(upbeat music) >> You got it, it's theCUBE. We are in Vegas. This is the Cube's live coverage day one of the full event coverage of AWS reInvent '22 from the Venetian Expo Center. Lisa Martin here with Dave Vellante. We love being in Vegas, Dave. >> Well, you know, this is where Super Cloud sort of was born. >> It is. >> Last year, just about a year ago. Steve Mullaney, CEO of of Aviatrix, you know, kind of helped us think it through. And we got some fun stories around. It's happening, but... >> It is happening. We're going to be talking about Super Cloud guys. >> I guess I just did the intro, Steve Mullaney >> You did my intro, don't do it again. >> Sorry I stole that from you, yeah. >> Steve Mullaney, joined just once again, one of our alumni. Steve, great to have you back on the program. >> Thanks for having me back. >> Dave: It's happening. >> It is happening. >> Dave: We talked about a year ago. Net Studio was right there. >> That was two years. Was that year ago, that was a year ago. >> Dave: It was last year. >> Yeah, I leaned over >> What's happening? >> so it's happening. It's happening. You know what, the thing I noticed what's happening now is the maturity of the cloud, right? So, if you think about this whole journey to cloud that has been, what, AWS 12 years. But really over the last few years is when enterprises have really kind of joined that journey. And three or four years ago, and this is why I came out of retirement and went to Aviatrix, was they all said, okay, now we're going to do cloud. You fast forward now three, four years from now, all of a sudden those five-year plans of evacuating the data center, they got one year left, two year left, and they're going, oh crap, we don't have five years anymore. We're, now the maturity's starting to say, we're starting to put more apps into the cloud. We're starting to put business critical apps like SAP into the cloud. This is not just like the low-hanging fruit anymore. So what's happening now is the business criticality, the scale, the maturity. And they're all now starting to hit a lot of limits that have been put into the CSPs that you never used to hit when you didn't have business critical and you didn't have that scale. They were always there. The rocks were always there. Just it was, you never hit 'em. People are starting to hit 'em now. So what's happening now is people are realizing, and I'm going to jump the gun, you asked me for my bumper sticker. The bumper sticker for Aviatrix is, "Good enough is no longer good enough." Now it's funny, it came in a keynote today, but what we see from our customers is it's time to upgrade the native constructs of networking and network security to be enterprise-grade now. It's no longer good enough to just use the native constructs because of a lack of visibility, the lack of controls, the lack of troubleshooting capabilities, all these things. "I now need enterprise grade networking." >> Let me ask you a question 'cause you got a good historical perspective on the industry. When you think about when Maritz was running VMWare. He was like any app, he said basically we're building a software mainframe. And they kind of did that, right? But then they, you know, hit the issue with scale, right? And they can't replicate the cloud. Are there things that we can draw from that experience and apply that to the cloud? What's the same, what's different? >> Oh yeah. So, 1992, do you remember what happened in 1992? I do this, weird German software company called SAP >> Yeah, R3. announced a release as R/3. Which was their first three-tier client-server application of SAP. Before that it ran on mainframes, TCP/IP. Remember that Protocol War? Guess what happened post-1992, everybody goes up like this. Infrastructure completely changes. Cisco, EMC, you name it, builds out these PCE client-server architectures. The WAN changes, MPLS, the campus, everything's home running back to that data center running SAP. That was the last 30 years ago. Great transformation of SAP. They've did it again. It's called S/4Hana. And now it's running and people are switching to S/4Hana and they're moving to the cloud. It's just starting. And that is going to alter how you build infrastructure. And so when you have that, being able to troubleshoot in hours versus minutes is a big deal. This is business critical, millions of dollars. This is not fun and games. So again, back to my, what was good enough for the last three or four years for enterprises no longer good enough, now I'm running business critical apps like SAP, and it's going to completely change infrastructure. That's happening in the cloud right now. And that's obviously a significant seismic shift, but what are some of the barriers that customers have been able to eliminate in order to get there? Or is it just good enough isn't good enough anymore? >> Barriers in terms of, well, I mean >> Lisa: The adoption. Yeah well, I mean, I think it's all the things that they go to cloud is, you know, the complexity, really, it's the agility, right? So the barrier that they have to get over is how do I keep the developer happy because the developer went to the cloud in the first place, why? Swipe the credit card because IT wasn't doing their job, 'cause every time I asked them for something, they said no. So I went around 'em. We need that. That's what they have to overcome in the move to the cloud. That is the obstacle is how do I deliver that visibility, that control, the enterprise, great functionality, but yet give the developer what they want. Because the minute I stop giving them that swipe the card operational model, what do you think they're going to do? They're going to go around me again and I can't, and the enterprise can't have that. >> That's a cultural shift. >> That's the main barrier they've got to overcome. >> Let me ask you another question. Is what we think of as mission critical, the definition changing? I mean, you mentioned SAP, obviously that's mission critical for operations, but you're also seeing new applications being developed in the cloud. >> I would say anything that's, I call business critical, same thing, but it's, business critical is internal to me, like SAP, but also anything customer-facing. That's business critical to me. If that app goes down or it has a problem, I'm not collecting revenue. So, you know, back 30 years ago, we didn't have a lot of customer-facing apps, right? It really was just SAP. I mean there wasn't a heck of a lot of cust- There were customer-facing things. But you didn't have all the digitalization that we have now, like the digital economy, where that's where the real explosion has come, is you think about all the customer-facing applications. And now every enterprise is what? A technology, digital company with a customer-facing and you're trying to get closer and closer to who? The consumer. >> Yeah, self-service. >> Self-service, B2C, everybody wants to do that. Get out of the middle man. And those are business critical applications for people. >> So what's needed under the covers to make all this happen? Give us a little double click on where you guys fit. >> You need consistent architecture. Obviously not just for one cloud, but for any cloud. But even within one cloud, forget multicloud, it gets worst with multicloud. You need a consistent architecture, right? That is automated, that is as code. I can't have the human involved. These are all, this is the API generation, you've got to be able to use automation, Terraform. And all the way from the application development platform you know, through Jenkins and all other software, through CICD pipeline and Terraform, when you, when that developer says, I want infrastructure, it has to go build that infrastructure in real time. And then when it says, I don't need it anymore it's got to take it away. And you cannot have a human involved in that process. That's what's completely changed. And that's what's giving the agility. And that's kind of a cloud model, right? Use software. >> Well, okay, so isn't that what serverless does, right? >> That's part of it. Absolutely. >> But I might still want control sometimes over the runtime if I'm running those mission critical applications. Everything in enterprise is a heterogeneous thing. It's like people, people say, well there's going to, the people going to repatriate back to on-prem, they are not repatriating back to on-prem. >> We were just talking about that, I'm like- >> Steve: It's not going to happen, right? >> It's a myth, it's a myth. >> And there's things that maybe shouldn't have ever gone into the cloud, I get that. Look, do people still have mainframes? Of course. There's certain things that you just, doesn't make sense to move to the new generation. There were things, certain applications that are very static, they weren't dynamic. You know what, keeping it on-prem it's, probably makes sense. So some of those things maybe will go back, but they never should have gone. But we are not repatriating ever, you know, that's not going to happen. >> No I agree. I mean, you know, there was an interesting paper by Andreessen, >> Yeah. >> But, I mean- >> Steve: Yeah it was a little self-serving for some company that need more funding, yeah. You look at the numbers. >> Steve: Yeah. >> It tells the story. It's just not happening. >> No. And the reason is, it's that agility, right? And so that's what people, I would say that what you need to do is, and in order to get that agility, you have to have that consistency. You have to have automation, you have to get these people out of the way. You have to use software, right? So it's that you have that swipe the card operational model for the developers. They don't want to hear the word no. >> Lisa: Right. >> What do you think is going to happen with AWS? Because we heard, I don't know if you heard Selipsky's keynote this morning, but you've probably heard the hallway talk. >> Steve: I did, yeah. >> Okay. You did. So, you know, connecting the dots, you know doubling down on all the primitives, that we expected. We kind of expected more of the higher level stuff, which really didn't see much of that, a little bit. >> Steve: Yeah. So, you know, there's a whole thing about, okay, does the cloud get commoditized? Does it not? I think the secret weapon's the ecosystem, right? Because they're able to sell through with guys like you. Make great margins on that. >> Steve: Yeah, well, yeah. >> What are your thoughts though on the future of AWS? >> IAS is going to get commoditized. So this is the fallacy that a lot of the CSPs have, is they thought that they were going to commoditize enterprise. It never happens that way. What's going to happen is infrastructure as a service, the lower level, which is why you see all the CSPs talking about what? Oracle Cloud, industry cloud. >> Well, sure, absolutely, yeah. >> We got to get to the apps, we got to get to SAP, we got to get to all that, because that's not going to get commoditized, right. But all the infrastructural service where AWS is king that is going to get commoditized, absolutely. >> Okay, so, but historically, you know Cisco's still got 60% plus gross margins. EMC always had good margin. How pure is the lone survivor in Flash? They got 70% gross margins. So infrastructure actually has always been a pretty good business. >> Yeah that's true. But it's a hell of a lot easier, particularly with people like Aviatrix and others that are building these common architectural things that create simplicity and abstract the way the complexities of underneath such that we allow your network to run an AWS, Azure, Google, Oracle, whatever, exactly the same. So it makes it a hell of a lot easier >> Dave: Super cloud. >> to go move. >> But I want to tap your brain because you have a good perspective of this because servers used to be a great margin business too on-prem and now it's not. It's a low margin business 'cause all the margin went to Intel. >> Yeah. But the cloud guys, you know, AWS in particular, makes a ton of dough on servers, so, or compute. So it's going to be interesting to see over time if that gets com- that's why they're going so hard after silicon. >> I think if they can, I think if you can capture the workload. So AWS and everyone else, as another example, this SAP, they call that a gravity workload. You know what gravity workload is? It's a black hole. It drags everything else with it. If you get SAP or Oracle or a mainframe app, it ain't going anywhere. And then what's going to happen is all your other apps are going to follow it. So that's what they're all going to fight for, is type of app. >> You said something earlier about, forget multicloud, for a moment, but, that idea of the super cloud, this abstraction layer, I mean, is that a real business value for customers other than, oh I got all these clouds, I need 'em to work together. You know, from your perspective from Aviatrix perspective, is it an opportunity for you to build on top of that? Or are you just looking at, look, I'm going to do really good work in AWS, in Azure? Now we're making the same experience. >> I hear this every single day from our customers is they look and they say, good enough isn't good enough. I've now hit the point, I'm hitting route limitations. I'm hitting, I'm doing things manually, and that's fine when I don't have that many applications or I don't have mission critical. The dogs are eating the dog food, we're going into the cloud and they're looking and then saying this is not an operational model for me. I've hit the point where I can't keep doing this, I can't throw bodies at this, I need software. And that's the opportunity for us, is they look and they say, I'm doing it in one cloud, but, and there's zero chance I'm going to be able to figure that out in the two or three other clouds. Every enterprise I talk to says multicloud is inevitable. Whether they're in it now, they all know they're going to go, because it's the business units that demand it. It's not the IT teams that demand it, it's the line of business that says, I like GCP for this reason. >> The driver's functionality that they're getting. >> It's the app teams that say, I have this service and GCP's better at it than AWS. >> Yeah, so it's not so much a cost game or the end all coffee mug, right? >> No, no. >> Google does this better than Microsoft, or better than- >> If you asked an IT person, they would rather not have multicloud. They actually tried to fight it. No, why would you want to support four clouds when you could support one right? That's insane. >> Dave and Lisa: Right. If they didn't have a choice and, and so it, the decision was made without them, and actually they weren't even notified until day before. They said, oh, good news, we're going to GCP tomorrow. Well, why wasn't I notified? Well, we're notifying you now. >> Yeah, you would've said, no. >> Steve: This is cloud bottle, let's go. >> Super cloud again. Did you see the Berkeley paper, sky computing I think they call it? Down at Berkeley, yep Dave Linthicum from Deloitte. He's talking about, I think he calls it meta cloud. It's happening. >> Yeah, yeah, yeah. >> It's happening. >> No, and because customers, customers want that. They... >> And talk about some customer example or two that you think really articulates the value of why it's happening and the outcomes that it's generating. >> I mean, I was just talking to Lamb Weston last night. So we had a reception, Lamb Weston, huge, frozen potatoes. They serve like, I dunno, some ungodly percentage of all the french fries to all the fast food. It's unbelievable what they do. Do you know, they have special chemicals they put on the french fries. So when you get your DoorDash, they stay crispy longer. They've invented that patented it. But anyway, it's all these businesses you've never heard of and they do all the, and again, they're moving to SAP or they're actually SAP in the cloud, they're one of the first ones. They did it through Accenture. They're pulling it back off from Accenture. They're not happy with the service they're getting. They're going to use us for their networking and network security because they're going to get that visibility and control back. And they're going to repatriate it back from a managed service and bring it back and run it in-house. And the SAP basis engineers want it to happen because they see the visibility and control that the infrastructure guy's going to get because of us, which leads to, all they care about is uptime and performance. That's it. And they're going to say the infrastructure team's going to lead to better uptime and better performance if it's running on Aviatrix. >> And business performance and uptime, business critical >> That is the business. That is the business. >> It is. So what are some of the things next coming down the pike from Aviatrix? Any secret sauce you can share? >> Lot of secrets. So, two secrets. One, the next thing people really want to do, embedded network security into the network. We've kind of talked about this. You're going to be seeing some things from us. Where does network security belong? In the network. Embedded in the fabric of the network, not as this dumb device called the next-gen firewall that you steer traffic to. It has to be into the fabric of what we do, what we call airspace. You're going to see us talk about that. And then the next thing, back to the maturity of the cloud, as they build out the core, guess what they're doing? It's this thing called edge, Dave, right? And guess what they're going to do? It's not about connecting the cloud to the edge to the cloud with dumb things like SD-WAN, right? Or SaaS. It's actually the other way around. Go into the cloud, turn around, look out at the edge and say, how do I extend the cloud out to the edge, and make it look like a VPC. That's what people are doing. Why, 'cause I want the operational model. I want all the things that I can do in the cloud out at the edge. And everyone knows it's been in networking. I've been in networking for 37 years. He who wins the core does what? Wins the edge, 'cause that's what happens. You do it first in the core and then you want one architecture, one common architecture, one consistent way of doing everything. And that's going to go out to the edge and it's going to look like a VPC from an operational model. >> And Amazon's going to support that, no doubt. >> Yeah, I mean every, you know, every, and then it's just how do you want to go do that? And us as the networking and network security provider, we're getting dragged to the edge by our customer. Because you're my networking provider. And that means, end to end. And they're trying to drag us into on-prem too, yeah. >> Lot's going on, you're going to have to come back- >> Because they want one networking vendor. >> But wait, and you say what? >> We will never do like switches and any of the keep Arista, the Cisco, and all that kind of stuff. But we will start sucking in net flow. We will start doing, from an operational perspective, we will integrate a lot of the things that are happening in on-prem into our- >> No halfway house. >> Copilot. >> No halfway house, no two architectures. But you'll take the data in. >> You want one architecture. >> Yeah. >> Yeah, totally. >> Right play. >> Amazing stuff. >> And he who wins the core, guess what's more strategic to them? What's more strategic on-prem or cloud? Cloud. >> It flipped three years ago. >> Dave: Yeah. >> So he who wins in the clouds going to win everywhere. >> Got it, We'll keep our eyes on that. >> Steve: Cause and effect. >> Thank you so much for joining us. We've got your bumper sticker already. It's been a great pleasure having you on the program. You got to come back, there's so, we've- >> You posting the bumper sticker somewhere? >> Lisa: It's going to be our Instagram. >> Oh really, okay. >> And an Instagram sto- This is new for you guys. Always coming up with new ideas. >> Raising the bar. >> It is, it is. >> Me advance, I mean, come on. >> I love it. >> All right, for our guest Steve Mullaney and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage.

(soft music) >> Hello, fellow hardware nerds and welcome back to Dallas Texas where we're reporting live from Supercomputing 2022. My name is Savannah Peterson, joined with the John Furrier on my left. >> Looking good today. >> Thank you, John, so are you. It's been a great show so far. >> We've had more hosts, more guests coming than ever before. >> I know. >> Amazing, super- >> We've got a whole thing going on. >> It's been a super computing performance. >> It, wow. And, we'll see how many times we can say super on this segment. Speaking of super things, I am in a very unique position right now. I am a flanked on both sides by people who have been doing content on theCUBE for 12 years. Yes, you heard me right, our next guest was on theCUBE 12 years ago, the third event, was that right, John? >> Man: First ever VM World. >> Yeah, the first ever VM World, third event theCUBE ever did. We are about to have a lot of fun. Please join me in welcoming Justin Emerson of Pure Storage. Justin, welcome back. >> It's a pleasure to be here. It's been too long, you never call, you don't write. (Savannah laughs) >> Great to see you. >> Yeah, likewise. >> How fun is this? Has the set evolved? Is everything looking good? >> I mean, I can barely remember what happened last week, so. (everyone laughs) >> Well, I remember lot's changed that VM world. You know, Paul Moritz was the CEO if you remember at that time. His actual vision actually happened but not the way, for VMware, but the industry, the cloud, he called the software mainframe. We were kind of riffing- >> It was quite the decade. >> Unbelievable where we are now, how we got here, but not where we're going to be. And you're with Pure Storage now which we've been, as you know, covering as well. Where's the connection into the supercomputing? Obviously storage performance, big part of this show. >> Right, right. >> What's the take? >> Well, I think, first of all it's great to be back at events in person. We were talking before we went on, and it's been so great to be back at live events now. It's been such a drought over the last several years, but yeah, yeah. So I'm very glad that we're doing in person events again. For Pure, this is an incredibly important show. You know, the product that I work with, with FlashBlade is you know, one of our key areas is specifically in this high performance computing, AI machine learning kind of space. And so we're really glad to be here. We've met a lot of customers, met a lot of other folks, had a lot of really great conversations. So it's been a really great show for me. And also just seeing all the really amazing stuff that's around here, I mean, if you want to find, you know, see what all the most cutting edge data center stuff that's going to be coming down the pipe, this is the place to do it. >> So one of the big themes of the show for us and probably, well, big theme of your life, is balancing power efficiency. You have a product in this category, Direct Flash. Can you tell us a little bit more about that? >> Yeah, so Pure as a storage company, right, what do we do differently from everybody else? And if I had to pick one thing, right, I would talk about, it's, you know, as the name implies, we're an all, we're purely flash, we're an all flash company. We've always been, don't plan to be anything else. And part of that innovation with Direct Flash is the idea of rather than treating a solid state disc as like a hard drive, right? Treat it as it actually is, treat it like who it really is and that's a very different kind of thing. And so Direct Flash is all about bringing native Flash interfaces to our product portfolio. And what's really exciting for me as a FlashBlade person, is now that's also part of our FlashBlade S portfolio, which just launched in June. And so the benefits of that are our myriad. But, you know, talking about efficiency, the biggest difference is that, you know, we can use like 90% less DRAM in our drives, which you know, everything uses, everything that you put in a drive uses power, it adds cost and all those things and so that really gives us an efficiency edge over everybody else and at a show like this, where, I mean, you walk the aisles and there's there's people doing liquid cooling and so much immersion stuff, and the reason they're doing that is because power is just increasing everywhere, right? So if you can figure out how do we use less power in some areas means you can shift that budget to other places. So if you can talk to a customer and say, well, if I could shrink your power budget for storage by two thirds or even, save you two-thirds of power, how many more accelerators, how many more CPUs, how much more work could you actually get done? So really exciting. >> I mean, less power consumption, more power and compute. >> Right. >> Kind of power center. So talk about the AI implications, where the use cases are. What are you seeing here? A lot of simulations, a lot of students, again, dorm room to the boardroom we've been saying here on theCUBE this is a great broad area, where's the action in the ML and the AI for you guys? >> So I think, not necessarily storage related but I think that right now there's this enormous explosion of custom silicon around AI machine learning which I as a, you said welcome hardware nerds at the beginning and I was like, ah, my people. >> We're all here, we're all here in Dallas. >> So wonderful. You know, as a hardware nerd we're talking about conferences, right? Who has ever attended hot chips and there's so much really amazing engineering work going on in the silicon space. It's probably the most exciting time for, CPU and accelerator, just innovation in, since the days before X 86 was the defacto standard, right? And you could go out and buy a different workstation with 16 different ISAs. That's really the most exciting thing, I walked past so many different places where you know, our booth is right next to Havana Labs with their gout accelerator, and they're doing this cute thing with one of the AI image generators in their booth, which is really cute. >> Woman: We're going to have to go check that out. >> Yeah, but that to me is like one of the more exciting things around like innovation at a, especially at a show like this where it's all about how do we move forward, the state of the art. >> What's different now than just a few years ago in terms of what's opening up the creativity for people to look at things that they could do with some of the scale that's different now. >> Yeah well, I mean, every time the state of the art moves forward what it means is, is that the entry level gets better, right? So if the high end is going faster, that means that the mid-range is going faster, and that means the entry level is going faster. So every time it pushes the boundary forward, it's a rising tide that floats all boats. And so now, the kind of stuff that's possible to do, if you're a student in a dorm room or if you're an enterprise, the world, the possible just keeps expanding dramatically and expanding almost, you know, geometrically like the amount of data that we are, that we have, as a storage guy, I was coming back to data but the amount of data that we have and the amount of of compute that we have, and it's not just about the raw compute, but also the advances in all sorts of other things in terms of algorithms and transfer learning and all these other things. There's so much amazing work going on in this area and it's just kind of this Kay Green explosion of innovation in the area. >> I love that you touched on the user experience for the community, no matter the level that you're at. >> Yeah. >> And I, it's been something that's come up a lot here. Everyone wants to do more faster, always, but it's not just that, it's about making the experience and the point of entry into this industry more approachable and digestible for folks who may not be familiar, I mean we have every end of the ecosystem here, on the show floor, where does Pure Storage sit in the whole game? >> Right, so as a storage company, right? What AI is all about deriving insights from data, right? And so everyone remembers that magazine cover data's the new oil, right? And it's kind of like, okay, so what do you do with it? Well, how do you derive value from all of that data? And AI machine learning and all of this supercomputing stuff is about how do we take all this data? How do we innovate with it? And so if you want data to innovate with, you need storage. And so, you know, our philosophy is that how do we make the best storage platforms that we can using the best technology for our customers that enable them to do really amazing things with AI machine learning and we've got different products, but, you know at the show here, what we're specifically showing off is our new flashlight S product, which, you know, I know we've had Pure folks on theCUBE before talking about FlashBlade, but for viewers out there, FlashBlade is our our scale out unstructured data platform and AI and machine learning and supercomputing is all about unstructured data. It's about sensor data, it's about imaging, it's about, you know, photogrammetry, all this other kinds of amazing stuff. But, you got to land all that somewhere. You got to process that all somewhere. And so really high performance, high throughput, highly scalable storage solutions are really essential. It's an enabler for all of the amazing other kinds of engineering work that goes on at a place like Supercomputing. >> It's interesting you mentioned data's oil. Remember in 2010, that year, our first year of theCUBE, Hadoop World, Hadoop just started to come on the scene, which became, you know kind of went away and, but now you got, Spark and Databricks and Snowflake- >> Justin: And it didn't go away, it just changed, right? >> It just got refactored and right size, I think for what the people wanted it to be easy to use but there's more data coming. How is data driving innovation as you bring, as people see clearly the more data's coming? How is data driving innovation as you guys look at your products, your roadmap and your customer base? How is data driving innovation for your customers? >> Well, I think every customer who has been, you know collecting all of this data, right? Is trying to figure out, now what do I do with it? And a lot of times people collect data and then it will end up on, you know, lower slower tiers and then suddenly they want to do something with it. And it's like, well now what do I do, right? And so there's all these people that are reevaluating you know, we, when we developed FlashBlade we sort of made this bet that unstructured data was going to become the new tier one data. It used to be that we thought unstructured data, it was emails and home directories and all that stuff the kind of stuff that you didn't really need a really good DR plan on. It's like, ah, we could, now of course, as soon as email goes down, you realize how important email is. But, the perspectives that people had on- >> Yeah, exactly. (all laughing) >> The perspectives that people had on unstructured data and it's value to the business was very different and so now- >> Good bet, by the way. >> Yeah, thank you. So now unstructured data is considered, you know, where companies are going to derive their value from. So it's whether they use the data that they have to build better products whether it's they use the data they have to develop you know, improvements in processes. All those kinds of things are data driven. And so all of the new big advancements in industry and in business are all about how do I derive insights from data? And so machine learning and AI has something to do with that, but also, you know, it all comes back to having data that's available. And so, we're working very hard on building platforms that customers can use to enable all of this really- >> Yeah, it's interesting, Savannah, you know, the top three areas we're covering for reinventing all the hyperscale events is data. How does it drive innovation and then specialized solutions to make customers lives easier? >> Yeah. >> It's become a big category. How do you compose stuff and then obviously compute, more and more compute and services to make the performance goes. So those seem to be the three hot areas. So, okay, data's the new oil refineries. You've got good solutions. What specialized solutions do you see coming out because once people have all this data, they might have either large scale, maybe some edge use cases. Do you see specialized solutions emerging? I mean, obviously it's got DPU emerging which is great, but like, do you see anything else coming out at that people are- >> Like from a hardware standpoint. >> Or from a customer standpoint, making the customer's lives easier? So, I got a lot of data flowing in. >> Yeah. >> It's never stopping, it keeps powering in. >> Yeah. >> Are there things coming out that makes their life easier? Have you seen anything coming out? >> Yeah, I think where we are as an industry right now with all of this new technology is, we're really in this phase of the standards aren't quite there yet. Everybody is sort of like figuring out what works and what doesn't. You know, there was this big revolution in sort of software development, right? Where moving towards agile development and all that kind of stuff, right? The way people build software change fundamentally this is kind of like another wave like that. I like to tell people that AI and machine learning is just a different way of writing software. What is the output of a training scenario, right? It's a model and a model is just code. And so I think that as all of these different, parts of the business figure out how do we leverage these technologies, what it is, is it's a different way of writing software and it's not necessarily going to replace traditional software development, but it's going to augment it, it's going to let you do other interesting things and so, where are things going? I think we're going to continue to start coalescing around what are the right ways to do things. Right now we talk about, you know, ML Ops and how development and the frameworks and all of this innovation. There's so much innovation, which means that the industry is moving so quickly that it's hard to settle on things like standards and, or at least best practices you know, at the very least. And that the best practices are changing every three months. Are they really best practices right? So I think, right, I think that as we progress and coalesce around kind of what are the right ways to do things that's really going to make customers' lives easier. Because, you know, today, if you're a software developer you know, we build a lot of software at Pure Storage right? And if you have people and developers who are familiar with how the process, how the factory functions, then their skills become portable and it becomes easier to onboard people and AI is still nothing like that right now. It's just so, so fast moving and it's so- >> Wild West kind of. >> It's not standardized. It's not industrialized, right? And so the next big frontier in all of this amazing stuff is how do we industrialize this and really make it easy to implement for organizations? >> Oil refineries, industrial Revolution. I mean, it's on that same trajectory. >> Yeah. >> Yeah, absolutely. >> Or industrial revolution. (John laughs) >> Well, we've talked a lot about the chaos and sort of we are very much at this early stage stepping way back and this can be your personal not Pure Storage opinion if you want. >> Okay. >> What in HPC or AIML I guess it all falls under the same umbrella, has you most excited? >> Ooh. >> So I feel like you're someone who sees a lot of different things. You've got a lot of customers, you're out talking to people. >> I think that there is a lot of advancement in the area of natural language processing and I think that, you know, we're starting to take things just like natural language processing and then turning them into vision processing and all these other, you know, I think the, the most exciting thing for me about AI is that there are a lot of people who are, you are looking to use these kinds of technologies to make technology more inclusive. And so- >> I love it. >> You know the ability for us to do things like automate captioning or the ability to automate descriptive, audio descriptions of video streams or things like that. I think that those are really,, I think they're really great in terms of bringing the benefits of technology to more people in an automated way because the challenge has always been bandwidth of how much a human can do. And because they were so difficult to automate and what AI's really allowing us to do is build systems whether that's text to speech or whether that's translation, or whether that's captioning or all these other things. I think the way that AI interfaces with humans is really the most interesting part. And I think the benefits that it can bring there because there's a lot of talk about all of the things that it does that people don't like or that they, that people are concerned about. But I think it's important to think about all the really great things that maybe don't necessarily personally impact you, but to the person who's not cited or to the person who you know is hearing impaired. You know, that's an enormously valuable thing. And the fact that those are becoming easier to do they're becoming better, the quality is getting better. I think those are really important for everybody. >> I love that you brought that up. I think it's a really important note to close on and you know, there's always the kind of terminator, dark side that we obsess over but that's actually not the truth. I mean, when we think about even just captioning it's a tool we use on theCUBE. It's, you know, we see it on our Instagram stories and everything else that opens the door for so many more people to be able to learn. >> Right? >> And the more we all learn, like you said the water level rises together and everything is magical. Justin, it has been a pleasure to have you on board. Last question, any more bourbon tasting today? >> Not that I'm aware of, but if you want to come by I'm sure we can find something somewhere. (all laughing) >> That's the spirit, that is the spirit of an innovator right there. Justin, thank you so much for joining us from Pure Storage. John Furrier, always a pleasure to interview with you. >> I'm glad I can contribute. >> Hey, hey, that's the understatement of the century. >> It's good to be back. >> Yeah. >> Hopefully I'll see you guys in, I'll see you guys in 2034. >> No. (all laughing) No, you've got the Pure Accelerate conference. We'll be there. >> That's right. >> We'll be there. >> Yeah, we have our Pure Accelerate conference next year and- >> Great. >> Yeah. >> I love that, I mean, feel free to, you know, hype that. That's awesome. >> Great company, great runs, stayed true to the mission from day one, all Flash, continue to innovate congratulations. >> Yep, thank you so much, it's pleasure being here. >> It's a fun ride, you are a joy to talk to and it's clear you're just as excited as we are about hardware, so thanks a lot Justin. >> My pleasure. >> And thank all of you for tuning in to this wonderfully nerdy hardware edition of theCUBE live from Dallas, Texas, where we're at, Supercomputing, my name's Savannah Peterson and I hope you have a wonderful night. (soft music)

>>Good afternoon and welcome back to Detroit, Lisa Martin here with John Furrier. We are live day two of our coverage of Coan Cloud Native Con North America. John, we've had great conversations. Yeah. All day yesterday. Half a day today. So far we're talking all things, Well, not all things Kubernetes so much more than that. We also have to talk about storage and data management solutions for Kubernetes projects, cuz that's obviously critical. >>Yeah, I mean the big trend here is Kubernetes going mainstream has been for a while. The adopt is crossing over, it's crossing the CADs and with that you're seeing security concerns. You're seeing things being gaps being filled. But enterprise grade is really the, the, the story. It's going enterprise, that's managed services, that's professional service, that's basically making things work at scale. This next segment hits that part and we are gonna talk about it in grade length >>With one of our alumni. Moral morale to Molly is back DP and GM of Port Work's Peer Storage. Great to have you back really? >>Yeah, absolutely. Delightful >>To be here. So I was looking on the website, number one in Kubernetes storage. Three years in a row. Yep. Awesome. What's Coworks doing here at KU Con? >>Well, I'll tell you, we, our engineering crew has been so productive and hard at work that I almost can't decide what to kind of tell you. But I thought what, what, what I thought I would do is kind of tell you that we are in forefront of two major trends in the world of Kubernetes. Right? And the, the two trends that I see are one is as a service, so is trend number one. So it's not software eating the world anymore. That's, that's old, old, old news. It's as a service unifying the world. The world wants easy, We all are, you know, subscribers to things like Netflix. We've been using Salesforce or other HR functions. Everything is as a service. And in the world of Kubernetes, it's a sign of that maturity that John was talking about as a platform that now as a service is the big trend. >>And so headline number one, if you will, is that Port Works is leading in the data management world for Kubernetes by providing, we're going all in on easy on as a service. So everything we do, we are satisfying it, right? So if you think, if you think about, if you think about this, that, that there are really, most of the people who are consuming Kubernetes are people who are building platforms for their dev users. And dev users want self service. That's one of the advantages of, of, of Kubernetes. And the more it is service size and made as a service, the more ready to consume it is. And so we are announcing at the show that we have, you know, the basic Kubernetes data management as a service, ha d r as a service. We have backup as a service and we have database as a service. So these are the three major components of data. And all of those are being made available as a service. And in fact, we're offering and announcing at the show our backup as a service freemium version where you can get free forever a terabyte of, of, you know, stuff to do for Kubernetes for forever. >>Congratulations on the announcement. Totally. In line with what the market wants. Developers want Selfer, they wanna also want simplicity by the way they'll leave if they don't like the service. Correct. So that you, you know that before we get into some more specifics, I want Yeah. Ask you on the industry and some of the point solutions you have, what, it's been two years since the acquisition with Pure Storage. Can you just give an update on how it's gone? Obviously as a service, you guys are hitting all your Marks, developers love it. Storage are big part of the game right now as well as these environments. Yeah. What's the update post acquisition two years. You had a great offering Stay right In >>Point Works. Yeah. So look, John, you're, you're, you're a veteran of the industry and have seen lots of acquisitions, right? And I've been acquired twice before myself. So, you know, there's, there's always best practices and poor practices in terms of acquisitions and I'm, you know, really delighted to say I think this, this acquisition has had some of the best practices. Let me just name a couple of them, right? One of them is just cultural fit, right? Cultural fit is great. Entrepreneurs, anybody, it's not just entrepreneurs. Everybody loves to work in a place they enjoy working with, with people that they, you know, thrive when they, when they interact with. And so the cultural fit with, with Pure is fantastic. The other one is the strategic intent that Pure had when they acquired us is still true. And so that goes a long way, you know, in terms of an investment profile, in terms of the ability to kind of leverage assets within the company. So Pure had kind of disrupted the world of storage using Flash and they wanted to disrupt higher up the stack using Kubernetes. And that's kind of been our role inside their strategy. And it's, it's still true. >>So culture, strategic intent. Yeah. Product market fit as well. You were, you weren't just an asset for customers or acquisition and then let the founders go through their next thing. You are part of their growth play. >>Absolutely. Right. The, the beauty of, of the kind of product market fit is, let's talk about the market is we have been always focused on the global two k and that is at the heart of, you know, purest 10,000 strong customer base, right? They have very strong presence in the, in the global two k. And we, we allow them to kind of go to those same folks with, with the offering. >>So satisfying everything that you do. What's for me as a business, whether I'm a financial services organization, I'm a hospital, I'm a retailer, what's in it for me >>As a customer? Yeah. So the, the what's in it for, for me is two things. It's speed and ease of use, which in a way are related. But, but, but you know, one is when something is provided as a service, it's much more consumable. It's instantly ready. It's like instant oatmeal, right? You just get it just ad hot water and it's there. Yep. So the world of of it has moved from owning large data centers, right? That used to be like 25 years ago and running those data centers better than everybody else to move to let me just consume a data center in the form of a cloud, right? So satisfying the cloud part of the data center. Now people are saying, well I expect that for software and services and I don't want it just from the public cloud, I want it from my own IT department. >>This is old news. And so the, the, the big news here is how fast Kubernetes has kind of moved everything. You know, you take a lot of these changes, Kubernetes is a poster child for things happening faster than the last wave. And in the last couple of years I would say that as a service model has really kind of thrived in the world of Kubernetes. And developers want to be able to get it fast. And the second thing is they want to be able to operate it fast. Self-service is the other benefit. Yeah. So speed and self-service are both benefits of, of >>This. Yeah. And, and the thing that's come up clearly in the cube, this is gonna be part of the headlines we'll probably end up getting a lot of highlights from telling my team to make a note of this, is that developers are gonna be be the, the business if you, if you take digital transformation to its conclusion, they're not a department that serves the business, they are the business that means Exactly. They have to be more productive. So developer productivity has been the top story. Yes. Security as a serves all these things. These are, these are examples to make developers more productive. But one of the things that came up and I wanna get your reaction to is, is that when you have disruption and, and the storage vision, you know what disruption it means. Cuz there's been a whole discussion around disruptive operations. When storage goes down, you have back m dr and failover. If there's a disruption that changes the nature of invisible infrastructure, developers want invisible infrastructure. That's the future steady state. So if there's a disruption in storage >>Yeah. It >>Can't affect the productivity and the tool chains and the workflows of developers. Yep. Right? So how do you guys look at that? Cuz you're a critical component. Storage is a service is a huge thing. Yeah. Storage has to, has to work seamlessly. And let's keep the developers out of the weeds. >>John. I think what, what what you put your finger on is another huge trend in the world of Kubernetes where at Cube Con, after all, which is really where, where all the leading practitioners both come and the leading vendors are. So here's the second trend that we are leading and, and actually I think it's happening not just with us, but with other, for folks in the industry. And that is, you know, the world of DevOps. Like DevOps has been such a catchphrase for all, all of us in the industry last five years. And it's been both a combination of cultural change as well as technology change. Here's what the latest is on the, in the world of DevOps. DevOps is now crystallized. It's not some kind of mysterious art form that you read about how people are practicing. DevOps is, it's broken into two, two things now. >>There is the platform part. So DevOps is now a bunch of platforms. And the other part of DevOps is a bunch of practices. So a little bit on both these, the platforms in the world of es there's only three platforms, right? There's the orchestration platforms, the, you know, eks, the open ships of the world and so on. There are the data management platforms, pro people like Port Works. And the third is security platforms, right? You know, Palo Alto Networks, others Aqua or all in this. So these are the three platforms and there are platform engineering teams now that many of our largest customers, some of the largest banks, the largest service providers, they're all operating as a ES platform engineering team. And then now developers, to your point, developers are in the practice of being able to use these platforms to launch new services. So the, the actual IT ops, the ops are run by developers now and they can do it on these platforms. And the platform engineering team provide that as an ease of use and they're there to troubleshoot when problems happen. So the idea of DevOps as a ops practice and a platform is the newest thing. E and, and ports and pure storage leading in the world of data management platforms >>There. Talk about a customer example that you think really articulates the value that Port Works and Pure Storage delivers from a data management perspective. >>Yeah, so there's so many examples. One of the, one of the longest running examples we have is a very, very large service provider that, you know, you all know and probably use, and they have been using us in the cable kinda set box or cable box business. They get streams of data from, from cable boxes all over the world. They collected all in a centralized large kind of thing and run elastic search and analytics on it. Now what they have done is they couldn't keep up with this at the scale and the depth, right? The speed of, of activity and the distributed nature of the activity. The only way to solve this was to use something like Kubernetes manage with Spark coming, bringing all the data in to deep, deep, deep silos of storage, which are all running not even on a sand, but on kind of, you know, very deep terabytes and terabytes of, of storage. So all of this is orchestrated with the Heco coworks and there's a platform engineering team. We are building that platform for them with some of these other components that allows them to kind of do analytics and, and make some changes in real time. Huge kind of setup for, for >>That. Yeah. Well, you guys have the right architecture. I love the vision. I love what you guys are doing. I think this is right in line with Pures. They've always been disruptors. I remember when we first interviewed the CEO when they started Yep. They, they stayed on path. They didn't waiver. EMC was the big player. They ended up taking their lunch and dinner as well and they beat 'em in the marketplace. But now you got this traction here. So I have to ask you, how's the business, what's the results look like? Either GM cloud native business unit of a storage company that's transformed and transforming? >>Yeah, you know, it's interesting, we just hit the two year anniversary, right John? And so what we did was just kind of like step back and hey, you know, we're running so hard, you just take a step back. And we've tripled the business in the two years since the acquisition, the two years before and, and we were growing through proven. So, you know, that that's quite a fe and we've tripled the number of people, the amount of engineering investments we have, the number of go to market investments have, have been, have been awesome. So business is going really well though, I will say. But I think, you know, we have, we can't be, we we're watching the market closely. You know, as a former ceo, I, you have to kind of learn to read the tea leaves when you invest. And I think, you know, what I would say is we're proceeding with caution in the next two quarters. I view business transformation as not a cancelable activity. So that's the, that's the good news, right? Our customers are large, it's, >>It's >>Right. All they're gonna do is say, Hey, they're gonna put their hand, their hand was always going right on the dial. Now they're kind of putting their hand on the dial going, hey, where, what is happening? But my, my own sense of this is that people will continue to invest through it. The question is at what level? And I also think that this is a six month kind of watch, the watch where, where we put the dial. So Q4 and q1 I think are kind of, you know, we have our, our watch kind of watch the market sign. But I have the highest confidence. What >>Does your gut tell you? You're an entrepreneur, >>Which my, my gut says that we'll go through a little bit of a cautious investment period in the next six months. And after that I think we're gonna be back in, back full, full in the crazy growth that we've always been. We're gonna grow by the way, in the next think >>It's core style. I think I'm, I'm more bullish. I think there's gonna be some, you know, weeding out of some overinvestment pre C or pre bubble. But I think tech's gonna continue to grow. I don't see >>It's stopping. Yeah. And, and the investment is gonna be on these core platforms. See, back to the platform story, it's gonna be in these core platforms and on unifying everything, let's consume it better rather than let's go kind of experiment with a whole bunch of things all over the map, right? So you'll see less experimentation and more kind of, let's harvest some of the investments we've made in the last couple >>Of years and actually be able to, to enable companies in any industry to truly be data companies. Because absolutely. We talked about as a service, we all have these expectations that any service we want, we can get it. Yes. There's no delay because patients has gone Yeah. From the pandemic. >>So it is kind of, you know, tightening up the screws on what they've built. They, you know, adding some polish to it, adding some more capability, like I said, a a a, a combination of harvesting and new investing. It's a combination I think is what we're gonna see. >>Yeah. What are some of the things that you're looking forward to? You talked about some of the, the growth things in the investment, but as we round out Q4 and head into a new year, what are you excited about? >>Yeah, so you know, I mentioned our, as a service kind of platform, the global two K for us has been a set of customers who we co-create stuff with. And so one of the other set of things that we are very excited about and announcing is because we're deployed at scale, we're, we're, we have upgraded our backend. So we have now the ability to go to million IOPS and more and, and for, for the right backends. And so Kubernetes is a add-on which will not slow down your, your core base infrastructure. Second thing that that we, we have is added a bunch of capability in the disaster recovery business continuity front, you know, we always had like metro kind of distance dr. We had long distance dr. We've added a near sync Dr. So now we can provide disaster recovery and business continuity for metro distances across continents and across the planet. Right? That's kind of a major change that we've done. The third thing is we've added the capability for file block and Object. So now by adding object, we're really a complete solution. So it is really that maturity of the business Yeah. That you start seeing as enterprises move to embracing a platform approach, deploying it much more widely. You talked about the early majority. Yeah. Right. And so what they require is more enterprise class capability and those are all the things that we've been adding and we're really looking forward >>To it. Well it sounds like tremendous evolution and maturation of Port Works in the two years since it's been with Pure Storage. You talked about the cultural alignment, great stuff that you're achieving. Congratulations on that. Yeah. Great stuff >>Ahead and having fun. Let's not forget that, that's too life's too short to do. It is right. >>You're right. Thank you. We will definitely, as always on the cube, keep our eyes on this space. Mur. Meley, it's been great to have you back on the program. Thank you for joining, John. >>Thank you so much. It's pleasure. Our, >>For our guests and John Furrier, Lisa Martin here live in Detroit with the cube about Coan Cloud Native Con at 22. We'll be back after a short break.

>>Good afternoon and welcome back to Detroit, Lisa Martin here with John Furrier. We are live day two of our coverage of Coan Cloud Native, Con North America. John, we've had great conversations. Yeah. All day yesterday. Half a day today. So far we're talking all things, Well, not all things Kubernetes so much more than that. We also have to talk about storage and data management solutions for Kubernetes projects, cuz that's obviously critical. >>Yeah, I mean the big trend here is Kubernetes going mainstream has been for a while. The adopt is crossing over, it's crossing the CADs and with that you're seeing security concerns. You're seeing things being gaps being filled. But enterprise grade is really the, the, the story. It's going enterprise, that's managed services, that's professional service, that's basically making things work at scale. This next segment hits that, that part, and we're gonna talk about it in grade length >>With one of our alumni morale to Molly is back VP and GM of Port Work's peer Storage. Great to have you back really? >>Yeah, absolutely. Delightful to >>Be here. So I was looking on the website, number one in Kubernetes storage. Three years in a row. Yep. Awesome. What's Coworks doing here at KU Con? >>Well, I'll tell you, we, our engineering crew has been so productive and hard at work that I almost can't decide what to kind of tell you. But I thought what, what, what I thought I would do is kind of tell you that we are in forefront of two major trends in the world of es. Right? And the, the two trends that I see are one is as a service, so is trend number one. So it's not software eating the world anymore. That's, that's old, old, old news. It's as a service, unifying the world. The world wants easy, We all are, you know, subscribers to things like Netflix. We've been using Salesforce or other HR functions. Everything is as a service. And in the world of Kubernetes, it's a sign of that maturity that John was talking about as a platform that now as a service is the big trend. >>And so headline number one, if you will, is that Port Works is leading in the data management world for the Kubernetes by providing, we're going all in on easy on as a service. So everything we do, we are satisfying it, right? So if you think, if you think about, if you think about this, that, that there are really, most of the people who are consuming Kubernetes are people who are building platforms for their dev users and their users want self service. That's one of the advantages of, of, of Kubernetes. And the more it is service size and made as a service, the more ready to consume it is. And so we are announcing at the show that we have, you know, the basic Kubernetes data management as a service, ha d r as a service. We have backup as a service and we have database as a service. So these are the three major components of data. And all of those are being made available as a service. And in fact, we're offering and announcing at the show our backup as a service freemium version where you can get free forever a terabyte of, of, you know, stuff to do for Kubernetes for forever. >>Congratulations on the announcement. Totally. In line with what the market wants. Developers want self serve, they wanna also want simplicity by the way they'll leave if they don't like the service. Correct. So that you, you know, that before we get into some more specifics, I want to Yeah. Ask you on the industry and some of the point solutions you have, what, it's been two years since the acquisition with Pure Storage. Can you just give an update on how it's gone? Obviously as a service, you guys are hitting all your Marks, developers love it. Storage a big part of the game right now as well as these environments. Yeah. What's the update post acquisition two years, You had a great offering Stay >>Right In Point Works. Yeah. So look, John, you're, you're, you're a veteran of the industry and have seen lots of acquisitions, right? And I've been acquired twice before myself. So, you know, there's, there's always best practices and poor practices in terms of acquisitions and I'm, you know, really delighted to say I think this, this acquisition has had some of the best practices. Let me just name a couple of them, right? One of them is just cultural fit, right? Cultural fit is great. Entrepreneurs, anybody, it's not just entrepreneurs. Everybody loves to work in a place they enjoy working with, with people that they, you know, thrive when they, when they interact with. And so the cultural fit with, with Pure is fantastic. The other one is the strategic intent that Pure had when they acquired us is still true. And so that goes a long way, you know, in terms of an investment profile, in terms of the ability to kind of leverage assets within the company. So Pure had kind of disrupted the world of storage using Flash and they wanted to disrupt higher up the stack using Kubernetes. And that's kind of been our role inside their strategy. And it's, it's still true. >>So culture, strategic intent. Yeah. Product market fit as well. You were, you weren't just an asset for customers or acquisition and then let the founders go through their next thing. You are part of their growth play. >>Absolutely. Right. The, the beauty of, of the kind of product market fit is, let's talk about the market is we have been always focused on the global two k and that is at the heart of, you know, purest 10,000 strong customer base, right? They have very strong presence in the, in the global two k. And we, we allow them to kind of go to those same folks with, with the offering. >>So satisfying everything that you do. What's for me as a business, whether I'm a financial services organization, I'm a hospital, I'm a retailer, what's in it for me >>As a customer? Yeah. So the, the what's in it for, for me is two things. It's speed and ease of use, which in a way are related. But, but, but you know, one is when something is provided as a service, it's much more consumable. It's instantly ready. It's like instant oatmeal, right? You just get it just adho water and it's there. Yep. So the world of of IT has moved from owning large data centers, right? That used to be like 25 years ago and running those data centers better than everybody else to move to let me just consume a data center in the form of a cloud, right? So satisfying the cloud part of the data center. Now people are saying, well I expect that for software and services and I don't want it just from the public cloud, I want it from my own IT department. >>This is old news. And so the, the, the big news here is how fast Kubernetes has kind of moved everything. You know, you take a lot of these changes, Kubernetes is a poster child for things happening faster than the last wave. And in the last couple of years I would say that as a service model has really kind of thrived in the world of Kubernetes. And developers want to be able to get it fast. And the second thing is they wanna be able to operate it fast. Self-service is the other benefit. Yeah. So speed and self-service are both benefits of, of >>This. Yeah. And, and the thing that's come up clearly in the cube, and this is gonna be part of the headlines, we'll probably end up getting a lot of highlights from telling my team to make a note of this, is that developers are gonna be be the business if you, if you take digital transformation to its conclusion, they're not a department that serves the business, they are the business that means Exactly. They have to be more productive. So developer productivity has been the top story. Yes. Security as a services, all these things. These are, these are examples to make developers more productive. But one of the things that came up and I wanna get your reaction to Yeah. Is, is that when you have disruption and, and the storage vision, you know what disruption it means. Cuz there's been a whole discussion around disruptive operations. When storage goes down, you have back DR. And failover. If there's a disruption that changes the nature of invisible infrastructure, developers want invisible infrastructure. That's the future steady state. So if there's a disruption in storage >>Yeah. It >>Can't affect the productivity and the tool chains and the workflows of developers. Yep. Right? So how do you guys look at that? Cause you're a critical component. Storage is a service, it's a huge thing. Yeah. Storage has to, has to work seamlessly. And let's keep the developers out of the weeds. >>John. I think what, what what you put your finger on is another huge trend in the world of Kubernetes where Atan after all, which is really where, where all the leading practitioners both come and the leading vendors are. So here's the second trend that we are leading and, and actually I think it's happening not just with us, but with other, for folks in the industry. And that is, you know, the world of DevOps. Like DevOps has been such a catchphrase for all of of us in the industry last five years. And it's been both a combination of cultural change as well as technology change. Here's what the latest is on the, in the world of DevOps. DevOps is now crystallized. It's not some kind of mysterious art form that you read about. Okay. How people are practicing. DevOps is, it's broken into two, two things now. >>There is the platform part. So DevOps is now a bunch of platforms. And the other part of DevOps is a bunch of practices. So a little bit on both these, the platforms in the world of es there's only three platforms, right? There's the orchestration platforms, the, you know, eks, the open ships of the world and so on. There are the data management platforms, pro people like Port Works. And the third is security platforms, right? You know, Palo Alto Networks, others Aqua are all in this. So these are the three platforms and there are platform engineering teams now that many of our largest customers, some of the largest banks, the largest service providers, they're all operating as a ES platform engineering team. And then now developers, to your point, developers are in the practice of being able to use these platforms to launch new services. So the, the actual IT ops, the ops are run by developers now and they can do it on these platforms. And the platform engineering team provide that as an ease of use and they're there to troubleshoot when problems happen. So the idea of DevOps as a ops practice and a platform is the newest thing. And, and ports and pure storage leading in the world of data management >>Platforms there. Talk about a customer example that you think really articulates the value that Port Works and Pure Storage delivers from a data management >>Perspective. Yeah, so there's so many examples. One of the, one of the longest running examples we have is a very, very large service provider that, you know, you all know and probably use, and they have been using us in the cable kind of set box or cable box business. They get streams of data from, from cable boxes all over the world. They collected all in a centralized large kind of thing and run elastic search and analytics on it. Now what they have done is they couldn't keep up with this at the scale and the depth, right? The speed of, of activity and the distributed nature of the activity. The only way to solve this was to use something like Kubernetes manage with Spark coming, bringing all the data in into deep, deep, deep silos of storage, which are all running not even on a sand, but on kind of, you know, very deep terabytes and terabytes of, of storage. So all of this is orchestrated with the he of Coworks and there's a platform engineering team. We are building that platform for them, them with some of these other components that allows them to kind of do analytics and, and make some changes in real time. Huge kind of setup for, for >>That. Yeah. Well, you guys have the right architecture. I love the vision. I love what you guys are doing. I think this is right in line with Pures. They've always been disruptors. I remember when we first interviewed the CEO and they started Yep. They, they stayed on path. They didn't waver. EMC was the big player. They ended up taking their lunch and dinner as well and they beat 'em in the marketplace. But now you got this traction here. So I have to ask you, how's the business, what's the results look like? You're a GM cloud native business unit of a storage company that's transformed and transforming. >>Yeah, you know, it's interesting, we just hit the two year anniversary, right John? And so what we did was just kind of like step back and hey to, you know, we're running so hard, you just take a step back and we've tripled the business in the two years since the acquisition, the two years before and, and we were growing through proven. So, you know, that that's quite a fee. And we've tripled the number of people, the amount of engineering investments we have, the number of go to market investments have been, have been awesome. So business is going really well though, I will say. But I think, you know, we have, we can't be, we're watching the market closely. You know, as a former ceo, I, you have to kind of learn to read the tea leaves when you invest. And I think, you know, what I would say is we're proceeding with caution in the next two quarters. I view business transformation as not a cancelable activity. So that's the, that's the good news, right? Our customers are large, >>It's >>Right. Never gonna stop prices, right? All they're gonna do is say, Hey, they're gonna put their hand, their hand was always going right on the dial. Now they're kind of putting their hand on the dial going, hey, where, what is happening? But my, my own sense of this is that people who continue to invest through it, the question is at what level? And I also think that this is a six month kind of watch, the watch where, where we put the dial. So Q4 and q1 I think are kind of, you know, we have our, our watch kind of watch the market sign. But I have the highest confidence. What >>Does your gut tell you? You're an >>Entrepreneur. My, my gut says that we'll go through a little bit of a cautious investment period in the next six months. And after that I think we're gonna be back in, back full, full in the crazy growth that we've always been. Yeah. We're gonna grow by the way, in the next, I think >>It's corn style. I think I'm, I'm more bullish. I think it's gonna be some, you know, weeding out of some overinvestment, pre covid or pre bubble. But I think tech's gonna continue to grow. I don't see >>It's stopping. Yeah. And, and the investment is gonna be on these core platforms. See, back to the platform story, it's gonna be in these lower platforms and on unifying everything, let's consume it better rather than let's go kind of experiment with a whole bunch of things all over the map, right? So you'll see less experimentation and more kind of, let's harvest some of the investments we've made in the last couple >>Of years and actually be able to, to enable companies in, in the industry to truly be data companies because absolutely. We talked about as a service, we all have these expectations that any service we want, we can get it. Yes. There's no delay because patients has gone Yeah. From the pandemic. >>So it is kind of, you know, tightening up the screws on what they've built. They, you know, adding some polish to it, adding some more capability, like I said, a, a a, a combination of harvesting and new investing. It's a combination I think is what we're gonna see. >>Yeah. What are some of the things that you're looking forward to? You talked about some of the, the growth things in the investment, but as we round out Q4 and head into a new year, what are you excited about? >>Yeah, so, you know, I mentioned our, as a service kind of platform. The global two K for us has been a set of customers who we co-create stuff with. And so one of the other set of things that we are very excited about and announcing is because we're deployed at scale, we're, we're, we have upgraded our backend. So we have now the ability to go to million IOPS and more and, and for, for the right backends. And so Kubernetes is a add-on, which will not slow down your, your core base infrastructure. Second thing that that we, we have is added a bunch of capability in the disaster recovery business continuity front, you know, we always had like metro kind of distance Dr. We had long distance dr. We've added a near sync Dr. So now we can provide disaster recovery and business continuity for metro distances across continents and across the planet. Right? That's kind of a major change that we've done. The third thing is we've added the capability for file block and Object. So now by adding object, we're really a complete solution. So it is really that maturity of the business Yeah. That you start seeing as enterprises move to embracing a platform approach, deploying it much more widely. You talked about the early majority. Yeah. Right. And so what they require is more enterprise class capability and those are all the things that we've been adding and we're really looking forward to it. >>Well it sounds like tremendous evolution and maturation of Port Works in the two years since it's been with Pure Storage. You talked about the cultural alignment, Great stuff that you are achieving. Congratulations on that. Great stuff >>Ahead and having fun. Let's not forget that that's too life's too short to do. It is. You're right. >>Right. Thank you. We will definitely, as always on the cube, keep our eyes on this space. Mur. Meley, it's been great to have you back on the program. Thank you for joining, John. >>Great. Thank you so much. It's a pleasure. Our, >>For our guests and John Furrier, Lisa Martin here live in Detroit with the cube about Cob Con Cloud native Con at 22. We'll be back after a short break.

>>Hey everyone, welcome to this cube conversation. I'm your host Lisa Martin, and I have the pleasure of welcoming back our most prolific guest on the cube in its history, the CMO of Fin Ad, Eric Herzog. Eric, it's great to see you. Welcome back, >>Lisa. It's great to be here. Love being on the cube. I think this might be number 55 or 56. Been doing 'em a long time with the Cube. You guys are great. >>You, you have, and we always recognize you lately with the Hawaiian shirts. It's your brand that's, that's the Eric Hizo brand. We love it. But I like the pin, the infin nut pin on brand. Thank you. >>Yeah. Oh, gotta be on brand. >>Exactly. So talk about the current IT landscape. So much change we've seen in the last couple of years. Specifically, what are some of the big challenges that you are talking with enterprise customers and cloud service providers? About what, what are some of those major things on their minds? >>So there's a couple things. First of all is obviously with the Rocky economy and even before covid, just for storage in particular, CIOs hate storage. I've been doing this now since 1986. I have never, ever, ever met a CIO at any company I've bid with. And I've been with four of the biggest storage companies on this planet. Never met a cio. Used to be a storage guy. So they know they need it, but boy, they really don't like it. So the storage admins have to manage more and more storage. Exabytes, exabytes, it just ballooning for what a storage admin has to do. Then you then have the covid and is it recession? No. Is it a growth? And then clearly what's happened in the last year with what's going on in Europe and the, is it a recession, the inflation. So they're always looking to, how do we cut money on storage yet still get what we need for our applications, workloads, and use cases. So that's definitely the biggest, the first topic. >>So never met a CIO that was a storage admin or as a fan, but as you point out, they need it. And we've seen needs changing in customer landscapes, especially as the threat landscape has changed so dramatically the last couple of years. Ransomware, you've said it before, I say it too. It's no longer if it's when it's how often. It's the frequency. We've gotta be able to recover. Backups are being targeted. Talk to me about some of, in that landscape, some of the evolutions of customer challenges and maybe those CIOs going, We've gotta make sure that our, our storage data is protected. >>So it's starting to change. However, historically with the cio and then when they started hiring CISOs or security directors, whatever they had, depending on the company size, it was very much about protecting the edge. Okay, if you will, the moat and the wall of the castle. Then it was the network in between. So keep the streets inside the castle clean. Then it was tracking down the bad guy. So if they did get over, the issue is, if I remember correctly, the sheriff of Nottingham never really caught Robinhood. So the problem is the dwell time where the ransomware malware's hidden on storage could be as much as 200 days. So I think they're starting to realize at the security level now, forget, forget the guys on the storage side, the security guys, the cso, the CIO, are starting to realize that if you're gonna have a comprehensive cybersecurity strategy, must include storage. And that is new >>That, well, that's promising then. That's new. I mean obviously promising given the, the challenges and the circumstances. So then from a storage perspective, customers that are in this multi-cloud hybrid cloud environment, you talked about the the edge cloud on-prem. What are some of the key things from a storage perspective that customers have to achieve these days to be secure as data volumes continue to grow and spread? >>So what we've done is implement on both primary storage and secondary storage and technology called infin safe. So Infin Safe has the four legs of the storage cyber security stool. So first of all is creating an air gap. In this case, a logical air gap can be local or remote. We create an immutable snapshot, which means it can't be changed, it can't be altered, so you can't change it. We have a fenced forensic environment to check out the storage because you don't wanna recover. Again, malware and rans square can is hidden. So you could be making amenable snapshots of actually malware, ransomware, and never know you're doing it right. So you have to check it out. Then you need to do a rapid recovery. The most important thing if you have an attack is how fast can you be up and going with recovery? So we have actually instituted now a number of cyber storage security guarantees. >>We will guarantee the SLAs on a, the snapshot is absolutely immutable. So they know that what they're getting is what they were supposed to be getting. And then also we are guaranteeing recovery times on primary storage. We're guaranteeing recovery of under one minute. We'll make the snapshot available under one minute and on secondary storage under 20 minutes. So those are things you gotta look for from a security perspective. And then the other thing you gotta practice, in my world, ransomware, malware, cyber tech is basically a disaster. So yes, you got the hurricane, yes, you got the flood, yes, you got the earthquake. Yes, you got the fire in the building. Yes you got whatever it may be. But if you don't practice malware, ransomware, recoveries and protection, then it might as well be a hurricane or earthquake. It will take your data, >>It will take your data on the numbers of customers that pay ransom is pretty high, isn't it? And and not necessarily able to recover their data. So it's a huge risk. >>So if you think about it, the government documented that last year, roughly $6 trillion was spent either protecting against ransomware and malware or paying ransomware attacks. And there's been several famous ones. There was one in Korea, 72 million ransom. It was one of the Korea's largest companies. So, and those are only the ones that make the news. Most of 'em don't make the news. Right. >>So talk to me then, speaking and making the news. Nobody wants to do that. We, we know every industry is vulnerable to this. Some of the ones that might be more vulnerable, healthcare, government, public sector education. I think the Los Angeles Unified School district was just hit as well in September. They >>Were >>What, talk to me about how infin out is helping customers really dial down the risk when the threat actors are becoming more and more sophisticated? >>Well, there's a couple things. First of all, our infin safe software comes free on our main product. So we have a product called infin Guard for Secondary Storage and it comes for free on that. And then our primary storage product's called the Infin Box. It also comes for free. So they don't have to use it, but we embed it. And then we have reference architectures that we give them our ses, our solutions architects and our technical advisors all up to speed on why they should do it, how they should do it. We have a number of customers doing it. You know, we're heavily concentrated the global Fortune 2000, for example, we publicly announced that 26% of the Fortune 50 use our technology, even though we're a small company. So we go to extra lengths to a B, educated on our own front, our own teams, and then B, make sure they portray that to the end users and our channel partners. But the end users don't pay a dime for the software that does what I just described, it's free, it's included when you get you're Infin box or you're ingar, it's included at no charge. >>That's pretty differentiating from a competitive standpoint. I might, I would guess >>It is. And also the guarantee. So for example, on primary storage, okay, whether you'd put your Oracle or put your SAP or I Mongo or your sequel or your highly transactional workloads, right? Your business finance workload, all your business critical stuff. We are the first and only storage company that offers a primary guarantee on cyber storage resilience. And we offer two of them on primary storage. No other vendor offers a guarantee, which we do on primary storage. Whether you the first and right now as of here we are sitting in the middle of October. We are still the only vendor that offers anything on primary storage from a guaranteed SLA on primary storage for cyber storage resilience. >>Let's talk about those guarantees. Walk me through what you just announced. There's been a a very, a lot of productivity at Infin DAT in 2022. A lot of things that you've announced but on crack some of the things you're announcing. Sure. Talk to me specifically about those guarantees and what's in it for me as a customer. It sounds pretty obvious, but I'd love to hear it from you. >>Okay, so we've done really three different types of guarantees. The first one is we have a hundred percent availability guarantee on our primary storage. And we've actually had that for the last, since 2019. So it's a hundred percent availability. We're guaranteed no downtime, a hundred percent availability, which for our customer base being heavily concentrated, the global Fortune 2000 large government enterprises, big universities and even smaller companies, we do a lot of business with CSPs and MSPs. In fact, at the Flash Memory Summit are Infin Box ssa All Flash was named the best product for hyperscaler deployment. Hyperscaler basically means cloud servers provider. So they need a hundred percent availability. So we have a guarantee on that. Second guarantee we have is a performance guarantee. We'll do an analysis, we look at all their workloads and then we will guarantee in writing what the performance should be based on which, which of our products they want to buy are Infin Box or Infin Box ssa, which is all flash. >>Then we have the third one is all about cyber resilience. So we have two on our Infin box, our Infin box SSA for primary storage, which is a one the immutability of the snapshot and immediately means you can't erase the data. Right? Camp tamper with it. Second one is on the recovery time, which is under a minute. We just announced in the middle of October that we are doing a similar cyber storage resilience guarantee on our ARD secondary product, which is designed for backup recovery, et cetera. We will also offer the immutably snapshot guarantee and also one on the recoverability of that data in under 20 minutes. In fact, we just did a demo at our live launch earlier this week and we demoed 20 petabytes of Veeam backup data recovered in 12 minutes. 12 >>Minutes 2012. >>20 petabytes In >>12 bytes in 12 minutes. Yes. That's massive. That's massively differentiating. But that's essential for customers cuz you know, in terms of backups and protecting the data, it's all about recovery >>A and once they've had the attack, it's how fast you get back online, right? That that's what happens if they've, if they can't stop the attack, can't stop the threat and it happens. They need to get that back as fast as they can. So we have the speed of recovery on primary stores, the first in the industry and we have speed on the backup software and we'll do the same thing for a backup data set recovery as well. Talk >>To me about the, the what's in it for me, For the cloud service providers, they're obviously the ones that you work with are competing with the hyperscalers. How does the guarantees and the differentiators that Fin out is bringing to market? How do you help those cloud SPS dial up their competitiveness against the big cheeses? >>Well, what we do is we provide that underlying infrastructure. We, first of all, we only sell things that are petabyte in scale. That's like always sell. So for example, on our in fitter guard product, the raw capacity is over four petabytes. And the effective capacity, cuz you do data reduction is over 85 petabytes on our newest announced product, on our primary storage product, we now can do up to 17 petabytes of effective capacity in a single rack. So the value to the service rider is they can save on what slots? Power and floor. A greener data center. Yeah, right. Which by the way is not just about environmentals, but guess what? It also translate into operational expense. >>Exactly. CapEx office, >>With a lot of these very large systems that we offer, you can consolidate multiple products from our competitors. So for example, with one of the competitors, we had a deal that we did last quarter 18 competitive arrays into one of ours. So talk about saving, not just on all of the operational expense, including operational manpower, but actually dramatically on the CapEx. In fact, one of our Fortune 500 customers in the telco space over the last five years have told us on CapEx alone, we've saved them $104 million on CapEx by consolidating smaller technology into our larger systems. And one of the key things we do is everything is automated. So we call it autonomous automation use AI based technology. So once you install it, we've got several public references who said, I haven't touched this thing in three or four years. It automatically configures itself. It automatically adjusts to changes in performance and new apps. When I put in point a new app at it automatically. So in the old days the storage admin would optimize performance for a new application. We don't do that, we automatically do it and autonomously the admin doesn't even click a button. We just sense there's new applications and we automate ourselves and configure ourselves without the admin having to do anything. So that's about saving operational expense as well as operational manpower. >>Absolutely. I was, one of the things that was ringing in my ear was workforce productivity and obviously those storage admins being able to to focus on more strategic projects. Can't believe the CIOs aren't coming around yet. But you said there's, there's a change, there's a wave coming. But if we think about the the, the what's in it for me as a customer, the positive business outcomes that I'm hearing, lower tco, your greener it, which is key. So many customers that we talk to are so focused on sustainability and becoming greener, especially with an on-prem footprint, workforce productivity. Talk about some of the other key business outcomes that you're helping customers achieve and how it helps them to be more competitive. >>Sure. So we've got a, a couple different things. First of all, storage can't go down. When the storage goes down, everyone gets blamed. Mission. When an app goes down, no one really thinks about it. It's always the storage guy's fault. So you want to be a hundred percent available. And that's today's businesses, and I'd actually argue it's been this way for 20 years are 24 by seven by 365. So that's one thing that we deliver. Second thing is performance. So we have public references talk about their SAP workload that used to take two hours, now takes 20 minutes, okay? We have another customer that was doing SAP queries. They improved their performance three times, Not 3%, not 3%, three times. So 300% better performance just by using our storages. They didn't touch the sap, they didn't touch the servers. All they do is to put our storage in there. >>So performance relates basically to applications, workloads and use cases and productivity beyond it. So think the productivity of supply chain guys, logistics guys, the shipping guys, the finance guys, right? All these applications that run today's enterprises. So we can automate all that. And then clearly the cyber threat. Yeah, that is a huge issue. And every CIO is concerned about the cyber threat. And in fact, it was interesting, Fortune magazine did a survey of CEOs, and this was last May, the number one concern, 66% in that may survey was cyber security number one concern. So this is not just a CIO thing, this is a CEO thing and a board level >>Thing. I was gonna say it's at at the board level that the cyber security threats are so real, they're so common. No one wants to be the next headline, like the colonial pipeline, right? Or the school districts or whatnot. And everybody is at risk. So then what you're enabling with what you've just announced, the all the guarantees on the SLAs, the massively fast recovery times, which is critical in cyber recovery. Obviously resilience is is key there. Modern data protection it sounds like to me. How do you define that and and what are customers looking for with respect to modern cyber resilience versus data protection? >>Yeah, so we've got normal data protection because we work with all the backup vendors. Our in ARD is what's known as a purpose built backup appliance. So that allows you to back at a much faster rate. And we work all the big back backup vendors, IBM spectrum Protect, we work with veritas vem com vault, oracle arm, anybody who does backup. So that's more about the regular side, the traditional backup. But the other part of modern data protection is infusing that with the cyber resilience. Cuz cyber resilience is a new thing. Yes, from a storage guy perspective, it hasn't been around a long time. Many of our competitors have almost nothing. One or two of our competitors have a pretty robust, but they don't guarantee it the way we guarantee it. So they're pretty good at it. But the fact that we're willing to put our money where our mouth is, we think says we price stand above and then most of the other guys in the storage industry are just starting to get on the bandwagon of having cyber resilience. >>So that changes what you do from data protection, what would call modern data protection is a combination of traditional backup recovery, et cetera. Now with this influence and this infusion of cybersecurity cyber resilience into a storage environment. And then of course we've also happened to add it on primary storage as well. So whether it's primary storage or backup and archive storage, we make sure you have that right cyber resilience to make it, if you will, modern data protection and diff different from what it, you know, the old backup of your grandfather, father, son backup in tape or however you used to do it. We're well beyond that now we adding this cyber resilience aspect. Well, >>From a cyber resilience perspective, ransomware, malware, cyber attacks are, that's a disaster, right? But traditional disaster recovery tools aren't really built to be able to pull back that data as quickly as it sounds like in Trinidad is able to facilitate. >>Yeah. So one of the things we do is in our reference architectures and written documentation as well as when we do the training, we'd sell the customers you need to practice, if you practice when there's a fire, a flood, a hurricane, an earthquake or whatever is the natural disaster you're practicing that you need to practice malware and ran somewhere. And because our recovery is so rapid and the case of our ingar, our fenced environment to do the testing is actually embedded in it. Several of our competitors, if you want the fenced environment, you have to buy a second product with us. It's all embedded in the one item. So A, that makes it more effective from a CapEx and opex perspective, but it also makes it easier. So we recommend that they do the practice recoveries monthly. Now whether they do it or not separate issue, but at least that's what we're recommending and say, you should be doing this on a monthly basis just like you would practice a disaster, like a hurricane or fire or a flood or an earthquake. Need to be practicing. And I think people are starting to hear it, but they don't still think more about, you know, the flood. Yeah. Or about >>The H, the hurricane. >>Yeah. That's what they think about. They not yet thinking about cybersecurity as really a disaster model. And it is. >>Absolutely. It is. Is is the theme of cyber resilience, as you said, this is a new concept, A lot of folks are talking about it, applying it differently. Is that gonna help dial up those folks just really being much more prepared for that type of cyber disaster? >>Well, we've made it so it's automated. Once you set up the immutable snapshots, it just does its thing. You don't set it and forget it. We create the logical air back. Once you do it, same thing. Set it and forget it. The fence forensic environment, easy to deploy. You do have to just configure it once and then obviously the recovery is almost instantaneous. It's under a minute guaranteed on primary storage and under 20 minutes, like I told you when we did our launch this week, we did 20 petabytes of Veeam backup data in 12 minutes. So that's pretty incredible. That's a lot of data to have recovered in 12 minutes. So the more automated we make it, which is what our real forte is, is this autonomous automation and automating as much as possible and make it easy to configure when you do have to configure. That's what differentiates what we do from our perspective. But overall in the storage industry, it's the recognition finally by the CISOs and the CIOs that, wait a second, maybe storage might be an essential part of my corporate cybersecurity strategy. Yes. Which it has not been historically, >>But you're seeing that change. Yes. >>We're starting to see that change. >>Excellent. So talk to me a little bit before we wrap here about the go to market one. Can folks get their hands on the updates to in kindergar and Finn and Safe and Penta box? >>So all these are available right now. They're available now either through our teams or through our, our channel partners globally. We do about 80% of our business globally through the channel. So whether you talk to us or talk to our channel partners, we're there to help. And again, we put our money where your mouth is with those guarantees, make sure we stand behind our products. >>That's awesome. Eric, thank you so much for joining me on the program. Congratulations on the launch. The the year of productivity just continues for infinit out is basically what I'm hearing. But you're really going in the extra mile for customers to help them ensure that the inevitable cyber attacks, that they, that they're complete storage environment on prem will be protected and more importantly, recoverable Very quickly. We appreciate your insights and your input. >>Great. Absolutely love being on the cube. Thank you very much for having us. Of >>Course. It's great to have you back. We appreciate it. For Eric Herzog, I'm Lisa Martin. You're watching this cube conversation live from Palo Alto.

>>Okay, welcome back. Everyone's Cube's coverage here in Las Vegas for Amazon re Mars machine learning, automation, robotics, and space out. John fir host of the queue. Got a great set of guests here talking about AI, Jason Montgomery CTO and co-founder man and Ryans CEO, founder guys. Thanks for coming on. We're just chatting, lost my train of thought. Cuz we were chatting about something else, your history with DataRobot and, and your backgrounds entrepreneurs. Welcome to the queue. Thanks >>Tur. Thanks for having >>Us. So first, before we get into the conversation, tell me about the company. You guys have a history together, multiple startups, multiple exits. What are you guys working on? Obviously AI is hot here as part of the show. M is Mars machine learning, which we all know is the basis for AI. What's the story. >>Yeah, really. We're we're here for two of the letters and Mars. We're here for the machine learning and the automation part. So at the high level, man is a no code AI application development platform. And basically anybody could log in and start making AI applications. It could be anything from just texting it with the Twilio integration to tell you that you're doing great or that you need to exercise more to integrating with zenes to get support tickets classified. >>So Jason, we were talking too about before he came on camera about the cloud and how you can spin up resources. The data world is coming together and I, and I like to see two flash points. The, I call it the 2010 big data era that began and then failed Hadoop crashed and burned. Yeah. Then out of the, out of the woodwork came data robots and the data stacks and the snowflakes >>Data break snowflake. >>And now you have that world coming back at scale. So we're now seeing a huge era of, I need to stand up infrastructure and platform to do all this heavy lifting. I don't have time to do. Right. That sounds like what you guys are doing. Is that kind of the case? >>That's absolutely correct. Yeah. Typically you would have to hire a whole team. It would take you months to sort of get the infrastructure automation in place, the dev ops DevOps pipelines together. And to do the automation to spin up, spin down, scale up scale down requires a lot of special expertise with, you know, Kubernetes. Yeah. And a lot of the other data pipelines and a lot of the AWS technologies. So we automate a lot of that. So >>If, if DevOps did what they did, infrastructure has code. Yeah. Data has code. This is kind of like that. It's not data ops per se. Is there a category? How do you see this? Cuz it's you could say data ops, but that's also it's DevOps dev. It's a lot going on. Oh yeah. It's not just seeing AI ops, right? There's a lot more, what, what would you call this? >>It's a good question. I don't know if we've quite come up with the name. I know >>It's not data ops. It's not >>Like we call it AI process automation >>SSPA instead of RPA, >>What RPA promised to be. Yes, >>Exactly. But what's the challenge. The number one problem is it's I would say not, not so much all on ever on undifferent heavy lifting. It's a lot of heavy lifting that for sure. Yes. What's involved. What's the consequences of not going this way. If I want to do it myself, can you take me through the, the pros and cons of what the scale scope, the scale of without you guys? >>Yeah. Historically you needed to curate all your data, bring it together and have some sort of data lake or something like that. And then you had to do really a lot of feature engineering and a lot of other sort of data science on the back end and automate the whole thing and deploy it and get it out there. It's a, it's a pretty rigorous and, and challenging problem that, you know, we there's a lot of automation platforms for, but they typically focus on data scientists with these large language models we're using they're pre-trained. So you've sort of taken out that whole first step of all that data collection to start out and you can basically start prototyping almost instantly because they've already got like 6 billion parameters, 10 billion parameters in them. They understand the human language really well. And a lot of other problems. I dunno if you have anything you wanna add to that, Ryan, but >>Yeah, I think the other part is we deal with a lot of organizations that don't have big it teams. Yeah. And it would be impossible quite frankly, for them to ever do something like deploy text, track as an example. Yeah. They're just not gonna do it, but now they can come to us. They know the problem they want solved. They know that they have all these invoices as an example and they wanna run it through a text track. And now with us they can just drag and drop and say, yeah, we want tech extract. Then we wanted to go through this. This is what we >>Want. Expertise is a huge problem. And the fact that it's changing too, right? Yeah. Put that out there. You guys say, you know, cybersecurity challenges. We guys do have a background on that. So you know, all the cutting edge. So this just seems to be this it, I hate to say transformation. Cause I not the word I'm looking for, I'd say stuck in the mud kind of scenario where they can't, they have to get bigger, faster. Yeah. And the scale is bigger and they don't have the people to do it. So you're seeing the rise of managed service. You mentioned Kubernetes, right? I know this young 21 year old kid, he's got a great business. He runs a managed service. Yep. Just for Kubernetes. Why? Because no, one's there to stand up the clusters. >>Yeah. >>It's a big gap. >>So this, you have these sets of services coming in now, where, where do you guys fit into that conversation? If I'm the customer? My problem is what, what is my, what is my problem that I need you guys for? What does it look like to describe my problem? >>Typically you actually, you, you kind of know that your employees are spending a lot of time, a lot of hours. So I'll just give you a real example. We have a customer that they were spending 60 hours a week just reviewing these accounts, payable, invoices, 60 hours a week on that. And they knew there had to be a better way. So manual review manual, like when we got their data, they were showing us these invoices and they had to have their people circle the total on the invoice, highlight the customer name, the >>Person who quit the next day. Right? >>No like they, they, Hey, you know, they had four people doing this, I think. And the point is, is they come to us and we say, well, you know, AI can, can just basically using something like text track can just do this. And then we can enrich those outputs from text track with the AI. So that's where the transformers come in. And when we showed them that and got them up and running in about 30 minutes, they were mind blown. Yeah. And now this is a company that doesn't have a big it department. So the >>Kind, and they had the ability to quantify the problem >>They knew. And, and in this case it was actually a business user. It was not a technical >>In is our she consequence technical it's hours. She consequences that's wasted. Manual, labor wasted. >>Exactly. Yeah. And, and to their point, it was look, we have way more high, valuable tasks that our people could be doing yeah. Than doing this AP thing. It takes 60 hours. And I think that's really important to remember about AI. What're I don't think it's gonna automate away people's jobs. Yeah. What it's going to do is it's going to free us up to focus on what really matters and focus on the high value stuff. And that's what people should >>Be doing. I know it's a cliche. I'm gonna say it again. Cause I keep saying, cause I keep saying for people to listen, the bank teller argument always was the big thing. Oh yeah. They're gonna get killed by the ATM machine. No, they're opening up more branches. That's right. That's right. So it's like, come on. People let's get, get over that. So I, I definitely agree with that. Then the question, next question is what's your secret sauce? I'm the customer I'm gonna like that value proposition. You make something go away. It's a pain relief. Then there's the growth side. Okay. You can solve from problems. Now I want this, the, the vitamin you got aspirin. And I want the vitamin. What's the growth angle for you guys with your customers. What's the big learnings. Once they get the beach head with problem solving. >>I think it, it, it it's the big one is let's say that we start with the account payable thing because it's so our platform's so approachable. They go in and then they start tinkering with the initial, we'll call it a template. So they might say, Hey, you know what, actually, in this edge case, I'm gonna play with this. And not only do I want it to go to our accounting system, but if it's this edge case, I want it to email me. So they'll just drag and drop an email block into our canvas. And now they're making it >>Their own. There is the no code, low code's situation. They're essentially building a notification engine under the covers. They have no idea what they're doing. That's >>Right. They get the, they just know that, Hey, you know what? When, when like the amount's over $10,000, I want an email. They know that's what they want. They don't, they don't know that's the notification engine. Of >>Course that's value email. Exactly. I get what I wanted. All right. So tell me about the secret sauce. What's under the covers. What's the big, big, big scale, valuable, valuable, secret sauce. >>I would say part of it. And, and honestly, the reason that we're able to do this now is transformer architecture. When the transformer papers came out and then of course the attention is all you need paper, those kind of unlocked it and made this all possible. Beyond that. I think the other secret sauce we've been doing this a long time. >>So we kind of, we know we're in the paid points. We went to those band points. Cause we weren't data scientists or ML people. >>Yeah. >>Yeah. You, you walked the snow and no shoes on in the winter. That's right. These kids now got boots on. They're all happy. You've installed machines. You've loaded OSS on, on top of rack switches. Yeah. I mean, it's unbelievable how awesome it's right now to be a developer and now a business user's doing the low code. Yep. If you have the system architecture set up, so back to the data engineering side, you guys had the experience got you here. This is a big discussion right now. We're having in, in, on the cube and many conversations like the server market, you had that go away through Amazon and Google was one of the first, obviously the board, but the idea that servers could be everywhere. So the SRE role came out the site reliability engineer, right. Which was one guy or gal and zillions of servers. Now you're seeing the same kind of role with data engineering. And then there's not a lot of people that fit the requirement of being a data engineer. It's like, yeah, it's very unique. Cause you're dealing with a system architecture, not data science. So start to see the role of this, this, this new persona, because they're taking on all the manual challenges of doing that. You guys are kind of replaced that I think. Well, do you agree with it about the data engineer? First of all? >>I think, yeah. Well and it's different cuz there's the older data engineer and then there's sort of the newer cloud aware one who knows how to use all the cloud technologies. And so when you're trying, we've tried to hire some of those and it's like, okay, you're really familiar with old database technology, but can you orchestrate that in a serverless environment with a lot of AWS technology for instance. And it's, and that's hard though. They don't, they don't, there's not a lot of people who know that space, >>So there's no real curriculum out there. That's gonna teach you how to handle, you know, ETL. And also like I got I'm on stream data from this source. Right. I'm using sequel I'm I got put all together. >>Yeah. So it's yeah, it's a lot of just not >>Data science. It's >>Figure that out. So its a large language models too. We don't have to worry about some of the data there too. It's it's already, you know, codified in the model. And then as we collect data, as people use our platform, they can then curate data. They want to annotate or enrich the model with so that it works better as it goes. So we're kind of curating, collecting the data as it's used. So as it evolves, it just gets better. >>Well, you guys obviously have a lot of experience together and congratulations on the venture. Thank you. What's going on here at re Mars. Why are you here? What's the pitch. What's the story. Where's your, you got two letters. You got the, you got the M for the machine learning and AI and you got the, a for automation. What's the ecosystem here for you? What are you doing? >>Well, I mean, I think you, you kind of said it right. We're here because the machine learning and the automation part, >>But >>More, more widely than that. I mean we work very, very closely with Amazon on a number of front things like text track, transcribe Alexa, basically all these AWS services are just integrations within our system. So you might want to hook up your AI to an Alexa so that you could say, Hey Alexa, tell me updates about my LinkedIn feed. I don't know, whatever, whatever your hearts content >>Is. Well what about this cube transcription? >>Yeah, exactly. A hundred percent. >>Yeah. We could do that. You know, feed all this in there and then we could do summarization of everything >>Here, >>Q and a extraction >>And say, Hey, these guys are >>Technicals. Yeah, >>There you go. No, they mentioned Kubernetes. We didn't say serverless chef puppet. Those are words straight, you know, and no linguistics matters right into that's a service that no one's ever gonna build. >>Well, and actually on that point, really interesting. We work with some healthcare companies and when you're basically, when people call in and they call into the insurance, they have a question about their, what like is this gonna be covered? And what they want to key in on are things like I just went to my doctor and got a cancer diagnosis. So the, the, the relevant thing here is they just got this diagnosis. And why is that important? Well, because if you just got a diagnosis, they want to start a certain triage to make you successful with your treatments. Because obviously there's an >>Incentive to do time. That time series matters and, and data exactly. And machine learning reacts to it. But also it could be fed back old data. It used to be time series to store it. Yeah. But now you could reuse it to see how to make the machine learning better. Are you guys doing anything, anything around that, how to make that machine learning smarter, look doing look backs or maybe not the right word, but because you have data, I might as well look back at it's happened. >>So part of, part of our platform and part of what we do is as people use these applications, to your point, there's lots of data that's getting generated, but we capture all that. And that becomes now a labeled data set within our platform. And you can take that label data set and do something called fine tuning, which just makes the underlying model more and more yours. It's proprietary. The more you do it. And it's more accurate. Usually the more you do it. >>So yeah, we keep all that. I wanna ask your reaction on this is a good point. The competitive advantage in the intellectual property is gonna be the workflows. And so the data is the IP. If this refinement happens, that becomes intellectual property. Yeah. That's kind of not software. It's the data modeling. It's the data itself is worth something. Are you guys seeing that? >>Yeah. And actually how we position the company is man team is a control plane and you retain ownership of the data plane. So it is your intellectual property. Yeah. It's in your system, it's in your AWS environment. >>That's not what everyone else is doing. Everyone wants to be the control plane and the data plan. We >>Don't wanna own your data. We don't, it's a compliance and security nightmare. Yeah. >>Let's be, Real's the question. What do you optimize for? Great. And I think that's a fair, a fair bet. Given the fact that clients want to be more agile with their data anyway, and the more restrictions you put on them, why would that this only gets you in trouble? Yeah. I could see that being a and plus lock. In's gonna be a huge factor. Yeah. I think this is coming fast and no one's talking about it in the press, but everyone's like run to silos, be a silo and that's not how data works. No. So the question is how do you create siloing of data for say domain specific applications while maintaining a horizontally scalable data plan or control plan that seems to be kind of disconnected everyone to lock in their data. What do you guys think about that? This industry transition we're in now because it seems people are reverting back to fourth grade, right. And to, you know, back to silos. >>Yeah. I think, well, I think the companies probably want their silo of data, their IP. And so as they refine their models and, and we give them the ability to deploy it in their own stage maker and their own VPC, they, they retain and own it. They can actually get rid of us and they still have that model. Now they may have to build, you know, a lot of pipelines and other technology to support it. But well, >>Your lock in is usability. Exactly. And value. Yeah. Value proposition is the lock in bingo. That's not counterintuitive. Exactly. Yeah. You say, Hey, more value. How do I wanna get rid of it? Valuable. I'll pay for it. Right. As long as you have multiple value, step up. And that's what cloud does. I mean, think that's the thing about cloud. That's gonna make all this work. In my opinion, the value enablement is much higher. Yeah. So good business model. Anything else here at the show that you observed that you like, that you think people would be interested in? What's the most important story coming out of the, the holistic, if you zoom up and look at re Mars, what's, what's coming out of the vibe. >>You know, one thing that I think about a lot is we're, you know, we have Artis here, humanity hopefully soon gonna be going to Mars. And I think that's really, really exciting. And I also think when we go to Mars, we're probably not gonna send a bunch of software engineers up there. >>Right. So like robots will do break fix now. So, you know, we're good. It's gone. So services are gonna be easy. >>Yeah. But I, oh, >>I left that device back at earth. I just think that's not gonna be good. Just >>Replicated it in one. I think there's like an eight >>Minute, the first monopoly on next day delivery in space. >>They'll just have a spaceship that sends out drones to Barss. Yeah. But I think that when we start going back to the moon and we go to Mars, people are gonna think, Hey, I need this application now to solve this problem that I didn't anticipate having. And in science fiction, we kind of saw this with like how, right? Like you had this AI on this computer or this, on this spaceship that could do all this stuff. We need that. And I haven't seen that here yet. >>No, it's not >>Here yet. And >>It's right now I think getting the hardware right first. Yep. But we did a lot of reporting on this with the D O D and the tactile edge, you know, military applications. It's a fundamental, I won't say it's a tech, religious argument. Like, do you believe in agile realtime data or do you believe in democratizing multi-vendor, you know, capability? I think, I think the interesting needs to sort itself out because sometimes multi vendor multi-cloud might not work for an application that needs this database or this application at the edge. >>Right. >>You know, so if you're in space, the back haul, it matters. >>It really does. Yeah. >>Yeah. Not a good time to go back and get that highly available data. You mean highly, is it highly available or there's two terms highly available, which means real time and available. Yeah. Available means it's on a dis, right? >>Yeah. >>So that's a big challenge. Well guys, thanks for coming on. Plug for the company. What are you guys up to? How much funding do you have? How old are you staff hiring? What's some of the details. >>We're about 45 people right now. We are a globally distributed team. So we hire every like from every country, pretty much we are fully remote. So if you're looking for that, hit us up, definitely always look for engineers, looking for more data scientists. We're very, very well funded as well. And yeah. So >>You guys headquarters out, you guys headquartered. >>So a lot of us live in Columbus, Ohio that's technically HQ, but like I said, we we're in pretty much every continent except in Antarctica. So >>You're for all virtual. >>Yeah. A hundred percent virtual, a hundred percent. >>Got it. Well, congratulations and love to hear that Datadog story at another time >>Or DataBot >>Yeah. I mean data, DataBot sorry. Let's get, get all confused >>Data dog data company. >>Well, thanks for coming on and congratulations for your success and thanks for sharing. Yeah. >>Thanks for having us for having >>Pleasure to be here. It's a cube here at rebars. I'm John furier host. Thanks for watching more coming back after this short break.

(upbeat music) >> Lately Infinidat has been on a bit of a Super cycle of product announcements. Adding features, capabilities, and innovations to its core platform that are applied across its growing install base. CEO, Phil Bollinger has brought in new management and really emphasized a strong and consistent cadence of product releases, a hallmark of successful storage companies. And one of those new executives is a CMO with a proven product chops, who seems to bring an energy and an acceleration of product output, wherever he lands. Eric Herzog joins us on "theCUBE". Hey, man. Great to see you. Awesome to have you again. >> Dave. Thank you. And of course, for "theCUBE", of course, I had to put on a Hawaiian shirt as always. >> They're back. All right, I love it.(laughs) Watch out for those Hawaiian shirt police, Eric. (both laughing) All right. I want to have you start by. Maybe you can make some comments on the portfolio over the past year. You heard my intro, InfiniBox is the core, the InfiniBox SSA, which announced last year. InfiniGuard you made some substantial updates in February of this year. Real focus on cyber resilience, which we're going to talk about with Infinidat. Give us the overview. >> Sure. Well, what we've got is it started really 11 years ago with the InfiniBox. High end enterprise solution, hybrid oriented really incredible magic fairy dust around the software and all the software technology. So for example, the Neural Cache technology, which has multiple patents on it, allowed the original InfiniBox to outperform probably 85% of the All-Flash Arrays in the industry. And it still does that today. We also of course, had our real, incredible ease-of-use the whole point of the way it was configured and set up from the beginning, which we continued to make sure we do is if you will a set it and forget it model. For example, When you install, you don't create lungs and raid groups and volumes it automatically and autonomously configures. And when you add new solutions, AKA additional applications or additional servers and point it at the InfiniBox. It automatically, again in autonomously, adjust to those new applications learning what it needs to configure everything. So you're not setting cash size and Q depth, or Stripes size, anything you would performance to you don't have to do any of that. So that entire set of software is on the InfiniBox. The InfiniBox SSA II, which we're of course launching today and then inside of the InfiniGuard platform, there's a actually an InfiniBox. So the commonality of snapshots replication, ease of use. All of that is identical across the platform of all-flash array, hybrid array and purpose-built backup secondary storage and no other vendor has that breadth of product that has the same exact software. Some make a similar GUI, but we're talking literally the same exact software. So once you learn it, all three platforms, even if you don't have them, you could easily buy one of the other platforms that you don't have yet. And once you've got it, you already know how to use it. 'Cause you've had one platform to start as an example. So really easy to use from a customer perspective. >> So ever since I've been following the storage business, which has been a long time now, three things that customers want. They want something that is rock solid, dirt cheap and super fast. So performance is something that you guys have always emphasized. I've had some really interesting discussions over the years with Infinidat folks. How do you get performance? If you're using this kind of architecture, it's been quite amazing. But how does this launch extend or affect performance? Why the focus on performance from your standpoint? >> Well, we've done a number of different things to bolster the performance. We've already been industry-leading performance again. The regular InfiniBox outperforms 80, 85% of the All-Flash Arrays. Then, when the announcement of the InfiniBox SSA our first all-flash a year ago, we took that now to the highest demanding workloads and applications in the industry. So what did it add to the super high end Oracle app or SAP or some custom app that someone's created with Mongo or Cassandra. We can absolutely meet the performance between either the InfiniBox or the InfiniBox all-flash with the InfiniBox SSA. However, we've decided to extend the performance even farther. So we added a whole bunch of new CPU cores into our tri part configuration. So we don't have two array controllers like many companies do. We actually have three everything's in threes, which gives us the capability of having our 100% availability guarantee. So we've extended that now we've optimized. We put a additional InfiniBand interconnects between the controllers, we've added the CPU core, we've taken if you will the InfiniBox operating system, Neural Cache and everything else we've had. And what we have done is we have optimized that to take advantage of all those additional cores. This has led us to increase performance in all aspects, IOPS bandwidth and in fact in latency. In latency we now are at 35 mikes of latency. Real world, not a hero number, but real-world on an array. And when you look end to end, if I Mr. Oracle, or SAP sitting in the server and I'll look across that bridge, of course the sand and over to the other building the storage building that entire traversing can be as fast as a 100 microseconds of latency across the entire configuration, not just the storage. >> Yeah. I think that's best in class for an external array. Well, so what's the spectrum you can now hit with the performance ranges. Can you hit all the aspects of the market with the two InfiniBoxes, your original, and then the SSA? >> Yes, even with the original SSA. In fact, we've had one of our end users, who's been first InfiniBox customer, then InfiniBox SSA actually has been running for the last two months. A better version of the SSA II. So they've had a better version and this customer's running high end Oracle rack configurations. So they decided, you know what? We're not going to run storage benchmarks. We're going to run only Oracle benchmarks. And in every benchmark IOPS, latency and bandwidth oriented, we outperformed the next nearest competition. So for example, 57% faster in IOPS, 58% faster in bandwidth and on the latency side using real-world Oracle apps, we were three times better performance on the latency aspect, which of course for a high end high performance workload, that's heavily transactional. Latency is the most important, but when you look across all three of those aspects dramatically outperform. And by the way, that was a beta unit that didn't of course have final code on it yet. So incredible performance angle with the InfiniBox SSA II. >> So I mean you earlier, you were talking about the ease of use. You don't have to provision lungs and all that sort of nonsense, and you've always emphasized ease-of-use. Can you double click on that a little bit? How do you think about that capability? And I'm really interested in why you think it's different from other vendors? >> Well, we make sure that, for example, when you install you don't have to do anything, you have to rack and stack, yes and cable. And of course, point the servers at the storage, but the storage just basically comes up. In fact, we have a customer and it's a public reference that bought a couple units many years ago and they said they were up and going in about two hours. So how many high-end enterprise storage array can be up and going in two hours? Almost I mean, basically nobody about us. So we wanted to make sure that we maintain that when we have customers, one of our big plays, particularly helping with CapEx and OpEx is because we are so performant. We can consolidate, we have a large customer in Europe that took 57 arrays from one of our competitors and consolidate it to five of the original InfiniBox. 57 to 5. They saved about $25 million in capital expense and they're saving about a million and a half a year in operational expense. But the whole point was as they kept adding more and more servers that were connected to those competitive arrays and pointing them at the InfiniBox, there's no performance tuning. Again, that's all ease-of-use, not only saving on operational expense, but obviously as we know, the headcount for storage admins is way down from its peak, which was probably in 2007. Yet every admin is managing what 25 to 50 times the amount of storage between 2007 and 2022. So the reality is the easier it is to use. Not only does of course the CIO love it because both the two of us together probably been storage, doing storage now for close to 80 years would be my guess I've been doing it for 40. You're a little younger. So maybe we're at 75 to 78. Have you ever met a CIO used to be a storage admin ever? >> No. >> And I can't think of one either so guess what? The easier it is to use the CIOs know that they need storage. They don't like it. They're all these days are all software guys. There used to be some mainframe guys in the old days, but they're long gone too. It's all about software. So when you say, not only can we help reduce your CapEx at OpEx, but the operational manpower to run the storage, we can dramatically reduce that because of our ease-of-use that they get and ease-of-use has been a theme on the software side ever since the Mac came out. I mean, Windows used to be a dog. Now it's easy to use and you know, every time the Linux distribution come out, someone's got something that's easier and easier to use. So, the fact that the storage is easy to use, you can turn that directly into, we can help you save on operational manpower and OPEX and CIOs. Again, none of which ever met are storage guys. They love that message. Of course the admins do too 'cause they're managing 25 to 50 times more storage than they had to manage back in 2007. So the easier it is for them at the tactical level, the storage admin, the storage manager, it's a huge deal. And we've made sure we've maintained that as you've added the SSA, as we brought up the InfiniGuard, as we've continue to push new feature function. We always make it easy to use. >> Yeah. Kind of a follow up on that. Just focus on software. I mean, I would think every storage company today, every modern storage company is going to have more software engineers than hardware engineers. And I think Infinidat obviously is no different. You got a strong set of software, it's across the portfolio. It's all included kind of thing. I wonder if you could talk about your software approach and how that is different from your competitors? >> Sure, so we started out 11 years ago when in Infinidat first got started. That was all about commodity hardware. So while some people will use custom this and custom that, yeah and I having worked at two of the biggest storage companies in the world before I came here. Yes, I know it's heavily software, but our percentage of hardware engines, softwares is even less hardware engineering than our competitors have. So we've had that model, which is why this whole what we call the set it and forget it mantra of ease-of-use is critical. We make sure that we've expanded that. For example, we're announcing today, our InfiniOps focus and Infini Ops all software allows us to do AIOps both inside of our storage system with our InfiniVerse and InfiniMetrics packages. They're easy to use. They come pre-installed and they manage capacity performance. We also now have heavy integration with AI, what I'll call data center, AIOps vendors, Vetana ServiceNow, VMware and others. And in that case, we make sure that we expose all of our information out to those AIOps data center apps so that they can report on the storage level. So we've made sure we do that. We have incredible support for the Ansible framework again, which is not only a software statement, but an ease-of-use statement as well. So for the Ansible framework, which is trying to allow an even simpler methodology for infrastructure deployment in companies. We support that extensively and we added some new features. Some more, if you will, what I'll say are more scripts, but they're not really scripts that Ansible hides all that. And we added more of that, whether that be configuration installations, that a DevOps guy, which of course just had all the storage guys listening to this video, have a heart attack, but the DevOps guy could actually configure storage. And I guess for my storage buddies, they can do it without messing up your storage. And that's what Ansible delivers. So between our AIOps focus and what we're doing with InfiniOps, that extends of course this ease-of-use model that we've had and includes that. And all this again, including we already talked about a little bit cyber resilience Dave, within InfiniSafe. All this is included when you buy it. So we don't piecemeal, which is you get this and then we try to upcharge you for that. We have the incredible pricing that delivers this CapEx and an OpEx. Not just for the array, but for the associated software that goes with it, whether that be Neural Cache, the ease-of-use, the InfiniOps, InfiniSafes. You get all of that package together in the way we deploy from a business now perspective, ease of doing business. You don't cut POS for all kinds of pieces. You cut APO and you just get all the pieces on the one PO when we deliver it. >> I was talking yesterday to a VC and we were chatting about AI And of course, everybody's chasing AI. It's a lot of investments go in there, but the reality is, AI is like containers. It's just getting absorbed into virtually every thing. And of course, last year you guys made a pretty robust splash into AIOps. And then with this launch, you're extending that pretty substantially. Tell us a little bit more about the InfiniOps announcement news. >> So the InfiniOps includes our existing in the box framework InfiniVerse and what we do there, by the way, InfiniVerse has the capability with the telemetry feed. That's how we could able to demo at our demo today and also at our demo for our channel partner pre-briefing. Again a hundred mics of latency across the entire configuration, not just to a hundred mics of latency on storage, which by the way, several of our competitors talk about a hundred mics of latency as their quote hero number. We're talking about a hundred mics of latency from the application through the server, through the SAN and out to the storage. Now that is incredible. But the monitoring for that is part of the InfiniOps packaging, okay. We support again with DevOps with all the integration that we do, make it easy for the DevOps team, such as with Ansible. Making sure for the data center people with our integration, with things like VMware and ServiceNow. The data center people who are obviously often not the storage centric person can also be managing the entire data center. And whether that is conversing with the storage admin on, we need this or that, or whether they're doing it themselves again, all that is part of our InfiniOps framework and we include things like the Ansible support as part of that. So InfiniOps is sort of an overarching theme and then overarching thing extends to AIops inside of the storage system. AIops across the data center and even integration with I'll say something that's not even considered an infrastructure play, but something like Ansible, which is clearly a red hat, software oriented framework that incorporates storage systems and servers or networks in the capability of having DevOps people manage them. And quite honestly have the DevOps people manage them without screwing them up or losing data or losing configuration, which of course the server guys, the network guys and the storage guys hate when the DevOps guys play with it. But that integration with Ansible is part of our InfiniOps strategy. >> Now our shift gears a little bit talk about cyber crime and I mean, it's a topic that we've been on for a long time. I've personally been writing about it now for the last few years. Periodically with my colleagues from ETR, we hit that pretty hard. It's top of mind, and now the house just approved what's called the Better Cybercrime Metrics Act. It was a bipartisan push. I mean, the vote was like 377 to 48 and the Senate approved this bill last year. Once president Biden signs it, it's going to be the law's going to be put into effect and you and many others have been active in this space Infinidat. You announced cyber resilience on your purpose bill backup appliance and secondary storage solution, InfiniGuard with the launch of InfiniSafe. What are you doing for primary storage from InfiniBox around cyber resilience? >> So the goal between the InfiniGuard and secondary storage and the InfiniBox and the InfiniBox SSA II, we're launching it now, but the InfiniSafe for InfiniBox will work on the original InfiniBox. It's a software only thing. So there's no extra hardware needed. So it's a software only play. So if you have an InfiniBox today, when you upgrade to the latest software, you can have the InfiniSafe reference architecture available to you. And the idea is to support the four key legs of the cybersecurity table from a storage perspective. When you look at it from a storage perspective, there's really four key things that the CISO and the CIO look for first is a mutable snapshot technology. An article can't be deleted, right? You can schedule it. You can do all kinds of different things, but the point is you can't get rid of it. Second thing of course, is an air gap. And there's two types of air gap, logical air gap, which is what we provide and physical the main physical air gaping would be either to tape or to course what's left of the optical storage market. But we've got a nice logical air gap and we can even do that logical air gaping remotely. Since most customers often buy for disaster recovery purposes, multiple arrays. We can then put that air gap, not just locally, but we can put the air gap of course remotely, which is a critical differentiator for the InfiniBox a remote logical air gap. Many other players have logical, we're logical local, but we're going remote. And then of course the third aspect is a fenced forensic environment. That fence forensic environment needs to be easily set up. So you can determine a known good copy to a restoration after you've had a cyber incident. And then lastly is rapid recovery. And we really pride ourself on this. When you go to our most recent launch in February of the InfiniGuard within InfiniSafe, we were able to demo live a recovery taking 12 minutes and 12 seconds of 1.5 petabytes of backup data from Veeam. Now that could have been any backup data. Convolt IBM spectrum tech Veritas. We happen to show with Veeam, but in 12 minutes and 12 seconds. Now on the primary storage side, depending on whether you're going to try to recover locally or do it from a remote, but if it's local, we're looking at something that's going to be 1 to 2 minutes recovery, because the way we do our snapshot technology, how we just need to rebuild the metadata tree and boom, you can recover. So that's a real differentiator, but those are four things that a CISO and a CIO look for from a storage vendor is this imutable snapshot capability, the air gaping capability, the fenced environment capability. And of course this near instantaneous recovery, which we have proven out well with the InfiniGuard. And now with the InfiniBox SSA II and our InfiniBox platform, we can make that recovery on primary storage, even faster than what we have been able to show customers with the InfiniGuard on the secondary data sets and backup data sets. >> Yeah. I love the four layer cake. I just want to clarify something on the air gap if I could so you got. You got a local air gap. You can do a remote air gap with your physical storage. And then you're saying there's I think, I'm not sure I directly heard that, but then the next layer is going to be tape with the CTA, the Chevy truck access method, right? >> Well, so while we don't actively support tape and go to that there's basically two air gap solutions out there that people talk about either physical, which goes to tape or optical or logical. We do logical air gaping. We don't do air gaping to tape 'cause we don't sell tape. So we make sure that it's a remote logical air gap going to a secondary DR Site. Now, obviously in today's world, no one has a true DR data center anymore, right. All data centers are both active and DR for another site. And because we're so heavily concentrated in the global Fortune 2000, almost all the InfiniBoxes in the field already are set up as in a disaster recovery configuration. So using a remote logical air gap would be is easy for us to do with our InfiniBox SSA II and the whole InfiniBox family. >> And, I get, you guys don't do tape, but when you say remote, so you've got a local air gap, right? But then you also you call a remote logical, but you've got a physical air gap, right? >> Yeah, they would be physically separated, but when you're not going to tape because it's fully removable or optical, then the security analysts consider that type of air gap, a logical air gap, even though it's physically at a remote. >> I understand, you spent a lot of time with the channel as well. I know, and they must be all over this. They must really be climbing on to the whole cyber resiliency. What do you say, do they set up? Like a lot of the guys, doing managed services as well? I'm just curious. Are there separate processes for the air gap piece than there are for the mainstream production environment or is it sort of blended together? How are they approaching that? >> So on the InfiniGuard product line, it's blended together, okay. On the InfiniBox with our InfiniSafe reference architecture, you do need to have an extra server where you create an scuzzy private VLAN and with that private VLAN, you set up your fenced forensic environment. So it's a slightly more complicated. The InfiniGuard is a 100% automated. On the InfiniBox we will be pushing that in the future and we will continue to have releases on InfiniSafe and making more and more automated. But the air gaping and the fence reference now are as a reference architecture configuration. Not with click on a gooey in the InfiniGuard case are original InfiniSafe. All you do is click on some windows and it just goes does. And we're not there yet, but we will be there in the future. But it's such a top of mind topic, as you probably see. Last year, Fortune did a survey of the Fortune 500 CEOs and the number one cited threat at 66% by the way was cybersecurity. So one of the key things store storage vendors do not just us, but all storage vendors is need to convince the CISO that storage is a critical component of a comprehensive cybersecurity strategy. And by having these four things, the rapid recovery, the fenced forensic environment, the air gaping technology and the immutable snapshots. You've got all of the checkbox items that a CISO needs to see to make sure. That said many CISOs still even today stood on real to a comprehensive cybersecurity strategy and that's something that the storage industry in general needs to work on with the security community from a partner perspective. The value is they can sell a full package, so they can go to their end user and say, look, here's what we have for edge protection. Here's what we've got to track the bad guide down once something's happened or to alert you that something's happened by having tools like IBM's, Q Radar and competitive tools to that product line. That can traverse the servers and the software infrastructure, and try to locate malware, ransomware akin to the way all of us have Norton or something like Norton on our laptop that is trolling constantly for viruses. So that's sort of software and then of course storage. And those are the elements that you really need to have an overall cybersecurity strategy. Right now many companies have not realized that storage is critical. When you think about it. When you talk to people in security industry, and I know you do from original insertion intrusion to solution is 287 days. Well guess what if the data sets thereafter, whether it be secondary InfiniGuard or primary within InfiniBox, if they're going to trap those things and they're going to take it. They might have trapped those few data sets at day 50, even though you don't even launch the attack until day 200. So it's a big deal of why storage is so critical and why CISOs and CIOs need to make sure they include it day one. >> It's where the data lives, okay. Eric. Wow.. A lot of topics we discovered. I love the agile sort of cadence. I presume you're not done for the year. Look forward to having you back and thanks so much for coming on today. >> Great. Thanks you, Dave. We of course love being on "theCUBE". Thanks again. And thanks for all the nice things about Infinidat. You've been saying thank you. >> Okay. Yeah, thank you for watching this cube conversation. This is Dave Vellante and we'll see you next time. (upbeat music)

>>mhm. >>With me now are Manish Agarwal, senior director of product management for Hyper Flex at Cisco at Flash for all number four. Love that on Twitter And Deron Williams, the director of business development and sales for Cisco Mister Hyper flex at Mr Hyper Flex on Twitter. Thanks, guys. Hey, we're going to talk about some news and and hyper flex and what role it plays in accelerating the hybrid cloud journey. Gentlemen, welcome to the Cube. Good to see you. >>Thanks, David. >>Thanks. Hi, >>Daryn. Let's start with you. So for hybrid cloud you gotta have on Prem Connection. Right? So you've got to have basically a private cloud. What are your thoughts on that? >>Yeah, we agree. You can't, but you can't have a hybrid cloud without that private element. And you've got to have a strong foundation in terms of how you set up the whole benefit of the cloud model you're building in terms of what you want to try and get back from the cloud, you need a strong foundation. I'm conversions provides that we see more and more customers requiring a private cloud, and they're building with hyper convergence in particular hyper flex no to make all that work. They need a good, strong Cloud operations model to be able to connect both the private and the public. And that's where we look at insight. We've got solution around that. To be able to connect that around a Saas offering that looks around simplified operations, gives them optimisation and also automation to bring both private and public together in that hybrid world. >>Darren, let's stay with you for a minute when you talk to your customers. What are they thinking these days, when it comes to implementing hyper converged infrastructure in both the the enterprise and and at the edge? What are they trying to achieve? >>So there's many things they're trying to achieve? Probably the most brutal honesty is they're trying to save money. That's probably the quickest answer, but I think they're trying to look at in terms of simplicity. How can they remove layers of components they've had before in their infrastructure? We see obviously collapsing of storage into hyper conversions and storage networking, and we've got customers that have saved 80% worth of savings by doing that, a collapse into hyper conversion infrastructure away from their three tier infrastructure. Also about scalability. They don't know the end game, so they're looking about how they can size for what they know now and how they can grow that with hyper conversions. Very easy is one of the major factors and benefits of hyper conversions. They also obviously need performance and consistent performance. They don't want to compromise performance around their virtual machines when they want to run multiple workloads. They need that consistency all the way through. And then probably one of the biggest ones is that around. The simplicity model is the management layer ease of management to make it easier for their operations that we've got customers that have told us they've saved 50% of costs in their operations model, deploying out flex also around the time savings. They make massive time savings which they can reinvest in their infrastructure and their operations teams in being able to innovate and go forward. And then I think that we one of the biggest pieces we've seen as people move away from three tier architecture is the deployment elements, and the ease of deployment gets easy with hyper converged, especially with edge edges of major key use case for us and what I want. What our customers want to do is get the benefit of the data centre at the edge without a big investment. They don't compromise in performance, and they want that simplicity in both management employment. And we've seen analysts recommendations around what their readers are telling them in terms of how management deployments key for it, operations teams and how much they're actually saving by deploying edge and taking the burden away when they deployed hyper conversions. As I said, the savings elements to keep it and again, not always, but obviously those are his studies around about public Cloud being quite expensive at times over time for the wrong workloads. So by bringing them back, people can make savings. We again have customers that have made 50% savings over three years compared to their public cloud usage. So I'd say that's the key things that customers looking for >>Great. Thank you for that, Darrin minutes. We have some hard news. You've been working a lot on evolving the hyper flex line. What's the big news that you've just announced? >>Yeah, Thanks. Leave. So there are several things that we are announcing today. the first one is a new offer, um, called hyper Flex Express. This is, you know, Cisco Inter site lead and Cisco and decide managed it Hyper flex configurations that we feel are the fastest part to hybrid cloud. The second is we're expanding our server portfolio by adding support for HX on AM Iraq, U. C s and Iraq. And the third is a new capability that we're introducing that we're calling local contemporaries witness. And let me take a minute to explain what this is. This is a very nifty capability to optimise for forage environments. So, you know, this leverages the Ciscos ubiquitous presence. Uh, the networking, um, you know, products that we have in the environments worldwide. So the smallest hyper flex configuration that we have is, uh it do not configuration, which is primarily used in edge environment. Think of a, you know, a back home in a department store or a oil rig. Or it might even be a smaller data centre, uh, somewhere, uh, on the globe. For these two not configurations. There is always a need for a third entity that, you know, industry term for that is either a witness or an arbitrator. Uh, we had that for hyper flex as well. The problem that customers faces where you host this witness it cannot be on the cluster because it's the job of the witnesses to when the when the infrastructure is going down, it basically breaks, um, sort of upgrade rates. Which note gets to survive, so it needs to be outside of the cluster. But finding infrastructure, uh, to actually host this is a problem, especially in the edge environments where these are resource constrained environment. So what we've done is we've taken that witness. We've converted it into a container reform factor and then qualified a very large a slew of Cisco networking products that we have right from S. R. S R. Texas catalyst, industrial routers, even even a raspberry pi that can host host this witness, eliminating the need for you to find yet another piece of infrastructure or doing any, um, you know, care and feeding of that infrastructure. You can host it on something that already exists in the environment. So those are the three things that we're announcing today. >>So I want to ask you about hyper Flex Express. You know, obviously the whole demand and supply chain is out of whack. Everybody's global supply chain issues are in the news. Everybody's dealing with it. Can you expand on that? A little bit more Can can hyper flex express help customers respond to some of these issues. >>Yeah, indeed. The, uh, you know, the primary motivation for hyper Flex Express was indeed, uh, an idea that, you know, one of the folks around my team had, which was to build a set of hyper flex configurations that are, you know, would have a shorter lead time. But as we were brainstorming, we were actually able to tag on multiple other things and make sure that, you know, there is in it for something in it for customers, for sales as well as our partners. So, for example, you know, for customers, we've been able to dramatically simplify the configuration and the instal for hyper flex express. These are still hypertext configurations, and you would, at the end of it, get a hyper flex cluster. But the part to that cluster is much much simplifying. Second is that we've added in flexibility where you can now deploy these, uh, these are data centre configurations But you can deploy these with or without fabric interconnects, meaning you can deploy it with your existing top of rack. Um, we've also, you know, already attract attractive price point for these. And of course, you know these will have better lead times because we made sure that, you know, we are using components that are that we have clear line of sight from a supply perspective for partner and sales. This is represents a high velocity sales motion, a faster turnaround time, Uh, and a frictionless sales motion for our distributors. Uh, this is actually a settled, risky, friendly configurations, which they would find very easy to stalk and with a quick turnaround time, this would be very attractive for the deceased as well. >>It's interesting many. So I'm looking at some fresh survey data. More than 70% of the customers that were surveyed this GTR survey again mentioned at the top. More than 70% said they had difficulty procuring, uh, server hardware and networking was also a huge problem. So so that's encouraging. What about Manisha AMG that's new for hyper flex? What's that going to give customers that they couldn't get before? >>Yeah, so you know, in the short time that we've had UCS am direct support, we've had several record breaking benchmark results that we've published. So it's a it's a It's a powerful platform with a lot of performance in it and hyper flex. Uh, you know, the differentiator that we've had from Day one is that it is. It has the industry leading storage performance. So with this, we're going to get the fastest compute together with the fastest storage and this we are hoping that will basically unlock, you know, a unprecedented level of performance and efficiency, but also unlock several new workloads that were previously locked out from the hyper converged experience. >>Yeah, cool. Uh, so, Darren, can >>you can you give us >>an idea as to how hyper flexes is doing in the field? >>Sure, Absolutely So both me and my initial been involved right from the start and before it was called Hyper Flex, and we've had a great journey, and it's very excited to see where we're taking where we've been with the technology. So we have over 5000 customers worldwide, and we're currently growing faster year over year than the market. The majority of our customers are repeat buyers, which is always a good sign in terms of coming back when they approved the technology and are comfortable with technology. They repeat by for expanding capacity, putting more workloads on. They're using different use cases on there. And from an energy perspective, more numbers of science so really good. Endorsement the technology. We get used across all verticals or segments, um, to house mission critical applications as well as the traditional virtual server infrastructures. Uh, and we are the lifeblood of our customers around those mission critical customers think one example, and I apologise for the worldwide audience. But this resonates with the American audiences the Super Bowl. So the sofa like stadium that housed the Super Bowl actually has Cisco hyper Flex running all the management services through from the entire stadium for digital signage. Four K video distribution, and it's complete completely cashless. So if that were to break during Super Bowl, that would have been a big, uh, news article, but it was run perfectly. We in the design of the solution, we're able to collapse down nearly 200 servers into a few notes across a few racks and have 100 120 virtual machines running the whole stadium without missing a heartbeat. And that is mission critical for you to run Super Bowl and not be on the front of the press afterwards for the wrong reasons. That's a win for us. So we really are really happy with High Flex where it's going, what it's doing. And some of the use cases were getting involved in very, very excited. >>Come on, Darren. It's Super Bowl NFL. That's a That's international now. And, you know, the NFL >>NFL. It's >>invading London. Of course I see the picture of the real football over your shoulder, But last question for many is give us a little roadmap. What's the future hold for hyper flex? >>Yeah, so you know, as Darren said, both Darren and I have been involved the type of flicks since the beginning, Uh, but I think the best is yet to come. There are three main pillars for for hyper Flex. One is in. The site is central to our strategy. It provides a lot of customer benefit from a single pane of glass management. But we're going to take this beyond the Lifecycle management, which is for hyper flex, which is integrated in winter side today and element management. We're going to take it beyond that and start delivering customer value on the dimensions of a job. Because Interstate really provides us an ideal platform to gather starts from all the clusters across the globe. Do AML and do some predictive analysis with that and return it back as, uh, you know, customer valued, um, actionable insights. So that is one. The second is you'll see us expand the hyper flex portfolio. Go beyond you see us to third party server platforms, and newer, you see a server platforms as well. But the highlight there is one that I'm really really excited about and think that there is a lot of potential in terms of the number of customers we can help is a checks on X CDs. Experience is another thing that we're able to, uh you know, uh, announcing a bunch of capabilities on in this particular launch. But a check sonic series. We'll have that by the end of this calendar year, and that should unlock with the flexibility of X series of hosting a multitude of workloads and the simplicity of hyper flex. We're hoping that would bring a lot of benefits to new workloads, that we're locked out previously. And then the last thing is hyper flex leader platform. This is the heart of the offering today, Uh, and you'll see the hyper flex data platform itself. It's a distributed architecture, unique distributed architecture primarily where we get our, you know, record breaking performance from you'll see it get faster, more scalable, more resilient. And we'll optimise it for, you know, containerised workloads, meaning it will get granular containerised container granular management capabilities and optimised for public. So those are some things that were the team is busy working on, and we should see that come to fruition. I'm hoping that we'll be back at this forum and maybe before the end of the year and talking about some of these new capabilities. >>That's great. Thank you very much for that. Okay, guys, we got to leave it there and you know many She was talking about the HX on X Series. That's huge. Customers are gonna love that, and it's a great transition because in a moment I'll be back with Vikas Ratna and Jim Leach and we're gonna dig into X series. Some real serious engineering went into this platform, and we're gonna explore what it all means. You're watching simplifying hybrid cloud on the cube, your leader in enterprise tech coverage.

(upbeat music) >> The Cloud essentially turned the data center into an API and ushered in the era of programmable infrastructure, no longer do we think about deploying infrastructure in rigid silos with a hardened, outer shell, rather infrastructure has to facilitate digital business strategies. And what this means is putting data at the core of your organization, irrespective of its physical location. It also means infrastructure generally and storage specifically must be accessed as sets of services that can be discovered, deployed, managed, secured, and governed in a DevOps model or OpsDev, if you prefer. Now, this has specific implications as to how vendor product strategies will evolve and how they'll meet modern data requirements. Welcome to this Cube conversation, everybody. This is Dave Vellante. And with me to discuss these sea changes is Ajay Singh, the Chief Product Officer of Pure Storage, Ajay welcome. >> Thank you, David, gald to be on. >> Yeah, great to have you, so let's talk about your role at Pure. I think you're the first CPO, what's the vision there? >> That's right, I just joined up Pure about eight months ago from VMware as the chief product officer and you're right, I'm the first our chief product officer at Pure. And at VMware I ran the Cloud management business unit, which was a lot about automation and infrastructure as code. And it's just great to join Pure, which has a phenomenal all flash product set. I kind of call it the iPhone or flash story super easy to use. And how do we take that same ease of use, which is a heart of a Cloud operating principle, and how do we actually take it up to really deliver a modern data experience, which includes infrastructure and storage as code, but then even more beyond that and how do you do modern operations and then modern data services. So super excited to be at Pure. And the vision, if you may, at the end of the day, is to provide, leveraging this moderate experience, a connected and effortless experience data experience, which allows customers to ultimately focus on what matters for them, their business, and by really leveraging and managing and winning with their data, because ultimately data is the new oil, if you may, and if you can mine it, get insights from it and really drive a competitive edge in the digital transformation in your head, and that's what be intended to help our customers to. >> So you joined earlier this year kind of, I guess, middle of the pandemic really I'm interested in kind of your first 100 days, what that was like, what key milestones you set and now you're into your second a 100 plus days. How's that all going? What can you share with us in and that's interesting timing because the effects of the pandemic you came in in a kind of post that, so you had experience from VMware and then you had to apply that to the product organization. So tell us about that sort of first a 100 days and the sort of mission now. >> Absolutely, so as we talked about the vision, around the modern data experience, kind of have three components to it, modernizing the infrastructure and really it's kudos to the team out of the work we've been doing, a ton of work in modernizing the infrastructure, I'll briefly talk to that, then modernizing the data, much more than modernizing the operations. I'll talk to that as well. And then of course, down the pike, modernizing data services. So if you think about it from modernizing the infrastructure, if you think about Pure for a minute, Pure is the first company that took flash to mainstream, essentially bringing what we call consumer simplicity to enterprise storage. The manual for the products with the front and back of a business card, that's it, you plug it in, boom, it's up and running, and then you get proactive AI driven support, right? So that was kind of the heart of Pure. Now you think about Pure again, what's unique about Pure has been a lot of our competition, has dealt with flash at the SSD level, hey, because guess what? All this software was built for hard drive. And so if I can treat NAND as a solid state drive SSD, then my software would easily work on it. But with Pure, because we started with flash, we released went straight to the NAND level, and as opposed to kind of the SSD layer, and what that does is it gives you greater efficiency, greater reliability and create a performance compared to an SSD, because you can optimize at the chip level as opposed to at the SSD module level. That's one big advantage that Pure has going for itself. And if you look at the physics, in the industry for a minute, there's recent data put out by Wikibon early this year, effectively showing that by the year 2026, flash on a dollar per terabyte basis, just the economics of the semiconductor versus the hard disk is going to be cheaper than hard disk. So this big inflection point is slowly but surely coming that's going to disrupt the hardest industry, already the high end has been taken over by flash, but hybrid is next and then even the long tail is coming up over there. And so to end to that extent our lead, if you may, the introduction of QLC NAND, QLC NAND powerful competition is barely introducing, we've been at it for a while. We just recently this year in my first a 100 days, we introduced the flasher AC, C40 and C60 drives, which really start to open up our ability to go after the hybrid story market in a big way. It opens up a big new market for us. So great work there by the team,. Also at the heart of it. If you think about it in the NAND side, we have our flash array, which is a scale-up latency centric architecture and FlashBlade which is a scale-out throughput architecture, all operating with NAND. And what that does is it allows us to cover both structured data, unstructured data, tier one apps and tier two apps. So pretty broad data coverage in that journey to the all flash data center, slowly but surely we're heading over there to the all flash data center based on demand economics that we just talked about, and we've done a bunch of releases. And then the team has done a bunch of things around introducing and NVME or fabric, the kind of thing that you expect them to do. A lot of recognition in the industry for the team or from the likes of TrustRadius, Gartner, named FlashRay, the Carton Peer Insights, the customer choice award and primary storage in the MQ. We were the leader. So a lot of kudos and recognition coming to the team as a result, Flash Blade just hit a billion dollars in cumulative revenue, kind of a leader by far in kind of the unstructured data, fast file an object marketplace. And then of course, all the work we're doing around what we say, ESG, environmental, social and governance, around reducing carbon footprint, reducing waste, our whole notion of evergreen and non-disruptive upgrades. We also kind of did a lot of work in that where we actually announced that over 2,700 customers have actually done non-disruptive upgrades over the technology. >> Yeah a lot to unpack there. And a lot of this sometimes you people say, oh, it's the plumbing, but the plumbing is actually very important too. 'Cause we're in a major inflection point, when we went from spinning disk to NAND. And it's all about volumes, you're seeing this all over the industry now, you see your old boss, Pat Gelsinger, is dealing with this at Intel. And it's all about consumer volumes in my view anyway, because thanks to Steve Jobs, NAND volumes are enormous and what two hard disk drive makers left in the planet. I don't know, maybe there's two and a half, but so those volumes drive costs down. And so you're on that curve and you can debate as to when it's going to happen, but it's not an if it's a when. Let me, shift gears a little bit. Because Cloud, as I was saying, it's ushered in this API economy, this as a service model, a lot of infrastructure companies have responded. How are you thinking at Pure about the as a service model for your customers? What's the strategy? How is it evolving and how does it differentiate from the competition? >> Absolutely, a great question. It's kind of segues into the second part of the moderate experience, which is how do you modernize the operations? And that's where automation as a service, because ultimately, the Cloud has validated and the address of this model, right? People are looking for outcomes. They care less about how you get there. They just want the outcome. And the as a service model actually delivers these outcomes. And this whole notion of infrastructure as code is kind of the start of it. Imagine if my infrastructure for a developer is just a line of code, in a Git repository in a program that goes through a CICD process and automatically kind of is configured and set up, fits in with the Terraform, the Ansibles, all that different automation frameworks. And so what we've done is we've gone down the path of really building out what I think is modern operations with this ability to have storage as code, disability, in addition modern operations is not just storage scored, but also we've got recently introduced some comprehensive ransomware protection, that's part of modern operations. There's all the threat you hear in the news or ransomware. We introduced what we call safe mode snapshots that allow you to recover in literally seconds. When you have a ransomware attack, we also have in the modern operations Pure one, which is maybe the leader in AI driven support to prevent downtime. We actually call you 80% of the time and fix the problems without you knowing about it. That's what modern operations is all about. And then also Martin operations says, okay, you've got flash on your on-prem side, but even maybe using flash in the public Cloud, how can I have seamless multi-Cloud experience in our Cloud block store we've introduced around Amazon, AWS and Azure allows one to do that. And then finally, for modern applications, if you think about it, this whole notion of infrastructure's code, as a service, software driven storage, the Kubernetes infrastructure enables one to really deliver a great automation framework that enables to reduce the labor required to manage the storage infrastructure and deliver it as code. And we have, kudos to Charlie and the Pure storage team before my time with the acquisition of Portworx, Portworx today is truly delivers true storage as code orchestrated entirely through Kubernetes and in a multi-Cloud hybrid situation. So it can run on EKS, GKE, OpenShift rancher, Tansu, recently announced as the leader by giggle home for enterprise Kubernetes storage. We were really proud about that asset. And then finally, the last piece are Pure as a service. That's also all outcome oriented, SLS. What matters is you sign up for SLS, and then you get those SLS, very different from our competition, right? Our competition tends to be a lot more around financial engineering, hey, you can buy it OPEX versus CapEx. And, but you get the same thing with a lot of professional services, we've really got, I'd say a couple of years and lead on, actually delivering and managing with SRE engineers for the SLA. So a lot of great work there. We recently also introduced Cisco FlashStack, again, flash stack as a service, again, as a service, a validation of that. And then finally, we also recently did a announcement with Aquaponics, with their bare metal as a service where we are a key part of their bare metal as a service offering, again, pushing the kind of the added service strategy. So yes, big for us, that's where the buck is skating, half the enterprises, even on prem, wanting to consume things in the Cloud operating model. And so that's where we're putting it lot. >> I see, so your contention is, it's not just this CapEx to OPEX, that's kind of the, during the economic downturn of 2007, 2008, the economic crisis, that was the big thing for CFOs. So that's kind of yesterday's news. What you're saying is you're creating a Cloud, like operating model, as I was saying upfront, irrespective of physical location. And I see that as your challenge, the industry's challenge, be, if I'm going to effect the digital transformation, I don't want to deal with the Cloud primitives. I want you to hide the underlying complexity of that Cloud. I want to deal with higher level problems, but so that brings me to digital transformation, which is kind of the now initiative, or I even sometimes call it the mandate. There's not a one size fits all for digital transformation, but I'm interested in your thoughts on the must take steps, universal steps that everybody needs to think about in a digital transformation journey. >> Yeah, so ultimately the digital transformation is all about how companies are gain a competitive edge in this new digital world or that the company are, and the competition are changing the game on, right? So you want to make sure that you can rapidly try new things, fail fast, innovate and invest, but speed is of the essence, agility and the Cloud operating model enables that agility. And so what we're also doing is not only are we driving agility in a multicloud kind of data, infrastructure, data operation fashion, but we also taking it a step further. We were also on the journey to deliver modern data services. Imagine on a Pure on-prem infrastructure, along with your different public Clouds that you're working on with the Kubernetes infrastructures, you could, with a few clicks run Kakfa as a service, TensorFlow as a service, Mongo as a service. So me as a technology team can truly become a service provider and not just an on-prem service provider, but a multi-Cloud service provider. Such that these services can be used to analyze the data that you have, not only your data, your partner data, third party public data, and how you can marry those different data sets, analyze it to deliver new insights that ultimately give you a competitive edge in the digital transformation. So you can see data plays a big role there. The data is what generates those insights. Your ability to match that data with partner data, public data, your data, the analysis on it services ready to go, as you get the digital, as you can do the insights. You can really start to separate yourself from your competition and get on the leaderboard a decade from now when this digital transformation settles down. >> All right, so bring us home, Ajay, summarize what does a modern data strategy look like and how does it fit into a digital business or a digital organization? >> So look, at the end of the day, data and analysis, both of them play a big role in the digital transformation. And it really comes down to how do I leverage this data, my data, partner data, public data, to really get that edge. And that links back to a vision. How do we provide that connected and effortless, modern data experience that allows our customers to focus on their business? How do I get the edge in the digital transformation? But easily leveraging, managing and winning with their data. And that's the heart of where Pure is headed. >> Ajay Singh, thanks so much for coming inside theCube and sharing your vision. >> Thank you, Dave, it was a real pleasure. >> And thank you for watching this Cube conversation. This is Dave Vellante and we'll see you next time. (upbeat music)

>>Please >>welcome back to HPD discovered 2021. The cubes virtual coverage, continuous coverage of H P. S H. P. S. Annual customer event. My name is Dave Volonte and we're going to dive into the intersection of high performance computing data and AI with DR Eng limb go who is the senior vice president and CTO for AI Hewlett Packard enterprise Doctor go great to see you again. Welcome back to the cube. >>Hello Dave, Great to talk to you again. >>You might remember last year we talked a lot about swarm intelligence and how AI is evolving. Of course you hosted the day two keynotes here at discover and you talked about thriving in the age of insights and how to craft a data centric strategy. And you addressed you know some of the biggest problems I think organizations face with data that's You got a data is plentiful but insights they're harder to come by. And you really dug into some great examples in retail banking and medicine and health care and media. But stepping back a little bit with zoom out on discovered 21, what do you make of the events so far? And some of your big takeaways? >>Mm Well you started with the insightful question, Right? Yeah, data is everywhere then. But we like the insight. Right? That's also part of the reason why that's the main reason why you know Antonio on day one focused and talked about that. The fact that we are now in the age of insight, right? Uh and uh and and how to thrive thrive in that in this new age. What I then did on the day to kino following Antonio is to talk about the challenges that we need to overcome in order in order to thrive in this new asia. >>So maybe we could talk a little bit about some of the things that you took away in terms I'm specifically interested in some of the barriers to achieving insights when customers are drowning in data. What do you hear from customers? What we take away from some of the ones you talked about today? >>Oh, very pertinent question. Dave You know the two challenges I spoke about right now that we need to overcome in order to thrive in this new age. The first one is is the current challenge and that current challenge is uh you know stated is no barriers to insight. You know when we are awash with data. So that's a statement. Right? How to overcome those barriers. What are the barriers of these two insight when we are awash in data? Um I in the data keynote I spoke about three main things. Three main areas that received from customers. The first one, the first barrier is in many with many of our customers. A data is siloed. All right. You know, like in a big corporation you've got data siloed by sales, finance, engineering, manufacturing, and so on, uh supply chain and so on. And uh there's a major effort ongoing in many corporations to build a federation layer above all those silos so that when you build applications above they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the that was the first barrier. We spoke about barriers to incite when we are washed with data. The second barrier is uh that we see amongst our customers is that uh data is raw and dispersed when they are stored and and uh and you know, it's tough to get tough to to get value out of them. Right? And I in that case I I used the example of uh you know the May 6 2010 event where the stock market dropped a trillion dollars in in tens of minutes. You know, we we all know those who are financially attuned with know about this uh incident, But this is not the only incident. There are many of them out there and for for that particular May six event, uh you know, it took a long time to get insight months. Yeah, before we for months we had no insight as to what happened, why it happened, right. Um, and and there were many other incidences like this and the regulators were looking for that one rule that could, that could mitigate many of these incidences. Um, one of our customers decided to take the hard road to go with the tough data right? Because data is rolling dispersed. So they went into all the different feeds of financial transaction information, took the took the tough took the tough road and analyze that data took a long time to assemble. And they discovered that there was quote stuffing right? That uh people were sending a lot of traits in and then cancelling them almost immediately. You have to manipulate the market. Um And why why why didn't we see it immediately? Well, the reason is the process reports that everybody sees the rule in there that says all trades, less than 100 shares don't need to report in there. And so what people did was sending a lot of less than 103 100 100 shares trades uh to fly under the radar to do this manipulation. So here is here the second barrier right? Data could be raw and dispersed. Um Sometimes you just have to take the hard road and um and to get insight And this is 1 1 great example. And then the last barrier is uh is has to do with sometimes when you start a project to to get insight to get uh to get answers and insight. You you realize that all the datas around you but you don't you don't seem to find the right ones to get what you need. You don't you don't seem to get the right ones. Yeah. Um here we have three quick examples of customers. 111 was it was a great example right? Where uh they were trying to build a language translator, a machine language translator between two languages. Right? By not do that. They need to get hundreds of millions of word pairs, you know, of one language compared uh with a corresponding other hundreds of millions of them. They say, well I'm going to get all these word pairs. Someone creative thought of a willing source. And you thought it was the United Nations, you see. So sometimes you think you don't have the right data with you, but there might be another source. And the willing one that could give you that data Right? The 2nd 1 has to do with uh there was uh the uh sometimes you you may just have to generate that data, interesting one. We had an autonomous car customer that collects all these data from their cars, right? Massive amounts of data, loss of sensors, collect loss of data. And uh, you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car uh in in um in fine weather and collected the car driving on this highway in rain and also in stone, but never had the opportunity to collect the car in hill because that's a rare occurrence. So instead of waiting for a time where the car can dr inhale, they build a simulation you by having the car collector in snow and simulated him. So, these are some of the examples where we have customers working to overcome barriers, right? You have barriers that is associated the fact that data silo the Federated it various associated with data. That's tough to get that. They just took the hard road, right? And, and sometimes, thirdly, you just have to be creative to get the right data. You need, >>wow, I I'll tell you, I have about 100 questions based on what you just said. Uh, there's a great example, the flash crash. In fact, Michael Lewis wrote about this in his book The Flash Boys and essentially right. It was high frequency traders trying to front run the market and sending in small block trades trying to get on the front end it. So that's and they, and they chalked it up to a glitch like you said, for months. Nobody really knew what it was. So technology got us into this problem. I guess my question is, can technology help us get out of the problem? And that maybe is where AI fits in. >>Yes, yes. Uh, in fact, a lot of analytics, we went in to go back to the raw data that is highly dispersed from different sources, right, assemble them to see if you can find a material trend, right? You can see lots of trends, right? Like, uh, you know, we if if humans look at things right, we tend to see patterns in clouds, right? So sometimes you need to apply statistical analysis, um math to to be sure that what the model is seeing is is real. Right? And and that required work. That's one area. The second area is uh you know, when um uh there are times when you you just need to to go through that uh that tough approach to to find the answer. Now, the issue comes to mind now is is that humans put in the rules to decide what goes into a report that everybody sees. And in this case uh before the change in the rules. Right? But by the way, after the discovery, uh authorities change the rules and all all shares, all traits of different any sizes. It has to be reported. No. Yeah. Right. But the rule was applied uh you know, to say earlier that shares under 100 trades under 100 shares need not be reported. So sometimes you just have to understand that reports were decided by humans and and under for understandable reasons. I mean they probably didn't want that for various reasons not to put everything in there so that people could still read it uh in a reasonable amount of time. But uh we need to understand that rules were being put in by humans for the reports we read. And as such there are times you just need to go back to the raw data. >>I want to ask, >>it's gonna be tough. >>Yeah. So I want to ask a question about AI is obviously it's in your title and it's something you know a lot about but and I want to make a statement, you tell me if it's on point or off point. So it seems that most of the Ai going on in the enterprise is modeling data science applied to troves of data but but there's also a lot of ai going on in consumer whether it's you know, fingerprint technology or facial recognition or natural language processing will a two part question will the consumer market as has so often in the enterprise sort of inform us uh the first part and then will there be a shift from sort of modeling if you will to more you mentioned autonomous vehicles more ai influencing in real time. Especially with the edge you can help us understand that better. >>Yeah, it's a great question. Right. Uh there are three stages to just simplify, I mean, you know, it's probably more sophisticated than that but let's simplify three stages. All right. To to building an Ai system that ultimately can predict, make a prediction right or to to assist you in decision making, have an outcome. So you start with the data massive amounts of data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data and the machine uh starts to evolve a model based on all the data is seeing. It starts to evolve right to the point that using a test set of data that you have separately kept a site that you know the answer for. Then you test the model uh you know after you trained it with all that data to see whether it's prediction accuracy is high enough and once you are satisfied with it, you you then deploy the model to make the decision and that's the influence. Right? So a lot of times depend on what what we are focusing on. We we um in data science are we working hard on assembling the right data to feed the machine with, That's the data preparation organization work. And then after which you build your models, you have to pick the right models for the decisions and prediction you wanted to make. You pick the right models and then you start feeding the data with it. Sometimes you you pick one model and the prediction isn't that robust, it is good but then it is not consistent right now. What you do is uh you try another model so sometimes it's just keep trying different models until you get the right kind. Yeah, that gives you a good robust decision making and prediction after which It is tested well Q eight. You would then take that model and deploy it at the edge. Yeah. And then at the edges is essentially just looking at new data, applying it to the model that you have trained and then that model will give you a prediction decision. Right? So uh it is these three stages. Yeah, but more and more uh your question reminds me that more and more people are thinking as the edge become more and more powerful. Can you also do learning at the edge? Right. That's the reason why we spoke about swarm learning the last time, learning at the edge as a swamp, right? Because maybe individually they may not have enough power to do so. But as a swamp they made >>is that learning from the edge? You're learning at the edge? In other words? >>Yes. >>Yeah, I understand the question. Yeah. >>That's a great question. That's a great question. Right? So uh the quick answer is learning at the edge, right? Uh and and also from the edge, but the main goal, right? The goal is to learn at the edge so that you don't have to move the data that the edge sees first back to the cloud or the core to do the learning because that would be the reason. One of the main reasons why you want to learn at the edge, right? Uh So so that you don't need to have to send all that data back and assemble it back from all the different Edge devices, assemble it back to the cloud side to to do the learning right. With someone you can learn it and keep the data at the edge and learn at that point. >>And then maybe only selectively send the autonomous vehicle example you gave us great because maybe there, you know, there may be only persisting, they're not persisting data that is inclement weather or when a deer runs across the front. And then maybe they they do that and then they send that smaller data set back and maybe that's where it's modelling done. But the rest can be done at the edges. It's a new world that's coming down. Let me ask you a question, is there a limit to what data should be collected and how it should be collected? >>That's a great question again, you know uh wow today, full of these uh insightful questions that actually touches on the second challenge. Right? How do we uh in order to thrive in this new age of insight? The second challenge is are you know the is our future challenge, right? What do we do for our future? And and in there is uh the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that I talk about what to collect right? When to organize it when you collect and where will your data be, you know, going forward that you are collecting from? So what, when and where for the what data for the what data to collect? That? That was the question you ask. Um it's it's a question that different industries have to ask themselves because it will vary, right? Um Let me give you the, you use the autonomous car example, let me use that. And We have this customer collecting massive amounts of data. You know, we're talking about 10 petabytes a day from the fleet of their cars. And these are not production autonomous cars, right? These are training autonomous cars, collecting data so they can train and eventually deploy commercial cars. Right? Um, so this data collection cars they collect as a fleet of them collect 10 petabytes a day and when it came to us uh building a storage system yeah, to store all of that data, they realized they don't want to afford to store all of it. Now here comes the dilemma, right? Should what should I after I spent so much effort building all these cars and sensors and collecting data, I've now decide what to delete. That's a dilemma right now in working with them on this process of trimming down what they collected. You know, I'm constantly reminded of the sixties and seventies, right? To remind myself 16 seventies we call a large part of our D. N. A junk DNA. Today we realize that a large part of that what we call john has function as valuable function. They are not jeans, but they regulate the function of jeans, you know? So, so what's jumped in the yesterday could be valuable today or what's junk today could be valuable tomorrow. Right? So, so there's this tension going on right between you decided not wanting to afford to store everything that you can get your hands on. But on the other hand, you you know, you worry you you you ignore the wrong ones, right? You can see this tension in our customers, right? And it depends on industry here. Right? In health care, they say I have no choice. I I want it. All right. One very insightful point brought up by one health care provider that really touched me was, you know, we are not we don't only care. Of course we care a lot. We care a lot about the people we are caring for, right? But you also care for the people were not caring for. How do we find them? Mhm. Right. And that therefore they did not just need to collect data that is uh that they have with from their patients. They also need to reach out right to outside data so that they can figure out who they are not caring for. Right? So they want it all. So I tell us them. So what do you do with funding if you want it all? They say they have no choice but to figure out a way to fund it and perhaps monetization of what they have now is the way to come around and find out. Of course they also come back to us rightfully that, you know, we have to then work out a way to help them build that system, you know, so that health care, right? And and if you go to other industries like banking, they say they can't afford to keep them on, but they are regulated. Seems like healthcare, they are regulated as to uh privacy and such. Like so many examples different industries having different needs but different approaches to how what they collect. But there is this constant tension between um you perhaps deciding not wanting to fund all of that uh all that you can stall right on the other hand, you know, if you if you kind of don't want to afford it and decide not to store some uh if he does some become highly valuable in the future right? Don't worry. >>We can make some assumptions about the future, can't we? I mean, we know there's gonna be a lot more data than than we've ever seen before. We know that we know. Well notwithstanding supply constraints on things like nand, we know the prices of storage is gonna continue to decline. We also know and not a lot of people are really talking about this but the processing power but he says moore's law is dead. Okay, it's waning. But the processing power when you combine the Cpus and N. P. U. S. And Gpus and accelerators and and so forth actually is is increasing. And so when you think about these use cases at the edge, you're going to have much more processing power, you're going to have cheaper storage and it's going to be less expensive processing. And so as an ai practitioner, what can you do with that? >>So the amount of data that's gonna come in, it's gonna we exceed right? Our drop in storage costs are increasing computer power. Right? So what's the answer? Right? So so the the answer must be knowing that we don't and and even the drop in price and increase in bandwidth, it will overwhelm the increased five G will overwhelm five G. Right? Given amount 55 billion of them collecting. Right? So the answer must be that there might need to be a balance between you needing to bring all that data from the 55 billion devices data back to a central as a bunch of central. Cause because you may not be able to afford to do that firstly band with even with five G. M and and SD when you'll still be too expensive given the number of devices out there, Were you given storage costs dropping? You'll still be too expensive to try and store them all. So the answer must be to start at least to mitigate the problem to some leave both a lot of the data out there. Right? And only send back the pertinent ones as you said before. But then if you did that, then how are we gonna do machine learning at the core and the cloud side? If you don't have all the data, you want rich data to train with. Right? Some sometimes you wanna mix of the uh positive type data and the negative type data so you can train the machine in a more balanced way. So the answer must be eventually right. As we move forward with these huge number of devices out of the edge to do machine learning at the edge today, we don't have enough power. Right? The edge typically is characterized by a lower uh energy capability and therefore lower compute power. But soon, you know, even with lower energy they can do more with compute power, improving in energy efficiency, Right? Uh So learning at the edge today we do influence at the edge. So we data model deploy and you do in France at the age, that's what we do today. But more and more I believe given a massive amount of data at the edge, you, you have to have to start doing machine learning at the edge and, and if when you don't have enough power then you aggregate multiple devices, compute power into a swamp and learn as a swan. >>Oh, interesting. So now of course, if, if I were sitting and fly, fly on the wall in hp board meeting, I said okay. HB is as a leading provider of compute how do you take advantage of that? I mean we're going, we're, I know its future, but you must be thinking about that and participating in those markets. I know today you are, you have, you know, edge line and other products. But there's, it seems to me that it's, it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that >>opportunity for the customers? The world will have to have a balance right? Where today the default? Well, the more common mode is to collect the data from the edge and train at uh at some centralized location or a number of centralized location um going forward. Given the proliferation of the edge devices, we'll need a balance. We need both. We need capability at the cloud side. Right? And it has to be hybrid and then we need capability on the edge side. Yeah. That they want to build systems that that on one hand, uh is uh edge adapted, right? Meaning the environmentally adapted because the edge different. They are on a lot of times. On the outside. Uh They need to be packaging adapted and also power adapted, right? Because typically many of these devices are battery power. Right? Um, so you have to build systems that adapt to it. But at the same time they must not be custom. That's my belief. They must be using standard processes and standard operating system so that they can run a rich set of applications. So yes. Um that's that's also the insightful for that Antonio announced in 2018 Uh the next four years from 2018, right $4 billion dollars invested to strengthen our edge portfolio. Edge product lines, Right. Edge solutions. >>I can doctor go, I could go on for hours with you. You're you're just such a great guest. Let's close. What are you most excited about in the future? Of of of it. Certainly H. P. E. But the industry in general. >>Yeah. I think the excitement is uh the customers, right? The diversity of customers and and the diversity in a way they have approached their different problems with data strategy. So the excitement is around data strategy, right? Just like you know uh you know, the the statement made was was so was profound, right? Um And Antonio said we are in the age of insight powered by data. That's the first line, right. Uh The line that comes after that is as such were becoming more and more data centric with data, the currency. Now the next step is even more profound. That is um You know, we are going as far as saying that you know um data should not be treated as cost anymore. No. Right. But instead as an investment in a new asset class called data with value on our balance sheet, this is a this is a step change right? In thinking that is going to change the way we look at data, the way we value it. So that's a statement that this is the exciting thing because because for for me, a city of Ai right uh machine is only as intelligent as the data you feed it with data is a source of the machine learning to be intelligent. So, so that's that's why when when people start to value data, right? And and and say that it is an investment when we collect it, it is very positive for AI because an AI system gets intelligent, get more intelligence because it has a huge amounts of data and the diversity of data. So it would be great if the community values values data. Well, >>you certainly see it in the valuations of many companies these days. Um and I think increasingly you see it on the income statement, you know, data products and people monetizing data services and maybe eventually you'll see it in the in the balance. You know, Doug Laney, when he was a gardener group wrote a book about this and a lot of people are thinking about it. That's a big change, isn't it? Dr >>yeah. Question is is the process and methods evaluation right. But I believe we'll get there, we need to get started and then we'll get there. Believe >>doctor goes on >>pleasure. And yeah. And then the Yeah, I will well benefit greatly from it. >>Oh yeah, no doubt people will better understand how to align you know, some of these technology investments, Doctor goes great to see you again. Thanks so much for coming back in the cube. It's been a real pleasure. >>Yes. A system. It's only as smart as the data you feed it with. >>Excellent. We'll leave it there, thank you for spending some time with us and keep it right there for more great interviews from HP discover 21 this is Dave Volonte for the cube. The leader in enterprise tech coverage right back

>>Welcome back to HPD discovered 2021 the cubes virtual coverage, continuous coverage of H P. S H. P. S. Annual customer event. My name is Dave Volonte and we're going to dive into the intersection of high performance computing data and AI with DR Eng limb go who is the senior vice president and CTO for AI at Hewlett Packard enterprise Doctor go great to see you again. Welcome back to the cube. >>Hello Dave, Great to talk to you again. >>You might remember last year we talked a lot about swarm intelligence and how AI is evolving. Of course you hosted the day two keynotes here at discover you talked about thriving in the age of insights and how to craft a data centric strategy and you addressed you know some of the biggest problems I think organizations face with data that's You got a data is plentiful but insights they're harder to come by. And you really dug into some great examples in retail banking and medicine and health care and media. But stepping back a little bit with zoom out on discovered 21, what do you make of the events so far? And some of your big takeaways? >>Mm Well you started with the insightful question, right? Yeah. Data is everywhere then. But we like the insight. Right? That's also part of the reason why that's the main reason why you know Antonio on day one focused and talked about that. The fact that we are now in the age of insight. Right? Uh and and uh and and how to thrive thrive in that in this new age. What I then did on the day to kino following Antonio is to talk about the challenges that we need to overcome in order in order to thrive in this new age. >>So maybe we could talk a little bit about some of the things that you took away in terms I'm specifically interested in some of the barriers to achieving insights when you know customers are drowning in data. What do you hear from customers? What we take away from some of the ones you talked about today? >>Oh, very pertinent question. Dave you know the two challenges I spoke about right now that we need to overcome in order to thrive in this new age. The first one is is the current challenge and that current challenge is uh you know stated is you know, barriers to insight, you know when we are awash with data. So that's a statement right? How to overcome those barriers. What are the barriers of these two insight when we are awash in data? Um I in the data keynote I spoke about three main things. Three main areas that received from customers. The first one, the first barrier is in many with many of our customers. A data is siloed. All right. You know, like in a big corporation you've got data siloed by sales, finance, engineering, manufacturing, and so on, uh supply chain and so on. And uh, there's a major effort ongoing in many corporations to build a federation layer above all those silos so that when you build applications above they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the that was the first barrier we spoke about barriers to incite when we are washed with data. The second barrier is uh, that we see amongst our customers is that uh data is raw and dispersed when they are stored and and uh and you know, it's tough to get tough to to get value out of them. Right? And I in that case I I used the example of uh you know the May 6 2010 event where the stock market dropped a trillion dollars in in tens of ministerial. We we all know those who are financially attuned with know about this uh incident But this is not the only incident. There are many of them out there and for for that particular May six event uh you know, it took a long time to get insight months. Yeah before we for months we had no insight as to what happened, why it happened, right. Um and and there were many other incidences like this. And the regulators were looking for that one rule that could, that could mitigate many of these incidences. Um one of our customers decided to take the hard road go with the tough data right? Because data is rolling dispersed. So they went into all the different feeds of financial transaction information. Uh took the took the tough uh took the tough road and analyze that data took a long time to assemble and they discovered that there was court stuffing right? That uh people were sending a lot of traits in and then cancelling them almost immediately. You have to manipulate the market. Um And why why why didn't we see it immediately? Well the reason is the process reports that everybody sees uh rule in there that says all trades. Less than 100 shares don't need to report in there. And so what people did was sending a lot of less than 103 100 100 shares trades uh to fly under the radar to do this manipulation. So here is here the second barrier right? Data could be raw and dispersed. Um Sometimes you just have to take the hard road and um and to get insight And this is 1 1 great example. And then the last barrier is uh is has to do with sometimes when you start a project to to get insight to get uh to get answers and insight. You you realize that all the datas around you but you don't you don't seem to find the right ones To get what you need. You don't you don't seem to get the right ones. Yeah. Um here we have three quick examples of customers. 111 was it was a great example right? Where uh they were trying to build a language translator, a machine language translator between two languages. Right? But not do that. They need to get hundreds of millions of word pairs, you know, of one language compared uh with the corresponding other hundreds of millions of them. They say we are going to get all these word pairs. Someone creative thought of a willing source and a huge, so it was a United Nations you see. So sometimes you think you don't have the right data with you, but there might be another source and a willing one that could give you that data right. The second one has to do with uh there was uh the uh sometimes you you may just have to generate that data, interesting one. We had an autonomous car customer that collects all these data from their cars, right, massive amounts of data, loss of senses, collect loss of data. And uh you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car uh in in um in fine weather and collected the car driving on this highway in rain and also in stone, but never had the opportunity to collect the car in hale because that's a rare occurrence. So instead of waiting for a time where the car can dr inhale, they build a simulation you by having the car collector in snow and simulated him. So these are some of the examples where we have customers working to overcome barriers, right? You have barriers that is associated the fact that data is silo Federated, it various associated with data. That's tough to get that. They just took the hard road, right? And sometimes, thirdly, you just have to be creative to get the right data you need, >>wow, I tell you, I have about 100 questions based on what you just said. Uh, there's a great example, the flash crash. In fact, Michael Lewis wrote about this in his book, The Flash Boys and essentially right. It was high frequency traders trying to front run the market and sending in small block trades trying to get on the front end it. So that's and they, and they chalked it up to a glitch like you said, for months, nobody really knew what it was. So technology got us into this problem. I guess my question is, can technology help us get out of the problem? And that maybe is where AI fits in. >>Yes, yes. Uh, in fact, a lot of analytics, we went in, uh, to go back to the raw data that is highly dispersed from different sources, right, assemble them to see if you can find a material trend, right? You can see lots of trends right? Like, uh, you know, we, if if humans look at things right, we tend to see patterns in clouds, right? So sometimes you need to apply statistical analysis, um math to be sure that what the model is seeing is is real. Right? And and that required work. That's one area. The second area is uh you know, when um uh there are times when you you just need to to go through that uh that tough approach to to find the answer. Now, the issue comes to mind now is is that humans put in the rules to decide what goes into a report that everybody sees in this case uh before the change in the rules. Right? But by the way, after the discovery, the authorities change the rules and all all shares, all traits of different any sizes. It has to be reported. No. Yeah. Right. But the rule was applied uh you know, to say earlier that shares under 100 trades under 100 shares need not be reported. So sometimes you just have to understand that reports were decided by humans and and under for understandable reasons. I mean they probably didn't want that for various reasons not to put everything in there so that people could still read it uh in a reasonable amount of time. But uh we need to understand that rules were being put in by humans for the reports we read. And as such, there are times you just need to go back to the raw data. >>I want to ask, >>albeit that it's gonna be tough. >>Yeah. So I want to ask a question about AI is obviously it's in your title and it's something you know a lot about but and I want to make a statement, you tell me if it's on point or off point. So it seems that most of the Ai going on in the enterprise is modeling data science applied to troves of data >>but >>but there's also a lot of ai going on in consumer whether it's you know, fingerprint technology or facial recognition or natural language processing. Will a two part question will the consumer market has so often in the enterprise sort of inform us uh the first part and then will there be a shift from sort of modeling if you will to more you mentioned autonomous vehicles more ai influencing in real time. Especially with the edge. She can help us understand that better. >>Yeah, it's a great question. Right. Uh there are three stages to just simplify, I mean, you know, it's probably more sophisticated than that but let's simplify three stages. All right. To to building an Ai system that ultimately can predict, make a prediction right or to to assist you in decision making, have an outcome. So you start with the data massive amounts data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data and the machine uh starts to evolve a model based on all the data is seeing. It starts to evolve right to the point that using a test set of data that you have separately campus site that you know the answer for. Then you test the model uh you know after you trained it with all that data to see whether it's prediction accuracy is high enough and once you are satisfied with it, you you then deploy the model to make the decision and that's the influence. Right? So a lot of times depend on what what we are focusing on. We we um in data science are we working hard on assembling the right data to feed the machine with, That's the data preparation organization work. And then after which you build your models, you have to pick the right models for the decisions and prediction you wanted to make. You pick the right models and then you start feeding the data with it. Sometimes you you pick one model and the prediction isn't that robust, it is good but then it is not consistent right now what you do is uh you try another model so sometimes it's just keep trying different models until you get the right kind. Yeah, that gives you a good robust decision making and prediction after which It is tested well Q eight. You would then take that model and deploy it at the edge. Yeah. And then at the edges is essentially just looking at new data, applying it to the model, you're you're trained and then that model will give you a prediction decision. Right? So uh it is these three stages. Yeah, but more and more uh you know, your question reminds me that more and more people are thinking as the edge become more and more powerful. Can you also do learning at the edge? Right. That's the reason why we spoke about swarm learning the last time, learning at the edge as a swamp, right? Because maybe individually they may not have enough power to do so. But as a swampy me, >>is that learning from the edge or learning at the edge? In other words? Yes. Yeah. Question Yeah. >>That's a great question. That's a great question. Right? So uh the quick answer is learning at the edge, right? Uh and also from the edge, but the main goal, right? The goal is to learn at the edge so that you don't have to move the data that the Edge sees first back to the cloud or the core to do the learning because that would be the reason. One of the main reasons why you want to learn at the edge, right? Uh So so that you don't need to have to send all that data back and assemble it back from all the different edge devices, assemble it back to the cloud side to to do the learning right? With swampland. You can learn it and keep the data at the edge and learn at that point. >>And then maybe only selectively send the autonomous vehicle example you gave us. Great because maybe there, you know, there may be only persisting, they're not persisting data that is inclement weather or when a deer runs across the front and then maybe they they do that and then they send that smaller data set back and maybe that's where it's modelling done. But the rest can be done at the edges. It's a new world that's coming down. Let me ask you a question, is there a limit to what data should be collected and how it should be collected? >>That's a great question again. You know uh wow today, full of these uh insightful questions that actually touches on the second challenge. Right? How do we uh in order to thrive in this new age of inside? The second challenge is are you know the is our future challenge, right? What do we do for our future? And and in there is uh the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that I talk about what to collect right? When to organize it when you collect and then where will your data be, you know going forward that you are collecting from? So what, when and where for the what data for the what data to collect? That? That was the question you ask. Um it's it's a question that different industries have to ask themselves because it will vary, right? Um let me give you the you use the autonomous car example, let me use that. And you have this customer collecting massive amounts of data. You know, we're talking about 10 petabytes a day from the fleet of their cars. And these are not production autonomous cars, right? These are training autonomous cars collecting data so they can train and eventually deploy commercial cars, right? Um so this data collection cars they collect as a fleet of them collect temporal bikes a day. And when it came to us building a storage system to store all of that data, they realized they don't want to afford to store all of it. Now, here comes the dilemma, right? What should I after I spent so much effort building all these cars and sensors and collecting data, I've now decide what to delete. That's a dilemma right now in working with them on this process of trimming down what they collected. You know, I'm constantly reminded of the sixties and seventies, right? To remind myself 60 and seventies, we call a large part of our D. N. A junk DNA. Today. We realize that a large part of that what we call john has function as valuable function. They are not jeans, but they regulate the function of jeans, you know, So, so what's jump in the yesterday could be valuable today or what's junk today could be valuable tomorrow, Right? So, so there's this tension going on right between you decided not wanting to afford to store everything that you can get your hands on. But on the other hand, you you know, you worry you you you ignore the wrong ones, right? You can see this tension in our customers, right? And it depends on industry here, right? In health care, they say I have no choice. I I want it. All right. One very insightful point brought up by one health care provider that really touched me was, you know, we are not we don't only care. Of course we care a lot. We care a lot about the people we are caring for, right? But you also care for the people were not caring for. How do we find them? Mhm. Right. And that therefore, they did not just need to collect data. That is that they have with from their patients. They also need to reach out right to outside data so that they can figure out who they are not caring for, right? So they want it all. So I tell us them, so what do you do with funding if you want it all? They say they have no choice but to figure out a way to fund it and perhaps monetization of what they have now is the way to come around and find that. Of course they also come back to us rightfully that you know, we have to then work out a way to help them build that system, you know? So that's health care, right? And and if you go to other industries like banking, they say they can't afford to keep them off, but they are regulated, seems like healthcare, they are regulated as to uh privacy and such. Like so many examples different industries having different needs, but different approaches to how what they collect. But there is this constant tension between um you perhaps deciding not wanting to fund all of that uh all that you can store, right? But on the other hand, you know, if you if you kind of don't want to afford it and decide not to store some uh if he does some become highly valuable in the future, right? Yeah. >>We can make some assumptions about the future, can't we? I mean, we know there's gonna be a lot more data than than we've ever seen before. We know that we know well notwithstanding supply constraints on things like nand. We know the prices of storage is going to continue to decline. We also know, and not a lot of people are really talking about this but the processing power but he says moore's law is dead okay. It's waning. But the processing power when you combine the Cpus and NP US and GPUS and accelerators and and so forth actually is is increasing. And so when you think about these use cases at the edge, you're going to have much more processing power, you're gonna have cheaper storage and it's going to be less expensive processing And so as an ai practitioner, what can you do with that? >>Yeah, it's highly again, another insightful questions that we touched on our keynote and that that goes up to the why I do the where? Right, When will your data be? Right. We have one estimate that says that by next year there will be 55 billion connected devices out there. Right. 55 billion. Right. What's the population of the world? Of the other? Of 10 billion? But this thing is 55 billion. Right? Uh and many of them, most of them can collect data. So what do you what do you do? Right. Um So the amount of data that's gonna come in, it's gonna weigh exceed right? Our drop in storage costs are increasing computer power. Right? So what's the answer? Right. So, so the the answer must be knowing that we don't and and even the drop in price and increase in bandwidth, it will overwhelm the increased five G will overwhelm five G. Right? Given amount 55 billion of them collecting. Right? So, the answer must be that there might need to be a balance between you needing to bring all that data from the 55 billion devices of data back to a central as a bunch of central Cause because you may not be able to afford to do that firstly band with even with five G. M and and SD when you'll still be too expensive given the number of devices out there. Were you given storage cause dropping will still be too expensive to try and store them all. So the answer must be to start at least to mitigate the problem to some leave both a lot of the data out there. Right? And only send back the pertinent ones as you said before. But then if you did that, then how are we gonna do machine learning at the core and the cloud side? If you don't have all the data you want rich data to train with. Right? Some sometimes you want a mix of the uh positive type data and the negative type data so you can train the machine in a more balanced way. So the answer must be eventually right. As we move forward with these huge number of devices out of the edge to do machine learning at the edge. Today, we don't have enough power. Right? The edge typically is characterized by a lower uh, energy capability and therefore lower compute power. But soon, you know, even with lower energy, they can do more with compute power improving in energy efficiency, Right? Uh, so learning at the edge today, we do influence at the edge. So we data model deploy and you do influence at the age, that's what we do today. But more and more, I believe, given a massive amount of data at the edge, you you have to have to start doing machine learning at the edge. And and if when you don't have enough power, then you aggregate multiple devices, compute power into a swamp and learn as a swan, >>interesting. So now, of course, if I were sitting and fly on the wall in HP board meeting, I said, okay, HP is as a leading provider of compute, how do you take advantage of that? I mean, we're going, I know it's future, but you must be thinking about that and participating in those markets. I know today you are you have, you know, edge line and other products. But there's it seems to me that it's it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that >>opportunity for your customers? Uh the world will have to have a balance right? Where today the default, Well, the more common mode is to collect the data from the edge and train at uh at some centralized location or a number of centralized location um going forward. Given the proliferation of the edge devices, we'll need a balance. We need both. We need capability at the cloud side. Right. And it has to be hybrid. And then we need capability on the edge side. Yeah. That they want to build systems that that on one hand, uh is uh edge adapted, right? Meaning the environmentally adapted because the edge different they are on a lot of times on the outside. Uh They need to be packaging adapted and also power adapted, right? Because typically many of these devices are battery powered. Right? Um so you have to build systems that adapt to it, but at the same time they must not be custom. That's my belief. They must be using standard processes and standard operating system so that they can run rich a set of applications. So yes. Um that's that's also the insightful for that Antonio announced in 2018, Uh the next four years from 2018, right, $4 billion dollars invested to strengthen our edge portfolio, edge product lines, right Edge solutions. >>I get a doctor go. I could go on for hours with you. You're you're just such a great guest. Let's close what are you most excited about in the future of of of it? Certainly H. P. E. But the industry in general. >>Yeah I think the excitement is uh the customers right? The diversity of customers and and the diversity in a way they have approached their different problems with data strategy. So the excitement is around data strategy right? Just like you know uh you know the the statement made was was so was profound. Right? Um And Antonio said we are in the age of insight powered by data. That's the first line right? The line that comes after that is as such were becoming more and more data centric with data the currency. Now the next step is even more profound. That is um you know we are going as far as saying that you know um data should not be treated as cost anymore. No right. But instead as an investment in a new asset class called data with value on our balance sheet, this is a this is a step change right in thinking that is going to change the way we look at data the way we value it. So that's a statement that this is the exciting thing because because for for me a city of AI right uh machine is only as intelligent as the data you feed it with. Data is a source of the machine learning to be intelligent. So so that's that's why when when people start to value data right? And and and say that it is an investment when we collect it. It is very positive for ai because an Ai system gets intelligent, more intelligence because it has a huge amounts of data and the diversity of data. So it'd be great if the community values values data. Well >>you certainly see it in the valuations of many companies these days. Um and I think increasingly you see it on the income statement, you know data products and people monetizing data services and maybe eventually you'll see it in the in the balance. You know Doug Laney when he was a gardener group wrote a book about this and a lot of people are thinking about it. That's a big change isn't it? Dr >>yeah. Question is is the process and methods evaluation. Right. But uh I believe we'll get there, we need to get started then we'll get their belief >>doctor goes on and >>pleasure. And yeah and then the yeah I will will will will benefit greatly from it. >>Oh yeah, no doubt people will better understand how to align you know, some of these technology investments, Doctor goes great to see you again. Thanks so much for coming back in the cube. It's been a real pleasure. >>Yes. A system. It's only as smart as the data you feed it with. >>Excellent. We'll leave it there. Thank you for spending some time with us and keep it right there for more great interviews from HP discover 21. This is dave a lot for the cube. The leader in enterprise tech coverage right back.

(upbeat music) >> Welcome back to HPE Discover 2021, theCube's virtual coverage, continuous coverage of HPE's annual customer event. My name is Dave Vellante and we're going to dive into the intersection of high-performance computing, data and AI with Dr. Eng Lim Goh who's a Senior Vice President and CTO for AI at Hewlett Packard Enterprise. Dr. Goh, great to see you again. Welcome back to theCube. >> Hey, hello, Dave. Great to talk to you again. >> You might remember last year we talked a lot about swarm intelligence and how AI is evolving. Of course you hosted the Day 2 keynotes here at Discover. And you talked about thriving in the age of insights and how to craft a data-centric strategy and you addressed some of the biggest problems I think organizations face with data. And that's, you got to look, data is plentiful, but insights, they're harder to come by and you really dug into some great examples in retail, banking, and medicine and healthcare and media. But stepping back a little bit we'll zoom out on Discover '21, you know, what do you make of the events so far and some of your big takeaways? >> Hmm, well, you started with the insightful question. Data is everywhere then but we lack the insight. That's also part of the reason why that's a main reason why, Antonio on Day 1 focused and talked about that, the fact that we are in the now in the age of insight and how to thrive in this new age. What I then did on the Day 2 keynote following Antonio is to talk about the challenges that we need to overcome in order to thrive in this new age. >> So maybe we could talk a little bit about some of the things that you took away in terms of, I'm specifically interested in some of the barriers to achieving insights when you know customers are drowning in data. What do you hear from customers? What were your takeaway from some of the ones you talked about today? >> Very pertinent question, Dave. You know, the two challenges I spoke about how to, that we need to overcome in order to thrive in this new age, the first one is the current challenge. And that current challenge is, you know state of this, you know, barriers to insight, when we are awash with data. So that's a statement. How to overcome those barriers. One of the barriers to insight when we are awash in data, in the Day 2 keynote, I spoke about three main things, three main areas that receive from customers. The first one, the first barrier is with many of our customers, data is siloed. You know, like in a big corporation, you've got data siloed by sales, finance, engineering, manufacturing, and so on supply chain and so on. And there's a major effort ongoing in many corporations to build a Federation layer above all those silos so that when you build applications above they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the first barrier we spoke about, you know, barriers to insight when we are awash with data. The second barrier is that we see amongst our customers is that data is raw and disperse when they are stored. And it's tough to get to value out of them. In that case I use the example of the May 6, 2010 event where the stock market dropped a trillion dollars in tens of minutes. We all know those who are financially attuned with, know about this incident. But that this is not the only incident. There are many of them out there. And for that particular May 6, event, you know it took a long time to get insight, months, yeah, before we, for months we had no insight as to what happened, why it happened. And there were many other incidences like this and the regulators were looking for that one rule that could mitigate many of these incidences. One of our customers decided to take the hard road to go with the tough data. Because data is raw and dispersed. So they went into all the different feeds of financial transaction information, took the tough, you know, took a tough road and analyze that data took a long time to assemble. And he discovered that there was quote stuffing. That people were sending a lot of trades in and then canceling them almost immediately. You have to manipulate the market. And why didn't we see it immediately? Well, the reason is the process reports that everybody sees had the rule in there that says all trades less than 100 shares don't need to report in there. And so what people did was sending a lot of less than 100 shares trades to fly under the radar to do this manipulation. So here is, here the second barrier. Data could be raw and disperse. Sometimes it's just have to take the hard road and to get insight. And this is one great example. And then the last barrier has to do with sometimes when you start a project to get insight, to get answers and insight, you realize that all the data's around you, but you don't seem to find the right ones to get what you need. You don't seem to get the right ones, yeah. Here we have three quick examples of customers. One was a great example where they were trying to build a language translator a machine language translator between two languages. But in order to do that they need to get hundreds of millions of word pairs of one language compare with the corresponding other hundreds of millions of them. They say, "Where I'm going to get all these word pairs?" Someone creative thought of a willing source and huge source, it was a United Nations. You see, so sometimes you think you don't have the right data with you, but there might be another source and a willing one that could give you that data. The second one has to do with, there was the, sometimes you may just have to generate that data. Interesting one. We had an autonomous car customer that collects all these data from their cars. Massive amounts of data, lots of sensors, collect lots of data. And, you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car in fine weather and collected the car driving on this highway in rain and also in snow. But never had the opportunity to collect the car in hail because that's a rare occurrence. So instead of waiting for a time where the car can drive in hail, they build a simulation by having the car collected in snow and simulated hail. So these are some of the examples where we have customers working to overcome barriers. You have barriers that is associated with the fact, that data silo, if federated barriers associated with data that's tough to get at. They just took the hard road. And sometimes thirdly, you just have to be creative to get the right data you need. >> Wow, I tell you, I have about 100 questions based on what you just said. And as a great example, the flash crash in fact Michael Lewis wrote about this in his book, the "Flash Boys" and essentially. It was high frequency traders trying to front run the market and sending in small block trades trying to get sort of front ended. So that's, and they chalked it up to a glitch. Like you said, for months, nobody really knew what it was. So technology got us into this problem. Can I guess my question is can technology help us get get out of the problem? And that maybe is where AI fits in. >> Yes. Yes. In fact, a lot of analytics work went in to go back to the raw data that is highly dispersed from different sources, assemble them to see if you can find a material trend. You can see lots of trends. Like, no, we, if humans at things we tend to see patterns in clouds. So sometimes you need to apply statistical analysis, math to be sure that what the model is seeing is real. And that required work. That's one area. The second area is, you know, when this, there are times when you just need to go through that tough approach to find the answer. Now, the issue comes to mind now is that humans put in the rules to decide what goes into a report that everybody sees. And in this case before the change in the rules. By the way, after the discovery, the authorities changed the rules and all shares all trades of different, any sizes it has to be reported. Not, yeah. But the rule was applied to to say earlier that shares under 100, trades under 100 shares need not be reported. So sometimes you just have to understand that reports were decided by humans and for understandable reasons. I mean, they probably didn't, wanted for various reasons not to put everything in there so that people could still read it in a reasonable amount of time. But we need to understand that rules were being put in by humans for the reports we read. And as such there are times we just need to go back to the raw data. >> I want to ask you-- Or be it that it's going to be tough there. >> Yeah, so I want to ask you a question about AI as obviously it's in your title and it's something you know a lot about and I'm going to make a statement. You tell me if it's on point or off point. Seems that most of the AI going on in the enterprise is modeling data science applied to troves of data. But there's also a lot of AI going on in consumer, whether it's fingerprint technology or facial recognition or natural language processing. Will, to two-part question, will the consumer market, let's say as it has so often in the enterprise sort of inform us is sort of first part. And then will there be a shift from sort of modeling, if you will, to more, you mentioned autonomous vehicles more AI inferencing in real-time, especially with the Edge. I think you can help us understand that better. >> Yeah, this is a great question. There are three stages to just simplify, I mean, you know, it's probably more sophisticated than that, but let's just simplify there're three stages to building an AI system that ultimately can predict, make a prediction. Or to assist you in decision-making, have an outcome. So you start with the data, massive amounts of data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data. And the machine starts to evolve a model based on all the data is seeing it starts to evolve. To a point that using a test set of data that you have separately kept a site that you know the answer for. Then you test the model, you know after you're trained it with all that data to see whether his prediction accuracy is high enough. And once you are satisfied with it, you then deploy the model to make the decision and that's the inference. So a lot of times depending on what we are focusing on. We in data science are we working hard on assembling the right data to feed the machine with? That's the data preparation organization work. And then after which you build your models you have to pick the right models for the decisions and prediction you wanted to make. You pick the right models and then you start feeding the data with it. Sometimes you pick one model and a prediction isn't that a robust, it is good, but then it is not consistent. Now what you do is you try another model. So sometimes you just keep trying different models until you get the right kind, yeah, that gives you a good robust decision-making and prediction. Now, after which, if it's tested well, Q8 you will then take that model and deploy it at the Edge, yeah. And then at the Edge is essentially just looking at new data applying it to the model that you have trained and then that model will give you a prediction or a decision. So it is these three stages, yeah. But more and more, your question reminds me that more and more people are thinking as the Edge become more and more powerful, can you also do learning at the Edge? That's the reason why we spoke about swarm learning the last time, learning at the Edge as a swarm. Because maybe individually they may not have enough power to do so, but as a swarm, they may. >> Is that learning from the Edge or learning at the Edge. In other words, is it-- >> Yes. >> Yeah, you don't understand my question, yeah. >> That's a great question. That's a great question. So answer is learning at the Edge, and also from the Edge, but the main goal, the goal is to learn at the Edge so that you don't have to move the data that Edge sees first back to the Cloud or the call to do the learning. Because that would be the reason, one of the main reasons why you want to learn at the Edge. So that you don't need to have to send all that data back and assemble it back from all the different Edge devices assemble it back to the Cloud side to do the learning. With swarm learning, you can learn it and keep the data at the Edge and learn at that point, yeah. >> And then maybe only selectively send the autonomous vehicle example you gave is great 'cause maybe they're, you know, there may be only persisting. They're not persisting data that is an inclement weather, or when a deer runs across the front and then maybe they do that and then they send that smaller data set back and maybe that's where it's modeling done but the rest can be done at the Edge. It's a new world that's coming to, let me ask you a question. Is there a limit to what data should be collected and how it should be collected? >> That's a great question again, yeah, well, today full of these insightful questions that actually touches on the second challenge. How do we, to in order to thrive in this new age of insight. The second challenge is our future challenge. What do we do for our future? And in there is the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that, I talk about what to collect, and when to organize it when you collect, and then where will your data be going forward that you are collecting from? So what, when, and where. For the what data, for what data to collect that was the question you asked. It's a question that different industries have to ask themselves because it will vary. Let me give you the, you use the autonomous car example. Let me use that and you have this customer collecting massive amounts of data. You know, we talking about 10 petabytes a day from a fleet of their cars and these are not production autonomous cars. These are training autonomous cars, collecting data so they can train and eventually deploy a commercial cars. Also these data collection cars, they collect 10 as a fleet of them collect 10 petabytes a day. And then when it came to us, building a storage system to store all of that data they realize they don't want to afford to store all of it. Now here comes the dilemma. What should I, after I spent so much effort building all this cars and sensors and collecting data, I've now decide what to delete. That's a dilemma. Now in working with them on this process of trimming down what they collected. I'm constantly reminded of the 60s and 70s. To remind myself 60s and 70s, we call a large part of our DNA, junk DNA. Today we realized that a large part of that, what we call junk has function has valuable function. They are not genes but they regulate the function of genes. So what's junk in yesterday could be valuable today, or what's junk today could be valuable tomorrow. So there's this tension going on between you deciding not wanting to afford to store everything that you can get your hands on. But on the other hand, you know you worry, you ignore the wrong ones. You can see this tension in our customers. And then it depends on industry here. In healthcare they say, I have no choice. I want it all, why? One very insightful point brought up by one healthcare provider that really touched me was you know, we are not, we don't only care. Of course we care a lot. We care a lot about the people we are caring for. But we also care for the people we are not caring for. How do we find them? And therefore, they did not just need to collect data that they have with, from their patients they also need to reach out to outside data so that they can figure out who they are not caring for. So they want it all. So I asked them, "So what do you do with funding if you want it all?" They say they have no choice but they'll figure out a way to fund it and perhaps monetization of what they have now is the way to come around and fund that. Of course, they also come back to us, rightfully that you know, we have to then work out a way to to help them build a system. So that healthcare. And if you go to other industries like banking, they say they can afford to keep them all. But they are regulated same like healthcare. They are regulated as to privacy and such like. So many examples, different industries having different needs but different approaches to how, what they collect. But there is this constant tension between you perhaps deciding not wanting to fund all of that, all that you can store. But on the other hand you know, if you kind of don't want to afford it and decide not to store some, maybe those some become highly valuable in the future. You worry. >> Well, we can make some assumptions about the future, can't we? I mean we know there's going to be a lot more data than we've ever seen before, we know that. We know, well not withstanding supply constraints and things like NAND. We know the price of storage is going to continue to decline. We also know and not a lot of people are really talking about this but the processing power, everybody says, Moore's Law is dead. Okay, it's waning but the processing power when you combine the CPUs and NPUs, and GPUs and accelerators and so forth, actually is increasing. And so when you think about these use cases at the Edge you're going to have much more processing power. You're going to have cheaper storage and it's going to be less expensive processing. And so as an AI practitioner, what can you do with that? >> Yeah, it's a highly, again another insightful question that we touched on, on our keynote and that goes up to the why, I'll do the where. Where will your data be? We have one estimate that says that by next year, there will be 55 billion connected devices out there. 55 billion. What's the population of the world? Well, off the order of 10 billion, but this thing is 55 billion. And many of them, most of them can collect data. So what do you do? So the amount of data that's going to come in is going to way exceed our drop in storage costs our increasing compute power. So what's the answer? The answer must be knowing that we don't and even a drop in price and increase in bandwidth, it will overwhelm the 5G, it'll will overwhelm 5G, given the amount of 55 billion of them collecting. So the answer must be that there needs to be a balance between you needing to bring all that data from the 55 billion devices of the data back out to a central, as a bunch of central cost because you may not be able to afford to do that. Firstly bandwidth, even with 5G and as the, when you still be too expensive given the number of devices out there. You know given storage costs dropping it'll still be too expensive to try and install them all. So the answer must be to start at least to mitigate the problem to some leave most a lot of the data out there. And only send back the pertinent ones, as you said before. But then if you did that then, how are we going to do machine learning at the core and the Cloud side, if you don't have all the data you want rich data to train with. Sometimes you want to a mix of the positive type data, and the negative type data. So you can train the machine in a more balanced way. So the answer must be you eventually, as we move forward with these huge number of devices are at the Edge to do machine learning at the Edge. Today we don't even have power. The Edge typically is characterized by a lower energy capability and therefore, lower compute power. But soon, you know, even with low energy, they can do more with compute power, improving in energy efficiency. So learning at the Edge today we do inference at the Edge. So we data, model, deploy and you do inference at age. That's what we do today. But more and more, I believe given a massive amount of data at the Edge you have to have to start doing machine learning at the Edge. And if when you don't have enough power then you aggregate multiple devices' compute power into a swarm and learn as a swarm. >> Oh, interesting, so now of course, if I were sitting in a flyer flying the wall on HPE Board meeting I said, "Okay, HPE is a leading provider of compute." How do you take advantage that? I mean, we're going, I know it's future but you must be thinking about that and participating in those markets. I know today you are, you have, you know, Edge line and other products, but there's, it seems to me that it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that opportunity for your customers? >> The wall will have to have a balance. Where today the default, well, the more common mode is to collect the data from the Edge and train at some centralized location or number of centralized location. Going forward, given the proliferation of the Edge devices, we'll need a balance, we need both. We need capability at the Cloud side. And it has to be hybrid. And then we need capability on the Edge side. Yeah that we need to build systems that on one hand is Edge-adapted. Meaning they environmentally-adapted because the Edge differently are on it. A lot of times on the outside, they need to be packaging-adapted and also power-adapted. Because typically many of these devices are battery-powered. So you have to build systems that adapts to it. But at the same time, they must not be custom. That's my belief. They must be using standard processes and standard operating system so that they can run a rich set of applications. So yes, that's also the insightful for that. Antonio announced in 2018 for the next four years from 2018, $4 billion invested to strengthen our Edge portfolio our Edge product lines, Edge solutions. >> Dr. Goh, I could go on for hours with you. You're just such a great guest. Let's close. What are you most excited about in the future of certainly HPE, but the industry in general? >> Yeah, I think the excitement is the customers. The diversity of customers and the diversity in the way they have approached their different problems with data strategy. So the excitement is around data strategy. Just like, you know, the statement made for us was so, was profound. And Antonio said we are in the age of insight powered by data. That's the first line. The line that comes after that is as such we are becoming more and more data-centric with data the currency. Now the next step is even more profound. That is, you know, we are going as far as saying that data should not be treated as cost anymore, no. But instead, as an investment in a new asset class called data with value on our balance sheet. This is a step change in thinking that is going to change the way we look at data, the way we value it. So that's a statement. So this is the exciting thing, because for me a CTO of AI, a machine is only as intelligent as the data you feed it with. Data is a source of the machine learning to be intelligent. So that's why when the people start to value data and say that it is an investment when we collect it it is very positive for AI because an AI system gets intelligent, get more intelligence because it has huge amounts of data and a diversity of data. So it'd be great if the community values data. >> Well, are you certainly see it in the valuations of many companies these days? And I think increasingly you see it on the income statement, you know data products and people monetizing data services, and yeah, maybe eventually you'll see it in the balance sheet, I know. Doug Laney when he was at Gartner Group wrote a book about this and a lot of people are thinking about it. That's a big change, isn't it? Dr. Goh. >> Yeah, yeah, yeah. Your question is the process and methods in valuation. But I believe we'll get there. We need to get started and then we'll get there, I believe, yeah. >> Dr. Goh it's always my pleasure. >> And then the AI will benefit greatly from it. >> Oh yeah, no doubt. People will better understand how to align some of these technology investments. Dr. Goh, great to see you again. Thanks so much for coming back in theCube. It's been a real pleasure. >> Yes, a system is only as smart as the data you feed it with. (both chuckling) >> Well, excellent, we'll leave it there. Thank you for spending some time with us so keep it right there for more great interviews from HPE Discover '21. This is Dave Vellante for theCube, the leader in enterprise tech coverage. We'll be right back (upbeat music)

(upbeat music) >> Welcome back to HPE Discover 2021, theCUBE's virtual coverage, continuous coverage of HPE's Annual Customer Event. My name is Dave Vellante, and we're going to dive into the intersection of high-performance computing, data and AI with Doctor Eng Lim Goh, who's a Senior Vice President and CTO for AI at Hewlett Packard Enterprise. Doctor Goh, great to see you again. Welcome back to theCUBE. >> Hello, Dave, great to talk to you again. >> You might remember last year we talked a lot about Swarm intelligence and how AI is evolving. Of course, you hosted the Day 2 Keynotes here at Discover. And you talked about thriving in the age of insights, and how to craft a data-centric strategy. And you addressed some of the biggest problems, I think organizations face with data. That's, you've got a, data is plentiful, but insights, they're harder to come by. >> Yeah. >> And you really dug into some great examples in retail, banking, in medicine, healthcare and media. But stepping back a little bit we zoomed out on Discover '21. What do you make of the events so far and some of your big takeaways? >> Hmm, well, we started with the insightful question, right, yeah? Data is everywhere then, but we lack the insight. That's also part of the reason why, that's a main reason why Antonio on day one focused and talked about the fact that we are in the now in the age of insight, right? And how to try thrive in that age, in this new age? What I then did on a Day 2 Keynote following Antonio is to talk about the challenges that we need to overcome in order to thrive in this new age. >> So, maybe we could talk a little bit about some of the things that you took away in terms of, I'm specifically interested in some of the barriers to achieving insights. You know customers are drowning in data. What do you hear from customers? What were your takeaway from some of the ones you talked about today? >> Oh, very pertinent question, Dave. You know the two challenges I spoke about, that we need to overcome in order to thrive in this new age. The first one is the current challenge. And that current challenge is, you know, stated is now barriers to insight, when we are awash with data. So that's a statement on how do you overcome those barriers? What are the barriers to insight when we are awash in data? In the Day 2 Keynote, I spoke about three main things. Three main areas that we receive from customers. The first one, the first barrier is in many, with many of our customers, data is siloed, all right. You know, like in a big corporation, you've got data siloed by sales, finance, engineering, manufacturing and so on supply chain and so on. And there's a major effort ongoing in many corporations to build a federation layer above all those silos so that when you build applications above, they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the first barrier we spoke about, you know? Barriers to insight when we are awash with data. The second barrier is that we see amongst our customers is that data is raw and disperse when they are stored. And you know, it's tough to get at, to tough to get a value out of them, right? And in that case, I use the example of, you know, the May 6, 2010 event where the stock market dropped a trillion dollars in terms of minutes. We all know those who are financially attuned with know about this incident but that this is not the only incident. There are many of them out there. And for that particular May 6 event, you know, it took a long time to get insight. Months, yeah, before we, for months we had no insight as to what happened. Why it happened? Right, and there were many other incidences like this and the regulators were looking for that one rule that could mitigate many of these incidences. One of our customers decided to take the hard road they go with the tough data, right? Because data is raw and dispersed. So they went into all the different feeds of financial transaction information, took the tough, you know, took a tough road. And analyze that data took a long time to assemble. And they discovered that there was caught stuffing, right? That people were sending a lot of trades in and then canceling them almost immediately. You have to manipulate the market. And why didn't we see it immediately? Well, the reason is the process reports that everybody sees, the rule in there that says, all trades less than a hundred shares don't need to report in there. And so what people did was sending a lot of less than a hundred shares trades to fly under the radar to do this manipulation. So here is the second barrier, right? Data could be raw and dispersed. Sometimes it's just have to take the hard road and to get insight. And this is one great example. And then the last barrier has to do with sometimes when you start a project to get insight, to get answers and insight, you realize that all the data's around you, but you don't seem to find the right ones to get what you need. You don't seem to get the right ones, yeah? Here we have three quick examples of customers. One was a great example, right? Where they were trying to build a language translator or machine language translator between two languages, right? By not do that, they need to get hundreds of millions of word pairs. You know of one language compare with the corresponding other. Hundreds of millions of them. They say, well, I'm going to get all these word pairs. Someone creative thought of a willing source and a huge, it was a United Nations. You see? So sometimes you think you don't have the right data with you, but there might be another source and a willing one that could give you that data, right? The second one has to do with, there was the sometimes you may just have to generate that data. Interesting one, we had an autonomous car customer that collects all these data from their their cars, right? Massive amounts of data, lots of sensors, collect lots of data. And, you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car in fine weather and collected the car driving on this highway in rain and also in snow. But never had the opportunity to collect the car in hill because that's a rare occurrence. So instead of waiting for a time where the car can drive in hill, they build a simulation by having the car collected in snow and simulated him. So these are some of the examples where we have customers working to overcome barriers, right? You have barriers that is associated. In fact, that data silo, they federated it. Virus associated with data, that's tough to get at. They just took the hard road, right? And sometimes thirdly, you just have to be creative to get the right data you need. >> Wow! I tell you, I have about a hundred questions based on what you just said, you know? (Dave chuckles) And as a great example, the Flash Crash. In fact, Michael Lewis, wrote about this in his book, the Flash Boys. And essentially, right, it was high frequency traders trying to front run the market and sending into small block trades (Dave chuckles) trying to get sort of front ended. So that's, and they chalked it up to a glitch. Like you said, for months, nobody really knew what it was. So technology got us into this problem. (Dave chuckles) I guess my question is can technology help us get out of the problem? And that maybe is where AI fits in? >> Yes, yes. In fact, a lot of analytics work went in to go back to the raw data that is highly dispersed from different sources, right? Assembled them to see if you can find a material trend, right? You can see lots of trends, right? Like, no, we, if humans look at things that we tend to see patterns in Clouds, right? So sometimes you need to apply statistical analysis math to be sure that what the model is seeing is real, right? And that required, well, that's one area. The second area is you know, when this, there are times when you just need to go through that tough approach to find the answer. Now, the issue comes to mind now is that humans put in the rules to decide what goes into a report that everybody sees. Now, in this case, before the change in the rules, right? But by the way, after the discovery, the authorities changed the rules and all shares, all trades of different any sizes it has to be reported. >> Right. >> Right, yeah? But the rule was applied, you know, I say earlier that shares under a hundred, trades under a hundred shares need not be reported. So, sometimes you just have to understand that reports were decided by humans and for understandable reasons. I mean, they probably didn't wanted a various reasons not to put everything in there. So that people could still read it in a reasonable amount of time. But we need to understand that rules were being put in by humans for the reports we read. And as such, there are times we just need to go back to the raw data. >> I want to ask you... >> Oh, it could be, that it's going to be tough, yeah. >> Yeah, I want to ask you a question about AI as obviously it's in your title and it's something you know a lot about but. And I'm going to make a statement, you tell me if it's on point or off point. So seems that most of the AI going on in the enterprise is modeling data science applied to, you know, troves of data. But there's also a lot of AI going on in consumer. Whether it's, you know, fingerprint technology or facial recognition or natural language processing. Well, two part question will the consumer market, as it has so often in the enterprise sort of inform us is sort of first part. And then, there'll be a shift from sort of modeling if you will to more, you mentioned the autonomous vehicles, more AI inferencing in real time, especially with the Edge. Could you help us understand that better? >> Yeah, this is a great question, right? There are three stages to just simplify. I mean, you know, it's probably more sophisticated than that. But let's just simplify that three stages, right? To building an AI system that ultimately can predict, make a prediction, right? Or to assist you in decision-making. I have an outcome. So you start with the data, massive amounts of data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data, and the machine starts to evolve a model based on all the data it's seeing. It starts to evolve, right? To a point that using a test set of data that you have separately kept aside that you know the answer for. Then you test the model, you know? After you've trained it with all that data to see whether its prediction accuracy is high enough. And once you are satisfied with it, you then deploy the model to make the decision. And that's the inference, right? So a lot of times, depending on what we are focusing on, we in data science are, are we working hard on assembling the right data to feed the machine with? That's the data preparation organization work. And then after which you build your models you have to pick the right models for the decisions and prediction you need to make. You pick the right models. And then you start feeding the data with it. Sometimes you pick one model and a prediction isn't that robust. It is good, but then it is not consistent, right? Now what you do is you try another model. So sometimes it gets keep trying different models until you get the right kind, yeah? That gives you a good robust decision-making and prediction. Now, after which, if it's tested well, QA, you will then take that model and deploy it at the Edge. Yeah, and then at the Edge is essentially just looking at new data, applying it to the model that you have trained. And then that model will give you a prediction or a decision, right? So it is these three stages, yeah. But more and more, your question reminds me that more and more people are thinking as the Edge become more and more powerful. Can you also do learning at the Edge? >> Right. >> That's the reason why we spoke about Swarm Learning the last time. Learning at the Edge as a Swarm, right? Because maybe individually, they may not have enough power to do so. But as a Swarm, they may. >> Is that learning from the Edge or learning at the Edge? In other words, is that... >> Yes. >> Yeah. You do understand my question. >> Yes. >> Yeah. (Dave chuckles) >> That's a great question. That's a great question, right? So the quick answer is learning at the Edge, right? And also from the Edge, but the main goal, right? The goal is to learn at the Edge so that you don't have to move the data that Edge sees first back to the Cloud or the Call to do the learning. Because that would be the reason, one of the main reasons why you want to learn at the Edge. Right? So that you don't need to have to send all that data back and assemble it back from all the different Edge devices. Assemble it back to the Cloud Site to do the learning, right? Some on you can learn it and keep the data at the Edge and learn at that point, yeah. >> And then maybe only selectively send. >> Yeah. >> The autonomous vehicle, example you gave is great. 'Cause maybe they're, you know, there may be only persisting. They're not persisting data that is an inclement weather, or when a deer runs across the front. And then maybe they do that and then they send that smaller data setback and maybe that's where it's modeling done but the rest can be done at the Edge. It's a new world that's coming through. Let me ask you a question. Is there a limit to what data should be collected and how it should be collected? >> That's a great question again, yeah. Well, today full of these insightful questions. (Dr. Eng chuckles) That actually touches on the the second challenge, right? How do we, in order to thrive in this new age of insight? The second challenge is our future challenge, right? What do we do for our future? And in there is the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that, I talked about what to collect, right? When to organize it when you collect? And then where will your data be going forward that you are collecting from? So what, when, and where? For what data to collect? That was the question you asked, it's a question that different industries have to ask themselves because it will vary, right? Let me give you the, you use the autonomous car example. Let me use that. And we do have this customer collecting massive amounts of data. You know, we're talking about 10 petabytes a day from a fleet of their cars. And these are not production autonomous cars, right? These are training autonomous cars, collecting data so they can train and eventually deploy commercial cars, right? Also this data collection cars, they collect 10, as a fleet of them collect 10 petabytes a day. And then when they came to us, building a storage system you know, to store all of that data, they realized they don't want to afford to store all of it. Now here comes the dilemma, right? What should I, after I spent so much effort building all this cars and sensors and collecting data, I've now decide what to delete. That's a dilemma, right? Now in working with them on this process of trimming down what they collected, you know, I'm constantly reminded of the 60s and 70s, right? To remind myself 60s and 70s, we called a large part of our DNA, junk DNA. >> Yeah. (Dave chuckles) >> Ah! Today, we realized that a large part of that what we call junk has function as valuable function. They are not genes but they regulate the function of genes. You know? So what's junk in yesterday could be valuable today. Or what's junk today could be valuable tomorrow, right? So, there's this tension going on, right? Between you deciding not wanting to afford to store everything that you can get your hands on. But on the other hand, you worry, you ignore the wrong ones, right? You can see this tension in our customers, right? And then it depends on industry here, right? In healthcare they say, I have no choice. I want it all, right? Oh, one very insightful point brought up by one healthcare provider that really touched me was you know, we don't only care. Of course we care a lot. We care a lot about the people we are caring for, right? But who also care for the people we are not caring for? How do we find them? >> Uh-huh. >> Right, and that definitely, they did not just need to collect data that they have with from their patients. They also need to reach out, right? To outside data so that they can figure out who they are not caring for, right? So they want it all. So I asked them, so what do you do with funding if you want it all? They say they have no choice but to figure out a way to fund it and perhaps monetization of what they have now is the way to come around and fund that. Of course, they also come back to us rightfully, that you know we have to then work out a way to help them build a system, you know? So that's healthcare, right? And if you go to other industries like banking, they say they can afford to keep them all. >> Yeah. >> But they are regulated, seemed like healthcare, they are regulated as to privacy and such like. So many examples different industries having different needs but different approaches to what they collect. But there is this constant tension between you perhaps deciding not wanting to fund all of that, all that you can install, right? But on the other hand, you know if you kind of don't want to afford it and decide not to start some. Maybe those some become highly valuable in the future, right? (Dr. Eng chuckles) You worry. >> Well, we can make some assumptions about the future. Can't we? I mean, we know there's going to be a lot more data than we've ever seen before. We know that. We know, well, not withstanding supply constraints and things like NAND. We know the prices of storage is going to continue to decline. We also know and not a lot of people are really talking about this, but the processing power, but the says, Moore's law is dead. Okay, it's waning, but the processing power when you combine the CPUs and NPUs, and GPUs and accelerators and so forth actually is increasing. And so when you think about these use cases at the Edge you're going to have much more processing power. You're going to have cheaper storage and it's going to be less expensive processing. And so as an AI practitioner, what can you do with that? >> Yeah, it's a highly, again, another insightful question that we touched on our Keynote. And that goes up to the why, uh, to the where? Where will your data be? Right? We have one estimate that says that by next year there will be 55 billion connected devices out there, right? 55 billion, right? What's the population of the world? Well, of the other 10 billion? But this thing is 55 billion. (Dave chuckles) Right? And many of them, most of them can collect data. So what do you do? Right? So the amount of data that's going to come in, it's going to way exceed, right? Drop in storage costs are increasing compute power. >> Right. >> Right. So what's the answer, right? So the answer must be knowing that we don't, and even a drop in price and increase in bandwidth, it will overwhelm the, 5G, it will overwhelm 5G, right? Given the amount of 55 billion of them collecting. So the answer must be that there needs to be a balance between you needing to bring all of that data from the 55 billion devices of the data back to a central, as a bunch of central cost. Because you may not be able to afford to do that. Firstly bandwidth, even with 5G and as the, when you'll still be too expensive given the number of devices out there. You know given storage costs dropping is still be too expensive to try and install them all. So the answer must be to start, at least to mitigate from to, some leave most a lot of the data out there, right? And only send back the pertinent ones, as you said before. But then if you did that then how are we going to do machine learning at the Core and the Cloud Site, if you don't have all the data? You want rich data to train with, right? Sometimes you want to mix up the positive type data and the negative type data. So you can train the machine in a more balanced way. So the answer must be eventually, right? As we move forward with these huge number of devices all at the Edge to do machine learning at the Edge. Today we don't even have power, right? The Edge typically is characterized by a lower energy capability and therefore lower compute power. But soon, you know? Even with low energy, they can do more with compute power improving in energy efficiency, right? So learning at the Edge, today we do inference at the Edge. So we data, model, deploy and you do inference there is. That's what we do today. But more and more, I believe given a massive amount of data at the Edge, you have to start doing machine learning at the Edge. And when you don't have enough power then you aggregate multiple devices, compute power into a Swarm and learn as a Swarm, yeah. >> Oh, interesting. So now of course, if I were sitting and fly on the wall and the HPE board meeting I said, okay, HPE is a leading provider of compute. How do you take advantage of that? I mean, we're going, I know it's future but you must be thinking about that and participating in those markets. I know today you are, you have, you know, Edge line and other products. But there's, it seems to me that it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that opportunity for the customers? >> Hmm, the wall will have to have a balance, right? Where today the default, well, the more common mode is to collect the data from the Edge and train at some centralized location or number of centralized location. Going forward, given the proliferation of the Edge devices, we'll need a balance, we need both. We need capability at the Cloud Site, right? And it has to be hybrid. And then we need capability on the Edge side that we need to build systems that on one hand is an Edge adapter, right? Meaning they environmentally adapted because the Edge differently are on it, a lot of times on the outside. They need to be packaging adapted and also power adapted, right? Because typically many of these devices are battery powered. Right? So you have to build systems that adapts to it. But at the same time, they must not be custom. That's my belief. It must be using standard processes and standard operating system so that they can run a rich set of applications. So yes, that's also the insight for that Antonio announced in 2018. For the next four years from 2018, right? $4 billion invested to strengthen our Edge portfolio. >> Uh-huh. >> Edge product lines. >> Right. >> Uh-huh, Edge solutions. >> I could, Doctor Goh, I could go on for hours with you. You're just such a great guest. Let's close. What are you most excited about in the future of, certainly HPE, but the industry in general? >> Yeah, I think the excitement is the customers, right? The diversity of customers and the diversity in the way they have approached different problems of data strategy. So the excitement is around data strategy, right? Just like, you know, the statement made for us was so was profound, right? And Antonio said, we are in the age of insight powered by data. That's the first line, right? The line that comes after that is as such we are becoming more and more data centric with data that currency. Now the next step is even more profound. That is, you know, we are going as far as saying that, you know, data should not be treated as cost anymore. No, right? But instead as an investment in a new asset class called data with value on our balance sheet. This is a step change, right? Right, in thinking that is going to change the way we look at data, the way we value it. So that's a statement. (Dr. Eng chuckles) This is the exciting thing, because for me a CTO of AI, right? A machine is only as intelligent as the data you feed it with. Data is a source of the machine learning to be intelligent. Right? (Dr. Eng chuckles) So, that's why when the people start to value data, right? And say that it is an investment when we collect it it is very positive for AI. Because an AI system gets intelligent, get more intelligence because it has huge amounts of data and a diversity of data. >> Yeah. >> So it'd be great, if the community values data. >> Well, you certainly see it in the valuations of many companies these days. And I think increasingly you see it on the income statement. You know data products and people monetizing data services. And yeah, maybe eventually you'll see it in the balance sheet. I know Doug Laney, when he was at Gartner Group, wrote a book about this and a lot of people are thinking about it. That's a big change, isn't it? >> Yeah, yeah. >> Dr. Goh... (Dave chuckles) >> The question is the process and methods in valuation. Right? >> Yeah, right. >> But I believe we will get there. We need to get started. And then we'll get there. I believe, yeah. >> Doctor Goh, it's always my pleasure. >> And then the AI will benefit greatly from it. >> Oh, yeah, no doubt. People will better understand how to align, you know some of these technology investments. Dr. Goh, great to see you again. Thanks so much for coming back in theCUBE. It's been a real pleasure. >> Yes, a system is only as smart as the data you feed it with. (Dave chuckles) (Dr. Eng laughs) >> Excellent. We'll leave it there. Thank you for spending some time with us and keep it right there for more great interviews from HPE Discover 21. This is Dave Vellante for theCUBE, the leader in Enterprise Tech Coverage. We'll be right back. (upbeat music)