>>Welcome back to The Cube's coverage of Supercomputing Conference 2022, otherwise known as SC 22 here in Dallas, Texas. This is day three of our coverage, the final day of coverage here on the exhibition floor. I'm Dave Nicholson, and I'm here with my co-host, tech journalist extraordinaire, Paul Gillum. How's it going, >>Paul? Hi, Dave. It's going good. >>And we have a wonderful guest with us this morning, Dr. Panda from the Ohio State University. Welcome Dr. Panda to the Cube. >>Thanks a lot. Thanks a lot to >>Paul. I know you're, you're chopping at >>The bit, you have incredible credentials, over 500 papers published. The, the impact that you've had on HPC is truly remarkable. But I wanted to talk to you specifically about a product project you've been working on for over 20 years now called mva, high Performance Computing platform that's used by more than 32 organ, 3,200 organizations across 90 countries. You've shepherded this from, its, its infancy. What is the vision for what MVA will be and and how is it a proof of concept that others can learn from? >>Yeah, Paul, that's a great question to start with. I mean, I, I started with this conference in 2001. That was the first time I came. It's very coincidental. If you remember the Finman Networking Technology, it was introduced in October of 2000. Okay. So in my group, we were working on NPI for Marinette Quadrics. Those are the old technology, if you can recollect when Finman was there, we were the very first one in the world to really jump in. Nobody knew how to use Infin van in an HPC system. So that's how the Happy Project was born. And in fact, in super computing 2002 on this exhibition floor in Baltimore, we had the first demonstration, the open source happy, actually is running on an eight node infinite van clusters, eight no zeros. And that was a big challenge. But now over the years, I means we have continuously worked with all infinite van vendors, MPI Forum. >>We are a member of the MPI Forum and also all other network interconnect. So we have steadily evolved this project over the last 21 years. I'm very proud of my team members working nonstop, continuously bringing not only performance, but scalability. If you see now INFIN event are being deployed in 8,000, 10,000 node clusters, and many of these clusters actually use our software, stack them rapid. So, so we have done a lot of, like our focuses, like we first do research because we are in academia. We come up with good designs, we publish, and in six to nine months, we actually bring it to the open source version and people can just download and then use it. And that's how currently it's been used by more than 3000 orange in 90 countries. And, but the interesting thing is happening, your second part of the question. Now, as you know, the field is moving into not just hvc, but ai, big data, and we have those support. This is where like we look at the vision for the next 20 years, we want to design this MPI library so that not only HPC but also all other workloads can take advantage of it. >>Oh, we have seen libraries that become a critical develop platform supporting ai, TensorFlow, and, and the pie torch and, and the emergence of, of, of some sort of default languages that are, that are driving the community. How, how important are these frameworks to the, the development of the progress making progress in the HPC world? >>Yeah, no, those are great. I mean, spite our stencil flow, I mean, those are the, the now the bread and butter of deep learning machine learning. Am I right? But the challenge is that people use these frameworks, but continuously models are becoming larger. You need very first turnaround time. So how do you train faster? How do you do influencing faster? So this is where HPC comes in and what exactly what we have done is actually we have linked floor fighters to our happy page because now you see the MPI library is running on a million core system. Now your fighters and tenor four clan also be scaled to to, to those number of, large number of course and gps. So we have actually done that kind of a tight coupling and that helps the research to really take advantage of hpc. >>So if, if a high school student is thinking in terms of interesting computer science, looking for a place, looking for a university, Ohio State University, bruns, world renowned, widely known, but talk about what that looks like from a day on a day to day basis in terms of the opportunity for undergrad and graduate students to participate in, in the kind of work that you do. What is, what does that look like? And is, and is that, and is that a good pitch to for, for people to consider the university? >>Yes. I mean, we continuously, from a university perspective, by the way, the Ohio State University is one of the largest single campus in, in us, one of the top three, top four. We have 65,000 students. Wow. It's one of the very largest campus. And especially within computer science where I am located, high performance computing is a very big focus. And we are one of the, again, the top schools all over the world for high performance computing. And we also have very strength in ai. So we always encourage, like the new students who like to really work on top of the art solutions, get exposed to the concepts, principles, and also practice. Okay. So, so we encourage those people that wish you can really bring you those kind of experience. And many of my past students, staff, they're all in top companies now, have become all big managers. >>How, how long, how long did you say you've been >>At 31 >>Years? 31 years. 31 years. So, so you, you've had people who weren't alive when you were already doing this stuff? That's correct. They then were born. Yes. They then grew up, yes. Went to university graduate school, and now they're on, >>Now they're in many top companies, national labs, all over the universities, all over the world. So they have been trained very well. Well, >>You've, you've touched a lot of lives, sir. >>Yes, thank you. Thank >>You. We've seen really a, a burgeoning of AI specific hardware emerge over the last five years or so. And, and architectures going beyond just CPUs and GPUs, but to Asics and f PGAs and, and accelerators, does this excite you? I mean, are there innovations that you're seeing in this area that you think have, have great promise? >>Yeah, there is a lot of promise. I think every time you see now supercomputing technology, you see there is sometime a big barrier comes barrier jump. Rather I'll say, new technology comes some disruptive technology, then you move to the next level. So that's what we are seeing now. A lot of these AI chips and AI systems are coming up, which takes you to the next level. But the bigger challenge is whether it is cost effective or not, can that be sustained longer? And this is where commodity technology comes in, which commodity technology tries to take you far longer. So we might see like all these likes, Gaudi, a lot of new chips are coming up, can they really bring down the cost? If that cost can be reduced, you will see a much more bigger push for AI solutions, which are cost effective. >>What, what about on the interconnect side of things, obvi, you, you, your, your start sort of coincided with the initial standards for Infin band, you know, Intel was very, very, was really big in that, in that architecture originally. Do you see interconnects like RDMA over converged ethernet playing a part in that sort of democratization or commoditization of things? Yes. Yes. What, what are your thoughts >>There for internet? No, this is a great thing. So, so we saw the infinite man coming. Of course, infinite Man is, commod is available. But then over the years people have been trying to see how those RDMA mechanisms can be used for ethernet. And then Rocky has been born. So Rocky has been also being deployed. But besides these, I mean now you talk about Slingshot, the gray slingshot, it is also an ethernet based systems. And a lot of those RMA principles are actually being used under the hood. Okay. So any modern networks you see, whether it is a Infin and Rocky Links art network, rock board network, you name any of these networks, they are using all the very latest principles. And of course everybody wants to make it commodity. And this is what you see on the, on the slow floor. Everybody's trying to compete against each other to give you the best performance with the lowest cost, and we'll see whoever wins over the years. >>Sort of a macroeconomic question, Japan, the US and China have been leapfrogging each other for a number of years in terms of the fastest supercomputer performance. How important do you think it is for the US to maintain leadership in this area? >>Big, big thing, significantly, right? We are saying that I think for the last five to seven years, I think we lost that lead. But now with the frontier being the number one, starting from the June ranking, I think we are getting that leadership back. And I think it is very critical not only for fundamental research, but for national security trying to really move the US to the leading edge. So I hope us will continue to lead the trend for the next few years until another new system comes out. >>And one of the gating factors, there is a shortage of people with data science skills. Obviously you're doing what you can at the university level. What do you think can change at the secondary school level to prepare students better to, for data science careers? >>Yeah, I mean that is also very important. I mean, we, we always call like a pipeline, you know, that means when PhD levels we are expecting like this even we want to students to get exposed to, to, to many of these concerts from the high school level. And, and things are actually changing. I mean, these days I see a lot of high school students, they, they know Python, how to program in Python, how to program in sea object oriented things. Even they're being exposed to AI at that level. So I think that is a very healthy sign. And in fact we, even from Ohio State side, we are always engaged with all this K to 12 in many different programs and then gradually trying to take them to the next level. And I think we need to accelerate also that in a very significant manner because we need those kind of a workforce. It is not just like a building a system number one, but how do we really utilize it? How do we utilize that science? How do we propagate that to the community? Then we need all these trained personal. So in fact in my group, we are also involved in a lot of cyber training activities for HPC professionals. So in fact, today there is a bar at 1 1 15 I, yeah, I think 1215 to one 15. We'll be talking more about that. >>About education. >>Yeah. Cyber training, how do we do for professionals? So we had a funding together with my co-pi, Dr. Karen Tom Cook from Ohio Super Center. We have a grant from NASA Science Foundation to really educate HPT professionals about cyber infrastructure and ai. Even though they work on some of these things, they don't have the complete knowledge. They don't get the time to, to learn. And the field is moving so fast. So this is how it has been. We got the initial funding, and in fact, the first time we advertised in 24 hours, we got 120 application, 24 hours. We couldn't even take all of them. So, so we are trying to offer that in multiple phases. So, so there is a big need for those kind of training sessions to take place. I also offer a lot of tutorials at all. Different conference. We had a high performance networking tutorial. Here we have a high performance deep learning tutorial, high performance, big data tutorial. So I've been offering tutorials at, even at this conference since 2001. Good. So, >>So in the last 31 years, the Ohio State University, as my friends remind me, it is properly >>Called, >>You've seen the world get a lot smaller. Yes. Because 31 years ago, Ohio, in this, you know, of roughly in the, in the middle of North America and the United States was not as connected as it was to everywhere else in the globe. So that's, that's pro that's, I i it kind of boggles the mind when you think of that progression over 31 years, but globally, and we talk about the world getting smaller, we're sort of in the thick of, of the celebratory seasons where, where many, many groups of people exchange gifts for varieties of reasons. If I were to offer you a holiday gift, that is the result of what AI can deliver the world. Yes. What would that be? What would, what would, what would the first thing be? This is, this is, this is like, it's, it's like the genie, but you only get one wish. >>I know, I know. >>So what would the first one be? >>Yeah, it's very hard to answer one way, but let me bring a little bit different context and I can answer this. I, I talked about the happy project and all, but recently last year actually we got awarded an S f I institute award. It's a 20 million award. I am the overall pi, but there are 14 universities involved. >>And who is that in that institute? >>What does that Oh, the I ici. C e. Okay. I cycle. You can just do I cycle.ai. Okay. And that lies with what exactly what you are trying to do, how to bring lot of AI for masses, democratizing ai. That's what is the overall goal of this, this institute, think of like a, we have three verticals we are working think of like one is digital agriculture. So I'll be, that will be my like the first ways. How do you take HPC and AI to agriculture the world as though we just crossed 8 billion people. Yeah, that's right. We need continuous food and food security. How do we grow food with the lowest cost and with the highest yield? >>Water >>Consumption. Water consumption. Can we minimize or minimize the water consumption or the fertilization? Don't do blindly. Technologies are out there. Like, let's say there is a weak field, A traditional farmer see that, yeah, there is some disease, they will just go and spray pesticides. It is not good for the environment. Now I can fly it drone, get images of the field in the real time, check it against the models, and then it'll tell that, okay, this part of the field has disease. One, this part of the field has disease. Two, I indicate to the, to the tractor or the sprayer saying, okay, spray only pesticide one, you have pesticide two here. That has a big impact. So this is what we are developing in that NSF A I institute I cycle ai. We also have, we have chosen two additional verticals. One is animal ecology, because that is very much related to wildlife conservation, climate change, how do you understand how the animals move? Can we learn from them? And then see how human beings need to act in future. And the third one is the food insecurity and logistics. Smart food distribution. So these are our three broad goals in that institute. How do we develop cyber infrastructure from below? Combining HP c AI security? We have, we have a large team, like as I said, there are 40 PIs there, 60 students. We are a hundred members team. We are working together. So, so that will be my wish. How do we really democratize ai? >>Fantastic. I think that's a great place to wrap the conversation here On day three at Supercomputing conference 2022 on the cube, it was an honor, Dr. Panda working tirelessly at the Ohio State University with his team for 31 years toiling in the field of computer science and the end result, improving the lives of everyone on Earth. That's not a stretch. If you're in high school thinking about a career in computer science, keep that in mind. It isn't just about the bits and the bobs and the speeds and the feeds. It's about serving humanity. Maybe, maybe a little, little, little too profound a statement, I would argue not even close. I'm Dave Nicholson with the Queue, with my cohost Paul Gillin. Thank you again, Dr. Panda. Stay tuned for more coverage from the Cube at Super Compute 2022 coming up shortly. >>Thanks a lot.

>> From every ISV to solve the problems. You want there to be tools in place that you can use, either open source tools or whatever it is that help you build it. And slowly over time, that building will become easier and easier. So my question to you was, where do you see you playing? Do you see yourself playing to ISVs as a set of tools, which will make their life a lot easier and provide that work? >> Absolutely. >> If they don't have, so they don't have to do it. Or you're providing this for the end users? Or both? >> So it's a progression. If you go to the ISVs first, you're doomed to starved before you have time for that other option. >> Yeah. >> Right? So it's a question of phase, the phasing of it. And also if you go directly to end users, you can demonstrate the power of it and get the attention of the ISVs. I believe that the ISVs, especially those with the biggest footprints and the most, you know, coveted estates, they have already made massive investments at trying to solve decentralization of their software stack. And I believe that they have used it as a hook to try to move to a software as a service model and rope people into leasing their infrastructure. So if you look at the clouds that have been propped up by Autodesk or by Adobe, or you name the company, they are building proprietary makeshift solutions for decentralizing or hybrid clouding. Or maybe they're not even doing that at all and all they're is saying hey, if you want to get location agnosticness, then what you should just, is just move into our cloud. >> Right. >> And then they try to solve on the background how to decentralize it between different regions so they can have decent offerings in each region. But those who are more advanced have already made larger investments and will be more averse to, you know, throwing that stuff away, all of their makeshift machinery away, and using a platform that gives them high performance parallel, low level file system access, while at the same time having metadata-driven, you know, policy-based, intent-based orchestration to manage the diffusion of data across a decentralized infrastructure. They are not going to be as open because they've made such an investment and they're going to look at how do they monetize it. So what we have found with like the movie studios who are using us already, many of the app they're using, many of those software offerings, the ISVs have their own cloud that offers that software for the cloud. But what we got when I asked about this, 'cause I was dealt specifically into this question because I'm very interested to know how we're going to make that leap from end user upstream into the ISVs where I believe we need to, and they said, look, we cannot use these software ISV-specific SAS clouds for two reasons. Number one is we lose control of the data. We're giving it to them. That's security and other issues. And here you're talking about we're doing work for Disney, we're doing work for Netflix, and they're not going to let us put our data on those software clouds, on those SAS clouds. Secondly, in any reasonable pipeline, the data is shared by many different applications. We need to be agnostic as to the application. 'Cause the inputs to one application, you know, the output for one application provides the input to the next, and it's not necessarily from the same vendor. So they need to have a data platform that lets them, you know, go from one software stack, and you know, to run it on another. Because they might do the rendering with this and yet, they do the editing with that, and you know, et cetera, et cetera. So I think the further you go up the stack in the structured data and dedicated applications for specific functions in specific verticals, the further up the stack you go, the harder it is to justify a SAS offering where you're basically telling the end users you need to park all your data with us and then you can run your application in our cloud and get this. That ultimately is a dead end path versus having the data be open and available to many applications across this supercloud layer. >> Okay, so-- >> Is that making any sense? >> Yes, so if I could just ask a clarifying question. So, if I had to take Snowflake as an example, I think they're doing exactly what you're saying is a dead end, put everything into our proprietary system and then we'll figure out how to distribute it. >> Yeah. >> And and I think if you're familiar with Zhamak Dehghaniis' data mesh concept. Are you? >> A little bit, yeah. >> But in her model, Snowflake, a Snowflake warehouse is just a node on the mesh and that mesh is-- >> That's right. >> Ultimately the supercloud and you're an enabler of that is what I'm hearing. >> That's right. What they're doing up at the structured level and what they're talking about at the structured level we're doing at the underlying, unstructured level, which by the way has implications for how you implement those distributed database things. In other words, implementing a Snowflake on top of Hammerspace would have made building stuff like in the first place easier. It would allow you to easily shift and run the database engine anywhere. You still have to solve how to shard and distribute at the transaction layer above, so I'm not saying we're a substitute for what you need to do at the app layer. By the way, there is another example of that and that's Microsoft Office, right? It's one thing to share that, to have a file share where you can share all the docs. It's something else to have Word and PowerPoint, Excel know how to allow people to be simultaneously editing the same doc. That's always going to happen in the app layer. But not all applications need that level of, you know, in-app decentralization. You know, many of them, many workflows are pipelined, especially the ones that are very data intensive where you're doing drug discovery or you're doing rendering, or you're doing machine learning training. These things are human in the loop with large stages of processing across tens of thousands of cores. And I think that kind of data processing pipeline is what we're focusing on first. Not so much the Microsoft Office or the Snowflake, you know, parking a relational database because that takes a lot of application layer stuff and that's what they're good at. >> Right. >> But I think... >> Go ahead, sorry. >> Later entrance in these markets will find Hammerspace as a way to accelerate their work so they can focus more narrowly on just the stuff that's app-specific, higher level sharing in the app. >> Yes, Snowflake founders-- >> I think it might be worth mentioning also, just keep this confidential guys, but one of our customers is Blue Origin. And one of the things that we have found is kind of the point of what you're talking about with our customers. They're needing to build this and since it's not commercially available or they don't know where to look for it to be commercially available, they're all building themselves. So this layer is needed. And Blue is just one of the examples of quite a few we're now talking to. And like manufacturing, HPC, research where they're out trying to solve this problem with their own scripting tools and things like that. And I just, I don't know if there's anything you want to add, David, but you know, but there's definitely a demand here and customers are trying to figure out how to solve it beyond what Hammerspace is doing. Like the need is so great that they're just putting developers on trying to do it themselves. >> Well, and you know, Snowflake founders, they didn't have a Hammerspace to lean on. But, one of the things that's interesting about supercloud is we feel as though industry clouds will emerge, that as part of company's digital transformations, they will, you know, every company's a software company, they'll begin to build their own clouds and they will be able to use a Hammerspace to do that. >> A super pass layer. >> Yes. It's really, I don't know if David's speaking, I don't want to speak over him, but we can't hear you. May be going through a bad... >> Well, a regional, regional talks that make that possible. And so they're doing these render farms and editing farms, and it's a cloud-specific to the types of workflows in the median entertainment world. Or clouds specifically to workflows in the chip design world or in the drug and bio and life sciences exploration world. There are large organizations that are kind of a blend of end users, like the Broad, which has their own kind of cloud where they're asking collaborators to come in and work with them. So it starts to even blur who's an end user versus an ISV. >> Yes. >> Right? When you start talking about the massive data is the main gravity is to having lots of people participate. >> Yep, and that's where the value is. And that's where the value is. And this is a megatrend that we see. And so it's really important for us to get to the point of what is and what is not a supercloud and, you know, that's where we're trying to evolve. >> Let's talk about this for a second 'cause I want to, I want to challenge you on something and it's something that I got challenged on and it has led me to thinking differently than I did at first, which Molly can attest to. Okay? So, we have been looking for a way to talk about the concept of cloud of utility computing, run anything anywhere that isn't addressed in today's realization of cloud. 'Cause today's cloud is not run anything anywhere, it's quite the opposite. You park your data in AWS and that's where you run stuff. And you pretty much have to. Same with with Azure. They're using data gravity to keep you captive there, just like the old infrastructure guys did. But now it's even worse because it's coupled back with the software to some degree, as well. And you have to use their storage, networking, and compute. It's not, I mean it fell back to the mainframe era. Anyhow, so I love the concept of supercloud. By the way, I was going to suggest that a better term might be hyper cloud since hyper speaks to the multidimensionality of it and the ability to be in a, you know, be in a different dimension, a different plane of existence kind of thing like hyperspace. But super and hyper are somewhat synonyms. I mean, you have hyper cars and you have super cars and blah, blah, blah. I happen to like hyper maybe also because it ties into the whole Hammerspace notion of a hyper-dimensional, you know, reality, having your data centers connected by a wormhole that is Hammerspace. But regardless, what I got challenged on is calling it something different at all versus simply saying, this is what cloud has always meant to be. This is the true cloud, this is real cloud, this is cloud. And I think back to what happened, you'll remember, at Fusion IO we talked about IO memory and we did that because people had a conceptualization of what an SSD was. And an SSD back then was low capacity, low endurance, made to go military, aerospace where things needed to be rugged but was completely useless in the data center. And we needed people to imagine this thing as being able to displace entire SAND, with the kind of capacity density, performance density, endurance. And so we talked IO memory, we could have said enterprise SSD, and that's what the industry now refers to for that concept. What will people be saying five and 10 years from now? Will they simply say, well this is cloud as it was always meant to be where you are truly able to run anything anywhere and have not only the same APIs, but you're same data available with high performance access, all forms of access, block file and object everywhere. So yeah. And I wonder, and this is just me throwing it out there, I wonder if, well, there's trade offs, right? Giving it a new moniker, supercloud, versus simply talking about how cloud is always intended to be and what it was meant to be, you know, the real cloud or true cloud, there are trade-offs. By putting a name on it and branding it, that lets people talk about it and understand they're talking about something different. But it also is that an affront to people who thought that that's what they already had. >> What's different, what's new? Yes, and so we've given a lot of thought to this. >> Right, it's like you. >> And it's because we've been asked that why does the industry need a new term, and we've tried to address some of that. But some of the inside baseball that we haven't shared is, you remember the Web 2.0, back then? >> Yep. >> Web 2.0 was the same thing. And I remember Tim Burners Lee saying, "Why do we need Web 2.0? "This is what the Web was always supposed to be." But the truth is-- >> I know, that was another perfect-- >> But the truth is it wasn't, number one. Number two, everybody hated the Web 2.0 term. John Furrier was actually in the middle of it all. And then it created this groundswell. So one of the things we wrote about is that supercloud is an evocative term that catalyzes debate and conversation, which is what we like, of course. And maybe that's self-serving. But yeah, HyperCloud, Metacloud, super, meaning, it's funny because super came from Latin supra, above, it was never the superlative. But the superlative was a convenient byproduct that caused a lot of friction and flack, which again, in the media business is like a perfect storm brewing. >> The bad thing to have to, and I think you do need to shake people out of their, the complacency of the limitations that they're used to. And I'll tell you what, the fact that you even have the terms hybrid cloud, multi-cloud, private cloud, edge computing, those are all just referring to the different boundaries that isolate the silo that is the current limited cloud. >> Right. >> So if I heard correctly, what just, in terms of us defining what is and what isn't in supercloud, you would say traditional applications which have to run in a certain place, in a certain cloud can't run anywhere else, would be the stuff that you would not put in as being addressed by supercloud. And over time, you would want to be able to run the data where you want to and in any of those concepts. >> Or even modern apps, right? Or even modern apps that are siloed in SAS within an individual cloud, right? >> So yeah, I guess it's twofold. Number one, if you're going at the high application layers, there's lots of ways that you can give the appearance of anything running anywhere. The ISV, the SAS vendor can engineer stuff to have the ability to serve with low enough latency to different geographies, right? So if you go too high up the stack, it kind of loses its meaning because there's lots of different ways to make due and give the appearance of omni-presence of the service. Okay? As you come down more towards the platform layer, it gets harder and harder to mask the fact that supercloud is something entirely different than just a good regionally-distributed SAS service. So I don't think you, I don't think you can distinguish supercloud if you go too high up the stack because it's just SAS, it's just a good SAS service where the SAS vendor has done the hard work to give you low latency access from different geographic regions. >> Yeah, so this is one of the hardest things, David. >> Common among them. >> Yeah, this is really an important point. This is one of the things I've had the most trouble with is why is this not just SAS? >> So you dilute your message when you go up to the SAS layer. If you were to focus most of this around the super pass layer, the how can you host applications and run them anywhere and not host this, not run a service, not have a service available everywhere. So how can you take any application, even applications that are written, you know, in a traditional legacy data center fashion and be able to run them anywhere and have them have their binaries and their datasets and the runtime environment and the infrastructure to start them and stop them? You know, the jobs, the, what the Kubernetes, the job scheduler? What we're really talking about here, what I think we're really talking about here is building the operating system for a decentralized cloud. What is the operating system, the operating environment for a decentralized cloud? Where you can, and that the main two functions of an operating system or an operating environment are the process scheduler, the thing that's scheduling what is running where and when and so forth, and the file system, right? The thing that's supplying a common view and access to data. So when we talk about this, I think that the strongest argument for supercloud is made when you go down to the platform layer and talk of it, talk about it as an operating environment on which you can run all forms of applications. >> Would you exclude--? >> Not a specific application that's been engineered as a SAS. (audio distortion) >> He'll come back. >> Are you there? >> Yeah, yeah, you just cut out for a minute. >> I lost your last statement when you broke up. >> We heard you, you said that not the specific application. So would you exclude Snowflake from supercloud? >> Frankly, I would. I would. Because, well, and this is kind of hard to do because Snowflake doesn't like to, Frank doesn't like to talk about Snowflake as a SAS service. It has a negative connotation. >> But it is. >> I know, we all know it is. We all know it is and because it is, yes, I would exclude them. >> I think I actually have him on camera. >> There's nothing in common. >> I think I have him on camera or maybe Benoit as saying, "Well, we are a SAS." I think it's Slootman. I think I said to Slootman, "I know you don't like to say you're a SAS." And I think he said, "Well, we are a SAS." >> Because again, if you go to the top of the application stack, there's any number of ways you can give it location agnostic function or you know, regional, local stuff. It's like let's solve the location problem by having me be your one location. How can it be decentralized if you're centralizing on (audio distortion)? >> Well, it's more decentralized than if it's all in one cloud. So let me actually, so the spectrum. So again, in the spirit of what is and what isn't, I think it's safe to say Hammerspace is supercloud. I think there's no debate there, right? Certainly among this crowd. And I think we can all agree that Dell, Dell Storage is not supercloud. Where it gets fuzzy is this Snowflake example or even, how about a, how about a Cohesity that instantiates its stack in different cloud regions in different clouds, and synchronizes, however magic sauce it does that. Is that a supercloud? I mean, so I'm cautious about having too strict of a definition 'cause then only-- >> Fair enough, fair enough. >> But I could use your help and thoughts on that. >> So I think we're talking about two different spectrums here. One is the spectrum of platform to application-specific. As you go up the application stack and it becomes this specific thing. Or you go up to the more and more structured where it's serving a specific application function where it's more of a SAS thing. I think it's harder to call a SAS service a supercloud. And I would argue that the reason there, and what you're lacking in the definition is to talk about it as general purpose. Okay? Now, that said, a data warehouse is general purpose at the structured data level. So you could make the argument for why Snowflake is a supercloud by saying that it is a general purpose platform for doing lots of different things. It's just one at a higher level up at the structured data level. So one spectrum is the high level going from platform to, you know, unstructured data to structured data to very application-specific, right? Like a specific, you know, CAD/CAM mechanical design cloud, like an Autodesk would want to give you their cloud for running, you know, and sharing CAD/CAM designs, doing your CAD/CAM anywhere stuff. Well, the other spectrum is how well does the purported supercloud technology actually live up to allowing you to run anything anywhere with not just the same APIs but with the local presence of data with the exact same runtime environment everywhere, and to be able to correctly manage how to get that runtime environment anywhere. So a Cohesity has some means of running things in different places and some means of coordinating what's where and of serving diff, you know, things in different places. I would argue that it is a very poor approximation of what Hammerspace does in providing the exact same file system with local high performance access everywhere with metadata ability to control where the data is actually instantiated so that you don't have to wait for it to get orchestrated. But even then when you do have to wait for it, it happens automatically and so it's still only a matter of, well, how quick is it? And on the other end of the spectrum is you could look at NetApp with Flexcache and say, "Is that supercloud?" And I would argue, well kind of because it allows you to run things in different places because it's a cache. But you know, it really isn't because it presumes some central silo from which you're cacheing stuff. So, you know, is it or isn't it? Well, it's on a spectrum of exactly how fully is it decoupling a runtime environment from specific locality? And I think a cache doesn't, it stretches a specific silo and makes it have some semblance of similar access in other places. But there's still a very big difference to the central silo, right? You can't turn off that central silo, for example. >> So it comes down to how specific you make the definition. And this is where it gets kind of really interesting. It's like cloud. Does IBM have a cloud? >> Exactly. >> I would say yes. Does it have the kind of quality that you would expect from a hyper-scale cloud? No. Or see if you could say the same thing about-- >> But that's a problem with choosing a name. That's the problem with choosing a name supercloud versus talking about the concept of cloud and how true up you are to that concept. >> For sure. >> Right? Because without getting a name, you don't have to draw, yeah. >> I'd like to explore one particular or bring them together. You made a very interesting observation that from a enterprise point of view, they want to safeguard their store, their data, and they want to make sure that they can have that data running in their own workflows, as well as, as other service providers providing services to them for that data. So, and in in particular, if you go back to, you go back to Snowflake. If Snowflake could provide the ability for you to have your data where you wanted, you were in charge of that, would that make Snowflake a supercloud? >> I'll tell you, in my mind, they would be closer to my conceptualization of supercloud if you can instantiate Snowflake as software on your own infrastructure, and pump your own data to Snowflake that's instantiated on your own infrastructure. The fact that it has to be on their infrastructure or that it's on their, that it's on their account in the cloud, that you're giving them the data and they're, that fundamentally goes against it to me. If they, you know, they would be a pure, a pure plate if they were a software defined thing where you could instantiate Snowflake machinery on the infrastructure of your choice and then put your data into that machinery and get all the benefits of Snowflake. >> So did you see--? >> In other words, if they were not a SAS service, but offered all of the similar benefits of being, you know, if it were a service that you could run on your own infrastructure. >> So did you see what they announced, that--? >> I hope that's making sense. >> It does, did you see what they announced at Dell? They basically announced the ability to take non-native Snowflake data, read it in from an object store on-prem, like a Dell object store. They do the same thing with Pure, read it in, running it in the cloud, and then push it back out. And I was saying to Dell, look, that's fine. Okay, that's interesting. You're taking a materialized view or an extended table, whatever you're doing, wouldn't it be more interesting if you could actually run the query locally with your compute? That would be an extension that would actually get my attention and extend that. >> That is what I'm talking about. That's what I'm talking about. And that's why I'm saying I think Hammerspace is more progressive on that front because with our technology, anybody who can instantiate a service, can make a service. And so I, so MSPs can use Hammerspace as a way to build a super pass layer and host their clients on their infrastructure in a cloud-like fashion. And their clients can have their own private data centers and the MSP or the public clouds, and Hammerspace can be instantiated, get this, by different parties in these different pieces of infrastructure and yet linked together to make a common file system across all of it. >> But this is data mesh. If I were HPE and Dell it's exactly what I'd be doing. I'd be working with Hammerspace to create my own data. I'd work with Databricks, Snowflake, and any other-- >> Data mesh is a good way to put it. Data mesh is a good way to put it. And this is at the lowest level of, you know, the underlying file system that's mountable by the operating system, consumed as a real file system. You can't get lower level than that. That's why this is the foundation for all of the other apps and structured data systems because you need to have a data mesh that can at least mesh the binary blob. >> Okay. >> That hold the binaries and that hold the datasets that those applications are running. >> So David, in the third week of January, we're doing supercloud 2 and I'm trying to convince John Furrier to make it a data slash data mesh edition. I'm slowly getting him to the knothole. I would very much, I mean you're in the Bay Area, I'd very much like you to be one of the headlines. As Zhamak Dehghaniis going to speak, she's the creator of Data Mesh, >> Sure. >> I'd love to have you come into our studio as well, for the live session. If you can't make it, we can pre-record. But you're right there, so I'll get you the dates. >> We'd love to, yeah. No, you can count on it. No, definitely. And you know, we don't typically talk about what we do as Data Mesh. We've been, you know, using global data environment. But, you know, under the covers, that's what the thing is. And so yeah, I think we can frame the discussion like that to line up with other, you know, with the other discussions. >> Yeah, and Data Mesh, of course, is one of those evocative names, but she has come up with some very well defined principles around decentralized data, data as products, self-serve infrastructure, automated governance, and and so forth, which I think your vision plugs right into. And she's brilliant. You'll love meeting her. >> Well, you know, and I think.. Oh, go ahead. Go ahead, Peter. >> Just like to work one other interface which I think is important. How do you see yourself and the open source? You talked about having an operating system. Obviously, Linux is the operating system at one level. How are you imagining that you would interface with cost community as part of this development? >> Well, it's funny you ask 'cause my CTO is the kernel maintainer of the storage networking stack. So how the Linux operating system perceives and consumes networked data at the file system level, the network file system stack is his purview. He owns that, he wrote most of it over the last decade that he's been the maintainer, but he's the gatekeeper of what goes in. And we have leveraged his abilities to enhance Linux to be able to use this decentralized data, in particular with decoupling the control plane driven by metadata from the data access path and the many storage systems on which the data gets accessed. So this factoring, this splitting of control plane from data path, metadata from data, was absolutely necessary to create a data mesh like we're talking about. And to be able to build this supercloud concept. And the highways on which the data runs and the client which knows how to talk to it is all open source. And we have, we've driven the NFS 4.2 spec. The newest NFS spec came from my team. And it was specifically the enhancements needed to be able to build a spanning file system, a data mesh at a file system level. Now that said, our file system itself and our server, our file server, our data orchestration, our data management stuff, that's all closed source, proprietary Hammerspace tech. But the highways on which the mesh connects are actually all open source and the client that knows how to consume it. So we would, honestly, I would welcome competitors using those same highways. They would be at a major disadvantage because we kind of built them, but it would still be very validating and I think only increase the potential adoption rate by more than whatever they might take of the market. So it'd actually be good to split the market with somebody else to come in and share those now super highways for how to mesh data at the file system level, you know, in here. So yeah, hopefully that answered your question. Does that answer the question about how we embrace the open source? >> Right, and there was one other, just that my last one is how do you enable something to run in every environment? And if we take the edge, for example, as being, as an environment which is much very, very compute heavy, but having a lot less capability, how do you do a hold? >> Perfect question. Perfect question. What we do today is a software appliance. We are using a Linux RHEL 8, RHEL 8 equivalent or a CentOS 8, or it's, you know, they're all roughly equivalent. But we have bundled and a software appliance which can be instantiated on bare metal hardware on any type of VM system from VMware to all of the different hypervisors in the Linux world, to even Nutanix and such. So it can run in any virtualized environment and it can run on any cloud instance, server instance in the cloud. And we have it packaged and deployable from the marketplaces within the different clouds. So you can literally spin it up at the click of an API in the cloud on instances in the cloud. So with all of these together, you can basically instantiate a Hammerspace set of machinery that can offer up this file system mesh. like we've been using the terminology we've been using now, anywhere. So it's like being able to take and spin up Snowflake and then just be able to install and run some VMs anywhere you want and boom, now you have a Snowflake service. And by the way, it is so complete that some of our customers, I would argue many aren't even using public clouds at all, they're using this just to run their own data centers in a cloud-like fashion, you know, where they have a data service that can span it all. >> Yeah and to Molly's first point, we would consider that, you know, cloud. Let me put you on the spot. If you had to describe conceptually without a chalkboard what an architectural diagram would look like for supercloud, what would you say? >> I would say it's to have the same runtime environment within every data center and defining that runtime environment as what it takes to schedule the execution of applications, so job scheduling, runtime stuff, and here we're talking Kubernetes, Slurm, other things that do job scheduling. We're talking about having a common way to, you know, instantiate compute resources. So a global compute environment, having a common compute environment where you can instantiate things that need computing. Okay? So that's the first part. And then the second is the data platform where you can have file block and object volumes, and have them available with the same APIs in each of these distributed data centers and have the exact same data omnipresent with the ability to control where the data is from one moment to the next, local, where all the data is instantiate. So my definition would be a common runtime environment that's bifurcate-- >> Oh. (attendees chuckling) We just lost them at the money slide. >> That's part of the magic makes people listen. We keep someone on pin and needles waiting. (attendees chuckling) >> That's good. >> Are you back, David? >> I'm on the edge of my seat. Common runtime environment. It was like... >> And just wait, there's more. >> But see, I'm maybe hyper-focused on the lower level of what it takes to host and run applications. And that's the stuff to schedule what resources they need to run and to get them going and to get them connected through to their persistence, you know, and their data. And to have that data available in all forms and have it be the same data everywhere. On top of that, you could then instantiate applications of different types, including relational databases, and data warehouses and such. And then you could say, now I've got, you know, now I've got these more application-level or structured data-level things. I tend to focus less on that structured data level and the application level and am more focused on what it takes to host any of them generically on that super pass layer. And I'll admit, I'm maybe hyper-focused on the pass layer and I think it's valid to include, you know, higher levels up the stack like the structured data level. But as soon as you go all the way up to like, you know, a very specific SAS service, I don't know that you would call that supercloud. >> Well, and that's the question, is there value? And Marianna Tessel from Intuit said, you know, we looked at it, we did it, and it just, it was actually negative value for us because connecting to all these separate clouds was a real pain in the neck. Didn't bring us any additional-- >> Well that's 'cause they don't have this pass layer underneath it so they can't even shop around, which actually makes it hard to stand up your own SAS service. And ultimately they end up having to build their own infrastructure. Like, you know, I think there's been examples like Netflix moving away from the cloud to their own infrastructure. Basically, if you're going to rent it for more than a few months, it makes sense to build it yourself, if it's at any kind of scale. >> Yeah, for certain components of that cloud. But if the Goldman Sachs came to you, David, and said, "Hey, we want to collaborate and we want to build "out a cloud and essentially build our SAS system "and we want to do that with Hammerspace, "and we want to tap the physical infrastructure "of not only our data centers but all the clouds," then that essentially would be a SAS, would it not? And wouldn't that be a Super SAS or a supercloud? >> Well, you know, what they may be using to build their service is a supercloud, but their service at the end of the day is just a SAS service with global reach. Right? >> Yeah. >> You know, look at, oh shoot. What's the name of the company that does? It has a cloud for doing bookkeeping and accounting. I forget their name, net something. NetSuite. >> NetSuite. NetSuite, yeah, Oracle. >> Yeah. >> Yep. >> Oracle acquired them, right? Is NetSuite a supercloud or is it just a SAS service? You know? I think under the covers you might ask are they using supercloud under the covers so that they can run their SAS service anywhere and be able to shop the venue, get elasticity, get all the benefits of cloud in the, to the benefit of their service that they're offering? But you know, folks who consume the service, they don't care because to them they're just connecting to some endpoint somewhere and they don't have to care. So the further up the stack you go, the more location-agnostic it is inherently anyway. >> And I think it's, paths is really the critical layer. We thought about IAS Plus and we thought about SAS Minus, you know, Heroku and hence, that's why we kind of got caught up and included it. But SAS, I admit, is the hardest one to crack. And so maybe we exclude that as a deployment model. >> That's right, and maybe coming down a level to saying but you can have a structured data supercloud, so you could still include, say, Snowflake. Because what Snowflake is doing is more general purpose. So it's about how general purpose it is. Is it hosting lots of other applications or is it the end application? Right? >> Yeah. >> So I would argue general purpose nature forces you to go further towards platform down-stack. And you really need that general purpose or else there is no real distinguishing. So if you want defensible turf to say supercloud is something different, I think it's important to not try to wrap your arms around SAS in the general sense. >> Yeah, and we've kind of not really gone, leaned hard into SAS, we've just included it as a deployment model, which, given the constraints that you just described for structured data would apply if it's general purpose. So David, super helpful. >> Had it sign. Define the SAS as including the hybrid model hold SAS. >> Yep. >> Okay, so with your permission, I'm going to add you to the list of contributors to the definition. I'm going to add-- >> Absolutely. >> I'm going to add this in. I'll share with Molly. >> Absolutely. >> We'll get on the calendar for the date. >> If Molly can share some specific language that we've been putting in that kind of goes to stuff we've been talking about, so. >> Oh, great. >> I think we can, we can share some written kind of concrete recommendations around this stuff, around the general purpose, nature, the common data thing and yeah. >> Okay. >> Really look forward to it and would be glad to be part of this thing. You said it's in February? >> It's in January, I'll let Molly know. >> Oh, January. >> What the date is. >> Excellent. >> Yeah, third week of January. Third week of January on a Tuesday, whatever that is. So yeah, we would welcome you in. But like I said, if it doesn't work for your schedule, we can prerecord something. But it would be awesome to have you in studio. >> I'm sure with this much notice we'll be able to get something. Let's make sure we have the dates communicated to Molly and she'll get my admin to set it up outside so that we have it. >> I'll get those today to you, Molly. Thank you. >> By the way, I am so, so pleased with being able to work with you guys on this. I think the industry needs it very bad. They need something to break them out of the box of their own mental constraints of what the cloud is versus what it's supposed to be. And obviously, the more we get people to question their reality and what is real, what are we really capable of today that then the more business that we're going to get. So we're excited to lend the hand behind this notion of supercloud and a super pass layer in whatever way we can. >> Awesome. >> Can I ask you whether your platforms include ARM as well as X86? >> So we have not done an ARM port yet. It has been entertained and won't be much of a stretch. >> Yeah, it's just a matter of time. >> Actually, entertained doing it on behalf of NVIDIA, but it will absolutely happen because ARM in the data center I think is a foregone conclusion. Well, it's already there in some cases, but not quite at volume. So definitely will be the case. And I'll tell you where this gets really interesting, discussion for another time, is back to my old friend, the SSD, and having SSDs that have enough brains on them to be part of that fabric. Directly. >> Interesting. Interesting. >> Very interesting. >> Directly attached to ethernet and able to create a data mesh global file system, that's going to be really fascinating. Got to run now. >> All right, hey, thanks you guys. Thanks David, thanks Molly. Great to catch up. Bye-bye. >> Bye >> Talk to you soon.

>>The Cube Presents UI Path Forward five. Brought to you by UI Path. >>Hi everybody. Welcome back to UI Path Forward at five. You're watching the Cubes coverage. Everybody here is automating everything. Mundane tasks, Enterprisewide Automation Platform Beats product. Dave Nicholson. Dave Ante, Garish Pie is here. He is the Global head of Intelligent Enterprise Automation at Cognizant Global. Si, good to see you. Thanks for coming to the queue. Thank you for having me. Tell us about your role. What are you focused on? So, >>So I lead the enterprise automation practice at Cognizant, and we are focused on three broad segments, right? So we help customers anchor to business outcomes in, in looking at the business outcomes, what we look to do is help them drive transformation at a process level, looking at it from a technology standpoint, and then helping them look at how they're trying to drive change across their entire enterprise and bringing that together, you know, and helping them harmonize both at a technology and at a process level in terms of, you know, the outcomes they're trying to achieve. So >>You guys are a partner, I see your booth over there, and you're also a customer, right? Yes, we are. So are you involved in the both sides? One side, what, what, what's your purpose? >>So we do, so we, we sort of work, So we have a full 360 degree relationship with the i p. So we work with them, you know, in a professional services capacity. They, they support us as a partnership in the marketplace where we go into a number of our customers jointly to drive turnkey transformational engagements from an automation standpoint and second from a, as a, as a, as a customer to UiPath, they've been supporting us, you know, drive a number of automation initiatives across our operations book over the course of the last two years. >>Okay. So tell us more about that. So you started your internal journey, we had you guys on last year. Yep. It were just getting started I think, I think I think you, your head count is what, 60,000 somewhere around? Yeah. 70,000. Yeah, $70,000 growing. I think at the time it was maybe less than 10% of the workforce was kind of automated and the goal was to automate everybody. How are you guys doing along >>The, I think it's starting to in industrialize quite significantly. So over the course of the last year since we last spoke to you, probably, you know, we've doubled the head count in terms of the number of people that are now, you know, officially what we call quote unquote citizen developers. And you know, how they are driving automation at a personal level, we've probably gone about 2.5 x in terms of the number of RS we've saved. So we've done about, I think 450,000 rs, you know, in terms of actual saves at a personal automation level. And look, it's, it's been a great, you know, last 12 months too, right? Because, you know, as we've sort of started to get the message percolated more and more, our teams have started to get energized. They are happy that they are, you know, getting a release in terms of what they're doing on a day to day basis, which is largely repetitive at times, very mundane. And now they have the ability to bring in technology to be able to embrace that and drive that, that you know, much more efficiently. >>Are you talking dozens of bots? Thousands of bots? What's the scope of? >>So I think we've, we've scaled to about 3,500 today in terms of the bots and, and it's, it's a journey that continues to evolve. For me, the number is probably something which I wouldn't anchor to because it's, look, it's end of the day what you end up releasing and what you end up freeing and what the teams are doing. And I think, you know, that's the way we are >>Leading. So you're saying like, we always talk about number of boss, but you're saying it's largely in a relevant metric? Well, and not if it's five versus a thousand. Okay. That's meaningful, right? But, but >>Yeah, I think look a number for me, I think it's not about the number, right? It's about the outcome and it's about what impact you're having in, in terms of, you know, what you're trying to get done at the end of the day, right? Because ultimately you're trying to better, you know, what you do on a day to day basis and you know, whether it's done through 10 or whether that's done through 10,000. >>Yeah. But you pay >>Form, >>Right? Exactly. So, so you better get some value out. Exactly. It's about the value. >>But is there, is there a, is there a curve in terms, you know, an s-curve in terms of scalability though? I mean we, we, we've heard organizations doing, from organizations doing an amazing amount of modernization and automation and they say they've got 15 bots running, you have 3,500. Is there a number where it becomes harder to manage or, or is there scalability involved? >>So look, for me, so let me answer it this way, right? I think, I think there are two aspects to it. I think the, the, the, the more you have, you know, the bigger the challenge in terms of how you run the controls, the governance and the residency in terms of, you know, how you manage the, you know, the, the setup of the bots itself. So I think, yeah, I mean we want to have it to a manageable number, but for us, in the way we've looked at the number of bots, one of the things that we've done is we also look at, you know, what's foundational versus what's nuanced in terms of the kind of use cases that you're trying to deliver. So, so any program of this nature, you need to have a setup, which is, you know, which allows you to sort of orchestrate it in the right manner so that as you sort of scale and you bring more people into that equation, you, it's, you're not just creating bots for the sake of it, but you're actually, you know, trying to look at what you can reuse, what you can orchestrate better. >>And then in the context of that, figuring out where you have the gaps and then hence, you know, sort of taking the delta approach of what else and what more you need to build it. >>So you guys have a big observation space. You work with a lot of customers and, and so what are you seeing as the trends when you look out there? How are you applying it to your own business and your customer's businesses? >>So look, for me, I think the last two years, if anything, the one thing I've taken away is that transformation is now extremely, extremely compressed, right? So, so it's almost, you know, what's true today is probably irrelevant tomorrow. So, which means you have to continually evolve in terms of what needs to be done, right? Second is experiences have become extremely, extremely crucial and critical and experiences of, in, in my mind, you know, two or three kinds, right? One the end customer second from an employee standpoint, and third, in terms of the partner ecosystem that you will have as an enterprise that you have to cater to, right? The other element that you know, which becomes true will always remain true is the whole outcome story in terms of, you know, how we have an anchor to why you're trying to do what you're trying to do. >>And that is, you know, core to what you need to get done. So in the way we've looked at it, as we've said, you know, as you sort of look at how transformation is now evolving and how compressed it's starting to become, the more you are able to orchestrate for what the enterprise is trying to get done in terms of modernization, in terms of digitization, in terms of end goals and end outcomes that they're trying to achieve. And the more you're able to sweat what sits within, you know, the enterprise bring that together as you think about automation is, you know, where the true value lies in terms of being able to create an agile enterprise. >>When you think about digital transformation, digital experiences, if it's, if it's a layer cake, where is automation in that, in that layer? Is it, is it sort of the bottom of the stack? Is it, is it the whole stack? >>So for me it's, I mean it's, it's evolved. If you take today's view, I think what's emerging is a very pervasive view of how you think about automation. It sits across, you know, the entire enterprise. It, it, it, it takes a people process, technology dimension, which is age old. It has to cover, you know, all forms of transformation. You know, whether you're looking at end, how do I say, impact in terms of how you're dealing with customers, whether you're looking at the infrastructure, whether you're looking at the data layer in between, it has to be embedded across the base, right? It, it, you have to take a pervasive approach. And for me, I think automation increasingly in the days ahead is gonna be an enterprise capability. You know, it has to be, you know, all pervasive in the way it needs to be set up. >>The key, the operative word there is pervasive. And that seems to be, you know, the era that we're entering, I don't know what you call it, call it the metaverse, I mean, you know, it's more than cloud and cloud is basically just the infrastructure, right? You're building on top of that, whether it's natural language processing or cryptography or virtual, I mean, there's so many different, you know, technology dimensions, right? But it, but the point about pervasive, okay, it's everywhere. It's sensing, it's anticipatory, it feels like there's this new, you know, construct, emerging of platform that is the basis for digital business, right? And I, and I feel like every 15 years our industry goes through some big transformation. How, how do you see it? You know, do you agree that you, it feels like, okay, something new is happening. It's, it's not gonna be the social media, you know, Facebook's not gonna continue to dominate the world as it does. You already seen some cracks in that armor. We saw Microsoft after the pc, and then of course it came back with cloud Amazon looks, you know, indestructible. But that, that's never the end story, right? In our, in our world, how do you see that? >>No, I think all of what you said, I, I would sort of tend to agree with, for me, look, I don't have a crystal ball to say, you know, what's gonna happen with Facebook or Amazon or >>Otherwise. Yeah. But that's what makes this fun. But >>I, Yeah, but, but I think for me, the, the core is I think you're dictated by, you know, us as end consumers, if you're a B2B or a b2b, b2c, you know, depending upon what business you're in, I think the end customer value dictates, you know, what evolves in terms of, you know, the, the manifestation of, you know, how you will two minutes sort of deliver services, the products that you'll get into. And in that context then, you know, whether you take a, a TikTok view to it or whether you take an Amazon view to it, or whether YouTube becomes relevant in the days ahead, I think it's gonna be dictated by, >>By customer, but it tends to be a technology that's the disruptor, it's the microprocessor or it's the social capability or, or maybe it's ai that, that is the catalyst for that. And then the customer adoption dictates, oh, you're right about that. But there, but the, the match is usually technology. Is that fair or not necessarily? Yeah, >>I still look, I mean you talked about metaverse earlier, right? I think we are, I think we are, it's probably hype more than it is reality right now, at least in my view. And it's, I think we are significantly out in terms of, you know, large scale adoption in terms of what needs to be done. You talk about blockchain, blockchains been around, you know, for at least a decade if not more in, in, right. The way it's being talked about, the adoption, you know, in terms of the, the, the applicability of the, you know, of what is that technology I think is understood, but the actual use cases in terms of how it can be taken into the market and how you can scale it across industries, I think is, you know, is still because >>The economics determine ultimately exactly the outcome. So, Okay, that makes sense. >>Yeah. Now you said you don't have a crystal ball. I, I have one, but when I look into it, it's sort of murky when I try to figure out the answer to the question, Is a platform necessary for this, for automation? I mean, this is really the direction, the question, the existential question in terms of the trajectory of UI path. It seems obvious that automation is critical. It's not as obvious where that automation is going to end up eventually because it's so critical. It feels like it's almost the same as, okay, there's an interface between my keystrokes and filling in a box with text. Well, of course there has to be, there has to be that interface, right? So why wouldn't everyone deliver that by default? So as you gaze into my crystal ball with me, tell me about the things that only a platform can do from your perspective. >>So, >>So, so, so think of it this way, right? I mean, any enterprise probably has hundreds of technologies that they've invested in some platform, some applications that you would've built and evolved over time, which are bespoke custom in nature. So for me, I think when you think about automation, I think it's the balance between the two. What a platform allows you to do is to be able to orchestrate, given the complexity and the, the spa that is any enterprise, you know, that's probably got the burden of, you know, what they've done over the course of the, the previous years. And then in that context then, you know, how do you sort of help get the, the best value out of that in terms of what you want to deliver as the end, end outcomes, if I can call it that, right? So for me, I don't think you can say it's, it's her platform versus the rest. >>I think it's gonna be, it's always gonna be a balance and to the question that you asked earlier. And in terms of saying where does an automation end up at? I think if it's gonna be a pervasive view, look, you know, if, if clients are trying to modernize and get onto the cloud, you can do automation at a cloud level too. Now, you know, do I say then, you know, is it, is it sort of inclusive or it's native to what the cloud providers offer? Or do I then go and say automation needs to be something which I will, you know, sort of overlay on top of what the cloud providers offer. So I think it depends upon what dimension that you come at it. So I don't think you can say it's one or the other. You have a platform, I think it helps you orchestrate quite significantly. But there are gonna be aspects within any enterprise, given the complexity that exists that you will have to balance out, you know, platform versus, you know, how you have to address it maybe in a more individual capacity. >>Garris, gotta go. Thank you so much. Appreciate your perspectives. Good conversation. All right, keep it right there. But trains will back it up. We'll be right back right after this short break. The cube live at UI path forward, five from Las Vegas.

(upbeat music) >> From theCUBE studios in Palo Alto, in Boston bringing you data-driven insights from theCUBE and ETR. This is braking analysis with Dave Vellante. >> Database is the heart of enterprise computing. The market is both exploding and it's evolving. The major force is transforming the space include Cloud and data, of course, but also new workloads, advanced memory and IO capabilities, new processor types, a massive push towards simplicity, new data sharing and governance models, and a spate of venture investment. Snowflake stands out as the gold standard for operational excellence and go to market execution. The company has attracted the attention of customers, investors, and competitors and everyone from entrenched players to upstarts once in the act. Hello everyone and welcome to this week's Wikibon CUBE Insights powered by ETR. In this breaking analysis, we'll share our most current thinking on the database marketplace and dig into Snowflake's execution. Some of its challenges and we'll take a look at how others are making moves to solve customer problems and try to get a piece of the growing database pie. Let's look at some of the factors that are driving market momentum. First, customers want lower license costs. They want simplicity. They want to avoid database sprawl. They want to run anywhere and manage new data types. These needs often are divergent and they pull vendors and technologies in different direction. It's really hard for any one platform to accommodate every customer need. The market is large and it's growing. Gardner has it at around 60 to 65 billion with a CAGR of somewhere around 20% over the next five years. But the market, as we know it is being redefined. Traditionally, databases have served two broad use cases, OLTP or transactions and reporting like data warehouses. But a diversity of workloads and new architectures and innovations have given rise to a number of new types of databases to accommodate all these diverse customer needs. Many billions have been spent over the last several years in venture money and it continues to pour in. Let me just give you some examples. Snowflake prior to its IPO, raised around 1.4 billion. Redis Labs has raised more than 1/2 billion dollars so far, Cockroach Labs, more than 350 million, Couchbase, 250 million, SingleStore formerly MemSQL, 238 million, Yellowbrick Data, 173 million. And if you stretch the definition of database a little bit to including low-code or no-code, Airtable has raised more than 600 million. And that's by no means a complete list. Now, why is all this investment happening? Well, in a large part, it's due to the TAM. The TAM is huge and it's growing and it's being redefined. Just how big is this market? Let's take a look at a chart that we've shown previously. We use this chart to Snowflakes TAM, and it focuses mainly on the analytics piece, but we'll use it here to really underscore the market potential. So the actual database TAM is larger than this, we think. Cloud and Cloud-native technologies have changed the way we think about databases. Virtually 100% of the database players that they're are in the market have pivoted to a Cloud first strategy. And many like Snowflake, they're pretty dogmatic and have a Cloud only strategy. Databases has historically been very difficult to manage, they're really sensitive to latency. So that means they require a lot of tuning. Cloud allows you to throw virtually infinite resources on demand and attack performance problems and scale very quickly, minimizing the complexity and tuning nuances. This idea, this layer of data as a service we think of it as a staple of digital transformation. Is this layer that's forming to support things like data sharing across ecosystems and the ability to build data products or data services. It's a fundamental value proposition of Snowflake and one of the most important aspects of its offering. Snowflake tracks a metric called edges, which are external connections in its data Cloud. And it claims that 15% of its total shared connections are edges and that's growing at 33% quarter on quarter. This notion of data sharing is changing the way people think about data. We use terms like data as an asset. This is the language of the 2010s. We don't share our assets with others, do we? No, we protect them, we secure or them, we even hide them. But we absolutely don't want to share those assets but we do want to share our data. I had a conversation recently with Forrester analyst, Michelle Goetz. And we both agreed we're going to scrub data as an asset from our phrasiology. Increasingly, people are looking at sharing as a way to create, as I said, data products or data services, which can be monetized. This is an underpinning of Zhamak Dehghani's concept of a data mesh, make data discoverable, shareable and securely governed so that we can build data products and data services that can be monetized. This is where the TAM just explodes and the market is redefining. And we think is in the hundreds of billions of dollars. Let's talk a little bit about the diversity of offerings in the marketplace. Again, databases used to be either transactional or analytic. The bottom lines and top lines. And this chart here describe those two but the types of databases, you can see the middle of mushrooms, just looking at this list, blockchain is of course a specialized type of database and it's also finding its way into other database platforms. Oracle is notable here. Document databases that support JSON and graph data stores that assist in visualizing data, inference from multiple different sources. That's is one of the ways in which adtech has taken off and been so effective. Key Value stores, log databases that are purpose-built, machine learning to enhance insights, spatial databases to help build the next generation of products, the next automobile, streaming databases to manage real time data flows and time series databases. We might've missed a few, let us know if you think we have, but this is a kind of pretty comprehensive list that is somewhat mind boggling when you think about it. And these unique requirements, they've spawned tons of innovation and companies. Here's a small subset on this logo slide. And this is by no means an exhaustive list, but you have these companies here which have been around forever like Oracle and IBM and Teradata and Microsoft, these are the kind of the tier one relational databases that have matured over the years. And they've got properties like atomicity, consistency, isolation, durability, what's known as ACID properties, ACID compliance. Some others that you may or may not be familiar with, Yellowbrick Data, we talked about them earlier. It's going after the best price, performance and analytics and optimizing to take advantage of both hybrid installations and the latest hardware innovations. SingleStore, as I said, formerly known as MemSQL is a very high end analytics and transaction database, supports mixed workloads, extremely high speeds. We're talking about trillions of rows per second that could be ingested in query. Couchbase with hybrid transactions and analytics, Redis Labs, open source, no SQL doing very well, as is Cockroach with distributed SQL, MariaDB with its managed MySQL, Mongo and document database has a lot of momentum, EDB, which supports open source Postgres. And if you stretch the definition a bit, Splunk, for log database, why not? ChaosSearch, really interesting startup that leaves data in S-3 and is going after simplifying the ELK stack, New Relic, they have a purpose-built database for application performance management and we probably could have even put Workday in the mix as it developed a specialized database for its apps. Of course, we can't forget about SAP with how not trying to pry customers off of Oracle. And then the big three Cloud players, AWS, Microsoft and Google with extremely large portfolios of database offerings. The spectrum of products in this space is very wide, with you've got AWS, which I think we're up to like 16 database offerings, all the way to Oracle, which has like one database to do everything not withstanding MySQL because it owns MySQL got that through the Sun Acquisition. And it recently, it made some innovations there around the heat wave announcement. But essentially Oracle is investing to make its database, Oracle database run any workload. While AWS takes the approach of the right tool for the right job and really focuses on the primitives for each database. A lot of ways to skin a cat in this enormous and strategic market. So let's take a look at the spending data for the names that make it into the ETR survey. Not everybody we just mentioned will be represented because they may not have quite the market presence of the ends in the survey, but ETR that capture a pretty nice mix of players. So this chart here, it's one of the favorite views that we like to share quite often. It shows the database players across the 1500 respondents in the ETR survey this past quarter and it measures their net score. That's spending momentum and is shown on the vertical axis and market share, which is the pervasiveness in the data set is on the horizontal axis. The Snowflake is notable because it's been hovering around 80% net score since the survey started picking them up. Anything above 40%, that red line there, is considered by us to be elevated. Microsoft and AWS, they also stand out because they have both market presence and they have spending velocity with their platforms. Oracle is very large but it doesn't have the spending momentum in the survey because nearly 30% of Oracle installations are spending less, whereas only 22% are spending more. Now as a caution, this survey doesn't measure dollar spent and Oracle will be skewed toward the big customers with big budgets. So you got to consider that caveat when evaluating this data. IBM is in a similar position although its market share is not keeping up with Oracle's. Google, they've got great tech especially with BigQuery and it has elevated momentum. So not a bad spot to be in although I'm sure it would like to be closer to AWS and Microsoft on the horizontal axis, so it's got some work to do there. And some of the others we mentioned earlier, like MemSQL, Couchbase. As shown MemSQL here, they're now SingleStore. Couchbase, Reddis, Mongo, MariaDB, all very solid scores on the vertical axis. Cloudera just announced that it was selling to private equity and that will hopefully give it some time to invest in this platform and get off the quarterly shot clock. MapR was acquired by HPE and it's part of HPE's Ezmeral platform, their data platform which doesn't yet have the market presence in the survey. Now, something that is interesting in looking at in Snowflakes earnings last quarter, is this laser focused on large customers. This is a hallmark of Frank Slootman and Mike Scarpelli who I know they don't have a playbook but they certainly know how to go whale hunting. So this chart isolates the data that we just showed you to the global 1000. Note that both AWS and Snowflake go up higher on the X-axis meaning large customers are spending at a faster rate for these two companies. The previous chart had an end of 161 for Snowflake, and a 77% net score. This chart shows the global 1000, in the end there for Snowflake is 48 accounts and the net score jumps to 85%. We're not going to show it here but when you isolate the ETR data, nice you can just cut it, when you isolate it on the fortune 1000, the end for Snowflake goes to 59 accounts in the data set and Snowflake jumps another 100 basis points in net score. When you cut the data by the fortune 500, the Snowflake N goes to 40 accounts and the net score jumps another 200 basis points to 88%. And when you isolate on the fortune 100 accounts is only 18 there but it's still 18, their net score jumps to 89%, almost 90%. So it's very strong confirmation that there's a proportional relationship between larger accounts and spending momentum in the ETR data set. So Snowflakes large account strategy appears to be working. And because we think Snowflake is sticky, this probably is a good sign for the future. Now we've been talking about net score, it's a key measure in the ETR data set, so we'd like to just quickly remind you what that is and use Snowflake as an example. This wheel chart shows the components of net score, that lime green is new adoptions. 29% of the customers in the ETR dataset that are new to Snowflake. That's pretty impressive. 50% of the customers are spending more, that's the forest green, 20% are flat, that's the gray, and only 1%, the pink, are spending less. And 0% zero or replacing Snowflake, no defections. What you do here to get net scores, you subtract the red from the green and you get a net score of 78%. Which is pretty sick and has been sick as in good sick and has been steady for many, many quarters. So that's how the net score methodology works. And remember, it typically takes Snowflake customers many months like six to nine months to start consuming it's services at the contracted rate. So those 29% new adoptions, they're not going to kick into high gear until next year, so that bodes well for future revenue. Now, it's worth taking a quick snapshot at Snowflakes most recent quarter, there's plenty of stuff out there that you can you can google and get a summary but let's just do a quick rundown. The company's product revenue run rate is now at 856 million they'll surpass $1 billion on a run rate basis this year. The growth is off the charts very high net revenue retention. We've explained that before with Snowflakes consumption pricing model, they have to account for retention differently than what a SaaS company. Snowflake added 27 net new $1 million accounts in the quarter and claims to have more than a hundred now. It also is just getting its act together overseas. Slootman says he's personally going to spend more time in Europe, given his belief, that the market is huge and they can disrupt it and of course he's from the continent. He was born there and lived there and gross margins expanded, do in a large part to renegotiation of its Cloud costs. Welcome back to that in a moment. Snowflake it's also moving from a product led growth company to one that's more focused on core industries. Interestingly media and entertainment is one of the largest along with financial services and it's several others. To me, this is really interesting because Disney's example that Snowflake often puts in front of its customers as a reference. And it seems to me to be a perfect example of using data and analytics to both target customers and also build so-called data products through data sharing. Snowflake has to grow its ecosystem to live up to its lofty expectations and indications are that large SIS are leaning in big time. Deloitte cross the $100 million in deal flow in the quarter. And the balance sheet's looking good. Thank you very much with $5 billion in cash. The snarks are going to focus on the losses, but this is all about growth. This is a growth story. It's about customer acquisition, it's about adoption, it's about loyalty and it's about lifetime value. Now, as I said at the IPO, and I always say this to young people, don't buy a stock at the IPO. There's probably almost always going to be better buying opportunities ahead. I'm not always right about that, but I often am. Here's a chart of Snowflake's performance since IPO. And I have to say, it's held up pretty well. It's trading above its first day close and as predicted there were better opportunities than day one but if you have to make a call from here. I mean, don't take my stock advice, do your research. Snowflake they're priced to perfection. So any disappointment is going to be met with selling. You saw that the day after they beat their earnings last quarter because their guidance in revenue growth,. Wasn't in the triple digits, it sort of moderated down to the 80% range. And they pointed, they pointed to a new storage compression feature that will lower customer costs and consequently, it's going to lower their revenue. I swear, I think that that before earnings calls, Scarpelli sits back he's okay, what kind of creative way can I introduce the dampen enthusiasm for the guidance. Now I'm not saying lower storage costs will translate into lower revenue for a period of time. But look at dropping storage prices, customers are always going to buy more, that's the way the storage market works. And stuff like did allude to that in all fairness. Let me introduce something that people in Silicon Valley are talking about, and that is the Cloud paradox for SaaS companies. And what is that? I was a clubhouse room with Martin Casado of Andreessen when I first heard about this. He wrote an article with Sarah Wang, calling it to question the merits of SaaS companies sticking with Cloud at scale. Now the basic premise is that for startups in early stages of growth, the Cloud is a no brainer for SaaS companies, but at scale, the cost of Cloud, the Cloud bill approaches 50% of the cost of revenue, it becomes an albatross that stifles operating leverage. Their conclusion ended up saying that as much as perhaps as much as the back of the napkin, they admitted that, but perhaps as much as 1/2 a trillion dollars in market cap is being vacuumed away by the hyperscalers that could go to the SaaS providers as cost savings from repatriation. And that Cloud repatriation is an inevitable path for large SaaS companies at scale. I was particularly interested in this as I had recently put on a post on the Cloud repatriation myth. I think in this instance, there's some merit to their conclusions. But I don't think it necessarily bleeds into traditional enterprise settings. But for SaaS companies, maybe service now has it right running their own data centers or maybe a hybrid approach to hedge bets and save money down the road is prudent. What caught my attention in reading through some of the Snowflake docs, like the S-1 in its most recent 10-K were comments regarding long-term purchase commitments and non-cancelable contracts with Cloud companies. And the companies S-1, for example, there was disclosure of $247 million in purchase commitments over a five plus year period. And the company's latest 10-K report, that same line item jumped to 1.8 billion. Now Snowflake is clearly managing these costs as it alluded to when its earnings call. But one has to wonder, at some point, will Snowflake follow the example of say Dropbox which Andreessen used in his blog and start managing its own IT? Or will it stick with the Cloud and negotiate hard? Snowflake certainly has the leverage. It has to be one of Amazon's best partners and customers even though it competes aggressively with Redshift but on the earnings call, CFO Scarpelli said, that Snowflake was working on a new chip technology to dramatically increase performance. What the heck does that mean? Is this Snowflake is not becoming a hardware company? So I going to have to dig into that a little bit and find out what that it means. I'm guessing, it means that it's taking advantage of ARM-based processes like graviton, which many ISVs ar allowing their software to run on that lower cost platform. Or maybe there's some deep dark in the weeds secret going on inside Snowflake, but I doubt it. We're going to leave all that for there for now and keep following this trend. So it's clear just in summary that Snowflake they're the pace setter in this new exciting world of data but there's plenty of room for others. And they still have a lot to prove. For instance, one customer in ETR, CTO round table express skepticism that Snowflake will live up to its hype because its success is going to lead to more competition from well-established established players. This is a common theme you hear it all the time. It's pretty easy to reach that conclusion. But my guess is this the exact type of narrative that fuels Slootman and sucked him back into this game of Thrones. That's it for now, everybody. Remember, these episodes they're all available as podcasts, wherever you listen. All you got to do is search braking analysis podcast and please subscribe to series. Check out ETR his website at etr.plus. We also publish a full report every week on wikinbon.com and siliconangle.com. You can get in touch with me, Email is David.vellante@siliconangle.com. You can DM me at DVelante on Twitter or comment on our LinkedIn posts. This is Dave Vellante for theCUBE Insights powered by ETR. Have a great week everybody, be well and we'll see you next time. (upbeat music)

from the cube studios in palo alto in boston bringing you data-driven insights from the cube and etr this is breaking analysis with dave vellante nvidia wants to completely transform enterprise computing by making data centers run 10x faster at one tenth the cost and video's ceo jensen wang is crafting a strategy to re-architect today's on-prem data centers public clouds and edge computing installations with a vision that leverages the company's strong position in ai architectures the keys to this end-to-end strategy include a clarity of vision massive chip design skills a new arm-based architecture approach that integrates memory processors i o and networking and a compelling software consumption model even if nvidia is unsuccessful at acquiring arm we believe it will still be able to execute on this strategy by actively participating in the arm ecosystem however if its attempts to acquire arm are successful we believe it will transform nvidia from the world's most valuable chip company into the world's most valuable supplier of integrated computing architectures hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll explain why we believe nvidia is in the right position to power the world's computing centers and how it plans to disrupt the grip that x86 architectures have had on the data center for decades the data center market is in transition like the universe the cloud is expanding at an accelerated pace no longer is the cloud an opaque set of remote services i always say somewhere out there sitting in a mega data center no rather the cloud is extending to on-premises data centers data centers are moving into the cloud and they're connecting through adjacent locations that create hybrid interactions clouds are being meshed together across regions and eventually will stretch to the far edge this new definition or view of cloud will be hyper distributed and run by software kubernetes is changing the world of software development and enabling workloads to run anywhere open apis external applications expanding the digital supply chains and this expanding cloud they all increase the threat surface and vulnerability to the most sensitive information that resides within the data center and around the world zero trust has become a mandate we're also seeing ai being injected into every application and it's the technology area that we see with the most momentum coming out of the pandemic this new world will not be powered by general purpose x86 processors rather it will be supported by an ecosystem of arm-based providers in our opinion that are affecting an unprecedented increase in processor performance as we have been reporting and nvidia in our view is sitting in the poll position and is currently the favorite to dominate the next era of computing architecture for global data centers public clouds as well as the near and far edge let's talk about jensen wang's clarity of vision for this new world here's a chart that underscores some of the fundamental assumptions that he's leveraging to expand his market the first is that there's a lot of waste in the data center he claims that only half of the cpu cores deployed in the data center today actually support applications the other half are processing the infrastructure all around the applications that run the software defined data center and they're terribly under utilized nvidia's blue field three dpu the data processing unit was described in a blog post on siliconangle by analyst zias caravala as a complete mini server on a card i like that with software defined networking storage and security acceleration built in this product has the bandwidth and according to nvidia can replace 300 general purpose x86 cores jensen believes that every network chip will be intelligent programmable and capable of this type of acceleration to offload conventional cpus he believes that every server node will have this capability and enable every packed of every packet and every application to be monitored in real time all the time for intrusion and as servers move to the edge bluefield will be included as a core component in his view and this last statement by jensen is critical in our opinion he says ai is the most powerful force of our time whether you agree with that or not it's relevant because ai is everywhere an invidious position in ai and the architectures the company is building are the fundamental linchpin of its data center enterprise strategy so let's take a look at some etr spending data to see where ai fits on the priority list here's a set of data in a view that we often like to share the horizontal axis is market share or pervasiveness in the etr data but we want to call your attention to the vertical axis that's really really what really we want to pay attention today that's net score or spending momentum exiting the pandemic we've seen ai capture the number one position in the last two surveys and we think this dynamic will continue for quite some time as ai becomes the staple of digital transformations and automations an ai will be infused in every single dot you see on this chart nvidia's architectures it just so happens are tailor made for ai workloads and that is how it will enter these markets let's quantify what that means and lay out our view of how nvidia with the help of arm will go after the enterprise market here's some data from wikibon research that depicts the percent of worldwide spending on server infrastructure by workload type here are the key points first the market last year was around 78 billion dollars worldwide and is expected to approach 115 billion by the end of the decade this might even be a conservative figure and we've split the market into three broad workload categories the blue is ai and other related applications what david floyer calls matrix workloads the orange is general purpose think things like erp supply chain hcm collaboration basically oracle saps and microsoft work that's being supported today and of course many other software providers and the gray that's the area that jensen was referring to is about being wasted the offload work for networking and storage and all the software defined management in the data centers around the world okay you can see the squeeze that we think compute infrastructure is gonna gonna occur around that orange area that general-purpose workloads that we think is going to really get squeezed in the next several years on a percentage basis and on an absolute basis it's really not growing nearly as fast as the other two and video with arm in our view is well positioned to attack that blue area and the gray area those those workload offsets and the new emerging ai applications but even the orange as we've reported is under pressure as for example companies like aws and oracle they use arm-based designs to service general purpose workloads why are they doing that cost is the reason because x86 generally and intel specifically are not delivering the price performance and efficiency required to keep up with the demands to reduce data center costs and if intel doesn't respond which we believe it will but if it doesn't act arm we think will get 50 percent of the general purpose workloads by the end of the decade and with nvidia it will dominate the blue the ai and the gray the offload work when we say dominate we're talking like capture 90 percent of the available market if intel doesn't respond now intel they're not just going to sit back and let that happen pat gelsinger is well aware of this in moving intel to a new strategy but nvidia and arm are way ahead in the game in our view and as we've reported this is going to be a real challenge for intel to catch up now let's take a quick look at what nvidia is doing with relevant parts of its pretty massive portfolio here's a slide that shows nvidia's three chip strategy the company is shifting to arm-based architectures which we'll describe in more detail in a moment the slide shows at the top line nvidia's ampere architecture not to be confused with the company ampere computing nvidia is taking a gpu centric approach no surprise obvious reasons there that's their sort of stronghold but we think over time it may rethink this a little bit and lean more into npus the neural processing unit we look at what apple's doing what tesla are doing we see opportunities for companies like nvidia to really sort of go after that but we'll save that for another day nvidia has announced its grace cpu a nod to the famous computer scientist grace hopper grace is a new architecture that doesn't rely on x86 and much more efficiently uses memory resources we'll again describe this in more detail later and the bottom line there that roadmap line shows the bluefield dpu which we described is essentially a complete server on a card in this approach using arm will reduce the elapsed time to go from chip design to production by 50 we're talking about shaving years down to 18 months or less we don't have time to do a deep dive into nvidia's portfolio it's large but we want to share some things that we think are important and this next graphic is one of them this shows some of the details of nvidia's jetson architecture which is designed to accelerate those ai plus workloads that we showed earlier and the reason is that this is important in our view is because the same software supports from small to very large including edge systems and we think this type of architecture is very well suited for ai inference at the edge as well as core data center applications that use ai and as we've said before a lot of the action in ai is going to happen at the edge so this is a good example of leveraging an architecture across a wide spectrum of performance and cost now we want to take a moment to explain why the moved arm-based architectures is so critical to nvidia one of the biggest cost challenges for nvidia today is keeping the gpu utilized typical utilization of gpu is well below 20 percent here's why the left hand side of this chart shows essentially racks if you will of traditional compute and the bottlenecks that nvidia faces the processor and dram they're tied together in separate blocks imagine there are thousands thousands of cores in a rack and every time you need data that lives in another processor you have to send a request and go retrieve it it's very overhead intensive now technologies like rocky are designed to help but it doesn't solve the fundamental architectural bottleneck every gpu shown here also has its own dram and it has to communicate with the processors to get the data i.e they can't communicate with each other efficiently now the right hand side side shows where nvidia is headed start in the middle with system on chip socs cpus are packaged in with npus ipu's that's the image processing unit you know x dot dot dot x pu's the the alternative processors they're all connected with sram which is think of that as a high speed layer like an layer one cache the os for the system on a chip lives inside of this and that's where nvidia has this killer software model what they're doing is they're licensing the consumption of the operating system that's running this system on chip in this entire system and they're affecting a new and really compelling subscription model you know maybe they should just give away the chips and charge for the software like a razer blade model talk about disruptive now the outer layer is the the dpu and the shared dram and other resources like the ampere computing the company this time cpus ssds and other resources these are the processors that will manage the socs together this design is based on nvidia's three chip approach using bluefield dpu leveraging melanox that's the networking component the network enables shared dram across the cpus which will eventually be all arm based grace lives inside the system on a chip and also on the outside layers and of course the gpu lives inside the soc in a scaled-down version like for instance a rendering gpu and we show some gpus on the outer layer as well for ai workloads at least in the near term you know eventually we think they may reside solely in the system on chip but only time will tell okay so you as you can see nvidia is making some serious moves and by teaming up with arm and leaning into the arm ecosystem it plans to take the company to its next level so let's talk about how we think competition for the next era of compute stacks up here's that same xy graph that we love to show market share or pervasiveness on the horizontal tracking against next net score on the vertical net score again is spending velocity and we've cut the etr data to capture players that are that are big in compute and storage and networking we've plugged in a couple of the cloud players these are the guys that we feel are vying for data center leadership around compute aws is a very strong position we believe that more than half of its revenues comes from compute you know ec2 we're talking about more than 25 billion on a run rate basis that's huge the company designs its own silicon graviton 2 etc and is working with isvs to run general purpose workloads on arm-based graviton chips microsoft and google they're going to follow suit they're big consumers of compute they sell a lot but microsoft in particular you know they're likely to continue to work with oem partners to attack that on-prem data center opportunity but it's really intel that's the provider of compute to the likes of hpe and dell and cisco and the odms which are the odms are not shown here now hpe let's talk about them for a second they have architectures and i hate to bring it up but remember the machine i know it's the butt of many jokes especially from competitors it had been you know frankly hpe and hp they deserve some of that heat for all the fanfare and then that they they put out there and then quietly you know pulled the machine or put it out the pasture but hpe has a strong position in high performance computing and the work that it did on new computing architectures with the machine and shared memories that might be still kicking around somewhere inside of hp and could come in handy for some day in the future so hpe has some chops there plus hpe has been known hp historically has been known to design its own custom silicon so i would not count them out as an innovator in this race cisco is interesting because it not only has custom silicon designs but its entry into the compute business with ucs a decade ago was notable and they created a new way to think about integrating resources particularly compute and networking with partnerships to add in the storage piece initially it was within within emc prior to the dell acquisition but you know it continues with netapp and pure and others cisco invests they spend money investing in architectures and we expect the next generation of ucs oh ucs2 ucs 2.0 will mark another notable milestone in the company's data center business dell just had an amazing quarterly earnings report the company grew top line revenue by around 12 percent and it wasn't because of an easy compare to last year dells is simply executing despite continued softness in the legacy emc storage business laptop the laptop demand continued to soar in dell server business it's growing again but we don't see dell as an architectural innovator per se in compute rather we think the company will be content to partner with suppliers whether it's intel nvidia arm-based partners or all of the above dell we think will rely on its massive portfolio its excellent supply chain and execution ethos to compete now ibm is notable for historical reasons with its mainframe ibm created the first great compute monopoly before it unwind and wittingly handed it to intel along with microsoft we don't see ibm necessarily aspiring to retake that compute platform mantle that once once held with mainframes rather red hat in the march to hybrid cloud is the path that we think in our view is ibm's approach now let's get down to the elephants in the room intel nvidia and china inc china is of course relevant because of companies like alibaba and huawei and the chinese chinese government's desire to be self-sufficient in semiconductor technology and technology generally but our premise here is that the trends are favoring nvidia over intel in this picture because nvidia is making moves to further position itself for new workloads in the data center and compete for intel's stronghold intel is going to attempt to remake itself but it should have been doing this seven years ago what pat gelsinger is doing today intel is simply far behind and it's going to take at least a couple years for them to really start to to make inroads in this new model let's stay on the nvidia v intel comparison for a moment and take a snapshot of the two companies here's a quick chart that we put together with some basic kpis some of these figures are approximations or they're rounded so don't stress over it too much but you can see intel is an 80 billion dollar company 4x the size of nvidia but nvidia's market cap far exceeds that of intel why is that of course growth in our view it's justified due to that growth and nvidia's strategic positioning intel used to be the gross margin king but nvidia has much higher gross margins interesting now when it comes down to free cash flow intel is still dominant as it pertains to the balance sheet intel is way more capital intensive than nvidia and as it starts to build out its foundries that's going to eat into intel's cash position now what we did is we put together a little pro forma on the third column of nvidia plus arm circa let's say the end of 2022. we think they could get to a run rate that is about half the size of intel and that can propel the company's market cap to well over half a trillion dollars if they get any credit for arm they're paying 40 billion dollars for arm a company that's you know sub 2 billion the risk is that because of the arm because the arm deal is based on cash plus tons of stock it could put pressure on the market capitalization for some time arm has 90 percent gross margins because it pretty much has a pure license model so it helps the gross margin line a little bit for this in this pro forma and the balance sheet is a swag arm has said that it's not going to take on debt to do the transaction but we haven't had time to really dig into that and figure out how they're going to structure it so we took a took a swag in in what we would do with this low interest rate environment but but take that with a grain of salt we'll do more research in there the point is given the momentum and growth of nvidia its strategic position in ai is in its deep engineering they're aimed at all the right places and its potential to unlock huge value with arm on paper it looks like the horse to beat if it can execute all right let's wrap up here's a summary look the architectures on which nvidia is building its dominant ai business are evolving and nvidia is well positioned to drive a truck right to the enterprise in our view the power has shifted from intel to the arm ecosystem and nvidia is leaning in big time whereas intel it has to preserve its current business while recreating itself at the same time this is going to take a couple of years but intel potentially has the powerful backing of the us government too strategic to fail the wild card is will nvidia be successful in acquiring arm certain factions in the uk and eu are fighting the deal because they don't want the u.s dictating to whom arm can sell its technology for example the restrictions placed on huawei for many suppliers of arm-based chips based on u.s sanctions nvidia's competitors like broadcom qualcomm at all are nervous that if nvidia gets armed they will be at a competitive disadvantage they being invidious competitors and for sure china doesn't want nvidia controlling arm for obvious reasons and it will do what it can to block the deal and or put handcuffs on how business can be done in china we can see a scenario where the u.s government pressures the uk and eu regulators to let this deal go through look ai and semiconductors you can't get much more strategic than that for the u.s military and the u.s long-term competitiveness in exchange for maybe facilitating the deal the government pressures nvidia to guarantee some feed to the intel foundry business while at the same time imposing conditions that secure access to arm-based technology for nvidia's competitors and maybe as we've talked about before having them funnel business to intel's foundry actually we've talked about the us government enticing apple to do so but it could also entice nvidia's competitors to do so propping up intel's foundry business which is clearly starting from ground zero and is going to need help outside of intel's own semiconductor manufacturing internally look we don't have any inside information as to what's happening behind the scenes with the us government and so forth but on its earning call on its earnings call nvidia said they're working with regulators that are on track to complete the deal in early 2022. we'll see okay that's it for today thank you to david floyer who co-created this episode with me and remember i publish each week on wikibon.com and siliconangle.com these episodes they're all available as podcasts all you're going to do is search breaking analysis podcast and you can always connect with me on twitter at dvalante or email me at david.valante siliconangle.com i always appreciate the comments on linkedin and in the clubhouse please follow me so you can be notified when we start a room and riff on these topics and don't forget to check out etr.plus for all the survey data this is dave vellante for the cube insights powered by etr be well and we'll see you next time [Music] you

>>From around the globe, it's the Cube with digital coverage of postgres Vision 2021 brought to you by >>enterprise DB. Hello everyone. This is Dave Volonte for the cube we're covering Postgres Vision 2021. The virtual cube edition. Welcome to our conversation with the Ceo Ed Boyajian is here is the Ceo of enterprise DB and we're gonna talk about what's happening in open source and database in the future of tech. Ed welcome. >>Hi Dave, Good to be here. >>Hey, several years ago, at a, at a Postgres Vision event, you put forth the premise that the industry was approaching a threshold moment, a digital transformation was the linchpin of that shift now. Ed Well you were correct and I have no doubt the audience agreed. Most people went back to their offices after that event and they returned to their hyper focus of their day to day jobs. Maybe a few accelerated their digital initiatives, but generally pre Covid, we moved in a pretty incremental pace and then the big bang hit. And if you were digital business, you are out of business. So that single event created the most rapid change that we've ever seen in the tech industry by far, nothing really compares. So the question is why is Postgres specifically and e d B generally the right fit for this new world? >>Yeah, I think, look, a couple of things are happening gave right along the bigger picture of digital transformation. We are seeing the database market in transformation and and I think the things that are driving that shift are the things that are resulting the success of Postgres and the success of B D B I think first and foremost we're seeing a dramatic re platform ng. And just like we saw in the world of Lennox where I was at red hat during that shift where people are moving from UNIX based systems to x 86 systems. We're seeing that similar re platform in happening. Whether that's from traditional infrastructures to cloud based infrastructures or container based infrastructures, it's a great opportunity for databases to be changed out. Postgres wins in that context because it's so easily deployed anywhere. I think the second thing that's changing is we're seeing a broad expansion of developers across the enterprise so they don't just live in I. T. Anymore. And I think as developers take on more power and control their defining the agenda and it's another place where Postgres shines, it's been a priority of the dBS to make postgres easier. Uh and that's coming to life. And I think the last Stack Overflow Developer Survey suggested that I think they survey 65 developers, the second most loved and the second most used database by developers, Postgres. And so I think there again Postgres shines in a moment of change. Uh and then I think the third is kind of obvious. It's always an elephant in the room, no pun intended. But it's this relentless nagging burden of the expenses of the incumbent proprietary databases and the need. And we especially saw this in Covid to start to change that more dramatically, change that economic equation here Again. PostGres shines. >>You know, I want to ask you, I'm gonna jump ahead to the future for a second because you're talking about the re platform NG and with your red hat chops, I kind of want to pick your brain on this because you're right, you saw it with red hat and you're kind of seeing it again when you think about open shift and where it's going my my question is related to replant forming around new types of workloads, new processing models at the edge. I mean you're seeing an explosion of processing power, GPU SNP us accelerators, dSPs and it appears that this is happening at a very low cost. I'm referring that you're saying Postgres can take advantage of that trend as well that that broader re platform ng trend to the edge, is that correct? >>It is. And I think you know this is, this has been one of the, I think the most interesting things with posters now I've been here almost 13 years. So if you put that in some perspective, I've watched Uh and participated in leading transformation in the category, you know, we've been squarely focused on postgres. So we've got 300 engineers who worry about making postgres better. And as you look across that landscape of time, not only as Postgres gotten more performant and more scalable, it's also proven to be the right database choice in the world of not just legacy migrations, but new application development. And I think that stack overflow developer survey is a good indicator of how developers feel about postgres. But you know, over that time frame I think if you went back to 2008 when I joined E D. B, post chris was considered a really good general purpose database. And today I think post chris is a great general purpose database. General purpose isn't sexy in the market broadly speaking, but Postgres capabilities across workloads in every area is really robust. Let me just spend a second on it. We look at our customer base is deploying in what we think of as systems of record, which are the traditional er, P type apps, uh you know where there's a single source of truth you might think of the RP apps there. We look at our customers deploying in systems of engagement. And those are apps that you might think of in the context of social media style apps or websites that are backed by a database in the third area Systems of analytics where you would typically think of data warehouse style applications interestingly. Postgres performs well and our customers report using us across that whole landscape of application areas. And I think that is one of postgres hidden superpowers. Is that ability to reach into each area of requirement on the workload side. >>And as always alluding to before that that itself is evolving as you now inject ai into the equation ai influencing and it's just a very exciting times ahead. There's no there's no database, You know, 20 years ago it was kind of boring. Now it's just exploding. I want to come back to that the notion of of post grass and maybe talk about other database models. Uh, I mean you mentioned that you've evolved from this, you know, system of record. You can take a system engagement on structured data etcetera. Jason. It's so how should we think about post grass in relation to other databases and specifically other business models of companies that provide database services? Why is Postgres attractive? Where is it winning? >>Yeah, I think a couple of places. So I mean first and foremost Postgres, you know, at his core, post chris is a sequel, relational databases in acid compliance, equal relational database. And that is inherently a strength of Postgres. But it's also a multi model database, which means we handle a lot of other, um, you know, database requirements, whether that's geospatial or or Jason, uh, for documents or time series, things like that. And so Postgres extensive bility is one of its inherent strengths and that's kind of been built in from the beginning of Postgres. So not surprisingly, people use postgres across the number of workloads because at the end of the day there's still value in having a database is able to do more. There are a lot of important specialty databases and I think they will remain important specialty databases, but Postgres thrives in its ability to cross cross over in that way. Um and I think that is, you know, one of the different key differentiators in how we've seen the market in the business development and that's the breadth of of workloads that Postgres succeeds in. But but our growth, if you kind of ventured it across vectors, we see growth happening, you know, in a few dimensions. First we see growth happening in new applications. About half of our customers that come to us today for new uh new postgres users are deploying us on new applications. The others are our second area migrating away from some existing legacy in companies often oracle. Not always. Um The third area of growth we see is in cloud, where Postgres is deployed very prolifically, both in the traditional cloud platforms, Uh like EC two, but then then again also uh in the database as a service environment. And then the fourth area growth we're seeing now is around uh container deployment, kubernetes deployment. >>Well, you may Oracle's prominent because it's just it's a big installed base and it's expensive and people, >>you >>know, they got a look at them. It's funny, I do a lot of TCO work and mostly, you know, usually TCO is about labor costs. When it comes to Oracle, it's about license costs and maintenance costs. And so to the extent that you can reduce that, at least for a portion of your state, you're gonna you're gonna drop right to the bottom line. But but but but I want to ask you about that kind of that spectrum that you think about the prevailing models for database you've got. On the one hand, You've got the right tool for the right job approach. It might be 10 or 12 data stores in the cloud. On the other hand, you've got, you know, kind of a converged approach. Oracle's going that direction clearly. Postgres with its open source innovation is going that direction. And it seems to me that at scale that's a more the latter is a more cost effective model. How do you think about that? >>Well, you know, I think at the end of the day, you kind of have to look at it. I mean, the business side of my brain looks at that as an addressable market question. Right? And you've heard me talk about three broad categories of workloads and you know, people define workloads in different bucket, but that's how we do it. But if you look at just a system of record in the system of engagement market, I think that's what would be traditionally viewed as the database market. Uh and there that's you know, let's just say for the sake of arguments of $45-$50 billion $18 billion dollar market. And you know, as we talk about that. So all in it's still between 60 and $70 billion market. And I think what happens there's so much heat and light poured on the valuation multiples of some of the specialty players. That the market gets confused, but the reality is our customers don't get confused. I mean if you look at those specialty players take that $48 billion market. I mean add up Mongo red is cockroach neo, all of those. I mean hugely valued companies. All unicorn companies. But combined to add up to a billion bucks don't get me wrong that's important revenue and meaningful in the workloads they support. But it's not. It doesn't define the full transformation of this category. Look at the systems of analysis again, another great great market example. I mean if you add up the consolidation of the Hadoop vendors add in there. Um Snowflake, you're still talking you know a billion five in revenue and an $18 billion market. So while those are all important technologies, the question is in this transformation move to the database market fully transform you. And my view is no it didn't were in the first maybe second inning of a $65 billion transformation. And I think this is where Postgres will ultimately shine. I think this is how Postgres wins because at the end of the day the nature of the workloads fits with postgres and the future tech that we're building in post schools will serve that broader set of needs I think more effectively >>well. And I love these tam expansion discussions because I think you're right on and I think it comes back to the data and we all talk about the data growth, the data explosion, we see the I. D. C. Numbers and you ain't seen nothing yet. And so data by its very nature is distributed. That's why I get so excited about these new platform models and and I want to tie it back to developers and open source because to me that is the linchpin of innovation um in the next decade it has been, I would even say for the last decade we've seen it, but it's gaining momentum, so, so in thinking about innovation and and specifically Postgres and an open source, you know, what can you share with us in terms of how we should think about your advantage, and again, what, where people are glomming leaning in to that advantage? >>Yeah, so, I mean, I think I think you bring up a really important topic for us as a company. Postgres we think is an incredibly powerful community, uh and when you step away from it again, I remember I told you I was at red hat before, now here at E D B, and there's a common thread that runs through those two experiences in both experiences. The companies are attached and prominent alongside a strong independent, open source community, and I think the notion of an independent community is really important to understand around postgres. There are hundreds and thousands of people contributing to Postgres now. E D B plays a big role in that. About approaching a third of the contributions. In the last release released, 13 of Postgres came from E D B. You might look at that and say gee, that sounds like a lot, but if you step away from it, you know, about 30% of those contributions, Most of the contributions come from a universe around D D. B. And that's inherently healthy for the community's ability to innovate and accelerate. And I think that while we play a strong role there, you can imagine that having and there are other great companies that are contributing to Postgres, I think having those companies participating and contributing gets the best, the best ideas to the front in innovation. So I think the inherent nature Postgres community makes it strong and healthy. And then contrast that to some of the other prominent high value open source companies, the companies and the communities are intimately intertwined. They're one and the same. They're actually not independent open source communities. And I think that therein lies one of the, one of the inherent weaknesses in those but postgres to rise because you know, we bring all those ideas from the DB, we bring a commercial contingent with us all the things we hope we emphasize and focus on in growth and postgres, whether that's in the areas of scalability, manageability, all hot topics, of course security, all of those areas. And then, you know, performance as always, all of those areas are informed to us by enterprise customers deploying post chris at scale. And I think that's the heart of what makes a successful independent project. >>Yeah. The combinatorial powers of of that ecosystem. Uh they their their multiplication, I've as opposed to the resources of one. I want to talk about postgres Vision 2021 sort of set up that a little bit. The theme this year is the future. Is you, what do you mean by that? >>So if you think about what we just said post the category is in transit database categories and transformation. And we know that many of our people are interested in. Postgres are early in their journey, their early in their experience. And so we want to focus this year's postcards vision on them that we understand as a company has been committed to postgres as long as we have and with the understanding we have the technology and best practices, we want to share that view those insights uh, with those who are coming to postgres, Some for the first time, some who are experienced >>Postgres. Vision 21 is june 22nd and 23rd. Go to enterprise db dot com and register the cube is going to be there. We hope you will be too. Ed, thanks for coming to the Cuban previewing the event. >>Thanks Dave. >>Thank you. We'll see you at Vision 21 >>mm mm.

>>From around the globe. It's the cube with digital coverage of IBM. Think 2021 brought to you by IBM. >>Hello everyone. Welcome back to the cubes. Ongoing virtual coverage of IBM. Think 2021. This is our second virtual think. And we're going to talk about what's on the minds of CTOs with a particular point of view from the EMEA region. I'm pleased to welcome rushy Parmer, who is an IBM fellow and vice-president of technology for AMEA that region. Hello Russia. Good to see you. >>Great to see you. So >>Let me start by, by asking, talk a little bit about the role of the CTO and why is it necessarily important to focus on the CTO role versus say some of the other technology practitioner roles? >>Yeah. You know, as you look at all the range of roles of the gut in the it department, the CTO is uniquely placed in looking forward at how technology and how digitization is going to make a difference in the business. But also at the same time, is there as the kind of thought leader for how they're going to really, um, reimagine the use of technology re-imagine automation, reimagining, how digitization helps them go to market different ways. So the CTO is a unique, a unique position from idea to impact. And in the past, we've kind of lost the CTO a little bit, but they're now reemerging as being the thought leader. That's only in driving digitization, going forward in our big clients. >>I, I would agree. I mean, it really has a deep understanding of that vision and can apply that vision to business success. So you obviously have a technical observation space and you also have some data, so maybe you could share with our audience how you inform yourself and your colleagues and IBM on, on what CTOs are thinking about and what they're worried about. >>Yeah. And so, so what we've done over the last four years now is gone out and interviewed CTOs. And can we do a very unstructured interviews? It's not, it's not a survey in the form of, uh, filling these, uh, these 10 questions and tell us yes or no. It reads a structured interview. We ask things like what's top of mind for you. What are the decisions you're making? Um, what's holding you back? What decisions do you think you shouldn't have made, or you wouldn't have liked to make? And, and it's that range of, um, of real input from the interview. So last year we interviewed a hundred CTOs. Um, this year we're actually doing a lot more, we're working with the IBM Institute of business value and we're gonna interview a lot more teachers, but for the material we're going to talk about today is really from those hundred CTO interviews. >>Yeah. And I think that, I mean, having done a lot of these myself, when you do those, we call them, you know, in depth interviews or ideas, you kind of have a structure and you do sort of follow that, but you learn so much and that maybe does inform those more structured interviews, uh, that, that, that you do down the road, you learn so much, but, but maybe you could summarize some of the concerns in the region what's on the minds of, of CTOs. Yeah. Yeah. The, >>The, the real decisions are being based around seven points, right? So the first one is we all know we're on a journey to the cloud. Um, but it's a hybrid multicloud. How do I think about the range of capabilities? I need to be able to unlock the latent potential of existing investments and the cloud-based capabilities we've got. So, so the, the hybrid cloud platform is, is, is one of the first and foundational pieces. The second challenge is that CTOs want to modernize their applications. And that modernization is a journey of, of moving towards microservices. That microservices journey has two parts. One is the business facing view, and that's what containers is all about choosing the right container platform. At the same time, they also want to use containers as a way of automation and management and reducing the effort and the infrastructure. So, so that's kind of two parts of that, that whole container journey. >>So Microsoft, this has really become the, the, the, the business developer view and containers become the operational view. At the same time, they wanna infuse new data to want to climb the AI ladder. They want to get the new, new insights from that data that plugs into those new workflows to get to those workflows. There's a decision around how do I isolate myself from some of the services of using that? And we've created a layer in the decisions around what called cloud services integration. So part service integration is, is kind of the, the modern day ESB as we might think about it. Um, but it's a way in which you choose which technology, which API I'm going to use from where, and then ultimately the CTOs are trying to build what are the new, um, uh, the new workflows, intelligent workflows. And they're really worried about how do I get the right level of automation that managing that issue between what becomes creepy and valuable, right. >>You know, there's some workflows that happen. You think, why the hell did that happen? Or I don't, that doesn't make sense. And, and, and it really sort of nerves the consumer, the user, whereas some which are wow, that's really cool. I really enjoyed that to try to get the intelligent workflows, right. Is a big concern. And then, um, on the two big, uh, parallel to that is how do we manage the systems operational automation, right from having the right data, the observability of all the infrastructure, recognizing they've got a spectrum of things from 30, 40, 50 year old systems to modern day cloud native systems, how to manage it, how to operationally automate that, keep that efficient, effective. And then of course, protecting from the perpetrator's rent business. A lot of people out there wanting to dig into the systems and, and, and, and draw all kinds of, um, you know, uh, data from their systems. So security, privacy, and making sure that align with the ethics and privacy of the business. So those are, those are the kind of range of issues, right? From the journey to cloud, through, to operational automation, through, through intelligent workflows, right. Into managing, protecting the services. >>That's interesting. Thank you for that. I mean, I remember, and you will, as well, some of the post wide thrust and sort of part of the modernization back then was during that they had budget to do that, but a lot of times organizations would make the mistake that they would, they're going to migrate off of a system that was working just fine. That was their sort of mental model of, of, of modernization. And it turned out to be disastrous in many cases. And so what, when I talk to CEOs, they talk about maybe, you know, I'd look at it as this, this abstraction layer. We want to protect what we have that works. Yes. Some stuff's going to go into the public cloud, but this hybrid connection that you talk about, and then we want control. And the way we're going to get control is we're going to use microservices to modernize and use modern API APIs. And so very, very sort of different thinking. And of course they want to avoid migration at all costs because it's so expensive and risky. I wonder if you could talk about, are there any patterns in terms of where people get started and the kinds of outcomes that they're working towards that they can measure? >>Yeah. And we kind of lump the, the learning from the work into three broad patterns, right. Um, one pattern is, is primarily around survival. They recognize that this journey, um, is, is very complex, that the pandemic has created tremendous challenges. Um, the market dynamics means that I've got to try and really be thoughtful in, in taking cost out and making sure they survive some of these issues and sort of the pattern is really around cost reduction. It may start with the hybrid cloud. It may start with in terms of workloads, but it's really about taking cost out of the systems. The second pattern is what we refer to as a simplification pattern. And this is about saying that we've got, we've got so much complexity because of technical debt, because of, you know, systems that we've half migrated in half done things with. Um, so how do I, how do I simplify my it landscape from applications through infrastructure to the data and make it more consistent and manageable and effective. >>And then the third one is that there are CTO saying, look, we've got a really pick that the time when we super scale something, we've got some things which we are unique and effective on. And I want to take that and really super scale that very quickly and make that consistent and really maximize the value of it. So that sort of pattern is really falling to those three categories of driving, driving cost reduction and survival simplification and modernization transformation. And then those that have got something which is unique and special and really super scaling up. >>Yeah. Right, right. Doubling down on those things that gave you unique, competitive advantage. Now, in this, in, in the studies that you've done over the years, you use this term ADP architectural decision points, and some of them are quite compelling. Maybe you could talk about some of those where there's some anxieties from the CTOs that, that you uncovered. >>Yeah. Yeah. The, the ADP's that we'll talk about the seven ATPs and it starts from the high rebuilt crowd through to, to intelligent workflows and so on. Um, and the ADP's themselves are really distilling the client's words in the client's, um, way of thinking about how they're going to drive those, those technologies. Um, and also how they're going to use those techniques to make a difference. But I think went through those interviews, um, what became the power is CTOs do have some anxieties as you refer to it. Um, and, and those anxiety, they couldn't necessarily put words on them and there were anxieties and like, are we thinking enough about the carbon footprint? Are we, are we being thoughtful in how we make sure we're reducing carbon footprint or reducing the environmental impact of the infrastructure you've got, we've got sprawling infrastructure, um, ripping out rare metals from the earth. >>Are we being thoughtful in how we reduce the, um, the amount of rare metals we have water consumption, uh, right through to is the code that we're producing efficient, secure and fit for, for the future? Um, are we being ethical in capturing the data for its right use, um, is the AI systems that we're building? Are they explainable? Are they ethical? Are they free from bias or are we kind of amplifying things that we shouldn't be able to find? So there was a whole bunch of those call anxieties and what we did along with the architectural decision report, um, a point after she decision report was, was identify what we call a set of responsibilities. And, and we've built a framework about around responsible computing, which is, uh, which is a basis for how you think through what your responsibilities are as a, as a CTO or as an it leader. Um, and we're right in the process of building out that, that kind of, um, responsible computing framework. >>Yeah, it's interesting. A lot of people may, may think about it. They think about the responsible computing and the sustainability, and they might think that's a, a one 80 from Milton Friedman economics, which said the job of business is to make profits. But in fact, responsible computing, there's a strong business case, uh, around it. It actually can help you reduce costs that can, can help you attract better employees because young people are passionate about this. I wonder if you could talk about how, how people can get involved with responsible computing in, in lean in. >>Yeah. So what we're about to publish is that he's actually a manifesto for responsible computing. So I think everybody, once we get that published, I'm hoping to do that in the next two to three months, we're working with a few clients, um, to there's actually three clients that have chosen, just click through your client's CTOs from the ones that we interviewed were very keen to collaborate with us in, in laying out that, um, that manifesto and the opportunity really is for anybody listening. If you, if you find this as a great value, please do come and reach out to me more than happy to collaborate with looking for more insights on this. Um, we've also had some, um, competitions. So in, in, in Mia, we've had a competition with, uh, with business partners looking for of how we can, um, really showcase examples or exemplars of being responsible computing provider, whether it's at the level of responsible data center, whether it's about responsible code data, use responsible systems, right through to responsible impact. And, you know, obviously a lot of our work around things like, um, your tech for good is, is tied directly to responsible impact. And of course, if you want to see what we IBM have been doing our responsible responsibility report, which we've been voluntarily publishing for the last 30 years, provides a tremendous set of insights on how we've done that over the years. And, and that's a, that's a great way for you to see how we've been doing things and see if that there are critical in your business. >>Yeah, so there's, so there's the, the re the ADP report is available. You can check it out on, on LinkedIn, um, go to go to Russia, LinkedIn profile, you'll find it. There's a blog post that talks about the next wave of digitization. Um, the learnings that you just talked about. So there's a lot of resources for, for people to get involved. I'll give you the last word rushy. >>Yeah. And th th this is, this is what I call job began. It's not job done. The whole ADP responsible computing is a digitization journey where we want to balance delivering business value and making a difference to the organization. But at the same time, being responsible, making sure that we're thoughtful of what's needed for the future. And we create impact that really matters. And, or we can feel proud that we've put a foundation for digitization, which will, which will serve the businesses for many years to come >>Love it, impact investing in your business and in the future. Russia, thanks so much for coming to the cube. Really appreciate it. Thank you. Okay. Keep it right there for more coverage from IBM. Think 2021. This is Dave Volante for the cube.

>>from >>Around the globe, it's the cube with digital coverage of IBM think 2020 >>one brought to you by IBM. Hello everyone and welcome back to the cubes ongoing virtual coverage of IBM think 2021 this is our second virtual think and we're going to talk about what's on the minds of C. T. O. S with a particular point of view from the EMEA region. I'm pleased to welcome rasheed Parmer, who is an IBM fellow and vice president of technology for Armenia that region. Hello rashid, Good to see you. >>Hey David, great to see you. >>So let me start by by asking talk a little bit about the role of the C. T. O. And why is it necessarily important to focus on the C. T. O. Role versus say some of the other technology practitioner roles? >>Yeah. You know, you know, they as you look at all the range of roles of the got in in the I. T. Department, the CTO is uniquely placed in looking forward how technology and how digitization is gonna make a difference in the business but also at the same time is there as the kind of thought leader for how they're going to really you re imagine the use of technology reimagine automation, reimagining, how digitalization helps them go to market different ways. So the CTO is a unique unique position from idea to impact. And in the past we've kind of lost the C. T. A little bit but they're now re emerging as being the thought leader that's owning and driving digitalization going forward in our big plants. >>Yeah I agree. And it really has a deep understanding of that vision and can apply that vision to business success. So you obviously have a technical observation space and you also have some data so maybe you could share with our audience how you inform yourself and your colleagues and IBM on on what C. T. O. S. Are thinking about and what they're worried about. >>Yeah. So what we've done over the last four years now is gone out and interviewed Cdos and we do a very unstructured interviews. It's not it's not a survey in the form of uh you know, filling these uh these 10 questions and tell us yes or no. It really is a structured interviews. We asked things like what's top of mind for you, what are the decisions you're making? What's holding you back? What decisions do you think you shouldn't have made or you wouldn't have liked to make? And it's that range of a real input from the the interview. So last year we interviewed 100 CTO s um this year we're actually doing a lot more. We're working with the IBM Institute Business Value and we're gonna interview a lot more teachers but but the material we're gonna talk about today is is really from those 100 CTO interviews. >>Yeah. And I think that having done a lot of these myself, when you do those, we call them, you know in depth interviews, our I. D. S. You kind of have a structure and you sort of follow that but you learn so much and that it maybe does inform those more structured interviews that you do down the road. You learn so much, but maybe you could summarize some of the concerns in the region. What's on the minds of Ceos? >>Yeah. And you know, the the real decisions are made based around seven points. Right? So the first one is we all know, we're on a journey to the cloud but it's a hybrid multi cloud. How do I think about the range of capabilities and need to be able to unlock the latent potential of existing investments and the cloud based capabilities of God. So, so the hybrid cloud platform is one of the the first and foundational pieces. The second challenge is the C e O s want to modernize their applications and that modernization is a journey of moving towards microservices. That microservices journey has two parts. One is the business facing view and that's what containers is all about, choosing the right container platform at the same time. They also want to use containers as a way of automation and management and reducing the effort in the infrastructure. So, so that's kind of two parts of the whole container journey. So Microsoft, this has really become the business developer view and containers become the operational view At the same time. They want infused new data, they want to climb the ladder, they want to get the new new insights from that data that plugs into those new workflows to get to those workflows. There's a decision around how do I isolate myself from some of the services of using that? And we created a layer in the decisions around what's called cloud services integration. So cloud services integration is kind of the modern day E S B as we might think about it, but it's a way in which you choose which technology, which a P I is. I'm going to use from where and then ultimately, the CTS are trying to build what are the new, the new workflows, intelligent workflows and they're really worried about how do I get the right level of automation that managing that issue between what becomes creepy and valuable, Right? You know, the some workflows that happen, you think, why the hell did that happen? Right. That doesn't make sense. And and and and it really sort of nerves. The consumer, the user where some which are, wow, that's really cool. I really enjoyed that. To try to get the intelligent workflows right is a big concern. And then on the two big perils of that is how do we manage the system, the operational automation right from having the right data observe ability of all the infrastructure, recognizing they've got a spectrum of things from 30 40 50 year old systems to modern day cloud native systems, how to manage how operationally automate that keep that efficient, effective. And then of course protecting from the perpetrators, right? Business, a lot of people out there wanting to begin to the systems and, and, and and draw all kinds of, you know, a data from their system. So security, privacy and making sure that align with the ethics and privacy of the business. So those are those are the kind of range of issues right from the journey to cloud, through to operational automation, through through intelligent workflows, right into manage and protecting the services. >>It's interesting. Thank you for that. I mean I remember and you will as well some of the post Y two K you know, thrust and part part of the modernization back then was during that they had budget to do that. But a lot of times organizations would make the mistake that they would they're going to migrate off of a system that was working just fine. That was there sort of mental model of of modernization. And it turned out to be disastrous in many cases. And so when I talk to Ceos they talk about maybe, you know, I'd look at it is this this abstraction layer we want to protect what we have that works. Yes. Some stuff is going to go into the public cloud, but this hybrid connection that you talk about and then we want control and the way we're gonna get control is we're gonna use microservices to modernize and use modern A. P. I. S. And so very very sort of different thinking. And of course they want to avoid migration at all costs because it's so expensive and risky. I wonder if you could talk about, are there any patterns in terms of where people get started and the kinds of outcomes that they're working towards that they can measure? >>Yeah, we we kind of lumped the learning from the work into three broad patterns, right? Um one pattern is primarily around survival. They recognize that this journey is very complex. The pandemic has created tremendous challenges. The market dynamics means they've got to try and really be thoughtful in in taking cost out and making sure they survive some of these issues. And so the pattern is really around cost reduction. It may start with a hybrid cloud, it may start with intelligent workflows but it's really about taking costs out of the systems. The second pattern is what is referred to as a simplification pattern and this is about saying but we've got we've got so much complexity because of technical debt because of you know systems that we've half migrated and half done things with. So how do I how do I simplify my I. T. Landscape from applications through infrastructure for data and make it more consistent, manageable and and effective. And then the 3rd 1 is their city is saying look we've got a really pick the time when we super scale something, we've got something which we are unique and effective on and I want to take that and really super scale that very quickly and make that consistent and really maximize value of it so that the pattern is really fall into three categories of driving, driving, cost reduction and survival, simplification and modernisation transformation. And then those that have got something which is unique and special and really super scaring up. >>Yeah. Right, right, doubling down on those things. That unique competitive advantage in the, in the studies that you've done over the years. You use this term ADP architectural decision points and some of them are quite compelling. Maybe you could talk about some of those. Were there some anxieties from the cdos that you uncovered? >>Yeah. You know, the, the NDP s talk about the 70 Gps and it starts from the higher ability crowd through to two intelligent workflows and so on. And the NDP s themselves are really distilling the client's words and the clients way of thinking about how they're going to drive those, those technologies, um and also how they're going to use those techniques to make a difference. But if we went through those interviews, what became apparent is, see us do have some anxieties as you refer to, and those anxieties, they couldn't necessarily put words on them and their anxieties. Like, are we thinking enough about the carbon footprint? Are we are we being thoughtful in how we make sure we're reducing carbon footprint or reducing the environmental impact of the infrastructure? You've got, we've got sprawling infrastructure um ripping out rare metals from the earth. Are we being thoughtful in how we reduce the amount of rare metals we have water consumption right through to is the code that we're producing efficient, secure and and fit for for the future. Are we being ethical in capturing the data for its right use? Um Is the ai systems that we're building? Are they explainable? Are they ethical? Are they free from bias or are we kind of amplifying things that we shouldn't be amplifying? So there was a whole bunch of those call anxieties and what we did along with the architectural decision report. A point after decision report was was identify what we call a set of responsibilities. And and we've built a framework about around responsible computing which is which is a basis for how you think through what your responsibilities are as a as a Ceo are as an I. T. Leader. And we're right in the process of building out that that kind of responsible computing framework. >>You know it's interesting a lot of people may may think about they think about the responsible computing and and and the sustainability and they might think that's a 1 80 from Milton Friedman Economics, which is the job of businesses to make profits. But in fact responsible computing, there's a strong business case around it. It actually can help you reduce costs that can help you attract better employees. Because young people are passionate about this. I wonder if you could talk about how how people can get involved with responsible computing and lean in. >>Yeah, so what we're about to publish it is actually manifesto for responsible computing. So I think everybody wants to get that published. I'm hoping to do that in the next two or three months. We're working with a few clients. So there's actually three clients that have chosen through your client cts from the ones that we interviewed were very keen to collaborate with us in laying out that that manifesto and the opportunity really is from anybody listening. If if you if you find this of great value, please do come and reach out to me more than happy to collaborate. We're looking for more insights on this. We've also had some competitions. So in in in a media we've had a competition with business partners, looking for ideas of how we can really showcase examples or exemplars of being responsible computing provider, whether it's at the level of responsible data center, whether it's about responsible code data, use Responsible systems right through the responsible impact. And obviously a lot of our work around things like your tech for good is tied directly to responsible impact. And of course, if you want to see what we have never been doing are responsible responsibility report, which we've been voluntarily publishing for the last 30 years, provides a tremendous set of insights on how we've done that over the years. And and that's a that's a great way for you to see how we've been doing things and see if there are people in your business. >>Yeah. So there's so there's the, the ADP report is available. You can check it out on on linkedin. Um, go to, go to Russia linked in profile, you'll find it. There's a blog post that talks about the next wave of, of digitization, uh, you know, the learnings that you just talked about. So there's a lot of resources for for people to get involved. I'll give you the last word. >>Yeah. And look, this is this is what I call job big and it's not job done that the whole ADP responsible computing is a digitization journey where we want to balance delivering business value and making a difference to the organization, but at the same time being responsible in making sure that we're thoughtful what's needed for the future and we create impact that really matters. And we can feel proud that we've put a foundation for digitization which will which will serve the businesses for many years to come, >>love it, impact investing in your business and in the future. Russia, thanks so much for coming on the cube. Really appreciate it. >>A pleasure. Thank you. >>Okay, keep it right there for more coverage from IBM think 2021 this is Dave Volonte for the Cube. Yeah, yeah.

>>Hello. I'm Professor Toru Suzuki Cherif cardiovascular medicine on associate dean of the College of Life Sciences at the University of Leicester in the United Kingdom, where I'm also director of the Lester Life Sciences accelerator. I'm also honorary consultant cardiologist within our university hospitals. It's part of the national health system NHS Trust. Today, I'd like to talk to you about our Lester Clinical Data Science Initiative. Now brief background on Lester. It's university in hospitals. Lester is in the center of England. The national health system is divided depending on the countries. The United Kingdom, which is comprised of, uh, England, Scotland to the north, whales to the west and Northern Ireland is another part in a different island. But national health system of England is what will be predominantly be discussed. Today has a history of about 70 years now, owing to the fact that we're basically in the center of England. Although this is only about one hour north of London, we have a catchment of about 100 miles, which takes us from the eastern coast of England, bordering with Birmingham to the west north just south of Liverpool, Manchester and just south to the tip of London. We have one of the busiest national health system trust in the United Kingdom, with a catchment about 100 miles and one million patients a year. Our main hospital, the General Hospital, which is actually called the Royal Infirmary, which can has an accident and emergency, which means Emergency Department is that has one of the busiest emergency departments in the nation. I work at Glen Field Hospital, which is one of the main cardiovascular hospitals of the United Kingdom and Europe. Academically, the Medical School of the University of Leicester is ranked 20th in the world on Lee, behind Cambridge, Oxford Imperial College and University College London. For the UK, this is very research. Waited, uh, ranking is Therefore we are very research focused universities as well for the cardiovascular research groups, with it mainly within Glenn Field Hospital, we are ranked as the 29th Independent research institution in the world which places us. A Suffield waited within our group. As you can see those their top ranked this is regardless of cardiology, include institutes like the Broad Institute and Whitehead Institute. Mitt Welcome Trust Sanger, Howard Hughes Medical Institute, Kemble, Cold Spring Harbor and as a hospital we rank within ah in this field in a relatively competitive manner as well. Therefore, we're very research focused. Hospital is well now to give you the unique selling points of Leicester. We're we're the largest and busiest national health system trust in the United Kingdom, but we also have a very large and stable as well as ethnically diverse population. The population ranges often into three generations, which allows us to do a lot of cohort based studies which allows us for the primary and secondary care cohorts, lot of which are well characterized and focused on genomics. In the past. We also have a biomedical research center focusing on chronic diseases, which is funded by the National Institutes of Health Research, which funds clinical research the hospitals of United Kingdom on we also have a very rich regional life science cluster, including med techs and small and medium sized enterprises. Now for this, the bottom line is that I am the director of the letter site left Sciences accelerator, >>which is tasked with industrial engagement in the local national sectors but not excluding the international sectors as well. Broadly, we have academics and clinicians with interest in health care, which includes science and engineering as well as non clinical researchers. And prior to the cove it outbreak, the government announced the £450 million investment into our university hospitals, which I hope will be going forward now to give you a brief background on where the scientific strategy the United Kingdom lies. Three industrial strategy was brought out a za part of the process which involved exiting the European Union, and part of that was the life science sector deal. And among this, as you will see, there were four grand challenges that were put in place a I and data economy, future of mobility, clean growth and aging society and as a medical research institute. A lot of the focus that we have been transitioning with within my group are projects are focused on using data and analytics using artificial intelligence, but also understanding how chronic diseases evolved as part of the aging society, and therefore we will be able to address these grand challenges for the country. Additionally, the national health system also has its long term plans, which we align to. One of those is digitally enabled care and that this hope you're going mainstream over the next 10 years. And to do this, what is envision will be The clinicians will be able to access and interact with patient records and care plants wherever they are with ready access to decision support and artificial intelligence, and that this will enable predictive techniques, which include linking with clinical genomic as well as other data supports, such as image ing a new medical breakthroughs. There has been what's called the Topol Review that discusses the future of health care in the United Kingdom and preparing the health care workforce for the delivery of the digital future, which clearly discusses in the end that we would be using automated image interpretation. Is using artificial intelligence predictive analytics using artificial intelligence as mentioned in the long term plans. That is part of that. We will also be engaging natural language processing speech recognition. I'm reading the genome amusing. Genomic announced this as well. We are in what is called the Midland's. As I mentioned previously, the Midland's comprised the East Midlands, where we are as Lester, other places such as Nottingham. We're here. The West Midland involves Birmingham, and here is ah collective. We are the Midlands. Here we comprise what is called the Midlands engine on the Midland's engine focuses on transport, accelerating innovation, trading with the world as well as the ultra connected region. And therefore our work will also involve connectivity moving forward. And it's part of that. It's part of our health care plans. We hope to also enable total digital connectivity moving forward and that will allow us to embrace digital data as well as collectivity. These three key words will ah Linkous our health care systems for the future. Now, to give you a vision for the future of medicine vision that there will be a very complex data set that we will need to work on, which will involve genomics Phanom ICS image ing which will called, uh oh mix analysis. But this is just meaning that is, uh complex data sets that we need to work on. This will integrate with our clinical data Platforms are bioinformatics, and we'll also get real time information of physiology through interfaces and wearables. Important for this is that we have computing, uh, processes that will now allow this kind of complex data analysis in real time using artificial intelligence and machine learning based applications to allow visualization Analytics, which could be out, put it through various user interfaces to the clinician and others. One of the characteristics of the United Kingdom is that the NHS is that we embrace data and captured data from when most citizens have been born from the cradle toe when they die to the grave. And it's important that we were able to link this data up to understand the journey of that patient. Over time. When they come to hospital, which is secondary care data, we will get disease data when they go to their primary care general practitioner, we will be able to get early check up data is Paula's follow monitoring monitoring, but also social care data. If this could be linked, allow us to understand how aging and deterioration as well as frailty, uh, encompasses thes patients. And to do this, we have many, many numerous data sets available, including clinical letters, blood tests, more advanced tests, which is genetics and imaging, which we can possibly, um, integrate into a patient journey which will allow us to understand the digital journey of that patient. I have called this the digital twin patient cohort to do a digital simulation of patient health journeys using data integration and analytics. This is a technique that has often been used in industrial manufacturing to understand the maintenance and service points for hardware and instruments. But we would be using this to stratify predict diseases. This'll would also be monitored and refined, using wearables and other types of complex data analysis to allow for, in the end, preemptive intervention to allow paradigm shifting. How we undertake medicine at this time, which is more reactive rather than proactive as infrastructure we are presently working on putting together what's it called the Data Safe haven or trusted research environment? One which with in the clinical environment, the university hospitals and curated and data manner, which allows us to enable data mining off the databases or, I should say, the trusted research environment within the clinical environment. Hopefully, we will then be able to anonymous that to allow ah used by academics and possibly also, uh, partnering industry to do further data mining and tool development, which we could then further field test again using our real world data base of patients that will be continually, uh, updating in our system. In the cardiovascular group, we have what's called the bricks cohort, which means biomedical research. Informatics Center for Cardiovascular Science, which was done, started long time even before I joined, uh, in 2010 which has today almost captured about 10,000 patients arm or who come through to Glenn Field Hospital for various treatments or and even those who have not on. We asked for their consent to their blood for genetics, but also for blood tests, uh, genomics testing, but also image ing as well as other consent. Hable medical information s so far there about 10,000 patients and we've been trying to extract and curate their data accordingly. Again, a za reminder of what the strengths of Leicester are. We have one of the largest and busiest trust with the very large, uh, patient cohort Ah, focused dr at the university, which allows for chronic diseases such as heart disease. I just mentioned our efforts on heart disease, uh which are about 10,000 patients ongoing right now. But we would wish thio include further chronic diseases such as diabetes, respiratory diseases, renal disease and further to understand the multi modality between these diseases so that we can understand how they >>interact as well. Finally, I like to talk about the lesser life science accelerator as well. This is a new project that was funded by >>the U started this January for three years. I'm the director for this and all the groups within the College of Life Sciences that are involved with healthcare but also clinical work are involved. And through this we hope to support innovative industrial partnerships and collaborations in the region, a swells nationally and further on into internationally as well. I realized that today is a talked to um, or business and commercial oriented audience. And we would welcome interest from your companies and partners to come to Leicester toe work with us on, uh, clinical health care data and to drive our agenda forward for this so that we can enable innovative research but also product development in partnership with you moving forward. Thank you for your time.

>> Narrator: From around the globe, it's theCUBE covering HPE Discover Virtual Experience, brought to you by HPE. >> Welcome to theCUBE's coverage of HPE's Discover 2020, the Virtual Experience. I'm Lisa Martin and I'm pleased to be joined by Harshul Asnani, the Global Head of the Technology Business at HPE partner, Tech Mahindra. Harshul, great to have you on the program. >> Glad to be here. Thanks for having me. >> So, tell me about Tech Mahindra. I see on the website abbreviated as Tech M, give our audience an overview of Tech Mahindra, what you guys do. >> Sure. So Tech Mahindra is digital transformation consulting and technology services company operating at the intersection of IT engineering networks and BPO services. We have about 125,000 people operating in our 90 countries with about 5.2 billion in revenue, and have about 1,000 customers across key strategic verticles our largest being communications, media, and entertainment. And then we have other strong word because like technology, manufacturing, HLS, BFSI, the retail, and energy, and utilities. So that's broadly what we do, being in existence for well over 30 years now. >> And tell me about your role as the Head of the Global Technology Business. What have you seen transpire and evolve over the last few years, and especially the last three months with COVID? >> Sure. No, absolutely. I think, you see, we have organized a company around six strategic business units. They are these customer facing business units and I lead the one that focuses on technology and the high tech industry, if you will. I'm based in the Bay Area. And in this business unit, a large part of our business is, in some sense, 360 degree relationship with our customers, where not only do we sell into our customers, we also sell with and sell through our customers and also buy from them. So in that sense, it's a little different model in which we operate as compared to, say, other verticals that we have like manufacturing or BFSI or healthcare, but the relationship is largely customer and a supplier relationship. We have a full blown 360 degree relationship. It's very unique from that standpoint. And things have, you know, in some sense, dramatically shifted in the last three years, rather three months where we are seeing that, you know, amount of digital transformation, which was to happen over the next two years, has kind of happened in the last two months. So this is kind of pivoting a lot of enterprises, and including the tech sector, into an era where we are saying, how do we reposition ourselves to bring in more COVID-related solutions, both from a commercial standpoint, as well as a humanitarian standpoint, to deal with this crisis. So that it does in terms of changes that are happening out there in the industry, as well as in Tech Mahindra, as we can't forget ready fore-global and post -lobal. >> If you look at some of the specific trends that you're seeing during the COVID crisis, in the high tech segment, what are they? >> So, a couple of things have, we've looked at very differently. Supply chain for example, which is very crucial to high tech, is undergoing, in some sense, a metamorphoses shift. It's undergoing a seismic shift in the way supply chains are kind of reconfiguring themselves. You're also seeing customer experience kind of dramatically changing. Another thing that is coming in very, very strongly from a change perspective, it's kind of a storm that is brewing out there is, is how do we enable people to work remotely? We at Tech Mahindra, ourselves, had to enable 80,000 people in India who work remotely in a matter of weeks. And it's by no means an easy task to do which in a country where working from home is not really a culture. And also where we work, out of secure customer premises, even in India, our secure offshore locations in India, and all those people have now moved to their homes, and work out with their living rooms and bedrooms. And that was a sizable shift in the way we had to deal with our engagements, and with our customers. And so far so good, knock on wood, We have not had any issues. >> So Harshul, pivoting so quickly, as Tech M did to get your 80,000 employees in India to be able to work from home connectivity, all the challenges associated with that, goes hand in hand with your business, being able to deliver an exceptional customer experience, customer experience being an issue that you say is a rising trend amongst your customers. Customer experience and work from home these days go hand in hand, right? >> Absolutely. No, I think we also surprised ourselves with the pace at which we could move these 80,000 people to work from home in a matter of days, as I was saying, and as without missing customers. Our task was unimaginable in the pre-COVID era. And we will also surprised ourselves at the pace at which we could turn around COVID-related solutions so quickly with the help of partners like HPE that are today helping us pivot ourselves from one kind of old age solutions to the new age solutions, to the new normal today. And yeah, of course, and at the same time, we are to ensure that we enable the customer experience, and doing this on that while we repurpose our people to work from home. It was a challenge, and frankly, we surprised ourselves the way we did. >> So Harshul, talk to me about what, in these COVID crisis times, HPE and Tech Mahindra are doing together to help your customers accelerate, maybe adoption of new technologies that they need to for their businesses to thrive. >> Yeah, sure. No, that's a great question, Lisa. Let me start by saying that HPE is a very strategic partnership for us, and we see it as a coming together of two market leaders to deliver a very differentiated playbook of solutions for our customers. There is a robust set of products and solutions and edge offerings, edge gateways, converged edge systems, and clear analytics, combined with HPE's great GreenLake offers, which is around flexible consumption-based services, which helps align our customers' IT spend to deliver pretty much everything as a service. We kind of have already robust partner in HPE. And when you combine this with a Tech Mahindra's industry domain and technology depth, and the systems integration wherewithal that we bring in, it makes form, I believe, a very potent combination to drive, serious value to our customers, right? And given the COVID situation, we have kind of defined our relationship along three broad vectors based on the mutual synergies and where we believe we can quickly drive value. Firstly, what the solution white spaces that we want to address together? Secondly, what are the geographies that you want to operate in and third is, what are the industry verticals that we believe we can quickly focus on? So from a solutioning standpoint, there are four broad trust areas that we want to sharply focus on. Firstly IoT. It's been a strong partnership with HP with IoT. And we would like to continue that followed. With HBE's edge offerings, and converged edge systems, we have kind of demonstrated the possibilities of IoT solutions across smart cities, factories of the future, of energy and utilities and of Costa Rico. And we have some good success stories we already have with HPE that would like to build on, we have won some for significant smart city projects in India, in four different cities of India. And we also, by the way, won the Systems Integrator Award for Edge and IoT from HPE last year, and also the SI Partner of the Year for HPE last year. So we would like to continue to build on that. We all see already have a COE on IoT set up in Bangalore. It's a very unique COE that we're built up where we have showcasing solutions around a smart city or IoT, and also brought in Aruba gear as well, but solutions that are smart campuses, so on and so forth. So, that's number one. Number two is data center transformation. As hybrid cloud kind of takes root through our customers are now looking at transforming their data centers as well. And particularly with HPE's GreenLake, it becomes a very strategic commercial tool for us to bring on demand paper, use models, elasticity, kind of the, as I was talking about, the flexible consumption services model, which is so unique today, as we help customers reduce their capex and get them to pay by the drink, if you will. Now that becomes very, very relevant in the COVID times. And last but not the least, our focus is also on network of the future. When I say that our partnership with HPE is really pivoted around 5G, as DNFE and private LTE solutions. For example, you know, HPE's private LTE network, which is essentially powered by HPE's EL300 and EL4000 converged edge systems. It's kind augmented by our industrial IoT expertise. And it includes a reintegrated, off the shelf, industrial IoT application from Tech Manhira. It's a kind of an end to end solution that uses the breakthrough innovation such as small sales EPC, and smart multi-access edge compute. So, we are staying sharply focused on these areas. And we started seeing the results, and given the goals in this scenario, we have evolved a bunch of use cases very quickly in multiple industry areas. And bought from a commercial library standpoint, and also importantly, on a humanitarian level, what we can do together. For example, in Italy, as the pandemic was raging. As many of you will know, a ship force can order into a hospital, probably 1,000 bed hospital, and HPE stepped in, and they brought in the Aruba gear to put up network together, the infrastructure and the connectivity to bring together, and take Manhira, which has a rapid response healthcare solution who help with remote patient diagnostics and monitoring. Kind of brought in that solution along with HPE, to bear in Italy as the pandemic was raging. So that's just an example of how we are partnering at multiple levels. You know, created a solution around workspace as a service, as an remote working becomes a new normal. >> Right. >> With HPE on that. So a bunch of other solutions as well, Lisa. >> Sounds like you guys have done a great job of, as you mentioned in the beginning of our time here, rapidly pivoting within Tech Mahindra, as you said, it actually kind of surprised ourselves to what you were doing with HPE to deploy rapidly in Italy, to I can only imagine helping customers accelerate projects like smart cities and smart factories where suddenly we need sensors on more things. Harshul, I thank you so much for spending time with us on theCUBE today. Exciting topics. We can't wait to see where this goes. >> Well, thank you so much, Lisa, for your time. It was great talking to you. >> Excellent. My pleasure. For Harshul, I'm Lisa Martin, and you're watching theCUBE's coverage of HPE Discover 2020. Thanks for watching. (gentle music)

(upbeat techno music) >> Narrator: Live from Las Vegas, it's theCUBE. Covering AWS re:Invent 2019. Brought to you buy Amazon Web Services and Intel, along with its equal system partners. >> Welcome back everyone. CUBE's live coverage here in Las Vegas for AWS, re:Invent 2019. I'm John Furrier and my cohost Dave Vellante. We're here extracting the signal from the noise with theCube covers for three days. Our next two guests, Mark Lohmeyer, Senior Vice President, General Manager, Cloud platform, business unit for VMWare. Ben Tanner, Director of Cloud Enable for IHS Market. Guys, thank you for coming on theCube. Good to see you again. >> Yeah, great to be here again. >> You got a customer here, customer at Momentum Store, but before we get into that I just want to get your quick take on the key note from Andy Jassy. Clearly, the VMWare relationship with AWS, really paying off well. >> Mark Lohmeyer: Right. >> Dave's going to dig into some customer spending data in the marketplace. Great momentum, I mean, looking back a few years when you guys launched this, I mean, come on. You got to be happy. (gentlemen laughing) >> Yeah, we're pleased. I mean, I think, as you said the partnership has never been stronger and I think the foundation of that is really the tremendous customer demand we're seeing for the service. And this initial idea that Pat and Andy had together of how do we create the best of both worlds here, right? The enterprise class capabilities of VMWare are combined with everything customers love about the AWS Cloud. I think that's really come to fruition and, you know, what's been great to sort of see over the last two years is, really the customer momentum and the use cases and the way they're able to take advantage of that service to really solve some really big challenges for their business, right? And for it to become a platform for them for innovation. So really pleased to see that momentum. >> John Furrier: Ben, talk about your use case. You obviously, the story here to reinvent is don't tire kick the Cloud, you got to kind of go all in as Chastity would say, but you've got to leverage the transformational aspects of the scale, but when you get in the reality, which you live, talk about what's real about the Cloud. >> Ben Tanner: We're an information company. Data is king to us so, you know, it's real hard for us to be part in on the Cloud. You know, we have a data gravity problem, so how do we get our workload to there without necessarily having to refactor them. How do we do it with a way that we can minimize the risks? So for me, you know, getting all in on the Cloud means getting the data to the Cloud and enabling the developers to work in a way that's going to deliver business value quicker to our customers. So, that's really where VMC kind of helps bridge that gap for us, I think. Originally, we were looking at it as like a short-term capacity first venue, but then we look under the covers. Actually, you know, we can go build a brace to VMC and really get to the Cloud quicker. >> John Furrier: VMC, VMWare Cloud? >> VMWare Cloud, sorry. >> I want to make sure I get it out there. >> I want to dive in on some of the spending data that we have access to from ETR, Enterprise Technology Research. And essentially, they do these these quarterly surveys. And a survey, the most recent one, there was 1,300 people who responded. 708 of U.S. customers, of which 150 said we are spending heavily on VMWare Cloud on AWS. So my first question is, to what do you attribute, sort of the momentum, maybe you can give us the update there. And then I want to follow up on the customer point of view. >> Mark Lohmeyer: Yeah, absolutely not. I'll sort of build on some of Ben's comments, because I think what he articulated is one of the killer use cases of VMWare Cloud on AWS that I think is driving that momentum, right, which is we think it's one of the best uses in the marketplace and customers have told us this, to enable them to migrate and modernize, right? So let's talk about the migrate piece first, right? I mean, you have customers that have these tremendous enterprise-class applications, running on vSphere in their data centers. They're built on top of that platform. They depend upon it for performance availability, everything else. With VMWare Cloud in AWS, we can migrate those applications with zero downtime, no refactoring, no additional costs, in a matter of weeks or months, as opposed to if you had to refactor everything, could take years and millions of dollars, right? So that Cloud migration use case I would say is the killer for us and that's, you know, exactly what Ben was referring to. >> John Furrier: We've got a special report on siliconangle.com called The Great Migration and it's about Cloud. Talk about this particular issue because this is like top of mind of everybody. How do you do it right if you're a VMWare customer, what do you pay attention to? What are some of the things that you learned and what are the things to watch out for? >> Ben Tanner: That's a great question. I think ultimately you have to listen to your customers. So for me, that sort of element community and then within IHS Market and then ultimately, their customers. So we cover like three broad sectors. Oil and gas, the energy division, we have transportation division and then we have our financial services division. So each one of those division's got a different risk appetite. So depending on that appetite, we'll very much govern how we take the approach of moving to the Cloud. We've done the classic lift and shift using tools like VMWare's HCX. We actually, as a kick the tires, we moved a thousand workloads in six weeks into VMC, which was kind of exciting. >> Mark Lohmeyer: Yeah, pretty impressive. >> We enjoyed that. And then in other areas we're looking at, well we don't want to take all that tentacle debt that lives in our data center with us, so can we do what we call a lift and fix approach, where we'll leverage sort of private Cloud ultimation tool and build over VMC to rapidly spin up new workloads there but without changing our operating model. And then that's one of the big things I call out about VMC, it allows you to get into that public Cloud space without having to drastically change how IT operates. And then you can start to shift to more of a public Cloud focus. So there's really that lift and shift, lift and fix, and then where we're developing new capabilities, or where there is definite business value, and that's the key thing, refactor of a Cloud native. So it's a spectrum. >> So you ultimately want to change your operating model- >> Ben Tanner: Absolutely. >> Just not today. >> Ben Tanner: Well no, I don't want to do it in a big bang. You know, that's very disruptive while we're doing that we're, you know, it takes our focus off away from delivering business value. So we're trying to find a way to do it in a more incremental manner. VMC's, VMWare Cloud Native is one of the things that's going to help us do that. >> John Furrier: Are you guys looking at Amazon's other services because you now, in AWS- >> Ben Tanner: Well we're heavy Amazon customers as it stands so we have a lot of Cloud Native Apps going out there. It was really interesting today, seeing where they're going with the HPC workloads, particularly where we're starting to look at ML and AI. We have a data late program that's at an AWS. So for our new developments, we're definitely embracing Cloud Native, but very much in the sort of hybrid Cloud methodology with the MC. >> John Furrier: Well Ben, I want to get your take on a meme that we've been kicking around all week around Cloud Native. The T, if we take the T out, which stands for trust, it's Cloud Naive. (laughter) So a lot of customers, they're trying, I think they're doing Cloud, they've got to factor into all these operational disruptions. >> Ben Tanner: Yep. >> You have staff issues, you have cost and inefficiencies that kick in. Disruption. Development choices. So where's the naivety, where's the native, savvy, where should people start thinking about when they start moving in the Cloud? >> Ben Tanner: It's a maturity conversation ultimately. I think if we look at, certainly within IHS Market, we've very much grown by acquisition. We have different sort of cultures within the firm. We have 650, 700 products, 700 different ways of doing things sometimes and they've all gone to the public Cloud at different rates and in different ways. So for us, it was assuming that we could do that in a manageable, controlled-cost, safely-governed way. And really understanding that, you know, you can't go out there as individual Dev teams and expect it all to be perfect. We need to start building almost a collabed community within the company and then starting to layer in governance. But again, that's if you say take the T out, trust. We within IT, we have to build up trust with our products teams because I think why they go to the Cloud is sometimes because IT hasn't been able to deliver on it. You know, it's customer's expectations. >> John Furrier: You can't move fast enough. >> Yeah, exactly. Yeah. And you know, we're never going to be able to compete with the likes of Amazon or VMWare in security and functionality and scalability. Why would we try to compete? Let's embrace that. Extend, enable it, and really try to give our customers a consistent, delightful experience. >> So Ben, where are you placing your bets? Obviously Cloud, Hybrid, those are two things. Any other places where you're really trying to focus? >> Ben Tanner: So I think that's interesting. Again, my job is to make life easy for my developers. So what do they need? And this is something that we're going through, again, internal transformation, starting to run IT more like a product management organization and actively listening and soliciting feedback and really delivering what they need. You know, we're getting a lot of talk around containers, what are our plays going to be in that space. Some of the development teams are on that. Some of them want to go and embrace the new stuff like Fargate and EKS and that's great as well, but ultimately, I want to get out of tickets and weight states and get out of the way of the developers. >> John Furrier: I want to ask you a question around developers, cause one of the trends we're seeing and we're kind of picking out of the announcements is when you look at the DevOps movement that started roughly around 2007-2008, '09 timeframe, that early wave of pioneers created infrastructure as code. >> Ben Tanner: Yeah. >> That essentially became, "I don't want to configure the software. Operating models like VMWare, make it easy." Things are just running under the covers. Now with the data modeling you're seeing, if you've got large scale infrastructure, you're seeing now all these data toolings. So there's almost a data as code kind of theme going on here where developers just want to access the data, they don't to have to get into the wrangling. >> Ben Tanner: I think that's where we're sort of seeing things like data late coming to the forefront. You know, again, IHS Market Information Company. How do we pool all that information together in a way that, you know, creates new business value, creates new ideas. You know, broad ease of access for our developers and our customers, but at the same time, how do we protect things like data sovereignty. If we've got PII data out there, you know, we have to think about that. Whether they're alter motive customers. You know, you've got different state legislation so again, it's how do we as the IT and sort of the develop community facilitate broad safe access to data. Data is a service. Yeah. >> John Furrier: Yeah. 100%. >> Absolutely. >> So Mark, as customers move to the Cloud and they want to change their operating model, what role is VMWare playing in terms of facilitating that? >> Mark Lohmeyer: Yeah, you know, I think essentially you said you wanted to make life as easy as possible for the developers, right? And I think we want to make life as easy as possible for Ben and IT so he can make it easy for developers. And I think we know one of the ways that we love to do that is, and the way I think about is, we want to provide him and customers like him the broadest, most powerful tool kit that they can choose from, right, as they're enabling their developers. If you think about VMWare Cloud and AWS, it can actually enable that, right? Because you have access to all of the VMWare tools and capabilities, not just your existing workloads, but also for modernized applications with things like Kubernetes and some of the capabilities we're bringing to bear there. So we provide all of those services in the VMWare environment, but then we also allow their IT teams and their development teams to also have access to all the Native AWS services and some of the data tools that they might want to leverage from AWS- >> So is it- >> All in a single environment. >> So you've got core VMWare, now you have pivotal- >> Mark Lohmeyer: That's right. >> For the developer angle and you've got all the security acquisitions you've made, not the least which is carbon black so that's the package that you're delivering to your customers. >> Mark Lohmeyer: Absolutely. Right. And we want to do all of that, obviously, as a service on top of AWS, right, bringing that same sort of simplicity of operations for all of those capabilities. >> John Furrier: Mark, talk about what's coming next for you guys at VMWare and the Cloud platform. Obviously, we saw that Outpost, Native Outpost, which is Amazon shipping, available now. >> Mark Lohmeyer: Yeah. >> 2020 we're going to see VMWare on AWS, VMWare Cloud and AWS roughly shipping behind it. So that's looking like good news too. Architectural shifts are happening, can you share any insight into what's next for you and your team? >> Mark Lohmeyer: Yeah, I mean, it's a really exciting time. I think, look at this point, I think the customer's have spoken, its a hybrid Cloud world, right? They want to have the flexibility to run apps across their own data centers, across public Clouds, across edge environments. It's a hybrid Cloud world. >> John Furrier: AWS agrees. >> Yeah, I mean, even AWS agrees. You know, as VMWare as a company, we're looking to really enable the most seamless, most consistent hybrid Cloud experience. Obviously, we're the standard in most enterprise customer's data centers today. With VMWare Cloud and AWS, we're bringing that capability to AWS. And then we're really excited, of course, about VMWare Cloud and AWS Outpost because we can now bring that same Cloud delivered model back, you know, on-prem and into edge environments, right? And so we think that full set of services, right, what you have in your data center today, what you can do on AWS with VMC and now back on-prem, it opens up a lot of possibilities for customers like IHS. >> John Furrier: And Chastity kind of hinted at it, well he talked specifically about networking- >> Mark Lohmeyer: Right. >> In context of 5G latency, different use cases around latency. So networking is going to be a big thing. >> Mark Lohmeyer: I mean networking, if you think about a hybrid Cloud world, right? I mean, networking is kind of at the heart of it, right? And if you look at technologies like NSX, right, that gives you a consistent software networking layer that can work across any hardware on-prem. Obviously, it's the heart of VMWare Cloud and AWS, also in Outpost, it's a really important construct that fundamentally enables things like the seamless migration of workloads between these different environments. >> John Furrier: On Open Source as well. Guys, thanks for coming on. Final word, your thoughts on the keynote, the presence here at AWS. What's your takeaway from the day one. >> Ben Tanner: I think for me for day one, it's really exciting to see the development in things like the HPCP's. How that's going to enable us as a customer to do more with things like AI and ML. I think, for me, Outpost is really fascinating. We were talking about this earlier, where we've got regulatory requirements, performance requirements. We can still deliver that consistent experience in the Cloud, in the data center. So those for me are going to be, potentially, really transformative. >> John Furrier: And this really highlights what we've been debating. I challenged Gelsinger, Pat Gelsinger, CEO of VMWare in 2013 about hybrid being a halfway house to the public Cloud. He's like, "What are you talking about? It is the model." Pat if you're watching, you were right, I was wrong. I admit it. (laughter) But hybrid Cloud is certainly a visibility, but the Cloud as an operating model and what Chastity's saying and what Microsoft and other's are saying is, "Hey, the Cloud is the operating model, not the old way." So center of gravity is Cloud, but the on-premise for these specific things like governance, compliance, use cases. This is the new normal. This is very clear, no one debates this. >> John Furrier: Congratulations. Congratulations on your success, so say hello to Ragu and the team. >> Will do. >> John Furrier: Thanks for coming on. VMWare and custom momentum. I'm John Furrier with Dave Vellante. AWS re:Invent. Be back with more coverage after the short break. (upbeat techno music)

(upbeat music) >> Hi, everybody, we're back. This is Dave Vellante with theCUBE. We're here talking storage at Amazon in Boston. Asa Kalavade's here, she's the general manager for Hybrid and Data Transfer services. >> Let me give you a perspective of how these services come together. We have DataSync, Storage Gateway, and Transfer. As a set of Hybrid and Data Transfer services. The problem that we're trying to address for customers is how to connect their on premises infrastructure to the cloud. And we have customers at different stages of their journey to the cloud. Some are just starting out to use the cloud, some are migrating, and others have migrated, but they still need access to the cloud from on-prem. So the broad charter for these services is to enable customers to use AWS Storage from on-premises. So for example, DataStorage Gateway today is used by customers to get unlimited access to cloud storage from on-premises. And they can do that with low latency, so they can run their on-prem workloads, but still leverage storage in the cloud. In addition to that, we have DataSync, which we launched at re:Invent last year, in 2018. And DataSync essentially is designed to help customers move a lot of their on-premises storage to the cloud, and back and forth for workloads that involve replication, migration, or ongoing data transfers. So together, Gateway and DataSync help solve the access and transfer problem for customers. >> Let's double down on the benefits. You started the segment just sort of describing the problem that you're solving, connecting on-prem to cloud, sort of helping create these hybrid environments. So that's really the other benefit for customers, really simplifying that sort of hybrid approach, giving them high performance confidence that it actually worked. >> Maybe talk a little bit more about that. >> So with DataSync, we see two broad use cases. There is a class of customers that have adopted DataSync for migration. So we have customers like Autodesk who've migrated hundreds of terabytes from their on-premises storage to AWS. And that has allowed them to shut down their data center, or retire their existing storage, because they're on their journey to the cloud. The other class of use cases is customers that have ongoing data that they need to move to the cloud for a workload. So it could be data from video cameras, or gene sequencers that they need to move to a data pipeline in the cloud, and they can do further processing there. And in some cases, bring the results back. So that's the second continuous data transfer use case, that DataSync allows customers to address. >> You're also talking today, about Storage Gateway high availability version of Storage Gateway. What's behind that? >> Storage Gateway today is used by customers to get access to data in the cloud, from on-premises. So if we continue this migration story that I mentioned with DataSync, now you have a customer that has moved a large amount of data to the cloud. They can now access that same data from on-premises for latency reasons, or if they need to distribute data across organizations and so on. So that's where the Gateway comes into play. Today we have 10's of thousands of customers that are using Gateway to do their back-ups, do archiving, or in some cases, use it as a target to replace their on-premises storage, with cloud backed storage. So a lot of these customers are running business critical applications today. But then some of our customers have told us they want to do additional workloads that are uninterruptible. So they can not tolerate downtime. So with that requirement in mind, we are launching this new capability around high availability. And we're quite excited, because that's solving, yet allowing us to do even more workloads on the Gateway. This announcement will allow customers to have a highly available Gateway, in a VMware environment. With that, their workloads can continue running, even if one of the Gateways goes down, if they have a hardware failure, a networking event, or software error such as the file shares becoming unavailable. The Gateway automatically restarts, so the workloads remain uninterrupted. >> So talk a little bit more about how it works, just in terms of anything customers have to do, any prerequisites they have. How does it all fit? >> Customers can essentially use this in their VMware H.A. environment today. So they would deploy their Gateway much like they do today. They can download the Gateway from the AWS console. If they have an existing Gateway, the software gets updated so they can take advantage of the high availability feature as well. The Gateway integrates into the VMware H.A. environment. It builds up a number of health checks, so we keep monitoring for the application up-time, network up-time, and so on. And if there is an event, the health check gets communicated back to VMware, and the Gateway gets restarted within, in most typical cases, under 60 seconds. >> So customers that are VMware customers, can take advantage of this, and to them, it's very non disruptive it sounds like. That's one of the benefits. But maybe talk about some of the other benefits. >> We saw a large number of our on-premises customers, especially in the enterprise environments, use VMware today. And they're using VMware HA for a number of their other applications. So we wanted to plug into that environment so the Gateway is as well highly available. So all their applications just work in that same framework. And then along with high availability, we're also introducing two additional capabilities. One is real time reports and visibility into the Gateway's resource consumption. So customers can now see embedded cloud watch graphs on how is their storage being consumed, what's their cache utilization, what's the network utilization. And then the administrators can use that to, in fairly real time, adapt the resources that they've allocated to the Gateway. So with that, as their workloads change, they can continue to adapt their Gateway resources, so they're getting the maximum performance out of the Gateway. >> So if they see a performance problem, and it's a high priority, they can put more resources on it-- >> They can attach more storage to it, or move it to a higher resourced VM, and they can continue to get the performance they need. Previously they could still do that, but they had to have manual checks. Now this is all automated, we can get this in a single pane of control. And they can use the AWS console today, like they do for their in cloud workloads. They can use that to look at performance of their on-premises Gateway's as well. So it's one pane of control. They can get CloudWatch health reports on their infrastructure on-prem. >> And if course it's cloud, so I can assume this is a service, I pay for it when I used it, I don't have to install any infrastructure, right? >> So the Gateways, again, consumption based, much like all AWS services. You download the Gateway, it doesn't cost you anything. And we charge one cent per gigabyte of data transfer through the Gateway, and it's capped at $125 a month. And you just pay for whatever storage is consumed by the Gateway. >> When you talk to senior exec's like Andy Jassy, always says "We focus on the customers." And sometimes people roll their eyes, but it's true. This is a hybrid world. Years ago, you didn't really hear much talk about hybrid. You talked to your customers and say, "Hey, we want to connect our on-prem to the public cloud." You're bringing services to do that. Asa, thanks so much for coming to theCUBE. Appreciate it. >> Thank you, thanks for your time. >> You're welcome. And thank you for watching everybody. This is Dave Vellante with theCUBE. We'll be back right after this short break. (upbeat music)

>> live from San Francisco celebrating 10 years of high tech coverage. It's the Cube covering Veum World 2019 brought to you by IBM Wear and its ecosystem partners. >> Welcome back to San Francisco. We continue our coverage here. Live on the Cube. 10th year John of covering Veum World This is 29 teens version John for John Wall's Got to have inside the Moscone Center. We're joined now by Varun Chabrol It was the vice president of marketing at Delhi M. C. Good to see you today. >> Thanks for having me. >> How's your week been? So far? >> It's been amazing. How can you don't get excited? All the innovation we're seeing this week >> we'll hear about some big announcements. Do you guys have made? And Moon Young Man Azzedine, who is the vice president of product marketing that for cloud security and works based solutions at Veum wear when you're good to see you. >> Good to see you again. You, By >> the way, you might be the busiest guy here. Yesterday, when you came into the set, you were coming in. Just spoken to 1300 people in a standing room only session You coming out? 500 folks, How many sessions have you done? The seven. So >> you don't count the the one on one with the analyst. And, uh, you know, the customers and partners and press. And tomorrow actually host ah 140 press media analyst on campus in Palo Alto from Asia Pacific because they float all the way from Asia >> plus 140. Yeah, it's a piece of cake. >> Yeah, hose them from 10 to 4. So, I mean, >> you're always smiling >> knowing that this is a pretty wide audience to whom you've been speaking. But just generally, what are you if there's a common thread at all about the kinds of questions that people are coming to you with, or or the concerns or maybe just the things they want to talk about being inspired. But what they're hearing here at the show, >> Okay. Now, according to two aspects of it, one obviously from analysts themselves, you know, they are actually have been very complimentary about the way we've taken our approach. I'm not sure if you could have paid attention. In the last couple of years, we've been talking especially the cloud side, the narrative, to be very much about use cases, solving problems. You know the key? No, we talked about hate my grade modernize. It wasn't about Hey, I've got the next big product here with all these features and capabilities. You do this and that. So we're gonna shifted out narrative. And it was very, you know, the the analyst across the boat. You know, we've been seeing an appreciative of the fact that you actually changing a narrative to be re compelling and we're gonna reflected. And we have some things here like Cloud City, where it's not a standard demo boot. It's a it's ah, Customers walk in and they touch and feel and see which we did it, Adele technology will, too. It's like, What's your business? Probably going through these applications. I'm sitting. I don't know if I should be modernizing them or should be migrating into Amazon. A ridge or so. So you know that narrative the analysts are appreciative off, and that reflects into the customer conversations I've been having in the briefings, like one on one with customers. They're really kind of lost us. D'oh! Hey, I've I'm working in this environment. There's a lot of pressure for me. Thio modernize my applications or go adopt my cloud. First strategy is where do I start? Where do I go? It's like, you know, there's a big pressure, so they just want clarity. I think in the end, everything we're gonna we're doing in our study that comes out obviously the buzzword for this weird world. It stanza, right? And, you know, >> we've won the product announcements was >> actually Brandon can Oh, yeah. Branding announcement, to be honest is yeah, because we're trying to bring together, as you know, in Tansy has landed in Bill Run Manage billed as in you know how our intent to acquire Pivotal Already acquired Big Tommy. How all our different acquisitions with different brand names are coming together to establish our bills portfolio again. The sphere. Everybody knows the sphere Project Pacific P ks. All of those create a good run time, environment and manageability like Adi manage with assets from ve Franta gain morbid Nami and you know it. So this multiple brands that are coming into this package off Iran. So we had a creative tan Xue too, you know, put forward statement together that yes is going to be 78 different brands coming into this, but going forward to stand. >> So so that's a great strategy on De Liam Seaside on Del Technology. Michael Dell was in here and I asked him. I said he could have been number one in everything you could. Let's talk about I'm number one in servers again. You kind of get on HP, little baby. But those air peace parts now. So we've got the cloud game. It's bringing despair it at parts together kind and making it coherent from a positioning standpoint and understandable and deployable. So you guys are going down there. That's your cloud strategy. Take a minute to explain that. >> Yeah, absolutely, John. So So what? What we've been doing. We announced this at Del Technologies will this year. But, you know, in the cloud infrastructure space, we're working very closely with the anywhere too tightly integrate our hardware solutions with their their cloud software. And we think that by combining these two in a tightly integrated joined engineer, jointly engineered solutions coupled with the service, is that you know, both of'em were and l e m c bring the customers we think we have. We're giving customers are very consistent experience both with their own premises, infrastructure with public cloud as well as with the edge cloud. And that's really what we're trying to do. That's what we've been building upon and uniting the announcements this week. You know, just just hopefully show customers that the sky's the limit, whether it's not just your infrastructure management. Also app development. Managing your APS both traditional and and cloud native. It's all here for And >> what's the big takeaway free from your standpoint that you'd like people to know about what's going on? Adele the emcee for the VM. Where relation. What's the big top item? >> Yeah, there's there's there's just so much good Doctor Wait forever drank the town about. If someone rises >> way, only have two hours >> time work. The most important thing that people should should know about it, >> you know, both deli M. C and V. M. R. I think, are very, very customer driven companies that we respond to customer feedback and we try to respond to them very fast. That's been true to our respective lifetimes and what we've done in the so that I think there's two broad areas of collaboration. One is in the cloud space, which is all about, you know, making sure that the the innovation that GM is bringing the market, we're providing that in a toy tightly integrated infrastructure solution. Right. So we announced from a deli in seaside support for Vienna, where p ks being deployed automatically on Vieques trail using VCF return. Our customers can you know, a lot of teams were telling us we have our developers and turning developers banging slash knocking on the door, saying we need to build a cloud. Native applications. You need to give us an environment that we can use. And you know, if if all righty, if these IittIe teams don't turn around and give them something relatively quickly Well, guess what? The developers will go somewhere else, right? Yeah, exactly. So And if you look at the kubernetes environment today, if you really look look at what the work that's required to set up kubernetes and ready infrastructure. So a lot of scripting a lot of manual, you know, work command line interface is testing stuff. And what what? V m r p k s does. And you know what times you will do as well is really makes it easy when we've taken that with the magic of the American Foundation sitting on top of the exhale to make it super easy for our customers to be able to deploy kubernetes ready infrastructure and then have it be ready for scale, right? And then the important thing here also is this is the same infrastructure of the expelling bcf that our customers are using for traditional applications as well, right? Trying to reduce that complexity. Give them the one platform. So this cloud, you know, we had we were doing the same integration on just with R A C I platform, but also with our best to breach storage or we're not working with the C f. And then we're also making investments on data protection like it's so important to be able to manage your data in this multi cloud world. We have applications sitting everywhere, data. We all know that it is a crown jewel. So >> it's really a king validating from the Vienna a point of view. How that works right is is about applications is about the infrastructure, and it's about the operation and it really kind of together as we talk about Han Xue p. K s is giving our customers that Chuy's off. You pick Cuban eighties, you know, environments, application choice. >> Um, >> it took us. Actually, we didn't We didn't arrive it in that order. Wait. Did it. In the outer off Infrastructure Plot Foundation is a critical piece of the joint engineering. But being aware and the Della Bella Technologies is really from aviary perspective. It took Locke Foundation, and that's the stack that runs in every public cloud. So, you know AWS as your G C P 4000 plus, you know, cloud provider partners. But Flat Foundation is a platform that was validated on. They'll take hardware and you know, that's the package. But now, as you see, we're lighting that it's same infrastructure up for traditional and culminated applications. >> I think the app sides important to point out, because if you could ve m wears heritage, you look at Dale's heritage. You had abs that ran on PCs absent, ran on servers, client server. And if you look at the fertilization that wasn't under the covers, apt an innovation that didn't require code changes. So that's the DNA that you guys have. Now, when you think about like cloud to point out which we've been riffing on that concept that's basically enterprise cloud mean donut. Hybrid cloud applications are gonna drive. The value on our premises is that they're going to be customer requirements that traditionally wouldn't have fit in the product. Marketing, management, featureless customs. Gonna define what they want. They'll build it, and then they'll dictate to the infrastructure to make it run. What? We can't do that yet. It'll be, Yes, we cannot be enabled to be dynamics. This is a a new cloud. 2.0, feature. This changes the complete game on suppliers >> completely agree. You know to your point, because, you know, you bring it thio back toward civilization. We've been going higher up the stack on So Day zero virtualization infrastructure will virtual eyes. So the line off abstraction has just been climbing from hardware retort realization next to like, you know, Pat platform of the service, and you kind of were working up our way down infrastructure. Now that base infrastructure platform looks like plants. Right? >> And there were times out a little bit over here. On the upside, you meet in the middle of >> it in the middle >> that is Hello, >> absolutely so ap and at middle wears shrinking down this way. Infrastructures. You know that the cloud incriminating stride in the middle to say, Well, that's a bit of, you know, infrastructure is a Kodak and pull. He's a bit of a AP AP eyes I can can I draw from And that's kind of nice future middleware. But our dad, I >> mean, I think applications air in charge, right? I mean, that's not sure That's the dynamic. That's the way it should be. But it never was that way before is basically the infrastructure was your gating factor. The network exact cloud two points Network security data. Yes, Dev Ops. A true Dev Ops Devane, Ops, Infrastructures Code. >> The only point I wanted to add is the reason the emphasis on abscess change acts in the past. Used to be a business support system after today is business. >> Yeah, I mean, it's >> really or you're you're gonna live or die based on the digital services you provide your customers. The other thing I was going to say about cloud 2.0, is that it's also becoming increasingly clear when we Dr customers that, um, customers are realizing Cloud is not a place right. There was this kind of cloud. One point it was okay. Big honking data centers, hyper skaters will be found now is that customers have gone through that process of and there's a lot more maturity in terms of understanding. What is good, better running on premises. What is what's better running in public Cloud? There's a place for both of them and that, um, and the cloud is actually the automation, the service delivery. It's Maurin operation and a way of being almost than a place. >> And what is it? Well, what does it do for you all? Then, in terms of challenge, especially at your teams, because you talk about all this customization, you're allowing the application to almost drive. You know, you're changing places in terms of who's the power of the relationship? Yes. Oh, me, yeah, How what? What does that do for you? Oh, in terms of how you approach that, how you change of mindset and how you change what you deliver? >> I think John, it's the way I think about it is that both daily emcee in Vienna, or any technology provider that's worth their salt is in the business of building platforms. Right? And platforms are essentially extensible. They're really they really provide a foundation that other people can innovate on top of it. And that's how I think you handled the customers issue. If one thing I think we can all agree on is that I t has always taught us there's no one size fits. All right? Right. So I think providing choice along every single dimension is super important for our >> customers. Yeah, I think that platform thing is a huge point. And I was gonna ask that question before John got jumped in because one of the things that you just brought up was platform is you guys have to build an enabling platform. One as suppliers. Okay, The successful cloud to point out cos are ones that are innovating in weird areas. Monitoring, for instance, they who will have thought that monitoring now observe ability would be such a massive, lucrative sector four. I pose M and A Why? Because it's data. It's instrumentation. This is operating system kind of thinking here is like network. So thinking like a platform on the supplier size one, the customers got to start thinking like a platform because their stakeholders air their internal developers or a P I shipping to suppliers. This is new for enterprises. This is news requires full hybrid capability. This requires date at the center of the value proposition. >> That's again the biggest value is business and I tr coming together on the area of applications and data. Yeah, that's starting up giving because the successful businesses are the ones who leveraged. Those guys have failed in the future, or the ones who don't pay attention to how critical applications are to the business logic and how critical data is to be able to mine and get the behavioral analytics to get ahead. And >> now the challenge in all this. But I'm learning and covering some of the public sector activity from the C I. A contract Jedi with Amazon to we had Raytheon Her here earlier is another customer example with another client is that procurement? And how they do business is not just a technical thing. There's like all this old legacy, things like, How do you procure technology, who you hire her and we hire developers? We build our own stack, so there's a lot of things going on. >> Yes, and you know, it's really interesting on the even on the procurement front, how our customers experience with Cloud has changed expectations, right, And that's really what we're doing with the McLaren DMC is what customers told us is, Hey, I love the agility of the cloud portal based access. Easy procurement. I love just being able to click a button and not have to navigate all this complexity. I need that for my own premises infrastructure. Imagine FRA structure. And that's, you know, in an example, while all of these dynamics are really all converging, >> well, if you can create abstraction, layer on a level of complexity and make things easy, simple and affordable, that's good business. Model >> one of our customers without taking the name right. The massive retailer you know they're spinning up, um, the retail outlets like crazy. They measure success in This was one truck roll, so they wanna have the entire infrastructure come into stand up one of the retail outlets in one truck roll. When everything comes in one button push that everything gets in a provision and up together. >> So that means I gotta have full software instrumentation automation Got intelligence. This is kind of where cloud 2.0, will lead us all >> likely. And that's expectation now that they go so fast and deploying this one Truck roll Hardware's there. Switch it on from the cloud it stood up and they're in operation 24 hours. >> Well, guys, we're going to get you on our power panels in our Palace of studio on this topic cloudy. But it's gonna be very aggressive and controversial topic because it's going to challenge the status quo. And that's really what this we're talking about >> that's in our DNA. >> And the good news is that that's more time with John. >> So as we before, we say so long, we've talked about clients. We talked about the folks you bet here. We talked about the presentation on this thing and what they're all getting out of it. What are you getting out of this? I mean, what are your takeaways? As you had back to your respective work orders, you get first. Okay? >> I think for me the biggest takeaway is just how incredibly vibrant via more user communities. I mean, it is unlike anything else I've seen before and now with the things like Project Pacific. I just feel like it's It's an opportunity for this community to be able to take the skills they have right now and actually go into this brave new world of containers with so much help forces having to do this all by yourself. Which means it's gonna be, you know, if you think about how largest community is, think about how much innovation this will spore in the container space and because of that in the application space and then because of that in business is I mean, this is a It just feels like a tipping point for me >> to me. Sure, I got high fives from every tech geek, you know, when we came out, you know, I also on our technical advisory boats for the company that these are the hot core geeks who were followed and you know us to the, you know, these were the fans and they were like, you know, they always kind of like if you walk out of them and you talk to them and they, uh how did it work? Because they my bar, you have a very high bar. They cut through all your marketing messaging. They go right to the hay. Is there meet in this And the high fives? I got the hajj. I got out. This is like, guys, you're nailing it. That's enough to tell me that a This is, like, 10 years ago. Yeah, that body. It's like you're so busy. I'm still smiling because the energy is I >> can't give you a hug. Give me a high five. Right. Good work, gentlemen. Thanks for the time. Always, he's still smiling to >> get you to a step. >> Good deal. Thanks for being with us. Thank you. Live on the Cube. You're watching our coverage in world 2019. Where? San Francisco. Back with more. Right after this.

(upbeat music) >> Hi everybody, welcome back to London. I'm Dave Vellante and you're watching theCUBE. We go out to the events, we extract the signal from the noise and we've been following AWS generally and the public sector specifically for a number of years now. We've seen the ascendancy of an expansion of public sector. We've covered the career of Teresa Carlson, and we're here in London ahead of AWS Summit London. There's a pre-day here, there's a number of public sector companies, there's a focus on healthcare. Kieron James is here, he's the founder of Wonderful.org. Wonderful.org is a fundraising vehicle, it's a really setup platform essentially for self-service. Kieron, welcome to theCUBE, thanks for coming up. >> Hello. >> So, tell me about Wonderful, why you started this organization? >> Wonderful was kicked off when I got to my 50th birthday, essentially, it's a way to give back. I've been involved in the tech sector for many years and we were sitting on quite a lot of infrastructure. We thought we had some spec paucity as well and we thought what we can do with the resource, human and physical, in terms of giving something back to charities. So, one of the things that looked like a great opportunity was to setup a completely fee-free fundraising platform. And essentially, that's what we kicked off with a brief of concept in 2016. >> So, fee-free meaning I can come in, I can setup my own fundraising vehicle so all the money goes to the recipients. >> A 100%, we have no charges whatsoever to charities, to donors, to fundraisers. And essentially, all the card processing fees as well are covered, and through the generosity of AWS and its NPO program, we've been able to also cover things like hosting as well which has been phenomenal for us, 'cause it really does enable us to give every single penny to charity. >> So, how do you fund your staff? >> The staff currently on our model going forward, if it's one that we continue, if we can continue to support is through secondment. So, we seconded our technical resource from my day job which is essentially running a telecoms business, and those guys are incredibly generous with their time. So, evenings and weekends have been devoted to setting up and maintaining the platform. We've called in favors from people we have networked with over the years. So again, when we moved beyond proof of concept into the current website now, the current build, we were able to get that done with some cost but albeit, a fraction of what we would've paid commercially. And essentially as we move forward, we want the whole concept to Wonderful.org to be something much bigger than just the organization. It's a vehicle for commercial organizations to do good. >> So, lots of in-kind contributions, lots of your time obviously so, when did you start the organization? >> 2016 and essentially, we went through what I describe as a proof of concept. We set three broad milestones, one was the first 100 charities onboard, first 100,000 pounds of a revenue or charity not really revenue but charity donations through the website. And we launched our first Wonderful week where we brought some sports celebrities including Phil Neville, now the manager of the England women's football team. He came on board to do some charity work for us with his family. Once we passed through those three milestones, it was then a case of saying, okay we've achieved all of these now, let's push the button and actually do this properly in inverted commas, and that's when we looked at hosting the thing properly, looked at the commercial build and so on. >> So that those milestones were really the prove the concept. >> Yeah. >> But they're pretty substantial milestone, >> Sure. >> And you hit them pretty fast though. >> We did hit them fast but again to give you some context on that, the first 100,000 through the website probably took us I would guess between 12 and 14 months. In the last 28 days, we've processed about a quarter of a million pounds through the website. So, the growth's been phenomenal and the appetite from the charities is enormous. What's particularly interesting about our sector is that whilst the lot of the events that take place like the London Marathon and so on, are very predictable, we know exactly the date and time that people are gonna be donating. Clearly, you get events that are completely unpredictable. We've gotta be able to respond and be available for donors to give when those kinds of things happen. >> Okay so, this leads me to the conversation about your infrastructure and obviously the Cloud. When you started the organization, you had your own owned premises infrastructure, correct? >> Correct. >> So take us through what that looked like and your decision to move to the Cloud. >> Expensive, disjointed, very complex. So, we were running essentially a full stock on a number of servers that were hosted independently. Co-location was expensive, maintenance was expensive, even things like getting to site were expensive, and if the rare occasions when you do have to do that in a hurry it can be quite time-consuming, particularly as I say given our profile where these guys are really doing it for love not money. So, it became apparent to us, I think learning from some scenarios that we've seen in the real world with other platforms as well when even the predictable events had still created some concerns for some of the charities in terms of availability. So, we've took a long hard look at what we had and said, are we scalable, are we fully available? Probably not, we need to look at this in some detail now. So, that was when we completely re-architected the website and looked at AWS. >> So, it was not only a matter of say scaling up for high demand and unpredictability but you had a fragile infrastructure. >> We did. >> And essentially, (chuckles) you're volunteers trying to keep it together. >> Exactly. >> So that's not a good formula for high availability, right? >> No, absolutely not. >> So, how does that change with the Cloud? >> With the Cloud, I think what we've got now is we've got a really good view of everything. We've got a view of the whole of our infrastructure in one place, so it gives our operations director a lot more peace of mind 'cause he can see all of the resources at his disposal. I think in terms of security, it's far far better for us as well, because we can manage access to various components, available US, depending on who needs access. So, our web developers are currently remote, they're not formally part of the organization. So, we can strict access to things that we don't want 'em to have access and so on and give them full access where it's required. So, I think that's been a lot of peace of mind for the operations director. And just having that confidence in clearly a brand that's got a huge reputation and people feel immensely confident about seeing. So, for us being to put the AWS badge on the website to reinforce to our users, to our donors that we're here, we're solid, we're stable, we're not going anywhere, it's really really important. >> Anyway, you said upfront that Adobe has some skin in the game, they're providing some services, >> They are. >> Some contributions. >> Yeah. >> So, that's gotta be pretty substantial. >> Massive. >> For you guys, yeah. >> Absolutely massive. I mean in all honesty, it's second only to card processing which is a significant cost of doing our business and one which is paid for by our other corporate sponsors. It's our second biggest cost without a doubt or would be if it were a cost but mercifully, AWS has come to the rescue and we're able to do what we're doing now. >> So corporate sponsors, give a little commercial, how does that work? >> Well essentially, our biggest corporate sponsor, our main partner at the moment is The Co Operative Bank and they have underwritten all of our card processing fees for the duration of that partnership. The big caveat with that is that we don't know what they will be and whilst we can provide some forecast based on empirical evidence, worst case scenario, there's another tragedy, people reach for their wallets and give, and suddenly that can go through the roof in the course of a couple of weeks. So, the difficulty in bringing corporate sponsors on for us is just that kind of unpredictability of the sector that we're operating in, but they've been tremendous. >> That's amazing right? >> Yeah. >> 'cause I could say that's a big junk of your cost >> For sure. >> Along with your infrastructure but, I'm fascinated by this organization and just wanna congratulate you on the mission and actually getting it off the ground because we all when we give to a charity, we always ask okay, what are the administrative cost behind this? You go to the website and you look it up and sometimes you just don't feel comfortable, and so what you've done is actually just eliminated that overhead. >> Completely. >> And where do you see this going? I mean you've got like 15 hundred registered charities now. >> Yes, yeah we're up to 15 hundred, again we've had a couple of fairly major events we were endorsed by the Money Saving Expert at number one but how could they not put us at number one. (they both laugh) Would've been very odd if they hadn't, given that we're the only completely fee-free platform. That clearly creates the demand and I think that endorsement was a huge catalyst to the growth. More recently, we've seen other things, BT MyDonate actually pulled out of this sector which has caused a lot of charities to migrate to our platform as well. In terms of where we see it going, we will need to continue to raise money from corporate sponsors to support it. But, there is a real step game in that, we have to manage that growth to meet their expectations as best as we can. But equally, new corporate sponsors coming onboard will want to see that we've got enough eyeballs to make it worth their while getting behind the organization. So, it's that constant game of trying to bring on the next round of funding and getting people through. >> How global do you see this getting? How is it today and in the future? >> Conceptually, there's no reason at all why this shouldn't be a global phenomenon but, we're now very concentrated on the UK, just because of our resource and we do get requests all of the time for international charities, for international fundraisers and so on, but we've gotta be realistic about what we can support. But going back to the point that I made earlier, it really isn't about Wonderful.org, it's about just corporations, fundraisers, charities, donors, we see all of the last three being wonderful all of the time by the nature of what they do, we're just trying to get more corporations to be as wonderful as, sounds terribly sick and fancy, but as AWS has been in supporting what we're doing, it's that sense of what we're trying to achieve here goes beyond one organization. >> Well, and the Cloud allows you to scale potentially to the extent that you can get the resource. There's no reason you can't go global. >> No. >> I'm gonna check it out and see even for a little local charity, can I (he chuckles), >> Absolutely. >> Can I participate, what does that involve? Do you have to have some minimum threshold or? >> No? >> No, anybody can-- >> Anybody, but you need to be a registered UK charity with one of the UK registrars. Beyond that, we go through a little bit of due diligence with the charity, so we will need to see some documentation. So, there's a little manual step in onboarding charities, but for all the right reasons, we wanna be diligent about the people using the platform to give the fundraisers the confidence that they're donating to a charity. So, we don't do any peer-to-peer fundraising, it is literally you'll register as a charity and the fundraisers can support your charity, often led by the fundraisers rather than the charities, interestingly, so the fundraisers will be saying to the charities, why are you not on this platform which gives you everything and you're already on this platform which doesn't. So, there's quite a lot of pressure now coming from the fundraisers to pull the charities in. >> So, there's a lot of word-of-mouth, a lot of peer-to-peer. >> Absolutely. >> Right, you don't really have the funding. >> There is no. >> The budget to go market. >> Not at all. >> Yeah, that's remote. >> Absolutely not. >> Well, hopefully this will help. >> Thank you very much. >> Thanks so much for coming to theCUBE, really appreciate your time. >> Thank you. >> Alright, thank you for watching everybody. This is Dave Vellante, you're watching theCUBE. We'll be back right after this short break from AWS HQ in London, right back. (upbeat music)

(intense orchestral music) >> Hey welcome back everybody, Jeff Frick here with theCUBE We are taking a short break from the madness of the conference season to do some CUBE Conversations here in the Palo Alto studio, which we always like to do and meet new people, and hear new stories, learn about new companies. And today we've got a new company, we've never had 'em on theCUBE before, it's Evan Kaplan, he's the CEO of InluxData. Evan, great to see you. >> Yeah, hey thanks for having me. >> Absolutely. So for people that aren't familiar with the company, give 'em kind of the 101 on Influx. >> Yeah so, InfluxData is an opensource platform for collecting metrics and events at scale. The company is about almost four years old, has a large selection of tier one customers, is broadly accepted by developers as the number one time-series platform out there, so. >> So a lot of people talk about collecting data, so we've been doing Splunk since 2012, and, they really found something interesting on log files, and took it a whole 'nother level, so there's a lot of people that are capturing events. So what do you guys do that's a little bit different, how are you slicing and dicing this opportunity? >> Yeah, to put this is even in the broader context of what we're looking at is the 20 year break-up of the Oracle, DB2 and Formex franchise that dominated and relational databases were the answer to all problems and so if you look at a company like Splunk working on logs, they optimized a platform for those logs, for that data set, Elastic also, really interesting space. I think our innovation has been in saying "Hey, where the world's going, where all of these complex systems are going?" Particularly IoT, is to real-time view of the data and so, rather than collect verbose logs, historical views of the data and things like that, real system operators, real developers and builders want to instrument their applications, their infrastructure, so you can view 'em in real time. The place where the rubber hits the road is IoT. Sensors spit out metrics and events, period, full stop. And so if you want to be performant in how you handle, your instrumentation of the physical world, and how you do your machine learning, and how you want to manage these systems, you use a fundamentally time-series based database. As opposed to Splunk or Elastic or, which are primarily search-based databases. >> And are you primarily capturing and standardizing the data to feed other analytics tools, or do you have the whole suite, where you're doing some of the analytics as well? >> Yeah, such a great question. So, the fundamental platform is called the TICK Stack, and it stands for Telegraf which is a collector, which has about 200 different collectors that sit out there in the world and collect everything from SNMP data, to Oracle data, to application, to micro-service data, to Kubernetes, to that sort of stuff. There's Influx, which is the DB, which is highly optimized for millions and millions of writes a second, so collecting data points and samples. There's Chronograf which is the visualization engine and so, it allows you as soon as the data comes input you can see how it's graphed, see it on time-series oriented graphing, and then there's Kapacitor which takes action on the data. What we don't do is the super high sophisticated analytics. There are lots of companies in Silicon Valley who take our data, pump it up, and then we put it back on the platform to build a control loop for it. >> Right. So when the Kapacitor, does your application then take action on those things? >> Yes. Yeah, so, it'd do everything from alerting, to sending out another machine request, to spinning up a new Kubernetes pod, to basically scaling the application, self healing. >> Right. So does it fit in between a lot of those other types of applications that are sending off notifications, and those types of things? >> Yes, yeah. so you're in between? >> And usually, we're instrumented the way a standard developer, or an architect or CTO does is they look at a complex application, or a complex set of sensors, they instrument with Influx and Telegraf, and collect that data, they view it in real time, and then they build control loops, automation loops, to make that easier so when you see a problem, it's got a tolerance you can self adjust for. So it's the beginning of kind of the self-healing system. >> Okay, and I know that Telegraf is definitely opensource, are the other three? >> All four are open-source All four are open-source. >> Everything, in our world, everything for a developer is free, so, and a single note of Influx can handle a couple million writes a second, which is really really performant to run in production. Where our business model is, where we make money is, our closed source clustering, sharding, distributing the database, if you decide you want to run highly available in the production environment, you would buy our closed-source stuff. We have about 430 customers who run our closed source stuff on top of the opensource. >> So, it is kind of like a MapR to Hadoop if you will, where, you know, it's built on, built on the opensource, and then they've got their proprietary stuff kind of wrapped around it, almost like an open core? Or is that a? >> Yeah, it's a little It's a little different than the normal Hadoop stuff. One is, our stuff doesn't have any external dependencies. It can work with other third party projects, but just, it's a platform onto itself, there aren't 25 projects. There are four different projects, we own them all, they come across as a single binary, and it's not part of Apache. >> So they're integrated So the TICK is the full TICK >> Yes, and then you put the clustering on top. So there's some similarity, but not being part of Apache, we can control and keep clean what that experience is. And we're about, the thing that's been most successful for us is, well Paul our founder who is my partner, it's called time to awesome, the idea that a developer in 10 minutes can very quickly be up and instrumenting an application or a set of sensors, and see that data pouring in within 10 minutes from going to the site and downloading the opensource. >> So it's interesting, the giant opportunity is really around IoT, just in terms of the explosion of the sensor data, and we see that coming, and we were at AT&T show a couple weeks ago, talking about 5G which is, slowly, slowly coming down the road, (Evan laughs) they've got the standards fixed. But in terms of the, you said the shorter term, nobody has budget, I always like to joke, nobody has budget for a new platform, they do have budget for new applications, because they've got real problems. So you said you're seeing, your main success now, your go to market application, is around application monitoring? Would that be accurate, or what is kind of your? >> Yeah, there are two broad things, and they're both very similar technology as a service. One is the central monitoring stuff so, Tesla's Power Wall, Seimens' Windmills, a variety of solar companies build Telegraf into their platforms and then use InluxData to collect and store that information and analyze it. On the software side, people like IBM's Cloud Service running their network and their fabric, SAP with Ariba, Cisco with all their collaboration stuff, they instrument their software applications. And that's the idea is it's a general purpose platform for collecting and instrumenting instrumenting the applications or the sensors, either one, or both. >> Okay, and so what are you guys working on now, what's next, kind of raise the profile, get some new stuff >> Yeah, so we are-- before the whole IoT thing completely explodes, we're not quite there yet but it's coming down the pike. >> But we're starting to see it really happen, so that's really exciting for us. And this is just a really, really big market, it's certainly a super set of the log market, it should be. As you think about just the instrumentation of the physical world, how much instrumentation is going on, your clothes, your cars, your homes, your industrial devices, my watch, how much sensor data there is. We think this is a tremendously large market, so we're doing a couple of things. One is, we're about to introduce a new language for querying these kinds of time-series data that's going to be opensource, that a bunch of other people can use with their data stores. We're rolling out a new API-driven service, so that people can store these things directly in the could natively, so all they have to do is know our API. So we're really trying to push from the technology limit we're a product-driven company, and so, and an opensource-driven company, so we're trying to push that, that community is super important to us. >> It's so wild to me, the opportunity to have a closed feedback loop between someone's product back to the barn, you're barely starting to see it, Tesla obviously, is a good example, they're slowly seeing it in other places. But what a fundamental change in manufacturing, from building a product, making some assumptions about use, shipping that product to your distribution, and then, maybe you get some feedback now an then, versus actually monitoring the way that that thing is actually used by your end user, whether it's a product like a car, or even a software application, as you're rolling out all these different apps and features in the apps, how are people using it, are they using it? Where do you double down, where do you back off? And that loop has not really been >> That's pretty insightful. >> opened up very wide. Yeah, no it's just starting to open up, and that whole notion of product telemetry, my prediction is is that, as development teams grow and things like that, you're going to have telemetry experts, people are going to be specializing. How do you instrument these products so you get maximum engagement, and usage, and things like that? So I think that's pretty insightful on your part. If you think about it from a systems point of view, right? Instrumentation is first. You can't do anything 'til you instrument, whether it's telemetry from a product, it's the engagement or this. So instrumentation is first, visibility in real time is second. So observability is the big thought in systems application and building now, this notion of observing your system in real time, because you don't know, apriori, it's impossible to know a complex system, how it's going to behave, then it's automation, right? So like, okay now I can see these behaviors, how do I automate something that makes the experience for you, the user, better? But lastly, we can see this with self-driving cars, it's autonomy. It's the idea that the system becomes self-healing, and AI, and those sorts of things, but that's kind of the last step. There's a lot of learning in that process to get there. >> And it has to be automated because at scale there's no way for people to keep up with this stuff, and then how do you separate signal from noise and how do you know what to do? So you've got to automate a whole bunch of this. >> And you know if we had an aspiration it would be we're not going to write the applications that do these things but what we want to do is be that system of record so that people have a really efficient, effective metrics and events store so they can really track and keep track of all that engagement. Time-stamped data, for lack of a better way to say it. >> It sounds like you're in a pretty good space, Evan. >> Pretty excited (chuckles), thank you. Thanks for saying that, but yeah, we're pretty excited. >> Alright, well thanks for taking a few minutes out of your day and sharing the story, we look forward to watching the journey. >> Yeah. Thanks man. Alright, take care. >> Alright, thanks. He's Evan, I'm Jeff, you're watching theCUBE. We're having a CUBE Conversation in Palo Alto, we'll see you next time, thanks for watching. (intense orchestral music)