>> Live from New York City it's theCube. Covering Lenovo Transform 2.0. Brought to you by Lenovo. >> Welcome back to theCube's live coverage of Lenovo Transform, I'm your host Rebecca Knight along with my co-host Stu Miniman. We're joined by Madhu Matta; He is the VP and GM High Performance Computing and Artificial Intelligence at Lenovo and Dr. Daniel Gruner the CTO of SciNet at University of Toronto. Thanks so much for coming on the show gentlemen. >> Thank you for having us. >> Our pleasure. >> So, before the cameras were rolling, you were talking about the Lenovo mission in this area to use the power of supercomputing to help solve some of society's most pressing challenges; and that is climate change, and curing cancer. Can you talk a little bit, tell our viewers a little bit about what you do and how you see your mission. >> Yeah so, our tagline is basically, Solving humanity's greatest challenges. We're also now the number one supercomputer provider in the world as measured by the rankings of the top 500 and that comes with a lot of responsibility. One, we take that responsibility very seriously, but more importantly, we work with some of the largest research institutions, universities all over the world as they do research, and it's amazing research. Whether it's particle physics, like you saw this morning, whether it's cancer research, whether it's climate modeling. I mean, we are sitting here in New York City and our headquarters is in Raleigh, right in the path of Hurricane Florence, so the ability to predict the next anomaly, the ability to predict the next hurricane is absolutely critical to get early warning signs and a lot of survival depends on that. So we work with these institutions jointly to develop custom solutions to ensure that all this research one it's powered and second to works seamlessly, and all their researchers have access to this infrastructure twenty-four seven. >> So Danny, tell us a little bit about SciNet, too. Tell us what you do, and then I want to hear how you work together. >> And, no relation with Skynet, I've been assured? Right? >> No. Not at all. It is also no relationship with another network that's called the same, but, it doesn't matter. SciNet is an organization that's basically the University of Toronto and the associated research hospitals, and we happen to run Canada's largest supercomputer. We're one of a number of computer sites around Canada that are tasked with providing resources and support, support is the most important, to academia in Canada. So, all academics, from all the different universities, in the country, they come and use our systems. From the University of Toronto, they can also go and use the other systems, it doesn't matter. Our mission is, as I said, we provide a system or a number of systems, we run them, but we really are about helping the researchers do their research. We're all scientists. All the guys that work with me, we're all scientists initially. We turned to computers because that was the way we do the research. You can not do astrophysics other than computationally, observationally and computationally, but nothing else. Climate science is the same story, you have so much data and so much modeling to do that you need a very large computer and, of course, very good algorithms and very careful physics modeling for an extremely complex system, but ultimately it needs a lot of horsepower to be able to even do a single simulation. So, what I was showing with Madhu at that booth earlier was results of a simulation that was done just prior us going into production with our Lenovo system where people were doing ocean circulation calculations. The ocean is obviously part of the big Earth system, which is part of the climate system as well. But, they took a small patch of the ocean, a few kilometers in size in each direction, but did it at very, very high resolution, even vertically going down to the bottom of the ocean so that the topography of the ocean floor can be taken into account. That allows you to see at a much smaller scale the onset of tides, the onset of micro-tides that allow water to mix, the cold water from the bottom and the hot water from the top; The mixing of nutrients, how life goes on, the whole cycle. It's super important. Now that, of course, gets coupled with the atmosphere and with the ice and with the radiation from the sun and all that stuff. That calculation was run by a group from, the main guy was from JPL in California, and he was running on 48,000 cores. Single runs at 48,000 cores for about two- to three-weeks and produced a petabyte of data, which is still being analyzed. That's the kind of resolution that's been enabled... >> Scale. >> It gives it a sense of just exactly... >> That's the scale. >> By a system the size of the one we have. It was not possible to do that in Canada before this system. >> I tell you both, when I lived on the vendor side and as an analyst, talking to labs and universities, you love geeking out. Because first of all, you always have a need for newer, faster things because the example you just gave is like, "Oh wait." "If I can get the next generation chipset." "If the networking can be improved." You know you can take that petabyte of data and process it so much faster. >> If I could only get more money to buy a bigger one. >> We've talked to the people at CERN and JPL and things like that. - Yeah. >> And it's like this is where most companies are it's like, yeah it's a little bit better, and it might make things a little better and make things nice, but no, this is critical to move along the research. So talk a little bit more about the infrastructure and what you look for and how that connects to the research and how you help close that gap over time. >> Before you go, I just want to also highlight a point that Danny made on solving humanity's greatest challenges which is our motto. He talked about the data analysis that he just did where they are looking at the surface of the ocean, as well as, going down, what is it, 264 nautical layers underneath the ocean? To analyze that much data, to start looking at marine life and protecting marine life. As you start to understand that level of nautical depth, they can start to figure out the nutrients value and other contents that are in that water to be able to start protecting the marine life. There again, another of humanity's greatest challenge right there that he's giving you... >> Nothing happens in isolation; It's all interconnected. >> Yeah. >> When you finally got a grant, you're able to buy a computer, how do you buy the computer that's going to give you the most bang for your buck? The best computer to do the science that we're all tasked with doing? It's tough, right? We don't fancy ourselves as computer architects; we engage the computer companies who really know about architecture to help us do it. The way we did our procurement was, 'Ok vendors, we have a set pot of money, we're willing to spend every last penny of this money, you give us the biggest and the baddest for our money." Now, it has to have a certain set of criteria. You have to be able to solve a number of benchmarks, some sample calculations that we provided. The ones that give you the best performance that's a bonus. It also has to be able to do it with the least amount of power, so we don't have to heat up the world and pay through the nose with power. Those are objective criteria that anybody can understand. But then, there's also the other criteria, so, how well will it run? How is it architected? How balanced is it? Did we get the iOS sub-system for all the storage that was the one that actually meets the criteria? What other extras do we have that will help us make the system run in a much smoother way and for a wide variety of disciplines because we run the biologists together with the physicists and the engineers and the humanitarians, the humanities people. Everybody uses the system. To make a long story short, the proposal that we got from Lenovo won the bid both in terms of what we got for in terms of hardware and also the way it was put together, which was quite innovative. >> Yeah. >> I want to hear about, you said give us the biggest, the baddest, we're willing to empty our coffers for this, so then where do you go from there? How closely do you work with SciNet, how does the relationship evolve and do you work together to innovate and kind of keep going? >> Yeah. I see it as not a segment or a division. I see High Performance Computing as a practice, and with any practice, it's many pieces that come together; you have a conductor, you have the orchestra, but the end of the day the delivery of that many systems is the concert. That's the way to look at it. To deliver this, our practice starts with multiple teams; one's a benchmarking team that understands the application that Dr. Gruner and SciNet will be running because they need to tune to the application the performance of the cluster. The second team is a set of solution architects that are deep engineers and understand our portfolio. Those two work together to say against this application, "Let's build," like he said, "the biggest, baddest, best-performing solution for that particular application." So, those two teams work together. Then we have the third team that kicks in once we win the business, which is coming on site to deploy, manage, and install. When Dr. Gruner talks about the infrastructure, it's a combination of hardware and software that all comes together and the software is open-source based that we built ourselves because we just felt there weren't the right tools in the industry to manage this level of infrastructure at that scale. All this comes together to essentially rack and roll onto their site. >> Let me just add to that. It's not like we went for it in a vacuum. We had already talked to the vendors, we always do. You always go, and they come to you and 'when's your next money coming,' and it's a dog and pony show. They tell you what they have. With Lenovo, at least the team, as we know it now, used to be the IBM team, iXsystems team, who built our previous system. A lot of these guys were already known to us, and we've always interacted very well with them. They were already aware of our thinking, where we were going, and that we're also open to suggestions for things that are non-conventional. Now, this can backfire, some data centers are very square they will only prescribe what they want. We're not prescriptive at all, we said, "Give us ideas about what can make this work better." These are the intangibles in a procurement process. You also have to believe in the team. If you don't know the team or if you don't know their track record then that's a no-no, right? Or, it takes points away. >> We brought innovations like DragonFly, which Dr. Dan will talk about that, as well as, we brought in for the first time, Excelero, which is a software-defined storage vendor and it was a smart part of the bid. We were able to flex muscles and be more creative versus just the standard. >> My understanding, you've been using water cooling for about a decade now, maybe? - Yes. >> Maybe you could give us a little bit about your experiences, how it's matured over time, and then Madhu will talk and bring us up to speed on project Neptune. >> Okay. Our first procurement about 10 years ago, again, that was the model we came up with. After years of wracking our brains, we could not decide how to build a data center and what computers to buy, it was like a chicken and egg process. We ended up saying, 'Okay, this is what we're going to do. Here's the money, here's is our total cost of operation that we can support." That included the power bill, the water, the maintenance, the whole works. So much can be used for infrastructure, and the rest is for the operational part. We said to the vendors, "You guys do the work. We want, again, the biggest and the baddest that we can operate within this budget." So, obviously, it has to be energy efficient, among other things. We couldn't design a data center and then put in the systems that we didn't know existed or vice-versa. That's how it started. The initial design was built by IBM, and they designed the data center for us to use water cooling for everything. They put rear door heat exchanges on the racks as a means of avoiding the use of blowing air and trying to contain the air which is less efficient, the air, and is also much more difficult. You can flow water very efficiently. You open the door of one of these racks. >> It's amazing. >> And it's hot air coming out, but you take the heat, right there in-situ, you remove it through a radiator. It's just like your car radiator. >> Car radiator. >> It works very well. Now, it would be nice if we could do even better by doing the hot water cooling and all that, but we're not in a university environment, we're in a strip mall out in the boonies, so we couldn't reuse the heat. Places like LRZ they're reusing the heat produced by the computers to heat their buildings. >> Wow. >> Or, if we're by a hospital, that always needs hot water, then we could have done it. But, it's really interesting how the option of that design that we ended up with the most efficient data center, certainly in Canada, and one of the most efficient in North America 10 years ago. Our PUE was 1.16, that was the design point, and this is not with direct water cooling through the chip. >> Right. Right. >> All right, bring us up to speed. Project Neptune, in general? >> Yes, so Neptune, as the name suggests, is the name of the God of the Sea and we chose that to brand our entire suite of liquid cooling products. Liquid cooling products is end to end in the sense that it's not just hardware, but, it's also software. The other key part of Neptune is a lot of these, in fact, most of these, products were built, not in a vacuum, but designed and built in conjunction with key partners like Barcelona Supercomputer, LRZ in Germany, in Munich. These were real-life customers working with us jointly to design these products. Neptune essentially allows you, very simplistically put, it's an entire suite of hardware and software that allows you to run very high-performance processes at a level of power and cooling utilization that's like using a much lower processor, it dissipates that much heat. The other key part is, you know, the normal way of cooling anything is run chilled water, we don't use chilled water. You save the money of chillers. We use ambient temperature, up to 50 degrees, 90% efficiency, 50 degree goes in, 60 degree comes out. It's really amazing, the entire suite. >> It's 50 Celsius, not Fahrenheit. >> It's Celsius, correct. >> Oh. >> Dr. Bruner talked about SciNet with the rado-heat exchanger. You actually got to stand in front of it to feel the magic of this, right? As geeky as that is. You open the door and it's this hot 60-, 65-degree C air. You close the door it's this cool 20-degree air that's coming out. So, the costs of running a data center drop dramatically with either the rado-heat exchanger, our direct to node product, which we just got released the SE650, or we have something call the thermal-transfer module, which replaces a normal heat sink. Where for an air cool we bring water cool goodness to an air cool product. >> Danny, I wonder if you can give us the final word, just the climate science in general, how's the community doing? Any technological things that are holding us back right now or anything that excites you about the research right now? >> Technology holds you back by the virtual size of the calculations that you need to do, but, it's also physics that hold you back. >> Yes. Because doing the actual modeling is very difficult and you have to be able to believe that the physics models actually work. This is one of the interesting things that Dick Peltier, who happens to be our scientific director and he's also one of the top climate scientists in the world, he's proven through some of his calculations that the models are actually pretty good. The models were designed for current conditions, with current data, so that they would reproduce the evolution of the climate that we can measure today. Now, what about climate that started happening 10,000 years ago, right? The climate was going on; it's been going on forever and ever. There's been glaciations; there's been all these events. It turns out that it has been recorded in history that there are some oscillations in temperature and other quantities that happen about every 1,000 years and nobody had been able to prove why they would happen. It turns out that the same models that we use for climate calculations today, if you take them back and do what's called paleoclimate, you start with approximating the conditions that happened 10,000 years ago, and then you move it forward, these things reproduce, those oscillations, exactly. It's very encouraging that the climate models actually make sense. We're not talking in a vacuum. We're not predicting the end of the world, just because. These calculations are right. They're correct. They're predicting the temperature of the earth is climbing and it's true, we're seeing it, but it will continue unless we do something. Right? It's extremely interesting. Now he's he's beginning to apply those results of the paleoclimate to studies with anthropologists and archeologists. We're trying to understand the events that happened in the Levant in the Middle East thousands of years ago and correlate them with climate events. Now, is that cool or what? >> That's very cool. >> So, I think humanity's greatest challenge is again to... >> I know! >> He just added global warming to it. >> You have a fun job. You have a fun job. >> It's all the interdisciplinarity that now has been made possible. Before we couldn't do this. Ten years ago we couldn't run those calculations, now we can. So it's really cool. - Amazing. Great. Well, Madhu, Danny, thank you so much for coming on the show. >> Thank you for having us. >> It was really fun talking to you. >> Thanks. >> I'm Rebecca Knight for Stu Miniman. We will have more from the Lenovo Transform just after this. (tech music)

>> from Washington, D. C. It's the queue covering science logic. Symposium twenty nineteen. Brought to you by Science Logic >> Hi, I'm student men and this is the Cubes coverage of Science Logic. Symposium twenty nineteen here at the Ritz Carlton in Washington, D. C. First of all, want Welcome back to the program. Roger Putnam, Who's the vice president of Global Solutions? That science logic Thanks for coming back and what with programme A first time Rob Gruner listed is this loosened architect from Telstra. But >> Rob, I actually had >> a chance to talk to some of your co ords there, they said. Arav robs a wizard. He's an engineer that does everything. So you know, solutions. Architect. Of course, we know that they're out there. They do a lot of different things and asleep, leased. Your peers say you're somebody that does quite a lot of different >> things. Did Jack of All trades master of none unfortunate >> way? It's all right, don't you know it is in vogue now to be, you know, a generalist. It's, you know, we've gone from specialties to well, oh no, it's it's platforms and everything's going to be everything, so I have plenty of background with Telstra, but maybe talk a little bit about you know, your role in the organization and what what kind of things you're involved in. Since you know some of those trades that you >> are jack of all, >> probably our spies have come into Telstra's an acquisition. So, you know, working for small company, you tend to do everything on. For some reason, I've been allowed to continue to do that on developing expertise around science logic. And that means I've been involved across a lot of areas of the business as we've been adopting science logic more widely, and it's been quite interesting. Process means eye contact, that expertise and then see how it's applied across the organization. So it's been quite interesting, >> awesome. One of things that's been interested in me and in talking to service Friday is talking to the enterprise customers is two. You know how many tools they had, how many they replaced with science logic, but also what things it's integrating with and working with. It was a big focus on the keynote this morning is, you know, integrations with Sam and you know all these various pieces, so maybe give us a little bit of kind of the scope. You know how long's tells me you've been using science logic, How broads the deployment and you know what? What? What does it do in? What does it tie into >> a tte? The mammoth is more enterprise focused. So on. That's the area. Tell Stur I come from so it's really around delivering services to her customers. Quite recently, we've seen then looking in deploying science logic across their carriage spokes and managing services there. That's quite a large deployment. You know, we're quite happy with that in terms of what is going to be doing for the business on the integrations, their endless. So Telstra, like a lot of large organizations, has a lot of different systems to talk to. A lot of different service dis, depending on the operational areas. So in service now is one of those. But it's a hollow of other stuff on, so that's a very challenging process. And sounds objects being pretty good at, you know, spreading itself around. Those >> give us a little insight as to you know, how fast things are changing. You know, hear Kafka and Streams and, you know, constantly moving I've been looking at the, you know, communities and container stuff that's happening, which is which is fast moving. So >> are definitely say it. And Telstra's trying as hard as akin to move as quickly as the market can allowed. So definitely it's virtual izing. ITT's automating II ops is a big component of what we're doing. It is extremely important for the business. >> Okay, so Alps is something you're doing have to We're not as mature as we'd like to video. I'm not sure if you saw the keynote this morning, but they put out a maturity models So would love for you to, you know, where are you when you look at that? They kind of had the three criterias there is. There's kind of the the machine learning, there's the automation and I'm trying to remember the third piece that was there, but you know where where are you today? You know, how'd you get there? And you know what? What's what's a little bit of the road map going forward? >> I think it might be probably our ambitions to be in that the upper end of the spectrum and into remediation, But that's an ambition and I think we've got a while to go with that. So, uh, more than that, I can't coming off >> its interests. So they have that The keynote tomorrow they're going. Jean Kim speaking on the deaf ops. And, you know, I'm a big fan of the Phoenix project and they talked about, you know, the jack of all trades that does it all. He could sometimes be the bottleneck in the system. Absolutely. Because you can't be up. I need something fixed. Well, we'LL go to Rob Rob all fix it. That's great. That fire floating mode. I know I've done that in my career, and it's one of those things. Oh, jeez, you're never going to move at this job because you're replaceable. It's like that's a dangerous place to be. >> It is s >> o. You know, we talk a little bit about, you know, you said, you know, science logic. You know that they position themselves as this is going to help you move that, you know, machine speed and keep up with that. Give us a little bit the reality of what you're seeing. How what does that impact your job? Your organization? >> Look, I think sounds logic has done a wonderful job within the organization. It's it's the legacy infrastructure within any organization, particularly tells her scale. That's really holding you back on. There's a lot of Well, I think people level with Intel Street. Move as quickly as we can, but we have such a large number of legacy systems to deal with. You know, we're looking at one deployment of Sands object. We were looking at IDing systems to kill, So it's a big task >> the wonderful technical death that we've all inherited. So So you know, Roger, you know, this something we hear from all customers. It'd be lovely if I had the mythical, you know, unicorn that, you know, start from the ground up and you know, he can start afresh. But we always have to have that mix and give it a little bit about what you're seeing. You know, about the Telstra in a little bit broader, You know, >> I think what tell us she has done really well with taking advantage of our technology was they didn't come in with this attitude of would rip out everything that we have and just have a magic easy bun. Software doesn't work that way. I think we've all learned the lessons of tough deployments when you try to stay out of fix everything. So they came in with a really gradual, phased approach of Get a couple pieces done where they had gaps. You start to fill those gaps. What's happening during the last few years as we've seen the shift greater change and they've taken advantage of the platforms, nationalities a hole as they go through their digitization efforts. And so as they digitize, they taking this step by step by step approach to you know what you were saying earlier with Rob does. He doesn't answer the question of being the one man band, but they did was they build it all process wise, using software to drive the automation. So once it's done one time, you're not stuck on the person anymore. And so I think when we look at our most successful customers like Telstra, it's because they've had this gradual, phased approach where they're using software rather than single person bottlenecks. And rather than having these tiger teams to try to solve problems and moving towards a better process to take advantage of the world, we're in today. So how >> do you measure success? You know, what are some of the business outcomes or, you know, k p I's that you understand how you're moving from kind of where you were to where you want to be. >> Uh, that's a difficult one to answer because particularly sounds, logic was used in so many different context. So for a certain part of the business, we might say, Are we monitoring the full stack? I were giving customers real value invisibility through the whole dynamic of the business. And then, in another context, we using sound subject. We were just saying, We just need to deploy its scale. We need two one board as quickly as possible. We need to keep the cost down to a minimum. We need to keep events that's allow as possible. Okay, so it's more about the efficiency argument, so it's really depends and way we're trying to use it and how we're deploying it. So >> how do you have visibility across how everybody is doing and getting trained on the latest things and keeping up to date and sharing best practices? How do you manage that internally, and how do you how do you do you network with your peers on some of that? >> Well, we've tried Teo really within. Tell us we have a concept of centre of excellence. So it's really about, you know, being recognizes the business experts in particular area and allowing the business to understand. That's that. That's where the expertise sits on a certain we've done a very good job with that and then allowing and communicating that after the business as well. So it's a very tough asked. It's a big business. We have thirty thousand people so often one person doesn't know about another person, another floor on the buildings, you know, to try and spread it across the biz, since we have fifty officers worldwide. So it's a process, you >> know? I mean, Roger just want one of things that here is, you know, science logic. It's not a widget, and it's, you know, can fit in a lot of different environments and a lot of different uses. You know, I heard of, you know, strong emphasis in into training had your CEO on where in his wizard tat for for for the that the learning knowledge that was gonna happen. So you know you talk a little bit about how science logic is looking to address this, especially for some you know, large customers like Telstra. >> You know, I think there's a general skills gap in is a whole beyond our technology beyond what's taking place in the world today. And you know, I've been in the business for quite a while, and we've long focused on training the operator on how to utilize the technology to solve their specific problems. And while that those aspects really powerful, some of the things we've done recently to go a step further is when we hear similar questions. We started record all of those so our customers could watch videos of how to solve problems instead of just going onto some form and let me type some question and hope somebody responds to in the future. You have read it for that. So we've got a look at a better mechanism and video based training handheld handling the customers we can build out these use cases drives the platform value, and what Telstra does it's really unique is they use the platform less so from a perspective of can I manage X y Z technology. But what can I build on top of it? How can I break the platform to some extend? And Rob is a mad scientist for us here. I mean, could jump into this more. But they've broken the platform to solve those business needs by addressing them individually. And what we've done is we've taken his best practices, and we rolled them back out to the rest of our customers. So with Robin, tell Hsia and a couple of other really great customers were driving a better community and sense of community so less question, answer form, less traditional support, more video, more community, more share ability. And that's where you're going to get additional quality. Coming out from the products are being delivered. Makes sense to you, Robert. Absolutely. >> Yeah, Rob. I mean, I love any commentary on that. You know, the network effect of software especially would talk about Sasser as a service type things, you know, that's what sales force really came out. It was like a weight one customer. Ask for something and wake everybody. You can take advantage of that or something similar. So are you seeing that kind of dynamics today with science logic and with others >> well, perfectly within the Telstra business. Absolutely so by building a capital into one area, you can share it across. And we found that we've been able to then sell the system internally, your internal stakeholders, so they appreciate the value of it and we can build on that. And then our customers, whilst we don't necessarily lady with the product they can. They see what's going on, and they basically then take it on as a service as well. So it's very, very interesting process. >> So one thing we haven't talked about yet, but you talk about data, you know, what's the role of data in your environment is something that you know key to the platform from science logic. How you leveraging it? How's that changing in your environment? One of the opportunities there. >> It's interesting questions. So as the telco, we collect a lot of data on DA. Obviously we have federal agencies who make that a requirement as well. So we have an existing data like initiative on that's very full of moment, and science logic is where we're looking at how we can add to that the value, valuable information and provides, but like everyone else, is a lot of data to collect, and it's an interesting process to try and make sense out of it and react accordingly. I mean, as a business, we were responding to millions and millions events of a day. So it's, you know, it's a difficult thing. >> Yeah, one of things. When we look at things like you know, anything that requires training like machine learning or the like, There's the balance between I want to learn from everybody. But you know, you're in a competitive marketplace. I don't want my competitors necessarily to get things. So you know the software products usually Well, I can isolate, and it doesn't have specific information. But how do you look at that dynamic of making sure that you gain from what the industry is doing, but that, you know, you could still stay competitive in ahead of your competition? >> Uh, >> no. I don't have a necessary can answer that. I suppose my head's tied into really what I could do with a platform and how I can then bring new technologies into the company's. So that's really are spies remind spaces on, Really, it's what I'm focused on. So you know what we do with the daughter probably is. He's not necessarily big concerns. How >> about that? There was quite a lot of announcements this week. The number of integrations as well as you know, update to the product. Anything specifically that you've been waiting for or that has caught your eye, >> the service now integration. I think it is far more advanced than has been in the past. On we have aspect of the business used thinks over quite heavily. So the fact that that's now matured and much more robust and you know which sort of offering that'LL have a lot of impact on the business. So I definitely mean the machine learning is another great thing on the question of then how that develops over time. So we'LL see how that goes. You >> know, Roger loves you know what? When I've been digging into some is the feedback you've been getting from customers and what's been leading toe, you know, some of the enhancement. So I would love, love your take on what you're saying. >> You know, I think one of the things that tell Sharpe pushed us towards a few years back was we're going to build. We already have a data like we don't need you to function. Is there Data Lake? So its multiple different Veda lakes And this concept of how do I move later From one day to lake to a different data Lake lakes within lakes ponds. Whatever the terminology is today the data ocean, our family perfect. And I'm getting to that data ocean from our lake. We have to go get streaming data. So now I'm going to extremes against really geographic here. But, you know, Rob really pushed us to make sure we could go right to Kaka buses and pushed data out. So what do you do with the data? And so tell Strip has been a, you know, an early adopter of a lot of our technology. And by being an early adopter, they've pushed us in a number of directions. So I think when you see a lot of the functionality that we've released this week and we've announced, it's been because of our customer base because of our partners like Telstra, that need to drive the business for further and forward, especially the industry like Telco World, where everything is mobile everything's moving so fast and aggressively. They're really like a good sounding board for where we need to go and how do we get there and and that drive And that partnership is What I think I'm most excited about working with tell sure is they demand from us to be excellent, and that gets great product coming out. And we see the results this week with all of our customers excitingly looking at stream treating capability that Rob was pushing us for well in advance of anyone else. >> Yeah, Robin, I want to give you the final word. You know, I can't help but notice you actually co branded shirts you've got tell star on your arm wither with science logic there. So, obviously, more than just a vendor relationship there, maybe close us out with you know how important science logic is. Two to your business >> job, Critical part of the business. I mean, particularly where we're looking at the commodity aspect of many services, you know, we can't survive unless we can provide quality, invaluable information where customers and really sounds. Logic has been the key platform for that. So in some respects, we're resting, you know, an aspect of the business entirely and Scientology's hands and we're hoping they'LL deliver >> well, Robin Raj, Thank you so much for joining us. Just sharing all the progress that you've made in. You know where things were going? Thanks so much, thanks to all right. And I'm student men. This is the Cube at Science Logic Symposium twenty nineteen. Thanks for watching.