(calm techno music) >> Hey, welcome to this Cube Conversation. I'm Lisa Martin. I've got two guests here with me. Please Welcome Philipp Niemietz, the intermediate head of the department for the Laboratory of Machine Tools and Production Engineering or WZL. Philipp, welcome to the program. >> Thank you. >> And we have Russ Caldwell here as well, senior product manager at Dell Technologies. Russ, great to see you. >> Thanks for the invite. >> Absolutely. We're going to be talking about how the enhanced video capabilities of Dell EMC's streaming data platform are enabling manufacturing, anomaly detection, and quality control through the use of sensors, cameras, and x-ray cameras. We're going to go ahead, Philipp, and start with you. We're abbreviating the lab as you guys do as WZL. Talk to us about the lab. What types of problems are you solving? >> Yeah, thank you. In the laboratory for machine tools, we are looking at actually all the other problems that arise in production engineering in general. So that's from the actual manufacturing of work pieces and that's getting used in aerospace or automotive industries, and really dig into the specifics of how those metal parts are manufactured, how they are formed, what are the mechanics of this. So this is a very traditional area where we are coming from. We're also looking at like how to manage all those production systems, how to come up with decision-making processes that's moving those engineering environments forward. But in our department, we recently get... 10 years ago... This Industry 4.0 scenario is getting more and more pushed into authentic research. So more and more data is gathered. We have to deal with a lot of data coming from various sources, and how to actually include this in the research, how to derive new findings from this, or even maybe, even physical equations from all the data that we are gathering around this manufacturing technologies. And this is something that we're, from the research perspective, looking at. >> And talk to me about when you were founded. You're based in Germany, but when was the lab founded? >> The lab was founded 100 years ago, about 100 years ago. It's like a very long history. It is the largest institute for production engineering in Germany, or maybe even in Europe. >> Got it. Okay. Well, 100 years. Amazing innovation that I'm sure the lab has seen. Russ, let's go over to you. Talk to us about the Dell EMC streaming data platform or SDP is what referred to it. >> Yeah. Thanks Lisa. So it's interesting that Philipp brings up Industry 4.0 because this is a prime area where the streaming data platform comes into play. Industry 4.0 for manufacturing really kind of encompasses a few things. It's real-time data analysis. It's automation, machine learning. SDP pulls all that together. So it's a software solution from Dell EMC. And one of the ways we make it all happen is we've unified this concept of time in data. Historical data and real-time data are typically analyzed very, very differently. And so we're trying to support Industry 4.0 manufacturing use cases. That's really important, right? Looking at historical data and real-time data, so you can learn from the past, work you've done on the factory floor, and apply that in real-time analytics. And the platform is used to ingest store and analyze data of this real-time and historical data. It leverages a high availability and dynamic scaling with Kubernetes. So that makes it possible to have lot different projects on the platform. And it really offers a lot of methods to automate this high speed and high precision activities that Philipp's talking about here. There's a lot of examples where it comes into play. It's really exciting to work with Philipp and the team there in Germany. But what's great about it is it's a general purpose platform that supports things like construction where they're doing drones with video ingestion, tracking resources on the ground, and things like that. Predictive maintenance and safety for amusement parks, and many other use cases. But with Industry 4.0 and manufacturing, RWTH and Philipp's team has really kind of pushed the boundaries of what's possible to automate and analyze data for the manufacturing process. >> What a great background. So we understand about the lab. We understand about Dell EMC SDP. Philipp, let's go back to you. How was the lab using this technology? >> Yeah, good question. Maybe, going a little bit back to the details of the use case that we are presenting. We started maybe five, six years ago where all this Industry 4.0 was put into research where you wanted to get more data out of the process now. So we started to apply a little census to the machine, starting with the more traditional ones, like energy consumption and some control information that we get from the machine tool itself. But the sensor system are quite like not that complex. And we could deal with the amount of data fairly easy now using just a USB sticks and some local devices, just a storage. But as it's getting more sophisticated, we're getting more sensor data. We're applying new sensor systems with the tool where the extra process is taking place, throughout the year, like delicious information is hidden. So we're getting really close to the process, applying video data, bigger data streams, more sensor data, and even like are not something like an IoT scenarios. We usually have some data points per second, but we're talking here about census that have like maybe a million data points a second now. So every high frequencies that we have to deal with, and of course, then we had to come up with some system that actually have to do this, help to deal with this data. And yeah, use the classic big data stack that we then set up for ourselves in our research facility to deal with this amount of streaming data to then apply historical analysis. Like Russ just talked about on this classic Hadoop data stack where we used Kafka and Storm for ingestion, and then for streaming processing, and Spark for this traditional historical analysis. And actually, this is exactly where the streaming data platform came into play because we had a meeting with one of the techy account at the university. And we were like talking about this. We were having a chat about this problem. And he's like, "Oh, we have something going on in America, in USA with this a streaming data platform. It was still under a code name or something." And then actually, Russ and I got into contact then talking about the streaming data platform, and how we could actually use it, and get getting part. We were taking part in the alpha program, really working with the system with the developers. And it was really an amazing experience. >> Were you having scale problems with the original kind of traditional big data platform that you talked about with Hadoop, Apache, Kafka, Spark? Was that scale issues, performance issues? Is that why you looked to Dell EMC? >> Yeah. There were several issues, like one is the scaling option now. And when we were not always using all of the sensors, we are just using some of the sensors. We're thinking about account process to different manufacturing technologies, different machines that we have in our laboratory so that we can quickly add sensors. They are shut down sensors. Do not have to take care about setting up new workers or stuff so that the work balance is handled. But that's not the only thing. We also had a lot of issues with administrating this Hadoop stacks. It's quite error prone if you do it yourself, like we are still in the university even though we are very big level laboratory. We still have limited resources. So we spend a lot of time dealing with the dev ops of the system. And actually, this is something where on the streaming data platform actually helped us to reduce the time that we invested into this administration processes. We were able to take more time into the analytics, which is actually what we are interested in. And specifically, the point that Russ talked about this unified concept of time, we now can just apply one and that type of analysis on historical and streaming data, and do not have to separate domains that we have to deal with. Now we dealt with Kafka, and Storm on one side, and Spark on the other side. And now, we can just put it into one model and actually reduce the time now to maintain and handle and implement the code. >> The time reduction is critical for the overall laboratory, the workforce productivity of the folks that are using it. Russ, let's go back to you. Tell us about, first of all, how long has the Dell EMC SDP been around? And what are some of the key features that WZL is leveraging that you're also seeing benefit other industries? >> So the product actually officially launched in early 2020. So in the first quarter of 2020. But what Philipp was just talking about, his organization was actually in the alpha and the beta programs earlier than that in 2019. And that's actually where we had a cross-section of very different kinds of companies in all sorts of industries all over the world; in Japan, and Germany, in the US. And that's where we started to see this pattern of commonality of challenges, and how we could solve those. So one of those things we mentioned that unified concept of time is really powerful because with one line of code, you can actually jump to any point on the timeline of your data, whether it's the real-time data coming off of the sensors right now or something minutes, hours, years ago. And so it's really, really powerful for the developers. But we saw the common challenges that Philipp was just talking about everywhere. So the SDP, one of the great things about it is it's a single piece of software that will install, manage, secure, upgrade, and be supported of all the components that you just heard Philipp talking about. So all the pieces for the ingestion, the storage and the analytics are all in there. And that makes it easier to focus on the problem there. There was other common challenges that our customers were seeing as well. Things like this concept of derived streams, so that you can actually bring in raw streams of data, leave it in its raw form because many times, regulatory reasons, audit reasons, you want to not touch that data. But you can create parallel streams of that data that are called derived streams that are versions that you've altered for some consumption or reporting purposes without affecting the others. And that's powerful when you have multiple teams analyzing different data. And then finally, the thing that Philipp mentioned we saw everywhere, which was a unified way to interact with sensors all the same way because there's sensors for IoT sensors, telemetry log files, video, X-ray, infrared, all sorts of things. But being able to simplify that so that the developers and the data scientists can really build models to solve a business problem was really where we started to focus on how we wanted to bring to market the value of SDP. >> So you launched this, right? And you said early 2020, right before the pandemic and all of the chaos that has- >> Don't recommend that by the way. Don't recommend launching into a pandemic. But yes. >> I'm sure that a lot of lessons learned from silver linings, I'm sure. >> That's right. >> But obviously, big challenges there. I'm curious thought if you thought. One of the things that we've learned from the pandemic is that for so many industries, the access to real-time data is no longer just a nice to have. It is a critical differentiator for those that needed to pivot multiple times to survive in the early days to thrive to continue pivoting. I'm curious, what other industries you saw Russ that came to you saying, "All right, guys. We've got challenges here. Help us figure this out."? Give me a snapshot of some of the other industries that were sort of leading Edge last year. >> Sure. There was some surprising ones. I've mentioned it a little bit, but it's interesting you give me a chance to talk about them. 'cause what was also shocking about this was not only that the same problems that I just mentioned happened in multiple industries. It was actually the prevalence of certain kinds of data. So for example, the construction example I gave you where a company was using drones to ingest streaming video as well as Telemetry of all the equipment on the ground. Drones are in all sorts of industries. So it turns out that's a pattern. But even a lower level than just drone data is actually video data or any kind of media data. And so Philipp talked about they're using that kind of data as well in manufacturing. We're seeing video data in every industry combined with other sensor data. And that's what's really surprised us in the beta program. So working with Philipp, we actually altered our roadmap after we launched to realize that we needed to escalate even more features about video analysis and actually be able to take the process even closer to the Edge where the data's being generated. So the other industries, including construction, logistics, medicine, network traffic, all sorts of data, that is a continuous unbounded stream of data falls into the category of being able to be analyzed, stored, playback like a DVR with SDP. >> Playback like a DVR. I like that. Philipp, back over to you. Talk to us about what's next. Obviously, a tremendous amount of innovation in the first 100 years of WZL. Talk to me about what some of the lab's plans are for the future from a streaming data perspective, got a great foundation infrastructure there with Dell EMC. What's next? >> Like we are working together with a large industry consortium, and then we get a lot of information. Not information, but they really want to see that all this big data stuff that's coming into Industry 4.0. And Russ already talked about it. And then, I'm pretty satisfied in having all the data and the data centers that they have, but they want to push it to the Edge. So all the analytics, it's getting more and more to the Edge because they see that the more data you gather, the more data has to be transferred via the network. So we have to come up with ways on, of course, deploy all the model on the Edge, maybe do some analytics on the Edge. I don't know, something like federated learning to see. Maybe you don't even need to transfer the data to the data center. You can start learning approaches on the Edge and combine them with different data sources that are actually sharing the data, which is the specific point in like corporations that want to corporate using the different data sources, but have some privacy issues. So this is something that we are looking into. And also, working like low-code or no-code environments, like different framework that we use here just in our laboratory, but this is also something that we see in the industry. And more and more people have to interact with the data management systems. So they have to somehow get a lower access point than just some pile from script that they need to write. Maybe, they just need drag and drop environment where they can modify some ingestion or some transformation to the data. So they're not always the people and all the data engineers or the computer science experts have to deal with those kind of stuff, and other people can do as well. So this is something that we are looking into this in the next future. But, yeah. But there are a lot of different things, and there's not enough time to talk about all of them. >> So it sounds like an idea to democratize that data to allow more data citizens to leverage that, analyze it and extract value from it because we all know data is oil, it's gold, but only if you can actually get those analysis quickly and make decisions that really affect and drive the business. Russ, last question for you. Talk to us about what you see next coming in the industry. Obviously, launching this technology at a very interesting time, a lot of things have changed in the last year. You've learned a lot. You said you modified the technology based on the WZL implementation. But what are some of the things that you see coming next? >> So it's really interesting 'cause my colleague at Dell constantly reminds me that people develop solutions with the technology they have at the time, right? It's a really obvious statement, but it's really powerful to realize what customers of ours have been doing so far. It's been based on batch tools and storage tools that were available at the time, but weren't necessarily the best match for the problem that we're trying to solve. And the world is moving completely to a real-time view of their data. If you can understand that answer sooner, there's higher value for higher revenue, lower costs, safety, all sorts of reasons, right? To do that, everyone's realizing you can't really count on... Like Philipp, he can't count on moving all the data somewhere else to make that decision, that latency; or sometimes, rules around controlling what data can go. Really, we'll keep it from that. So being able to move code closer to the data is where we see things are really happening. This is actually why the streaming data platform has really focused heavily on Edge implementations. We have SDP Core for the core data center. We also have SDP Edge that runs on single node in three node configurations for a headless environments for all sorts of use cases where you need to move the code and make the decisions right when the data is generated at the sensors. The other things we see happening in the industry that are really important is everything's moving to a fully software-defined solution. This idea of being able to have software-defined stream ingestion, analytics and storage. You can deploy the solution you want in the form factor that you have available at your location is important, right? And so, fully software-defined solutions is really going to be where things are at, and which gives you this kind of cloud-like experience, but you can deploy it anywhere at the Edge, Core or cloud, right? And that's really, really powerful. Philipp picked up on the one that we see a lot of this idea of low-code, no-code whether it's things like node red in the IoT world, where you're being able to stitch together a sequence of functions to answer questions in real time or other more sophisticated tools. That ability to, like you said, democratize what people can do with the data in real time is going to be extremely valuable as things move forward. And then the biggest thing we see that we're really focused on is we need to make it as easy as possible to ingest any kind of data. The more data types that you can bring in, the more problems you can solve. And so bringing on as many on-ramps and connectivity into other solutions is really, really important. And for all that, SDP's team is really focused on trying to prioritize the customers like Philipp's team in the RWTH WZL labs there. But finding those common patterns everywhere so that we can actually kind of make it the norm to be analyzing streaming data, not just historical batch data. >> Right. That's outstanding. As you said, the world is moving to real-time analytics. Real-time data ingestion is absolutely critical on there. Just think of the problems that we don't even know about that we could solve. Guys, thank you for joining me today, talking about what WZL is doing with the Dell EMC streaming data platform, and all the innovations you've done so far, and what's coming in the future. We'll have to catch up in the next six months or so, and see what great progress you've made. Thank you for your time. >> Thanks, Lisa. >> Thank you. >> For my guests, I'm Lisa Martin. You're watching a Cube Conversation. (calm techno music)

>> Announcer: Live from Madrid, Spain, it's theCUBE, covering HPE Discover Madrid 2017, brought to you by Hewlett Packard Enterprise. >> Welcome back to Madrid, everybody, this is theCUBE, the leader in live tech coverage. My name is Dave Vellante, I'm here with Peter Burris, this is day one of HPE Discover Madrid. Olivier Frank is here, he's the Worldwide Senior Sales Director for Alliances for IoT at HPE, and Kurt Bayer, otherwise known as Bager in English, in America. He's Vice President of IoT Solutions for EMEA PTC, did I get that right? >> Yeah you did it. >> Bayer? All right, well thank you for sharing that with me. Welcome to theCUBE, gentlemen. Olivier, let me start with you. The relationship between PTC and HPE is not brand new. You guys got together a while back. What catalyzed that getting together? >> Yeah, it's a great question, and thank you for inviting us, it's great pleasure to be on theCUBE, and for me the first time, so thank you for that. >> Welcome. >> Yeah, you know, the partnership is all about action and doing things together, so we did start about a year ago with, you may remember flow serve and industrial pump that we showcased, and since then we've been working very closely together to actually allow our customers to go an test the technology themselves. So I would say the partnership has matured, we now have two live environments that customer can visit, one in Europe, in Germany, in Aachen, with the RWTH University, and one in the US, near Houston, with Texmark who you know because you also came to the show. >> Right, okay, Kurt give us the update on PTC. Company's been in business for a long time, IoT is like a tailwind. >> It is, that's right. PTC is mostly known for CAD and PLM, so for 30 years they made 3D CAD software for when you design and make an aircraft or car engine. But over the last five years, PTC have moved heavily into IoT, spent a billion on acquiring and designing software platform that can connect and calculate and show in augmented reality. >> So let me build on that, because PTC as a CAD company, as a PLM company, has done a phenomenal job of using software and technology to be able to design things to a level of specificity and tolerance that just wasn't able to be done before, and it's revolutionized how people build products. But now, because technology's advanced, you can leverage that information in your drawings, in your systems to create a new kind of an artifact, a digital twin that allows a business that's working closely with you to actually render that in an IoT sense and add intelligence to it. Have I got that right? >> You got it exactly right. So making the copy. We can draw it and we can design the physical part, and we can make the digital twin of the physical part with sensors. So in that way you can loop back and see if the calculation, the design, the engineering you have made is the right fit, or you need to change things. You can optimize product with having the live digital twin of the things that you've designed physically. >> So it's like a model, except it's not a model. It's like a real world instantiation. Model is an estimate, right? A digital twin is actual real data. >> It's feeded by live data, so you have a real copy of what's going on. And we use it for not only closing the loop of designing products, but also to optimize in the industrial fold, to optimize operation and creating manufacturing of things, and we use it to connect things, so you can do predictive maintenance or you can turn products to be a service, instead of selling an asset, the company can buy by click, by use, plus the product are connected. >> I want to really amplify this, Dave, 'cause it's really important, I want to test this with you, 'cause the whole concept of using technology, IoT technology to improve the operational efficiency, to improve the serviceability, to evolve your business models, your ability to do that is tied back to the fidelity of the models you're using for things that are delivering the services, and I don't think the world fully understands the degree to which it's a natural leap from CAD and related technologies, into building the digital artifacts that are gonna be necessary to make that all work. Have I got that right? >> You got it completely right. So it is moving from having live informations from the physical object. So if you go to augmented reality, so you have the opportunity to look at things and get live information about temperature, power, streaming of water, and all these things that goes on inside the product, you also have the opportunity to understand if there's something wrong with the product, you can click on it and you can be directed on how to change and service things like when the augmented reality, all built by the CAD drawing in the beginning that is combined with sensor information and >> And simulate, and test, and all the other things that are hard, but obviously to do that, you need a whole bunch of other technology, and I guess that's where HPE comes in. >> Exactly. >> Absolutely. In fact to bounce on that thought, we talk a lot about connected operation, where you know, we are showing the digital twin, but one of the new use case that we're showing on the floor here is what we call smart product engineering. So we're basically using the CAD environment of (mumbles), running on that edge line with edge compute, you know, enterprise compute capability, manageability and security, and running on that same platform then, simulation from companies like Ensys, right, and then doing 3D printing, print prototyping, and basically instrumenting the prototype, we're using a bike, the saddle stem of a bike showcase, and they are able to connect and collect the data, we're partnering with National Instruments who are also well-known, and reinject the real data into the digital model. So again, the engineers can compare their thought and their design assumptions with the real physical prototype, and accelerate time to market. >> PTC's been a leader in starting with the CAD and then pulling it through product life cycle management, PLM. So talk about this is going to alter the way PLM becomes a design tool for digital business. If I'm right. >> You're right, it becomes industrial innovation platform from creating the product to the full life cycle of it. >> Peter: All the way up to the business model. >> All the way up to the business model. And talking about analytics, so if you have a lot of data and you want to make sure you get some decision made fast about predictive maintenance, that's an area where we are partnering with HP so we have a lot of power close in the edge, close to the products that can do the calculations from the devices, from the product, and do some fast results in order to do predictive maintenance and only send the results away from the location. >> So what are some of the things you guys are most excited about, Olivier? >> Well, really excited about making those use cases, being the smart product engineering, or the predictive maintenance, you know, work for our customers so behind the scenes we have great solutions, now we're partnering on the sales front to kind of go together to customers, we have huge install base on both sides, and picking the right customers interested in this digital transformation, and make it real for them, because we know it's a journey, we know it's kind of the crawl, walk, run, and it's really about accelerating, you know, turning insights into information and into actions, and that's really where we are very much excited to work together. >> So it's not just, so the collaboration's extending to go to market is what I'm hearing. And so what's the uptake been like, what are customers, customers must be asking you, "Where do I start?" What do you tell them? >> Before you start, it's important that you have a business case, a business value, you understand what you wanted to achieve, by integrating an IoT solution. That's important. Then you need to figure out what is the data, what is the fast solution I need to take, and then you can start deciding on the planning of your implementation of the IoT. >> Can I go back one step further, >> Yep. >> You tell me if I got that. And that one step further is, look, every... Innovation and adoption happens faster when you can take an existing asset and create new value. >> Kurt: Exactly. >> So isn't PTC actually starting by saying, hey, you've already got these designs, you've already got these models. Reuse them, create new life, give 'em new life, create new value with 'em. Do things in ways that now you can work with your customers totally differently, and isn't that kind of where it starts? >> It does, and you already have a good portion of what you need, so in order to make a fast value out of your new product or the new thing you can do with the product, connecting the products, then PTC and HP is a good platform to move on. >> Yeah but the pretesting, precertify, packaging, the software with the hardware, is allowing our customer to go faster to proof of concept and then to production. So we have a number of workshops, customers can come, again as I mentioned at the beginning, in Germany, in Aachen or in Houston at our Texmark facility, where we can basically walk the talk with customers and start those early POCs, defining the business success factors, business value they want to take out of it, and basically get the ball rolling. But it's really exciting because we have, we're touching really some of the key digital transformation of our enterprise customers. >> And don't forget that you need a partner that can do a good job in service, because you need a organization that can help you get it through, and HP are a strong service organization too. >> Well this idea of the intelligent edge has a lot of obviously executive support at Hewlett Packard Enterprise, that keeps buzzing at theCUBE today, Meg Whitman's in the house, she's right next door, and we're gonna do a quick cutaway to Meg, give her a shoutout, trying to get her over here to talk about her six-year tenure here, but you know, that top-down executive support has been so critical in terms of HPE getting early into the edge, IoT, intelligent edge you call it, Tom Bradicich obviously a leader, he's coming on. You mentioned National Instruments, PTC, you guys were first, really, from a traditional IT business to really get into that space. >> We're also the first to converge OT and IT, so we're showing on the floor what we're doing in end of line quality testing for automotive for example, taking PX higher standard, which is like instrumentation and real-time data position into our converged systems. So what I found is really amazing. You take the same architecture, and we can do it edge to core to cloud, right, that's very powerful. One software framework, one IT architecture that's pan out. >> Peter: Not some time in the future, but right now. >> Yeah, right now. >> So we talk about a three, maybe even a 3A, four-tier data model, where you've got data at the edge, real time, maybe you don't persist all of it or a lot of it. >> We call it experience data or primary data at the edge. vet data, or secondary data, and then business optimization data at the top level, that's at the cloud. >> So let's unpack that a little bit and get your perspective. So the edge, obviously you're talking about real time decision making, autonomous cars, you're not gonna go back to the cloud to make that decision. That, well you call it core, that's what did you call it? >> The hybrid IT. >> The vet, the vet. That's an aggregation point, right, to collect a lot of the data from the edge, and then cloud maybe is where you do the deep analysis and do the deep modeling. And that cloud can be on-prem, or it can be on the public cloud. Is that a reasonable data model for the flow of data for edge and IoT? >> I believe it is, because some of these products generate a lot of data, and you need to be able to handle that data, and honestly, connectivity is not for free, and sometimes it's difficult if it's in the industry floor, manufacturing floor, you need good connectivity, but you still have limitations. So if you can do the local analytics and then you only send the results to the core, then it's a perfect model. And then there's a lot of regulations around data, so for many countries, and especially in Europe, there's boundaries around the data, it's not all that you can move to a cloud, especially if it's out of the country. So the model makes a good hybrid in between speed, connectivity, analytics and the legislation problem. >> Dave: And you've both got solutions at each layer? >> Absolutely, so in fact... So PTC can run at the edge, at the core or in the cloud, and of course we are powering the three pillars. And I think what's also interesting to know is that with the advance in artificial intelligence, as was explored during the main session, there it is pivotal you need to keep a lot of data in order to learn from those data, right? So I think it's quite fascinating that we're going to store more and more data, probably make some useful right away, and maybe store some that we come back to it. That's why we're working also with companies like OSIsoft, an historian, which is collecting this time stamp data for later utilization. But I wanted also to say that what's great working with PTC is that it's kind of a workflow in media, in terms of collecting the data, contextualizing them and then visualization and then analytics. But we're developing a rich ecosystem, because in this complex world of IoT, again it's kind of an art and a science, and the ability to partner ourselves, but also our let's say friendly partners is very, very critical. >> Dave: Guys, oh good, last word. >> I will say we started with a digital twin, and for some companies they might be late to get the digital twin. The longer you have had collecting data from a live product >> The better the model gets >> The stronger you will be, >> Peter: Better fidelity. >> The better model you can do, because you have the bigger data. So it's a matter of getting the data into the twin. >> That's exactly what our research suggests. We've got a lot of examples of this. >> It's the difference between sampling and having an entire corpus of data. >> Kurt: Exactly. >> Kurt, Olivier, thanks very much for coming on the theCUBE. >> Thank you. >> Thank you so much. >> Great segment guys. Okay, keep it right there everybody, Dave Vellante for Peter Burris, we'll be back in Madrid right after this short break.