the GDPR is concerned, the customer has a right to withhold consent for you and the enterprise that holds that data to use that personal data of theirs which they own for various and sundry reasons. Many enterprises and many of Hortonworks customers are using their big data for things like AI and machine learning. Won't this compliance with GDPR limit their ability to seize the opportunity to build deep learning and so forth? What are customers saying about that? Is that going to be kind of a downer or a chilling effect on their investments in AI and so forth? >> So there's two elements around it. The first thing which you said, there are customers, there's machine learning in AI, yes, there are. But broadly speaking, before you're able to do machine learning and AI, you need to get your data sets onto a particular platform in a particular fashion, clean data, otherwise, you can't do AI or machine learning on top of it. >> James: Right. So the reason why I say it's an opportunity is that because you're being forced by compliance to get that data from every other place onto this platform. So obviously those capabilities will get enhanced. Having said, I do agree if I'm an organization which does targeting, retargeting of customers based on multiple segmentations and then one of the things is online advertisements. In that case, yes, your ability might get affected, but I don't think you'll get prohibited. And that affected time span will be only small because you just adapt. So the good thing about machine learning and AI is that you don't create rules, you don't create manual rules. They pick up the rules based on the patterns and how the data sets have been performing. So obviously once you have created those structures in place, initially, yes, you'll have to make an investment to alter your programs of work. However, going forward, it will be even better. Because guess what? You just cleaned your entire data supply chain. So that's how I would see that, yes, a lot of companies, ecommerce you do targeting and retargeting based on the customer DNA, based on their shopping profiles, based on their shopping ad libs and then based off that, you give them the next best offer or whatever. So, yes, that might get affected initially, but that's not because GDPR is there or not. That's just because you're changing your program software. You're changing the fundamental way by which you're sourcing the data, the way they are coming from and which data can you use. But once you have tags against each of those attributes, once you have access controls, once you know exactly which customer attributes you can touch and you cannot for the purposes, do you have consent or not, your life's even better. The AI tools or the machine learning algorithms will learn from themselves. >> Right, so essentially, once you have a tight ship in terms of managing your data in line with the GDPR strictures and so forth, it sounds like what you're saying is that it gives you as an enterprise the confidence and assurance that if you want to use that data and need to use that data, you know exactly how you've the processes in place to gain the necessary consents from customers. So there won't be any nasty surprises later on of customers complaining because you've got legal procedures for getting the consent and that's great. You know, one of the things, Abhas, we're hearing right now in terms of compliance requirements that are coming along, maybe not apart of GDPR directly yet, but related to it is the whole notion of algorithmic transparency. As you build machine learning models and these machine learning models are driven into working applications, being able to transparently identify if those models make, in particular, let's say autonomous action based on particular data and particular variables, and then there is some nasty consequences like crashing an autonomous vehicle, the ability, they call it explicably AI to roll that back and determine who's liable for that event. Does Hortonworks have any capability within your portfolio to enable more transparency into the algorithmic underpinnings of a given decision? Is that something that you enable in your solutions or that your partner IBM enables through DSX and so forth? Give us a sense whether that's a capability currently that you guys offer and whether that's something in terms of your understand, are customers asking for that yet or is that too futuristic? >> So I would say that it's a two-part question. >> James: Yeah. >> The first one, yes, there are multiple regulations coming in, like Vilica Financial Markets, there's Mid Fair, the BCBS, etc. and organizations have to comply. You've got the IFRS which span to brokers, the insurance, etc., etc. So, yes, a lot of organizations across industries are getting affected by compliance use cases. Where does Hortonworks come into the picture is to be able to be compliant from a data standpoint, A you need to be able to identify which of those data sources you need to implement a particular use case. B you need to get them to a certain point whereby you can do analytics on that And then there's the whole storage and processing and all of that. But also which you might have heard at the keynote today, from a cloud perspective, it's starting to get more and more complex because everyone's moving to the cloud which means, if you look at any large multi-national organization, most of them have a hybrid cloud structure because they work with two or three cloud vendors which makes the process even more complex because now you have multiple clusters, you have have on premise and you have multiple different IT systems who need to talk to each other. Which is where the Hortonworks data plan services come into the picture because it gives you a unified view of your global data assets. >> James: Yes. >> Think of it like a single pane of glass which whereby you can do security and governance across all data assets. So from those angles, yes, we definitely enable those use cases which will help with compliance. >> Making the case to the customer for a big data catalog along the lines of what you guys offer, in making the case, there's a lot of upfront data architectural work that needs to be done to get all you data assets into shape within the context of the catalog. How do they justify making that expense in terms of hiring the people, the data architects and so forth needed to put it all in shape. I mean, how long does it take before you can really stand up in your working data catalog in most companies? >> So again, you've asked two questions. First of all is how do they justify it? Which is where we say that the platform is a means to an end. It's enabling you to deliver use cases. So I look at it in terms of five key value drivers. Either it's a risk reduction or it's a cost reduction or it's a cost avoidance. >> James: Okay. >> Or it's a revenue optimization, or it's time to market. Against each one of these value drivers, or multiple of them or a combination of them, each of the use cases that you're delivering on the platform will lead you to benefits around that. My job, obviously, is to work with the customers and executes to understand what will that be to quantify the potential impact which will then form the basis and give my customer champions enough ammunition so that they can go back and justify those investments. >> James: Abhas, we're going to have to cut it short, but I'm going to let you finish your point here, but we have to end this segment so go ahead. >> That's fine. >> Okay, well, anyway, have had Abhas Ricky who is the director of strategy and innovation at Hortonworks. We're here at Dataworks Summit Berlin. And thank you very much Sorry to cut it short, but we have to move to the next guest. >> No worries, pleasure, thank you very much. >> Take care, have a good one. >> Thanks a lot, yes. (upbeat music)

>> From San Jose, in the heart of Silicon Valley, it's theCube. Covering DataWorks Summit 2018. Brought to you by Hortonworks. (upbeat music) >> Welcome back to theCUBE's live coverage of Data Works here in sunny San Jose, California. I'm your host Rebecca Knight along with my co-host James Kobielus. We're joined by Pandit Prasad. He is the analytics, projects, strategy, and management at IBM Analytics. Thanks so much for coming on the show. >> Thanks Rebecca, glad to be here. >> So, why don't you just start out by telling our viewers a little bit about what you do in terms of in relationship with the Horton Works relationship and the other parts of your job. >> Sure, as you said I am in Offering Management, which is also known as Product Management for IBM, manage the big data portfolio from an IBM perspective. I was also working with Hortonworks on developing this relationship, nurturing that relationship, so it's been a year since the Northsys partnership. We announced this partnership exactly last year at the same conference. And now it's been a year, so this year has been a journey and aligning the two portfolios together. Right, so Hortonworks had HDP HDF. IBM also had similar products, so we have for example, Big Sequel, Hortonworks has Hive, so how Hive and Big Sequel align together. IBM has a Data Science Experience, where does that come into the picture on top of HDP, so it means before this partnership if you look into the market, it has been you sell Hadoop, you sell a sequel engine, you sell Data Science. So what this year has given us is more of a solution sell. Now with this partnership we go to the customers and say here is NTN experience for you. You start with Hadoop, you put more analytics on top of it, you then bring Big Sequel for complex queries and federation visualization stories and then finally you put Data Science on top of it, so it gives you a complete NTN solution, the NTN experience for getting the value out of the data. >> Now IBM a few years back released a Watson data platform for team data science with DSX, data science experience, as one of the tools for data scientists. Is Watson data platform still the core, I call it dev ops for data science and maybe that's the wrong term, that IBM provides to market or is there sort of a broader dev ops frame work within which IBM goes to market these tools? >> Sure, Watson data platform one year ago was more of a cloud platform and it had many components of it and now we are getting a lot of components on to the (mumbles) and data science experience is one part of it, so data science experience... >> So Watson analytics as well for subject matter experts and so forth. >> Yes. And again Watson has a whole suit of side business based offerings, data science experience is more of a a particular aspect of the focus, specifically on the data science and that's been now available on PRAM and now we are building this arm from stack, so we have HDP, HDF, Big Sequel, Data Science Experience and we are working towards adding more and more to that portfolio. >> Well you have a broader reference architecture and a stack of solutions AI and power and so for more of the deep learning development. In your relationship with Hortonworks, are they reselling more of those tools into their customer base to supplement, extend what they already resell DSX or is that outside of the scope of the relationship? >> No it is all part of the relationship, these three have been the core of what we announced last year and then there are other solutions. We have the whole governance solution right, so again it goes back to the partnership HDP brings with it Atlas. IBM has a whole suite of governance portfolio including the governance catalog. How do you expand the story from being a Hadoop-centric story to an enterprise data-like story, and then now we are taking that to the cloud that's what Truata is all about. Rob Thomas came out with a blog yesterday morning talking about Truata. If you look at it is nothing but a governed data-link hosted offering, if you want to simplify it. That's one way to look at it caters to the GDPR requirements as well. >> For GDPR for the IBM Hortonworks partnership is the lead solution for GDPR compliance, is it Hortonworks Data Steward Studio or is it any number of solutions that IBM already has for data governance and curation, or is it a combination of all of that in terms of what you, as partners, propose to customers for soup to nuts GDPR compliance? Give me a sense for... >> It is a combination of all of those so it has a HDP, its has HDF, it has Big Sequel, it has Data Science Experience, it had IBM governance catalog, it has IBM data quality and it has a bunch of security products, like Gaurdium and it has some new IBM proprietary components that are very specific towards data (cough drowns out speaker) and how do you deal with the personal data and sensitive personal data as classified by GDPR. I'm supposed to query some high level information but I'm not allowed to query deep into the personal information so how do you blog those queries, how do you understand those, these are not necessarily part of Data Steward Studio. These are some of the proprietary components that are thrown into the mix by IBM. >> One of the requirements that is not often talked about under GDPR, Ricky of Formworks got in to it a little bit in his presentation, was the notion that the requirement that if you are using an UE citizen's PII to drive algorithmic outcomes, that they have the right to full transparency. It's the algorithmic decision paths that were taken. I remember IBM had a tool under the Watson brand that wraps up a narrative of that sort. Is that something that IBM still, it was called Watson Curator a few years back, is that a solution that IBM still offers, because I'm getting a sense right now that Hortonworks has a specific solution, not to say that they may not be working on it, that addresses that side of GDPR, do you know what I'm referring to there? >> I'm not aware of something from the Hortonworks side beyond the Data Steward Studio, which offers basically identification of what some of the... >> Data lineage as opposed to model lineage. It's a subtle distinction. >> It can identify some of the personal information and maybe provide a way to tag it and hence, mask it, but the Truata offering is the one that is bringing some new research assets, after GDPR guidelines became clear and then they got into they are full of how do we cater to those requirements. These are relatively new proprietary components, they are not even being productized, that's why I am calling them proprietary components that are going in to this hosting service. >> IBM's got a big portfolio so I'll understand if you guys are still working out what position. Rebecca go ahead. >> I just wanted to ask you about this new era of GDPR. The last Hortonworks conference was sort of before it came into effect and now we're in this new era. How would you say companies are reacting? Are they in the right space for it, in the sense of they're really still understand the ripple effects and how it's all going to play out? How would you describe your interactions with companies in terms of how they're dealing with these new requirements? >> They are still trying to understand the requirements and interpret the requirements coming to terms with what that really means. For example I met with a customer and they are a multi-national company. They have data centers across different geos and they asked me, I have somebody from Asia trying to query the data so that the query should go to Europe, but the query processing should not happen in Asia, the query processing all should happen in Europe, and only the output of the query should be sent back to Asia. You won't be able to think in these terms before the GDPR guidance era. >> Right, exceedingly complicated. >> Decoupling storage from processing enables those kinds of fairly complex scenarios for compliance purposes. >> It's not just about the access to data, now you are getting into where the processing happens were the results are getting displayed, so we are getting... >> Severe penalties for not doing that so your customers need to keep up. There was announcement at this show at Dataworks 2018 of an IBM Hortonwokrs solution. IBM post-analytics with with Hortonworks. I wonder if you could speak a little bit about that, Pandit, in terms of what's provided, it's a subscription service? If you could tell us what subset of IBM's analytics portfolio is hosted for Hortonwork's customers? >> Sure, was you said, it is a a hosted offering. Initially we are starting of as base offering with three products, it will have HDP, Big Sequel, IBM DB2 Big Sequel and DSX, Data Science Experience. Those are the three solutions, again as I said, it is hosted on IBM Cloud, so customers have a choice of different configurations they can choose, whether it be VMs or bare metal. I should say this is probably the only offering, as of today, that offers bare metal configuration in the cloud. >> It's geared to data scientist developers and machine-learning models will build the models and train them in IBM Cloud, but in a hosted HDP in IBM Cloud. Is that correct? >> Yeah, I would rephrase that a little bit. There are several different offerings on the cloud today and we can think about them as you said for ad-hoc or ephemeral workloads, also geared towards low cost. You think about this offering as taking your on PRAM data center experience directly onto the cloud. It is geared towards very high performance. The hardware and the software they are all configured, optimized for providing high performance, not necessarily for ad-hoc workloads, or ephemeral workloads, they are capable of handling massive workloads, on sitcky workloads, not meant for I turned this massive performance computing power for a couple of hours and then switched them off, but rather, I'm going to run these massive workloads as if it is located in my data center, that's number one. It comes with the complete set of HDP. If you think about it there are currently in the cloud you have Hive and Hbase, the sequel engines and the stories separate, security is optional, governance is optional. This comes with the whole enchilada. It has security and governance all baked in. It provides the option to use Big Sequel, because once you get on Hadoop, the next experience is I want to run complex workloads. I want to run federated queries across Hadoop as well as other data storage. How do I handle those, and then it comes with Data Science Experience also configured for best performance and integrated together. As a part of this partnership, I mentioned earlier, that we have progress towards providing this story of an NTN solution. The next steps of that are, yeah I can say that it's an NTN solution but are the product's look and feel as if they are one solution. That's what we are getting into and I have featured some of those integrations. For example Big Sequel, IBM product, we have been working on baking it very closely with HDP. It can be deployed through Morey, it is integrated with Atlas and Granger for security. We are improving the integrations with Atlas for governance. >> Say you're building a Spark machine learning model inside a DSX on HDP within IH (mumbles) IBM hosting with Hortonworks on HDP 3.0, can you then containerize that machine learning Sparks and then deploy into an edge scenario? >> Sure, first was Big Sequel, the next one was DSX. DSX is integrated with HDP as well. We can run DSX workloads on HDP before, but what we have done now is, if you want to run the DSX workloads, I want to run a Python workload, I need to have Python libraries on all the nodes that I want to deploy. Suppose you are running a big cluster, 500 cluster. I need to have Python libraries on all 500 nodes and I need to maintain the versioning of it. If I upgrade the versions then I need to go and upgrade and make sure all of them are perfectly aligned. >> In this first version will you be able build a Spark model and a Tesorflow model and containerize them and deploy them. >> Yes. >> Across a multi-cloud and orchestrate them with Kubernetes to do all that meshing, is that a capability now or planned for the future within this portfolio? >> Yeah, we have that capability demonstrated in the pedestal today, so that is a new one integration. We can run virtual, we call it virtual Python environment. DSX can containerize it and run data that's foreclosed in the HDP cluster. Now we are making use of both the data in the cluster, as well as the infrastructure of the cluster itself for running the workloads. >> In terms of the layers stacked, is also incorporating the IBM distributed deep-learning technology that you've recently announced? Which I think is highly differentiated, because deep learning is increasingly become a set of capabilities that are across a distributed mesh playing together as is they're one unified application. Is that a capability now in this solution, or will it be in the near future? DPL distributed deep learning? >> No, we have not yet. >> I know that's on the AI power platform currently, gotcha. >> It's what we'll be talking about at next year's conference. >> That's definitely on the roadmap. We are starting with the base configuration of bare metals and VM configuration, next one is, depending on how the customers react to it, definitely we're thinking about bare metal with GPUs optimized for Tensorflow workloads. >> Exciting, we'll be tuned in the coming months and years I'm sure you guys will have that. >> Pandit, thank you so much for coming on theCUBE. We appreciate it. I'm Rebecca Knight for James Kobielus. We will have, more from theCUBE's live coverage of Dataworks, just after this.

>> Narrator: Live from San Jose, in the heart of Silicon Valley, it's theCUBE. Covering DataWorks Summit 2017, brought to you by Hortonworks. (digitalized music) >> Welcome back to theCUBE. We are live at day one of the DataWorks Summit, in the heart of Silicon Valley. I'm Lisa Martin with theCUBE; my co-host George Gilbert. We're very excited to be joined by our two next guests. Going to be talking about a lot of the passion and the energy that came from the keynote this morning and some big announcements. Please welcome Madhu Kochar, VP of analytics and product development and client success at IBM, and Jamie Engesser, VP of product management at Hortonworks. Welcome guys! >> Thank you. >> Glad to be here. >> First time on theCUBE, George and I are thrilled to have you. So, in the last six to eight months doing my research, there's been announcements between IBM and Hortonworks. You guys have been partners for a very long time, and announcements on technology partnerships with servers and storage, and presumably all of that gives Hortonworks Jamie, a great opportunity to tap into IBM's enterprise install base, but boy today? Socks blown off with this big announcement between IBM and Hortonworks. Jamie, kind of walk us through that, or sorry Madhu I'm going to ask you first. Walk us through this announcement today. What does it mean for the IBM-Hortonworks partnership? Oh my God, what an exciting, exciting day right? We've been working towards this one, so three main things come out of the announcement today. First is really the adoption by Hortonworks of IBM data sciences machine learning. As you heard in the announcement, we brought the machine learning to our mainframe where the most trusted data is. Now bringing that to the open source, big data on Hadoop, great right, amazing. Number two is obviously the whole aspects around our big sequel, which is bringing the complex-query analytics, where it brings all the data together from all various sources and making that as HDP and Hadoop and Hortonworks and really adopting that amazing announcement. Number three, what we gain out of this humongously, obviously from an IBM perspective is the whole platform. We've been on this journey together with Hortonworks since 2015 with ODPI, and we've been all champions in the open source, delivering a lot of that. As we start to look at it, it makes sense to merge that as a platform, and give to our clients what's most needed out there, as we take our journey towards machine learning, AI, and enhancing the enterprise data warehousing strategy. >> Awesome, Jamie from your perspective on the product management side, what is this? What's the impact and potential downstream, great implications for Hortonworks? >> I think there's two things. I think Hortonworks has always been very committed to the open source community. I think with Hortonworks and IBM partnering on this, number one is it brings a much bigger community to bear, to really push innovation on top of Hadoop. That innovation is going to come through the community, and I think that partnership drives two of the biggest contributors to the community to do more together. So I think that's number one is the community interest. The second thing is when you look at Hadoop adoption, we're seeing that people want to get more and more value out of Hadoop adoption, and they want to access more and more data sets, to number one get more and more value. We're seeing the data science platform become really fundamental to that. They're also seeing the extension to say, not only do I need data science to get and add new insights, but I need to aggregate more data. So we're also seeing the notion of, how do I use big sequel on top of Hadoop, but then I can federate data from my mainframe, which has got some very valuable data on it. DB2 instances and the rest of the data repositories out there. So now we get a better federation model, to allow our customers to access more of the data that they can make better business decisions on, and they can use data science on top of that to get new learnings from that data. >> Let me build on that. Let's say that I'm a Telco customer, and the two of you come together to me and say, we don't want to talk to you about Hadoop. We want to talk to you about solving a problem where you've got data in applications and many places, including inaccessible stuff. You have a limited number of data scientists, and the problem of cleaning all the data. Even if you build models, the challenge of integrating them with operational applications. So what do the two of you tell me the Telco customer? >> Yeah, so maybe I'll go first. So the Telco, the main use case or the main application as I've been talking to many of the largest Telco companies here in U.S. and even outside of U.S. is all about their churn rate. They want to know when the calls are dropping, why are they dropping, why are the clients going to the competition and such? There's so much data. The data is just streaming and they want to understand that. I think if you bring the data science experience and machine learning to that data. That as said, it doesn't matter now where the data resides. Hadoop, mainframes, wherever, we can bring that data. You can do a transformation of that, cleanup the data. The quality of the data is there so that you can start feeding that data into the models and that's when the models learn. More data it is, the better it is, so they train, and then you can really drive the insights out of it. Now data science the framework, which is available, it's like a team sport. You can bring in many other data scientists into the organization who could have different analyst reports to go render for or provide results into. So being a team support, being a collaboration, bringing together with that clean data, I think it's going to change the world. I think the business side can have instant value from the data they going to see. >> Let me just test the edge conditions on that. Some of that data is streaming and you might apply the analytics in real time. Some of it is, I think as you were telling us before, sort of locked up as dark data. The question is how much of that data, the streaming stuff and the dark data, how much do you have to land in a Hadoop repository versus how much do you just push the analytics out too and have it inform a decision? >> Maybe I can take a first thought on it. I think there's a couple things in that. There's the learnings, and then how do I execute the learnings? I think the first step of it is, I tend to land the data, and going to the Telecom churn model, I want to see all the touch points. So I want to see the person that came through the website. He went into the store, he called into us, so I need to aggregate all that data to get a better view of what's the chain of steps that happened for somebody to churn? Once I end up diagnosing that, go through the data science of that, to learn the models that are being executed on that data, and that's the data at rest. What I want to do is build the model out so that now I can take that model, and I can prescriptively run it in this stream of data. So I know that that customer just hung up off the phone, now he walked in the store and we can sense that he's in the store because we just registered that he's asking about his billing details. The system can now dynamically diagnose by those two activities that this is a churn high-rate, so notify that teller in the store that there's a chance of him rolling out. If you look at that, that required the machine learning and data science side to build the analytical model, and it required the data-flow management and streaming analytics to consume that model to make a real-time insight out of it, to ultimately stop the churn from happening. Let's just give the customer a discount at the end of the day. That type of stuff; so you need to marry those two. >> It's interesting, you articulated that very clearly. Although then the question I have is now not on the technical side, but on the go-to market side. You guys have to work very very closely, and this is calling at a level that I assume is not very normal for Hortonworks, and it's something that is a natural sales motion for IBM. >> So maybe I'll first speak up, and then I'll let you add some color to that. When I look at it, I think there's a lot of natural synergies. IBM and Hortonworks have been partnered since day one. We've always continued on the path. If you look at it, and I'll bring up community again and open source again, but we've worked very well in the community. I think that's incubated a really strong and fostered a really strong relationship. I think at the end of the day we both look at what's going to be the outcome for the customer and working back from that, and we tend to really engage at that level. So what's the outcome and then how do we make a better product to get to that outcome? So I think there is a lot of natural synergies in that. I think to your point, there's lots of pieces that we need to integrate better together, and we will join that over time. I think we're already starting with the data science experience. A bunch of integration touchpoints there. I think you're going to see in the information governance space, with Atlas being a key underpinning and information governance catalog on top of that, ultimately moving up to IBM's unified governance, we'll start getting more synergies there as well and on the big sequel side. I think when you look at the different pods, there's a lot of synergies that our customers will be driving and that's what the driving factors, along with the organizations are very well aligned. >> And VPF engineering, so there's a lot of integration points which were already identified, and big sequel is already working really well on the Hortonworks HDP platform. We've got good integration going, but I think more and more on the data science. I think in end of the day we end up talking to very similar clients, so going as a joined go-to market strategy, it's a win-win. Jamie and I were talking earlier. I think in this type of a partnership, A our community is winning and our clients, so really good solutions. >> And that's what it's all about. Speaking of clients, you gave a great example with Telco. When we were talking to Rob Thomas and Rob Bearden earlier on in the program today. They talked about the data science conversation is at the C-suite, so walk us through an example of whether it's a Telco or maybe a healthcare organization, what is that conversation that you're having? How is a Telco helping foster what was announced today and this partnership? >> Madhu: Do you want to take em? >> Maybe I'll start. When we look in a Telco, I think there's a natural revolution, and when we start looking at that problem of how does a Telco consume and operate data science at a larger scale? So at the C-suite it becomes a people-process discussion. There's not a lot of tools currently that really help the people and process side of it. It's kind of an artist capability today in the data science space. What we're trying to do is, I think I mentioned team sport, but also give the tooling to say there's step one, which is we need to start learning and training the right teams and the right approach. Step two is start giving them access to the right data, etcetera to work through that. And step three, giving them all the tooling to support that, and tooling becomes things like TensorFlow etcetera, things like Zeppelin, Jupiter, a bunch of the open source community evolved capabilities. So first learn and training. The second step in that is give them the access to the right data to consume it, and then third, give them the right tooling. I think those three things are helping us to drive the right capabilities out of it. But to your point, elevating up to the C-suite. It's really they think people-process, and I think giving them the right tooling for their people and the right processes to get them there. Moving data science from an art to a science, is I would argue at a top level. >> On the client success side, how instrumental though are your clients, like maybe on the Telco side, in actually fostering the development of the technology, or helping IBM make the decision to standardize on HDP as their big data platform? >> Oh, huge, huge, a lot of our clients, especially as they are looking at the big data. Many of them are actually helping us get committers into the code. They're adding, providing; feet can't move fast enough in the engineering. They are coming up and saying, "Hey we're going to help" "and code up and do some code development with you." They've been really pushing our limits. A lot of clients, actually I ended up working with on the Hadoop site is like, you know for example. My entire information integration suite is very much running on top of HDP today. So they are saying, OK what's next? We want to see better integration. So as I called a few clients yesterday saying, "Hey, under embargo this is something going to get announced." Amazing, amazing results, and they're just very excited about this. So we are starting to get a lot of push, and actually the clients who do have large development community as well. Like a lot of banks today, they write a lot of their own applications. We're starting to see them co-developing stuff with us and becoming the committers. >> Lisa: You have a question? >> Well, if I just were to jump in. How do you see over time the mix of apps starting to move from completely custom developed, sort of the way the original big data applications were all written, down to the medal-ep in MapReduce. For shops that don't have a lot of data scientists, how are we going to see applications become more self-service, more pre-packaged? >> So maybe I'll give a little bit of perspective. Right now I think IBM has got really good synergies on what I'll call vertical solutions to vertical organizations, financial, etcetera. I would say, Hortonworks has took a more horizontal approach. We're more of a platform solution. An example of one where it's kind of marrying the two, is if you move up the stack from Hortonworks as a platform to the next level up, which is Hortonworks as a solution. One of the examples that we've invested heavily in is cybersecurity, and in an Apache project called Metron. Less about Metron and more about cybersecurity. People want to solve a problem. They want to defend an attacker immediately, and what that means is we need to give them out-of-the-box models to detect a lot of common patterns. What we're doing there, is we're investing in some of the data science and pre-packaged models to identify attack vectors and then try to resolve that or at least notify you that there's a concern. It's an example where the data science behind it, pre-packaging that data science to solve a specific problem. That's in the cybersecurity space and that case happens to be horizontal where Hortonwork's strength is. I think in the IBM case, there's a lot more vertical apps that we can apply to. Fraud, adjudication, etcetera. >> So it sounds like we're really just hitting the tip of the iceberg here, with the potential. We want to thank you both for joining us on theCUBE today, sharing your excitement about this deepening, expanding partnership between Hortonworks and IBM. Madhu and Jamie, thank you so much for joining George and I today on theCUBE. >> Thank you. >> Thank you Lisa and George. >> Appreciate it. >> Thank you. >> And for my co-host George Gilbert, I am Lisa Martin. You're watching us live on theCUBE, from day one of the DataWorks Summit in Silicon Valley. Stick around, we'll be right back. (digitalized music)

(Rhythm music) >> Narrator: Live, from Munich, Germany, it's The Cube. Coverage, DataWorks Summit Europe, 2017. Brought to you by Hortonworks. >> Okay, welcome back everyone. We are live in Munich, Germany for DataWorks 2017, formally known as Hadoop Summit. This is The Cube special coverage of the Big Data world. I'm John Furrier my co-host Dave Vallente. Two days of live coverage, day one wrapping up. Now, Dave, we're just kind of reviewing the scene here. First of all, Europe is a different vibe. But the game is still the same. It's about Big Data evolving from Hadoop to full open source penetration. Puppy's now public in markets Hortonworks, Cloudera is now filing an S-1, Neosoft, Talon, variety of the other public companies. Alteryx. Hadoop is not dead, it's not dying. It certainly is going to have a position in the industry, but the Big Data conversation is front and center. And one thing that's striking to me is that in Europe, more than in the North America, is IOT is more centrally themed in this event. Europe is on the Internet of Things because of the manufacturing, smart cities. So this is a lot of IOT happening here, and I think this is a big discovery certainly, Hortonworks event is much more of a community event than Strata Hadoop. Which is much more about making money and modernization. This show's got a lot more engagement with real conversations and developers sessions. Very engaging audience. Well, yeah, it's Europe. So you've go a little bit different smaller show than North America but to me, IOT, Internet of Things, is bringing the other cloud world with Big Data. That's the forcing function. And real time data is the center of the action. I think is going to be a continuing theme as we move forward. >> So, in 2010 John, it was all about 'What is Hadoop?' With the middle part of that decade was all about Hadoop's got to go into the enterprise. It's gone mainstream in to the enterprise, and now it's sort of 'what's next?' Same wine new bottle. But I will say this, Hadoop, as you pointed out, is not dead. And I liken it to the early web. Web one dot O it was profound. It was a new paradigm. The profundity of Hadoop was that you could ship five megabytes of code to a petabyte of data. And that was the new model and that's spawned, that's catalyzed the Big Data movement. That is with us now and it's entrenched, and now you're seeing layers of innovation on top of that. >> Yeah, and I would just reiterate and reinforce that point by saying that Cloudera, the founders of this industry if you will, with Hadoop the first company to be commercially funded to do what Hortonworks came in after the fact out of Yahoo, came out of a web-scale world. So you have the cloud native DevOps culture, Amar Ujala's at Yahoo, Mike Olson, Jeff Hammerbacher, Christopher Vercelli. These guys were hardcore large-scale data guys. Again, this is the continuation of the evolution, and I think nothing is changed it that regard because those pioneers have set the stage for now the commercialization and now the conversation around operationalizing this cloud is big. And having Alan Nance, a practitioner, rock-star, talking about radical deployments that can drop a billion dollars at a cost savings to the bottom line. This is the kind of conversations we're going to see more of this is going to change the game from, you know, "Hey, I'm the CFO buyer" or "CIO doing IT", to an operational CEO, chief operating officer level conversation. That operational model of cloud is now coming into the view what ERP did in software, those kinds of megatrends, this is happening right now. >> As we talk about the open, the people who are going to make the real money on Big Data are the practitioners, those people applying it. We talked about Alan Nance's example of billion dollar, half a billion dollar cost-savings revenue opportunities, that's where the money's being made. It's not being made, yet anyway with these public companies. You're seeing it Splunk, Tableau, now Cloudera, Hortonworks, MapR. Is MapR even here? >> Haven't seen 'em. >> No I haven't seen MapR, they used to have pretty prominent display at the show. >> You brought up point I want to get back to. This relates to those guys, which is, profitless prosperity. >> Yeah. >> A term used for open source. I think there's a trend happening and I can't put a finger on it but I can kind of feel it. That is the ecosystems of open source are now going to a dimension where they're not yet valued in the classic sense. Most people that build platforms value ecosystems, that's where developers came from. Developer ecosystems fuel open source. But if you look at enterprise, at transformations over the decades, you'd see the successful companies have ecosystems of channel partners; ecosystems of indirect sales if you will. We're seeing the formation, at least I can start seeing the formation of an indirect engine of value creation, vis-à-vis this organic developer community where the people are building businesses and companies. Shaun Connolly pointed to Thintech as an example. Where these startups became financial services businesses that became Thintech suppliers, the banks. They're not in the banking business per se, but they're becoming as important as banks 'cuz they're the providers in Thintech, Thintech being financial tech. So you're starting to see this ecosystem of not "channel partners", resell my equipment or software in the classic sense as we know them as they're called channel partners. But if this continues to develop, the thousand flower blooming strategy, you could argue that Hortonworks is undervalued as a company because they're not realizing those gains yet or those gains can't be measured. So if you're an MBA or an investment banker, you've got to be looking at the market saying, "wow, is there a net-present value to an ecosystem?" It begs the question Dave. >> Dave: It's a great question John. >> This is a wealth creation. A rising tide floats all boats, in that rising tide is a ecosystem value number there. No one has their hands on that, no one's talked about that. That is the upshot in my mind, the silver-lining to what some are saying is the consolidation of Hadoop. Some are saying Cloudera is going to get a huge haircut off their four point one billion dollar value. >> Dave: I think that's inevitable. >> Which is some say, they may lose two to three billion in value, in the IPO. Post IPO which would put them in line with Hortonworks based on the numbers. You know, is that good or bad? I don't think it's bad because the value shifts to the ecosystem. Both Cloudera and Hortonworks both play in open source so you can be glass half-full on one hand, on the haircut, upcoming for Cloudera, two saying "No, the glass is half-full because it's a haircut in the short-term maybe", if that happens. I mean some said Pure Storage was going to get a haircut, they never really did Dave. So, again, no one yet has pegged the valuation of an ecosystem. >> Well, and I think that is a great point, personally I think, I've been sort of racking my brain, will this Big Data hike be realized. Like the internet. You remember the internet hyped up, then it crashed; no one wanted to own any of these companies. But it actually lived up to the hype. It actually exceeded the hype. >> You can get pet food online now, it's called amazon. [Co-Hosts Chuckle Together] All the e-commerce played out. >> Right, e-commerce played out. But I think you're right. But everybody's expecting sort of, was expecting a similar type of cycle. "Oh, this will replace that." And that's now what's going to happen. What's going to happen is the ecosystem is going to create a flywheel effect, is really what you're saying. >> Jeff: Yes. >> And there will be huge valuations that emerge out of this. But today, the guys that we know and love, the Hortonworks, the Clouderas, et cetera, aren't really on the winners list, I mean some of their founders maybe are. But who are the winners? Maybe the customers because they saw a big drop in cost. Apache's a big winner here. Wouldn't ya say? >> Yeah. >> Apache's looking pretty good, Apache Foundation. I would say AWS is a pretty big winner. They're drifting off of this. How about Microsoft and IBM? I mean I feel in a way IBM is sort of co-opted this Big Data meme, and said, "okay, cognitive." And layered all of it's stuff on top of it. Bought the weather company, repositioned the company, now it hasn't translated in to growth, but certainly has profitability implications. >> IBM plays well here, I'll tell you why. They're very big in open source, so that's positive. Two, they have huge track record and staff dealing with professional services in the enterprise. So if transformation is the journey conversation, IBM's right there. You can't ignore IBM on this one. Now, the stack might be different, but again, beauty is in the eye of the beholder because depending on what work clothes you have it depends. IBM is not going to leave you high and dry 'cuz they have a really you need for what they can do with their customers. Where people are going to get blindsided in my opinion, the IBMs and Oracles of the world, and even Microsoft, is what Alan Nance was talking about, the radical transformation around the operating model is going to force people to figure out when to start cannibalizing their own stacks. That's going to be a tell sign for winners and losers in the big game. Because if IBM can shift quickly and co-op the megatrends, make it their own, get out in front of that next wave as Pat Gelsinger would say, they could surf that wave and then tweak, and then get out in front. If they don't get behind that next wave, they're driftwood. It really is all about where you are in the spectrum, and analytics is one of those things in data where, you've got to have a cohesive horizontal strategy. You got to be horizontally scalable with data. You got to make data freely available. You have to have an abstraction layer of software that will allow free movement of data, across systems. That's the number one thing that comes out of seeing the Hortonwork's data platform for instance. Shaun Connolly called it 'connective tissue'. Cloudera is the same thing, they have to start figuring out ways to be better at the data across the horizontal view. Cloudera like IBM has an opportunity as well, to get out in front of the next wave. I think you can see that with AI and machine learning, clearly they're going to go after that. >> Just to finish off on the winners and losers; I mean, the other winner is systems integrators to service these companies. But I like what you said about cannibalizing stacks as an indicator of what's happening. So let's talk about that. Oracle clearly cannibalizing it's stacks, saying, "okay, we're going to the red stack to the cloud, go." Microsoft has made that decision to do that. IBM? To a large degree is cannibalizing it's stack. HP sold off it's stack, said, "we don't want to cannibalize our stack, we want to sell and try to retool." >> So, your question, your point? >> So, haven't they already begun to do that, the big legacy companies? >> They're doing their tweaking the collet and mog, as an example. At Oracle Open World and IBM Interconnect, all the shows we, except for Amazon, 'cuz they're pure cloud. All are taking the unique differentiation approach to their own stuff. IBM is putting stuff that's relate to IBM in their cloud. Oracle differentiates on their stack, for instance, I have no problem with Oracle because they have a huge database business. And, you're high as a kite if you think Oracle's going to lose that database business when data is the number one asset in the world. What Oracle's doing which I think is quite brilliant on Oracle's part is saying, "hey, if you want to run on premise with hardware, we got Sun, and oh by the way, our database is the fastest on our stuff." Check. Win. "Oh you want to move to the cloud? Come to the Oracle cloud, our database runs the fastest in our cloud", which is their stuff in the cloud. So if you're an Oracle customer you just can't lose there. So they created an inimitability around their own database. So does that mean they're going to win the new database war? Maybe not, but they can coexist as a system of records so that's a win. Microsoft Office 365, tightly coupling that with Azure is a brilliant move. Why wouldn't they do that? They're going to migrate their customer base to their own clouds. Oracle and Microsoft are going to migrate their customers to their own cloud. Differentiate and give their customers a gateway to the cloud. VVMware is partnering with Amazon. Brilliant move and they just sold vCloud Air which we reported at Silicon Angle last night, to a French company recently so vCloud Air is gone. Now that puts the VMware clearly in bed with Amazon web services. Great move for VMware, benefit to AWS, that's a differentiation for VMware. >> Dave: Somebody bought vCloud Air? >> I think you missed that last night 'cuz you were traveling. >> Chuckling: That's tongue-in-cheek, I mean what did they get for vCloud Air? >> OVH bought them, French company. >> More de-levering by Michael. >> Well, they're inter-clouding right? I mean de-leveraging the focus, right? So OVH, French company, has a very much coexisted... >> What'd they pay? >> ... strategy. It's undisclosed. >> Yeah, well why? 'Cuz it wasn't a big number. That's my point. >> Back to the other cloud players, Google. I think Google's differentiating on their technology. Great move, smart move. They just got to get, as someone who's been following them, and you know, you and I both love an enterprise experience. They got to speak the enterprise language and execute the language. Not through 19 year olds and interns or recent smart college grads ad and say, "we're instantly enterprise." There's a dis-economies of scale for trying to ramp up and trying to be too heavy on the enterprise. Amazon's got the same problem, you can't hire sales guy fast enough, and oh by the way, find me a sales guy that has ten 15 years executive selling experience to a complex strategic sales, like the enterprise where you now have stakeholders that are in multiple roles and changing roles as Alan Nance pointed out. So the enterprise game is very difficult. >> Yup. >> Very very difficult. >> Well, I think these dupe startups are seeing that. None of them are making money. Shaun Connolly basically said, "hey, it used to be growth they would pay for growth, but now their punishing you if you don't have growth plus profitability." By the way, that's not all totally true. Amazon makes no money, unless stock prices go through the roof. >> There is no self-service, there is no self-service business model for digital transformation for enterprise customers today. It doesn't exist. The value proposition doesn't resinate with customers. It works good for Shadow IT, and if you want to roll out G Suite in some pockets of your organization, but an ad-sense sales force doesn't work in the enterprise. Everyone's finding that out right now because they're basically transforming their enterprise. >> I think Google's going to solve their problem. I think Google has to solve their problem 'cuz... >> I think they will, but to me it's, buy a company, there's a zillion company out there they could buy tomorrow that are private, that have like 300 sales people that are senior people. Pay the bucks, buy a sales force, roll your stuff out and start speaking the language. I think Dianne Green gets this. So, I think, I expect to see Google ... >> Dave: Totally. >> do some things in that area. >> And I think, to you're point, I've always said the rich get richer. The traditional legacy companies, they're holding servant in this. They waited they waited they waited, and they said, "okay now we're going to go put our chips on the table." Oracle made it's bets. IBM made it's bets. HP, not really, betting on hardware. Okay. Fine. Cisco, Microsoft, they're all making their bets. >> It's all about bets on technology and profitability. This is what I'm looking at right now Dave. We talked about it on our intro. Shaun Connolly who's in charge of strategy at Hortonworks clarified it that clearly revenue, losing money is not going to solve the problem for credibility. Profitability matters. This comes back to the point we've said on The Cube multiple years ago and even just as recently as last year, that the world's flipping back down to credibility. Customers in the enterprise want to see credibility and track record. And they're going to evaluate the suppliers based upon key fundamentals in their business. Can they make money? Can they deliver SLAs? These are going to be key requirements, not the shiny new toy from Silicon Valley. Or the cool machine learning algorithm. It has to apply to their product, their value, and they're going to look to companies on the scoreboard and say, "are you profitable?" As a proxy for relevance. >> Well I want to keep it, but I do want to, we've been kind of critical of some of the Hadoop players. Cloudera and Hortonworks specifically. But I want to give them props 'cuz you remember well John, when the legacy enterprise guys started coming into the Hadoop market they all said that they had the same messaging, "we're going to make Hadoop enterprise ready." You remember that well, and I have to say that Hortonworks, Cloudera, I would say MapR as well and the ecosystem, have done a pretty good job of making Hadoop and Big Data enterprise ready. They were already working on it very hard, I think they took it seriously and I think that that's why they are in the mix and they are growing as they are. Shaun Connolly talked about them being operating cashflow positive. Eking out some plus cash. On the next earnings call, pressures on. But we want to see, you know, rocket ships. >> I think they've done a good job, I mean, I don't think anyone's been asleep at the switch. At all, enterprise ready. The questions always been "can they get there fast enough?" I think everyone's recognized that cost of ownership's down. We still solicit on the OpenStack ecosystem, and that they move right from the valley properties. So we'll keep an eye on it, tomorrow we'll be checking in. We got a great day tomorrow. Live coverage here in Munich, Germany for DataWorks 2017. More coverage tomorrow, stay with us. I'm John Furrier with Dave Vallente. Be right back with more tomorrow, day two. Keep following us.