>>Welcome back here on the Cube. I'm John Walls. We are in Las Vegas at the Venetian, and this is Reinvent 22 in the Executive Summit sponsored by Accenture. Glad to have you with us here as we continue our conversations. I'm joined by Paul Puckett, who's the former director of the Enterprise Cloud Management Services at the US Army. Paul, good to see you sir. Hey, you as well, John. Thank you. And Justin, she who is managing director and cloud go to market lead at Accenture Federal Services. Justin, good morning to you. Good morning, John. Yeah, glad to have you both here on the cube. First time too, I believe, right? Yes sir. Well, welcome. I wish we had some kind of baptism or indoctrination, but I'll see what I can come up with in the next 10 minutes for you. Let's talk about the Army, Paul. So enterprise cloud management, US Army. You know, I can't imagine the scale we're talking about here. I can't imagine the solutions we're talking about. I can't imagine the users we're talking about. Just for our folks at home, paint the picture a little bit of what kind of landscape it is that you have to cover with that kind of title. >>Sure. The United States Army, about 1.4 million people. Obviously a global organization responsible for protecting and defending the United States as part of our sister services in the Department of Defense. And scale often comes up a lot, right? And we talk about any capability to your solution for the United States Army scale is the, the number one thing, but oftentimes people overlook quality first. And actually when you think of the partnership between the Army and Accenture Federal, we thought a lot when it came to establishing the enterprise Cloud management agency that we wanted to deliver quality first when it came to adopting cloud computing and then scale that quality and not so much be afraid of the, the scale of the army and the size that forces us to make bad decisions. Cuz we wanted to make sure that we proved that there was opportunity and value in the cloud first, and then we wanted to truly scale that. And so no doubt, an immense challenge. The organization's been around for now three years, but I think that we've established irreversible momentum when it comes to modernization, leveraging cloud computing >>For the army. So let's back up. You kind of threw it in there, the ecma. So this agency was, was your a collaboration, right? To create from the ground up and it's in three years in existence. So let's just talk about that. What went into that thinking? What went into the planning and then how did you actually get it up and run into the extent that it is today? >>Sure. Well, it was once the enterprise cloud management office. It was a directorate within the, the CIO G six of the United States Army. So at the headquarters, the army, the chief information Officer, and the G six, which is essentially the military arm for all IT capability were once a joint's organization and the ECMO was created to catalyze the adoption of cloud computing. The army had actually been on a, a cloud adoption journey for many years, but there wasn't a lot of value that was actually derived. And so they created the ecma, well, the ECMO at the time brought me in as the director. And so we were responsible for establishing the new strategy for the adoption of cloud. One of the components of that strategy was essentially we needed an opportunity to be able to buy cloud services at scale. And this was part of our buy secure and build model that we had in place. And so part of the buy piece, we put an acquisition strategy together around how we wanted to buy cloud at scale. We called it the cloud account management optimization. OTA >>Just rolls right off the >>Tongue, it just rolls right off the tongue. And for those that love acronyms, camo, >>Which I liked it when I was say cama, I loved that. That was, that was, >>You always have to have like a tundra, a little >>Piece of that. Very good. It was good. >>But at the time it was novetta, no, Nevada's been bought up by afs, but Novea won that agreement. And so we've had this partnership in place now for just about a year and a half for buying cloud computing net scale. >>So let's talk about, about what you deal with on, on the federal services side here, Justin, in terms of the army. So obviously governance, a major issue, compliance, a major issue, security, you know, paramount importance and all that STEM leads up to quality that Paul was talking about. So when you were looking at this and keeping all those factors in, in your mind, right? I mean, how many, like, oh my God, what kind of days did you have? Oh, well, because this was a handful. >>Well, it was, but you could see when we were responding to the acquisition that it was really, you know, forward thinking and forward leaning in terms of how they thought about cloud acquisition and cloud governance and cloud management. And it's really kind of a sleepy area like cloud account acquisition. Everyone's like, oh, it's easy to get in the cloud, you know, run your credit card on Amazon and you're in, in 30 seconds or less. That's really not the case inside the federal government, whether it's the army, the Air Force or whoever, right? Those, those are, they're real challenges in procuring and acquiring cloud. And so it was clear from, you know, Paul's office that they understood those challenges and we were excited to really meet them with them. >>And, and how, I guess from an institutional perspective, before this was right, I I assume very protective, very tight cloistered, right? You, you, in terms of being open to or, or a more open environment, there might have been some pushback was they're not. Right? So dealing with that, what did you find that to be the case? Well, so >>There's kind of a few pieces to unpacking that. There's a lot of fear in trepidation around something you don't understand, right? And so part of it is the teaching and training and the, and the capability and the opportunity in the cloud and the ability to be exceptionally secure when it comes to no doubt, the sensitivity of the information of the Department of Defense, but also from an action acquisition strategy perspective, more from a financial perspective, the DOD is accustomed to buying hardware. We make these big bets of these big things to, to live in today's centers. And so when we talk about consuming cloud as a utility, there's a lot of fear there as well, because they don't really understand how to kind of pay for something by the drink, if you will, because it incentivizes them to be more efficient with their utilization of resources. >>But when you look at the budgeting process of the d od, there really is not that much of incentive for efficiency. The p PPE process, the planning program, budgeting, execution, they care about execution, which is spending money and you can spend a lot of money in the cloud, right? But how are you actually utilizing that? And so what we wanted to do is create that feedback loop and so the utilization is actually fed into our financial systems that help us then estimate into the future. And that's the capability that we partnered with AFS on is establishing the closing of that feedback loop. So now we can actually optimize our utilization of the cloud. And that's actually driving better incentives in the PPE >>Process. You know, when you think about these keywords here, modernized, digitized, data driven, so on, so forth, I, I don't think a lot of people might connect that to the US government in general just because of, you know, it's a large intentionally slow moving bureaucratic machine, right? Is that fair to characterize it that way? It >>Is, but not in this case. Right? So what we done, >>You you totally juxtapose that. Yeah. >>Yeah. So what we've done is we've really enabled data driven decision making as it relates to cloud accounts and cloud governance. And so we have a, a tool called Cloud Tracker. We deployed for the army at a number of different classifications, and you get a full 360 view of all of your cloud utilization and cloud spend, you know, really up to date within 24 hours of it occurring, right? And there a lot of folks, you know, they didn't never went into the console, they never looked at what they were spending in cloud previously. And so now you just go to a simple web portal and see the entire entirety of the army cloud spend right there at your fingertips. So that really enables like better decision making in terms of like purchasing savings plans and reserved instances and other sorts of AWS specific tools to help you save money. >>So Paul, tell me about Cloud Tracker then. Yeah, I mean from the client side then, can you just say this dashboard lays it out for you right? In great detail about what kind of usage, what kind of efficiencies I assume Yeah. What's working, what's not? >>Absolutely. Well, and, and I think a few things to unpack that's really important here is listen, any cloud service provider has a concept. You can see what you're actually spending. But when it comes to money in the United States government, there are different colors of money. There's regulations when it comes to how money is identified for different capabilities or incentives. And you've gotta be very explicit in how you track and how you spend that money from an auditability perspective. Beyond that, there is a move when it comes to the technology business management, which is the actual labeling of what we actually spend money on for different services or labor or software. And what Cloud Tracker allows us to do is speak the language of the different colors of money. It allows us to also get very fine grain in the actual analysis of, from a TBM perspective, what we're spending on. >>But then also it has real time hooks into our financial systems for execution. And so what that really does for us is it allows us to complete the picture, not just be able to see our spend in the cloud, but also be able to able to see that spending context of all things in the P P P E process as well as the execution process that then really empowers the government to make better investments. And all we're seeing is either cost avoidance or cost savings simply because we're able to close that loop, like I said. Yep. And then we're able to redirect those funds, retag them, remove them through our actual financial office within the headquarters of the army, and be able to repurpose that to other modernization efforts that Congress is essentially asking us to invest >>In. Right. So you know how much money you have, basically. Exactly. Right. You know how much you've already spent, you know how you're spending it, and now you how much you have left, >>You can provide a reliable forecast for your spend. >>Right. You know, hey, we're, we're halfway through this quarter, we're halfway through the, the fiscal year, whatever the case might be. >>Exactly. And the focus on expenditures, you know, the government rates you on, you know, how much have you spent, right? So you have a clear total transparency into what you're going to spend through the rest of the fiscal. Sure. >>All right. Let's just talk about the relationship quickly then about going forward then in terms of federal services and then what on, on the, the US Army side. I mean, what now you've laid this great groundwork, right? You have a really solid foundation where now what next? >>We wanna be all things cloud to the army. I mean, we think there's tremendous opportunity to really aid the modernization efforts and governance across the holistic part of the army. So, you know, we just, we want to, we wanna do it all with the Army as much as we can. It's, it's, it's a fantastic >>Opportunity. Yeah. AFS is, is in a very kind of a strategic role. So as part of the ecma, we own the greater strategy and execution for adoption of cloud on behalf of the entire army. Now, when it comes to delivery of individual capabilities for mission here and there, that's all specific to system owners and different organizations. AFS plays a different role in this instance where they're able to more facilitate the greater strategy on the financial side of the house. And what we've done is we've proven the ability to adopt cloud as a utility rather than this fixed thing, kind of predict the future, spend a whole bunch of money and never use the resource. We're seeing the efficiency for the actual utilization of cloud as a utility. This actually came out as one of the previous NDAs. And so how we actually address nda, I believe it was 2018 in the adoption of cloud as a utility, really is now cornerstone of modernization across all of the do d and really feeds into the Jo Warfighting cloud capability, major acquisition on behalf of all of the D O D to establish buying cloud as just a common service for everyone. >>And so we've been fortunate to inform that team of some of our lessons learned, but when it comes to the partnership, we just see camo moving into production. We've been live for now a year and a half. And so there's another two and a half years of runway there. And then AFS also plays a strategic role at part of our cloud enablement division, which is essentially back to that teaching part, helping the Army understand the opportunity of cloud computing, align the architectures to actually leverage those resources and then deliver capabilities that save soldier's >>Lives. Well, you know, we've, we've always known that the Army does its best work on the ground, and you've done all this groundwork for the military, so I'm not surprised, right? It's, it's a winning formula. Thanks to both of you for being with us here in the executive summit. Great conversation. Awesome. Thanks for having us. A good deal. All right. Thank you. All right. You are watching the executive summit sponsored by Accenture here at Reinvent 22, and you're catching it all on the cube, the leader in high tech coverage.

(electronic music) (graphics whoosh) (graphics tinkle) >> Welcome to Las Vegas! It's theCUBE live at AWS re:Invent '22. Lisa Martin here with Dave Vellante. Dave, it is not only great to be back, but this re:Invent seems to be bigger than last year for sure. >> Oh, definitely. I'd say it's double last year. I'd say it's comparable to 2019. Maybe even a little bigger, I've heard it's the largest re:Invent ever. And we're going to talk data, one of our favorite topics. >> We're going to talk data products. We have some great guests. One of them is an alumni who's back with us. Justin Borgman, the CEO of Starburst, and Ashwin Patil also joins us, Principal AI and Data Engineering at Deloitte. Guys, welcome to the program. >> Thank you. >> Together: Thank you. >> Justin, define data products. Give us the scoop, what's goin' on with Starburst. But define data products and the value in it for organizations of productizing data. >> Mm-hmm. So, data products are curated data sets that are able to span across multiple data sets. And I think that's what's makes it particularly unique, is you can span across multiple data sources to create federated data products that allow you to really bring together the business value that you're seeking. And I think ultimately, what's driving the interest in data products is a desire to ultimately facilitate self-service consumption within the enterprise. I think that's the holy grail that we've all been building towards. And data products represents a framework for sort of how you would do that. >> So, monetization is not necessarily a criterion? >> Not necessarily. (Dave's voice drowns) >> But it could be. >> It could be. It can be internal data products or external data products. And in either case, it's really intended to facilitate easier discovery and consumption of data. >> Ashwin, bringing you into the conversation, talk about some of the revenue drivers that data products can help organizations to unlock. >> Sure. Like Justin said, there are internal and external revenue drivers. So internally, a lot of clients are focused around, hey, how do I make the most out of my modernization platform? So, a lot of them are thinking about what AI, what analytics, what can they run to drive consumption? And when you think about consumption, consumption typically requires data from across the enterprise, right? And data from the enterprise is sometimes fragmented in pieces, in places. So, we've gone from being data in too many places to now, data products, helping bring all of that together, and really aid, drive business decisions faster with more data and more accuracy, right? Externally, a lot of that has got to do with how the ecosystems are evolving for data products that use not only company data, but also the ecosystem data that includes customers, that include suppliers and vendors. >> I mean, conceptually, data products, you could say have been around a long time. When I think of financial services, I think that's always been a data product in a sense. But suddenly, there's a lot more conversation about it. There's data mesh, there's data fabric, we could talk about that too, but why do you think now it's coming to the fore again? >> Yeah, I mean, I think it's because historically, there's always been this disconnect between the people that understand data infrastructure, and the people who know the right questions to ask of the data. Generally, these have been two very distinct groups. And so, the interest in data mesh as you mentioned, and data products as a foundational element of it, is really centered around how do we bring these groups together? How do we get the people who know the data the best to participate in the process of creating data to be consumed? Ultimately, again, trying to facilitate greater self-service consumption. And I think that's the real beauty behind it. And I think increasingly, in today's world, people are realizing the data will always be decentralized to some degree. That notion of bringing everything together into one single database has never really been successfully achieved, and is probably even further from the truth at this point in time, given you've got data on-prem and multiple clouds, and multiple different systems. And so, data products and data mesh represents, again, a framework for you to sort of think about data that lives everywhere. >> We did a session this summer with (chuckles) Justin and I, and some others on the data lies. And that was one of the good ol' lies, right? There's a single source of truth. >> Justin: Right. >> And all that is, we've probably never been further from the single source of truth. But actually, you're suggesting that there's maybe multiple truths that the same data can support. Is that a right way to think about it? >> Yeah, exactly. And I think ultimately, you want a single point of access that gives you, at your fingertips, everything that your organization knows about its business today. And that's really what data products aims to do, is sort of curate that for you, and provide high quality data sets that you can trust, that you can now self-service to answer your business question. >> One of the things that, oh, go ahead. >> No, no, I was just going to say, I mean, if you pivot it from the way the usage of data has changed, right? Traditionally, IT has been in the business of providing data to the business users. Today, with more self-service being driven, we want business users to be the drivers of consumption, right? So if you take that backwards one step, it's basically saying, what data do I need to support my business needs, such that IT doesn't always have to get involved in providing that data, or providing the reports on top of that data? So, the data products concept, I think supports that thinking of business-led technology-enabled, or IT-enabled really well. >> Business led. One of the things that Adam Zelinsky talked with John Furrier about just a week or so ago in their pre re:Invent interview, was talking about the role of the data analyst going away. That everybody in an organization, regardless of function, will be able to eventually be a data analyst, and need to evaluate and analyze data for their roles. Talk about data products as a facilitator of that democratization. >> Yeah. We are seeing more and more the concept of citizen data scientists. We are seeing more and more citizens AI. What we are seeing is a general trend, as we move towards self-service, there is going to be a need for business users to be able to access data when they want, how they want, and merge data across the enterprise in ways that they haven't done before, right? Technology today, through products like data products, right, provides you the access to do that. And that's why we are going to see this movement of people of seeing people become more and more self-service oriented, where you're going to democratize the use of AI and analytics into the business users. >> Do you think, when you talk to a data analyst, by the way, about that, he or she will be like, yeah, mm, maybe, good luck with that. So, do ya think maybe there's a sort of an interim step? Because we've had these highly, ZeMac lays this out very well. We've had these highly-centralized, highly-specialized teams. The premise being, oh, that's less expensive. Perhaps data analysts, like functions, get put into the line of business. Do you see that as a bridge or a stepping stone? Because it feels like it's quite a distance between what a data analyst does today, and this nirvana that we talk about. What are your thoughts on that? >> Yeah, I mean, I think there's possibly a new role around a data product manager. Much the way you have product managers in the products you actually build to sell, you might need data product managers to help facilitate and curate the high quality data products that others can consume. And I think that becomes an interesting and important, a skill set. Much the way that data scientist was created as a occupation, if you will, maybe 10 years ago, when previously, those were statisticians, or other names. >> Right. A big risk that many clients are seeing around data products is, how do you drive governance? And to that, to the point that Justin's making, we are going to see that role evolve where governance in the world, where data products are getting democratized is going to become increasingly important in terms of how are data products being generated, how is the propensity of data products towards a more governed environment being managed? And that's going to continue to play an important role as data products evolve. >> Okay, so how do you guys fit, because you take ZeMac's four principles, domain ownership, data as product. And that creates two problems. Governance. (chuckles) Right? How do you automate, and self-service, infrastructure and automated governance. >> Yep. >> Tell us what role Starburst plays in solving all of those, but the latter two in particular. >> Yeah. Well, we're working on all four of those dimensions to some degree, but I think ultimately, what we're focused today is the governance piece, providing fine-grained access controls, which is so important, if you're going to have a single point of access, you better have a way of controlling who has access to what. But secondly, data products allows you to really abstract away or decouple where the data is stored from the business meaning of the data. And I think that's what's so key here is, if we're going to ultimately democratize data as we've talked about, we need to change the conversation from a very storage-centric world, like, oh, that table lives in this system or that system, or that system. And make it much more about the data, and the value that it represents. And I think that's what data products aims to do. >> What about data fabric? I have to say, I'm confused by data fabric. I read this, I feel like Gartner just threw it in there to muck it up. And say, no, no, we get to make up the terms, but I've read data mesh versus data fabric, is data fabric just more sort of the physical infrastructure? And data mesh is more of an organizational construct, or how do you see it? >> Yeah, I'm happy to take that or. So, I mean, to me, it's a little bit of potato potato. I think there are some subtle differences. Data fabric is a little bit more about data movement. Whereas, I think data mesh is a little bit more about accessing the data where it lies. But they're both trying to solve the similar problem, which is that we have data in a wide variety of different data sets. And for us to actually analyze it, we need to have a single view. >> Because Gartner hype cycle says data mesh is DOA- >> Justin: I know. >> Which I think is complete BS, I think is real. You talk to customers that are doing it, they're doing it on AWS, they're trying to extend it across clouds, I mean, it's a real trend. I mean, anyway, that's how I see it. >> Yeah. I feel the word data fabric many a times gets misused. Because when you think about the digitization movement that happened, started almost a decade ago, many companies tried to digitize or create digital twins of their systems into the data world, right? So, everything has an underlying data fabric that replicates what's happening transactionally, or otherwise in the real world. What data mesh does is creates structure that works complimentary to the data fabric, that then lends itself to data products, right? So to me, data products becomes a medium, which drives the connection between data mesh and data fabric into the real world for usage and consumption. >> You should write for Gartner. (all laugh) That's the best explanation I've heard. That made sense! >> That really did. That was excellent. So, when we think about any company these days has to be a data company, whether it's your grocery store, a gas station, a car dealer, what can companies do to start productizing their data, so that they can actually unlock new revenue streams, new routes to market? What are some steps and recommendations that you have? Justin, we'll start with you. >> Sure. I would say the first thing is find data that is ultimately valuable to the consumers within your business, and create a product of it. And the way you do that at Starburst is allow you to essentially create a view of your data that can span multiple data sources. So again, we're decoupling where the data lives. That might be a table that lives in a traditional data warehouse, a table that lives in an operational system like Mongo, a table that lives in a data lake. And you can actually join those together, and represent it as a view, and now make it easily consumable. And so, the end user doesn't need to know, did that live in a data warehouse, an operational database, or a data lake? I'm just accessing that. And I think that's a great, easy way to start in your journey. Because I think if you absorb all the elements of data mesh at once, it can feel overwhelming. And I think that's a great way to start. >> Irrespective of physical location. >> Yes. >> Right? >> Precisely. Yep, multicloud, hybrid cloud, you name it. >> And when you think about the broader landscape, right? For the traditionally, companies that only looked at internal data as a way of driving business decisions. More and more, as things evolve into industry, clouds, or ecosystem data, and companies start going beyond their four walls in terms of the data that they manage or the data that they use to make decisions, I think data products are going to play more and more an important part in that construct where you don't govern all the data that our entities within that ecosystem will govern parts of their data, but that data lives together in the form of data products that are governed somewhat centrally. I mean, kind of like a blockchain system, but not really. >> Justin, for our folks here, as we kind of wrap the segment here, what's the bumper sticker for Starburst, and how you're helping organizations to really be able to build data products that add value to their organization? >> I would say analytics anywhere. Our core ethos is, we want to give you the ability to access data wherever it lives, and understand your business holistically. And our query engine allows you to do that from a query perspective, and data products allows you to bring that up a level and make it consumable. >> Make it consumable. Ashwin, last question for you, here we are, day one of re:Invent, loads of people behind us. Tomorrow all the great keynotes kick up. What are you hoping to take away from re:Invent '22? >> Well, I'm hoping to understand how all of these different entities that are represented here connect with each other, right? And to me, Starburst is an important player in terms of how do you drive connectivity. And to me, as we help plans from a Deloitte perspective, drive that business value, connectivity across all of the technology players is extremely important part. So, integration across those technology players is what I'm trying to get from re:Invent here. >> And so, you guys do, you're dot connectors. (Ashwin chuckles) >> Exactly, excellent. Guys, thank you so much for joining David and me on the program tonight. We appreciate your insights, your time, and probably the best explanation of data fabric versus data mesh. (Justin chuckles) And data products that we've maybe ever had on the show! We appreciate your time, thank you. >> Together: Thank you- >> Thanks, guys. >> All right. For our guests and Dave Vellante, I'm Lisa Martin, you're watching theCUBE, the leader in enterprise and emerging tech coverage. (electronic music)

(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)

>> Announcer: theCUBE presents HPE Discover 2022. Brought to you by HPE. >> Okay, we're back here at HPE Discover 2022, theCUBE's continuous coverage. This is day two, Dave Vellante with John Furrier. John Frey's here. He is the chief technologist for sustainable transformation at Hewlett Packard Enterprise and Justin Murrill who's the director of corporate responsibility for AMD. Guys, welcome to theCUBE. Good to see you. >> Thank you. >> Thank you. It's great to be here. >> So again, I remember the days where, you know, CIOs didn't really care about the power budget. They didn't pay the power budget. You had, you know, facilities over here, IT over here and they didn't talk to each other. That's changed. Why is there so much discussion around sustainable IT today? >> It's exciting to see how much it's up leveled, as you say. I think there are a couple different trends happening but mainly, you know, the IT teams and IT leaders that are making decisions are seeing to your point how their decisions are affecting enterprise level, greenhouse gas emission reduction goals. So that connection is becoming very clear. Everything from the server processor to beyond it, those decisions have a key role. And importantly we're seeing, you know, 60% of the Fortune 500 now have climate or energy efficiency related goals. So there's a perfect storm of sorts happening where more companies setting goals, IT decision makers looking particularly at the data center because as the computational heart of an organization, it has a wealth of opportunity from an energy and a mission savings perspective. >> I'm surprised it's only 60%. I mean, that number really shocked me. So it's got to be a 100% within the next couple of years here. I would think, I mean, it's not trivial, right? You've got responsibilities in terms of reporting and you can't just mail it in, right? >> Yeah, absolutely. So there's a lot more disclosure happening but the goal setting is really upleveling as well. >> And the metrics involved too. Can you just scope the scale and challenge of like getting the right metrics, not when you have the goals. Does that factor in, how do you see there? What's your commentary on that? >> Yeah, I think there's, the aperture is continuing to open as metrics go, so to speak. So from an operations perspective, companies are reporting on what's referred to as scope one and scope two. And scope two is the big one from electricity, right? And then scope three is everything else. That's the supply chain and the outside of that. So a lot of implications there as well from IT decision making. >> Is there a business case for sustainable IT? I mean, you're probably not going to lower the power budget, right? But is it just, hey, it's the right thing to do. We have to do it, it's good for the brand. It'll allow us to attract people or is there a a more of a rich business case? >> So there really is a business case even just within inside the data center walls, for example. There's inefficiencies that are inherent in many of these data centers. There's really low utilization levels as well. And by reducing over provisioning and increasing utilization, there's real money to be saved in terms of equipment costs, maintenance agreement costs, software licensing costs. So actually the power consumption and the environmental piece is an added benefit but it's not the main reason. So we actually had IDC do a survey for us last year and we asked IT executives, 500 senior IT executives, were you implementing sustainable IT programs and why? My guess initially was about 40% of them would say yes. Actually the number was 96% of them. And when we asked them why they fell into three categories. The digital leaders, those that are the early adopters moving the quickest. They said we do it to attract and retain institutional investors. They've been hearing from their boards. They've been hearing from their investor relations teams and investors are starting to ask and even in a couple cases board seats are becoming contentious based on the environmental perspective of the person being nominated. This digital mainstream, the folks in the middle about 80% of the total pie, they're doing it to attract and retain customers because customers are asking them about their sustainable IT programs. If they're a non-manufacturing customer, their data center consumption is probably the largest part of their company. It's also by the way usually the most expensive real estate the company owns. So customers are asking and customers are not only asking, do you have basic programs in place? But they're asking, what are your goals to Justin's point? The customers are starting to realize that carbon goals have been vaguely defined historically. So they're asking for specificity, they're asking for transparency and by the way the science-based target initiative recently released their requirements for net zero science-based targets. And that requires significant reduction to your point before you start considering renewable energy in that balance. The third reason those digital followers, that slowest group or folks that are in industries that move the slowest, they said they were doing this to attract and retain employees. Because they recognize the data scientists, the computer science, computer engineering students that they're trying to attract want to work at a company where they can see how what they do directly contributes to purpose. And they vote with their feet. If they come on and they can't make that connection pretty quickly or if they spend a lot of their time chasing down inefficiencies in a technology infrastructure, they're not going to stay there very long. >> I mean, the mission-driven organization is definitely an employee factor. People are interested in that. The work for company is responsible, doing the right thing but that business case is interesting because I think there's recognition now more than ever before. You think you're right on. It used to be kind of like mailed it in before. Okay, we're doing some stuff. Now it's like, we all have to do it. And it's a board issue. It's a financing issue. It might be a filing issue as you guys mentioned. So that's all great. So I got to ask how you guys specifically are working together, AMD and HPE. What are you guys doing to make it more efficient? And then I'll see with Cloud and Cloud scale, there's more servers being shipped now than ever before. And more devices at the edge. What are you guys doing together specifically? >> Yeah, we've been working together, AMD and HPE on advancing sustainability for many years. I've had the opportunity to working directly with John for many years and I've learned a lot from him and your team. It's fantastic to see all the developments here. I mean, so most recently the top 500 and the green 500 list of supercomputers came out. And at the top of that list is AMD HPE systems. And it shows kind of the pinnacle of what can be possible for other data centers looking to modernize and scale. So the number one system, the fastest system in the world and the most energy efficient system in the world, the Frontier supercomputer has AMD HPE technology in it. And it just passed the exit scale barrier. I mean, I'm still just blown away by this. A billion, billion calculations per second. It's just amazing. And the research is doing around clean energy, alternative energy sources, scientific research is really exciting. So there's that. The other system that really jumps out is the LUMI system, the number three system because it's a 100% powered by renewable energy. So not only that, it takes the heat and it channels it to a nearby town and covers 20% of that town's heating needs thereby avoiding 12,400 metric tons of carbon emissions. So this system is carbon negative, right? And you just go down the list. I mean, AMD is in the top eight out of 10 most green... >> Rewind that second. So you have the heat and the power shifting to a town? >> Yes, the LUMI supercomputer has the heat from the system to an nearby town. It's like a closed loop, the idea of circular economy but with energy. And it takes that waste and it makes it an input, a resource. >> But this is the kind of innovation that's going on, right? This is the scale, this is where scale and efficiency kind of come together. That's huge. Where's that going to go? What's your perspective on where that goes next because that's a blueprint that could be replicated. >> You bet. So I think we're going to continue to see overall power consumption go up at the system level. But performance per wat is climbing much more dramatically. So I think that's going to continue to scale. It's going to require a new cooling technology. So direct liquid cooling is becoming more and more in use and customers really interested in that. There's shifting from industry standard architectures to lower end high performance computer architectures to get direct liquid cooling, higher core processors and get the performance they want in a smaller footprint. And at the same time, they're really thinking about how do we operate the infrastructure as a system not as individual piece parts. And one of the things that Frontier and LUMI do so well is they were designed from the start as a system, not as piece parts making up the system. So I think that happens. The other thing that's really critical is no one company is going to solve these challenges ourself. So one of the things I love about our partnership with AMD is we look at each other's sustainability goals before we launch 'em. We say, well, how can we help? One of AMD's goals that I'll let Justin talk about came about because HPE at the time of separation laid a really aggressive product, energy efficiency goal out, said but we're not sure how we're going to make this. And AMD said we can help. So that collaboration, we critique each other's programs, we push each other, but we work together. I like to say partnership is leadership in this. >> Well, that's a nuance point. Before you get to that solution there Justin, this system's thinking is really important. You're seeing that now with Cloud. Some of the things that GreenLake and the systems are pointing out, this holistic systems' thinking is applied to partnerships, not just the company. >> Yep. >> This is a really nuanced point but we're seeing that more and more. >> Yeah, absolutely. In fact, Justin mentioned the heat reuse, same way with the national renewable energy lab. They actually did snow removal and building heating with the heat reuse. So if you're designing for example, a liquid cold system from the start, how do you make it a symbiotic relationship? There's more and more interest in co-locating data centers and greenhouses in colder environments for example. Because the principle of the circular economy is nothing is waste. So if you think it's waste or you think it's a byproduct, think about how can that be an input to something else. >> Right, so you might put a data center so you can use ambient cooling or in somewhere in the Columbia River so you can, you know, take advantage of, you know, renewable energy. What are some goals that you guys can share with us? >> So we've got some great momentum and a track record coming off of, going back to 2014, we set a 25 by 20 goal to improve the energy efficiency for our mobile processors and mobile devices, right? So laptops. And we were able to achieve a 31.7x in that timeframe. So which was twice the industry trend to that. And then moving on, we've doubled down on data center and we've set a new goal of a 30x increase in energy efficiency for our server processors and accelerators to really focused on HPC and AI training. So that's a 30x goal over 2020 to 2025 focused on these really important workloads 'cause they're fast growing. We heard yesterday 150 billion devices connected by 2025 generating a lot of data, right? So that's one of the reasons why we focused on that. 'Cause these are demanding workloads. And this represents a 2.5x increase over the historical trend, right? And fundamentally speaking, that's a 97% reduction in energy use per computation in five years. So we're very pleased. We announced an update recently. We're at 6.8x. We're on track for this goal and making great progress and showing how these, you know, solutions at a processor level and an accelerator level can be amplified, taken into HPE technology. >> Generally tech companies, you know, that compete want to rip each other's faces off. And is that the case in this space or do you guys collaborate with your competitors to share best practice? Is that beginning? Is it already there? >> There's much more collaboration in this space. This is one of the safe places I think where collaboration does occur more. >> Yeah. And we've all got to work together. A great example that was in the supply chain. When HPE first set our supply chain expectations for our suppliers around things like worker rights and environment and worker protection from a health and safety perspective. We initially had our code of conduct asked their suppliers to comply with it. Started auditing in event. And we quickly got into the factories and saw they were doing it for our workloads. But if you looked around the factory, they weren't doing in other places. And we took a step back and said, well, wait a minute. Why is that? And they said that vendor doesn't require it. So we took a step back and said let's get the industry together. We share a common supply chain. How do we have a common set of expectations and push them out to our supply chain? How to now do third party audits so the same supplier doesn't get audited by each of the major vendors and then share those audit results. And what we found was that really had a large lever effect of moving the electronic supply chain much more rapidly towards our expectations in all those areas. Well then other industries looked and said, well, wait a minute, if that worked for electronics, it'll probably work broader. And so now, the output of that is what's called the responsible business alliance across many industries taking that same approach. So that's a pre-competitive. We all have the same challenge. In many cases we share a common supply chain. So that's a great example of electronic companies coming together, design standards for things. There's a green grid group at the moment looking at liquid cooling connects. You know, we don't want every vendor to have a different connection point for liquid cooling for example. So how do we standardize that to make our customers have a easier time about looking at the technologies they want from any vendor and having common connection points. >> Right. Okay. So a lot of collaboration. Last question. How much of a difference do you think it can make? In other words, what percent of the blame pie goes to information technology? And I think regardless, you got to do your part. Will it make a dent? >> I think the sector has done a really good job of keeping that increase from going up while exponentially increasing performance. So it's been a really amazing industry effort. And moving forward, I think this is more important than ever, right? And with the slowdown of Moore's law we're seeing more gains that need to come from beyond process architecture to include packaging innovations, to power management, to just the architecture here. So the challenge of mitigating and minimizing energy growth is important. And we believe like with that 30x energy efficiency goal that it is doable but it does take a lot of collaboration and focus. >> That's a great point. I mean, if you didn't pay attention to this, IT could really become a big piece of the pie. Guys thanks so much for coming on theCUBE. Really appreciate. >> People are watching. They're paying attention at all levels. Congratulations. >> Absolutely. >> All right, Dave Vellante, John Furrier and our guests. Don't forget to go to SiliconANGLE.com for all the news. Our YouTube channel, actually go to CUBE.net. You'll get all these videos in our YouTube channel, youtube.com/SiliconANGLE. You can check out everything on demand. Keep it right there. We'll be right back. HPE Discover 2022 from Las Vegas. You're watching theCUBE. (soft music)

>>The cube presents HPE discover 2022 brought to you by HPE. >>Hey everyone. Welcome back to the Cube's coverage of HPE. Discover 22 live from the Sans expo center in Las Vegas. Lisa Martin, here with Dave Velante. We've an alumni back joining us to talk about high performance computing and AI, Justin ARD, EVP, and general manager of HPC and AI at HPE. That's a mouthful. Welcome back. >>It is no, it's great to be back and wow, it's great to be back in person as well. >>It's it's life changing to be back in person. The keynote this morning was great. The Dave was saying the energy that he's seen is probably the most out of, of any discover that you've been at and we've been feeling that and it's only day one. >>Yeah, I, I, I agree. And I think it's a Testament to the places in the market that we're leading the innovation we're driving. I mean, obviously the leadership in HPE GreenLake and, and enabling as a service for, for every customer, not just those in the public cloud, providing that, that capability. And then obviously what we're doing at HPC and AI breaking, uh, you know, breaking records and, uh, advancing the industry. So >>I just saw the Q2 numbers, nice revenue growth there for HPC and AI. Talk to us about the lay of the land what's going on, what are customers excited about? >>Yeah. You know, it's, it's a, it's a really fascinating time in this, in this business because we're, you know, we just, we just delivered the first, the world's first exo scale system. Right. And that's, uh, you know, that's a huge milestone for our industry, a breakthrough, you know, 13 years ago, we did the first Petta scale system. Now we're doing the first exo scale system, huge advance forward. But what's exciting too, is these systems are enabling new applications, new workloads, breakthroughs in AI, the beginning of being able to do proper quantum simulations, which will lead us to a much, you know, brighter future with quantum and then actually better and more granular models, which have the ability to really change the world. >>I was telling Lisa that during the pandemic we did, uh, exo scale day, it was like this co yep. You know, produce event. And we weren't quite at exo scale yet, but we could see it coming. And so it was great to see in frontier and, and the keynote you guys broke through that, is that a natural evolution of HPC or is this we entering a new era? >>Yeah, I, I think it's a new era and I think it's a new era for a few reasons because that, that breakthrough really, it starts to enable a different class of use cases. And it's combined with the fact that I think, you know, you look at where the rest of the enterprises data set has gone, right? We've got a lot more data, a lot more visibility to data. Um, but we don't know how to use it. And now with this computing power, we can start to create new insights and new applications. And so I think this is gonna be a path to making HPC more broadly available. And of course it introduces AI models at scale. And that's, that's really critical cause AI is a buzzword. I mean, lots of people say they're doing AI, but when you know, to, to build true intelligence, not, not effectively, you know, a machine that learns data and then can only handle that data, but to build true intelligence where you've got something that can continue to learn and understand and grow and evolve, you need this class of system. And so I think we're at, we're at the forefront of a lot of exciting innovation. H how, >>In terms of innovation, how important is it that you're able to combine as a service and HPC? Uh, what does that mean for, for customers for experimentation and innovation? >>You know, a couple things I've been, I've actually been talking to customers about that over the last day and a half. And, you know, one is, um, you think about these, these systems are, they're very large and, and they're, they're pretty, you know, pretty big bets if you're a customer. So getting early access to them right, is, is really key, making sure that they're, they can migrate their software, their applications, again, in our space, most of our applications are custom built, whether you're a, you know, a government or a private sector company, that's using these systems, you're, you're doing these are pretty specialized. So getting that early access is important. And then actually what we're seeing is, uh, with the growth and explosion of insight that we can enable. And some of the diversity of, you know, new, um, accelerator partners and new processors that are on the market is actually the attraction of diversity. And so making things available where customers can use multimodal systems. And we've seen that in this era, like our customer Lumi and Finland number, the number three fastest system in the world actually has two sides to their system. So there's a compute side, dense compute side and a dense accelerator side. >>So Oak Ridge national labs was on stage with Antonio this morning, the, the talking about frontier, the frontier system, I thought what a great name, very apropo, but it was also just named the number one to the super computing, top 500. That's a pretty big accomplishment. Talk about the impact of what that really means. >>Yeah. I, I think a couple things, first of all, uh, anytime you have this breakthrough of number one, you see a massive acceleration of applications. And if you really, if you look at the applications that were built, because when the us department of energy funded these Exoscale products or platforms, they also funded app a set of applications. And so it's the ability to get more accurate earth models for long term climate science. It's the ability to model the electrical grid and understand better how to build resiliency into that grid. His ability is, um, Dr. Te Rossi talked about a progressing, you know, cancer research and cancer breakthroughs. I mean, there's so many benefits to the world that we can bring with these systems. That's one element. The other big part of this breakthrough is actually a list, a lesser known list from the top 500 called the green 500. >>And that's where we measure performance over power consumption. And what's a huge breakthrough in this system. Is that not only to frontier debut at number one on the top 500, it's actually got the top two spots, uh, because it's got a small test system that also is up there, but it's got the top two spots on the green 500 and that's actually a real huge breakthrough because now we're doing a ton more computation at far lesser power. And that's really important cuz you think about these systems, ultimately you can, you can't, you know, continue to consume power linearly with scaling up performance. There's I mean, there's a huge issue on our impact on our environment, but it's the impact to the power grid. It's the impact to heat dissipation. There's a lot of complexities. So this breakthrough with frontier also enables us no pun intended to really accelerate, you know, the, the capacity and scale of these systems and what we can deliver. >>It feels like we're entering a new Renaissance of HPC. I mean, I'm old enough to remember. I, it was, it wasn't until recently my wife, not recently, maybe five, six years ago, my wife threw out my, my green thinking machines. T-shirt that Danny Hillis gave you guys probably both too young to remember, but you had thinking machines, Ken to square research convex tried to mini build a mini computer HPC. Okay. And there was a lot of innovation going on around that time and then it just became too expensive and, and, and other things X 86 happened. And, and, but it feels like now we're entering a, a new era of, of HPC. Is that valid or is it true? What's that mean for HPC as an industry and for industry? >>Yeah, I think, I think it's a BR I think it's a breadth. Um, it's a market that's opening and getting much more broader the number of applications you can run, you know, and we've traditionally had, you know, scientific applications, obviously there's a ton in energy and, and you know, physics and some of the traditional areas that obviously the department of energy sponsor, but, you know, we saw this with, with even the COVID pandemic, right? Our, our supercomputers were used to identify the spike protein to, to help and validate and test these vaccines and bring them to market and record time. We saw some of the benefits of these breakthroughs. And I think it's this combination of that, that we actually have the data, you know, it's, it's digital, it's captured, um, we're capturing it at, you know, at the edge, we're capturing it and, and storing it obviously more broadly. So we have the access to the data and now we have the compute power to run it. And the other big thing is the techniques around artificial intelligence. I mean, what we're able to do with neural networks, computer vision, large language models, natural language processing. These are breakthroughs that, um, one require these large systems, but two, as you give them a large systems, you can actually really enable acceleration of how sophisticated these, these applications can get. >>Let's talk about the impact of the convergence of HPC and AI. What are some of the things that you're seeing now and what are some of the things that we're gonna see? >>Yeah. So, so I, one thing I like to talk about is it's, it's really, it's not a convergence. I think it's it. Sometimes it gets a little bit oversimplified. It's actually, it's traditional modeling and simulation leveraging machine learning to, to refine the simulation. And this is a, is one of the things we talk about a lot in AI, right? It's using machine learning to actually create code in real time, rather than humans doing it, that ability to refine the model as you're running. So we have an example. We did a, uh, we, we actually launched an open source solution called smart SIM. And the first application of that was climate science. And it's what it's doing is it's actually learning the data from the model as the simulation is running to provide more accurate climate prediction. But you think about that, that could be run for, you know, anything that has a complex model. >>You could run that for financial modeling, you can use AI. And so we're seeing things like that. And I think we'll continue to see that the other side of that is using modeling and simulation to actually represent what you see in AI. So we were talking about the grid. This is one of the Exoscale compute projects you could actually use once you actually get, get the data and you can start modeling the behavior of every electrical endpoint in a city. You know, the, the meter in your house, the substation, the, the transformers, you can start measuring the FX of that. You can then build equations. Well, once you build those equations, you can then take a model, cuz you've learned what actually happens in the real world, build the equation. And then you can provide that to someone who doesn't need a extra scale supercomputer to run it, but that, you know, your local energy company can better understand what's happening and they'll know, oh, there's a problem here. We need to shift the grid or respond more, more dynamically. And hopefully that avoids brownouts or, you know, some of the catastrophic outages we've >>Seen so they can deploy that model, which, which inherently has that intelligence on sort of more cost effective systems and then apply it to a much broader range. Do any of those, um, smart simulations on, on climate suggest that it's, it's all a hoax. You don't have to answer that question. <laugh> um, what, uh, >>The temperature outside Dave might, might give you a little bit of an argument to that. >>Tell us about quantum, what's your point of view there? Is it becoming more stable? What's H HPE doing there? >>Yeah. So, so look, I think there's, there's two things to understand with quantum there's quantum hardware, right? Fundamentally, um, how, um, how that runs very differently than, than how we run traditional computers. And then there's the applications. And ultimately a quantum application on quantum hardware will be far more efficient in the future than, than anything else. We, we see the opportunity for, uh, much like we see with, you know, with HPC and AI, we just talked about for quantum to be complimentary. It runs really well with certain applications that fabricate themselves as quantum problems and some great examples are, you know, the, the life sciences, obviously quantum chemistry, uh, you see some, actually some opportunities in, in, uh, in AI and in other areas where, uh, quantum has a very, very, it, it just lends itself more naturally to the behavior of the problem. And what we believe is that in the short term, we can actually model quantum effectively on these, on these super computers, because there's not a perfect quantum hardware replacement over time. You know, we would anticipate that will evolve and we'll see quantum accelerators much. Like we see, you know, AI accelerators today in this space. So we think it's gonna be a natural evolution in progression, but there's certain applications that are just gonna be solved better by quantum. And that's the, that's the future we'll we'll run into. And >>You're suggesting if I understood it correctly, you can start building those applications and, and at least modeling what those applications look like today with today's technology. That's interesting because I mean, I, I think it's something rudimentary compared to quantum as flash storage, right? When you got rid of the spinning disc, it changed the way in which people thought about writing applications. So if I understand it, new applications that can take advantage of quantum are gonna change the way in which developers write, not one or a zero it's one and virtually infinite <laugh> combinations. >>Yeah. And I actually, I think that's, what's compelling about the opportunity is that you can, if you think about a lot of traditional the traditional computing industry, you always had to kind of wait for the hardware to be there, to really write, write, and test the application. And we, you know, we even see that with our customers and HPC and, and AI, right? They, they build a model and then they, they actually have to optimize it across the hardware once they deploy it at scale. And with quantum what's interesting is you can actually, uh, you can actually model and, and, and make progress on the software. And then, and then as the hardware becomes available, optimize it. And that's, you know, that's why we see this. We talk about this concept of quantum accelerators as, as really interesting, >>What are the customer conversations these days as there's been so much evolution in HPC and AI and the technology so much change in the world in the last couple of years, is it elevating up the CS stack in terms of your conversations with customers wanting to become familiar with Exoscale computing? For example? >>Yeah. I, I think two things, uh, one, one is we see a real rise in digital sovereignty and Exoscale and HPC as a core fund, you know, fundamental foundation. So you see what, um, you know, what Europe is doing with the, the, the Euro HPC initiative, as one example, you know, we see the same kind of leadership coming out of the UK with the system. We deployed with them in Archer two, you know, we've got many customers across the globe deploying next generation weather forecasting systems, but everybody feels, they, they understand the foundation of having a strong supercomputing and HPC capability and competence and not just the hardware, the software development, the scientific research, the, the computational scientists to enable them to remain competitive economically. It's important for defense purposes. It's important for, you know, for helping their citizens, right. And providing, you know, providing services and, and betterment. >>So that's one, I'd say that's one big theme. The other one is something Dave touched on before around, you know, as a service and why we think HP GreenLake will be, uh, a beautiful marriage with our, with our HPC and AI systems over time, which is customers also, um, are going to scale up and build really complex models. And then they'll simplify them and deploy them in other places. And so there's a number of examples. We see them, you know, we see them in places like oil and gas. We see them in manufacturing where I've gotta build a really complex model, figure out what it looks like. Then I can reduce it to a, you know, to a, uh, certain equation or application that I can then deploy. So I understand what's happening and running because you, of course, as much as I would love it, you're not gonna have, uh, every enterprise around the world or every endpoint have an exit scale system. Right. So, so that ability to, to, to really provide an as a service element with HP GreenLake, we think is really compelling. >>HP's move into HPC, the acquisitions you've made it really have become a differentiator for the company. Hasn't it? >>Yeah. And I, and I think what's unique about us today. If you look at the landscape is we're, we're really the only system provider globally. Yeah. You know, there are, there are local players that we compete with. Um, but we are the one true global system provider. And we're also the only, I would say the only holistic innovator at the system level to, to, you know, to credit my team on the work they're doing. But, you know, we're, we're also very committed to open standards. We're investing in, um, you know, in a number of places where we contribute the dev the software assets to open source, we're doing work with standards bodies to progress and accelerate the industry and enable the ecosystem. And, uh, and I think that, you know, ultimately the, the, the last thing I'd say is we, we are so connected in, um, with, through our, through the legacy or the, the legend of H Hewlett Packard labs, which now also reports into me that we have these really tight ties into advanced research and that some of that advanced research, which isn't just, um, around kind of core processing Silicon is really critical to enabling better applications, better use cases and accelerating the outcomes we see in these systems going forward. >>Can >>You double click on that? I, I, I wasn't aware that kind of reported into your group. Yeah. So, you know, the roots of HP are invent, right? Yeah. HP labs are, are renowned. It kinda lost that formula for a while. And now it's sounds like it's coming back. What, what, what are some of the cool things that you guys are working on? Well, >>You know, let me, let me start with a little bit of recent history. So we just talked about the exo scale program. I mean, that was a, that's a great example of where we had a public private partnership with the department of energy and it, and it wasn't just that we, um, you know, we built a system and delivered it, but if you go back a decade ago, or five years ago, there were, there were innovations that were built, you know, to accelerate that system. One is our Slingshot fabric as an example, which is a core enable of, of acceler, you know, of, of this accelerated computing environment, but others in software applications and services that allowed us to, you know, to really deliver a, a complete solution into the market. Um, today we're looking at things around trustworthy and ethical AI, so trustworthy AI in the sense that, you know, the models are accurate, you know, and that's, that's a challenge on two dimensions, cuz one is the, model's only as good as the data it's studying. >>So you need to validate that the data's accurate and then you need to really study how, you know, how do I make sure that even if the data is accurate, I've got a model that then, you know, is gonna predict the right things and not call a, a dog, a cat, or a, you know, a, a cat, a mouse or whatever that is. But so that's important. And, uh, so that's one area. The other is future system architectures because, um, as we've talked about before, Dave, you have this constant tension between the fabric, uh, you know, the interconnect, the compute and the, and the storage and, you know, constant, constantly balancing it. And so we're really looking at that, how do we do more, you know, shared memory access? How do we, you know, how do we do more direct rights? Like, you know, looking at some future system architectures and thinking about that. And we, you know, we think that's really, really critical in this part of the business because these heterogeneous systems, and not saying I'm gonna have one monolithic application, but I'm gonna have applications that need to take advantage of different code, different technologies at different times. And being able to move that seamlessly across the architecture, uh, we think is gonna be the, you know, a part of the, the hallmark of the Exoscale era, including >>Edge, which is a completely different animal. I think that's where some disruption is gonna gonna bubble up here in the next decade. >>So, yeah know, and, and that's, you know, that's the last thing I'd say is, is we look at AI at scale, which is another core part of the business that can run on these large clusters. That means getting all the way down to the edge and doing inference at scale, right. And, and inference at scale is, you know, I, I was, um, about a month ago, I was at the world economic forum. We were talking about the space economy and it's a great, you know, to me, it's the perfect example of inference, because if you get a set of data that you know, is, is out at Mars, it doesn't matter whether, you know, whether you wanna push all that data back to, uh, to earth for processing or not. You don't really have a choice, cuz it's just gonna take too long. >>Don't have that time. Justin, thank you so much for spending some of your time with Dave and me talking about what's going on with HBC and AI. The frontier just seems endless and very exciting. We appreciate your time on your insights. >>Great. Thanks so much. Thanks. >>Yes. And don't call a dog, a cat that I thought I learned from you. A dog at no, Nope. <laugh> Nope. <laugh> for Justin and Dave ante. I'm Lisa Martin. You're watching the Cube's coverage of day one from HPE. Discover 22. The cube is, guess what? The leader, the leader in live tech coverage will be right back with our next guest.

>>Okay, welcome back everyone. This is the Cube's coverage here in Las Vegas. Back at events re Mars, Amazon re Mars. I'm your host, John fur with the cube. Mars stands for machine learning, automation, robotics, and space. It's great event brings together a lot of the industrial space machine learning and all the new changes in scaling up from going on the moon to, you know, doing great machine learning. And we've got two great guests here with kinda called lunar outpost, Justin Sears, CEO, Lauren, man. He's the co-founder and chief strategy officer lunar outpost. They're right next to us, watching their booth. Love the name, gentlemen. Welcome to the cube. >>Yeah. Thanks for having us, John. >>All right. So lunar outpost, I get the clues here. Tell us what you guys do. Start with that. >>Absolutely. So lunar outpost, we're a company based outta Colorado that has two missions headed to the moon over the course of the next 24 months. We're currently operating on Mars, which forest will tell you a little bit more about here in a second. And we're really pushing out towards expanding the infrastructure on the lunar surface. And then we're gonna utilize that to provide sustainable access to other planetary bodies. >>All right, far as teeing it up for you. Go, how cool is this? We don't, we wanna use every minute. What's the lunar surface look like? What's the infrastructure roads. You gonna pave it down. You what's going on. Well, >>Where we're going. No one has ever been. So, um, our first mission is going to Shackleton connecting Ridge on the south pole, the moon, and that's ripe to add infrastructure such as landing pads and other things. But our first Rover will be primarily driving across the surface, uh, exploring, uh, what the material looks like, prospecting for resources and testing new technologies. >>And you have a lot of technology involved. You're getting data in, you're just doing surveillance. What's the tech involved there. >>Yeah. So the primary technology that we're demonstrating is a 4g network for NOK. Um, we're providing them mobility services, which is basically like the old Verizon commercial. Can you hear me now? Uh, where the Rover drives farther and farther away from the Lander to test their signal strength, and then we're gonna have some other payloads ride sharing along with us for the ride >>Reminds me the old days of wifi. We used to call it war drive and you go around and try to find someone's wifi hotspot <laugh> inside the thing, but no, this is kind of cool. It brings up the whole thing. Now on lunar outpost, how big is the company? What's how what's to some of the stats heres some of the stats. >>Absolutely. So lunar outpost, 58 people, uh, growing quite quickly on track to double. So any of you watching, you want a job, please apply <laugh>. But with lunar outpost, uh, very similar to how launch companies provide people access to different parts of space. Lunar outpost provides people access to different spots on planetary bodies, whether it's the moon, Mars or beyond. So that's really where we're starting. >>So it's kinda like a managed service for all kinds of space utilities. If you kind of think about it, you're gonna provide services. Yeah, >>Absolutely. Yeah. It, it's definitely starting there and, and we're pushing towards building that infrastructure and that long term vision of utilizing space resources. But I can talk about that a little bit more here in a sec. >>Let's get into that. Let's talk about Mars first. You guys said what's going on with >>Mars. Absolutely. >>Yeah. So right now, uh, lunar outpost is part of the science team for, uh, Moxi, which is an instrument on the perseverance Rover. Yeah. Moxi is the first demonstration of space resource utilization on another planet. And what space resource utilization is basically taking resources on another planet, turning them into something useful. What Moxi does is it takes the CO2 from the atmosphere of Mars and atmosphere of Mars is mostly CO2 and it uses a process called solid oxide electrolysis to basically strip oxygen off of that CO2 to produce oh two and carbon monoxide. >>So it's what you need to self sustain on the surface. >>Exactly. It's not just sustaining, um, the astronauts, but also for producing oxygen for propellant. So it'll actually produce, um, it's a, it's a technology that'll produce a propellant for return rockets, um, to come back for Mars. So >>This is the real wildcard and all this, this, this exploration is how fast can the discoveries invent the new science to provide the life and the habitat on the surface. And that seems to be the real focus in the, in the conversations I heard on the keynote as well, get the infrastructure up so you can kinda land and, and we'll pull back and forth. Um, where are we on progress? You guys have the peg from one zero to 10, 10 being we're going, my grandmother's going, everyone's going to zero. Nothing's moving. >>We're making pretty rapid >>Progress. A three six, >>You know, I'll, I'll put it on an eight, John an >>Eight, I'll put it on >>Eight. This is why the mission force was just talking about that's launching within the next 12 months. This is no longer 10 years out. This is no longer 20 years away, 12 months. And then we have mission two shortly after, and that's just the beginning. We have over a dozen Landers that are headed to line surface this decade alone and heavy lift Landers and launchers, uh, start going to the moon and coming back by 2025. >>So, and you guys are from Colorado. You mentioned before you came on camera, right with the swap offices. So you got some space in Colorado, then the rovers to move around. You get, you get weird looks when people drive by and see the space gear. >>Oh yeah, definitely. So we have, um, you know, we have our facility in golden and our Nevada Colorado, and we'll take the vehicles out for strolls and you'll see construction workers, building stuff, and looking over and saying, what's >>Good place to work too. So you're, you're hiring great. You're doubling on the business model side. I can see a lot of demand. It's cheaper to launch stuff now in space. Is there becoming any rules of engagement relative to space? I don't wanna say verified, but like, you know, yet somehow get to the point where, I mean, I could launch a satellite, I could launch something for a couple hundred grand that might interfere with something legitimate. Do you see that on the radar because you guys are having ease of use so smaller, faster, cheaper to get out there. Now you gotta refine the infrastructure, get the services going. Is there threats from just random launches? >>It's a, it's a really interesting question. I mean, current state of the art people who have put rovers on other planetary bodies, you're talking like $3 billion, uh, for the March perseverance Rover. So historically there hasn't been that threat, but when you start talking about lowering the cost and the access to some of these different locations, I do think we'll get to the point where there might be folks that interfere with large scale operations. And that's something that's not very well defined in international law and something you won't really probably get any of the major space powers to agree to. So it's gonna be up to commercial companies to operate responsibly so we can make that space sustainable. And if there is a bad actor, I think it they'll weed themselves out over time. >>Yeah. It's gonna be of self govern, I think in the short term. Good point. Yeah. What about the technology? Where are we in the technology? What are some of the big, uh, challenges that we're overcoming now and what's that next 20 M stare in terms of the next milestone? Yeah, a tech perspective. >>Yeah. So the big technology technological hurdle that has been identified by many is the ability to survive the LUN night. Um, it gets exceptionally cold, uh, when the sun on the moon and that happens every 14 days for another, for, you know, for 14 days. So these long, cold lunar nights, uh, can destroy circuit boards and batteries and different components. So lunar outpost has invested in developing thermal technologies to overcome this, um, both in our offices, in the United States, but we also have opened a new office in, uh, Luxembourg in Europe. That's focusing specifically on thermal technologies to survive the lunar night, not just for rovers, but all sorts of space assets. >>Yeah. Huge. That's a hardware, you know, five, nine kind of like meantime between failure conversation, right. >><laugh> and it's, it gets fun, right? Because you talk five nines and it's such like, uh, you know, ingrained part of the aerospace community. But what we're pitching is we can send a dozen rovers for the cost of one of these historical rovers. So even if 25% of 'em fail, you still have eight rovers for the cost of one of the old rovers. And that's just the, economy's a scale. >>I saw James Hamilton here walking around. He's one of the legendary Amazonians who built out the data center. You might come by the cube. That's just like what they did with servers. Hey, if one breaks throw it away. Yeah. Why buy the big mainframe? Yeah. That's the new model. All right. So now about, uh, space space, that's a not space space, but like room to move around when you start getting some of these habitats going, um, how does space factor into the size of the location? Um, cuz you got the, to live there, solve some of the thermal problems. How do I live on space? I gotta have, you know, how many people gonna be there? What's your forecast? You think from a mission standpoint where there'll be dozens of people or is it still gonna be small teams? >>Yeah. >>Uh, what's that look like? >>I mean you >>Can guess it's okay. >>I mean, my vision's thousands of people. Yep. Uh, living and working in space because it's gonna be, especially the moon I think is a destination that's gonna grow, uh, for tourism. There's an insane drive from people to go visit a new destination. And the moon is one of the most unique experiences you could imagine. Yep. Um, in the near term for Artis, we're gonna start by supporting the Artis astronauts, which are gonna be small crews of astronauts. Um, you know, two to six in the near term. >>And to answer your question, uh, you know, in a different way, the habitat that we're actually gonna build, it's gonna take dozens of these robotic systems to build and maintain over time. And when we're actually talking, timelines, force talks, thousands of people living and working in space, I think that's gonna happen within the next 10 to 15 years. The first few folks are gonna be on the moon by 2025. And we're pushing towards having dozens of people living and working in space and by 2030. >>Yeah. I think it's an awesome goal. And I think it's doable question I'll have for you is the role of software in all this. I had a conversation with, uh, space nerd and we were talking and, and I said open sources everywhere now in the software. Yeah. How do you repair in space? Does you know, you don't want to have a firmware be down. So send down backhoe back to the United States. The us, wait a minute, it's the planet. I gotta go back to earth. Yeah. To get apart. So how does break fix work in space? How, how do you guys see that problem? >>So this one's actually quite fun. I mean, currently we don't have astronauts that can pick up a or change a tire. Uh, so you have to make robots that are really reliable, right. That can continuously operate for years at a time. But when you're talking about long-term repairs, there's some really cool ideas and concepts about standardization of some of these parts, you know, just like Lu knots on your car, right? Yeah. If everyone has the same Lu knots on their wheel, great. Now I can go change it out. I can switch off different parts that are available on the line surface. So I think we're moving towards, uh, that in the long >>Term you guys got a great company. Love the mission. Final question for both of you is I noticed that there's a huge community development around Mars, living on Mars, living on the moon. I mean, there's not a chat group that clubhouse app used, used to be around just kind of dying. But now it's when the Twitter spaces Reddit, you name it, there's a fanatical fan base that loves to talk about an engineer and kind of a collective intelligence, not, may not be official engineering, but they just love to talk about it. So there's a huge fan base for space. How does someone get involved if they really want to dive in and then how do you nurture that audience? How does that, is it developing? What's your take on this whole movement? It's it's beyond just being interested. It's it's become, I won't say cult-like but it's been, there's very, a lot of people in young people interested in space. >>Yeah. >>Yeah. There's, there's a whole, lots of places to get involved. There's, you know, societies, right? Like the Mar society there's technical committees, um, there's, you know, even potentially learning about these, you know, taking a space, resources master program and getting into the field and, and joining the company. So, um, we really, uh, thrive on that energy from the community and it really helps press us forward. And we hope to, uh, have a way to take everyone with us on the mission. And so stay tuned, follow our website. We'll be announcing some of that stuff soon. >>Awesome. And just one last, uh, quick pitch for you, John, I'll leave you with one thought. There are two things that space has an infinite amount of the first is power and the second is resources. And if we can find a way to access either of those, we can fundamentally change the way humanity operates. Yeah. So when you're talking about living on Mars long term, we're gonna need to access the resource from Mars. And then long term, once we get the transportation infrastructure in place, we can start bringing those resources back here to earth. So of course there are gonna be those people that sign up for that first mission out to Mars with SpaceX. But, uh, we'd love for folks to join on with us at lunar outpost and be a part of that kind of next leap accessing those resources. >>I love the mission, as always said, once in the cube, everything in star Trek will be invented someday. <laugh>, we're almost there except for the, the, uh, the transporter room. We don't have that done yet, but almost soon be there. All right. Well, thanks for coming. I, I really appreciate Justin for us for sharing. Great story. Final minute. Give a plug for the company. What are you guys looking for? You said hiring. Yep. Anything else you'd like to share? Put a plug in for lunar outpost. >>Absolutely. So we're hiring across the board, aerospace engineering, robotics engineering, sales marketing. Doesn't really matter. Uh, we're doubling as a company currently around 58 people, as we said, and we're looking for the top people that want to make an impact in aerospace. This is truly a unique moment. First time we've ever had continuous reliable operations. First time NASA is pushing really hard on the public private partnerships for commercial companies like ours to go out and create this sustainable presence on the moon. So whether you wanna work with us, our partner with us, we'd be excited to talk to you and, uh, yeah. Please contact us at info. Lunar outpost.com. >>We'll certainly follow up. Thanks for coming. I love the mission we're behind you and everyone else is too. You can see the energy it's gonna happen. It's the cube coverage from re Mars new actions happening in space on the ground, in the, on the moon you name it's happening right here in Vegas. I'm John furrier. Thanks for watching.

we're back at vemma in 2022 we're here at the aria hotel in las vegas this is thecube's continuous coverage we're day two welcome to the cxo session we have ceo cto cso chief strategy officer brett diamond is the ceo justin jardina is the cto and dante orsini is the chief strategy officer for 11 11 systems recently named i guess today the impact cloud service provider of the year congratulations guys welcome thank you welcome back to the cube great to see you again thank you great likewise so okay brett let's start with you tell give us the overview of 11 1111 uh your focus area talk about the the the island acquisition what that what that's all about give us the setup yeah so we started 11-11 uh really with a focus on taking the three core pillars of our business which are cloud connectivity and security bring them together into one platform allowing a much easier way for our customers and our partners to procure those three solution sets through a single company and really focus on uh the three main drivers of the business uh which you know have a litany of other services associated with them under each platform okay so so justin cloud connectivity and security they all dramatically changed in march of 2020 everybody had to go to the cloud the rather rethink the network had a secure remote worker so what did you see from a from a cto's perspective what changed and how did 11 respond sure so early on when we built our cloud even back into 2008 we really focused on enterprise great features one of which being uh very flexible in the networking so we found early on was that we would be able to architect solutions for customers that were dipping their toe in the cloud and set ourselves apart from some of the vendors at the time so if you fast forward from 2008 until today we still see that as a main component for iaz and draz and the ability to start taking into some of the things brett talked about where customers may need a point-to-point circuit to offload data connectivity to us or develop sd-wan and multi-cloud solutions to connect to their resources in the cloud in my opinion it's just the natural progression of what we set out to do in 2008 and to couple that with the security um if you think about what that opens up from a security landscape now you have multiple clouds you have different ingress and egress points you have different people accessing workloads in each one of these clouds so the idea or our idea is that we can layer a comprehensive security solution over this new multi-cloud networking world and then provide visibility and manageability to our customer base so what does that mean specifically for your customers because i mean we saw obviously a rapid move toward endpoint um cloud security uh identity access you know people really started thinking rethinking that as opposed to trying to just you know build a moat around the castle right um what does that mean for for your customer you take care of all that you partner with whomever you need to partner in the ecosystem and then you provide the managed service how does that work right it does and that's a great analogy you know we have a picture of a hamburger in our office exploded with all the components and they say a good security policy is all the pieces and it's really synonymous with what you said so to answer your question yes we have all that baked in the platform we can offer managed services around it but we also give the consumer the ability to access that data whether it's a ui or api so dante i know you talk to a lot of customers all you do is watch the stock market go like this and like that you say okay the pandemic drove all these but but when you talk to csos and customers a lot of things are changing permanently first of all they were forced to march to digital when previously they were like we'll get there i mean a lot of customers were let's face it i mean some were serious about it but many weren't now if you're not a digital business you're out of business what have you seen when you talk to customers in terms of the permanence of some of these changes what are they telling you well i think we go through this for ourselves right the business continues to grow you've got tons of people that are working remotely and that are going to continue to work remotely right as much as we'd like to offer up hybrid workspace and things like that some folks are like hey i've worked it out i'm working out great from home right and also i think what justin was saying also is we've seen time go on that operating environment has gotten much more complex you've got stuff in the data center stuff it's somebody's you know endpoint you've got various different public clouds different sas services right that's why it's been phenomenal to work with veeam because we can protect that data regardless of where it exists but when you start to look at some of the managed security services that we're talking about we're helping those csos you get better visibility better control and take proactive action against the infrastructure um when we look at threat mitigation and how to actually respond when when something does happen right and i think that's the key because there's no shortage of great security vendors right but how do you tie it all together into a single solution right with a vendor that you can actually partner with to help secure the environment while you go focus on the things they're more strategic to the business i was talking to jim mercer at um red hat summit last week he's an idc analyst and he said we did a survey i think it was last summer and we asked customers to your point about there's no shortage of security tools how do you want to buy your security and you know do you want you know best to breed bespoke tools and you sort of put it together or do you kind of want your platform provider to do it now surprisingly they said platform provider the the problem is that's aspirational for a lot of platforms providers so they've got to look to a managed service provider so brett talk about the the island acquisition what green cloud is how that all fits together so we acquired island and green cloud last year and the reality is that the people at both of those companies and the technology is what drove us to making those acquisitions they were the foundational pieces to eleven eleven uh obviously the things that justin has been able to create from an automation and innovation perspective uh at the company is transforming this business in a litany of different ways as well so those two acquisitions allow us at this point to take a cloud environment on a geographic footprint not only throughout the us but globally uh have a security product that was given to us from from the green cloud acquisition of cascade and add-on connectivity to allow us to have all three platforms in one all three pillars so i like 11 11 11 is near and dear to my heart i am so where'd the name come from uh everybody asked me this question i think five times a day so uh growing up as a kid everyone in my family would always say 11 11 make a wish whenever you'd see it on the clock and uh during coven we were coming up with a new name for the business my daughter looked at the microwave said dad it's 11 11. make a wish the reality was though i had no idea why i'd been doing it for all that time and when you look up kind of the background origination derivation of the word uh it means the time of day when everything's in line um and when things are complex especially with running all the different businesses that we have aligning them so that they're working together it seemed like a perfect man when i had the big corner office at idc i had my staff meetings at 11 11. because the universe was aligned and then the other thing was nobody could forget the time so they gave him 11 minutes to be there now you'll see it all the time even when you don't want to so justin we've been talking a lot about ransomware and and not just backup but recovery my friend fred moore who you know coined the phrase backup is one thing recovery is everything and recovery time network speeds and and the like are critical especially when you're thinking cloud how are you architecting recovery for your clients maybe you could dig into that a little bit sure so it's really a multitude of things you know you mentioned ransomware seeing the ransomware landscape evolve over time especially in our business with backup and dr it's very singular you know people protecting against host nodes now we're seeing ransomware be able to get into an environment land and expand actually delete backups target backup vendors so the ransomware point i guess um trying to battle that is a multi-step process right you need to think about how data flows into the organization from a security perspective from a networking perspective you need to think about how your workloads are protected and then when you think about backups i know we're at veeam vmon now talking about veeam there's a multitude of ways to protect that data whether it's retention whether it's immutability air gapping data so while i know we focus a lot sometimes on protecting data it's really that hamburg analogy where the sum of the parts make up the protection so how do you provide services i mean you say okay you want immutability there's a there's a line item for that um you want faster or you know low rpo fast rto how does that all work for as a customer what what am i buying from you is it just a managed service we'll take care of everything platinum gold silver or is it if if you don't mind so i'm glad you asked that question because this is something that's very unique about us years ago his team actually built the ip because we were scaling at such an incredible rate globally through all our joint partners with veeam that how do we take all the intelligence that we have in his team and all of our solution architects and scale it so they actually developed a tool called catalyst and it's a pre-sales tool it's an application you download it you install it it basically takes a snapshot of your environment you start to manipulate the data what are you trying to do dave are you trying to protect that data are you backing up to us are you trying to replicate for dr purposes um you know what are you doing for production or maybe it's a migration it analyzes the network it analyzes all your infrastructure it helps the ses know immediately if we're a feasible solution based on what you are trying to do so nobody in the space is doing this and that's been a huge key to our growth because the channel community as well as the customer they're working with real data so we can get past all the garbage and get right to what's important for them for the outcome yeah that's huge who do you guys sell to is it is it more mid-sized businesses that maybe don't have the large teams is it larger enterprises who want to complement to their business is it both well i would say with the two acquisitions that we made the go-to-market sales strategies and the clientele were very different when you look at green cloud they're selling predominantly wholesale through msps and those msps are mostly selling to smbs right so we covered that smb market for the most part through our acquisition of green cloud island on the other hand was more focused on selling direct inbound through vars through the channel mid enterprise big enterprise so really those two acquisitions outside of the ip that we got from the systems we have every single go-to-market sale strategy and we're aligned from smb all the way up to the fortune 500. i heard a stat a couple months ago that that less than 50 of enterprises have a sock it blew me away and you know even small businesses need one they may not be able to afford but certainly a medium size or larger business should have some kind of sock is it does that stat jive with what you're seeing in the marketplace 100 if that's true the need for a managed service like this is just it's going to explode it is exploding yeah i mean 100 right there is zero unemployment in the cyberspace right just north america alone there's about a million or so folks in that space and right now you've got about 600 000 open wrecks just in north america right so earlier we talked about no shortage of tools right but the shortage of head count is a significant challenge big time right most importantly the people that you do have on staff they've got alert fatigue from the tools that they do have that's why you're seeing this massive insurgence in the managed security services provider lack of talent is number one challenge for csos that's what they'll tell you and there's no end in sight to that and it's you know another tool and and it's amazing because you see security companies popping up all the time billion dollar evaluations i mean lacework did a billion dollar raise and so so there's no shortage of funding now maybe that'll change you know with the market but i wanted to turn our attention to the keynotes this morning you guys got some serious love up on stage um there was a demo uh it was a pretty pretty cool demo fast recovery very very tight rpo as i recall it was i think four minutes of data loss is that right was that the right knit stat i was happy it wasn't zero data loss because there's really you know no such thing uh but so you got to feel good about that tell us about um how that all came about your relationship with with veeam who wants to take it sure i can i can take a step at it so one of the or two of the things that i'm um most excited about at least with this vmon is our team was able to work with veeam on that demo and what that demo was showing was some cdp-based features for cloud providers so we're really happy to see that and the reason why we're happy to see that is that with the veeam platform it's now given the customers the ability to do things like snapshot replication cdp replication on-prem backup cloud backup immutability air gap the list goes on and on and in our opinion having a singular software vendor that can provide all that through you know with a cloud provider on prem or not is really like the icing on the cake so for us it's very exciting to see that and then also coupled with a lot of the innovation that veeam's doing in the sas space right so again having that umbrella product that can cover all those use cases i'll tell you if you guys can get a that was a very cool demo if we can get a youtube of that that that demo i'll make sure we put it in the the show notes and uh of this video or maybe pop it into one of the blogs that we write about it um so so how you guys feel i mean this is a new chapter for you very cool with a couple of acquisitions that are now the main mainspring of your strategy so the first veeam on in a couple years so what's the vibe been like for you what's the nighttime activity the customer interaction i know you guys are running a lot of the back end demos so you're everywhere what's the what's the vibe like at veeamon and how does it feel to be back look at that one at dante as far as yeah you got a lot of experience here yeah let me loose on this one dave i'm like so excited about this right it's been it's been far too long to get face to face again and um veeam always does it right and i think that uh for years we've been back-ending like all the hands-on lab infrastructure here but forget about that i think the part that's really exciting is getting face-to-face with such a great team right we have phenomenal architects that we work with at veeam day in and day out they put up with us pushing them pushing and pushing them and together we've been able to create a lot of magic together right but i think it's you can't replace the human interaction that we've all been starving for for the last two years but the vibe's always fantastic at veeam if you're going to be around tonight i'll be looking forward to enjoying some of that veeam love with you at the after party yeah that's well famous after parties we'll see if that culture continues i have a feeling it will um brett where do you want to take 11 11. a new new phase in all of your careers you got a great crew out here it looks like i i love that you're all out and uh make some noise here people let's hear it all right let's see you this is the biggest audience we've had all week where do you want to take 11 11. i think you know if uh if you look at what we've done so far in the short six months since the acquisitions of green cloud and ireland obviously the integration is a key piece we're going to be laser focused on growing organically across those three pillars we've got to put more capital and resources into the incredible ip like i said earlier that just and his team have created on those front ends the user experience but you know we made two large acquisitions obviously mna is a is a key piece for us we're going to be diligent and we're probably going to be very aggressive on that front as well to be able to grow this business into the global leader of cloud connectivity and security and i think we've really hit a void in the industry that's been looking for this for a very long time and we want to be the first ones to be able to collaborate and combine those three into one when the when the cloud started to hit the steep part of the s-curve kind of early part of the last decade people thought oh wow these managed service providers are toast the exact opposite happened it created such a tailwind and need for consistent services and integration and managed services we've seen it all across the stack so guys wish you best of luck congratulations on the acquisitions thank you uh hope to have you back soon yeah thank you around the block all right keep it right there everybody dave vellante for the cube's coverage of veeamon 2022 we'll be right back after this short break

(pleasant music) >> We're back at Veeamon 2022. We're here at the Aria hotel in Las Vegas. This is theCube's continuous coverage. We're in day two. Welcome to the CXO session. We have CEO, CTO, CSO, chief strategy officer. Brett Diamond is the CEO, Justin Giardina is the CTO, and Dante Orsini is the chief strategy officer for 11:11 Systems recently named, I guess today, the impact cloud service provider of the year. Congratulations, guys. Welcome to theCube. Welcome back to theCube. Great to see you again. >> Thank you. >> Great. >> Likewise. >> Thanks for having us. Okay, Brett, let's start with you. Give us the overview of 11:11, your focus area, talk about the Island acquisition, what that's all about, give us the setup. >> Yeah, so we started 11:11, really, with a focus on taking the three core pillars of our business, which are cloud, connectivity, and security, bring them together into one platform, allowing a much easier way for our customers and our partners to procure those three solution sets through a single company and really focus on the three main drivers of the business, which, you know, have a litany of other services associated with them under each platform. >> Okay, so Justin, cloud connectivity and security, they all dramatically changed in March of 2020. Everybody had to go to the cloud, had to rethink the network, had to secure remote workers. So what did you see, from a CTO's perspective, what changed and how did 11:11 respond? >> Sure, so early on, when we built our cloud, even back into 2008, we really focused on enterprise grade features, one of which being very flexible in the networking. So we found early on was that we would be able to architect solutions for customers that were dipping their toe in the cloud and set ourselves apart from some of the vendors at the time. So if you fast forward from 2008 until today, we still see that as a main component for IaaS and DRaaS and the ability to start taking into some of the things Brett talked about, where customers may need a point to point circuit to offload data connectivity to us, or develop SD-WAN and multi-cloud solutions to connect to their resources in the cloud. In my opinion, it's just the natural progression of what we set out to do in 2008. And to couple that with the security, if you think about what that opens up from a security landscape, now you have multiple clouds, you have different ingress and egress points, you have different people accessing workloads in each one of these clouds, so the idea or our idea is that we can layer a comprehensive security solution over this new multi-cloud networking world and then provide visibility and manageability to our customer base. >> So what does that mean specifically for your customers? Because, I mean, we saw obviously a rapid move toward end point, cloud security, identity access. You know, people really started rethinking that as opposed to trying to just, you know, build a moat around the castle. >> Right. >> What does that mean for your customer? You take care of all that? You partner with whomever you need to partner in the ecosystem and then you provide the managed service? How does that work? >> Right. It does and that's a great analogy. You know, we have a picture of a hamburger in our office, exploded with all the components and they say, a good security policy has all the pieces and it's really synonymous with what you said. So to answer your question, yes. We have all that baked in the platform. We can offer managed services around it, but we also give the consumer the ability to access that data, whether it's a UI or API. >> So Dante, I know you talked to a lot of customers. All you do is watch the stock market go like this and like that and you say, okay, the pandemic drove all these, but when you talk to CISOs and customers, a lot of things are changing permanently. First of all, they were forced to march to digital when previously, they were like, eh, we'll get there. I mean, a lot of customers were. Let's face it. I mean, some were serious about it, but many weren't. Now, if you're not a digital business, you're out of business. What have you seen when you talk to customers in terms of the permanence of some of these changes? What are they telling you? >> Well, I think, you know, we go through this ourselves, right? The business continues to grow. You've got tons of people that are working remotely and they are going to continue to work remotely, right? As much as we'd like to offer up hybrid workspace and things like that, some folks are like, hey, I've worked it out. I'm working out great from home, right? And also, I think what Justin was saying also is, as we've seen time go on, that operating environment has gotten much more complex. You've got stuff in the data center, stuff in somebody's, you know, endpoint, you've got various different public clouds, different SAS services, right? That's why it's been phenomenal to work with Veeam because we can protect that data regardless of where it exists. But when you start to look at some of the managed security services that we're talking about, we're helping those CSOs, you know, get better visibility, better control, and take proactive action against the infrastructure when we look at threat mitigation and how to actually respond when something does happen, right? And I think that's the key because there's no shortage of great security vendors, right? But how do you tie it all together into a single solution, right, with a vendor that you can actually partner with to help secure the environment while you go focus on the things that are more strategic to the business? >> I was talking to Jim Mercer at Red Hat Summit last week. He's an IDC analyst and we did a survey, I think it was last summer, and we asked customers to your point about, there's no shortage of security tools. How do you want to buy your security? And, you know, do you want, you know, best to breed bespoke tools and you sort of put it together or do you kind of want your platform provider to do it? Now surprisingly, they said platform provider. The problem is, that's aspirational for a lot of platform providers, so they got to look to a managed service provider. So Brett, talk about the Island acquisition, what Green Cloud is, how that all fits together. >> So we acquired Island and Green Cloud last year and the reality is, the people at both of those companies and the technology is what drove us to making those acquisitions. They were the foundational pieces to 11:11. Obviously, the things that Justin has been able to create from an automation and innovation perspective at the company is transforming this business in a litany of different ways, as well. So, those two acquisitions allow us at this point to take a cloud environment on a geographic footprint, not only throughout the US but globally, have a security product that was given to us from the Green Cloud acquisition of Cascade, and add on connectivity to allow us to have all three platforms in one, all three pillars in one. >> So I like 11:11. 11:11 is near and dear to my heart. So where'd the name come from? >> Everybody asked me this question, I think, five times a day. So growing up as a kid, everyone in my family would always say 11:11 make a wish whenever you'd see it on the clock. And during COVID, we were coming up with a new name for the business. My daughter looked at the microwave, said, dad, it's 11:11, make a wish. The reality was though, I had no idea why I'd been doing it for all that time and when you look up kind of the background origination, derivation of the word, it means the time of day when everything's in line and when things are complex, especially with running all the different businesses that we have, aligning them so that they're working together, it seemed like the perfect thing >> So when I had the big corner office at IDC, I had my staff meetings at 11:11. >> Yep. >> Because the universe was aligned and then the other thing was, nobody could forget the time. So they gave me 11 minutes to be there, so they were never late. >> And now you'll see it all the time, even when you don't want to. (chuckles) >> So Justin, we've been talking a lot about ransomware and not just backup, but recovery. My friend, Fred Moore, who, you know, coined the phrase backup is one thing, recovery is everything, and recovery time, network speeds and the like are critical, especially when you're thinking cloud. How are you architecting recovery for your clients? Maybe you could dig into that a little bit. >> Sure. So it's really a multitude of things. You know, you mention ransomware. Seeing the ransomware landscape evolve over time, especially in our business with backup NDR, is very singular, you know, people protecting against host nodes. Now we're seeing ransomware be able to get into an environment, land and expand, actually delete backups, target backup vendors. So the ransomware point, I guess, trying to battle that is a multi-step process, right? You need to think about how data flows into the organization from a security perspective, from a networking perspective, you need to think about how your workloads are protected, and then when you think about backups, I know we're at Veeamon now talking about Veeam, there's a multitude of ways to protect that data, whether it's retention, whether it's immutability, air gapping data. So, while I know we focus a lot sometimes on protecting data, it's really that hamburger analogy where the sum of the parts make up the protection. >> So how do you provide services? I mean, do you say, okay, do you want immutability? There's a line item for that. You want low RPO, fast RTO? How does that all work as a customer? What am I buying from you? Is it just a managed service? We'll take care of everything, platinum, gold, silver, or is it? >> If you don't mind, so I'm glad you asked that question because this is something that's very unique about us. Years ago, his team actually built the IP because we were scaling at such an incredible rate globally through all our joint partners with Veeam that, how do we take all the intelligence that we have and his team and all of our solution architects and scale it? So they actually developed a tool called Catalyst, and it's a pre-sales tool. It's an application. You download it, you install it. It basically takes a snapshot of your environment. You start to manipulate the data. What are you trying to do, Dave? Are you trying to protect that data? Are you backing up to us? Are you trying to replicate it for DR purposes? You know, what are you doing for production, or maybe it's a migration? It analyzes the network. It analyzes all your infrastructure. It helps the SEs know immediately if we're a feasible solution based on what you are trying to do. So, nobody in the space is doing this and that's been a huge key to our growth because the channel community, as well as the customer, they're working with real data. So we can get past all the garbage, you get right to what's important for them for the outcome. >> Yeah, that's huge. Who do you guys sell to? Is it more mid-size businesses that maybe don't have the large teams? Is it larger enterprises who want to compliment to their business? Is it both? >> Well, I would say with the two acquisitions that we made to go to market sales strategies and the clientele were very different, when you look at Green Cloud, they're selling predominantly wholesale through MSPs and those MSPs are mostly selling to SMBs, right? So we covered that SMB market for the most part through our acquisition of Green Cloud. Island, on the other hand, was more focused on selling direct, inbound, through VARs through the channel, mid-enterprise, big enterprise. So really, those two acquisitions outside of the IP that we got from the systems, we have every single go to market sales strategy and we're aligned from SMB all the way up to the Fortune 500. >> I heard a stat a couple months ago that less than 50% of enterprises have a SAQ. That blew me away. And, you know, even small businesses need one. They may not be able to afford, but there's certainly a medium size or a larger business should have some kind of SAQ. Does that stat jive with what you're seeing in the marketplace? >> A hundred percent. >> If that's true, the need for a managed service like this, it's going to explode. It is exploding, I mean. >> Yeah, I mean, a hundred percent, right? There is zero unemployment in the cyberspace, right? Just North America alone, there's about a million or so folks in that space and right now you've got about 600,000 open recs just in North America, right? So earlier, we talked about no shortage of tools, right? But the shortage of headcount is a significant challenge, big time, right? Most importantly, the people that you do have on staff, they've got alert fatigue from the tools that they do have. That's why you're seeing this massive surgence in the managed security services provider. >> Lack of talent is number one challenge for CISOs. That's what they'll tell you and there's no end in sight to that. And it's, you know, another tool and it's amazing 'cause you see security companies popping up all the time. I mean, billion dollar valuations, I mean, Lacework did a billion dollar raise. And so, there's no shortage of funding. Now, maybe that'll change, you know, with the market but I wanted to turn our attention to the keynotes this morning. You guys got some serious love up on stage. There was a demo. It was a pretty cool demo, fast recovery, very tight RPO, as I recall. It was, I think, four minutes of, of data loss? Is that right? Is that the right stat? I was happy it wasn't zero data loss 'cause there's really, you know, no such thing, but so you got to feel good about that. Tell us about how that all came about, your relationship with Veeam. Who wants to take it? >> Sure, I can take a stab at it. So two of the things that I'm most excited about, at least with this Veeamon, is our team was able to work with Veeam on that demo, and what that demo was showing was some CDP based features for cloud providers. So we're really happy to see that and the reason why we're happy to see that is that with the Veeam platform, it's now given the customers the ability to do things like snapshot replication, CDP replication, on-prem backup, cloud backup, immutability air gap, the list goes on and on. And in our opinion, having a singular software vendor that can provide all that, you know, with a cloud provider on-prem or not is really like, the icing on the cake. So for us, it's very exciting to see that, and then also coupled with a lot of the innovation that's Veeam's doing in the SAS space, right? So again, having that umbrella product that can cover all those use cases. >> I'll tell you, that was a very cool demo. If you can get a YouTube of that demo, I'll make sure we put it in the show notes of this video or maybe pop it into one of the blogs that we write about it. So, how do you guys feel? I mean, this is a new chapter for you. Very cool, with a couple of acquisitions that are now the main spring of your strategy, so the first Veeamon in a couple years. So what's the vibe been like for you? What's the nighttime activity, the customer interaction? I know you guys are running a lot of the backend demos, so you're everywhere. What's the vibe like at Veeamon and how does it feel to be back? >> I'll give that one to Dante as far as the vibes, so far. >> Yeah, yeah, you got a lot of experience. >> Yeah, let me loose on this one, Dave. I'm like, so excited about this, right? It's been far too long to get face to face again and Veeam always does it right. And I think that for years, we've been back ending like, all the hands on lab infrastructure here, but forget about that. I think the part that's really exciting is getting face to face with such a great team, right? We have phenomenal architects that we work with at Veeam day in and day out. They put up with us, pushing them, pushing them, pushing them and together, we've been able to create a lot of magic together, right? But I think you can't replace the human interaction that we've all been starving for, for the last two years. But the vibe's always fantastic at Veeam. If you're going to be around tonight, I'll be looking forward to enjoying some of that Veeam love with you at the after party. >> Yeah, well, famous after parties. We'll see if that culture continues. I have a feeling it will. Brett, where do you want to take 11:11? New phase in all of your careers. You got a great crew out here, it looks like. I love that you're all out and, make some noise here, people. Let's hear it! (audience cheering) You see, this is the biggest audience we've had all week. Where do you want to take 11:11? >> I think, you know, if you look at what we've done so far in the short six months since the acquisitions of Green Cloud and Island, obviously the integration is a key piece. We're going to be laser focused on growing organically across those three pillars. We've got to put more capital and resources into the incredible IP, like I said earlier, that Justin and his team have created on those front ends, the user experience. But, you know, we made two large acquisitions, obviously M and A is a key piece for us. We're going to be diligent and we're probably going to be very aggressive on that front as well, to be able to grow this business into the global leader of cloud connectivity and security. And I think we've really hit a void in the industry that's been looking for this for a very long time and we want to be the first ones to be able to collaborate and combine those three into one. >> When the cloud started to hit the steep part of the S-curve, kind of early part of last decade, people thought, oh wow, these managed service providers are toast. The exact opposite happened. It created such a tailwind and need for consistent services and integration and managed services. We've seen it all across the stacks. So guys, wish you best of luck. Congratulations on the acquisitions, >> Thank you. >> And hope to have you back soon. >> Absolutely, thanks for having us. >> All right, keep it right there everybody. Dave Vellante for theCube's coverage of Veeamon 2022. We'll be right back after this short break. (pleasant music)

(upbeat music) >> We're back. You're watching theCUBE's coverage of Red Hat Summit 2022 here in the Seaport in Boston. I'm Dave Vellante with my co-host, Paul Gillin. Justin Boitano is here. He's the Vice President of Enterprise and Edge Computing at NVIDIA. Maybe you've heard of him. And Tushar Katarki who's the Director of Product Management at Red Hat. Gentlemen, welcome to theCUBE, good to see you. >> Thank you. >> Great to be here, thanks >> Justin, you are a keynote this morning. You got interviewed and shared your thoughts on AI. You encourage people to got to think bigger on AI. I know it's kind of self-serving but why? Why should we think bigger? >> When you think of AI, I mean, it's a monumental change. It's going to affect every industry. And so when we think of AI, you step back, you're challenging companies to build intelligence and AI factories, and factories that can produce intelligence. And so it, you know, forces you to rethink how you build data centers, how you build applications. It's a very data centric process where you're bringing in, you know, an exponential amount of data. You have to label that data. You got to train a model. You got to test the model to make sure that it's accurate and delivers business value. Then you push it into production, it's going to generate more data, and you kind of work through that cycle over and over and over. So, you know, just as Red Hat talks about, you know, CI/CD of applications, we're talking about CI/CD of the AI model itself, right? So it becomes a continuous improvement of AI models in production which is a big, big business transformation. >> Yeah, Chris Wright was talking about basically take your typical application development, you know, pipeline, and life cycle, and apply that type of thinking to AI. I was saying those two worlds have to come together. Actually, you know, the application stack and the data stack including AI need to come together. What's the role of Red Hat? What's your sort of posture on AI? Where do you fit with OpenShift? >> Yeah, so we're really excited about AI. I mean, a lot of our customers obviously are looking to take that data and make meaning out of it using AI is definitely a big important tool. And OpenShift, and our approach to Open Hybrid Cloud really forms a successful platform to base all your AI journey on with the partners such as NVIDIA whom we are working very closely with. And so the idea really is as Justin was saying, you know, the end to end, when you think about life of a model, you've got data, you mine that data, you create models, you deploy it into production. That whole thing, what we call CI/CD, as he was saying DevOps, DevSecOps, and the hybrid cloud that Red Hat has been talking about, although with OpenShift as the center forms a good basis for that. >> So somebody said the other day, I'm going to ask you, is INVIDIA a hardware company or a software company? >> We are a company that people know for our hardware but, you know, predominantly now we're a software company. And that's what we were on stage talking about. I mean, ultimately, a lot of these customers know that they've got to embark on this journey to apply AI, to transform their business with it. It's such a big competitive advantage going into, you know, the next decade. And so the faster they get ahead of it, the more they're going to win, right? But some of them, they're just not really sure how to get going. And so a lot of this is we want to lower the barrier to entry. We built this program, we call it Launchpad to basically make it so they get instant access to the servers, the AI servers, with OpenShift, with the MLOps tooling, with example applications. And then we walk them through examples like how do you build a chatbot? How do you build a vision system for quality control? How do you build a price recommendation model? And they can do hands on labs and walk out of, you know, Launchpad with all the software they need, I'll say the blueprint for building their application. They've got a way to have the software and containers supported in production, and they know the blueprint for the infrastructure and operating that a scale with OpenShift. So more and more, you know, to come back to your question is we're focused on the software layers and making that easy to help, you know, either enterprises build their apps or work with our ecosystem and developers to buy, you know, solutions off the shelf. >> On the harbor side though, I mean, clearly NVIDIA has prospered on the backs of GPUs, as the engines of AI development. Is that how it's going to be for the foreseeable future? Will GPUs continue to be core to building and training AI models or do you see something more specific to AI workloads? >> Yeah, I mean, it's a good question. So I think for the next decade, well, plus, I mean not forever, we're going to always monetize hardware. It's a big, you know, market opportunity. I mean, Jensen talks about a $100 billion, you know, market opportunity for NVIDIA just on hardware. It's probably another a $100 billion opportunity on the software. So the reality is we're getting going on the software side, so it's still kind of early days, but that's, you know, a big area of growth for us in the future and we're making big investments in that area. On the hardware side, and in the data center, you know, the reality is since Moore's law has ended, acceleration is really the thing that's going to advance all data centers. So I think in the future, every server will have GPUs, every server will have DPUs, and we can talk a bit about what DPUs are. And so there's really kind of three primary processors that have to be there to form the foundation of the enterprise data center in the future. >> Did you bring up an interesting point about DPUs and MPUs, and sort of the variations of GPUs that are coming about? Do you see those different PU types continuing to proliferate? >> Oh, absolutely. I mean, we've done a bunch of work with Red Hat, and we've got a, I'll say a beta of OpenShift 4.10 that now supports DPUs as the, I'll call it the control plane like software defined networking offload in the data center. So it takes all the software defined networking off of CPUs. When everybody talks about, I'll call it software defined, you know, networking and core data centers, you can think of that as just a CPU tax up to this point. So what's nice is it's all moving over to DPU to, you know, offload and isolate it from the x86 cores. It increases security of data center. It improves the throughput of your data center. And so, yeah, DPUs, we see everybody copying that model. And, you know to give credit where credit is due, I think, you know, companies like AWS, you know, they bought Annapurna, they turned it into Nitro which is the foundation of their data centers. And everybody wants the, I'll call it democratized version of that to run their data centers. And so every financial institution and bank around the world sees the value of this technology, but running in their data centers. >> Hey, everybody needs a Nitro. I've written about it. It's Annapurna acquisition, 350 million. I mean, peanuts in the grand scheme of things. It's interesting, you said Moore's law is dead. You know, we have that conversation all the time. Pat Gelsinger promised that Moore's law is alive and well. But the interesting thing is when you look at the numbers, that's, you know, Moore's law, we all know it, doubling of the transistor densities every 18 to 24 months. Let's say that, that promise that he made is true. What I think the industry maybe doesn't appreciate, I'm sure you do, being in NVIDIA, when you combine what you were just saying, the CPU, the GPU, Paul, the MPU, accelerators, all the XPUs, you're talking about, I mean, look at Apple with the M1, I mean 6X in 15 months versus doubling every 18 to 24. The A15 is probably averaging over the last five years, a 110% performance improvement each year versus the historical Moore's law which is 40%. It's probably down to the low 30s now. So it's a completely different world that we're entering now. And the new applications are going to be developed on these capabilities. It's just not your general purpose market anymore. From an application development standpoint, what does that mean to the world? >> Yeah, I mean, yeah, it is a great point. I mean, from an application, I mean first of all, I mean, just talk about AI. I mean, they are all very compute intensive. They're data intensive. And I mean to move data focus so much in to compute and crunch those numbers. I mean, I'd say you need all the PUs that you mentioned in the world. And also there are other concerns that will augment that, right? Like we want to, you know, security is so important so we want to secure everything. Cryptography is going to take off to new levels, you know, that we are talking about, for example, in the case of DPUs, we are talking about, you know, can that be used to offload your encryption and firewalling, and so on and so forth. So I think there are a lot of opportunities even from an application point of view to take of this capacity. So I'd say we've never run out of the need for PUs if you will. >> So is OpenShift the layer that's going to simplify all that for the developer. >> That's right. You know, so one of the things that we worked with NVIDIA, and in fact was we developed this concept of an operator for GPUs, but you can use that pattern for any of the PUs. And so the idea really is that, how do you, yeah-- (all giggle) >> That's a new term. >> Yeah, it's a new term. (all giggle) >> XPUs. >> XPUs, yeah. And so that pattern becomes very easy for GPUs or any other such accelerators to be easily added as a capacity. And for the Kubernetes scaler to understand that there is that capacity so that an application which says that I want to run on a GPU then it becomes very easy for it to run on that GPU. And so that's the abstraction to your point about how we are making that happen. >> And to add to this. So the operator model, it's this, you know, open source model that does the orchestration. So Kubernetes will say, oh, there's a GPU in that node, let me run the operator, and it installs our entire run time. And our run time now, you know, it's got a MIG configuration utility. It's got the driver. It's got, you know, telemetry and metering of the actual GPU and the workload, you know, along with a bunch of other components, right? They get installed in that Kubernetes cluster. So instead of somebody trying to chase down all the little pieces and parts, it just happens automatically in seconds. We've extended the operator model to DPUs and networking cards as well, and we have all of those in the operator hub. So for somebody that's running OpenShift in their data centers, it's really simple to, you know, turn on Node Feature Discovery, you point to the operators. And when you see new accelerated nodes, the entire run time is automatically installed for you. So it really makes, you know, GPUs and our networking, our advanced networking capabilities really first class citizens in the data center. >> So you can kind of connect the dots and see how NVIDIA and the Red Hat partnership are sort of aiming at the enterprise. I mean, NVIDIA, obviously, they got the AI piece. I always thought maybe 25% of the compute cycles in the data center were wasted doing storage offloads or networking offload, security. I think Jensen says it's 30%, probably a better number than I have. But so now you're seeing a lot of new innovation in new hardware devices that are attacking that with alternative processors. And then my question is, what about the edge? Is that a blue field out at the edge? What does that look like to NVIDIA and where does OpenShift play? >> Yeah, so when we talk about the edge, we always going to start talking about like which edge are we talking about 'cause it's everything outside the core data center. I mean, some of the trends that we see with regard to the edges is, you know, when you get to the far edge, it's single nodes. You don't have the guards, gates, and guns protection of the data center. So you start having to worry about physical security of the hardware. So you can imagine there's really stringent requirements on protecting the intellectual property of the AI model itself. You spend millions of dollars to build it. If I push that out to an edge data center, how do I make sure that that's fully protected? And that's the area that we just announced a new processor that we call Hopper H100. It supports confidential computing so that you can basically ensure that model is always encrypted in system memory across the bus, of the PCI bus to the GPU, and it's run in a confidential way on the GPU. So you're protecting your data which is your model plus the data flowing through it, you know, in transit, wallet stored, and then in use. So that really adds to that edge security model. >> I wanted to ask you about the cloud, correct me if I'm wrong. But it seems to me that that AI workloads have been slower than most to make their way to the cloud. There are a lot of concerns about data transfer capacity and even cost. Do you see that? First of all, do you agree with that? And secondly, is that going to change in the short-term? >> Yeah, so I think there's different classes of problems. So we'll take, there's some companies where their data's generated in the cloud and we see a ton of, I'll say, adoption of AI by cloud service providers, right? Recommendation engines, translation engines, conversational AI services, that all the clouds are building. That's all, you know, our processors. There's also problems that enterprises have where now I'm trying to take some of these automation capabilities but I'm trying to create an intelligent factory where I want to, you know, merge kind of AI with the physical world. And that really has to run at the edge 'cause there's too much data being generated by cameras to bring that all the way back into the cloud. So, you know, I think we're seeing mass adoption in the cloud today. I think at the edge a lot of businesses are trying to understand how do I deploy that reliably and securely and scale it. So I do think, you know, there's different problems that are going to run in different places, and ultimately we want to help anybody apply AI where the business is generating the data. >> So obviously very memory intensive applications as well. We've seen you, NVIDIA, architecturally kind of move away from the traditional, you know, x86 approach, take better advantage of memories where obviously you have relationships with Arm. So you've got a very diverse set of capabilities. And then all these other components that come into use, to just be a kind of x86 centric world. And now it's all these other supporting components to support these new applications and it's... How should we think about the future? >> Yeah, I mean, it's very exciting for sure, right? Like, you know, the future, the data is out there at the edge, the data can be in the data center. And so we are trying to weave a hybrid cloud footprint that spans that. I mean, you heard Paul come here, talk about it. But, you know, we've talked about it for some time now. And so the paradigm really that is, that be it an application, and when I say application, it could be even an AI model as a service. It can think about that as an application. How does an application span that entire paradigm from the core to the edge and beyond is where the future is. And, of course, there's a lot of technical challenges, you know, for us to get there. And I think partnerships like this are going to help us and our customers to get there. So the world is very exciting. You know, I'm very bullish on how this will play out, right? >> Justin, we'll give you the last word, closing thoughts. >> Well, you know, I think a lot of this is like I said, it's how do we reduce the complexity for enterprises to get started which is why Launchpad is so fundamental. It gives, you know, access to the entire stack instantly with like hands on curated labs for both IT and data scientists. So they can, again, walk out with the blueprints they need to set this up and, you know, start on a successful AI journey. >> Just a position, is Launchpad more of a Sandbox, more of a school, or more of an actual development environment. >> Yeah, think of it as it's, again, it's really for trial, like hands on labs to help people learn all the foundational skills they need to like build an AI practice and get it into production. And again, it's like, you don't need to go champion to your executive team that you need access to expensive infrastructure and, you know, and bring in Red Hat to set up OpenShift. Everything's there for you so you can instantly get started. Do kind of a pilot project and then use that to explain to your executive team everything that you need to then go do to get this into production and drive business value for the company. >> All right, great stuff, guys. Thanks so much for coming to theCUBE. >> Yeah, thanks. >> Thank you for having us. >> All right, thank you for watching. Keep it right there, Dave Vellante and Paul Gillin. We'll be back right after this short break at the Red Hat Summit 2022. (upbeat music)

>>Everyone. Welcome to the cube here. Live in San Francisco, California for AWS summit, 2022. We're live we're back with events. Also we're a virtual, we got hybrid all kinds of events this year, of course, summit in New York city happening this summer. We'll be there with the cube as well. I'm John, again, John host of the queue. Got a great guest here, Justin Colby, owner and CEO of innovative solutions. Their booth is right behind us. Justin, welcome to the cube. >>Thank you. Thank you for having me. >>So we're just chatting, uh, uh, off camera about some of the work you're doing the owner of and CEO. Yeah. Of innovative. Yeah. So tell us the story. What do you guys do? What's the elevator pitch. Yeah. >><laugh> so elevator pitch is we are, are, uh, a hundred percent focused on small to mid-size businesses that are moving to the cloud or have already moved to the cloud and really trying to understand how to best control, cost, security, compliance, all the good stuff, uh, that comes along with it. Um, exclusively focused on AWS and, um, you know, about 110 people, uh, based in Rochester, New York, that's where our headquarters is. Now. We have offices down in Austin, Texas, up in Toronto, uh, Canada, as well as Chicago. Um, and obviously in New York, uh, you know, the, the business was never like this, uh, five years ago, um, founded in 1989, made the decision in 2018 to pivot and go all in on the cloud. And, uh, I've been a part of the company for about 18 years, bought the company about five years ago. And yeah, it's been a great ride. >>It's interesting. The manages services are interesting with cloud cause a lot of the heavy liftings done by AWS. So we had Matt on your team on earlier talking about some of the edge stuff. Yeah. But you guys are a managed cloud service. You got cloud advisory, you know, the classic service that's needed, but the demand coming from cloud migrations and application modernization and obviously data is a huge part of it. Huge. How is this factoring into what you guys do and your growth cuz you guys are the number one partner on the SMB side for edge. Yeah. For AWS, you got results coming in. Where's the, where's the forcing function. What's the pressure point. What's the demand like? >>Yeah. It's a great question. Every CEO I talk to, it's a small to midsize business. They're all trying to understand how to leverage technology better to help either drive a revenue target for their own business, uh, help with customer service as so much has gone remote now. And we're all having problems or troubles or issues trying to hire talent. And um, you know, tech is really at the, at the forefront and the center of that. So most cut customers are coming to us and they're like, listen, we gotta move to the cloud or we move some things to the cloud and we want to do that better. And um, there's this big misnomer that when you move to the cloud, you gotta automatically modernize. Yeah. And what we try to help as many customers understand as possible is lifting and shifting, moving the stuff that you maybe currently have OnPrem and a data center to the cloud first is a first step. And then, uh, progressively working through a modernization strategy is always the better approach. And so we spent a lot of time with small to mid-size businesses who don't have the technology talent on staff to be able to do >>That. Yeah. And they want to get set up. But the, the dynamic of like latency is huge. We're seeing that edge product is a big part of it. This is not a one-off it's happening around everywhere. It is. And it's not, it's manufacturing, it's the physical plant or location >>Literally. >>And so, and you're seeing more, I O T devices, what's that like right now from a channel engine problem statement standpoint, are the customers, not staff, is the it staff kind of old school? Is it new skills? What's the core problem you guys solve >>In the SMB space? The core issue nine outta 10 times is people get enamored with the latest and greatest. And the reality is not everything that's cloud based. Not all cloud services are the latest and greatest. Some things have been around for quite some time and are hardened solutions. And so, um, what we try to do with technology staff that has traditional on-prem, uh, let's just say skill sets and they're trying to move to a cloud-based workload is we try to help those customers through education and through some practical, let's just call it use case. Um, whether that's a proof of concept that we're doing or whether that's, we're gonna migrate a small workload over, we try to give them the confidence to be able to not, not necessarily go it alone, but to, to, to have the, uh, the Gusto and to really have the, um, the, the opportunity to, to do that in a wise way. Um, and what I find is that most CEOs that I tell, yeah, they're like, listen, at the end of the day, I'm gonna be spending money in one place or another, whether that's OnPrem or in the cloud. I just want to know that I'm doing that in a way that helps me grow as quickly as possible status quo. I think every, every business owner knows that COVID taught us anything that status quo is, uh, is no. No. Good. >>How about factoring in the, the agility and speed equation? Does that come up a lot? >>It does. I think, um, I think there's also this idea that if, uh, if we do a deep dive analysis and we really take a surgical approach to things, um, we're gonna be better off. And the reality is the faster you move with anything cloud based, the better you are. And so there's this assumption that we gotta get it right the first time in the cloud. If you start down your journey in one way and you realize midway that it's not the right, let's just say the right place to go. It's not like buying a piece of iron that you put in the closet and now you own it in the cloud. You can turn those services on and off. It's a, gives you a much higher density for making decisions and failing forward. >>Well actually shutting down the abandoning, the projects that early, not worrying about it, you got it. I mean, most people don't abandon stuff cuz they're like, oh, I own >>It. Exactly. And >>They get, they get used to it. Like, and then they wait too long. >>That's exactly. Yeah. >>Frog and boiling water, as we used to say, oh, it's a great analogy. So I mean, this, this is a dynamic that's interesting. I wanna get more thoughts on it because like I'm, if I'm a CEO of a company, like, okay, I gotta make my number. Yeah. I gotta keep my people motivated. Yeah. And I gotta move faster. So this is where you guys come. I get the whole thing. And by the way, great service, um, professional services in the cloud right now are so hot because so hot, you can build it and then have option optionality. You got path decisions, you got new services to take advantage of. It's almost too much for customers. It is. I mean, everyone I talk to at reinvent, that's a customer. Well, how many announcements did Andy Jessey announce or Adam, you know, the 5,000 announcement or whatever. They did huge amounts. Right. Keeping track of it all. Oh, is huge. So what's the, what's the, um, the mission of, of your company. How does, how do you talk to that alignment? Yeah. Not just product. I can get that like values as companies, cuz they're betting on you and your people. >>They are, they are what's >>What's the values. >>Our mission is, is very simple. We want to help every small to mid-size business, leverage the power of the cloud. Here's the reality. We believe wholeheartedly. This is our vision that every company is going to become a technology company. So we, the market with this idea that every customer's trying to leverage the power of the cloud in some way, shape or form, whether they know it or don't know it. And number two, they're gonna become a tech company in the process of that because everything is so tech-centric. And so when you talk about speed and agility, when you talk about the, the endless options and the endless permutations of solutions that a customer can buy in the out, how are you gonna ask a team of one or two people in your it department to make all of those decisions going it alone or trying to learn it as you go, it only gets you so far working with a partner. >>I'll just give you some perspective. We work with about a thousand small to midsize business customers. More than 50% of those customers are on our manage services. Meaning they know that we have their back and we're the safety net. So when a customer is saying, right, I'm gonna spend a couple thousand dollars a month in the cloud. They know that that bill, isn't gonna jump to $10,000 a month going in alone. Who's there to help protect that. Number two, if you have a security posture and let's just say your high profile and you're gonna potentially be more vulnerable to security attacks. If you have a partner that's offering you some managed services. Now you, again, you've got that backstop and you've got those services and tooling. We, we offer, um, seven different products, uh, that are part of our managed services that give the customer the tooling, that for them to go out and buy on their own for a customer to go out today and go buy a new Relic solution on their own, it would cost them a fortune. If >>The training alone would be insane, a risk factor not mean the cost. Yes, absolutely. Opportunity cost is huge, >>Huge, absolutely enormous training and development. Something. I think that is often, you know, it's often overlooked technologists. Typically they want to get their skills up. They, they love to get the, the stickers and the badges and the pins, um, at innovative in 28 team. When, uh, when we made the decision to go all in on the club, I said to the organization, you know, we have this idea that we're gonna pivot and be aligned with AWS in such a way that it's gonna really require us all to get certified. My executive assistant at the time looks at me. She said, even me, I said, yeah, even you, why can't you get certified? Yeah. And so we made, uh, a conscious decision. It wasn't requirement. It still isn't today to make sure everybody in the company has the opportunity to become certified. Even the people that are answering the phones at the front desk >>And she could be running the Kubernetes clusters. I >>Love it. >>It's amazing. But I'll tell >>You what, when that customer calls and they have a real Kubernetes issue, she'll be able to assist and get the right >>People involved. And that's a cultural factor that you guys have. So, so again, this is back to my whole point about SMBs and businesses in general, small and large, it staffs are turning over the gen Z and millennials are in the workforce. They were a provisioning top of rack switches. Right? First of all. And so if you're a business is also the, I call the buildout, um, uh, return factor, ROI piece. At what point in time as an owner or SMB, do I get the ROI? Yeah. I gotta hire a person to manage it. That person's gonna have five zillion job offers. Yep. Uh, maybe who knows? Right. I got cyber security issues. Where am I gonna find a cyber person? Yeah. A data compliance. I need a data scientist and a compliance person. Right. Maybe one and the same. Right. Good luck. Trying to find a data scientist. Who's also a compliance person. Yep. And the list goes on. I can just continue. Absolutely. I need an SRE to manage the, the, uh, the sock report and we can pen test. Right. >>Right. >>These are, these are like >>Critical issues. This is >>Just like, these are the table stakes. >>Yeah. And, and every, every business owner's thinking about this. So >>That's, that's what at least a million in loading, if not three or more Just to get that going. Yeah. Then it's like, where's the app. Yeah. So there's no cloud migration. There's no modernization on the app side though. No. And then remind AI and ML. That's >>Right. That's right. So to try to it alone, to me, it's hard. It it's incredibly difficult. And the other thing is, is there's not a lot of partners, so the partner, >>No one's raising their hand boss. I'll do all that. Exactly. An it department. >>Exactly. >>Like, can we just call up, uh, you know, our old vendor that's right, >>Right. Our old vendor. I like it. >><laugh> but that's so true. I mean, when I think about how, if I was a business owner starting a business today and I had to build my team, um, and the amount of investment that it would take to get those people skilled up and then the risk factor of those people now having the skills and being so much more in demand and being recruited away, that's a real, that's a real issue. And so how you build your culture around the, at is, is very important. And it's something that we talk about every, with every one of our small to mid-size >>Business. So just, I wanna get, I want to get your story as CEO. Okay. Take us through your journey. You said you bought the company and your progression to, to being the owner and CEO of innovative yeah. Award winning guys doing great. Uh, great bet on a good call. Yeah. Things a good tell your, your story. What's your journey. >>It's real simple. I was, uh, I was a sophomore at the Rochester Institute of technology in 2003. And, uh, I knew that I, I was going to school for it and I, I knew I wanted to be in tech. I didn't know what I wanted to do, but I knew I didn't wanna code or configure routers and switches. So I had this great opportu with the local it company that was doing managed services. We didn't call it at that time innovative solutions to come in and, uh, jump on the phone and dial for dollars. I was gonna cold call and introduce other, uh, small to midsize businesses locally in Rochester, New York go to Western New York, um, who innovative was now. We were 19 people at the time. And I came in, I did an internship for six months and I loved it. I learned more in those six months than I probably did in my first couple of years at, uh, at RT long story short. >>Um, for about seven years, I worked, uh, to really help develop, uh, process and methodology for the business so that we could grow and scale. And we grew to about 30 people. And, um, I went to the owners at the time in 2010 and I was like, Hey, I'm growing the value of this business. And who knows where you guys are gonna be another five years? What do you think about making me an owner? And they were like, listen, you got a long ways before you're gonna be an owner, but if you stick it out in your patient, we'll, um, we'll work through a succession plan with you. And I said, okay, there were four other individuals at the time that were gonna also buy into the business with me. >>And they were the owners, no outside capital, >>None zero, well, 2014 comes around. And, uh, the other folks that were gonna buy into the business with me that were also working at innovative for different reasons. They all decided that it wasn't for them. One started a family. The other didn't wanna put capital in. Didn't wanna write a check. Um, the other had a real big problem with having to write a check. If we couldn't make payroll, I'm like, well, that's kind of like if we're owners, we're gonna have to like cover that stuff. <laugh> well, so >>It's called the pucker factor. >>Exactly. So, uh, I sat down with the CEO in early 2015, and, uh, we made the decision that I was gonna buy the three partners out, um, go through an earn out process, uh, coupled with, uh, an interesting financial strategy that wouldn't strap the business, cuz they cared very much. The company still had the opportunity to keep going. So in 2016 I bought the business, um, became the sole owner. And, and at that point we, um, we really focused hard on what do we want this company to be had built this company to this point? Yeah. And, uh, and by 2018 we knew that pivoting all going all in on the cloud was important for us and we haven't looked back. >>And at that time, the proof points were coming clearer and clearer 2012 through 15 was the early adopters, the builders, the startups and early enterprises. Yes. The capital ones of the world. Exactly the, uh, and those kinds of big enterprises. The GA I don't wanna say gamblers, but ones that were very savvy. The innovators, the FinTech folks. Yep. The hardcore glass eating enterprises >>Agreed, agreed to find a small to midsize business, to migrate completely to the cloud as, as infrastructure was considered. That just didn't happen as often. Um, what we were seeing where a lot of our small to mid-size business customers, they wanted to leverage cloud based backup, or they wanted to leverage a cloud for disaster recovery because it lent itself. Well, early days, our most common cloud customer though, was the customer that wanted to move messaging and collaboration, the, the Microsoft suite to the cloud and that a lot of 'em dipped their toe in the water. But by 2017 we knew interest structure was around the corner. Yeah. Yeah. And so, uh, we only had two customers on AWS at the time. Um, and we, uh, we, we made the decision to go all in >>Justin. Great to have you on the cube. Thank you. Let's wrap up. Uh, tell me the hottest product that you have. Is it migrations? Is it the app? Modernization is the data. What's the hot product and then put a plugin for the company. Awesome. >>So, uh, there's no question. Every customer is looking to migrate workloads and try to figure out how to modernize for the future. We have very interesting, sophisticated yet elegant funding solutions to help customers with the cash flow, uh, constraints that come along with those migration. So any SMB that's thinking about migrating to the cloud, they should be talking innovative solutions. We know how to do it in a way that allows those customers not to be cash strapped and gives them an opportunity to move forward in a controlled, contained way so that they can modernize. >>So like insurance, basically for them not insurance classic in the classic sense, but you help them out on the, on the cash exposure. >>Absolutely. We are known for that and we're known for being creative with those customers and being empathetic to where they are in their >>Journey. And that's the cloud upside is all about doubling down on the variable wind. That's right. Seeing the value and doubling down on it. Absolutely not praying for it. Yeah. <laugh> all right, Justin. Thanks for coming on. You really appreciate it. >>Thank you very much for having me. Okay. >>This is the cube coverage here live in San Francisco, California for AWS summit tour 22. I'm John for your host. Thanks for watching. We're back with more great coverage for two days after this short break.

(upbeat music) >> Okay, welcome back everyone to theCUBE's coverage of AWS re:Invent 2021. We're here live in Las Vegas for an in-person event. Of course, it's a hybrid event, virtual online. Many people online. A lot of people here on the event, a lot of action. cloud going next generation, mainframe transformation, more analytics, more chips, everything's faster and cheaper and getting better and better in the cloud. We've got the great coverage. We've got two great guests. We got Justin Hooper vice president of Global Operations Infrastructure Ingram Micro and JP Perot, EVP of Telecom Media and Technology and AWS executive sponsor for Atos. Gentlemen, thank you for joining me on theCUBE. >> Thank you. >> Thank you, glad to be here. >> So what is it about the conference so far? Pretty good, like a lot of people here? >> Super exciting. >> 27,000 people showing up. >> It's amazing. >> Real people not workers. >> Yeah, it feels almost normal. >> Yeah I can't believe the turnout. >> Got a great topic, you guys working together you got mainframe, you got analytics, transformation. Let's get into it. Let's start by introducing what you guys do, your company, and why you are here. JP, we'll start with you. >> Yeah, I can start. Okay, so yeah, so I'm leading, you know, in Atos all the cloud business and telecom media technology, Atos is a big company is a service and technology company, 11 billion Euro business, and we are leading all the transformation to cloud and also, all what is related to hybrid cloud transformation. Also with security. We are number two in the world in terms of security and cybersecurity. We have developed for so long lasting relationship with AWS. We have been an advanced technology partner of AWS for now many years, since 2013. And we have developed a specific program, you know, regarding manufacturing transformation and mega trends. So this is why we are super excited to be here with you Justin today, thank you, by the way having Ingram Micro with us, Justin. >> I appreciate it, it's good talking to you. Good to be here. >> And I'm Justin Hooper, I work for a Ingram Micro. I run their Global Infrastructure and IT operations. Ingram Micro is one of the world's largest technology suppliers, technology solutions, and cloud platform and services. >> Well, great for the intro. Thanks for that set up, a lot of action going on. You guys have recently purchased, you guys got the transformation in the cloud. What is the movement to the cloud? Take us through the current situation. >> Yeah, we see, a big acceleration and especially due to COVID situation. We have seen acceleration of transformation to cloud. There is a lot to do, a lot to do because many business critical application, which have not been transformed. And also, especially all the applications which are sitting on top of mainframe that is why executive discussion we are with Ingram at the moment is how we can help Ingram with some of the applications that are on mainframe to transform and design the future. >> Justin, take us through the transformation that they're helping you with, what's the key challenge? What problem are they solving? Take us through the specifics. >> Yeah, it's interesting because I'm actually a lover of the mainframe, I think most of the people at AWS would think, the mainframes, didn`t they put the rockets on the moon. That's old technology, but mainframes are still fantastic platforms. We, have great success, great resiliency with Atos . We are on the most modern chips, but there are a lot of restrictions. You know, you really have to size your mainframe for your peak workloads. You don't have the ability to separate segments, scale horizontally, and then there are really nuanced. Like it's hard to get resources that know mainframes, to be honest with you, it is just, there's a people issue there industry-wide, and we're not different than any other company and compete for resources, both on the system side and on the programming side. And we really want to look at how we can make a massive leave, if we're going to leave the mainframe, how do we start a journey where we can end up really being able to take advantage of horizontal scalability, the ability to have a utility compute model where we're really paying as we go, because that is the opposite how the mainframe model works today. >> Talk about the refactor 'cause I know I've covered a lot of mainframe stuff with IBM in the past and how banks are still using it. Everyone is they're real applications. They're mission critical. How are they integrating into the digital transformation. Containers, kubernetes are hot right now. You're starting to see a lot more integration. How do your customers and how do you guys see refactoring happening? I can see the integration, but that as the refactoring come in. >> There's really what we've learned with our partnership is there are really a couple of ways to do the refactoring. You can convert your old Cobalt code to something else like Java. And there are tools and companies out there that do that. Or you can really build a plan where you can effectively emulate your mainframe on commodity computing and through a relatively deep analysis with Atos, the recommendation that we're looking at is that ability to first get off of the hardware, get off of that reliance on the platform, but not jump all the way to modify your code to Java. There's not a lot of value in going from Cobalt to Java, for example, if you're not making improvements in your programs, adding business capabilities. So the journey starts with get off the hardware, but then it allows you to go and say, we are going to break up those complex programs. We are going to separate the data differently. And once you do that, you can start to take advantage of containers and start to separate yourself even more and really get into the cloud. But, there are ways to piece your way through that. >> You know that's really good insight, JP, I'd love to get your reaction on this too, because what Justin's getting at is what we hear a lot from experienced CIOs of large companies that have a lot of existing stuff. And the theme is and the word they use is you don't want to touch the white hot core. Meaning it's so mission critical that if you mangle it too hard, touch it too hard a lot of bad things happen. So this idea of push things out to the cloud that around the edges, and then work your way slowly in is a risk management and practical approach. >> Not exactly, you said it right, its exactly a risk management So, and Justin explain it different factors, which come to the end point in terms of decision there is one issue regarding competencies, because their lack of competency in the markets. There is what kind of business critical apps you have. There is a transformation as such, you know, as Justin explain you can record, or you can move, you know, to a different platform. So, each time it's a program, it's a program and analysis that we do with our customer advising the customer about, hey, based on your critical application, what is the right journey, what is the right transformation to do the right risk management? >> Justin you're taking the hot core I saw you nodding your head. You're like, yeah, what's your take on that? >> Well, yeah, I agree with you. And that fear, uncertainty and doubt it's kept people on the mainframe for a really long time. The technology has caught up and the expertise where just as JP said, you've got to be really careful in the way you plan it, and you've got to make sure that you can always get back. And so the approach that we're looking at taking is, you're able to accommodate more risk if there's an easy roll back plan, and some of the new new technologies and processes are going to allow that, so, you know, if I screw this up, I get to go find a job someplace else. So I'm aware of the white hot core, but I am confident it can be done. >> Yeah, and then technology, if you don't have to kill the old to bring in the new, you can do both, and I like your approach. I think that's a success path that people are talking about it's well documented. But at the end of the day, we're back in distributed computing, I mean, I want to get this at the end, but I want to give you guys some time to think about it. As cloud becomes everywhere as Adam Selipsky talks about, it's not just about mainframes, this it's a distributed computing paradigm. So we're going to come back to that, but let's get into the SAP Redshift `cause I think that's something that you guys are working on I think that's worth calling out here. Analytics is huge, tell us what's going on there, how you guys are working together on that. >> Yeah, so we have designed a high level of competency around SAP, and especially the migration and the SAP Redshift. So we have designed this program also with AWS and is something we are discussing at the moment also with Ingram. So we see an acceleration of this trajectory. It has been also highly pushed by SAP as well. And also we are one of the strongest partner of SAP and we see many many customers engage into this transformation at the moment. >> The cloud really gives you a lot of advantages when you're doing migration, especially around a pre-existing software, like SAP was pretty big, complex mission critical So you can throw compute at it, a lot of cool capabilities. >> Yeah, that's true, but you need, you still need to configure a lot of things as well, because it's, you need to customize and you have to really fine tune, you know, what is it, what is going to be available to the customer needs and to what you need in your company as well. >> Okay, lets get back to what Justin was saying about emulation, I mean, I can run SAP (laughs) on Amazon. I mean, we've talked about it, I wrote a story about this prior to the event called Superclouds. You can build these super applications that combine things that you never would have thought was possible. SAP running on Amazon or Ford can come to Redshift when you need it, so you have a lot more flexibility. This is now the new normal. >> Correct. >> Correct. Or Justin maybe you want also to comment on that, you know? >> You know, the great thing about being on Redshift is we pick that as a platform for our data warehouse a number of years ago, and there were basically analytics capabilities, but what we're seeing a lot here at re:Invent is as Amazon is catching up with their out of the box ML and AI capabilities in Redshift. So it feels good that we pick the platform that they're growing the capabilities in right, as we're advancing out of more of the traditional analytics and trying to go to that machine learning. And one of the things that we work at the Atos on is migrating off of SAPBW and saying, maybe we don't need that as a data and reporting platform, if we're solid with Redshift and we certainly don't need both. So we're working with them to look at the case to move all the way to Redshift, and then we can run our analytics and build the ML in that. >> And you know, that was a big theme in the keynote. This purpose-built capabilities, it's almost like having, you know, if you're building a building, you got iron steel girders made for you. You got this now better value in the platform to build on. This brings up the notion of distributed computing. >> JP: Yeah. >> In a way there's the same game, different generation. I mean, isn't it? You've got to integrate this still transformations inflection points, this is current. I feel like this now more than ever is a time where you can actually roll it all together with a little help from your friends or if Amazon's got somebody, you know, who want to reinvent the wheel. What's your reaction to that? 'Cause we've seen the movie before, when it's hard. Now, it seems easier, maybe its not. >> Yeah, we see three layers co-existing in more more. We see that, see application on data will be spread over three dimensions there will be edge computing there will be private cloud and there will be public cloud. And we see more more pressure in the direction that many customers are saying, hey, where should I put my data? This spot will go on and be processed at the edge, this spot will be process in the public cloud. There is also one of the capability where we in Atos we are able to advise customers about what is the best way to process your data if you have a lot of latency, you can process at the edge. You know, there's less equipment, you can process the public cloud. So, we see this coexistence of model. >> Justin what's your take on this distributed computing throwback concept, because look at the rise of companies like Snowflake. Where'd they come from? They're on Amazon, they pick the cloud. Now they're on going to other clouds. You can build a supercloud. You can actually build this out now faster. What's your take? >> Yeah, I, my take is that it's a pendulum and it swings back and forth, and like you said, there was client server and then everything was web-based. And a lot of things look a lot like the mainframe, put everything in the cloud and then attached to it. And then all of a sudden, you know what? We need edge computing, pull some stuff back out of the cloud and put it where we need it. So, it's going to continue to evolve. What I have noticed that I like more is, the major home one swings and statements like I want to get out of the data center business and go to the cloud, I hear those less. And people are realizing there's there's hybrid. There's purpose build computing like you said, and we need to make sure that we're putting our data where it needs to be, putting our compute where it needs to be. And that's going to change on our customer base and evolving technologies 5G is changing the whole world around edge computing. So I'm enjoying the ride, I'm glad I'm in technology because I get to move with the ebbs and flows, but I don't think we're ever going to land. I think it's going to keep moving. >> Yeah that's a totally fair point where it's fun as a technologist, but you're right. If you're operating with cloud, it doesn't matter if you're on premises or edge or public cloud, it's the same thing (laughs). >> Justin: Yeah. >> It's just pick your, pick, your use case. >> Justin: Right, your use case- >> I want a low latency at the edge. I'm not going to move my data, send it to cloud. Of course, we're going to leave it there. >> Yeah. >> If you're on premise before- >> We've already move workloads back and forth. And a lot of companies are doing that too, you know? >> I think that's what they're getting at when they talk about cloud everywhere. I think that's their way of saying, okay, hybrid is real. They won't ever say multicloud though. (all laughing) Not yet. (all laughing) >> Won't hear that here. >> All right, quick summary. What do you guys do? And what's the future hold for the relationship. You guys got a good thing going on? Take us through what's the future look like. >> Yeah, for me it's a big thank you for the partnership with Ingram. So we are extremely excited by what we can do for you. And in terms of advising your transformation and, you know, I hope that you get the right service and the right advice, you know, from Atos. >> Yeah, it's been great. We, we've appreciated. We've been, we started as a mainframe customer and now they're helping to advise on how we could get off the mainframe. I mean really cannibalizing one of the other areas there in the spirit of evolving and partnership, and you guys are bringing a ton of expertise and the way you guys attack the account and we centralized through our account team is very helpful. You're very aware of what's going on on all sides of the business. >> Well, congratulations. >> That's a big part of the theme, the keynote today on, on ad about mainframe transmission. But at the end of the day, it's about modern infrastructure, modern application development. >> Justin: Absolutely. >> Getting out, having set the table for the next generation. >> JP: Yeah. >> Thanks for coming on theCUBE and you guys want to get a quick plug in real quick for the company talk about Atos and Ingram. Give a quick plug for what you guys are working on. >> Sorry- >> Give a quick plug about what you`re working on. Give a quick commercial one minute about the company. >> One minute. >> Yeah. >> At Atos we're really transforming or leading the industry in terms of secured transformation to cloud security will be more and more important as data is everything about where is the value. So we are really making sure that our customers, they get to maximize, they can maximize the value around data transformation to cloud in a secured way. >> Justin, JP, thanks for coming on theCUBE. Really appreciate the insights. Don't touch the hot core and make, take your time. Have a good time, you're watching theCUBE, the official broadcasting of AWS re:Invent leader in tech coverage, theCUBE. Thanks for watching. (upbeat music)

>>Hey, welcome back to the cubes. Continuing coverage of AWS reinvent 2021. I'm your host, Lisa Martin. This is day two, our first full day of coverage. But day two, we have two life sets here with AWS and its ecosystem partners to remote sets over a hundred guests on the program. We're going to be talking about the next decade of cloud innovation, and I'm pleased to welcome back to cube alumni to the program. Justin Borkman is here, the co-founder and CEO of Starburst and Teresa Tung, the cloud first chief technologist at Accenture guys. Welcome back to the queue. Thank you. Thank you for having me. Good to have you back. So, so Teresa, I was doing some research on you and I see you are the most prolific prolific inventor at Accenture with over 220 patents and patent applications. That's huge. Congratulations. Thank you. Thank you. And I love your title. I think it's intriguing. I'd like to learn a little bit more about your role cloud-first chief technologist. Tell me about, >>Well, I get to think about the future of cloud and if you think about clouded powers, everything experiences in our everyday lives and our homes and our car in our stores. So pretty much I get to be cute, right? The rest of Accenture's James Bond >>And your queue. I like that. Wow. What a great analogy. Just to talk to me a little bit, I know service has been on the program before, but give me a little bit of an overview of the company, what you guys do. What were some of the gaps in the markets that you saw a few years ago and said, we have an idea to solve this? Sure. >>So Starburst offers a distributed query engine, which essentially means we're able to run SQL queries on data anywhere, uh, could be in traditional relational databases, data lakes in the cloud on-prem. And I think that was the gap that we saw was basically that people had data everywhere and really had a challenge with how they analyze that data. And, uh, my co-founders are the creators of an open source project originally called Presto now called Trino. And it's how Facebook and Netflix and Airbnb and, and a number of the internet companies run their analytics. And so our idea was basically to take that, commercialize that and make it enterprise grade for the thousands of other companies that are struggling with data management, data analytics problems. >>And that's one of the things we've seen explode during the last 22 months, among many other things is data, right? In every company. These days has to be a data company. If they're not, there's a competitor in the rear view rear view mirror, ready to come and take that place. We're going to talk about the data mesh Teresa, we're going to start with you. This is not a new car. This is a new concept. Talk to us about what a data mesh is and why organizations need to embrace this >>Approach. So there's a canonical definition about data mesh with four attributes and any data geek or data architect really resonates with them. So number one, it's really routed decentralized domain ownership. So data is not within a single line of business within a single entity within a single partner has to be across different domains. Second is publishing data as products. And so instead of these really, you know, technology solutions, data sets, data tables, really thinking about the product and who's going to use it. The third one is really around self-service infrastructure. So you want everybody to be able to use those products. And finally, number four, it's really about federated and global governance. So even though their products, you really need to make sure that you're doing the right things, but what's data money. >>We're not talking about a single tool here, right? This is more of a, an approach, a solution. >>It is a data strategy first and foremost, right? So companies, they are multi-cloud, they have many projects going on, they are on premise. So what do you do about it? And so that's the reality of the situation today, and it's first and foremost, a business strategy and framework to think about the data. And then there's a new architecture that underlines and supports that >>Just didn't talk to me about when you're having customer conversations. Obviously organizations need to have a core data strategy that runs the business. They need to be able to, to democratize really truly democratized data access across all business units. What are some of the, what are some of your customer conversations like are customers really embracing the data strategy, vision and approach? >>Yeah, well, I think as you alluded to, you know, every business is data-driven today and the pandemic, if anything has accelerated digital transformation in that move to become data-driven. So it's imperative that every business of every shape and size really put the power of data in the hands of everyone within their organization. And I think part of what's making data mesh resonates so well, is that decentralization concept that Teresa spoke about? Like, I think companies acknowledge that data is inherently decentralized. They have a lot of different database systems, different teams and data mesh is a framework for thinking about that. Then not only acknowledges that reality, but also braces it and basically says there's actually advantages to this decentralized approach. And so I think that's, what's driving the interest level in the data mesh, uh, paradigm. And it's been exciting to work with customers as they think about that strategy. And I think that, you know, essentially every company in the space is, is in transition, whether they're moving from on cloud to the prem, uh, to, uh, sorry, from on-prem to the cloud or from one cloud to another cloud or undergoing that digital transformation, they have left behind data everywhere. And so they're, they're trying to wrestle with how to grasp that. >>And there's, we know that there's so much value in data. The, the need is to be able to get it, to be able to analyze it quickly in real time. I think another thing we learned in the pandemic is it real-time is no longer a nice to have. It is essential for businesses in every organization. So Theresa let's talk about how Accenture and servers are working together to take the data mesh from a concept of framework and put this into production into execution. >>Yeah. I mean, many clients are already doing some aspect of the data mesh as I listed those four attributes. I'm sure everybody thought like I'm already doing some of this. And so a lot of that is reviewing your existing data projects and looking at it from a data product landscape we're at Amazon, right? Amazon famous for being customer obsessed. So in data, we're not always customer obsessed. We put up tables, we put up data sets, feature stores. Who's actually going to use this data. What's the value from it. And I think that's a big change. And so a lot of what we're doing is helping apply that product lens, a literal product lens and thinking about the customer. >>So what are some w you know, we often talk about outcomes, everything being outcomes focused and customers, vendors wanting to help customers deliver big outcomes, you know, cost reduction, et cetera, things like that. How, what are some of the key outcomes Theresa that the data mesh framework unlocks for organizations in any industry to be able to leverage? >>Yeah. I mean, it really depends on the product. Some of it is organizational efficiency and data-driven decisions. So just by the able to see the data, see what's happening now, that's great. But then you have so beyond the, now what the, so what the analytics, right. Both predictive prescriptive analytics. So what, so now I have all this data I can analyze and drive and predict. And then finally, the, what if, if I have this data and my partners have this data in this mesh, and I can use it, I can ask a lot of what if and, and kind of game out scenarios about what if I did things differently, all of this in a very virtualized data-driven fashion, >>Right? Well, we've been talking about being data-driven for years and years and years, but it's one thing to say that it's a whole other thing to actually be able to put that into practice and to use it, to develop new products and services, delight customers, right. And, and really achieve the competitive advantage that businesses want to have. Just so talk to me about how your customer conversations have changed in the last 22 months, as we've seen this massive acceleration of digital transformation companies initially, really trying to survive and figure out how to pivot, not once, but multiple times. How are those customer conversations changing now is as that data strategy becomes core to the survival of every business and its ability to thrive. >>Yeah. I mean, I think it's accelerated everything and, and that's been obviously good for companies like us and like Accenture, cause there's a lot of work to be done out there. Um, but I think it's a transition from a storage centric mindset to more of an analytics centric mindset. You know, I think traditionally data warehousing has been all about moving data into one central place. And, and once you get it there, then you can analyze it. But I think companies don't have the time to wait for that anymore. Right there, there's no time to build all the ETL pipelines and maintain them and get all of that data together. We need to shorten that time to insight. And that's really what we, what we've been focusing on with our, with our customers, >>Shorten that time to insight to get that value out of the data faster. Exactly. Like I said, you know, the time is no longer a nice to have. It's an absolute differentiator for folks in every business. And as, as in our consumer lives, we have this expectation that we can get whatever we want on our phone, on any device, 24 by seven. And of course now in our business lives, we're having the same expectation, but you have to be able to unlock that access to that data, to be able to do the analytics, to make the decisions based on what the data say. Are you, are you finding our total? Let's talk about a little bit about the go to market strategy. You guys go in together. Talk to me about how you're working with AWS, Theresa, we'll start with you. And then Justin we'll head over to you. Okay. >>Well, a lot of this is powered by the cloud, right? So being able to imagine a new data business to run the analytics on it and then push it out, all of that is often cloud-based. But then the great thing about data mesh it's it gives you a framework to look at and tap into multi-cloud on-prem edge data, right? Data that can't be moved because it is a private and secure has to be at the edge and on-prem so you need to have that's their data reality. And the cloud really makes this easier to do. And then with data virtualization, especially coming from the digital natives, we know it scales >>Just to talk to me about it from your perspective that the GTL. >>Yeah. So, I mean, I think, uh, data mesh is really about people process and technology. I think Theresa alluded to it as a strategy. It's, it's more than just technology. Obviously we bring some of that technology to bear by allowing customers to query the data where it lives. But the people in process side is just as important training people to kind of think about how they do data management, data analytics differently is essential thinking about how to create data as a product. That's one of the core principles that Theresa mentioned, you know, that's where I think, um, you know, folks like Accenture can be really instrumental in helping people drive that transformational change within their organization. And that's >>Hard. Transformational change is hard with, you know, the last 22 months. I've been hard on everyone for every reason. How are you facilitating? I'm curious, like to get Theresa, we'll start with you, your perspectives on how our together as servers and Accenture, with the power of AWS, helping to drive that cultural change within organizations. Because like we talked about Justin there, nobody has extra time to waste on anything these days. >>The good news is there's that imperative, right? Every business is a digital business. We found that our technology leaders, right, the top 10% investors in digital, they are outperforming are the laggards. So before pandemic, it's times to post pep devek times five, so there's a need to change. And so data is really the heart of the company. That's how you unlock your technical debt into technical wealth. And so really using cloud and technologies like Starburst and data virtualization is how we can actually do that. >>And so how do you, Justin, how does Starburst help organizations transfer that technical debt or reduce it? How does the D how does the data much help facilitate that? Because we talk about technical debt and it can, it can really add up. >>Yeah, well, a lot of people use us, uh, or think about us as an abstraction layer above the different data sources that they have. So they may have legacy data sources today. Um, then maybe they want to move off of over time, um, could be classical data, warehouses, other classical, uh, relational databases, perhaps they're moving to the cloud. And by leveraging Starburst as this abstraction, they can query the data that they have today, while in the background, moving data into the cloud or moving it into the new data stores that they want to utilize. And it sort of hides that complexity. It decouples the end user experience, the business analyst, the data scientists from where the data lives. And I think that gives people a lot of freedom and a lot of optionality. And I think, you know, the only constant is change. Um, and so creating an architecture that can stand the test of time, I think is really, really important. >>Absolutely. Speaking of change, I just saw the announcement about Starburst galaxy fully managed SAS platform now available in all three major clouds. Of course, here we are at AWS. This is a, is this a big directional shift for servers? >>It is, you know, uh, I think there's great precedent within open source enterprise software companies like Mongo DB or confluent who started with a self managed product, much the way that we did, and then moved in the direction of creating a SAS product, a cloud hosted, fully managed product that really I think, expands the market. And that's really essentially what we're doing with galaxy galaxy is designed to be as easy as possible. Um, you know, Starburst was already powerful. This makes it powerful and easy. And, uh, and, and in our view, can, can hopefully expand the market to thousands of potential customers that can now leverage this technology in a, in a faster, easier way, >>Just in sticking with you for a minute. Talk to me about kind of where you're going in, where services heading in terms of support for the data mesh architecture across industries. >>Yeah. So a couple of things that we've, we've done recently, and whether we're doing, uh, as we speak, one is, uh, we introduced a new capability. We call star gate. Now star gate is a connector between Starburst clusters. So you're going to have a Starbucks cluster, and let's say Azure service cluster in AWS, a Starbucks cluster, maybe an AWS west and AWS east. And this basically pushes the processing to where the data lives. So again, living within this construct of, uh, of decentralized data that a data mesh is all about, this allows you to do that at an even greater level of abstraction. So it doesn't even matter what cloud region the data lives in or what cloud entirely it lives in. And there are a lot of important applications for this, not only latency in terms of giving you fast, uh, ability to join across those different clouds, but also, uh, data sovereignty constraints, right? >>Um, increasingly important, especially in Europe, but increasingly everywhere. And, you know, if your data isn't Switzerland, it needs to stay in Switzerland. So starting date as a way of pushing the processing to Switzerland. So you're minimizing the data that you need to pull back to complete your analysis. And, uh, and so we think that's a big deal about, you know, kind of enabling a data mash on a, on a global scale. Um, another thing we're working on back to the point of data products is how do customers curate and create these data products and share them within their organization. And so we're investing heavily in our product to make that easier as well, because I think back to one of the things, uh, Theresa said, it's, it's really all about, uh, making this practical and finding quick wins that customers can deploy, deploy in their data mess journey, right? >>This quick wins are key. So Theresa, last question to you, where should companies go to get started today? Obviously everybody has gotten, we're still in this work from anywhere environment. Companies have tons of data, tons of sources of data, did it, infrastructure's already in place. How did they go and get started with data? >>I think they should start looking at their data projects and thinking about the best data products. I think just that mindset shift about thinking about who's this for what's the business value. And then underneath that architecture and support comes to bear. And then thinking about who are the products that your product could work better with just like any other practice partnerships, like what we have with AWS, right? Like that's a stronger together sort of thing, >>Right? So there's that kind of that cultural component that really strategic shift in thinking and on the architecture. Awesome guys, thank you so much for joining me on the program, coming back on the cube at re-invent talking about data mesh really help. You can help organizations and industry put that together and what's going on at service. We appreciate your time. Thanks again. All right. For my guests, I'm Lisa Martin, you're watching the cubes coverage of AWS reinvent 2021. The cube is the leader in global live tech coverage. We'll be right back.

(upbeat music) >> Okay, welcome back to theCUBES's coverage of Dockercon 2021. I'm John Furrier, your host of theCUBE. We have Justin Cormack, CTO of Docker. Was also involved in the CNCF technical oversight and variety of other technical activities. Justin, great to see you. Thanks for coming on theCUBE Virtual this year, again, twice in a row and maybe next year will be in person but certainly hybrid, great to see you. >> Yeah, great to see you too. Yeah, in person would be nice one of these days, yes. >> Yeah, when we get real life back. It's almost there, I can feel it, but there's so much activity. One of the things that we've been talking about, certainly in theCUBE and even here at DockerCon, same story. The pandemic really hasn't truly impacted developer community, because most of the people have been working remotely and virtually for many, many decades. And if you think about just in the past 10 years, all the innovation in cloud has come from virtual teams, open-source softwares, always had good kind of governance and a democratization of kind of how it becomes built. So not a bit's been skipped during the pandemic. In fact, if anything supply chain of software development has increased. So- >> Yeah, I think that it's definitely true that open-source was really the place that pioneered remote working. And a lot of the work methods the people worked out to do open-source as in communication and things like that, were things that people have adopted. It's a slightly different community. I'd say open-source projects like meetings less than some other organizations, but there was definitely that pioneering thing. And a lot of the companies that started off remote first, were in open-source software, and they started off for those reasons as well because developers were already working like that, and they could just hire them and they could continue to work like that. >> Yeah, one of the upsides of all this is that people won't tolerate even zoom or in person meetings that just go on, 15, 30 minutes good call. Why do we have a meeting? What's the purpose? (faintly speaking) the way to go. Let's get into the developer community. One of the things I love about DockerCon this year 2021 is the envelopes being pushed again almost to another level, it's almost a new level, this next level of containers is bringing more innovation to the table and productivity and simplicity. Some of the same messages last year but now more than ever, stuff's going on. What are you hearing directly from the community? You talk to a lot of the developers out of the millions of developers in the Docker ecosystem. What are they saying now in 2021? What's going on in their mind? >> Yeah, I think it's an area... More and more people are using Docker, and they're using it every day and it's a change that's been going on, obviously for a while, but it begins to sort of, as it spreads, the kind of developers using Docker, so different from... When I started at Docker, coming up for six years ago, it was a very bleeding edge type thing for early adopters. Now it's everywhere, millions and millions of ordinary developers are using Docker every day. And the kinds of things that's telling us is, well, some of this stuff that we thought, well, five years ago was an amazing breakthrough and simplicity. Now that's on its own still too hard. One of the things I mentioned in my keynote was that, we're talking to developers who just primarily have been working windows all their life but more and more applications being shipped on Linux. And they using Linux containers, but they find Docker files really hard because they have really, Linux shell scrapes and not a windows developer doesn't know how to use a Linux shell script. And it's bringing it down to that next level of use where you can adopt these things more easily, the pitched to the kind of level of developer who is just thinking about their language, their APIs and they don't want to have to learn kind of lots of new things to do Docker. They'll learn some, but they really wanted to kind of integrate better into the environments they work in and help them more. We've been working on a lot of detailed instructions about like how to use Docker better with JavaScript and Python, because people have told us, be specific about these things, tell us exactly how I do make things work well with the way I'm doing things now. >> What is the big upside for containers for the folks watching? And last year, one of the most popular sessions was the one-on-one Peter McKay did, which was fascinating, packed with people. And the adoption of containers is going everywhere and enabling a lot of growth. What's the main message to these new developers that are coming on board to ecosystem. >> I think what's happening is that people are gradually, very slowly starting to think about containers in a different way. When we started, the question everyone kept asking was about containers and VMS, what's the difference? That question didn't really, kind of really address what the big fundamental changes that containers made to how people work was. I'd like to think about it in terms of the physical shipping containers, like people are concerned about like, can you escape from the box? Can I get out of a container? These kinds of questions. This is not really the important question about containers is kind of escape from the box. The question is, what does it enable you to build? The shipping container let us build the supply chains that let people build products and factories and things that would never have been possible without the ability to actually just ship things in a routine and predictable and reliable and secure way, getting that content and the things that come in the container and you actually work more effectively. And, so I think that now we're talking about like what's the effect of containers on the industry as a whole? What are the things that we can learn about repeatability and documentation and metadata and reliability, that we kind of talked about a little bit before, but these are becoming the important use cases for containers. Containers are really about, they're not about that kind of security and escape piece, there're about the content, the supply chain and your actual process of working. >> What do you, first of all, great call out on the security piece. I want to get that in a second. I think that's a killer one. You've mentioned supply chain, can you define software supply chain, and is that where the automation value comes in? Because a lot of people are talking about automation is improving the developer experience. So can you clarify quickly, what do you mean by the software supply chain? And is that where automation comes in? Am I getting that right? >> Yeah, so the software supply chain is really that process by which you get components of software to build your applications. Around 99% of companies are using open-source software to build applications. And the vast majority of the pieces of any modern application art consists mainly of open-source software and some tries source software, and some software that people are writing themselves. But you've got to get these components in, you've got to make sure that they're updated and scanned and they're reliable. And that's the software supply chain is that process for bringing in components that you're using to build your applications. And so, the way automation comes in, is just because there's so much of the software dealing with it manually is just difficult, and it's an ongoing process of build and test and CI and all those scanning and all those processes. And I think as software developers, we fundamentally know that the most valuable things are the things that we automate. They're the things that we do all the time and they're important. And that a lot of building a software is about building repeatable processes, rather than just doing things one by one, because we know that we have to keep updating software, we have to keep fixing bags, we have to keep improving software. And so you've got to be able to keep doing these things, and automation is what helps us do that. >> I was talking to Dana Lawson the VP of Engineering at GitHub, and she and I were chatting about this one topic. I want to get your thoughts on it, because she was definitely of the camp of automation helps with productivity. No doubt, check, double check there. The question I have for you is how do you see the impact on say the developer experience and innovation specifically? Because, okay, I can see the productivity, okay, something happens a bunch of times automated. Then you start thinking about supply chain, then you thought about developer experience and ultimately with Kubernetes around the corner, with the relationship with containers, you can see the cloud-native benefits from an innovation standpoint. Can you share your thoughts on the automation impact to experience for the developer and the innovation strategies they need? >> Well, I think that one of the ways we're trying to think about everything we do at Docker is that we should be helping build processes rather than helping you do something once, because, if you do something three times, you want to automate it, but what if the first time you did it, that could also build that automated process. And if it was, why isn't it as easy to make something automated as it is to do it once? There's no real reason why it shouldn't be. And I think that kind of... I was having a conversation with someone the other day about how they would... They had kind of reversed their thinking and they found that often it was easier to start with automation and harder to do things manually. And that's a kind of real reversal of that kind of role between automation and doing stuff run, so, and it's not how we think about it, but I think it's really interesting to think about that kind of thing, and how could we make automation really, really simple. >> Well, that's a great example when you have that kind of environment, and certainly the psychology is better to have automation but if everyone's saying it's hard to do manual, that means they're at some sort of scale, right? So scale matters, right? So as you start getting the SRE vibes going, and you start getting Cloud Scale in cloud-native apps, that's going to be cool. Now, the question I want to ask you, because while the other thing that's happening is more people are coming into open-source than ever before, not just young developers, but also end users. Not like the hardcore-end users, looking like classic enterprises are coming in. So as more developers come in and increase over the year, what does that mean for the experience for developers? Now you have, does that change it? How do you view that? Because as more developers come in, you have institutional knowledge, you have scale, you have learnings, what's your thoughts on on the impact as the population of developers increase? How does Docker view that? >> Yeah, now, I think it's really interesting trend. It's been very visible in CNCF for the last few years. We've been seeing a lot more active end-user, company's doing open-source. Spotify has been one of the examples with a backstage project they brought into CNCF and other areas where they work. And I think it's part of this growing trend that's really important to Docker, Docker is a bottom up technology adoption company. Developers are using Docker because it works for them and they love it. And developers are doing open-source in their companies because open-source works for them and they love it. And it works for their business as well. And whereas historically like the the model was, you would buy kind of large enterprise products, with big procurement deals that were often not what the developers wanted, but now you're getting developers saying, what we want to do is adopt these open-source projects, because we know how they work, we already understand that we know how to integrate them better into our processes. And I think it's that developer lad demand that's really important, and it's the kind of integration that developers want to do, the kind of products that they want to work with, because they understand them and love them, and they had targeted at developers and that's incredibly important. And I think that's very much where Docker's focused and we really want to... Open-source is of the core of everything we've always done. We've built with the open-source community, and we've kind of come from that kind of environment. And we built things that we love as developers and that other developers love. >> Talk about your thoughts on security. Obviously it's always built in from the beginning, Shift-Left is the ethos, day two operations, AI apps, whatever people want to call that. Post-deployment mode, security has to be at the center of this, containers can be a great solution and give some great flexibility for developers. Can you talk about your view and Docker view on the security posture and situation? >> Yeah, I think Shift-Left is incredibly important because just doing things late, everyone knows is the wrong thing from the point of view of productivity. But I think Shift-Left can just mean, ask the developers to do everything, which is really a bit too much. I think that sometimes things need to be shifted even further left than people have actually thought. So like, why are you expecting developers to scan components to see if they're allowed to use? If they should be using them or they should be updated, why hasn't that happened before the developer even gets there? I think there's a, I sorted my keynote about this whole piece, about trusted content. And it's really important that we really shift that even further left, so it's long before it gets to the developer, those things that are happening. Security, it's a huge area, of course, but it's very much, we need to help developers because security is non-obvious. I think the more you understand about security, the more you understand that it doesn't come naturally to people and they need to be helped with it, and they need to learn a lot about things in a way to, I found myself that, learning how to think like an attacker is a really important way of thinking about how to secure softwares, like what what would they do rather than just thinking about the normal kind of, oh, this works in the (faintly speaking) What happens if things go wrong? That you have to think about as well. So there's a lot of work to do to educate and help and build tools that help developers there. And it's been really good working with Snyk, cause they're a very developer focused security company, that's why we chose to work with them. Whereas historically, security companies have been very oriented towards kind of the operator side of it, not the development side, not the developer experience. And the other piece is really around supply chain security. That's just kind of a new security area. And it's very important from the container point of view, because one of the things containers let you do is really control the components that you're using to build applications and manage them better. And so we can really build tooling that helps you manage, that helps you understand what's in a container, helps you understand where it came from, how it was built and automate those processes and sign and authenticate them as well. And we've been working with CNCF on Nature V2, which is for signing revamp of the container signing process, because people really want to know who originated this container? Where did it come from? What did they say is in it? There's a lot of work about build up materials and composition analysis and all those things that you need to know about. What's in a container, and the... >> Everyone wants to know what's in a container. If you've got a Kubernetes cluster for instance, that's all highly secure and in comes a container, how do you know what the... There's no perimeter, right? So again, as you said, thinking like an attack vector there, you got to understand that, this is where the action is, right? This is where a lot of work's being done on this idea of always on security. You don't know when the container's coming in. during the run stage, you're running a business now, it's not just build and share, your running infrastructure. >> Absolutely, you really want full control about everything that goes into it, and you want to know where everything that you're running in production came from, and you pretty tired of this, and that's your end to end supply chain. It's everything from developer inputs through the build process and grow to production. And in production, understanding whether it needs to be updated and whether there's new discover vulnerabilities and whether it's being attacked and how that relates back to what came into it in the first place. >> Lot more intelligence, lot more monitoring. You guys are enabling all that I know it's cool. Great stuff. Hey, I want to get your thoughts on just what got you here on the calendar, looking at the DockerCon '21 event, and we're having a fun time here with, we're on theCUBE track, get the keynote track. But if you look at the sessions that's going on, you got, and I'll get your comment on this, cause it's really interesting how it's cleverly laid out this is. You've got the classic run share build and then you've got a track called accelerate, interesting metadata around these labels. Take us through, because this basically shows the maturation of containers. We already talked about the relationship, somewhat with Kubernetes, everyone kind of sees that direction clearly, but you got acceleration, which is a key new track, but run, share, build, what's your reaction to that? Share your observations of what the layout of those names and what it means to an enterprise and people building. >> Yeah, (faintly speaking) has been Docker's kind of motto for a long time. It kind of encapsulates that kind of process of like, the developer building application, the collaborative piece that's really important about sharing content in containers and then obviously putting into production because that's the aim. But, accelerate is incredibly important too. Developers are just being asked to do a lot. Everything is software, there's a lot of software, and a lot of software has to be created and we've got to make it easier to do this. And that kind of getting quickly from idea to business outcomes and results is what modern software teams are really driving at. And, I think we've really been focused this last year on what the team needs to succeed, and especially, small focused teams delivering business value. It's how we're structured internally as well and is how our customers, to a large extent are structured. And there's that kind of focus on accelerating those business outcomes and the feedback loops from your ideas to what the feedback that your customers give you at helping you understand that it's really important. >> Talk about final question for you in terms of the topic here, cloud, hybrid cloud, multicloud, this is, put multicloud asides more hype. Everyone has multiple clouds, but it speaks to the general distributed computing architecture when you talk about public cloud and on-premises cloud operations. So modern developers looking at that as, okay, distributed environment, edge, whatever you're going to call it. What's your view of Docker as it goes forward for the folks watching, who have experience with Docker, loved the vibe, loved the open-source, but now I've got to start thinking about putting the containers everywhere. What's the Docker pitch, so to speak, with a tech story that they should walk away with from you? What's the story, what's the pitch? >> Yeah, so containers everywhere has been a sort of emerging trend for a while, the last year or so. The whole Kubernetes at the edge thing has really exploded with people experimenting with lots and lots of different architectures for different kinds of environments at the edge. What's totally clear is that people want to be able to update software really easily at the edge the way you can in the cloud. We can't have the sort of, there's no point in shipping a modern piece of manufacturing equipment that you can't update the software on, because the software is how it works, more and more equipment is becoming very general purpose, people making general purpose robots, general purpose factories, general purpose everything which need to be specialized into the application they're going to run that week. And also people are getting more and more feedback and data and feedback from the data. So if you're building something that runs on a farm, you're getting permanent feedback about how well it's doing and how well the crops are growing was coming back. And so everywhere you've got this, we need to update. And everywhere you need to update, you want containers because containers are the simple reliable way to update software. >> I know you talked about CNCF and your role there. Also the CTO of Docker, I have to ask cause we were just covered Coop con and cloud-native con just last month and this month. And it's clear that Kubernetes is becoming boringly good in a way that's good to be boring, right? It means it's working. And it's becoming more cloud-native con than Coop-con. That has been kind of editorial observation, which speaks to what we feel is a trend towards more cloud-native discussions, less about Kubernetes. So, it's still Kubernetes stuff going on, don't get me wrong, just saying it's not as controversial in the sense that people kind of clearly understand why that's important, and all the discussions now seem to be on cloud-native modern developer workflows. What's your reaction to that? Do you agree, if not, what's your take? >> Yeah, I think that's definitely true. Kubernetes is definitely much more boring. Everyone is using it. They're using it in production now vastly more than they were a few years ago, when it was just experiment, experiment, experiment, now it's production scale out. The ecosystem in CNCF is kind of huge. There's so many little bits that have to be filled in storage and networking and all that. So there's actually a lot of pieces that are around Kubernetes, but, there's definitely more of a focus coming on the developer experience there. Compared to DockerCon, the audience at Coop Con is incarnated kind of still much more operator focused rather than developer focused. And it's very nice coming to DockerCon, just to feel like being amongst that developer community, Coop Con still has a way to gauge to have more of a real developer audience, but the project is starting to pair with a more developer focused kind of aim or things like backstage from Spotify is a really interesting one where it's about operations, but it's a developer portal focused things. So, I think it's happening, and there's a lot more talk about that. There's a whole bunch of infrastructure, there's a lot more security projects in CNCF than they were before. And we're doing a lot of work on supply chain security and CNCF just released a white paper on that few days ago. So there's a lot of work there that touches on developer needs. I still think that audience (faintly speaking) that much different from DockerCon which is I think 80% developers and maybe 10% infrastructure rather than the other way round. >> I think if you're going to get operators it can be SRE/platformleads. The platform leads are definitely inside DockerCon now than they've ever been before from my observation. So, but that speaks to the sign of the times. Most development teams have an SRE in the team, not an SRE team. They're just starting to see much more integration amongst the kind of a threaded or threaded teams or whatnot. So... >> Yeah. (faintly speaking) Operate your apps is the model. And I think that it's going to lead to more and more crossover between these communities. It's what DevOps was supposed to be about, somehow got diverted into building DevOps teams instead of working together, but we'll get there. >> It's clear from my standpoint, at least from reporting here is that, from the DockerCon and community at large, cloud-native community, having end-to-end work-load visibility on developer test run, everything seems to be the consensus, without a doubt. And then having multiple teams, and then having some platform, have some flexing people moving between teams for the most part, but built insecurity, built in SRE, built in DevOps, DevSecOps, all the way from end-to-end. >> Absolutely, we know that that's what does work best, it's where most organizations are heading at different speeds, because it's very different from the traditional architecture. It takes time to get there, but that's the model that has come out of microservices that really containers enabled and allow that model to happen. And it's the team architecture of containers. >> Hey, monolithic applications have monolithic organizations, microservices have microservices teams. Justin, great to have you on theCUBE for this conversation. If folks watching this interview, check out Justin's keynote, came from the main stage, great stuff. Justin, thanks for coming on theCUBE, we really appreciate your time and insight. >> Thank you, good to see you again. >> Okay, this is theCUBES's coverage of DockerCon 2021 Virtual. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>>Well, good day. And thank you for joining us here on the cube, John Walls here, uh, bringing you to this conversation as part of the AWS startup showcase. And we're joined by Justin bough, who is the SVP of product for amplitude and Justin. Good to see you today. How are you? >>I'm doing great. Thank you for having me, John. >>No pleasure. Looking forward to it. Um, you know, personalization that everybody's talking about these days and then how do we better personalize our, our digital presence, our digital products, um, you know, how do we get much more acutely aware of the end user at the end of the day and grow? I know that's what Amplitude's all about. So maybe if you just give us a 30,000 foot, um, perspective on that, about your thoughts about personalization today and how amplitude tries to affect >>For sure. Yeah. So I think first off personalization matters because it actually works. I think we live in a world where, as you know, we're drowning in content and distraction, uh, and it's been proven that customers respond better to digital experiences that are more personalized, that are more relevant for them. And frankly just save them time. Um, and the nice thing about this is not only the customers benefit, but companies do too. Uh, we actually see that a big impact on a company's bottom line, if they're able to, uh, deliver a more relevant customer experience to them because that leads to better engagement, better return, higher loyalty and lifetime value, uh, for those customers. >>So, um, well, let's, let's just go right to an example then, uh, I know you worked with a lot of different people, um, but there's anybody in particular that stands out, um, maybe give us an idea of a case study here about what practices you put into place, the kind of evaluations that you do, and ultimately the service that you're providing that allows them to increase sales and, and get a little more stickiness with them. >>Yeah, that's great. That's great. So I think one, uh, company customer of ours we're working with right now on this is actually Chick-fil-A. Uh, so people probably familiar with Chick-fil-A. Their mission is to be the most customer caring company in the world, uh, which I love in personalization is critical to that strategy because it helps them create a more relevant and seamless experience for their customers. Um, and the experience itself, and the app is actually pretty simple, which is the magic of personalization. So you open the Chick-fil-A app, uh, you see a list of menu items and those items are relevant to you based on your previous behavior. Um, after you order your entree, you're then offered a list of personalized sides. And then after that Alyssa personalized drinks, um, and the great thing is that as new items, uh, get introduced to the menu by Chick-fil-A you see the ones that are most relevant to you based on predicted affinity and all of the machine learning that we're doing in the background. And so really now Chick-fil-A is actually they're able to deliver a customized menu for everyone that automatically updates based on your behavior and your preferences. Um, and I think the real beauty of this is that they're able to configure all of this by a marketer through a simple UI. This did not require an army of data scientists or engineers. Uh, they're able to use the amplitude platform, uh, to build out this entire experience for their customers. >>Right. Cause I mean, it seems like there'd be an enormous amount of analytics that you have to apply here, right. Um, because you got all this structured and unstructured data, uh, you know, it's, it's all over the place, right. And a lot of times people don't even know what they have on hand. Um, and so you gotta, you gotta help them sift through all this. Right. So let's talk about that process a little bit for somebody who's watching and thinking about, well, that's all sounds well and good, but, but how do you kind of automate this? How do you make it so that we don't have to invest a lot in a team dedicated solely to, you know, sipping through our data and making it valuable for us? >>Yeah. I mean, I think that's the beauty of, uh, of amplitude actually offering this in that that's actually our original first product product analytics. That's what we've done. Um, so we've actually made an out of the box system that can read from all your different data sources. Um, so whether those be your product sources, marketing channels, data that sits in your data warehouse, um, but it's not just piping that data. Uh, we then combine that into a unique identity, uh, profile for that customer, um, across all those different touch points, um, and also have out of the box data governance, um, so that you can make sure you maintain, uh, the quality of that data profile, uh, over time. And then that gets fed into, um, our, what we call our behavioral graph. It's our database, uh, that's actually built to both understand and predict future behavior. And so all of this happens effectively out of the box for our customer. They don't need to do any of this, uh, themselves. Uh, we're managing all this for them. And then what they experience is, uh, an analytics application. So they can analyze that user behavior understand kind of what the drivers of different things like engage in retention are, and then use that to actually personalize the product experience. >>And, and you mentioned machine learning, um, talk about that aspect of this. I mean, how much more capability you have now because of what I know can deliver and, and, um, in some ways it adds some complexity, um, but also obviously it delivers exponentially, I would think in benefit at the end of the day. >>Yeah, for sure. I mean, it's just not possible to do one to one personalization without machine learning. I think that's actually, when we talk about the benefits and the advantages of personalization, it's probably even worth taking a step back. Like there's a lot of different types of personalization. Um, I think when you want to do behavioral personalization where you truly getting to one-to-one experiences, you have to use machine learning. Now you compare that to maybe like demographic personalization, which is actually, I think when most companies talk about when they're doing personalization, they're actually doing demographic personalization. That's like, are you a male or female? Um, what's your, you live in a city or a suburb. Um, uh, but the reality is like that light segmentation, it's not really that effective. Like do all women who live in a city behave the same, obviously not. Uh, and so, uh, we want instead to use behavior because your past behavior is the best predictor of your future behavior. >>Um, and, uh, and you need machine learning to be able to actually come up with, for an individual. What is their likelihood propensity to actually engage on any piece of content of which think about for you think about Chick-fil-A, how many different items they have in a menu. Um, you can think about like, we work with, um, a content company that has millions of different articles and they want to figure out what's the right article to put in front of you. Like, that's just not possible to actually analyze that by hand, uh, nor actually work working straight that, uh, uh, in real time without actually leveraging machine learning. Um, and so that's the exciting thing that's happened with, uh, new advances in, uh, supervisor and supervised learning models that we can actually do those in generalizable ways, uh, for our customers, >>Wait, we've talked a lot about behavioral, so that's obviously metrics you've been tracked. Right. I saw something and I clicked on something and I acted on something or watch something. These are all very measurable activities. On the other hand, though, as you know, in the consumer space, a lot of it's emotion too, you know, I make decisions based on, on my feelings or my thoughts or whatever. Can you, can you do any kind of unpeeling of my motivation in this almost like empathetic, uh, investigation so that you have an idea of what social cues on emanating or sending off? So, Hey, yeah, we can, we can get John this way too. >>Yeah. So I think a lot of it is, I mean, we're talking a lot about the science of, uh, product development, uh, for sure. And how do you bring personalization leveraging data? There is then the art of actually understanding, like what are the emotional States that users are in and like this isn't to say that the ability to personalize the product means that you're not actually bringing the heart as well. Like you act, it actually is a, both about the art and the science coming together. Um, and so you still need to, like, you're still gonna talk to your customers. You're still going to understand, uh, them and kind of what their, uh, different need States are, but this is then taking what you have, which you've built as a great product, then how do you optimize that? So we call it an optimization system, um, and actually deliver, uh, the best experience, uh, based on that customer's behavior. >>So just to kind of flip this a little bit, then what are you doing? Amplitude? What are you doing that, um, that hasn't been done before? I couldn't, I didn't understand that a lot of people think personalization just hasn't has a great horizon, has a lot of great promise. Well, but we're not there yet. I mean, what haven't we delivered on yet that you think amplitude is improving on and refining this capability? >>Yeah. So I think there are a couple of things there as to why we haven't fully seen the promise of personalization deliver no way. And I would say we're really starting to see that chasm emerge, where there are some companies that, you know, you think of, um, you know, Netflix, like obviously Amazon and others, who've done, who've been really successful here, but they've done it through armies of people. Um, what hasn't happened is a self-serve way of doing this so that it does not require massive investments, uh, in technical resources. Um, and so what we've solved for three things, um, one we've already talked about it, but it's just so true. Like this actually in and of itself is not an ML problem. First, it's actually a trustworthy data problem. Do you actually have the behavioral data that you can trust? Can you actually capture that across the entire customer journey because you can't personalize a journey if you don't even know what your users are doing to begin with. >>So you have to start there at that foundational level. Um, and that is a big part of our secret sauce is that we've built a database specifically catered to helping you understand that journey of that customer across all the different platforms and channels that they do. That's not easy to actually unify behavior in that fashion and allow you to analyze that in real time. Um, so that's the first thing that we did, um, is build that, uh, that database. So that's number one. And that's just the foundation. You have to have that, like, I, I think so many companies fail because they think we can go hire ML engineers, but if you don't have the foundation, it's not going to work. Um, the second thing isn't necessarily technological. It's more cultural, but it is really critical. And I think our analytics applications helped, uh, helped a lot here, which is you gotta break down the silos between marketing product engineering and data science. >>You actually have, you have to have all of them working together, um, to really be able to fulfill the promise of personalization because you have to be aligned and what's the outcome we're trying to drive, but that's actually how I literally can walk you through like the, how the, how the actual product works. But the first starting point is what are we trying to accomplish? Like in the Chick-fil-A example, it is, we want people to buy more than one item. Okay. So that's your goal. Like you have to get alignment that that is the goal. Cause if everyone's arguing about different goals, it doesn't matter what ammo model, like the model needs to know what we're trying to actually focus in on. Uh, and so how do you bring people together? And you do that through shared understanding of data. You do that through, we call it a North star, like we're aligned in what is the North star that we're focused on. >>And can you measure that? And that's analytics is focused in on that. And then when you have both of those, you've got behavioral data, you understand the journey of a customer you're aligned in the goals and outcomes you care about. Then you can leverage machine learning to actually deliver that personalized experience. And the advances that we're making there are actually doing that in a generalizable fashion. And so that does not have to be custom built for every single use case. Um, and our models are now able that we can run a model basically, uh, every hour to update for a customer. Um, and that scales horizontally, >>Well, I know of Chick-fil-A certainly has a track record that, um, is an arguable, right? And, and, and you've had a lot to do with satisfying that appetite for success. So, uh, Justin, uh, congratulations to amplitude. It's been a real pleasure speaking with you and thanks for the time today. >>Of course. >>Excellent speaking with Justin Bauer, the senior vice president of product at amplitude, and you've been watching the AWS startup showcase here on the cube.