(upbeat music) >> Hello everyone. This is Dave Vellante. We're diving into the deep end with AMD and Oracle on the topic of mySQL HeatWave performance. And we want to explore the important issues around machine learning. As applications become more data intensive and machine intelligence continues to evolve, workloads increasingly are seeing a major shift where data and AI are being infused into applications. And having a database that simplifies the convergence of transaction and analytics data without the need to context, switch and move data out of and into different data stores. And eliminating the need to perform extensive ETL operations is becoming an industry trend that customers are demanding. At the same time, workloads are becoming more automated and intelligent. And to explore these issues further, we're happy to have back in theCUBE Nipun Agarwal, who's the Senior Vice President of mySQL HeatWave and Kumaran Siva, who's the Corporate Vice President Strategic Business Development at AMD. Gents, hello again. Welcome back. >> Hello. Hi Dave. >> Thank you, Dave. >> Okay. Nipun, obviously machine learning has become a must have for analytics offerings. It's integrated into mySQL HeatWave. Why did you take this approach and not the specialized database approach as many competitors do right tool for the right job? >> Right? So, there are a lot of customers of mySQL who have the need to run machine learning on the data which is store in mySQL database. So in the past, customers would need to extract the data out of mySQL and they would take it to a specialized service for running machine learning. Now, the reason we decided to incorporate machine learning inside the database, there are multiple reasons. One, customers don't need to move the data. And if they don't need to move the data, it is more secure because it's protected by the same access controlled mechanisms as rest of the data There is no need for customers to manage multiple services. But in addition to that, when we run the machine learning inside the database customers are able to leverage the same service the same hardware, which has been provisioned for OTP analytics and use machine learning capabilities at no additional charge. So from a customer's perspective, they get the benefits that it is a single database. They don't need to manage multiple services. And it is offered at no additional charge. And then as another aspect, which is kind of hard to learn which is based on the IP, the work we have done it is also significantly faster than what customers would get by having a separate service. >> Just to follow up on that. How are you seeing customers use HeatWaves machine learning capabilities today? How is that evolving? >> Right. So one of the things which, you know customers very often want to do is to train their models based on the data. Now, one of the things is that data in a database or in a transaction database changes quite rapidly. So we have introduced support for auto machine learning as a part of HeatWave ML. And what it does is that it fully automates the process of training. And this is something which is very important to database users, very important to mySQL users that they don't really want to hire or data scientists or specialists for doing training. So that's the first part that training in HeatWave ML is fully automated. Doesn't require the user to provide any like specific parameters, just the source data and the task which they want to train. The second aspect is the training is really fast. So the training is really fast. The benefit is that customers can retrain quite often. They can make sure that the model is up to date with any changes which have been made to their transaction database. And as a result of the models being up to date, the accuracy of the prediction is high. Right? So that's the first aspect, which is training. The second aspect is inference, which customers run once they have the models trained. And the third thing, which is perhaps been the most sought after request from the mySQL customers is the ability to provide explanations. So, HeatWave ML provides explanations for any model which has been generated or trained by HeatWave ML. So these are the three capabilities- training, inference and explanations. And this whole process is completely automated, doesn't require a specialist or a data scientist. >> Yeah, that's nice. I mean, training obviously very popular today. I've said inference I think is going to explode in the coming decade. And then of course, AI explainable AI is a very important issue. Kumaran, what are the relevant capabilities of the AMD chips that are used in OCI to support HeatWave ML? Are they different from say the specs for HeatWave in general? >> So, actually they aren't. And this is one of the key features of this architecture or this implementation that is really exciting. Um, there with HeatWave ML, you're using the same CPU. And by the way, it's not a GPU, it's a CPU for both for all three of the functions that Nipun just talked about- inference, training and explanation all done on CPU. You know, bigger picture with the capabilities we bring here we're really providing a balance, you know between the CPU cores, memory and the networking. And what that allows you to do here is be able to feed the CPU cores appropriately. And within the cores, we have these AVX instruc... extensions in with the Zen 2 and Zen 3 cores. We had AVX 2, and then with the Zen 4 core coming out we're going to have AVX 512. But we were able to with that balance of being able to bring in the data and utilize the high memory bandwidth and then use the computation to its maximum we're able to provide, you know, build pride enough AI processing that we are able to get the job done. And then we're built to build a fit into that larger pipeline that that we build out here with the HeatWave. >> Got it. Nipun you know, you and I every time we have a conversation we've got to talk benchmarks. So you've done machine learning benchmarks with HeatWave. You might even be the first in the industry to publish you know, transparent, open ML benchmarks on GitHub. I mean, I, I wouldn't know for sure but I've not seen that as common. Can you describe the benchmarks and the data sets that you used here? >> Sure. So what we did was we took a bunch of open data sets for two categories of tasks- classification and regression. So we took about a dozen data sets for classification and about six for regression. So to give an example, the kind of data sets we used for classifications like the airlines data set, hex sensors bank, right? So these are open data sets. And what we did was for on these data sets we did a comparison of what would it take to train using HeatWave ML? And then the other service we compared with is that RedShift ML. So, there were two observations. One is that with HeatWave ML, the user does not need to provide any tuning parameters, right? The HeatWave ML using RML fully generates a train model, figures out what are the right algorithms? What are the right features? What are the right hyper parameters and sets, right? So no need for any manual intervention not so the case with Redshift ML. The second thing is the performance, right? So the performance of HeatWave ML aggregate on these 12 data sets for classification and the six data sets on regression. On an average, it is 25 times faster than Redshift ML. And note that Redshift ML in turn involves SageMaker, right? So on an average, HeatWave ML provides 25 times better performance for training. And the other point to note is that there is no need for any human intervention. That's fully automated. But in the case of Redshift ML, many of these data sets did not even complete in the set duration. If you look at price performance, one of the things again I want to highlight is because of the fact that AMD does pretty well in all kinds of workloads. We are able to use the same cluster users and use the same cluster for analytics, for OTP or for machine learning. So there is no additional cost for customers to run HeatWave ML if they have provision HeatWave. But assuming a user is provisioning a HeatWave cluster only to run HeatWave ML, right? That's the case, even in that case the price performance advantage of HeatWave ML over Redshift ML is 97 times, right? So 25 times faster at 1% of the cost compared to Redshift ML And all these scripts and all this information is available on GitHub for customers to try to modify and like, see, like what are the advantages they would get on their workloads? >> Every time I hear these numbers, I shake my head. I mean, they're just so overwhelming. Um, and so we'll see how the competition responds when, and if they respond. So, but thank you for sharing those results. Kumaran, can you elaborate on how the specs that you talked about earlier contribute to HeatWave ML's you know, benchmark results. I'm particularly interested in scalability, you know Typically things degrade as you push the system harder. What are you seeing? >> No, I think, I think it's good. Look, yeah. That's by those numbers, just blow me, blow my head too. That's crazy good performance. So look from, from an AMD perspective, we have really built an architecture. Like if you think about the chiplet architecture to begin with, it is fundamentally, you know, it's kind of scaling by design, right? And, and one of the things that we've done here is been able to work with, with the HeatWave team and heat well ML team, and then been able to, to within within the CPU package itself, be able to scale up to take very efficient use of all of the course. And then of course, work with them on how you go between nodes. So you can have these very large systems that can run ML very, very efficiently. So it's really, you know, building on the building blocks of the chiplet architecture and how scaling happens there. >> Yeah. So it's you're saying it's near linear scaling or essentially. >> So, let Nipun comment on that. >> Yeah. >> Is it... So, how about as cluster sizes grow, Nipun? >> Right. >> What happens there? >> So one of the design points for HeatWave is scale out architecture, right? So as you said, that as we add more data set or increase the size of the data, or we add the number of nodes to the cluster, we want the performance to scale. So we show that we have near linear scale factor, or nearly near scale scalability for SQL workloads in the case of HeatWave ML, as well. As users add more nodes to the cluster so the size of the cluster the performance of HeatWave ML improves. So I was giving you this example that HeatWave ML is 25 times faster compared to Redshift ML. Well, that was on a cluster size of two. If you increase the cluster size of HeatWave ML to a larger number. But I think the number is 16. The performance advantage over Redshift ML increases from 25 times faster to 45 times faster. So what that means is that on a cluster size of 16 nodes HeatWave ML is 45 times faster for training these again, dozen data sets. So this shows that HeatWave ML skills better than the computation. >> So you're saying adding nodes offsets any management complexity that you would think of as getting in the way. Is that right? >> Right. So one is the management complexity and which is why by features like last customers can scale up or scale down, you know, very easily. The second aspect is, okay What gives us this advantage, right, of scalability? Or how are we able to scale? Now, the techniques which we use for HeatWave ML scalability are a bit different from what we use for SQL processing. So in the case of HeatWave ML, they really like, you know, three, two trade offs which we have to be careful about. One is the accuracy. Because we want to provide better performance for machine learning without compromising on the accuracy. So accuracy would require like more synchronization if you have multiple threads. But if you have too much of synchronization that can slow down the degree of patterns that we get. Right? So we have to strike a fine balance. So what we do is that in HeatWave ML, there are different phases of training, like algorithm selection, feature selection, hyper probability training. Each of these phases is analyzed. And for instance, one of the ways techniques we use is that if you're trying to figure out what's the optimal hyper parameter to be used? We start up with the search space. And then each of the VMs gets a part of the search space. And then we synchronize only when needed, right? So these are some of the techniques which we have developed over the years. And there are actually paper's filed, research publications filed on this. And this is what we do to achieve good scalability. And what that results to the customer is that if they have some amount of training time and they want to make it better they can just provision a larger cluster and they will get better performance. >> Got it. Thank you. Kumaran, when I think of machine learning, machine intelligence, AI, I think GPU but you're not using GPU. So how are you able to get this type of performance or price performance without using GPU's? >> Yeah, definitely. So yeah, that's a good point. And you think about what is going on here and you consider the whole pipeline that Nipun has just described in terms of how you get you know, your training, your algorithms And using the mySQL pieces of it to get to the point where the AI can be effective. In that process what happens is you have to have a lot of memory to transactions. A lot of memory bandwidth comes into play. And then bringing all that data together, feeding the actual complex that does the AI calculations that in itself could be the bottleneck, right? And you can have multiple bottlenecks along the way. And I think what you see in the AMD architecture for epic for this use case is the balance. And the fact that you are able to do the pre-processing, the AI, and then the post-processing all kind of seamlessly together, that has a huge value. And that goes back to what Nipun was saying about using the same infrastructure, gets you the better TCO but it also gets you gets you better performance. And that's because of the fact that you're bringing the data to the computation. So the computation in this case is not strictly the bottleneck. It's really about how you pull together what you need and to do the AI computation. And that is, that's probably a more, you know, it's a common case. And so, you know, you're going to start I think the least start to see this especially for inference applications. But in this case we're doing both inference explanation and training. All using the the CPU in the same OCI infrastructure. >> Interesting. Now Nipun, is the secret sauce for HeatWave ML performance different than what we've discussed before you and I with with HeatWave generally? Is there some, you know, additive engine additive that you're putting in? >> Right? Yes. The secret sauce is indeed different, right? Just the way I was saying that for SQL processing. The reason we get very good performance and price performance is because we have come up with new algorithms which help the SQL process can scale out. Similarly for HeatWave ML, we have come up with new IP, new like algorithms. One example is that we use meta-learn proxy models, right? That's the technique we use for automating the training process, right? So think of this meta-learn proxy models to be like, you know using machine learning for machine learning training. And this is an IP which we developed. And again, we have published the results and the techniques. But having such kind of like techniques is what gives us a better performance. Similarly, another thing which we use is adaptive sampling that you can have a large data set. But we intelligently sample to figure out that how can we train on a small subset without compromising on the accuracy? So, yes, there are many techniques that you have developed specifically for machine learning which is what gives us the better performance, better price performance, and also better scalability. >> What about mySQL autopilot? Is there anything that differs from HeatWave ML that is relevant? >> Okay. Interesting you should ask. So mySQL Autopilot is think of it to be an application using machine learning. So mySQL Autopilot uses machine learning to automate various aspects of the database service. So for instance, if you want to figure out that what's the right partitioning scheme to partition the data in memory? We use machine learning techniques to figure out that what's the right, the best column based on the user's workload to partition the data in memory Or given a workload, if you want to figure out what is the right cluster size to provision? That's something we use mySQL autopilot for. And I want to highlight that we don't aware of any other database service which provides this level of machine learning based automation which customers get with mySQL Autopilot. >> Hmm. Interesting. Okay. Last question for both of you. What are you guys working on next? What can customers expect from this collaboration specifically in this space? Maybe Nipun, you can start and then Kamaran can bring us home. >> Sure. So there are two things we are working on. One is based on the feedback we have gotten from customers, we are going to keep making the machine learning capabilities richer in HeatWave ML. That's one dimension. And the second thing is which Kamaran was alluding to earlier, We are looking at the next generation of like processes coming from AMD. And we will be seeing as to how we can more benefit from these processes whether it's the size of the L3 cache, the memory bandwidth, the network bandwidth, and such or the newer effects. And make sure that we leverage the all the greatness which the new generation of processes will offer. >> It's like an engineering playground. Kumaran, let's give you the final word. >> No, that's great. Now look with the Zen 4 CPU cores, we're also bringing in AVX 512 instruction capability. Now our implementation is a little different. It was in, in Rome and Milan, too where we use a double pump implementation. What that means is, you know, we take two cycles to do these instructions. But the key thing there is we don't lower our speed of the CPU. So there's no noisy neighbor effects. And it's something that OCI and the HeatWave has taken full advantage of. And so like, as we go out in time and we see the Zen 4 core, we can... we see up to 96 CPUs that that's going to work really well. So we're collaborating closely with, with OCI and with the HeatWave team here to make sure that we can take advantage of that. And we're also going to upgrade the memory subsystem to get to 12 channels of DDR 5. So it should be, you know there should be a fairly significant boost in absolute performance. But more important or just as importantly in TCO value for the customers, the end customers who are going to adopt this great service. >> I love their relentless innovation guys. Thanks so much for your time. We're going to have to leave it there. Appreciate it. >> Thank you, David. >> Thank you, David. >> Okay. Thank you for watching this special presentation on theCUBE. Your leader in enterprise and emerging tech coverage.

(upbeat music) >> For those of you who've been following the cloud database space, you know that MySQL HeatWave has been on a technology tear over the last 24 months with Oracle claiming record breaking benchmarks relative to other database platforms. So far, those benchmarks remain industry leading as competitors have chosen not to respond, perhaps because they don't feel the need to, or maybe they don't feel that doing so would serve their interest. Regardless, the HeatWave team at Oracle has been very aggressive about its performance claims, making lots of noise, challenging the competition to respond, publishing their scripts to GitHub. But so far, there are no takers, but customers seem to be picking up on these moves by Oracle and it's likely the performance numbers resonate with them. Now, the other area we want to explore, which we haven't thus far, is the engine behind HeatWave and that is AMD. AMD's epic processors have been the powerhouse on OCI, running MySQL HeatWave since day one. And today we're going to explore how these two technology companies are working together to deliver these performance gains and some compelling TCO metrics. In fact, a recent Wikibon analysis from senior analyst Marc Staimer made some TCO comparisons in OLAP workloads relative to AWS, Snowflake, GCP, and Azure databases, you can find that research on wikibon.com. And with that, let me introduce today's guest, Nipun Agarwal senior vice president of MySQL HeatWave and Kumaran Siva, who's the corporate vice president for strategic business development at AMD. Welcome to theCUBE gentlemen. >> Welcome. Thank you. >> Thank you, Dave. >> Hey Nipun, you and I have talked a lot about this. You've been on theCUBE a number of times talking about MySQL HeatWave. But for viewers who may not have seen those episodes maybe you could give us an overview of HeatWave and how it's different from competitive cloud database offerings. >> Sure. So MySQL HeatWave is a fully managed MySQL database service offering from Oracle. It's a single database, which can be used to run transactional processing, analytics and machine learning workloads. So, in the past, MySQL has been designed and optimized for transaction processing. So customers of MySQL when they had to run, analytics machine learning, would need to extract the data out of MySQL, into some other database or service, to run analytics or machine learning. MySQL HeatWave offers a single database for running all kinds of workloads so customers don't need to extract data into some of the database. In addition to having a single database, MySQL HeatWave is also very performant compared to one up databases and also it is very price competitive. So the advantages are; single database, very performant, and very good price performance. >> Yes. And you've published some pretty impressive price performance numbers against competitors. Maybe you could describe those benchmarks and highlight some of the results, please. >> Sure. So one thing to notice that the performance of any database is going to like vary, the performance advantage is going to vary based on, the size of the data and the specific workloads, so the mileage varies, that's the first thing to know. So what we have done is, we have published multiple benchmarks. So we have benchmarks on PPCH or PPCDS and we have benchmarks on different data sizes because based on the customer's workload, the mileage is going to vary, so we want to give customers a broad range of comparisons so that they can decide for themselves. So in a specific case, where we are running on a 30 terabyte PPCH workload, HeatWave is about 18 times better price performance compared to Redshift. 18 times better compared to Redshift, about 33 times better price performance, compared to Snowflake, and 42 times better price performance compared to Google BigQuery. So, this is on 30 Terabyte PPCH. Now, if the data size is different, or the workload is different, the characteristics may vary slightly but this is just to give a flavor of the kind of performance advantage MySQL HeatWave offers. >> And then my last question before we bring in Kumaran. We've talked about the secret sauce being the tight integration between hardware and software, but would you add anything to that? What is that secret sauce in HeatWave that enables you to achieve these performance results and what does it mean for customers? >> So there are three parts to this. One is HeatWave has been designed with a scale out architecture in mind. So we have invented and implemented new algorithms for skill out query processing for analytics. The second aspect is that HeatWave has been really optimized for cloud, commodity cloud, and that's where AMD comes in. So for instance, many of the partitioning schemes we have for processing HeatWave, we optimize them for the L3 cache of the AMD processor. The thing which is very important to our customers is not just the sheer performance but the price performance, and that's where we have had a very good partnership with AMD because not only does AMD help us provide very good performance, but the price performance, right? And that all these numbers which I was showing, big part of it is because we are running on AMD which provides very good price performance. So that's the second aspect. And the third aspect is, MySQL autopilot, which provides machine learning based automation. So it's really these three things, a combination of new algorithms, design for scale out query processing, optimized for commodity cloud hardware, specifically AMD processors, and third, MySQL auto pilot which gives us this performance advantage. >> Great, thank you. So that's a good segue for AMD and Kumaran. So Kumaran, what is AMD bringing to the table? What are the, like, for instance, relevance specs of the chips that are used in Oracle cloud infrastructure and what makes them unique? >> Yeah, thanks Dave. That's a good question. So, OCI is a great customer of ours. They use what we call the top of stack devices meaning that they have the highest core count and they also are very, very fast cores. So these are currently Zen 3 cores. I think the HeatWave product is right now deployed on Zen 2 but will shortly be also on the Zen 3 core as well. But we provide in the case of OCI 64 cores. So that's the largest devices that we build. What actually happens is, because these large number of CPUs in a single package and therefore increasing the density of the node, you end up with this fantastic TCO equation and the cost per performance, the cost per for deployed services like HeatWave actually ends up being extraordinarily competitive and that's a big part of the contribution that we're bringing in here. >> So Zen 3 is the AMD micro architecture which you introduced, I think in 2017, and it's the basis for EPIC, which is sort of the enterprise grade that you really attacked the enterprise with. Maybe you could elaborate a little bit, double click on how your chips contribute specifically to HeatWave's, price performance results. >> Yeah, absolutely. So in the case of HeatWave, so as Nipun alluded to, we have very large L3 caches, right? So in our very, very top end parts just like the Milan X devices, we can go all the way up to like 768 megabytes of L3 cache. And that gives you just enormous performance and performance gains. And that's part of what we're seeing with HeatWave today and that not that they're currently on the second generation ROM based product, 'cause it's a 7,002 based product line running with the 64 cores. But as time goes on, they'll be adopting the next generation Milan as well. And the other part of it too is, as our chip led architecture has evolved, we know, so from the first generation Naples way back in 2017, we went from having multiple memory domains and a sort of NUMA architecture at the time, today we've really optimized that architecture. We use a common I/O Die that has all of the memory channels attached to it. And what that means is that, these scale out applications like HeatWave, are able to really scale very efficiently as they go from a small domain of CPUs to, for example the entire chip, all 64 cores that scaling, is been a key focus for AMD and being able to design and build architectures that can take advantage of that and then have applications like HeatWave that scale so well on it, has been, a key aim of ours. >> And Gen 3 moving up the Italian countryside. Nipun, you've taken the somewhat unusual step of posting the benchmark parameters, making them public on GitHub. Now, HeatWave is relatively new. So people felt that when Oracle gained ownership of MySQL it would let it wilt on the vine in favor of Oracle database, so you lost some ground and now, you're getting very aggressive with HeatWave. What's the reason for publishing those benchmark parameters on GitHub? >> So, the main reason for us to publish price performance numbers for HeatWave is to communicate to our customers a sense of what are the benefits they're going to get when they use HeatWave. But we want to be very transparent because as I said the performance advantages for the customers may vary, based on the data size, based on the specific workloads. So one of the reasons for us to publish, all these scripts on GitHub is for transparency. So we want customers to take a look at the scripts, know what we have done, and be confident that we stand by the numbers which we are publishing, and they're very welcome, to try these numbers themselves. In fact, we have had customers who have downloaded the scripts from GitHub and run them on our service to kind of validate. The second aspect is in some cases, they may be some deviations from what we are publishing versus what the customer would like to run in the production deployments so it provides an easy way, for customers to take the scripts, modify them in some ways which may suit their real world scenario and run to see what the performance advantages are. So that's the main reason, first, is transparency, so the customers can see what we are doing, because of the comparison, and B, if they want to modify it to suit their needs, and then see what is the performance of HeatWave, they're very welcome to do so. >> So have customers done that? Have they taken the benchmarks? And I mean, if I were a competitor, honestly, I wouldn't get into that food fight because of the impressive performance, but unless I had to, I mean, have customers picked up on that, Nipun? >> Absolutely. In fact, we have had many customers who have benchmarked the performance of MySQL HeatWave, with other services. And the fact that the scripts are available, gives them a very good starting point, and then they've also tweaked those queries in some cases, to see what the Delta would be. And in some cases, customers got back to us saying, hey the performance advantage of HeatWave is actually slightly higher than what was published and what is the reason. And the reason was, when the customers were trying, they were trying on the latest version of the service, and our benchmark results were posted let's say, two months back. So the service had improved in those two to three months and customers actually saw better performance. So yes, absolutely. We have seen customers download the scripts, try them and also modify them to some extent and then do the comparison of HeatWave with other services. >> Interesting. Maybe a question for both of you how is the competition responding to this? They haven't said, "Hey, we're going to come up "with our own benchmarks." Which is very common, you oftentimes see that. Although, for instance, Snowflake hasn't responded to data bricks, so that's not their game, but if the customers are actually, putting a lot of faith in the benchmarks and actually using that for buying decisions, then it's inevitable. But how have you seen the competition respond to the MySQL HeatWave and AMD combo? >> So maybe I can take the first track from the database service standpoint. When customers have more choice, it is invariably advantages for the customer because then the competition is going to react, right? So the way we have seen the reaction is that we do believe, that the other database services are going to take a closer eye to the price performance, right? Because if you're offering such good price performance, the vendors are already looking at it. And, you know, instances where they have offered let's say discount to the customers, to kind of at least like close the gap to some extent. And the second thing would be in terms of the capability. So like one of the things which I should have mentioned even early on, is that not only does MySQL HeatWave on AMD, provide very good price performance, say on like a small cluster, but it's all the way up to a cluster size of 64 nodes, which has about 1000 cores. So the point is, that HeatWave performs very well, both on a small system, as well as a huge scale out. And this is again, one of those things which is a differentiation compared to other services so we expect that even other database services will have to improve their offerings to provide the same good scale factor, which customers are now starting to expectancy, with MySQL HeatWave. >> Kumaran, anything you'd add to that? I mean, you guys are an arms dealer, you love all your OEMs, but at the same time, you've got chip competitors, Silicon competitors. How do you see the competitive-- >> I'd say the broader answer and the big picture for AMD, we're very maniacally focused on our customers, right? And OCI and Oracle are huge and important customers for us, and this particular use cases is extremely interesting both in that it takes advantage, very well of our architecture and it pulls out some of the value that AMD bring. I think from a big picture standpoint, our aim is to execute, to build to bring out generations of CPUs, kind of, you know, do what we say and say, sorry, say what we do and do what we say. And from that point of view, we're hitting, the schedules that we say, and being able to bring out the latest technology and bring it in a TCO value proposition that generationally keeps OCI and HeatWave ahead. That's the crux of our partnership here. >> Yeah, the execution's been obvious for the last several years. Kumaran, staying with you, how would you characterize the collaboration between, the AMD engineers and the HeatWave engineering team? How do you guys work together? >> No, I'd say we're in a very, very deep collaboration. So, there's a few aspects where, we've actually been working together very closely on the code and being able to optimize for both the large L3 cache that AMD has, and so to be able to take advantage of that. And then also, to be able to take advantage of the scaling. So going between, you know, our architecture is chip like based, so we have these, the CPU cores on, we call 'em CCDs and the inter CCD communication, there's opportunities to optimize an application level and that's something we've been engaged with. In the broader engagement, we are going back now for multiple generations with OCI, and there's a lot of input that now, kind of resonates in the product line itself. And so we value this very close collaboration with HeatWave and OCI. >> Yeah, and the cadence, Nip, and you and I have talked about this quite a bit. The cadence has been quite rapid. It's like this constant cycle every couple of months I turn around, is something new on HeatWave. But for question again, for both of you, what new things do you think that organizations, customers, are going to be able to do with MySQL HeatWave if you could look out next 12 to 18 months, is there anything you can share at this time about future collaborations? >> Right, look, 12 to 18 months is a long time. There's going to be a lot of innovation, a lot of new capabilities coming out on in MySQL HeatWave. But even based on what we are currently offering, and the trend we are seeing is that customers are bringing, more classes of workloads. So we started off with OLTP for MySQL, then it went to analytics. Then we increased it to mixed workloads, and now we offer like machine learning as alike. So one is we are seeing, more and more classes of workloads come to MySQL HeatWave. And the second is a scale, that kind of data volumes people are using HeatWave for, to process these mixed workloads, analytics machine learning OLTP, that's increasing. Now, along the way we are making it simpler to use, we are making it more cost effective use. So for instance, last time, when we talked about, we had introduced this real time elasticity and that's something which is a very, very popular feature because customers want the ability to be able to scale out, or scale down very efficiently. That's something we provided. We provided support for compression. So all of these capabilities are making it more efficient for customers to run a larger part of their workloads on MySQL HeatWave, and we will continue to make it richer in the next 12 to 18 months. >> Thank you. Kumaran, anything you'd add to that, we'll give you the last word as we got to wrap it. >> No, absolutely. So, you know, next 12 to 18 months we will have our Zen 4 CPUs out. So this could potentially go into the next generation of the OCI infrastructure. This would be with the Genoa and then Bergamo CPUs taking us to 96 and 128 cores with 12 channels at DDR five. This capability, you know, when applied to an application like HeatWave, you can see that it'll open up another order of magnitude potentially of use cases, right? And we're excited to see what customers can do do with that. It certainly will make, kind of the, this service, and the cloud in general, that this cloud migration, I think even more attractive. So we're pretty excited to see how things evolve in this period of time. >> Yeah, the innovations are coming together. Guys, thanks so much, we got to leave it there really appreciate your time. >> Thank you. >> All right, and thank you for watching this special Cube conversation, this is Dave Vellante, and we'll see you next time. (soft calm music)

(upbeat music) >> When Oracle acquired Sun in 2009, it paid $5.6 billion net of Sun's cash and debt. Now I argued at the time that Oracle got one of the best deals in the history of enterprise tech, and I got a lot of grief for saying that because Sun had a declining business, it was losing money, and its revenue was under serious pressure as it tried to hang on for dear life. But Safra Catz understood that Oracle could pay Sun's lower profit and lagging businesses, like its low index 86 product lines, and even if Sun's revenue was cut in half, because Oracle has such a high revenue multiple as a software company, it could almost instantly generate $25 to $30 billion in shareholder value on paper. In addition, it was a catalyst for Oracle to initiate its highly differentiated engineering systems business, and was actually the precursor to Oracle's Cloud. Oracle saw that it could capture high margin dollars that used to go to partners like HP, it's original exit data partner, and get paid for the full stack across infrastructure, middleware, database, and application software, when eventually got really serious about cloud. Now there was also a major technology angle to this story. Remember Sun's tagline, "the network is the computer"? Well, they should have just called it cloud. Through the Sun acquisition. Oracle also got a couple of key technologies, Java, the number one programming language in the world, and MySQL, a key ingredient of the LAMP stack, that's Linux, Apache, MySQL and PHP, Perl or Python, on which the internet is basically built, and is used by many cloud services like Facebook, Twitter, WordPress, Flicker, Amazon, Aurora, and many other examples, including, by the way, Maria DB, which is a fork of MySQL created by MySQL's creator, basically in protest to Oracle's acquisition; the drama is Oscar worthy. It gets even better. In 2020, Oracle began introducing a new version of MySQL called MySQL HeatWave, and since late 2020 it's been in sort of a super cycle rolling, out three new releases in less than a year and a half in an attempt to expand its Tam and compete in new markets. Now we covered the release of MySQL Autopilot, which uses machine learning to automate management functions. And we also covered the bench marketing that Oracle produced against Snowflake, AWS, Azure, and Google. And Oracle's at it again with HeatWave, adding machine learning into its database capabilities, along with previously available integrations of OLAP and OLTP. This, of course, is in line with Oracle's converged database philosophy, which, as we've reported, is different from other cloud database providers, most notably Amazon, which takes the right tool for the right job approach and chooses database specialization over a one size fits all strategy. Now we've asked Oracle to come on theCUBE and explain these moves, and I'm pleased to welcome back Nipun Agarwal, who's the senior vice president for MySQL Database and HeatWave at Oracle. And today, in this video exclusive, we'll discuss machine learning, other new capabilities around elasticity and compression, and then any benchmark data that Nipun wants to share. Nipun's been a leading advocate of the HeatWave program. He's led engineering in that team for over 10 years, and he has over 185 patents in database technologies. Welcome back to the show Nipun. Great to see you again. Thanks for coming on. >> Thank you, Dave. Very happy to be back. >> Yeah, now for those who may not have kept up with the news, maybe to kick things off you could give us an overview of what MySQL HeatWave actually is so that we're all on the same page. >> Sure, Dave, MySQL HeatWave is a fully managed MySQL database service from Oracle, and it has a builtin query accelerator called HeatWave, and that's the part which is unique. So with MySQL HeatWave, customers of MySQL get a single database which they can use for transactional processing, for analytics, and for mixed workloads because traditionally MySQL has been designed and optimized for transaction processing. So in the past, when customers had to run analytics with the MySQL based service, they would need to move the data out of MySQL into some other database for running analytics. So they would end up with two different databases and it would take some time to move the data out of MySQL into this other system. With MySQL HeatWave, we have solved this problem and customers now have a single MySQL database for all their applications, and they can get the good performance of analytics without any changes to their MySQL application. >> Now it's no secret that a lot of times, you know, queries are not, you know, most efficiently written, and critics of MySQL HeatWave will claim that this product is very memory and cluster intensive, it has a heavy footprint that adds to cost. How do you answer that, Nipun? >> Right, so for offering any database service in the cloud there are two dimensions, performance and cost, and we have been very cognizant of both of them. So it is indeed the case that HeatWave is a, in-memory query accelerator, which is why we get very good performance, but it is also the case that we have optimized HeatWave for commodity cloud services. So for instance, we use the least expensive compute. We use the least expensive storage. So what I would suggest is for the customers who kind of would like to know what is the price performance advantage of HeatWave compared to any database we have benchmark against, Redshift, Snowflake, Google BigQuery, Azure Synapse, HeatWave is significantly faster and significantly lower price on a multitude of workloads. So not only is it in-memory database and optimized for that, but we have also optimized it for commodity cloud services, which makes it much lower price than the competition. >> Well, at the end of the day, it's customers that sort of decide what the truth is. So to date, what's been the customer reaction? Are they moving from other clouds from on-prem environments? Both why, you know, what are you seeing? >> Right, so we are definitely a whole bunch of migrations of customers who are running MySQL on-premise to the cloud, to MySQL HeatWave. That's definitely happening. What is also very interesting is we are seeing that a very large percentage of customers, more than half the customers who are coming to MySQL HeatWave, are migrating from other clouds. We have a lot of migrations coming from AWS Aurora, migrations from RedShift, migrations from RDS MySQL, TerriData, SAP HANA, right. So we are seeing migrations from a whole bunch of other databases and other cloud services to MySQL HeatWave. And the main reason we are told why customers are migrating from other databases to MySQL HeatWave are lower cost, better performance, and no change to their application because many of these services, like AWS Aurora are ETL compatible with MySQL. So when customers try MySQL HeatWave, not only do they get better performance at a lower cost, but they find that they can migrate their application without any changes, and that's a big incentive for them. >> Great, thank you, Nipun. So can you give us some names? Are there some real world examples of these customers that have migrated to MySQL HeatWave that you can share? >> Oh, absolutely, I'll give you a few names. Stutor.com, this is an educational SaaS provider raised out of Brazil. They were using Google BigQuery, and when they migrated to MySQL HeatWave, they found a 300X, right, 300 times improvement in performance, and it lowered their cost by 85 (audio cut out). Another example is Neovera. They offer cybersecurity solutions and they were running their application on an on-premise version of MySQL when they migrated to MySQL HeatWave, their application improved in performance by 300 times and their cost reduced by 80%, right. So by going from on-premise to MySQL HeatWave, they reduced the cost by 80%, improved performance by 300 times. We are Glass, another customer based out of Brazil. They were running on AWS EC2, and when they migrated, within hours they found that there was a significant improvement, like, you know, over 5X improvement in database performance, and they were able to accommodate a very large virtual event, which had more than a million visitors. Another example, Genius Senority. They are a game designer in Japan, and when they moved to MySQL HeatWave, they found a 90 times percent improvement in performance. And there many, many more like a lot of migrations, again, from like, you know, Aurora, RedShift and many other databases as well. And consistently what we hear is (audio cut out) getting much better performance at a much lower cost without any change to their application. >> Great, thank you. You know, when I ask that question, a lot of times I get, "Well, I can't name the customer name," but I got to give Oracle credit, a lot of times you guys have at your fingertips. So you're not the only one, but it's somewhat rare in this industry. So, okay, so you got some good feedback from those customers that did migrate to MySQL HeatWave. What else did they tell you that they wanted? Did they, you know, kind of share a wishlist and some of the white space that you guys should be working on? What'd they tell you? >> Right, so as customers are moving more data into MySQL HeatWave, as they're consolidating more data into MySQL HeatWave, customers want to run other kinds of processing with this data. A very popular one is (audio cut out) So we have had multiple customers who told us that they wanted to run machine learning with data which is stored in MySQL HeatWave, and for that they have to extract the data out of MySQL (audio cut out). So that was the first feedback we got. Second thing is MySQL HeatWave is a highly scalable system. What that means is that as you add more nodes to a HeatWave cluster, the performance of the system improves almost linearly. But currently customers need to perform some manual steps to add most to a cluster or to reduce the cluster size. So that was other feedback we got that people wanted this thing to be automated. Third thing is that we have shown in the previous results, that HeatWave is significantly faster and significantly lower price compared to competitive services. So we got feedback from customers that can we trade off some performance to get even lower cost, and that's what we have looked at. And then finally, like we have some results on various data sizes with TPC-H. Customers wanted to see if we can offer some more data points as to how does HeatWave perform on other kinds of workloads. And that's what we've been working on for the several months. >> Okay, Nipun, we're going to get into some of that, but, so how did you go about addressing these requirements? >> Right, so the first thing is we are announcing support for in-database machine learning, meaning that customers who have their data inside MySQL HeatWave can now run training, inference, and prediction all inside the database without the data or the model ever having to leave the database. So that's how we address the first one. Second thing is we are offering support for real time elasticity, meaning that customers can scale up or scale down to any number of nodes. This requires no manual intervention on part of the user, and for the entire duration of the resize operation, the system is fully available. The third, in terms of the costs, we have double the amount of data that can be processed per node. So if you look at a HeatWave cluster, the size of the cluster determines the cost. So by doubling the amount of data that can be processed per node, we have effectively reduced the cluster size which is required for planning a given workload to have, which means it reduces the cost to the customer by half. And finally, we have also run the TPC-DS workload on HeatWave and compared it with other vendors. So now customers can have another data point in terms of the performance and the cost comparison of HeatWave with other services. >> All right, and I promise, I'm going to ask you about the benchmarks, but I want to come back and drill into these a bit. How is HeatWave ML different from competitive offerings? Take for instance, Redshift ML, for example. >> Sure, okay, so this is a good comparison. Let's start with, let's say RedShift ML, like there are some systems like, you know, Snowflake, which don't even offer any, like, processing of machine learning inside the database, and they expect customers to write a whole bunch of code, in say Python or Java, to do machine learning. RedShift ML does have integration with SQL. That's a good start. However, when customers of Redshift need to run machine learning, and they invoke Redshift ML, it makes a call to another service, SageMaker, right, where so the data needs to be exported to a different service. The model is generated, and the model is also outside RedShift. With HeatWave ML, the data resides always inside the MySQL database service. We are able to generate models. We are able to train the models, run inference, run explanations, all inside the MySQL HeatWave service. So the data, or the model, never have to leave the database, which means that both the data and the models can now be secured by the same access control mechanisms as the rest of the data. So that's the first part, that there is no need for any ETL. The second aspect is the automation. Training is a very important part of machine learning, right, and it impacts the quality of the predictions and such. So traditionally, customers would employ data scientists to influence the training process so that it's done right. And even in the case of Redshift ML, the users are expected to provide a lot of parameters to the training process. So the second thing which we have worked on with HeatWave ML is that it is fully automated. There is absolutely no user intervention required for training. Third is in terms of performance. So one of the things we are very, very sensitive to is performance because performance determines the eventual cost to the customer. So again, in some benchmarks, which we have published, and these are all available on GitHub, we are showing how HeatWave ML is 25 times faster than Redshift ML, and here's the kicker, at 1% of the cost. So four benefits, the data all remain secure inside the database service, it's fully automated, much faster, much lower cost than the competition. >> All right, thank you Nipun. Now, so there's a lot of talk these days about explainability and AI. You know, the system can very accurately tell you that it's a cat, you know, or for you Silicon Valley fans, it's a hot dog or not a hot dog, but they can't tell you how the system got there. So what is explainability, and why should people care about it? >> Right, so when we were talking to customers about what they would like from a machine learning based solution, one of the feedbacks we got is that enterprise is a little slow or averse to uptaking machine learning, because it seems to be, you know, like magic, right? And enterprises have the obligation to be able to explain, or to provide a answer to their customers as to why did the database make a certain choice. With a rule based solution it's simple, it's a rule based thing, and you know what the logic was. So the reason explanations are important is because customers want to know why did the system make a certain prediction? One of the important characteristics of HeatWave ML is that any model which is generated by HeatWave ML can be explained, and we can do both global explanations or model explanations as well as we can also do local explanations. So when the system makes a specific prediction using HeatWave ML, the user can find out why did the system make such a prediction? So for instance, if someone is being denied a loan, the user can figure out what were the attribute, what were the features which led to that decision? So this ensures, like, you know, fairness, and many of the times there is also like a need for regulatory compliance where users have a right to know. So we feel that explanations are very important for enterprise workload, and that's why every model which is generated by HeatWave ML can be explained. >> Now I got to give Snowflakes some props, you know, this whole idea of separating compute from storage, but also bringing the database to the cloud and driving elasticity. So that's been a key enabler and has solved a lot of problems, in particular the snake swallowing the basketball problem, as I often say. But what about elasticity and elasticity in real time? How is your version, and there's a lot of companies chasing this, how is your approach to an elastic cloud database service different from what others are promoting these days? >> Right, so a couple of characteristics. One is that we have now fully automated the process of elasticity, meaning that if a user wants to scale up or scale down, the only thing they need to specify is the eventual size of the cluster and the system completely takes care of it transparently. But then there are a few characteristics which are very unique. So for instance, we can scale up or scale down to any number of nodes. Whereas in the case of Snowflake, the number of nodes someone can scale up or scale down to are the powers of two. So if a user needs 70 CPUs, well, their choice is either 64 or 128. So by providing this flexibly with MySQL HeatWave, customers get a custom fit. So they can get a cluster which is optimized for their specific portal. So that's the first thing, flexibility of scaling up or down to any number of nodes. The second thing is that after the operation is completed, the system is fully balanced, meaning the data across the various nodes is fully balanced. That is not the case with many solutions. So for instance, in the case of Redshift, after the resize operation is done, the user is expected to manually balance the data, which can be very cumbersome. And the third aspect is that while the resize operation is going on, the HeatWave cluster is completely available for queries, for DMLS, for loading more data. That is, again, not the case with Redshift. Redshift, suppose the operation takes 10 to 15 minutes, during that window of time, the system is not available for writes, and for a big part of that chunk of time, the system is not even available for queries, which is very limiting. So the advantages we have are fully flexible, the system is in a balanced state, and the system is completely available for the entire duration operation. >> Yeah, I guess you got that hypergranularity, which, you know, sometimes they say, "Well, t-shirt sizes are good enough," but then I think of myself, some t-shirts fit me better than others, so. Okay, I saw on the announcement that you have this lower price point for customers. How did you actually achieve this? Could you give us some details around that please? >> Sure, so there are two things for announcing this service, which lower the cost for the customers. The first thing is that we have doubled the amount of data that can be processed by a HeatWave node. So if we have doubled the amount of data, which can be a process by a node, the cluster size which is required by customers reduces to half, and that's why the cost drops to half. The way we have managed to do this is by two things. One is support for Bloom filters, which reduces the amount of intermediate memory. And second is we compress the base data. So these are the two techniques we have used to process more data per node. The second way by which we are lowering the cost for the customers is by supporting pause and resume of HeatWave. And many times you find customers of like HeatWave and other services that they want to run some other queries or some other workloads for some duration of time, but then they don't need the cluster for a few hours. Now with the support for pause and resume, customers can pause the cluster and the HeatWave cluster instantaneously stops. And when they resume, not only do we fetch the data, in a very, like, you know, a quick pace from the object store, but we also preserve all the statistics, which are used by Autopilot. So both the data and the metadata are fetched, extremely fast from the object store. So with these two capabilities we feel that it'll drive down the cost to our customers even more. >> Got it, thank you. Okay, I promised I was going to get to the benchmarks. Let's have it. How do you compare with others but specifically cloud databases? I mean, and how do we know these benchmarks are real? My friends at EMC, they were back in the day, they were brilliant at doing benchmarks. They would produce these beautiful PowerPoints charts, but it was kind of opaque, but what do you say to that? >> Right, so there are multiple things I would say. The first thing is that this time we have published two benchmarks, one is for machine learning and other is for SQL analytics. All the benchmarks, including the scripts which we have used are available on GitHub. So we have full transparency, and we invite and encourage customers or other service providers to download the scripts, to download the benchmarks and see if they get any different results, right. So what we are seeing, we have published it for other people to try and validate. That's the first part. Now for machine learning, there hasn't been a precedence for enterprise benchmarks so we talk about aiding open data sets and we have published benchmarks for those, right? So both for classification, as well as for aggression, we have run the training times, and that's where we find that HeatWave MLS is 25 times faster than RedShift ML at one percent of the cost. So fully transparent, available. For SQL analytics, in the past we have shown comparisons with TPC-H. So we would show TPC-H across various databases, across various data sizes. This time we decided to use TPC-DS. the advantage of TPC-DS over TPC-H is that it has more number of queries, the queries are more complex, the schema is more complex, and there is a lot more data skew. So it represents a different class of workloads, and which is very interesting. So these are queries derived from the TPC-DS benchmark. So the numbers we have are published this time are for 10 terabyte TPC-DS, and we are comparing with all the four majors services, Redshift, Snowflake, Google BigQuery, Azure Synapse. And in all the cases, HeatWave is significantly faster and significantly lower priced. Now one of the things I want to point out is that when we are doing the cost comparison with other vendors, we are being overly fair. For instance, the cost of HeatWave includes the cost of both the MySQL node as well as the HeatWave node, and with this setup, customers can run transaction processing analytics as well as machine learning. So the price captures all of it. Whereas with the other vendors, the comparison is only for the analytic queries, right? So if customers wanted to run RDP, you would need to add the cost of that database. Or if customers wanted to run machine learning, you would need to add the cost of that service. Furthermore, with the case of HeatWave, we are quoting pay as you go price, whereas for other vendors like, you know, RedShift, and like, you know, where applicable, we are quoting one year, fully paid upfront cost rate. So it's like, you know, very fair comparison. So in terms of the numbers though, price performance for TPC-DS, we are about 4.8 times better price performance compared to RedShift We are 14.4 times better price performance compared to Snowflake, 13 times better than Google BigQuery, and 15 times better than Synapse. So across the board, we are significantly faster and significantly lower price. And as I said, all of these scripts are available in GitHub for people to drive for themselves. >> Okay, all right, I get it. So I think what you're saying is, you could have said this is what it's going to cost for you to do both analytics and transaction processing on a competitive platform versus what it takes to do that on Oracle MySQL HeatWave, but you're not doing that. You're saying, let's take them head on in their sweet spot of analytics, or OLTP separately and you're saying you still beat them. Okay, so you got this one database service in your cloud that supports transactions and analytics and machine learning. How much do you estimate your saving companies with this integrated approach versus the alternative of kind of what I called upfront, the right tool for the right job, and admittedly having to ETL tools. How can you quantify that? >> Right, so, okay. The numbers I call it, right, at the end of the day in a cloud service price performance is the metric which gives a sense as to how much the customers are going to save. So for instance, for like a TPC-DS workload, if we are 14 times better price performance than Snowflake, it means that our cost is going to be 1/14th for what customers would pay for Snowflake. Now, in addition, in other costs, in terms of migrating the data, having to manage two different databases, having to pay for other service for like, you know, machine learning, that's all extra and that depends upon what tools customers are using or what other services they're using for transaction processing or for machine learning. But these numbers themselves, right, like they're very, very compelling. If we are 1/5th the cost of Redshift, right, or 1/14th of Snowflake, these numbers, like, themselves are very, very compelling. And that's the reason we are seeing so many of these migrations from these databases to MySQL HeatWave. >> Okay, great, thank you. Our last question, in the Q3 earnings call for fiscal 22, Larry Ellison said that "MySQL HeatWave is coming soon on AWS," and that caught a lot of people's attention. That's not like Oracle. I mean, people might say maybe that's an indication that you're not having success moving customers to OCI. So you got to go to other clouds, which by the way I applaud, but any comments on that? >> Yep, this is very much like Oracle. So if you look at one of the big reasons for success of the Oracle database and why Oracle database is the most popular database is because Oracle database runs on all the platforms, and that has been the case from day one. So very akin to that, the idea is that there's a lot of value in MySQL HeatWave, and we want to make sure that we can offer same value to the customers of MySQL running on any cloud, whether it's OCI, whether it's the AWS, or any other cloud. So this shows how confident we are in our offering, and we believe that in other clouds as well, customers will find significant advantage by having a single database, which is much faster and much lower price then what alternatives they currently have. So this shows how confident we are about our products and services. >> Well, that's great, I mean, obviously for you, you're in MySQL group. You love that, right? The more places you can run, the better it is for you, of course, and your customers. Okay, Nipun, we got to leave it there. As always it's great to have you on theCUBE, really appreciate your time. Thanks for coming on and sharing the new innovations. Congratulations on all the progress you're making here. You're doing a great job. >> Thank you, Dave, and thank you for the opportunity. >> All right, and thank you for watching this CUBE conversation with Dave Vellante for theCUBE, your leader in enterprise tech coverage. We'll see you next time. (upbeat music)

(peaceful music) >> Welcome to this cube conversation. I'm Dave Nicholson, and I am delighted to welcome back to the cube, cube veteran, Chen Goldberg, VP of engineering from Google. Chen, welcome back to the cube. >> Hey Dave, super happy to be here. >> Absolutely delighted to have you here. Let's dive right into this conversation. There was a, there was a blog post this week, talking about Google Cloud putting a lot of weight behind this idea of principles for software development. What are those principles and why are they important? >> The three principles that we put in that blog post is open, easy, and transformative. And I think what's really important to recognize with the three principles that those are not new principles, not for Google Cloud, and definitely not for me. I joined Google about, a little bit over five years ago. Right when just Kubernetes started to lead Kubernetes and Google Kubernetes engine team. And we immediately recognized, the idea of open and the importance of flexibility and choice is a foundation to the idea of Kubernetes and portability workloads. But pretty early on, it was clear that it's not enough just to have portability and flexibility because it creates a lot of complexity. So how can we still have that without creating a trade-off or tension for our customers? So really making sure that everything is also easy. You know, and one of the things, I use, I like to say it's not just portability of workloads, but also portability of skills and you achieve that through consistent experience, right? A lot of automation. And when you bring all of those things together, what I love about Google Cloud is that, you know, I'm an infrastructure person. I've always been infrastructure person. And what excites me the most is seeing others take this innovation and, and really empowers developers to make amazing, or, you know, unique ideas, a reality. And that's really the foundation principles for Google Cloud. >> So how does that translate into, from a customer perspective? >> So I would just start with some customer examples, right? Starting from, their perspective. So when we think about open, this is actually part of the, our customers cloud strategy, right? You say cloud, you immediately think only about public cloud, but from our customer perspectives, right? They think about public clouds, right? Most of them have more than one cloud, but they also think about the private cloud, you know, IOT edge and having that openness and flexibility to choose where they can run their workload, is critical. It's critical for them. What I hear mostly is of course, innovation, managing costs, and also making sure that they are not locked out of innovation that happens for example, in any cloud or, or somewhere else. So that's a really a key consideration for our customers when they think about their cloud strategy. The second thing that open matters is that it's really hard to hire talent that is expert and has the right skills. And we see that by using a leveraging open source technologies, it actually makes it easier to our customers to hire the best talent there is in the industry. At one of the previous Google Cloud Next sessions, we had the Loblaw for example, which is the biggest grocery in Canada. And, you know, we were joking on stage, that even though at our hiring for grocery shop, they still can hire the best talent because they are using the best technologies out there in the industry. So that's one, if you think about the importance of easy, I would just call out Western Digital that we've just announced how they decided to standardize on Anthos for their cloud strategy, right? Both of course, Google cloud platform, but On Prem and the Edge. And for them what's important is that when they have all of their amazing developers and operators, how can they provide them reach experience, right. We don't want our developers or operators to spend time on things that can be automated or managed by others. So having a smooth, intuitive experience is really critical. And we we've been announcing some new stuff like a, a Google Cloud deploy and really integrating the entire experience, especially integration for managing, deploying directly to Google Kubernetes engine. And of course, one of my favorite is Jiechi autopilot, which really takes all the goodness with Kubernetes and automatically managing. And then transformative, this is like what I said before, unleashing innovation. And we see Wendy's, for example, right, they want to actually have AI machine learning at run time at their branches, which will allow them to create a new experience for their customers. So this is how we see customers really appreciate these three principles. >> So whenever the subject of Kubernetes and Google comes up, we have to talk Anthos. We're now into what year three of Anthos. How has adoption looked what's the latest on that front? >> That has been really great. We actually have been seeing a 500% growth on the end of Q2 of year over year. And it's important you know to mention that the journey with Anthos is not something new, but something that we have built with our customers when they really love the experience they have on GCP, but needed to innovate elsewhere and not just on Google Cloud. So we've been seeing that, you know, I mentioned the Western Digital, blah, blah, and Wendy's we also have customers like MLB, which is really exciting how they've changed their entire fans' experience using Anthos. And for them, again, it was both the easy part, right? How can I deal with that complexity of having compute and storage everywhere in every one of the stadiums, but also how can I use AI and machine learning, which is unique to Google Cloud in order to create unique experiences for the fans, at real time, of course. >> Yeah. Now you've, touched on this a bit already, if you had to, if you thought about someone reviewing Anthos, their Anthos experience, because we're in the midst of people adopting Anthos and becoming new to Anthos at this point. What does a delighted customers response sound like to you? What is that Yelp review that they would write? If they were telling people we, doubled down on Anthos and we are thrilled because, fill in the blank for a second. >> The first thing that comes to mind is that it works everywhere and the developer experience that comes with it, right? So we have, of course the platform and the infrastructure, but where Anthos really shine is that experience, on top of thinking about all developers and operators that can really work in every environment without paying too much attention to that. And just having that intuitive experience, right? If you go to the Google Cloud console, you see all your clusters, and now we're actually also going to add your VMs into that view, and you can use tools like Anthos config managers, and Anthos service mash to manage your security posture or the configuration in all of those environments. >> So we hear a lot about Multicloud. Multicloud is fantastic, but it sounds like, dealing with the complexity associated with Multicloud is something that Anthos definitely helps with. >> Yes, you know, Google is best with complexity at scale, we've been running containers and really large environments for many years. And some of those principles really, you know, have been fundamental to the way we've started with Kubernetes. So the idea of the declarative intent and automation is really critical in managing large environment and high complexity because in those environments, lots of things can change, but with the declarative approach, you don't have to anticipate everything that is going to change, but you need to know what is your desired state. And that's really one way that Anthos is leveraging the Kubernetes primitives and those ideas to manage different types of environments. In addition to that, it's actually really adding that layer that I talked about before, around the easy can I make sure that my tools, right, if it's, for example, a cloud hybrid build or cloud deploy or Anthos service manager, Anthos config manager, can I make sure that this UI, the CLI the API will be consistent in all of those environments? Can I view in one place, all of my clusters, all of my applications, and this is really where Anthos shines. >> So the cloud data foundation had a, had to get together at the same time as, Google Cloud Next. And there's been a lot of discussion around topics like security. I just like to get your thoughts on, you know, what what's at the forefront of your mind, working in engineering at Google, working in this world where people are deploying Anthos, working in a world where in a multi-cloud environment, you don't necessarily have control as vice president of engineering at Google over what's happening in these other clouds. So what are some of the things that are at the front of your mind is security one of them, what are your thoughts? >> Security is top of mine. Similar to all of our customers and definitely internally. And there are many things that we are very worried about or create some risks. You know, we've just started talking about the secure central supply chain, by building with open source, how can we make sure that everything is secure, right? Then we know what is the contribution that's from the software that we are delivering, how can we make sure that the security posture is portable, right? We talked about workloads portability. We talked about skills portability, and experience, but really I think the next phase for us as an industry is to think about security posture portability. Can I really apply the same policy everywhere and still make sure that I have the right controls in place, which will have to be different depending on the environment, and to make sure that that really is the case. So lots of work around that, and again, talking about the other things we talked about. We talked about open and flexibility, how can you make sure that it's easy? One of the areas that we are very excited about is really around binary authorization, for example. So when you use our tools like cloud build, cloud deploy, artifact, registry, you can get your container images automatically scanned for vulnerabilities and tools like onto service mesh, which allows you to actually manage your security posture, traffic management, who can access what without doing any changes to your applications. >> Fantastic stuff. As we, as we wrap up our time here, do you have any final thoughts on the direction of cloud where we are in the adoption curve? You know, by some estimates, something like 75% of IT is still happening on premises. There've been some announcements coming out of Cloud Next regarding the ability to run all sorts of Google goodness on premises. So we seem to all be acknowledging that we're going to be in a bit of a hybrid world, in addition to a multicloud world, moving forward. Do you want to place any bets on, on when we'll hit the 50, 50 mark or the 25% on premises, 75% cloud mark. What do you think? >> Yeah, I'm not the best gambler to be honest, but I do have a thought about that. I think what's interesting is that customers started to talk, you know, few years back, it was, hey, I have my on-prem environment and I have the cloud. How can they, these two work together. And now what we see our customers talking, you know, they're on premises, their edge is part of their cloud strategy. It's not separated. And I think this is what we'll see more and more of, right? Regardless if this is your private cloud or public cloud, your edge, we would like to have a cloud like experience in that environment and consistency. And of course, we would love to leverage all the goodness of the cloud. If it's like machine learning, AI, and other capabilities, automation, everywhere we go. So I think this is the biggest change we're starting to see. And in addition to that, I think we will see, today everybody are already multicloud cloud, right? If it's recquisitions and just by cause of bottom up culture, you know, people choose different services. And I expect we'll see more strategic thinking about our customers multicloud strategy. Where do I deploy my workloads? What are the benefits? If it's latency, if it's specific services that are available, maybe cost, we'll see the customers becoming more intentional about that and this is really exciting. >> Well Chen, amazing insights. It's obvious why you're a cube veteran. It's obviously why we seek you out for your counsel and guidance on a variety of subjects. Thank you so much for spending time with us today in this cube conversation. With that I'd like to thank you for joining us. Until next time, I'm Dave Nicholson, thanks for joining (peaceful music)

(bright music) >> Surprising many people, including myself, Oracle last year began investing pretty heavily in the MySQL space. Now those investments continue today. Let me give you a brief history. Last December, Oracle made its first HeatWave announcement. Where converged OLTP and OLAP together in a single MySQL database. Now, what wasn't surprising was the approach Oracle took. They leveraged hardware to improve the performance and lower the cost. You see when Oracle acquired Sun more than a decade ago, rather than rely on loosely coupled partnerships with hardware vendors to speed up its databases. Oracle set out on a path to tightly integrate hardware and software innovations using its own in-house engineering. So with his first, MySQL HeatWave announcement, Oracle leaned heavily on developing software on top of an in-memory database technology to create an embedded OLAP capability that eliminates the need for ETL and data from a transaction system into a separate analytics database. Now in doing so, Oracle is taking a similar approach with its MySQL today, as it does for its, or back then, whereas it does for its mainstream Oracle database. And today extends that. And what I mean by that is it's converging capabilities in a single platform. So the argument is this simplifies and accelerates analytics that lowers the costs and allows analytics, things like analytics to be run on data that is more fresh. Now, as many of you know, this is a different strategy than how, for example, an AWS approaches database where it creates purpose-built database services, targeted at specific workloads. These are philosophical design decisions made for a variety of reasons, but it's very clear which direction Oracle is headed in. Today, Oracle continues its HeatWave announcement cadence with a focus on increased automation as well. The company is continuing the trend of using clustering technology to scale out for both performance and capacity. And again, that theme of marrying hardware with software Oracle is also making announcements that focus on security. Hello everyone and welcome to this video exclusive. This is Dave Vellante. We're going to dig into these capabilities, Nipun Agarwal here. He's VP of MySQL HeatWave and advanced development in Oracle. Nipun has been leading the MySQL and HeatWave development effort for nearly a decade. He's got 180 patents to his name about half of which are associated with HeatWave. Nipun, welcome back to the show. Great to have you. >> Thank you, Dave. >> So before we get into the new news, if we could, maybe you could give us all a quick overview of HeatWave again, and what problems you originally set out to solve with it? >> Sure. So HeatWave is a in-memory query accelerator for MySQL. Now, as most people are aware, MySQL was originally designed and optimized for transactional processing. So when customers had the need to run analytics, they would need to extract data from the, MySQL database into another database and run analytics. With MySQL HeatWave, customers get a single database, which can be used both for transactional processing and for analytics. There's no need to move the data from one database to another database and all existing tools and applications, which are compatible with MySQL, continue to work as is. So in-memory query accelerator for MySQL and this is significantly faster than any version of MySQL database. And also it's much faster than specialized databases for analytics. >> Yeah, we're going to talk about that. And so obviously when you made the announcement last December, you had, I'm sure, a core group of, of early customers and beta customers, but then you opened it up to the world. So what was the reaction once you expose that to customers? >> The reaction has been very positive, Dave. So initially we're thinking that they're going to be a lot of customers who are on premise users of MySQL, who are going to migrate to the service. And surely that was the case. But the part which was very interesting and surprising is that we see many customers who are migrating from other cloud vendors or migrating from other cloud services to MySQL HeatWave. And most notably the biggest number of migrations we are seeing are from AWS Aurora and AWS RDS. >> Interesting. Okay. I wonder if you've got other feedback you're obviously responding in a pretty, pretty fast cadence here, you know, seven, eight month cadence. What are the feedback that you get, were there gaps that customers wanted you to to close? >> Sure. Yes. So as customers starting moving in to HeatWave they found that HeatWave is much faster, much cheaper. And when it's so much faster, they told us that there are some classes of queries, which could just not run earlier, which they can now with HeatWave. So it makes the applications richer because they can write new classes of queries with which they could not in the past. But in terms of the feedback or enhancement requests we got, I would say they will categorize the number one was automation. There've been customers move their database from on-premise to the cloud. They expect more automation. So that was the number one thing. The second thing was people wanted the ability to run analytics on larger sizes of data with MySQL HeatWave because they like what they saw and they wanted us to increase the data size limit, which can be processed by HeatWave. Third one was they wanted more classes of queries to be accessed with HeatWave. Initially, when we went out, HeatWave was designed to be an accelerator for analytic queries but more and more customers started seeing the benefit of beyond just analytics. More towards mixed workloads. So that was a third request. And then finally they wanted us to scale to a larger cluster size. And that's what we have done over the last several months that incorporating this feedback, which you've gotten from customers. >> So you're addressing those, those, those gaps. And thank you for sharing that with us. I got the press release here. I wonder if we could kind of go through these. Let's start with AutoPilot, you know, what's, what's that all about? What's different about AutoPilot? >> That's right. So MySQL AutoPilot provides machine learning based automation. So the first difference is that not only is it automating things, where and as a cloud provider as a service provider, we feel there are a lot of opportunities for us to automate, but the big difference about the approach we've taken with MySQL AutoPilot is that it's all driven based on the data and the queries. It's machine learning based automation. That's the first aspect. The second thing is this is all done natively in the server, right? So we are enhancing the, MySQL engine. We're enhancing the HeatWave engine and that's where all the logic and all the processing resides. In order to do this, we have had to collect new kinds of data. So for instance, in the past, people would collect statistics, which are based on just the data. Now we also collect statistics based on queries, for instance, what is the compilation time? What is the execution time? And we have augmented this with new machine learning models. And finally we have made a lot of innovations, a lot of inventions in the process where we collect data in a smart way. We process data in a smart way and the machine learning models we are talking about, also have a lot of innovation. And that's what gives us an edge over what other vendors may try to do. >> Yeah. I mean, I'm just, again, I'm looking at this meat, this pretty meaty preference, press release. Auto-provisioning, auto parallel load, auto data placement, auto encoding, auto error, auto recovery, auto scheduling, and you know, using a lot of, you know, computer science techniques that are well-known, first in first out, auto change propagation. So really focusing on, on driving that automation for customers. The other piece of it that struck me, and I said this in my intro is, you know, using clustering technology, clustering technology has been around for a long time as, as in-memory database, but applying it and integrating it. My sense is that's really about scale and performance and taking advantage of course, cloud being able to drive that scale instantaneously, but talk about scale a little bit in your philosophy there and why so much emphasis on scalability? >> Right. So what we want to do is to provide the fastest engine for running analytics. And that's why we do the processing in memory. Now, one of the issues with in process, in-memory processing is that the amount of data which you're processing has to reside in memory. So when we went out in the version one, given the footprint of the MySQL customers we spoke to, we thought 12 terabytes of processing at any given point in time, would be adequate. In the very first month, we got feedback that customers wanted us to process larger amounts of data with HeatWave, because they really like what they saw and they wanted us to increase. So if we have increased deployment from 12 terabytes to 32 terabytes and in order to do so, we now have a HeatWave cluster, which can be up to 64 nodes. That's one aspect on the query processing side. Now to answer the question as to why so much of an emphasis it's because this is something which is extremely difficult to do in query processing that as you scale the size of the cluster, the kind of algorithms, the kind of techniques you have to use so that you achieve a very high efficiency with a very large cluster. These are things which are easy to do, because what we want to make sure is that as customers have the need for like, like a processing larger amount of data, one of the big benefits customers get by using a cloud as opposed to on-premise is that they don't need to worry about provisioning gear ahead of time. So if they have more data with the cloud, they should be able to like process pool data easily. But when they process more data, they should expect the same kind of performance. So same kind of efficiency on a larger data size, similar to a smaller data size. And this is something traditionally other database vendors have struggled to provide. So this is a important problem. This is a tough engineering problem. And that's why a lot of emphasis on this to make sure that we provide our customers with very high efficiency of processing as they increase the size of the data. >> You're saying, traditionally, you'll get diminishing returns as you scale. So sort of as, as the volume grows, you're not able to take as much advantage or you're less efficient. And you're saying you've, you've largely solved that problem you're able to use. I mean, people always talk about scaling linearly and I'm always skeptical, but, but you're saying, especially in database, that's been a challenge, but you're, you're saying you've solved that problem largely. >> Right. What I would say is that we have a system which is very efficient, more efficient than like, you know, any of the database we are aware of. So as you said, perfect scaling is hard with you, right? I mean, that's a critical limit of scale factor one. That's very hard to achieve. We are now close to 90% efficiency for n2n queries. This is not for primitives. This is for n2n queries, both on industry benchmarks, as well as real world customer workloads. So this 90% efficiency we believe is very good and higher than what many of the vendors provide. >> Yeah. Right. So you're not, not just primitives the whole end to end cycle. I think 0.89, I think was the number that I, that I saw just to be technically correct there, but that's pretty, pretty good. Now let's talk about the benchmarks. It wouldn't be an Oracle announcement with some, some benchmarks. So you laid out today in your announcement, some, some pretty outstanding performance and price performance numbers, particularly you called out it's, it's. I feel like it's a badge of honor. If, if Oracle calls me out, I feel like I'm doing well. You called out Snowflake and Amazons. So maybe you could go over those benchmark results that we could peel the onion on that a little bit. >> Right. So the first thing to realize is that we want to have benchmarks, which are credible, right? So it's not the case that we have taken some specific unique workloads where HeatWave shines. That's not the case. What we did was we took a industry standard benchmark, which is like, you know, TPC-H. And furthermore, we had a third party, independent firm do this comparison. So let's first compare with Snowflake. On a 10 terabyte TPC-H benchmark HeatWave is seven times faster and one fifth the cost. So with this, it is 35 times better price performance compared to Snowflake, right? So seven times faster than Snowflake and one fifth of the cost. So HeatWave is 35 times better price performance compared to Snowflake. Not just that, Snowflake only does analytics, whereas MySQL HeatWave does both transactional processing and analytics. It's not a specialized database, MySQL HeatWave is a general purpose database, which can do both OLTP analytics whereas Snowflake can only do analytics. So to be 35 times more efficient than a database service, which is specialized only for one case, which is analytics, we think it's pretty good. So that's a comparison with Snowflake. >> So that's, that's you're using, I presume you got to be using list prices for that, obviously. >> That is correct. >> So there's discounts, let's put that into context of maybe 35 X better. You're not going to get that kind of discount. I wouldn't think. >> That is correct. >> Okay. What about Redshift? Aqua for Redshift has gained a lot of momentum in the marketplace. How do you compare against that? >> Right. So we did a comparison with Redshift, Aqua, same benchmark, 10 terabytes, TPC-H. And again, this was done by a third party. Here, HeatWave is six and a half times faster at half the cost. So HeatWave is 13 times better price performance compared to Redshift Aqua. And the same thing for Redshift. It's a specialized database only for analytics. So customers need to have two databases, one for transaction processing, one for analytics, with Redshift. Whereas with MySQL HeatWave, it's a single database for both. And it is so much faster than Redshift. That again, we feel is a pretty remarkable. >> Now, you mentioned earlier, but you're not, you're obviously I presume not, you're not cheating here. You're not including the cost of the transaction processing data store. Right? We're, we're, we're ignoring that for a minute. Ignoring that you got to, you know, move data, ETL, we're just talking about like the like, is that correct? >> Right. This is extremely fair and extremely generous comparison. Not only are we not including the cost of the source OLTP database, the cost in the case of the Redshift I'm talking about is the cost for one year paid full upfront. So this is a best pricing. A customer can get for one year subscription with Redshift. Whereas when I'm talking about HeatWave, this is the pay as you go price. And the third aspect is, this is Redshift when it is completely fully optimized. I don't think anyone else can get much better numbers on Redshift than we have. Right? So fully optimized configuration of Redshift looking at the one year pre-pay cost of Redshift and not including the source database. >> Okay. And then speaking of transaction processing database, what about Aurora? You mentioned earlier that that you're seeing a lot of migration from Aurora. Can you add some color to that? >> Right. And this is a very interesting question in a, it was a very interesting observation for us when we did the launch back in December, we had numbers on four terabytes, TPC-H with Aurora. So if you look at the same benchmark, four terabytes TPC-H HeatWave is 1,400 times faster than Aurora at half the cost, which makes it 2,800 times better price performance compared to Aurora. So very good number. What we have found is that many customers who are running on Aurora started migrating to HeatWave, and these customers had a mix of transaction processing and analytics, and the data sizes are much smaller. Even those customers found that there was a significant improvement in performance and reduction in costs when they migrated to HeatWave. In the announcement today, many of the references are those class of customers. So for that, we decided to choose another benchmark, which is called CH-benchmark on a much smaller data size. And even for that, even for mixed workloads, we find that HeatWave is 18 times faster, provides over a hundred times higher throughput than Aurora at 42% of the cost. So in terms of price performance gain, it is much, much better than Aurora, even for mixed workloads. And then if you consider a pure OLTP assume you have an application, which has only OLTP, which by the way is like, you know, a very uncommon scenario, but even if that were be the case, in that case for pure OLTP only, MySQL HeatWave is at par with Aurora, with respect to performance, but MySQL HeatWave costs 42% of Aurora. So the point is that in the whole spectrum, pure OLTP, mixed workloads or analytics, MySQL HeatWave is going to be fraction of the cost of a Aurora. And depending upon your query workload, your acceleration can be anywhere from 14,000 times to 18 times faster. >> That's interesting. I mean, you've been at this for the better part of a decade, because my sense is that HeatWave is all about OLAP. And that's really where you've put the majority, if not all of the innovation. But you're saying just coming into December's announcement, you were at par with a, in a, in a, in a, in a rare, but, but hypothetical OLTP workload. >> That is correct. >> Yeah. >> Well, you know, I got to push you still on this because a lot of times these benchmarks are a function of the skills of the individuals performing these tests, right? So can I, if I want to run them myself, you know, if you publish these benchmarks, what if a customer wants to replicate these tests and try to see if they can tune up, you know, Redshift better than you guys did? >> Sure. So I'll say a couple of things. One is all the numbers which I'm talking about both for Redshift and Snowflake were done by a third party firm, but all the numbers we is talking about, TPC-H, as well has CH-benchmark. All the scripts are published on GitHub. So anyone is very welcome. In fact, we encourage customers to go and try it for themselves, and they will find that the numbers are absolutely as advertised. In fact, we had couple of companies like in the last several months who went to GitHub, they downloaded our TPCH scripts and they reported that the performance numbers they were seeing with HeatWave were actually better than we had published back in December. And the reason was that since December we had new code, which was running. So our numbers were actually better than advertised. So all the benchmarks are published. They are all available on GitHub. You can go to the HeatWave website on oracle.com and get the link for it. And we welcome anyone to come and try these numbers for themselves. >> All right. Good. Great. Thank you for that. Now you mentioned earlier that you were somewhat surprised, not surprised that you got customers migrating from on-prem databases, but you also saw migration from other clouds. How do you expect the trend with regard to this new announcement? Do you have any sense as to how that's going to go? >> Right. So one of the big changes from December to now is that we have now focused quite a bit on mixed workloads. So in the past, in December, when we first went out, HeatWave was designed primarily for analytics. Now, what we have found is that there's a very large class of customers who have mixed workloads and who also have smaller data sizes. We now have introduced a lot of technology, including things like auto scheduling, definitely improvement in performance, where MySQL HeatWave is a very superior solution compared to Aurora or other databases out there, both in terms of performance as well as price for these mixed workloads and better latency, better throughput, lower costs. So we expect this trend of migration to MySQL HeatWave, to accelerate. So we are seeing customers migrate from Azure. We are seeing customers migrate from GCP and by far the number one migrations we are seeing are from AWS. So I think based on the new features and technologies, we have announced today, this migration is going to accelerate. >> All right, last question. So I said earlier, it's, it's, it seems like you're applying what are generally well understood and proven technologies, like in-memory, you like clustering to solve these problems. And I think about, you know, the, the things that you're doing, and I wonder, you know, I mean, these things have been around for awhile and why has this type of approach not been introduced by others previously? >> Right. Well, so the main thing is it takes time, right? That we designed HeatWave from the ground up for the cloud. And as a part of that, we had to invent new algorithms for distributed query processing for the cloud. We put in the hooks for machine learning processes. We're sealing processing right from the ground up. So this has taken us close to a decade. It's been hundreds of person-years of investment, dozens of patents which have gone in. Another aspect is it takes talent from different areas. So we have like, you know, people working in distributed query processing, we have people who have a lot of like background in machine learning. And then given that we are like the custodians of the MySQL database, we have a very rich set of customers we can reach out to, to get feedback from them as to what are the pinpoints. So culmination of these trends, which we have this talent, the customer base and the time, so we spent almost close to a decade to make this thing work. So that's what it takes. It takes time, patience, patience, and talent. >> A lot of software innovation bringing together, as I said, that hardware and software strategy. Very interesting. Nipun, thanks so much. I appreciate your, your insights and coming on this video exclusive. >> Thank you, Dave. Thank you for the opportunity. >> My pleasure. And thank you for watching everybody. This is Dave Vellante for theCUBE. We'll see you next time. (bright music)

>>from New York. It's the Q covering Escape. 19. >>Welcome back to the Cube coverage here in New York City for the first inaugural multi cloud conference called Escape, where in New York City was staying in New York, were not escaping from New York were in New York. It's all about multi Cloud, and we're here. Lisa Marie Nancy, developer advocate for Port Works, and Eric Conn, vice president of Products Works. Welcome back. Q. >>Thank you, John. Good to see >>you guys. So, um, whenever the first inaugural of anything, we want to get into it and find out why. Multi clouds certainly been kicked around. People have multiple clouds, but is there really multi clouding going on? So this seems to be the theme here about setting the foundation, architecture and data of the two kind of consistent themes. What shared guys take Eric, What's your take on this multi cloud trend? Yeah, >>I think it's something we've all been actively watching for a couple years, and suddenly it is becoming the thing right? So every we just had ah, customer event back in Europe last week, and every customer there is already running multi cloud. It's always something on their consideration. So there's definitely it's not just a discussion topic. It's now becoming a practical reality. So this event's been perfect because it's both the sense of what are people doing, What are they trying to achieve and also the business sense. So it's definitely something that is not necessarily mainstream, but it's becoming much more how they're thinking about building all their applications. Going forward, >>you know, you have almost two camps in the world. Want to get your thoughts on this guy's Because, like you have cloud native and people that are cloud native, they love it. They born the cloud that get it. Everything's cracking along. The developers air on Micro Service's They're agile train with their own micro service's. Then you got the hybrid I t. Trying to be hybrid developer, right? So you kind of have to markets coming together. So to me, I see multi cloud as kind of a combination of old legacy Data center types of I t with cloud native, not just ops and dead. But how about like trying to build developer teams inside enterprises? This seems to be a big trend, and multi club fits into that because now the reality is that I got azure. I got Amazon. Well, let's take a step back and think about the architecture. What's the foundation? So that to me, is more my opinion. But I want to get your thoughts and reactions that because if it's true, that means some new thinking has to come around around. What's the architecture? What are you trying to do? What's the workloads behavior outcome look like? What's the work flows? So there's a whole nother set of conversations that happened. >>I agree. I think the thing that the fight out there right now that we want to make mainstream is that it's a platform choice, and that's the best way to go forward. So it's still an active debate. But the idea could be I want to do multi club, but I'm gonna lock myself into the Cloud Service is if that's the intent or that's the design architecture pattern. You're really not gonna achieve the goals we all set out to do right, So in some ways we have to design ourselves or have the architecture that will let us achieve the business schools that were really going for and that really means from our perspective or from a port works perspective. There's a platform team. That platform team should run all the applications and do so in a multi cloud first design pattern. And so from that perspective, that's what we're doing from a data plan perspective. And that's what we do with Kubernetes etcetera. So from that idea going forward, what we're seeing is that customers do want to build a platform team, have that as the architecture pattern, and that's what we think is going to be the winning strategy. >>Thank you. Also, when you have the definition of cod you have to incorporate, just like with hybrid I t the legacy applications. And we saw that you throughout the years those crucial applications, as we call them People don't always want them to refer to his legacy. But those are crucial applications, and our customers were definitely thinking about how we're gonna run those and where is the right places it on Prem. We're seeing that a lot too. So I think when we talk about multi cloud, we also talk about what What is in your legacy? What is it? Yeah, I >>like I mean I use legacy. I think it's a great word because I think it really puts nail in the coffin of that old way because remember, if you think about some of the large enterprises, these legacy applications, they've been optimized for hardware and optimize their full stack. They've been build up from the ground up, so they're cool. They're running stuff, but it doesn't always translate to see a new platform designed point. So how do you mean Containers is great fit for their Cooper names. Obviously, you know is the answer. We you guys see that as well, but okay, I can keep that and still get this design point. So I guess what I want to ask you guys, as you guys are digging into some of the customer facing conversations, what are they talking about? The day talking about? The platform? Specifically? Certainly, on the security side, we're seeing everyone running away from buying tools to thinking about platforms. What's the conversation like on the cloud side >>way? Did a talk are multiplied for real talk at Barcelona? Q. Khan put your X three on Sudden. Andrew named it for reals of Izzy, but we really wanted to talk about multiplied in the real world. And when we said show of hands in Barcelona, who's running multi cloud? It was very, very few. And this was in, what, five months? Four months ago? Whereas maybe our customers are just really super advanced because of our 100 plus customers. At four words, we Eric is right. A lot of them are already running multi cloud or if not their plan, in the planning stage right now. So even in the last +56 months, this has become a reality. And we're big fans of communities. I don't know if you know Eric was the first product manager for Pernetti. Hey, he's too shy to say it on Dhe. So yeah, and we think, you know, and criminal justice to be the answer to making all They caught a reality right now. >>Well, I want to get back into G, K, E and Cooper. Very notable historic moment. So congratulations, But to your point about multi cloud, it's interesting because, you know, having multiple clouds means things, right? So, for instance, if I upgrade to office 3 65 and I kill my exchange server, I'm essentially running azure by their definition. If I'm building it, stack on AWS. I'm a native, this customer. Let's just say I want to do some tensorflow or play with big table or spanner on Google. Now >>we have three >>clouds now they're not. So they have work clothes, specific objectives. I am totally no problem. I see that like for the progressive customers, some legacy be to be people who like maybe they put their toe in the cloud. But anyone doing meaningful cloud probably has multiple clouds. But that's workload driven when you get into tying them together and is interesting. And I think that's where I think you guys have a great opportunity in this community because if open source convene the gateway to minimize the lock in and when I say lock and I mean like locking them propriety respect if his value their great use it. But if I want to move my data out of the Amazon, >>you brought up so many good points. So let me go through a few and Lisa jumping. I feel like locking. People don't wanna be locked >>in at the infrastructure level. So, like you said, if >>there's value at the higher levels of Stack, and it helps me do my business faster. That's an okay thing to exchange, but it is just locked in and it's not doing anything. They're that's not equal exchange, right, So there's definitely a move from infrastructure up the platform. So locking in >>infrastructure is what people are trying to move away from. >>From what we see from the perspective of legacy, there is a lot of things happening in industry that's pretty exciting of how legacy will also start to running containers. And I'm sure you've seen that. But containers being the basis you could run a BM as well. And so that will mean a lot for in terms of how V EMS can start >>to be matched by orchestrators like kubernetes. So that is another movement for legacy, and I wanted to acknowledge that point >>now, in terms of the patterns, there are definitely applications, like a hybrid pattern where connect the car has to upload all its data once it docks into its location and move it to the data center. So there are patterns where the workflow does move the ups are the application data between on Prem into a public cloud, for instance, and then coming back from that your trip with Lisa. There is also examples where regulations require companies to enterprise is to be able to move to another cloud in a reasonable time frame. So there's definitely a notion of Multi Cloud is both an architectural design pattern. But it's also a sourcing strategy, and that sourcing strategy is more regulation type o. R. In terms of not being locked in. And that's where I'm saying it's all those things. I'd >>love to get your thoughts on this because I like where you're going with this because it kind of takes it to a level of okay, standardization, kubernetes nights, containers, everyone knows what that is. But then you start talking about a P I gateways, for instance, right? So if I'm a car and I have five different gateways on my device, I ot devices or I have multiple vendors dealing with control playing data that could be problematic. I gotta do something like that. So I'm starting. Envision them? I just made that news case up, but my point is is that you need some standards. So on the a p I side was seeing some trends there. One saying, Okay, here's my stuff. I'll just pass parameters with FBI State and stateless are two dynamics. What do you make of that? What, What? What has to happen next to get to that next level of happiness and goodness? Because Bernays, who's got it, got it there, >>right? I feel that next level. I feel like in Lisa, Please jump. And I feel like from automation perspective, Kubernetes has done that from a P I gateway. And what has to happen next. There's still a lot of easy use that isn't solved right. There's probably tons of opportunities out there to build a much better user experience, both from the operations point of view and from what I'm trying to do is an intense because what people aren't gonna automate right now is the intent. They automate a lot of the infrastructure manual tasks, and that's goodness. But from how I docked my application, how the application did it gets moved. We're still at the point of making policy driven, easy to use, and I think there's a lot of opportunities for everyone to get better there. That's like low priority loving fruitcake manual stuff >>and communities was really good at the local food. That's a really use case that you brought up. Really. People were looking at the data now and when you're talking about persistent mean kun is his great for stateless, but for state full really crucial data. So that's where we really come in. And a number of other companies in the cloud native storage ecosystem come in and have really fought through this problem and that data management problem. That's where this platform that Aaron was talking to that >>state problem. Talk about your company. I want to go back to to, um, Google Days. Um, many war stories around kubernetes will have the same fate as map reduce. Yeah, the debates internally at Google. What do we do with it? You guys made the good call. Congratulations on doing that. What was it like to be early on? Because you already had large scale. You were already had. Borg already had all these things in place. Um, it wasn't like there was what was, >>Well, a few things l say one is It was intense, right? It was intense in the sense that amazing amount of intelligence amazing amount of intent, and right back then a lot of things were still undecided, right? We're still looking at how containers or package we're still looking at how infrastructure kit run and a lot of service is were still being rolled out. So what it really meant is howto build something that people want to build, something that people want to run with you and how to build an ecosystem community. A lot of that the community got was done very well, right? You have to give credit to things like the Sig. A lot of things like how people like advocates like Lisa had gone out and made it part of what they're doing. And that's important, right? Every ecosystem needs to have those advocates, and that's what's going well, a cz ah flip side. I think there's a lot of things where way always look back, in which we could have done a few things differently. But that's a different story for different. Today >>I will come back in the studio Palop of that. I gotta ask you now that you're outside. Google was a culture shock. Oh my God! People actually provisioning software provisioning data center culture shock when there's a little >>bit of culture shock. One thing is, and the funny thing is coming full circle in communities now, is that the idea of an application? Right? The idea of what is an application eyes, something that feels very comfortable to a lot of legacy traditional. I wanna use traditional applications, but the moment you're you've spent so much time incriminates and you say, What's the application? It became a very hard thing, and I used to have a lot of academic debates. Where is saying there is no application? It's It's a soup of resources and such. So that was a hard thing. But funny thing is covered, as is now coming out with definitions around application, and Microsoft announced a few things in that area to so there are things that are coming full circle, but that just shows how the movement has changed and how things are becoming in some ways meeting each other halfway. >>Talk about the company, what you guys are doing. Take a moment. Explain in context to multi cloud. We're here. Port works. What's the platform? It's a product. What's the value proposition? What's the state of the company. >>So the companies? Uh well, well, it's grown from early days when Lisa and I joined where we're probably a handful now. We're in four or five cities. Geography ease over 100 people over 150 customers and there. It's been a lot of enterprises that are saying, like, How do I take this pattern of doing containers and micro service is And how do I run it with my mission? Critical business crinkle workloads. And at that point, there is no mission critical business critical workload that isn't stable so suddenly they're trying to say, How do I run These applications and containers and data have different life cycles. So what they're really looking for is a data plane that works with the control planes and how controlled planes are changing the behavior. So a lot of our technology and a lot of our product innovation has been around both the data plane but a storage control plane that integrates with a computer controlled plane. So I know we like to talk about one control plane. There's actually multiple control planes, and you mentioned security, right? If I look at how applications are running way after now securely access for applications, and it's no longer have access to the data. Before I get to use it, you have to now start to do things like J W. T. Or much higher level bearer tokens to say, I know how to access this application for this life cycle for this use case and get that kind of resiliency. So it's really around having that storage. More complexity absolutely need abstraction >>layers, and you got compute. Look, leading work there. But you gotta have >>software to do it from a poor works perspective. Our products entirely software right down loans and runs using kubernetes. And so the point here is we make remarries able to run all the staple workloads out of the box using the same comment control plane, which is communities. So that's the experiences that we really want to make it so that Dev Ops teams can run anywhere close. And that's that's in some ways been part of the mix. Lisa, >>we've been covering Dev up, going back to 2010. Remember when I first was hanging around San Francisco 2008 joint was coming out the woodwork and all that early days and you look at the journey of how infrastructures code We talked about that in 2008 and now we'll get 11 years later. Look at the advancements you've been through this now The tipping point. It's just seems like this wave is big and people are on it. The developers air getting it. It's a modern renaissance of application developers, and the enterprises it's happening in the enterprise is not just like the nerds Tier one, the Alfa Geeks or >>the Cloud native. It's happening in the >>everyone's on board this time, and you and I have been in the trenches in the early stages of many open source projects. And I think with with kubernetes Arab reference of community earlier, I'm super proud to be running the world's largest CNC F for user group. And it's a great community, a diverse community, super smart people. One of my favorite things about working for works is we have some really smart engineers that have figured out what companies want, how to solve problems, and then we'll go creative. It'll open source projects. We created a project called autopilot, really largely because one of our customers, every who's in the G s space and who's running just incredible application. You can google it and see what the work they're doing. It's all there publicly, Onda We built, you know, we built an open source project for them to help them get the most out of kubernetes. We can say so. There's a lot of people in the community system doing that. How can we make communities better halfway make commitments, enterprise grade and not take years to do that? Like some of the other open source projects that we worked on, it took. So it's a super exciting time to be here, >>and open source is growing so fast now. I mean, just think about how these projects being structured. Maur and Maur projects are coming online and user price, but a lot more vendor driven projects to use be mostly and used, but now you have a lot of vendors who are users. So the line is blurring between Bender User in Open source is really fascinating. >>Well, you look at the look of the landscape on the C N. C f. You know the website. I mean, it's what 400 that are already on board. It's really important. >>They don't have enough speaking slasher with >>right. I know, and it's just it. It is users and vendors. Everybody's in this community together. It's one of things that makes it super exciting. And it it's how we know this is This was the right choice for us to base this on communities because that's what everybody, you guys >>are practically neighbors. So we're looking for seeing the studio. Palo Alto Eric, I want to ask you one final question on the product side. Road map. What you guys thinking As Kubernetes goes, the next level state, a lot of micro service is observe abilities becoming a key part of it, Obviously, automation, configuration management things are developing fast. State. What's the What's the road map for you guys? >>For us, it's been always about howto handle the mission critical and make that application run seamlessly. And then now we've done a lot of portability. So disaster recovery has been one of the biggest things for us is that customers are saying, How do I do a hybrid pattern back to your earlier question of running on Prem and in Public Cloud and do a d. R. Pale over into some of the things at least, is pointing out that we're announcing soon is non series autopilot in the idea, automatically managing applications scale from a volume capacity. And then we're actually going to start moving a lot more into some of the what you do with data after the life cycle in terms of backup and retention. So those are the things that everyone's been pushing us and the customers are all asking for. You >>know, I think data they were back in recovery is interesting. I think that's going to change radically. And I think we look at the trend of how yeah, data backup and recovery was built. It was built because of disruption of business, floods, our gains, data center failure. But I think the biggest disruptions ransomware that malware. So security is now a active disruptor. So it's not like it after the hey, if we ever have, ah, fire, we can always roll back. So you're infected and you're just rolling back infected code. That's a ransomware dream. That's what's going on. So I think data protection it needs to be >>redefined. What do you think? Absolutely. I think there's a notion of How do I get last week's data last month? And then oftentimes customers will say, If I have a piece of data volume and I suddenly have to delete it, I still need to have some record of that action for a long time, right? So those are the kinds of things that are happening and his crew bearnaise and everything. It gets changed. Suddenly. The important part is not what was just that one pot it becomes. How do I reconstruct everything? What action is not one thing. It's everywhere. That's right and protected all through the platform. If it was a platform decision, it's not some the cattlemen on the side. You can't be a single lap. It has to be entire solution. And it has to handle things like, Where do you come from? Where is it allowed to go? And you guys have that philosophy. We absolutely, and it's based on the enterprises that are adopting port works and saying, Hey, this is my romance. I'm basing it on Kubernetes. You're my date a partner. We make it happen. >>This speaks to your point of why the enterprise is in. The vendors jumped in this is what people care about Security. How do you solve this last mile problem? Storage. Networking. How do you plug those holes in Kubernetes? Because that is crucial to our >>personal private moment. Victory moment for me personally, was been a big fan of Cuban is absolutely, you know, for years. Then there were created, talked about one. The moments that got me that was really kind of a personal, heartfelt moment was enterprise buyer. And, you know, the whole mindset in the Enterprise has always been You gotta kill the old to bring in the new. And so there's always been that tension of a you know, the shiny new toy from Silicon Valley or whatever. You know, I'm not gonna just trash this and have a migration za paying that. But for I t, they don't want that to do that. They hate doing migrations, but with containers and kubernetes that could actually they don't to end of life to bring in the new project. They can do it on their own timetable or keep it around. So that took a lot of air out of the tension in on the I t. Side because they say great I can deal with the lifecycle management, my app on my own terms and go play with Cloud native and said to me, that's like that was to be like, Okay, there it is. That was validation. That means this Israel because now they can innovate without compromising. >>I think so. And I think some of that has been how the ecosystems embrace it, right. So now it's becoming all the vendors are saying my internal stack is also based on community. So even if you as an application owner or not realizing it, you're gonna take a B M next year and you're gonna run it and it's gonna be back by something like awesome. Lisa >>Marie Nappy Eric on Thank you for coming on Port Works Hot start of multiple cities Kubernetes big developer Project Open Source. Talking about multi cloud here at the inaugural Multi cloud conference in New York City. It's the Cube Courage of escape. 2019. I'm John Period. Thanks for watching

>>from New York. It's the Q covering escape. 19. Hey, welcome back to the Cube coverage here in New York City for the first inaugural multi cloud conference called Escape. We're in New York City. Was staying in New York, were not escapee from New York were in New York. So about Multi Cloud. And we're here. Lisa Marie Nancy, developer advocate for report works, and Eric Conn, vice president of products. Welcome back with you. >>Thank you, John. >>Good to see you guys. So whenever the first inaugural of anything, we want to get into it and find out why. Multiplied certainly been kicked around. People have multiple clouds, but is there really multi clouding going on? So this seems to be the theme here about setting the foundation, architecture and data to kind of consistent themes. What's your guys take? Eric, What's your take on this multi cloud trend? >>Yeah, I think it's something we've all been actively watching for a couple years, and suddenly it is becoming the thing right? So every we just had a customer event back in Europe last week, and every customer there is already running multi cloud. It's always something on their consideration. So there's definitely it's not just a discussion topic. It's now becoming a practical reality. So this event's been perfect because it's both the sense of what are people doing, What are they trying to achieve and also the business sense. So it's definitely something that is not necessarily mainstream, but it's becoming much more how they're thinking about building all their applications Going forward. >>You know, you have almost two camps in the world to get your thoughts on this guy's because like you have a cloud native people that are cloud needed, they love it. They're born in the cloud that get it. Everything's bringing along. The developers are on micro service's They're agile train with their own micro service is when you got the hybrid. I t trying to be hybrid developer, right? So you kind of have to markets coming together. So to me, Essie multi Cloud as a combination of old legacy Data Center types of I t with cloud native not just optioned. It was all about trying to build developer teams inside enterprises. This seems to be a big trend, and multi cloud fits into them because now the reality is that I got azure, I got Amazon. Well, let's take a step back and think about the architecture. What's the foundation? So that to me, is more my opinion. But I want to get your thoughts and reactions that because if it's true, that means some new thinking has to come around around. What's the architecture, What we're trying to do? What's the workloads behavior outcome look like? What's the workflow? So there's a whole nother set of conversations. >>Yeah, that happened. I agree. I think the thing that the fight out there right now that we want to make mainstream is that it's a platform choice, and that's the best way to go forward. So it's still an active debate. But the idea could be I want to do multi club, but I'm gonna lock myself into the Cloud Service is if that's the intent or that's the design architecture pattern. You're really not gonna achieve the goals we all set out to do right, So in some ways we have to design ourselves or have the architecture that will let us achieve the business schools that were really going for and that really means from our perspective or from a port Works perspective. There's a platform team. That platform team should run all the applications and do so in a multi cloud first design pattern. And so from that perspective, that's what we're doing from a data plane perspective. And that's what we do with Kubernetes etcetera. So from that idea going forward, what we're seeing is that customers do want to build a platform team, have that as the architecture pattern, and that's what we think is going to be the winning strategy. >>Thank you. Also, when you have the death definition of cod, you have to incorporate, just like with hybrid a teeny the legacy applications. And we saw that you throughout the years those crucial applications, as we call them. People don't always want them to refer to his legacy. But those are crucial applications, and our customers were definitely thinking about how we're gonna run those and where is the right places it on Prem. We're seeing that a lot, too. So I think when we talk about multi cloud, we also talk about what what is in your legacy? What is your name? I mean, I >>like you use legacy. I think it's a great word because I think it really nail the coffin of that old way because remember, if you think about some of the large enterprises these legacy applications didn't optimized for harden optimize their full stack builds up from the ground up. So they're cool. They're running stuff, but it doesn't translate to see a new platform design point. So how do you continue? This is a great fit for that, cos obviously is the answer. You guys see that? Well, okay, I can keep that and still get this design point. So I guess what I want to ask you guys, as you guys are digging into some of the customer facing conversations, what are they talking about? The day talking about? The platform? Specifically? Certainly on the security side, we're seeing everyone running away from buying tools were thinking about platform. What's the conversation like on the outside >>before your way? Did a talk are multiplied for real talk at Barcelona. Q. Khan put your X three on son. Andrew named it for reals of busy, but we really wanted to talk about multiplied in the real world. And when we said show of hands in Barcelona, who's running multi pod. It was very, very few. And this was in, what, five months? Four months ago? Whereas maybe our customers are just really super advanced because of our 100 plus customers. At four words, we Eric is right. A lot of them are already running multi cloud or if not their plan, in the planning stage right now. So even in the last +56 months, this has become a reality. And we're big fans of your vanities. I don't know if you know, Eric was the first product manager for Pernetti. T o k. He's too shy to say it on dhe. So yeah, and we think, you know, And when it does seem to be the answer to making all they caught a reality right now. >>Well, I want to get back into G k e. And Cooper was very notable historical. So congratulations. But your point about multi cloud is interesting because, you know, having multiple clouds means things, right? So, for instance, if I upgrade to office 3 65 and I killed my exchange server, I'm essentially running azure by their definition. If I'm building a stack I need of us, I'm a Navy best customer. Let's just say I want to do some tensorflow or play with big table. Are spanner on Google now? I have three clouds. No, they're not saying they have worked low specific objectives. I am totally no problem. I see that all the progressive customers, some legacy. I need to be people like maybe they put their tone a file. But anyone doing meaningful cloud probably has multiple clouds, but that's workload driven when you get into tying them together. It's interesting. I think that's where I think you guys have a great opportunity in this community because it open source convene the gateway to minimize the locket. What locket? I mean, like locking the surprise respect if its value, their great use it. But if I want to move my data out of the Amazon, >>you brought up so many good points. So let me go through a few and Lisa jumping. I feel like locking. People don't wanna be locked in at the infrastructure level. So, like you said, if there's value at the higher levels of Stack and it helps me do my business faster, that's an okay thing to exchange. But if it's just locked in and it's not doing anything. They're that's not equal exchange, right? So there's definitely a move from infrastructure up the platform. So locking in infrastructure is what people are trying to move away from. From what we see from the perspective of legacy, there is a lot of things happening in industry that's pretty exciting. How legacy will also start to run in containers, and I'm sure you've seen that. But containers being the basis you could run a BM as well. And so that will mean a lot for in terms of how VM skin start to be matched by orchestrators like kubernetes. So that is another movement for legacy, and I wanted to acknowledge that point now, in terms of the patterns, there are definitely applications, like a hybrid pattern where connect the car has to upload all its data once it docks into its location and move it to the data center. So there are patterns where the workflow does move the ups are the application data between on Prem into a public cloud, for instance, and then coming back from that your trip with Lisa. There is also examples where regulations require companies to enterprise is to be able to move to another cloud in a reasonable time frame. So there's definitely a notion of Multi Cloud is both an architectural design pattern. But it's also a sourcing strategy and that sourcing strategies Maura regulation type o. R in terms of not being locked in. And that's where I'm saying it's all those things. >>You love to get your thoughts on this because I like where you're going with this because it kind of takes it to a level of Okay, standardization kubernetes nights containing one does that. But then you're something about FBI gateways, for instance. Right? So if I'm a car, have five different gig weighs on my device devices or I have multiple vendors dealing with control playing data that could be problematic. I gotta do something. So I started envisioned. I just made that this case up. But my point is, is that you need some standards. So on the A p I side was seeing some trends there once saying, Okay, here's my stuff. I'll just pass Paramus with FBI, you know, state and stateless are two dynamics. What do you make of that? What? What what has to happen next to get to that next level of happiness and goodness because Ruben is has got it, got it there, >>right? I feel like next level. I feel like in Lisa. Please jump. And I feel like from automation perspective, Kubernetes has done that from a P I gateway. And what has to happen next. There's still a lot of easy use that isn't solved right. There's probably tons of opportunities out there to build a much better user experience, both from operations point of view and from what I'm trying to do is an intense because what people aren't gonna automate right now is the intent to automate a lot of the infrastructure manual tasks, and that's goodness. But from how I docked my application, how the application did, it gets moved. We're still at the point of making policy driven, easy to use, and I think there's a lot of opportunities for everyone to get better there. >>That's like Logan is priority looking fruity manual stuff >>and communities was really good at the food. That's a really use case that you brought up really. People were looking at the data now, and when you're talking about persistent mean Cooney's is great for stateless, but for St Paul's really crucial data. So that's where we really come in. And a number of other companies in the cloud native storage ecosystem come in and have really fought through this problem and that data management problem. That's where this platform that Aaron was talking about >>We'll get to that state problem. Talk about your company. I wanna get back Thio, Google Days, um, many war stories around kubernetes. We'll have the same fate as map reduce. You know, the debates internally and Google. What do we do with it? You guys made a good call. Congratulations doing that. What was it like to be early on? Because you already had large scale. You already had. Borg already had all these things in place. Was it like there was >>a few things I'll say One is. It was intense, right? It was intense in the sense that amazing amount of intelligence, amazing amount of intent, and right back then a lot of things were still undecided, right? We're still looking at how containers are package. We're still looking at how infrastructure Kate run and a lot of the service's were still being rolled out. So what it really meant is howto build something that people want to build, something that people want to run with you and how to build an ecosystem community. A lot of that the community got was done very well, right? You have to give credit to things like the Sig. A lot of things like how people like advocates like Lisa had gone out and made it part of what they're doing. And that's important, right? Every ecosystem needs to have those advocates, and that's what's going well, a cz ah flip side. I think there's a lot of things where way always look back, in which we could have done a few things differently. But that's a different story for different >>will. Come back and get in the studio fellow that I gotta ask you now that you're outside. Google was a culture shock. Oh my God. People actually provisioning software. Yeah, I was in a data center. Cultures. There's a little >>bit of culture shock. One thing is, and the funny thing is coming full circle in communities now, is that the idea of an application, right? The idea of what is an application eyes something that feels very comfortable to a lot of legacy traditional. I wanna use traditional applications, but the moment you're you've spent so much time incriminates and you say, What's the application? It became a very hard thing, and I used to have a lot of academic debates wise saying there is no application. It's it's a soup of resources and such. So that was a hard thing. But funny thing is covered, as is now coming out with definitions around application, and Microsoft announced a few things in that area to so there are things that are coming full circle, but that just shows how the movement has changed and how things are becoming in some ways meeting each other halfway. >>Talk about the company. What you guys are doing. Taking moments explaining contacts. Multi Cloud were here. Put worse. What's the platform? It's a product. What's the value proposition? What's the state of the company? >>Yes. So the companies? Uh well, well, it's grown from early days when Lisa and I joined where we're probably a handful now. We're in four or five cities. Geography is over 100 people over 150 customers and there. It's been a lot of enterprises that are saying, like, How do I take this pattern? Doing containers and micro service is, and how do I run it with my mission? Critical business crinkle workloads And at that point, there is no mission critical business critical workload that isn't stable so suddenly they're trying to say, How do I run These applications and containers and data have different life cycles. So what they're really looking for is a data plane that works with the control planes and how controlled planes are changing the behavior. So a lot of our technology and a lot of our product innovation has been around both the data plane but a storage control plane that integrates with a computer controlled plane. So I know we like to talk about one control plane. There's actually multiple control planes, and you mentioned security, right? If I look at how applications are running way, acting now securely access for applications and it's no longer have access to the data. Before I get to use it, you have to now start to do things like J W. T. Or much higher level bear tokens to say I know how to access this application for this life cycle for this use case and get that kind of resiliency. So it's really around having that >>storage. More complexity, absolutely needing abstraction layers and you compute. Luckily, work there. But you gotta have software to do it >>from a poor box perspective. Our products entirely software right down loans and runs using kubernetes. And so the point here is we make remarries able to run all the staple workloads out of the box using the same comment control plane, which is communities. So that's the experiences that we really want to make it so that Dev Ops teams can run anywhere close. And that's that's in some ways been part of the mix. >>Lisa, we've been covering Jeff up. Go back to 2010. Remember when I first I was hanging around? San Francisco? Doesn't eight Joint was coming out the woodwork and all that early days. You look at the journey of how infrastructures code. We'll talk about that in 2008 and now we'll get 11 years later. Look at the advancements you've been through this now the tipping point just seems like this wave is big and people are on developers air getting it. It's a modern renaissance of application developers, and the enterprise it's happening in the enterprise is not just like the energy. You're one Apple geeks or the foundation. It's happening in >>everyone's on board this time, and you and I have been in the trenches in the early stages of many open source projects. And I think with kubernetes Arab reference of community earlier, I'm super proud to be running the world's largest CNC F for user group. And it's a great community, a diverse community, super smart people. One of my favorite things about working poor works is we have some really smart engineers that have figured out what companies want, how to solve problems, and then we'll go credible open source projects. We created a project called autopilot, really largely because one of our customers, every who's in the G s space and who's running just incredible application, you can google it and see what the work they're doing. It's all out there publicly. Onda we built, you know, we've built an open source project for them to help them get the most out of kubernetes we can say so there's a lot of people in the community system doing that. How can we make communities better? Half We make competitive enterprise grade and not take years to do that. Like some of the other open source projects that we worked on, it took. So it's a super exciting time to be here, >>and open source is growing so fast. Now just think about having project being structured. More and more projects are coming online and user profit a lot more. Vendor driven projects, too used mostly and used with. Now you have a lot of support vendors who are users, so the line is blurring between then their user in open source is really fast. >>Will you look at the look of the landscape on the C N. C. F? You know the website. I mean, it's what 400 that are already on board. It's really important. >>They don't have enough speaking slasher with >>right. I know, and it's just it. It is users and vendors. Everybody's in the community together. It's one of things that makes it super exciting, and it's how we know this is This was the right choice for us. Did they communities because that's what? Everybody? >>You guys are practically neighbors. We look for CNN Studio, Palo Alto. I wanna ask you one final question on the product side. Road map. What you guys thinking As Kubernetes goes, the next level state, a lot of micro service is observe. Ability is becoming a key part of it. The automation configuration management things are developing fast. State. What's the road for you guys? For >>us, it's been always about howto handle the mission critical and make that application run seamlessly. And then now we've done a lot of portability. So disaster recovery is one of the biggest things for us is that customers are saying, How do I do a hybrid pattern back to your earlier question of running on Prem and in Public Cloud and do a D. R fail over into a Some of the things, at least, is pointing out. That we're announcing soon is non Terry's autopilot in the idea of automatically managing applications scale from a volume capacity. And then we're actually going to start moving a lot more into some of what you do with data after the life cycle in terms of backup and retention. So those are the things that everyone's been pushing us, and the customers are all asking, >>You know, I think data that recovery is interesting. I think that's going to change radically. And I think we look at the trend of how yeah, data backup recovery was built. It was built because of disruption of business, floods, our games. That's right. It is in their failure. But I think the biggest disruptions ransomware that malware. So security is now a active disruptor, So it's not like it After today. If we hadn't have ah, fire, we can always roll back. So you're infected and you're just rolling back infected code. That's a ransomware dream. That's what's going on. So I think data protection needs to redefine. >>What do you think? Absolutely. I think there's a notion of how do I get last week's data last month and then oftentimes customers will say If I have a piece of data volume and I suddenly have to delete it, I still need to have some record of that action for a long time, right? So those are the kinds of things that are happening and his crew bearnaise and everything, it gets changed. Suddenly, the important part is not what was just that one pot it becomes. How do I reconstruct everything? Action >>is not one thing. It's everywhere That's right, protected all through the platform. It is a platform decision. It's not some cattlemen on the side. >>You can't be a single lap. It has to be entire solution. And it has to handle things like, Where do you come from? Where is it allowed to go? >>You guys have that philosophy? >>We absolutely. And it's based on the enterprises that are adopting port works and saying, Hey, this is my romance. I'm basing it on Kubernetes here, my data partner. How do you make it happen? >>This speaks to your point of why the enterprise is in the vendors jumped in. This is what people care about security. How do you solve this last mile problem? Storage, Networking. How do you plug those holes and kubernetes? Because that is crucial. >>One personal private moment. Victory moment for me personally, Waas been a big fan of Cuban, is actually, you know, for years in there when it was created, talked about one of moments that got me was personal. Heartfelt moment was enterprise buyer on. The whole mindset in the enterprise has always been You gotta kill the old to bring in the new. And so there's always been that tension of a you know, the shame, your toy from Silicon Valley or whatever. You know, I'm not gonna just trash this and have a migration is a pain in the butt fried. You don't want that to do that. They hate doing migrations, but with containers and kubernetes, they actually they don't end of life to bring in the new project they could do on their own or keep it around. So that took a lot of air out of the tension in on the I t. Side. Because it's a great I can deal with the life cycle of my app on my own terms and go play with Cloud native and said to me, I was like, That was to be like, Okay, there it is. That was validation. That means this is real because now they will be without compromising. >>I think so. And I think some of that has been how the ecosystems embraced it, right, So now it's becoming all the vendors are saying My internal stack is also based on company. So even if you as an application owner or not realizing it, you're gonna take a B M next year and you're gonna run it and it's gonna be back by something like >>the submarine and the aircon. Thank you for coming on court. Worse Hot started Multiple cities Kubernetes Big developer Project Open Source Talking about multi cloud here at the inaugural Multi Cloud Conference in New York City Secu Courage of Escape Plan 19 John Corey Thanks for watching.

(electronic dance music) (Intel Jingle) (ethereal electronic dance music) ♪ Okay ♪ (upbeat techno dance music) ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh oh ♪ ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh oh ♪ ♪ Take it back take it back ♪ ♪ Take it back ♪ ♪ Take it back take it back ♪ ♪ Take it back ♪ ♪ Take it back take it back ♪ ♪ Yeah everybody get loose yeah ♪ ♪ Yeah ♪ ♪ Ye-yeah yeah ♪ ♪ Yeah yeah ♪ ♪ Everybody everybody yeah ♪ ♪ Whoo whoo ♪ ♪ Whoo whoo ♪ ♪ Whoo yeah ♪ ♪ Everybody get loose whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ >> As a courtesy to the presenters and those around you, please silence all mobile devices, thank you. (electronic dance music) ♪ Everybody get loose ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ (upbeat salsa music) ♪ Ha ha ha ♪ ♪ Ah ♪ ♪ Ha ha ha ♪ ♪ So happy ♪ ♪ Whoo whoo ♪ (female singer scatting) >> Ladies and gentlemen, please take your seats. Our program will begin momentarily. ♪ Hey ♪ (female singer scatting) (male singer scatting) ♪ Hey ♪ ♪ Whoo ♪ (female singer scatting) (electronic dance music) ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ Red don't go ♪ ♪ All hands are in don't go ♪ ♪ In don't go ♪ ♪ Oh red go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are red don't go ♪ ♪ All hands are in red red red red ♪ ♪ All hands are in don't go ♪ ♪ All hands are in red go ♪ >> Ladies and gentlemen, there are available seats. Towards house left, house left there are available seats. If you are please standing, we ask that you please take an available seat. We will begin momentarily, thank you. ♪ Let go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ (upbeat electronic dance music) ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ I live ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Hey ♪ ♪ Yeah ♪ ♪ Oh ♪ ♪ Ah ♪ ♪ Ah ah ah ah ah ah ♪ ♪ Just make me ♪ ♪ Just make me ♪ (bouncy techno music) >> Ladies and gentlemen, once again we ask that you please take the available seats to your left, house left, there are many available seats. If you are standing, please make your way there. The program will begin momentarily, thank you. Good morning! This is Lenovo Transform 2.0! (keyboard clicks) >> Progress. Why do we always talk about it in the future? When will it finally get here? We don't progress when it's ready for us. We need it when we're ready, and we're ready now. Our hospitals and their patients need it now, our businesses and their customers need it now, our cities and their citizens need it now. To deliver intelligent transformation, we need to build it into the products and solutions we make every day. At Lenovo, we're designing the systems to fight disease, power businesses, and help you reach more customers, end-to-end security solutions to protect your data and your companies reputation. We're making IT departments more agile and cost efficient. We're revolutionizing how kids learn with VR. We're designing smart devices and software that transform the way you collaborate, because technology shouldn't just power industries, it should power people. While everybody else is talking about tomorrow, we'll keep building today, because the progress we need can't wait for the future. >> Please welcome to the stage Lenovo's Rod Lappen! (electronic dance music) (audience applauding) >> Alright. Good morning everyone! >> Good morning. >> Ooh, that was pretty good actually, I'll give it one more shot. Good morning everyone! >> Good morning! >> Oh, that's much better! Hope everyone's had a great morning. Welcome very much to the second Lenovo Transform event here in New York. I think when I got up just now on the steps I realized there's probably one thing in common all of us have in this room including myself which is, absolutely no one has a clue what I'm going to say today. So, I'm hoping very much that we get through this thing very quickly and crisply. I love this town, love New York, and you're going to hear us talk a little bit about New York as we get through here, but just before we get started I'm going to ask anyone who's standing up the back, there are plenty of seats down here, and down here on the right hand side, I think he called it house left is the professional way of calling it, but these steps to my right, your left, get up here, let's get you all seated down so that you can actually sit down during the keynote session for us. Last year we had our very first Lenovo Transform. We had about 400 people. It was here in New York, fantastic event, today, over 1,000 people. We have over 62 different technology demonstrations and about 15 breakout sessions, which I'll talk you through a little bit later on as well, so it's a much bigger event. Next year we're definitely going to be shooting for over 2,000 people as Lenovo really transforms and starts to address a lot of the technology that our commercial customers are really looking for. We were however hampered last year by a storm, I don't know if those of you who were with us last year will remember, we had a storm on the evening before Transform last year in New York, and obviously the day that it actually occurred, and we had lots of logistics. Our media people from AMIA were coming in. They took the, the plane was circling around New York for a long time, and Kamran Amini, our General Manager of our Data Center Infrastructure Group, probably one of our largest groups in the Lenovo DCG business, took 17 hours to get from Raleigh, North Carolina to New York, 17 hours, I think it takes seven or eight hours to drive. Took him 17 hours by plane to get here. And then of course this year, we have Florence. And so, obviously the hurricane Florence down there in the Carolinas right now, we tried to help, but still Kamran has made it today. Unfortunately, very tragically, we were hoping he wouldn't, but he's here today to do a big presentation a little bit later on as well. However, I do want to say, obviously, Florence is a very serious tragedy and we have to take it very serious. We got, our headquarters is in Raleigh, North Carolina. While it looks like the hurricane is just missing it's heading a little bit southeast, all of our thoughts and prayers and well wishes are obviously with everyone in the Carolinas on behalf of Lenovo, everyone at our headquarters, everyone throughout the Carolinas, we want to make sure everyone stays safe and out of harm's way. We have a great mixture today in the crowd of all customers, partners, industry analysts, media, as well as our financial analysts from all around the world. There's over 30 countries represented here and people who are here to listen to both YY, Kirk, and Christian Teismann speak today. And so, it's going to be a really really exciting day, and I really appreciate everyone coming in from all around the world. So, a big round of applause for everyone whose come in. (audience applauding) We have a great agenda for you today, and it starts obviously a very consistent format which worked very successful for us last year, and that's obviously our keynote. You'll hear from YY, our CEO, talk a little bit about the vision he has in the industry and how he sees Lenovo's turned the corner and really driving some great strategy to address our customer's needs. Kirk Skaugen, our Executive Vice President of DCG, will be up talking about how we've transformed the DCG business and once again are hitting record growth ratios for our DCG business. And then you'll hear from Christian Teismann, our SVP and General Manager for our commercial business, get up and talk about everything that's going on in our IDG business. There's really exciting stuff going on there and obviously ThinkPad being the cornerstone of that I'm sure he's going to talk to us about a couple surprises in that space as well. Then we've got some great breakout sessions, I mentioned before, 15 breakout sessions, so while this keynote section goes until about 11:30, once we get through that, please go over and explore, and have a look at all of the breakout sessions. We have all of our subject matter experts from both our PC, NBG, and our DCG businesses out to showcase what we're doing as an organization to better address your needs. And then obviously we have the technology pieces that I've also spoken about, 62 different technology displays there arranged from everything IoT, 5G, NFV, everything that's really cool and hot in the industry right now is going to be on display up there, and I really encourage all of you to get up there. So, I'm going to have a quick video to show you from some of the setup yesterday on a couple of the 62 technology displays we've got on up on stage. Okay let's go, so we've got a demonstrations to show you today, one of the greats one here is the one we've done with NC State, a high-performance computing artificial intelligence demonstration of fresh produce. It's about modeling the population growth of the planet, and how we're going to supply water and food as we go forward. Whoo. Oh, that is not an apple. Okay. (woman laughs) Second one over here is really, hey Jonas, how are you? Is really around virtual reality, and how we look at one of the most amazing sites we've got, as an install on our high-performance computing practice here globally. And you can see, obviously, that this is the Barcelona supercomputer, and, where else in New York can you get access to being able to see something like that so easily? Only here at Lenovo Transform. Whoo, okay. (audience applauding) So there's two examples of some of the technology. We're really encouraging everyone in the room after the keynote to flow into that space and really get engaged, and interact with a lot of the technology we've got up there. It seems I need to also do something about my fashion, I've just realized I've worn a vest two days in a row, so I've got to work on that as well. Alright so listen, the last thing on the agenda, we've gone through the breakout sessions and the demo, tonight at four o'clock, there's about 400 of you registered to be on the cruise boat with us, the doors will open behind me. the boat is literally at the pier right behind us. You need to make sure you're on the boat for 4:00 p.m. this evening. Outside of that, I want everyone to have a great time today, really enjoy the experience, make it as experiential as you possibly can, get out there and really get in and touch the technology. There's some really cool AI displays up there for us all to get involved in as well. So ladies and gentlemen, without further adieu, it gives me great pleasure to introduce to you a lover of tennis, as some of you would've heard last year at Lenovo Transform, as well as a lover of technology, Lenovo, and of course, New York City. I am obviously very pleasured to introduce to you Yang Yuanqing, our CEO, as we like to call him, YY. (audience applauding) (upbeat funky music) >> Good morning, everyone. >> Good morning. >> Thank you Rod for that introduction. Welcome to New York City. So, this is the second year in a row we host our Transform event here, because New York is indeed one of the most transformative cities in the world. Last year on this stage, I spoke about the Fourth Industrial Revolution, and our vision around the intelligent transformation, how it would fundamentally change the nature of business and the customer relationships. And why preparing for this transformation is the key for the future of our company. And in the last year I can assure you, we were being very busy doing just that, from searching and bringing global talents around the world to the way we think about every product and every investment we make. I was here in New York just a month ago to announce our fiscal year Q1 earnings, which was a good day for us. I think now the world believes it when we say Lenovo has truly turned the corner to a new phase of growth and a new phase of acceleration in executing the transformation strategy. That's clear to me is that the last few years of a purposeful disruption at Lenovo have led us to a point where we can now claim leadership of the coming intelligent transformation. People often asked me, what is the intelligent transformation? I was saying this way. This is the unlimited potential of the Fourth Industrial Revolution driven by artificial intelligence being realized, ordering a pizza through our speaker, and locking the door with a look, letting your car drive itself back to your home. This indeed reflect the power of AI, but it just the surface of it. The true impact of AI will not only make our homes smarter and offices more efficient, but we are also completely transformed every value chip in every industry. However, to realize these amazing possibilities, we will need a structure built around the key components, and one that touches every part of all our lives. First of all, explosions in new technology always lead to new structures. This has happened many times before. In the early 20th century, thousands of companies provided a telephone service. City streets across the US looked like this, and now bundles of a microscopic fiber running from city to city bring the world closer together. Here's what a driving was like in the US, up until 1950s. Good luck finding your way. (audience laughs) And today, millions of vehicles are organized and routed daily, making the world more efficient. Structure is vital, from fiber cables and the interstate highways, to our cells bounded together to create humans. Thankfully the structure for intelligent transformation has emerged, and it is just as revolutionary. What does this new structure look like? We believe there are three key building blocks, data, computing power, and algorithms. Ever wondered what is it behind intelligent transformation? What is fueling this miracle of human possibility? Data. As the Internet becomes ubiquitous, not only PCs, mobile phones, have come online and been generating data. Today it is the cameras in this room, the climate controls in our offices, or the smart displays in our kitchens at home. The number of smart devices worldwide will reach over 20 billion in 2020, more than double the number in 2017. These devices and the sensors are connected and generating massive amount of data. By 2020, the amount of data generated will be 57 times more than all the grains of sand on Earth. This data will not only make devices smarter, but will also fuel the intelligence of our homes, offices, and entire industries. Then we need engines to turn the fuel into power, and the engine is actually the computing power. Last but not least the advanced algorithms combined with Big Data technology and industry know how will form vertical industrial intelligence and produce valuable insights for every value chain in every industry. When these three building blocks all come together, it will change the world. At Lenovo, we have each of these elements of intelligent transformations in a single place. We have built our business around the new structure of intelligent transformation, especially with mobile and the data center now firmly part of our business. I'm often asked why did you acquire these businesses? Why has a Lenovo gone into so many fields? People ask the same questions of the companies that become the leaders of the information technology revolution, or the third industrial transformation. They were the companies that saw the future and what the future required, and I believe Lenovo is the company today. From largest portfolio of devices in the world, leadership in the data center field, to the algorithm-powered intelligent vertical solutions, and not to mention the strong partnership Lenovo has built over decades. We are the only company that can unify all these essential assets and deliver end to end solutions. Let's look at each part. We now understand the important importance data plays as fuel in intelligent transformation. Hundreds of billions of devices and smart IoTs in the world are generating better and powering the intelligence. Who makes these devices in large volume and variety? Who puts these devices into people's home, offices, manufacturing lines, and in their hands? Lenovo definitely has the front row seats here. We are number one in PCs and tablets. We also produces smart phones, smart speakers, smart displays. AR/VR headsets, as well as commercial IoTs. All of these smart devices, or smart IoTs are linked to each other and to the cloud. In fact, we have more than 20 manufacturing facilities in China, US, Brazil, Japan, India, Mexico, Germany, and more, producing various devices around the clock. We actually make four devices every second, and 37 motherboards every minute. So, this factory located in my hometown, Hu-fi, China, is actually the largest laptop factory in the world, with more than three million square feet. So, this is as big as 42 soccer fields. Our scale and the larger portfolio of devices gives us access to massive amount of data, which very few companies can say. So, why is the ability to scale so critical? Let's look again at our example from before. The early days of telephone, dozens of service providers but only a few companies could survive consolidation and become the leader. The same was true for the third Industrial Revolution. Only a few companies could scale, only a few could survive to lead. Now the building blocks of the next revolution are locking into place. The (mumbles) will go to those who can operate at the scale. So, who could foresee the total integration of cloud, network, and the device, need to deliver intelligent transformation. Lenovo is that company. We are ready to scale. Next, our computing power. Computing power is provided in two ways. On one hand, the modern supercomputers are providing the brute force to quickly analyze the massive data like never before. On the other hand the cloud computing data centers with the server storage networking capabilities, and any computing IoT's, gateways, and miniservers are making computing available everywhere. Did you know, Lenovo is number one provider of super computers worldwide? 170 of the top 500 supercomputers, run on Lenovo. We hold 89 World Records in key workloads. We are number one in x86 server reliability for five years running, according to ITIC. a respected provider of industry research. We are also the fastest growing provider of hyperscale public cloud, hyper-converged and aggressively growing in edge computing. cur-ges target, we are expand on this point soon. And finally to run these individual nodes into our symphony, we must transform the data and utilize the computing power with advanced algorithms. Manufactured, industry maintenance, healthcare, education, retail, and more, so many industries are on the edge of intelligent transformation to improve efficiency and provide the better products and services. We are creating advanced algorithms and the big data tools combined with industry know-how to provide intelligent vertical solutions for several industries. In fact, we studied at Lenovo first. Our IT and research teams partnered with our global supply chain to develop an AI that improved our demand forecasting accuracy. Beyond managing our own supply chain we have offered our deep learning supply focused solution to other manufacturing companies to improve their efficiency. In the best case, we have improved the demand, focused the accuracy by 30 points to nearly 90 percent, for Baosteel, the largest of steel manufacturer in China, covering the world as well. Led by Lenovo research, we launched the industry-leading commercial ready AR headset, DaystAR, partnering with companies like the ones in this room. This technology is being used to revolutionize the way companies service utility, and even our jet engines. Using our workstations, servers, and award-winning imaging processing algorithms, we have partnered with hospitals to process complex CT scan data in minutes. So, this enable the doctors to more successfully detect the tumors, and it increases the success rate of cancer diagnosis all around the world. We are also piloting our smart IoT driven warehouse solution with one of the world's largest retail companies to greatly improve the efficiency. So, the opportunities are endless. This is where Lenovo will truly shine. When we combine the industry know-how of our customers with our end-to-end technology offerings, our intelligent vertical solutions like this are growing, which Kirk and Christian will share more. Now, what will drive this transformation even faster? The speed at which our networks operate, specifically 5G. You may know that Lenovo just launched the first-ever 5G smartphone, our Moto Z3, with the new 5G Moto model. We are partnering with multiple major network providers like Verizon, China Mobile. With the 5G model scheduled to ship early next year, we will be the first company to provide a 5G mobile experience to any users, customers. This is amazing innovation. You don't have to buy a new phone, just the 5G clip on. What can I say, except wow. (audience laughs) 5G is 10 times the fast faster than 4G. Its download speed will transform how people engage with the world, driverless car, new types of smart wearables, gaming, home security, industrial intelligence, all will be transformed. Finally, accelerating with partners, as ready as we are at Lenovo, we need partners to unlock our full potential, partners here to create with us the edge of the intelligent transformation. The opportunities of intelligent transformation are too profound, the scale is too vast. No company can drive it alone fully. We are eager to collaborate with all partners that can help bring our vision to life. We are dedicated to open partnerships, dedicated to cross-border collaboration, unify the standards, share the advantage, and market the synergies. We partner with the biggest names in the industry, Intel, Microsoft, AMD, Qualcomm, Google, Amazon, and Disney. We also find and partner with the smaller innovators as well. We're building the ultimate partner experience, open, shared, collaborative, diverse. So, everything is in place for intelligent transformation on a global scale. Smart devices are everywhere, the infrastructure is in place, networks are accelerating, and the industries demand to be more intelligent, and Lenovo is at the center of it all. We are helping to drive change with the hundreds of companies, companies just like yours, every day. We are your partner for intelligent transformation. Transformation never stops. This is what you will hear from Kirk, including details about Lenovo NetApp global partnership we just announced this morning. We've made the investments in every single aspect of the technology. We have the end-to-end resources to meet your end-to-end needs. As you attend the breakout session this afternoon, I hope you see for yourself how much Lenovo has transformed as a company this past year, and how we truly are delivering a future of intelligent transformation. Now, let me invite to the stage Kirk Skaugen, our president of Data Center growth to tell you about the exciting transformation happening in the global Data C enter market. Thank you. (audience applauding) (upbeat music) >> Well, good morning. >> Good morning. >> Good morning! >> Good morning! >> Excellent, well, I'm pleased to be here this morning to talk about how we're transforming the Data Center and taking you as our customers through your own intelligent transformation journey. Last year I stood up here at Transform 1.0, and we were proud to announce the largest Data Center portfolio in Lenovo's history, so I thought I'd start today and talk about the portfolio and the progress that we've made over the last year, and the strategies that we have going forward in phase 2.0 of Lenovo's transformation to be one of the largest data center companies in the world. We had an audacious vision that we talked about last year, and that is to be the most trusted data center provider in the world, empowering customers through the new IT, intelligent transformation. And now as the world's largest supercomputer provider, giving something back to humanity, is very important this week with the hurricanes now hitting North Carolina's coast, but we take this most trusted aspect very seriously, whether it's delivering the highest quality products on time to you as customers with the highest levels of security, or whether it's how we partner with our channel partners and our suppliers each and every day. You know we're in a unique world where we're going from hundreds of millions of PCs, and then over the next 25 years to hundred billions of connected devices, so each and every one of you is going through this intelligent transformation journey, and in many aspects were very early in that cycle. And we're going to talk today about our role as the largest supercomputer provider, and how we're solving humanity's greatest challenges. Last year we talked about two special milestones, the 25th anniversary of ThinkPad, but also the 25th anniversary of Lenovo with our IBM heritage in x86 computing. I joined the workforce in 1992 out of college, and the IBM first personal server was launching at the same time with an OS2 operating system and a free mouse when you bought the server as a marketing campaign. (audience laughing) But what I want to be very clear today, is that the innovation engine is alive and well at Lenovo, and it's really built on the culture that we're building as a company. All of these awards at the bottom are things that we earned over the last year at Lenovo. As a Fortune now 240 company, larger than companies like Nike, or AMEX, or Coca-Cola. The one I'm probably most proud of is Forbes first list of the top 2,000 globally regarded companies. This was something where 15,000 respondents in 60 countries voted based on ethics, trustworthiness, social conduct, company as an employer, and the overall company performance, and Lenovo was ranked number 27 of 2000 companies by our peer group, but we also now one of-- (audience applauding) But we also got a perfect score in the LGBTQ Equality Index, exemplifying the diversity internally. We're number 82 in the top working companies for mothers, top working companies for fathers, top 100 companies for sustainability. If you saw that factory, it's filled with solar panels on the top of that. And now again, one of the top global brands in the world. So, innovation is built on a customer foundation of trust. We also said last year that we'd be crossing an amazing milestone. So we did, over the last 12 months ship our 20 millionth x86 server. So, thank you very much to our customers for this milestone. (audience applauding) So, let me recap some of the transformation elements that have happened over the last year. Last year I talked about a lot of brand confusion, because we had the ThinkServer brand from the legacy Lenovo, the System x, from IBM, we had acquired a number of networking companies, like BLADE Network Technologies, et cetera, et cetera. Over the last year we've been ramping based on two brand structures, ThinkAgile for next generation IT, and all of our software-defined infrastructure products and ThinkSystem as the world's highest performance, highest reliable x86 server brand, but for servers, for storage, and for networking. We have transformed every single aspect of the customer experience. A year and a half ago, we had four different global channel programs around the world. Typically we're about twice the mix to our channel partners of any of our competitors, so this was really important to fix. We now have a single global Channel program, and have technically certified over 11,000 partners to be technical experts on our product line to deliver better solutions to our customer base. Gardner recently recognized Lenovo as the 26th ranked supply chain in the world. And, that's a pretty big honor, when you're up there with Amazon and Walmart and others, but in tech, we now are in the top five supply chains. You saw the factory network from YY, and today we'll be talking about product shipping in more than 160 countries, and I know there's people here that I've met already this morning, from India, from South Africa, from Brazil and China. We announced new Premier Support services, enabling you to go directly to local language support in nine languages in 49 countries in the world, going directly to a native speaker level three support engineer. And today we have more than 10,000 support specialists supporting our products in over 160 countries. We've delivered three times the number of engineered solutions to deliver a solutions orientation, whether it's on HANA, or SQL Server, or Oracle, et cetera, and we've completely reengaged our system integrator channel. Last year we had the CIO of DXE on stage, and here we're talking about more than 175 percent growth through our system integrator channel in the last year alone as we've brought that back and really built strong relationships there. So, thank you very much for amazing work here on the customer experience. (audience applauding) We also transformed our leadership. We thought it was extremely important with a focus on diversity, to have diverse talent from the legacy IBM, the legacy Lenovo, but also outside the industry. We made about 19 executive changes in the DCG group. This is the most senior leadership team within DCG, all which are newly on board, either from our outside competitors mainly over the last year. About 50 percent of our executives were now hired internally, 50 percent externally, and 31 percent of those new executives are diverse, representing the diversity of our global customer base and gender. So welcome, and most of them you're going to be able to meet over here in the breakout sessions later today. (audience applauding) But some things haven't changed, they're just keeping getting better within Lenovo. So, last year I got up and said we were committed with the new ThinkSystem brand to be a world performance leader. You're going to see that we're sponsoring Ducati for MotoGP. You saw the Ferrari out there with Formula One. That's not a surprise. We want the Lenovo ThinkSystem and ThinkAgile brands to be synonymous with world record performance. So in the last year we've gone from 39 to 89 world records, and partners like Intel would tell you, we now have four times the number of world record workloads on Lenovo hardware than any other server company on the planet today, with more than 89 world records across HPC, Java, database, transaction processing, et cetera. And we're proud to have just brought on Doug Fisher from Intel Corporation who had about 10-17,000 people on any given year working for him in workload optimizations across all of our software. It's just another testament to the leadership team we're bringing in to keep focusing on world-class performance software and solutions. We also per ITIC, are the number one now in x86 server reliability five years running. So, this is a survey where CIOs are in a blind survey asked to submit their reliability of their uptime on their x86 server equipment over the last 365 days. And you can see from 2016 to 2017 the downtime, there was over four hours as noted by the 750 CXOs in more than 20 countries is about one percent for the Lenovo products, and is getting worse generation from generation as we went from Broadwell to Pearlie. So we're taking our reliability, which was really paramount in the IBM System X heritage, and ensuring that we don't just recognize high performance but we recognize the highest level of reliability for mission-critical workloads. And what that translates into is that we at once again have been ranked number one in customer satisfaction from you our customers in 19 of 22 attributes, in North America in 18 of 22. This is a survey by TVR across hundreds of customers of us and our top competitors. This is the ninth consecutive study that we've been ranked number one in customer satisfaction, so we're taking this extremely seriously, and in fact YY now has increased the compensation of every single Lenovo employee. Up to 40 percent of their compensation bonus this year is going to be based on customer metrics like quality, order to ship, and things of this nature. So, we're really putting every employee focused on customer centricity this year. So, the summary on Transform 1.0 is that every aspect of what you knew about Lenovo's data center group has transformed, from the culture to the branding to dedicated sales and marketing, supply chain and quality groups, to a worldwide channel program and certifications, to new system integrator relationships, and to the new leadership team. So, rather than me just talk about it, I thought I'd share a quick video about what we've done over the last year, if you could run the video please. Turn around for a second. (epic music) (audience applauds) Okay. So, thank you to all our customers that allowed us to publicly display their logos in that video. So, what that means for you as investors, and for the investor community out there is, that our customers have responded, that this year Gardner just published that we are the fastest growing server company in the top 10, with 39 percent growth quarter-on-quarter, and 49 percent growth year-on-year. If you look at the progress we've made since the transformation the last three quarters publicly, we've grown 17 percent, then 44 percent, then 68 percent year on year in revenue, and I can tell you this quarter I'm as confident as ever in the financials around the DCG group, and it hasn't been in one area. You're going to see breakout sessions from hyperscale, software-defined, and flash, which are all growing more than a 100 percent year-on-year, supercomputing which we'll talk about shortly, now number one, and then ultimately from profitability, delivering five consecutive quarters of pre-tax profit increase, so I think, thank you very much to the customer base who's been working with us through this transformation journey. So, you're here to really hear what's next on 2.0, and that's what I'm excited to talk about today. Last year I came up with an audacious goal that we would become the largest supercomputer company on the planet by 2020, and this graph represents since the acquisition of the IBM System x business how far we were behind being the number one supercomputer. When we started we were 182 positions behind, even with the acquisition for example of SGI from HP, we've now accomplished our goal actually two years ahead of time. We're now the largest supercomputer company in the world. About one in every four supercomputers, 117 on the list, are now Lenovo computers, and you saw in the video where the universities are said, but I think what I'm most proud of is when your customers rank you as the best. So the awards at the bottom here, are actually Readers Choice from the last International Supercomputing Show where the scientific researchers on these computers ranked their vendors, and we were actually rated the number one server technology in supercomputing with our ThinkSystem SD530, and the number one storage technology with our ThinkSystem DSS-G, but more importantly what we're doing with the technology. You're going to see we won best in life sciences, best in data analytics, and best in collaboration as well, so you're going to see all of that in our breakout sessions. As you saw in the video now, 17 of the top 25 research institutions in the world are now running Lenovo supercomputers. And again coming from Raleigh and watching that hurricane come across the Atlantic, there are eight supercomputers crunching all of those models you see from Germany to Malaysia to Canada, and we're happy to have a SciNet from University of Toronto here with us in our breakout session to talk about what they're doing on climate modeling as well. But we're not stopping there. We just announced our new Neptune warm water cooling technology, which won the International Supercomputing Vendor Showdown, the first time we've won that best of show in 25 years, and we've now installed this. We're building out LRZ in Germany, the first ever warm water cooling in Peking University, at the India Space Propulsion Laboratory, at the Malaysian Weather and Meteorological Society, at Uninett, at the largest supercomputer in Norway, T-Systems, University of Birmingham. This is truly amazing technology where we're actually using water to cool the machine to deliver a significantly more energy-efficient computer. Super important, when we're looking at global warming and some of the electric bills can be millions of dollars just for one computer, and could actually power a small city just with the technology from the computer. We've built AI centers now in Morrisville, Stuttgart, Taipei, and Beijing, where customers can bring their AI workloads in with experts from Intel, from Nvidia, from our FPGA partners, to work on their workloads, and how they can best implement artificial intelligence. And we also this year launched LICO which is Lenovo Intelligent Compute Orchestrator software, and it's a software solution that simplifies the management and use of distributed clusters in both HPC and AI model development. So, what it enables you to do is take a single cluster, and run both HPC and AI workloads on it simultaneously, delivering better TCO for your environment, so check out LICO as well. A lot of the customers here and Wall Street are very excited and using it already. And we talked about solving humanity's greatest challenges. In the breakout session, you're going to have a virtual reality experience where you're going to be able to walk through what as was just ranked the world's most beautiful data center, the Barcelona Supercomputer. So, you can actually walk through one of the largest supercomputers in the world from Barcelona. You can see the work we're doing with NC State where we're going to have to grow the food supply of the world by 50 percent, and there's not enough fresh water in the world in the right places to actually make all those crops grow between now and 2055, so you're going to see the progression of how they're mapping the entire globe and the water around the world, how to build out the crop population over time using AI. You're going to see our work with Vestas is this largest supercomputer provider in the wind turbine areas, how they're working on wind energy, and then with University College London, how they're working on some of the toughest particle physics calculations in the world. So again, lots of opportunity here. Take advantage of it in the breakout sessions. Okay, let me transition to hyperscale. So in hyperscale now, we have completely transformed our business model. We are now powering six of the top 10 hyperscalers in the world, which is a significant difference from where we were two years ago. And the reason we're doing that, is we've coined a term called ODM+. We believe that hyperscalers want more procurement power than an ODM, and Lenovo is doing about $18 billion of procurement a year. They want a broader global supply chain that they can get from a local system integrator. We're more than 160 countries around the world, but they want the same world-class quality and reliability like they get from an MNC. So, what we're doing now is instead of just taking off the shelf motherboards from somewhere, we're starting with a blank sheet of paper, we're working with the customer base on customized SKUs and you can see we already are developing 33 custom solutions for the largest hyperscalers in the world. And then we're not just running notebooks through this factory where YY said, we're running 37 notebook boards a minute, we're now putting in tens and tens and tens of thousands of server board capacity per month into this same factory, so absolutely we can compete with the most aggressive ODM's in the world, but it's not just putting these things in in the motherboard side, we're also building out these systems all around the world, India, Brazil, Hungary, Mexico, China. This is an example of a new hyperscale customer we've had this last year, 34,000 servers we delivered in the first six months. The next 34,000 servers we delivered in 68 days. The next 34,000 servers we delivered in 35 days, with more than 99 percent on-time delivery to 35 data centers in 14 countries as diverse as South Africa, India, China, Brazil, et cetera. And I'm really ashamed to say it was 99.3, because we did have a forklift driver who rammed their forklift right through the middle of the one of the server racks. (audience laughing) At JFK Airport that we had to respond to, but I think this gives you a perspective of what it is to be a top five global supply chain and technology. So last year, I said we would invest significantly in IP, in joint ventures, and M and A to compete in software defined, in networking, and in storage, so I wanted to give you an update on that as well. Our newest software-defined partnership is with Cloudistics, enabling a fully composable cloud infrastructure. It's an exclusive agreement, you can see them here. I think Nag, our founder, is going to be here today, with a significant Lenovo investment in the company. So, this new ThinkAgile CP series delivers the simplicity of the public cloud, on-premise with exceptional support and a marketplace of essential enterprise applications all with a single click deployment. So simply put, we're delivering a private cloud with a premium experience. It's simple in that you need no specialists to deploy it. An IT generalist can set it up and manage it. It's agile in that you can provision dozens of workloads in minutes, and it's transformative in that you get all of the goodness of public cloud on-prem in a private cloud to unlock opportunity for use. So, we're extremely excited about the ThinkAgile CP series that's now shipping into the marketplace. Beyond that we're aggressively ramping, and we're either doubling, tripling, or quadrupling our market share as customers move from traditional server technology to software-defined technology. With Nutanix we've been public, growing about more than 150 percent year-on-year, with Nutanix as their fastest growing Nutanix partner, but today I want to set another audacious goal. I believe we cannot just be Nutanix's fastest growing partner but we can become their largest partner within two years. On Microsoft, we are already four times our market share on Azure stack of our traditional business. We were the first to launch our ThinkAgile on Broadwell and on Skylake with the Azure Stack Infrastructure. And on VMware we're about twice our market segment share. We were the first to deliver an Intel-optimized Optane-certified VSAN node. And with Optane technology, we're delivering 50 percent more VM density than any competitive SSD system in the marketplace, about 10 times lower latency, four times the performance of any SSD system out there, and Lenovo's first to market on that. And at VMworld you saw CEO Pat Gelsinger of VMware talked about project dimension, which is Edge as a service, and we're the only OEM beyond the Dell family that is participating today in project dimension. Beyond that you're going to see a number of other partnerships we have. I'm excited that we have the city of Bogota Columbia here, an eight million person city, where we announced a 3,000 camera video surveillance solution last month. With pivot three you're going to see city of Bogota in our breakout sessions. You're going to see a new partnership with Veeam around backup that's launching today. You're going to see partnerships with scale computing in IoT and hyper-converged infrastructure working on some of the largest retailers in the world. So again, everything out in the breakout session. Transitioning to storage and data management, it's been a great year for Lenovo, more than a 100 percent growth year-on-year, 2X market growth in flash arrays. IDC just reported 30 percent growth in storage, number one in price performance in the world and the best HPC storage product in the top 500 with our ThinkSystem DSS G, so strong coverage, but I'm excited today to announce for Transform 2.0 that Lenovo is launching the largest data management and storage portfolio in our 25-year data center history. (audience applauding) So a year ago, the largest server portfolio, becoming the largest fastest growing server OEM, today the largest storage portfolio, but as you saw this morning we're not doing it alone. Today Lenovo and NetApp, two global powerhouses are joining forces to deliver a multi-billion dollar global alliance in data management and storage to help customers through their intelligent transformation. As the fastest growing worldwide server leader and one of the fastest growing flash array and data management companies in the world, we're going to deliver more choice to customers than ever before, global scale that's never been seen, supply chain efficiencies, and rapidly accelerating innovation and solutions. So, let me unwrap this a little bit for you and talk about what we're announcing today. First, it's the largest portfolio in our history. You're going to see not just storage solutions launching today but a set of solution recipes from NetApp that are going to make Lenovo server and NetApp or Lenovo storage work better together. The announcement enables Lenovo to go from covering 15 percent of the global storage market to more than 90 percent of the global storage market and distribute these products in more than 160 countries around the world. So we're launching today, 10 new storage platforms, the ThinkSystem DE and ThinkSystem DM platforms. They're going to be centrally managed, so the same XClarity management that you've been using for server, you can now use across all of your storage platforms as well, and it'll be supported by the same 10,000 plus service personnel that are giving outstanding customer support to you today on the server side. And we didn't come up with this in the last month or the last quarter. We're announcing availability in ordering today and shipments tomorrow of the first products in this portfolio, so we're excited today that it's not just a future announcement but something you as customers can take advantage of immediately. (audience applauding) The second part of the announcement is we are announcing a joint venture in China. Not only will this be a multi-billion dollar global partnership, but Lenovo will be a 51 percent owner, NetApp a 49 percent owner of a new joint venture in China with the goal of becoming in the top three storage companies in the largest data and storage market in the world. We will deliver our R and D in China for China, pooling our IP and resources together, and delivering a single route to market through a complementary channel, not just in China but worldwide. And in the future I just want to tell everyone this is phase one. There is so much exciting stuff. We're going to be on the stage over the next year talking to you about around integrated solutions, next-generation technologies, and further synergies and collaborations. So, rather than just have me talk about it, I'd like to welcome to the stage our new partner NetApp and Brad Anderson who's the senior vice president and general manager of NetApp Cloud Infrastructure. (upbeat music) (audience applauding) >> Thank You Kirk. >> So Brad, we've known each other a long time. It's an exciting day. I'm going to give you the stage and allow you to say NetApp's perspective on this announcement. >> Very good, thank you very much, Kirk. Kirk and I go back to I think 1994, so hey good morning and welcome. My name is Brad Anderson. I manage the Cloud Infrastructure Group at NetApp, and I am honored and privileged to be here at Lenovo Transform, particularly today on today's announcement. Now, you've heard a lot about digital transformation about how companies have to transform their IT to compete in today's global environment. And today's announcement with the partnership between NetApp and Lenovo is what that's all about. This is the joining of two global leaders bringing innovative technology in a simplified solution to help customers modernize their IT and accelerate their global digital transformations. Drawing on the strengths of both companies, Lenovo's high performance compute world-class supply chain, and NetApp's hybrid cloud data management, hybrid flash and all flash storage solutions and products. And both companies providing our customers with the global scale for them to be able to meet their transformation goals. At NetApp, we're very excited. This is a quote from George Kurian our CEO. George spent all day yesterday with YY and Kirk, and would have been here today if it hadn't been also our shareholders meeting in California, but I want to just convey how excited we are for all across NetApp with this partnership. This is a partnership between two companies with tremendous market momentum. Kirk took you through all the amazing results that Lenovo has accomplished, number one in supercomputing, number one in performance, number one in x86 reliability, number one in x86 customers sat, number five in supply chain, really impressive and congratulations. Like Lenovo, NetApp is also on a transformation journey, from a storage company to the data authority in hybrid cloud, and we've seen some pretty impressive momentum as well. Just last week we became number one in all flash arrays worldwide, catching EMC and Dell, and we plan to keep on going by them, as we help customers modernize their their data centers with cloud connected flash. We have strategic partnerships with the largest hyperscalers to provide cloud native data services around the globe and we are having success helping our customers build their own private clouds with just, with a new disruptive hyper-converged technology that allows them to operate just like hyperscalers. These three initiatives has fueled NetApp's transformation, and has enabled our customers to change the world with data. And oh by the way, it has also fueled us to have meet or have beaten Wall Street's expectations for nine quarters in a row. These are two companies with tremendous market momentum. We are also building this partnership for long term success. We think about this as phase one and there are two important components to phase one. Kirk took you through them but let me just review them. Part one, the establishment of a multi-year commitment and a collaboration agreement to offer Lenovo branded flash products globally, and as Kurt said in 160 countries. Part two, the formation of a joint venture in PRC, People's Republic of China, that will provide long term commitment, joint product development, and increase go-to-market investment to meet the unique needs to China. Both companies will put in storage technologies and storage expertise to form an independent JV that establishes a data management company in China for China. And while we can dream about what phase two looks like, our entire focus is on making phase one incredibly successful and I'm pleased to repeat what Kirk, is that the first products are orderable and shippable this week in 160 different countries, and you will see our two companies focusing on the here and now. On our joint go to market strategy, you'll see us working together to drive strategic alignment, focused execution, strong governance, and realistic expectations and milestones. And it starts with the success of our customers and our channel partners is job one. Enabling customers to modernize their legacy IT with complete data center solutions, ensuring that our customers get the best from both companies, new offerings the fuel business success, efficiencies to reinvest in game-changing initiatives, and new solutions for new mission-critical applications like data analytics, IoT, artificial intelligence, and machine learning. Channel partners are also top of mind for both our two companies. We are committed to the success of our existing and our future channel partners. For NetApp channel partners, it is new pathways to new segments and to new customers. For Lenovo's channel partners, it is the competitive weapons that now allows you to compete and more importantly win against Dell, EMC, and HP. And the good news for both companies is that our channel partner ecosystem is highly complementary with minimal overlap. Today is the first day of a very exciting partnership, of a partnership that will better serve our customers today and will provide new opportunities to both our companies and to our partners, new products to our customers globally and in China. I am personally very excited. I will be on the board of the JV. And so, I look forward to working with you, partnering with you and serving you as we go forward, and with that, I'd like to invite Kirk back up. (audience applauding) >> Thank you. >> Thank you. >> Well, thank you, Brad. I think it's an exciting overview, and these products will be manufactured in China, in Mexico, in Hungary, and around the world, enabling this amazing supply chain we talked about to deliver in over 160 countries. So thank you Brad, thank you George, for the amazing partnership. So again, that's not all. In Transform 2.0, last year, we talked about the joint ventures that were coming. I want to give you a sneak peek at what you should expect at future Lenovo events around the world. We have this Transform in Beijing in a couple weeks. We'll then be repeating this in 20 different locations roughly around the world over the next year, and I'm excited probably more than ever about what else is coming. Let's talk about Telco 5G and network function virtualization. Today, Motorola phones are certified on 46 global networks. We launched the world's first 5G upgradable phone here in the United States with Verizon. Lenovo DCG sells to 58 telecommunication providers around the world. At Mobile World Congress in Barcelona and Shanghai, you saw China Telecom and China Mobile in the Lenovo booth, China Telecom showing a video broadband remote access server, a VBRAS, with video streaming demonstrations with 2x less jitter than they had seen before. You saw China Mobile with a virtual remote access network, a VRAN, with greater than 10 times the throughput and 10x lower latency running on Lenovo. And this year, we'll be launching a new NFV company, a software company in China for China to drive the entire NFV stack, delivering not just hardware solutions, but software solutions, and we've recently hired a new CEO. You're going to hear more about that over the next several quarters. Very exciting as we try to drive new economics into the networks to deliver these 20 billion devices. We're going to need new economics that I think Lenovo can uniquely deliver. The second on IoT and edge, we've integrated on the device side into our intelligent devices group. With everything that's going to consume electricity computes and communicates, Lenovo is in a unique position on the device side to take advantage of the communications from Motorola and being one of the largest device companies in the world. But this year, we're also going to roll out a comprehensive set of edge gateways and ruggedized industrial servers and edge servers and ISP appliances for the edge and for IoT. So look for that as well. And then lastly, as a service, you're going to see Lenovo delivering hardware as a service, device as a service, infrastructure as a service, software as a service, and hardware as a service, not just as a glorified leasing contract, but with IP, we've developed true flexible metering capability that enables you to scale up and scale down freely and paying strictly based on usage, and we'll be having those announcements within this fiscal year. So Transform 2.0, lots to talk about, NetApp the big news of the day, but a lot more to come over the next year from the Data Center group. So in summary, I'm excited that we have a lot of customers that are going to be on stage with us that you saw in the video. Lots of testimonials so that you can talk to colleagues of yourself. Alamos Gold from Canada, a Canadian gold producer, Caligo for data optimization and privacy, SciNet, the largest supercomputer we've ever put into North America, and the largest in Canada at the University of Toronto will be here talking about climate change. City of Bogota again with our hyper-converged solutions around smart city putting in 3,000 cameras for criminal detection, license plate detection, et cetera, and then more from a channel mid market perspective, Jerry's Foods, which is from my home state of Wisconsin, and Minnesota which has about 57 stores in the specialty foods market, and how they're leveraging our IoT solutions as well. So again, about five times the number of demos that we had last year. So in summary, first and foremost to the customers, thank you for your business. It's been a great journey and I think we're on a tremendous role. You saw from last year, we're trying to build credibility with you. After the largest server portfolio, we're now the fastest-growing server OEM per Gardner, number one in performance, number one in reliability, number one in customer satisfaction, number one in supercomputing. Today, the largest storage portfolio in our history, with the goal of becoming the fastest growing storage company in the world, top three in China, multibillion-dollar collaboration with NetApp. And the transformation is going to continue with new edge gateways, edge servers, NFV solutions, telecommunications infrastructure, and hardware as a service with dynamic metering. So thank you for your time. I've looked forward to meeting many of you over the next day. We appreciate your business, and with that, I'd like to bring up Rod Lappen to introduce our next speaker. Rod? (audience applauding) >> Thanks, boss, well done. Alright ladies and gentlemen. No real secret there. I think we've heard why I might talk about the fourth Industrial Revolution in data and exactly what's going on with that. You've heard Kirk with some amazing announcements, obviously now with our NetApp partnership, talk about 5G, NFV, cloud, artificial intelligence, I think we've hit just about all the key hot topics. It's with great pleasure that I now bring up on stage Mr. Christian Teismann, our senior vice president and general manager of commercial business for both our PCs and our IoT business, so Christian Teismann. (techno music) Here, take that. >> Thank you. I think I'll need that. >> Okay, Christian, so obviously just before we get down, you and I last year, we had a bit of a chat about being in New York. >> Exports. >> You were an expat in New York for a long time. >> That's true. >> And now, you've moved from New York. You're in Munich? >> Yep. >> How does that feel? >> Well Munich is a wonderful city, and it's a great place to live and raise kids, but you know there's no place in the world like New York. >> Right. >> And I miss it a lot, quite frankly. >> So what exactly do you miss in New York? >> Well there's a lot of things in New York that are unique, but I know you spent some time in Japan, but I still believe the best sushi in the world is still in New York City. (all laughing) >> I will beg to differ. I will beg to differ. I think Mr. Guchi-san from Softbank is here somewhere. He will get up an argue very quickly that Japan definitely has better sushi than New York. But obviously you know, it's a very very special place, and I have had sushi here, it's been fantastic. What about Munich? Anything else that you like in Munich? >> Well I mean in Munich, we have pork knuckles. >> Pork knuckles. (Christian laughing) Very similar sushi. >> What is also very fantastic, but we have the real, the real Oktoberfest in Munich, and it starts next week, mid-September, and I think it's unique in the world. So it's very special as well. >> Oktoberfest. >> Yes. >> Unfortunately, I'm not going this year, 'cause you didn't invite me, but-- (audience chuckling) How about, I think you've got a bit of a secret in relation to Oktoberfest, probably not in Munich, however. >> It's a secret, yes, but-- >> Are you going to share? >> Well I mean-- >> See how I'm putting you on the spot? >> In the 10 years, while living here in New York, I was a regular visitor of the Oktoberfest at the Lower East Side in Avenue C at Zum Schneider, where I actually met my wife, and she's German. >> Very good. So, how about a big round of applause? (audience applauding) Not so much for Christian, but more I think, obviously for his wife, who obviously had been drinking and consequently ended up with you. (all laughing) See you later, mate. >> That's the beauty about Oktoberfest, but yes. So first of all, good morning to everybody, and great to be back here in New York for a second Transform event. New York clearly is the melting pot of the world in terms of culture, nations, but also business professionals from all kind of different industries, and having this event here in New York City I believe is manifesting what we are trying to do here at Lenovo, is transform every aspect of our business and helping our customers on the journey of intelligent transformation. Last year, in our transformation on the device business, I talked about how the PC is transforming to personalized computing, and we've made a lot of progress in that journey over the last 12 months. One major change that we have made is we combined all our device business under one roof. So basically PCs, smart devices, and smart phones are now under the roof and under the intelligent device group. But from my perspective makes a lot of sense, because at the end of the day, all devices connect in the modern world into the cloud and are operating in a seamless way. But we are also moving from a device business what is mainly a hardware focus historically, more and more also into a solutions business, and I will give you during my speech a little bit of a sense of what we are trying to do, as we are trying to bring all these components closer together, and specifically also with our strengths on the data center side really build end-to-end customer solution. Ultimately, what we want to do is make our business, our customer's businesses faster, safer, and ultimately smarter as well. So I want to look a little bit back, because I really believe it's important to understand what's going on today on the device side. Many of us have still grown up with phones with terminals, ultimately getting their first desktop, their first laptop, their first mobile phone, and ultimately smartphone. Emails and internet improved our speed, how we could operate together, but still we were defined by linear technology advances. Today, the world has changed completely. Technology itself is not a limiting factor anymore. It is how we use technology going forward. The Internet is pervasive, and we are not yet there that we are always connected, but we are nearly always connected, and we are moving to the stage, that everything is getting connected all the time. Sharing experiences is the most driving force in our behavior. In our private life, sharing pictures, videos constantly, real-time around the world, with our friends and with our family, and you see the same behavior actually happening in the business life as well. Collaboration is the number-one topic if it comes down to workplace, and video and instant messaging, things that are coming from the consumer side are dominating the way we are operating in the commercial business as well. Most important beside technology, that a new generation of workforce has completely changed the way we are working. As the famous workforce the first generation of Millennials that have now fully entered in the global workforce, and the next generation, it's called Generation Z, is already starting to enter the global workforce. By 2025, 75 percent of the world's workforce will be composed out of two of these generations. Why is this so important? These two generations have been growing up using state-of-the-art IT technology during their private life, during their education, school and study, and are taking these learnings and taking these behaviors in the commercial workspace. And this is the number one force of change that we are seeing in the moment. Diverse workforces are driving this change in the IT spectrum, and for years in many of our customers' focus was their customer focus. Customer experience also in Lenovo is the most important thing, but we've realized that our own human capital is equally valuable in our customer relationships, and employee experience is becoming a very important thing for many of our customers, and equally for Lenovo as well. As you have heard YY, as we heard from YY, Lenovo is focused on intelligent transformation. What that means for us in the intelligent device business is ultimately starting with putting intelligence in all of our devices, smartify every single one of our devices, adding value to our customers, traditionally IT departments, but also focusing on their end users and building products that make their end users more productive. And as a world leader in commercial devices with more than 33 percent market share, we can solve problems been even better than any other company in the world. So, let's talk about transformation of productivity first. We are in a device-led world. Everything we do is connected. There's more interaction with devices than ever, but also with spaces who are increasingly becoming smart and intelligent. YY said it, by 2020 we have more than 20 billion connected devices in the world, and it will grow exponentially from there on. And users have unique personal choices for technology, and that's very important to recognize, and we call this concept a digital wardrobe. And it means that every single end-user in the commercial business is composing his personal wardrobe on an ongoing basis and is reconfiguring it based on the work he's doing and based where he's going and based what task he is doing. I would ask all of you to put out all the devices you're carrying in your pockets and in your bags. You will see a lot of you are using phones, tablets, laptops, but also cameras and even smartwatches. They're all different, but they have one underlying technology that is bringing it all together. Recognizing digital wardrobe dynamics is a core factor for us to put all the devices under one roof in IDG, one business group that is dedicated to end-user solutions across mobile, PC, but also software services and imaging, to emerging technologies like AR, VR, IoT, and ultimately a AI as well. A couple of years back there was a big debate around bring-your-own-device, what was called consumerization. Today consumerization does not exist anymore, because consumerization has happened into every single device we build in our commercial business. End users and commercial customers today do expect superior display performance, superior audio, microphone, voice, and touch quality, and have it all connected and working seamlessly together in an ease of use space. We are already deep in the journey of personalized computing today. But the center point of it has been for the last 25 years, the mobile PC, that we have perfected over the last 25 years, and has been the undisputed leader in mobility computing. We believe in the commercial business, the ThinkPad is still the core device of a digital wardrobe, and we continue to drive the success of the ThinkPad in the marketplace. We've sold more than 140 million over the last 26 years, and even last year we exceeded nearly 11 million units. That is about 21 ThinkPads per minute, or one Thinkpad every three seconds that we are shipping out in the market. It's the number one commercial PC in the world. It has gotten countless awards but we felt last year after Transform we need to build a step further, in really tailoring the ThinkPad towards the need of the future. So, we announced a new line of X1 Carbon and Yoga at CES the Consumer Electronics Show. And the reason is not we want to sell to consumer, but that we do recognize that a lot of CIOs and IT decision makers need to understand what consumers are really doing in terms of technology to make them successful. So, let's take a look at the video. (suspenseful music) >> When you're the number one business laptop of all time, your only competition is yourself. (wall shattering) And, that's different. Different, like resisting heat, ice, dust, and spills. Different, like sharper, brighter OLA display. The trackpoint that reinvented controls, and a carbon fiber roll cage to protect what's inside, built by an engineering and design team, doing the impossible for the last 25 years. This is the number one business laptop of all time, but it's not a laptop. It's a ThinkPad. (audience applauding) >> Thank you very much. And we are very proud that Lenovo ThinkPad has been selected as the best laptop in the world in the second year in a row. I think it's a wonderful tribute to what our engineers have been done on this one. And users do want awesome displays. They want the best possible audio, voice, and touch control, but some users they want more. What they want is super power, and I'm really proud to announce our newest member of the X1 family, and that's the X1 extreme. It's exceptionally featured. It has six core I9 intel chipset, the highest performance you get in the commercial space. It has Nvidia XTX graphic, it is a 4K UHD display with HDR with Dolby vision and Dolby Atmos Audio, two terabyte in SSD, so it is really the absolute Ferrari in terms of building high performance commercial computer. Of course it has touch and voice, but it is one thing. It has so much performance that it serves also a purpose that is not typical for commercial, and I know there's a lot of secret gamers also here in this room. So you see, by really bringing technology together in the commercial space, you're creating productivity solutions of one of a kind. But there's another category of products from a productivity perspective that is incredibly important in our commercial business, and that is the workstation business . Clearly workstations are very specifically designed computers for very advanced high-performance workloads, serving designers, architects, researchers, developers, or data analysts. And power and performance is not just about the performance itself. It has to be tailored towards the specific use case, and traditionally these products have a similar size, like a server. They are running on Intel Xeon technology, and they are equally complex to manufacture. We have now created a new category as the ultra mobile workstation, and I'm very proud that we can announce here the lightest mobile workstation in the industry. It is so powerful that it really can run AI and big data analysis. And with this performance you can go really close where you need this power, to the sensors, into the cars, or into the manufacturing places where you not only wannna read the sensors but get real-time analytics out of these sensors. To build a machine like this one you need customers who are really challenging you to the limit. and we're very happy that we had a customer who went on this journey with us, and ultimately jointly with us created this product. So, let's take a look at the video. (suspenseful music) >> My world involves pathfinding both the hardware needs to the various work sites throughout the company, and then finding an appropriate model of desktop, laptop, or workstation to match those needs. My first impressions when I first seen the ThinkPad P1 was I didn't actually believe that we could get everything that I was asked for inside something as small and light in comparison to other mobile workstations. That was one of the I can't believe this is real sort of moments for me. (engine roars) >> Well, it's better than general when you're going around in the wind tunnel, which isn't alway easy, and going on a track is not necessarily the best bet, so having a lightweight very powerful laptop is extremely useful. It can take a Xeon processor, which can support ECC from when we try to load a full car, and when we're analyzing live simulation results. through and RCFT post processor or example. It needs a pretty powerful machine. >> It's come a long way to be able to deliver this. I hate to use the word game changer, but it is that for us. >> Aston Martin has got a lot of different projects going. There's some pretty exciting projects and a pretty versatile range coming out. Having Lenovo as a partner is certainly going to ensure that future. (engine roars) (audience applauds) >> So, don't you think the Aston Martin design and the ThinkPad design fit very well together? (audience laughs) So if Q, would get a new laptop, I think you would get a ThinkPad X P1. So, I want to switch gears a little bit, and go into something in terms of productivity that is not necessarily on top of the mind or every end user but I believe it's on top of the mind of every C-level executive and of every CEO. Security is the number one threat in terms of potential risk in your business and the cost of cybersecurity is estimated by 2020 around six trillion dollars. That's more than the GDP of Japan and we've seen a significant amount of data breach incidents already this years. Now, they're threatening to take companies out of business and that are threatening companies to lose a huge amount of sensitive customer data or internal data. At Lenovo, we are taking security very, very seriously, and we run a very deep analysis, around our own security capabilities in the products that we are building. And we are announcing today a new brand under the Think umbrella that is called ThinkShield. Our goal is to build the world's most secure PC, and ultimately the most secure devices in the industry. And when we looked at this end-to-end, there is no silver bullet around security. You have to go through every aspect where security breaches can potentially happen. That is why we have changed the whole organization, how we look at security in our device business, and really have it grouped under one complete ecosystem of solutions, Security is always something where you constantly are getting challenged with the next potential breach the next potential technology flaw. As we keep innovating and as we keep integrating, a lot of our partners' software and hardware components into our products. So for us, it's really very important that we partner with companies like Intel, Microsoft, Coronet, Absolute, and many others to really as an example to drive full encryption on all the data seamlessly, to have multi-factor authentication to protect your users' identity, to protect you in unsecured Wi-Fi locations, or even simple things like innovation on the device itself, to and an example protect the camera, against usage with a little thing like a thinkShutter that you can shut off the camera. SO what I want to show you here, is this is the full portfolio of ThinkShield that we are announcing today. This is clearly not something I can even read to you today, but I believe it shows you the breadth of security management that we are announcing today. There are four key pillars in managing security end-to-end. The first one is your data, and this has a lot of aspects around the hardware and the software itself. The second is identity. The third is the security around online, and ultimately the device itself. So, there is a breakout on security and ThinkShield today, available in the afternoon, and encourage you to really take a deeper look at this one. The first pillar around productivity was the device, and around the device. The second major pillar that we are seeing in terms of intelligent transformation is the workspace itself. Employees of a new generation have a very different habit how they work. They split their time between travel, working remotely but if they do come in the office, they expect a very different office environment than what they've seen in the past in cubicles or small offices. They come into the office to collaborate, and they want to create ideas, and they really work in cross-functional teams, and they want to do it instantly. And what we've seen is there is a huge amount of investment that companies are doing today in reconfiguring real estate reconfiguring offices. And most of these kind of things are moving to a digital platform. And what we are doing, is we want to build an entire set of solutions that are just focused on making the workspace more productive for remote workforce, and to create technology that allow people to work anywhere and connect instantly. And the core of this is that we need to be, the productivity of the employee as high as possible, and make it for him as easy as possible to use these kind of technologies. Last year in Transform, I announced that we will enter the smart office space. By the end of last year, we brought the first product into the market. It's called the Hub 500. It's already deployed in thousands of our customers, and it's uniquely focused on Microsoft Skype for Business, and making meeting instantly happen. And the product is very successful in the market. What we are announcing today is the next generation of this product, what is the Hub 700, what has a fantastic audio quality. It has far few microphones, and it is usable in small office environment, as well as in major conference rooms, but the most important part of this new announcement is that we are also announcing a software platform, and this software platform allows you to run multiple video conferencing software solutions on the same platform. Many of you may have standardized for one software solution or for another one, but as you are moving in a world of collaborating instantly with partners, customers, suppliers, you always will face multiple software standards in your company, and Lenovo is uniquely positioned but providing a middleware platform for the device to really enable multiple of these UX interfaces. And there's more to come and we will add additional UX interfaces on an ongoing base, based on our customer requirements. But this software does not only help to create a better experience and a higher productivity in the conference room or the huddle room itself. It really will allow you ultimately to manage all your conference rooms in the company in one instance. And you can run AI technologies around how to increase productivity utilization of your entire conference room ecosystem in your company. You will see a lot more devices coming from the node in this space, around intelligent screens, cameras, and so on, and so on. The idea is really that Lenovo will become a core provider in the whole movement into the smart office space. But it's great if you have hardware and software that is really supporting the approach of modern IT, but one component that Kirk also mentioned is absolutely critical, that we are providing this to you in an as a service approach. Get it what you want, when you need it, and pay it in the amount that you're really using it. And within UIT there is also I think a new philosophy around IT management, where you're much more focused on the value that you are consuming instead of investing into technology. We are launched as a service two years back and we already have a significant number of customers running PC as a service, but we believe as a service will stretch far more than just the PC device. It will go into categories like smart office. It might go even into categories like phone, and it will definitely go also in categories like storage and server in terms of capacity management. I want to highlight three offerings that we are also displaying today that are sort of building blocks in terms of how we really run as a service. The first one is that we collaborated intensively over the last year with Microsoft to be the launch pilot for their Autopilot offering, basically deploying images easily in the same approach like you would deploy a new phone on the network. The purpose really is to make new imaging and enabling new PC as seamless as it's used to be in the phone industry, and we have a complete set of offerings, and already a significant number customers have deployed Autopilot with Lenovo. The second major offering is Premier Support, like in the in the server business, where Premier Support is absolutely critical to run critical infrastructure, we see a lot of our customers do want to have Premier Support for their end users, so they can be back into work basically instantly, and that you have the highest possible instant repair on every single device. And then finally we have a significant amount of time invested into understanding how the software as a service really can get into one philosophy. And many of you already are consuming software as a service in many different contracts from many different vendors, but what we've created is one platform that really can manage this all together. All these things are the foundation for a device as a service offering that really can manage this end-to-end. So, implementing an intelligent workplace can be really a daunting prospect depending on where you're starting from, and how big your company ultimately is. But how do you manage the transformation of technology workspace if you're present in 50 or more countries and you run an infrastructure for more than 100,000 people? Michelin, famous for their tires, infamous for their Michelin star restaurant rating, especially in New York, and instantly recognizable by the Michelin Man, has just doing that. Please welcome with me Damon McIntyre from Michelin to talk to us about the challenges and transforming collaboration and productivity. (audience applauding) (electronic dance music) Thank you, David. >> Thank you, thank you very much. >> We on? >> So, how do you feel here? >> Well good, I want to thank you first of all for your partnership and the devices you create that helped us design, manufacture, and distribute the best tire in the world, okay? I just had to say it and put out there, alright. And I was wondering, were those Michelin tires on that Aston Martin? >> I'm pretty sure there is no other tire that would fit to that. >> Yeah, no, thank you, thank you again, and thank you for the introduction. >> So, when we talk about the transformation happening really in the workplace, the most tangible transformation that you actually see is the drastic change that companies are doing physically. They're breaking down walls. They're removing cubes, and they're moving to flexible layouts, new desks, new huddle rooms, open spaces, but the underlying technology for that is clearly not so visible very often. So, tell us about Michelin's strategy, and the technology you are deploying to really enable this corporation. >> So we, so let me give a little bit a history about the company to understand the daunting tasks that we had before us. So we have over 114,000 people in the company under 170 nationalities, okay? If you go to the corporate office in France, it's Clermont. It's about 3,000 executives and directors, and what have you in the marketing, sales, all the way up to the chain of the global CIO, right? Inside of the Americas, we merged in Americas about three years ago. Now we have the Americas zone. There's about 28,000 employees across the Americas, so it's really, it's really hard in a lot of cases. You start looking at the different areas that you lose time, and you lose you know, your productivity and what have you, so there, it's when we looked at different aspects of how we were going to manage the meeting rooms, right? because we have opened up our areas of workspace, our CIO, CEOs in our zones will no longer have an office. They'll sit out in front of everybody else and mingle with the crowd. So, how do you take those spaces that were originally used by an individual but now turn them into like meeting rooms? So, we went through a large process, and looked at the Hub 500, and that really met our needs, because at the end of the day what we noticed was, it was it was just it just worked, okay? We've just added it to the catalog, so we're going to be deploying it very soon, and I just want to again point that I know everybody struggles with this, and if you look at all the minutes that you lose in starting up a meeting, and we know you know what I'm talking about when I say this, it equates to many many many dollars, okay? And so at the end the day, this product helps us to be more efficient in starting up the meeting, and more productive during the meeting. >> Okay, it's very good to hear. Another major trend we are seeing in IT departments is taking a more hands-off approach to hardware. We're seeing new technologies enable IT to create a more efficient model, how IT gets hardware in the hands of end-users, and how they are ultimately supporting themselves. So what's your strategy around the lifecycle management of the devices? >> So yeah you mentioned, again, we'll go back to the 114,000 employees in the company, right? You imagine looking at all the devices we use. I'm not going to get into the number of devices we have, but we have a set number that we use, and we have to go through a process of deploying these devices, which we right now service our own image. We build our images, we service them through our help desk and all that process, and we go through it. If you imagine deploying 25,000 PCs in a year, okay? The time and the daunting task that's behind all that, you can probably add up to 20 or 30 people just full-time doing that, okay? So, with partnering with Lenovo and their excellent technology, their technical teams, and putting together the whole process of how we do imaging, it now lifts that burden off of our folks, and it shifts it into a more automated process through the cloud, okay? And, it's with the Autopilot on the end of the project, we'll have Autopilot fully engaged, but what I really appreciate is how Lenovo really, really kind of got with us, and partnered with us for the whole process. I mean it wasn't just a partner between Michelin and Lenovo. Microsoft was also partnered during that whole process, and it really was a good project that we put together, and we hope to have something in a full production mode next year for sure. >> So, David thank you very, very much to be here with us on stage. What I really want to say, customers like you, who are always challenging us on every single aspect of our capabilities really do make the big difference for us to get better every single day and we really appreciate the partnership. >> Yeah, and I would like to say this is that I am, I'm doing what he's exactly said he just said. I am challenging Lenovo to show us how we can innovate in our work space with your devices, right? That's a challenge, and it's going to be starting up next year for sure. We've done some in the past, but I'm really going to challenge you, and my whole aspect about how to do that is bring you into our workspace. Show you how we make how we go through the process of making tires and all that process, and how we distribute those tires, so you can brainstorm, come back to the table and say, here's a device that can do exactly what you're doing right now, better, more efficient, and save money, so thank you. >> Thank you very much, David. (audience applauding) Well it's sometimes really refreshing to get a very challenging customers feedback. And you know, we will continue to grow this business together, and I'm very confident that your challenge will ultimately help to make our products even more seamless together. So, as we now covered productivity and how we are really improving our devices itself, and the transformation around the workplace, there is one pillar left I want to talk about, and that's really, how do we make businesses smarter than ever? What that really means is, that we are on a journey on trying to understand our customer's business, deeper than ever, understanding our customer's processes even better than ever, and trying to understand how we can help our customers to become more competitive by injecting state-of-the-art technology in this intelligent transformation process, into core processes. But this cannot be done without talking about a fundamental and that is the journey towards 5G. I really believe that 5G is changing everything the way we are operating devices today, because they will be connected in a way like it has never done before. YY talked about you know, 20 times 10 times the amount of performance. There are other studies that talk about even 200 times the performance, how you can use these devices. What it will lead to ultimately is that we will build devices that will be always connected to the cloud. And, we are preparing for this, and Kirk already talked about, and how many operators in the world we already present with our Moto phones, with how many Telcos we are working already on the backend, and we are working on the device side on integrating 5G basically into every single one of our product in the future. One of the areas that will benefit hugely from always connected is the world of virtual reality and augmented reality. And I'm going to pick here one example, and that is that we have created a commercial VR solution for classrooms and education, and basically using consumer type of product like our Mirage Solo with Daydream and put a solution around this one that enables teachers and schools to use these products in the classroom experience. So, students now can have immersive learning. They can studying sciences. They can look at environmental issues. They can exploring their careers, or they can even taking a tour in the next college they're going to go after this one. And no matter what grade level, this is how people will continue to learn in the future. It's quite a departure from the old world of textbooks. In our area that we are looking is IoT, And as YY already elaborated, we are clearly learning from our own processes around how we improve our supply chain and manufacturing and how we improve also retail experience and warehousing, and we are working with some of the largest companies in the world on pilots, on deploying IoT solutions to make their businesses, their processes, and their businesses, you know, more competitive, and some of them you can see in the demo environment. Lenovo itself already is managing 55 million devices in an IoT fashion connecting to our own cloud, and constantly improving the experience by learning from the behavior of these devices in an IoT way, and we are collecting significant amount of data to really improve the performance of these systems and our future generations of products on a ongoing base. We have a very strong partnership with a company called ADLINK from Taiwan that is one of the leading manufacturers of manufacturing PC and hardened devices to create solutions on the IoT platform. The next area that we are very actively investing in is commercial augmented reality. I believe augmented reality has by far more opportunity in commercial than virtual reality, because it has the potential to ultimately improve every single business process of commercial customers. Imagine in the future how complex surgeries can be simplified by basically having real-time augmented reality information about the surgery, by having people connecting into a virtual surgery, and supporting the surgery around the world. Visit a furniture store in the future and see how this furniture looks in your home instantly. Doing some maintenance on some devices yourself by just calling the company and getting an online manual into an augmented reality device. Lenovo is exploring all kinds of possibilities, and you will see a solution very soon from Lenovo. Early when we talked about smart office, I talked about the importance of creating a software platform that really run all these use cases for a smart office. We are creating a similar platform for augmented reality where companies can develop and run all their argumented reality use cases. So you will see that early in 2019 we will announce an augmented reality device, as well as an augmented reality platform. So, I know you're very interested on what exactly we are rolling out, so we will have a first prototype view available there. It's still a codename project on the horizon, and we will announce it ultimately in 2019, but I think it's good for you to take a look what we are doing here. So, I just wanted to give you a peek on what we are working beyond smart office and the device productivity in terms of really how we make businesses smarter. It's really about increasing productivity, providing you the most secure solutions, increase workplace collaboration, increase IT efficiency, using new computing devices and software and services to make business smarter in the future. There's no other company that will enable to offer what we do in commercial. No company has the breadth of commercial devices, software solutions, and the same data center capabilities, and no other company can do more for your intelligent transformation than Lenovo. Thank you very much. (audience applauding) >> Thanks mate, give me that. I need that. Alright, ladies and gentlemen, we are done. So firstly, I've got a couple of little housekeeping pieces at the end of this and then we can go straight into going and experiencing some of the technology we've got on the left-hand side of the room here. So, I want to thank Christian obviously. Christian, awesome as always, some great announcements there. I love the P1. I actually like the Aston Martin a little bit better, but I'll take either if you want to give me one for free. I'll take it. We heard from YY obviously about the industry and how the the fourth Industrial Revolution is impacting us all from a digital transformation perspective, and obviously Kirk on DCG, the great NetApp announcement, which is going to be really exciting, actually that Twitter and some of the social media panels are absolutely going crazy, so it's good to see that the industry is really taking some impact. Some of the publications are really great, so thank you for the media who are obviously in the room publishing right no. But now, I really want to say it's all of your turn. So, all of you up the back there who are having coffee, it's your turn now. I want everyone who's sitting down here after this event move into there, and really take advantage of the 15 breakouts that we've got set there. There are four breakout sessions from a time perspective. I want to try and get you all out there at least to use up three of them and use your fourth one to get out and actually experience some of the technology. So, you've got four breakout sessions. A lot of the breakout sessions are actually done twice. If you have not downloaded the app, please download the app so you can actually see what time things are going on and make sure you're registering correctly. There's a lot of great experience of stuff out there for you to go do. I've got one quick video to show you on some of the technology we've got and then we're about to close. Alright, here we are acting crazy. Now, you can see obviously, artificial intelligence machine learning in the browser. God, I hate that dance, I'm not a Millenial at all. It's effectively going to be implemented by healthcare. I want you to come around and test that out. Look at these two guys. This looks like a Lenovo management meeting to be honest with you. These two guys are actually concentrating, using their brain power to race each others in cars. You got to come past and give that a try. Give that a try obviously. Fantastic event here, lots of technology for you to experience, and great partners that have been involved as well. And so, from a Lenovo perspective, we've had some great alliance partners contribute, including obviously our number one partner, Intel, who's been a really big loyal contributor to us, and been a real part of our success here at Transform. Excellent, so please, you've just seen a little bit of tech out there that you can go and play with. I really want you, I mean go put on those black things, like Scott Hawkins our chief marketing officer from Lenovo's DCG business was doing and racing around this little car with his concentration not using his hands. He said it's really good actually, but as soon as someone comes up to speak to him, his car stops, so you got to try and do better. You got to try and prove if you can multitask or not. Get up there and concentrate and talk at the same time. 62 different breakouts up there. I'm not going to go into too much detai, but you can see we've got a very, very unusual numbering system, 18 to 18.8. I think over here we've got a 4849. There's a 4114. And then up here we've got a 46.1 and a 46.2. So, you need the decoder ring to be able to understand it. Get over there have a lot of fun. Remember the boat leaves today at 4:00 o'clock, right behind us at the pier right behind us here. There's 400 of us registered. Go onto the app and let us know if there's more people coming. It's going to be a great event out there on the Hudson River. Ladies and gentlemen that is the end of your keynote. I want to thank you all for being patient and thank all of our speakers today. Have a great have a great day, thank you very much. 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