(gentle upbeat music) >> Announcer: theCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (bright upbeat music) >> We're back in Barcelona at the Fira. My name is Dave Vellante. I'm here with David Nicholson. We're live at MWC23, day four of the coverage. The show is still rocking. You walk the floor, it's jamming. People are lined up to get in the copter, in the right. It's amazing. Planes, trains, automobiles, digitization of analog businesses. We're going to talk private wireless here with Dell. Sarvesh Sharma, the Global Director for Edge and Private Mobility Solutions practice at Dell. And John McCready is a Senior Director for 5G Solutions and product management at Dell Technologies. Guys, good to see you. >> Likewise, likewise. >> Good to see you too. >> Private wireless. It's the buzz of the show. Everybody's talking about it. What's Dell's point of view on that? >> So Dell is, obviously, interested entering the private wireless game, as it's a good part of the overall enterprise IT space. As you move more and more into the different things. What we announced here, is sort of our initial partnerships with some key players like Airspan and expedo and AlphaNet. Players that are important in the space. Dell's going to provide an overall system integration solution wrap along with our Edge BU as well. And we think that we can bring really good solutions to our enterprise customers. >> Okay, I got to ask you about AlphaNet. So HPE pulled a little judo move they waited till you announced your partnership and then they bought the company. What, you know, what's your opinion on that? You going to, you going to dump AlphaNet, you're going to keep 'em? >> No. >> We're open Ecosystem. >> Yeah, it's an open ecosystem. We announce these are our initial partners, you know we're going to announce additional partners that was always the case. You know, there's a lot of good players in this space that bring different pros and cons. We got to be able to match the solution requirements of all our customers. And so we'll continue to partner with them and with others. >> Good, good answer, I like that. So some of these solutions are sort of out of the box, others require more integration. Can you talk about your, the spectrum of your portfolio? >> So I'm glad you brought up the integration part, right? I mean, if you look at private wireless, private mobility it is not a sell by itself. At the end of the day what the enterprise wants is not just private mobility. They're looking for an outcome. Which means from an integration perspective, you need somebody who can integrate the infrastructure stack. But that's not enough. You need somebody who can bring in the application stack to play and integrate that application stack with the enterprises IT OT. And that's not enough. You need somebody to put those together. And Dell is ideally suited to do all of this, right? We have strong partners that can bring the infrastructure stack to play. We have a proven track record of managing the IT and the enterprise stack. So we are very excited to say, "Hey, this is the sweet spot for us. And if there was a right to win the edge, we have it." >> Can you explain, I mean, people might be saying, well, why do I even need private wireless? I got Wi-Fi. I know it's kind of a dumb question for people who are in the business, but explain to folks in the audience who may not understand the intersection of the two. >> So, yeah, so I think, you know, wireless is a great techno- pardon me, Wi-Fi is a great technology for taking your laptop to the conference room. You know, it's effectively wireless LAN Where private 5G and before that private LTE had come into play is where there's a number of attributes of your application, what you're using it for, for which Wi-Fi is not as well suited. And so, you know, that plays out in different verticals in different ways. Either maybe you need a much higher capacity than Wi-Fi, better security than Wi-Fi, wider coverage like outdoor, and in many cases a more predictable reliability. So cellular is just a different way of handling the wireless interface that provides those attributes. So, you know, I think at the beginning, the first several years, you know Wi-Fi and 5G are going to live side by side in the enterprise for their different roles. How that plays out in the long term? We'll see how they each evolve. >> But I think anybody can relate to that. I mean, Wi-Fi's fine, you know, we have our issues with Wi-Fi. I'm having a lot of issues with Wi-Fi this week, but generally speaking, it works just fine. It's ubiquitous, it's cheap, okay. But I would not want to run my factory on it and rely on it for my robots that are shipping products, right? So that really is kind of the difference. It's really an industry 4.0 type. >> Yeah, exactly. So I mean, manufacturing's an important vertical, but things of energy and mining and things like that they're all outdoor, right? So you actually need the scale that comes, with a higher power technology, and even, you know just basic things like running cameras in a retail store and using AI to watch for certain things. You get a much better latency performance on private 5G and therefore are able to run more sophisticated applications. >> So I could be doing realtime inference. I can imagine Dave, I got an arm processor I'm doing some realtime inference AI at the Edge. You know, you need something like 5G to be able to do that, you can't be doing that over Wi-Fi. >> Yeah >> You nailed it. I mean that's exactly the difference, right? I mean if you look at Wi-Fi, it grow out from a IT enabled mode, right? You got to replace an ethernet. It was an IT extension. A LAN extension. Cellular came up from the mode of, "Hey, when I have that call, I need for it to be consistent and I need for it to be always available," right? So it's a different way of looking at it. Not to say one is better, the other is not better. It's just a different philosophy behind the technologies and they're going to coexist because they meet diverse needs. >> Now you have operators who embrace the idea of 5G obviously, and even private 5G. But the sort of next hurdle to overcome for some, is the idea of open standards. What does the landscape look like right now in terms of those conversations? Are you still having to push people over that hump, to get them beyond the legacy of proprietary closed stacks? >> Yeah, so I think I look, there are still people who are advocating that. And I think in the carrier's core networks it's going to take a little longer their main, you know macro networks that they serve the general public. In the private network though, the opportunity to use open standard and open technology is really strong because that's how you bring the innovation. And that's what we need in order to be able to solve all these different business problems. You know, the problems in retail, and healthcare and energy, they're different. And so you need to be able to use this open stack and be able to bring different elements of technology and blend it together in order to serve it. Otherwise we won't serve it. We'll all fail. So that's why I think it's going to have a quicker path in private. >> And the only thing to add to that is if you look at private 5G and the deployment of private LTE or private 5G, right? There is no real technology debt that you carry. So it's easy for us to say, "Hey, the operators are not listening, they're not going open." But hey, they have a technical debt, they have 2G, 3G, 4G, 5G, systems, right? >> Interviewer: Sure. >> But the reason we are so excited about private 5G and private 4G, is right off the bat when we go into an enterprise space, we can go open. >> So what exactly is Dell's role here? How do you see, obviously you make hardware and you have solutions, but you got to open ecosystems. You got, you know, you got labs, what do you see your role in the ecosystem? Kind of a disruptor here in this, when I walk around this show. >> Well a disruptor, also a solution provider, and system integrator. You know, Sarvesh and I are part of the telecom practice. We have a big Edge practice in Dell as well. And so for this space around private 5G, we're really teamed up with our cohort in the Edge business unit. And think about this as, it's not just private 5G. It's what are you doing with it? That requires storage, it requires compute, it requires other applications. So Dell brings that entire package. There definitely are players who are just focused on the connectivity, but our view is, that's not enough. To ask the enterprise to integrate that all themself. I don't think that's going to work. You need to bring the connectivity and the application to storage compute the whole solution. >> Explain Telecom and and Edge. They're different but they're like cousins in the Dell organization. Where do you guys divide the two? >> You're saying within Dell? >> Yeah, within Dell. >> Yeah, so if you look at Dell, right? Telecom is one of our most newest business units. And the way it has formed is like we talk Edge all the time, right? It's not new. Edge has always been around. So our enterprise Edge has always been around. What has changed with 5G is now you can seamlessly move between the enterprise Edge and the telecom Edge. And for that happen you had to bring in a telecom systems business unit that can facilitate that evolution. The next evolution of seamless Edge that goes across from enterprise all the way into the telco and other places where Edge needs to be. >> Same question for the market, because I remember at Dell Tech World last year, I interviewed Lowe's and the discussion was about the Edge. >> John: Yep. >> What they're doing in their Edge locations. So that's Edge. That's cool. But then I had, I had another discussion with an agriculture firm. They had like the massive greenhouses and they were growing these awesome tomatoes. Well that was Edge too. It was actually further Edge. So I guess those are both Edge, right? >> Sarvesh: Yeah, yeah, yeah. >> Spectrum there, right? And then the telecom business, now you're saying is more closely aligned with that? >> Right. >> Depending on what you're trying to do. The appropriate place for the Edge is different. You, you nailed it exactly, right. So if you need wide area, low latency, the Edge being in the telecom network actually makes a lot of sense 'cause they can serve wide area low latency. If you're just doing your manufacturing plant or your logistics facility or your agricultural growing site, that's the Edge. So that's exactly right. And the tech, the reason why they're close cousins between telecom and that is, you're going to need some kind of connectivity, some kind of connectivity from that Edge, in order to execute whatever it's you're trying to do with your business. >> Nature's Fresh was the company. I couldn't think of Nature's Fresh. They're great. Keith awesome Cube guest. >> You mentioned this mix of Wi-Fi and 5G. I know it's impossible to predict with dates certain, you know, when this, how's this is going to develop. But can you imagine a scenario where at some point in time we don't think in terms of Wi-Fi because everything is essentially enabled by a SIM or am I missing a critical piece there, in terms of management of spectrum and the complicated governmental? >> Yeah, there is- >> Situation, am I missing something? It seems like a logical progression to me, but what am I missing? >> Well, there is something to be said about spectrum, right? If you look at Wi-Fi, as I said, the driver behind the technology is different. However, I fully agree with you that at some point in time, whether it's Wi-Fi behind, whether it's private 5G behind becomes a moot point. It's simply a matter of, where is my data being generated? What is the best technology for me to use to ingest that data so I can derive value out of that data. If it means Wi-Fi, so be it. If it means cellular, so be it. And if you look at cellular right? The biggest thing people talk about SIMs. Now if you look at 5G standard. In 5G standard, you have EAPTLS, which means there is a possibility that SIMs in the future go away for IoT devices. I'm not saying they need to go away for consumer devices, they probably need to be there. But who's to say going ahead for IoT devices, they all become SIM free. So at that point, whether you Wi-Fi or 5G doesn't matter. >> Yeah, by the way, on the spectrum side people are starting to think about the concept. You might have heard this NRU, new radio unlicensed. So it's running the Wi-Fi standard, but in the unlicensed bands like Wi-Fi. So, and then the last piece is of course you know, the cost, the reality it stays 5G still new technology, the endpoints, you know, what would go in your laptop or a sensor et cetera. Today that's more expensive than Wi-Fi. So we need to get the volume curve down a little bit for that to really hit every application. I would guess your vision is correct. >> David: Yep >> But who can predict? >> Yeah, so explain more about what the unlicensed piece means for organizations. What does that for everybody? >> That's more of a future thing. So you know, just- >> No, right, but let's put on our telescope. >> Okay, so it's true today that Wi-Fi traditionally runs in the bands that have been licensed by the government and it's a country by country thing, right? >> Dave: Right. >> What we did in the United States was CBRS, is different than what they've done in Germany where they took part of the Zurich C-band and gave it to the enterprises. The telco's not involved. And now that's been copied in Japan and Korea. So it's one of the complications unfortunately in the market. Is that you have this different approach by regulators in different countries. Wi-Fi, the unlicensed band is a nice global standard. So if you could run NR just as 5G, right? It's another name for 5G, run that in the unlicensed bands, then you solve the spectrum problem that Dave was asking about. >> Which means that the market really opens up and now. >> It would be a real enabler >> Innovation. >> Exactly. >> And the only thing I would add to that is, right, there are some enterprises who have the size and scale to kind of say, "Hey, I'm going the unlicensed route. I can do things on my own." There are some enterprises that still are going to rely on the telcos, right? So I don't want to make a demon out of the telcos that you own the spectrum, no. >> David: Sure. >> They will be offering a very valuable service to a massive number of small, medium enterprises and enterprises that span regional boundaries to say, hey we can bring that consistent experience to you. >> But the primary value proposition has been connectivity, right? >> Yes. >> I mean, we can all agree on that. And you hear different monetization models, we can't allow the OTT vendors to do it again. You know, we want to tax Netflix. Okay, we've been talking about that all week. But there may be better models. >> Sarvesh: Yes. >> Right, and so where does private network fit into the monetization models? Let's follow the money here. >> Actually you've brought up an extremely important point, right? Because if you look at why haven't 5G networks taken off, one of the biggest things people keep contrasting is what is the cost of a Wi-Fi versus the cost of deploying a 5G, right? And a portion of the cost of deploying a 5G is how do you commercialize that spectrum? What is going to be the cost of that spectrum, right? So the CSPs will have to eventually figure out a proper commercialization model to say, hey listen, I can't just take what I've been doing till date and say this is how I make. Because if you look at 5G, the return of investment is incremental. Any use case you take, unless, let's take smart manufacturing, unless the factory decides I'm going to rip and replace everything by a 5G, they're going to introduce a small use case. You look at the investment for that use case, you'll say Hmm, I'm not making money. But guess what? Once you've deployed it and you bring use case number two, three, four, five, now it starts to really add value. So how can a CSP acknowledge that and create commercial models to enable that is going to be key. Like one of the things that Dell does in terms of as a service solution that we offer. I think that is a crucial way of really kick starting 5G adoption. >> It's Metcalfe's Law in this world, right? The first telephone, not a lot of value, second, I can call one person, but you know if I can call a zillion now it's valuable. >> John: Now you got data. >> Yeah, right, you used a phrase, rip and replace. What percentage of the market that you are focusing on is the let's go in and replace something, versus the let's help you digitally transform your business. And this is a networking technology that we can use to help you digitally transform? The example that you guys have with the small breweries, a perfect example. >> Sarvesh: Yeah. >> You help digitize, you know, digitally transform their business. You weren't going in and saying, I see that you have these things connected via Wi-Fi, let's rip those out and put SIMs in. >> No. >> Nope, so you know- >> That's exactly right. It's enabling new things that either couldn't be achieved before or weren't. So from a private 5G perspective, it's not going to be rip and replaced. As I said, I think we'll coexist with Wi-Fi, it's still got a great role. It's enabling those, solving those business problems that either hadn't been solved before or could not be solved with other technology. >> How are you guys using AI? Everybody's talking about ChatGPT. I love ChatGPT, we use it all the time. Love it, hate it, you know, whatever. It's a fun topic. But AI generally is here in a way that it wasn't when the enterprise disaggregated. >> John: Right. >> So there's AI, there's automation, there's opportunities there. How do they fit into private 5G? >> So if you look at it, right, AI, AI/ML is actually crucial to value extraction from that data, because all private 5G is doing is giving you access to that precious data. But that data by itself means nothing, right? You get access to the data, extracting value out of the data that bring in business value is all going to be AI/ML. Whether it's computer vision, whether it's data analytics on the fly so that you can, you know do your closed loop controls or what have you. All of these are going to be AI/ML models. >> Dave: Does it play into automation as well? >> Absolutely, 'cause they drive the automation, right? You learn your AI models, drive their automation. Control, closed loop control systems are a perfect example of their automation. >> Explain that further. Like give us an example. >> So for example, let's say we're talking about a smart manufacturing, right? So you have widgets coming down the pipe, right? You have your computer vision, you have your AI/ML model that says, "Hey, I'm starting to detect a consistent error in the product being manufactured. I'm going to close loop that automation and either tweak the settings of the machine, shut down the machine, open a workflow, escalate it for human intervention." All that automation is facilitated by the AI/ML models >> And that, and by the way, there's real money in that, right? If you're making your power and you're making it wrong, you don't detect it for hours, there's real money in fixing that >> Right. >> So I've got a, I've got an example albeit a slight, not even slightly, but a tragic one. Let's say you have a train that's rolling down the tracks at every several miles or so, temperature readings are taken from bearings in the train. >> Sarvesh: Yes, yes. >> Wouldn't it be nice to have that be happening in real time? >> Sarvesh: Yes. >> So it doesn't reach that critical point >> Yes. >> Where then you have a derailment. >> Yes. >> Yeah, absolutely. >> I mean, those are, it's doesn't sound sexy in terms of "Hey, what a great business use case that we can monetize." >> John: Yeah. >> But I'll bet you in hindsight that operator would've loved to have that capability. >> John: Yeah. >> Sarvesh: Right. >> To be able to shut the train down and not run. >> That's a great example where the carrier is actually, probably in a good position, right? Cause you got wide area, you want low latency. So the traditional carriers would be able in great position to provide that exact service. Telemetry is another great example. We've been talking about other kinds of automation, but just picking up measurements and so on. The other example of that is in oil and gas, right? As you've got pipelines running around you're measuring pressure, temperature, you detect a leak, >> David: Right. >> in minutes, not weeks. >> David: Right. >> So there's a lot of good examples of things like that >> To pick up in a point, Dave. You know, it's like you look at these big huge super tankers, right? They have big private networks on that super tanker to monitor everything. If on this train we had, you know, we hear about so many Edges, let's call one more the rolling Edge. >> Yeah. >> Right, that, that Edge is right on that locomotive tracking everything with AI/ML models, detecting things, warning people ahead of time shutting it down as needed. And that connectivity doesn't have to be wired. It can be a rolling wireless. It potentially could be a spectrum that's you know, open spectrum in the future. Or as you said, an operator could facilitate that. So many options, right? >> Yeah, got to double down on this. Look, I know 'cause I've been involved in some of these projects. Amusement park operators are doing this for rides. >> John: Yes. >> Sarvesh: Yep. >> So that they can optimize the amount of time the ride is up, so they can shorten lines >> Yes. >> So that they can get people into shops to buy food and souvenirs. >> John: Yes. >> Certainly we should be able to do it to protect infrastructure. >> Sarvesh: Absolutely. >> Right, so- >> But I think the ultimate point you're making is, it's actually quite finally segmented. There's so many different applications. And so that's why again, we come back to what we started with is at Dell, we're bringing the solution from Edge, compute, application, connectivity, and be able to bring that across all these different verticals and these different solutions. The other amusement park example, by the way, is as the rides start to invest in virtual reality, so you're moving, but you're seeing something, you need some technology like 5G to have low latency and keep that in sync and have a good experience on the ride. >> To 5G and beyond, gents. Thanks so much for coming on theCUBE. >> All right, thank you Dave. >> It was great to have you. >> Thank, thank you guys. >> Great to meet you guys. Thank you very much. >> Great, all right. Keep it right there. For David Nicholson and Dave Vellante, This is theCUBE's coverage of MWC23. Check out siliconangle.com for all the news. theCUBE.net is where all these videos live. John Furrier is in our Palo Alto office, banging out that news. Keep it right there. Be right back after this short break. (gentle upbeat music)

(bright, upbeat music) >> Hello, brilliant humans and welcome to this special programming on theCUBE featuring Women of the Cloud, brought to you by AWS. My name is Savannah Peterson, and I am very excited to be joined by a brilliant woman both in supply chain as well as digital transformation. Please welcome Jeanette Barlow, VP of Product at Instacart. Jeanette, thank you so much for joining us from Boston today. How you doing? >> Thank you. I'm doing well, thank you. And thank you to the Amazon team for letting me join you. I'm excited to participate in this. I think it's such an important topic to learn all about how as women we're helping shape the future of business, supply chain, consumer experiences. So thank you very much. >> That's fantastic to have you and to be really celebrating women of the cloud properly. To start us off, how long, let's just, let's run with this. How long have you been a woman of the cloud? (Jeanette and Savannah laugh) >> Oh, probably since there, before there was a cloud, actually I have spent my entire career in enterprise technology and I spent nearly 25 years actually with IBM. And, you know, I remember when the internet really took off as far as a highly accessible thing and then the very beginnings of e-commerce where it was really the wild west and it was such a different experience than you get now. And I've been very fortunate throughout that journey to have a variety of roles from sales, marketing, communications. I eventually landed in product management and that's pretty much where I stayed. >> Savannah: At least for now. >> At least for now. >> Sounds like you're very curious. I can tell that you are a very curious person. Since you've been around for what I would consider a, an impressive period of time in an industry, especially when there were not a ton of women to reference or receive mentorship from, what was the initial catalyst or spark or inspiration for you to pursue a career in technology? >> I'll be really honest, getting out of college with college debt, money. (Savannah laughs) The best salary, I'm not going to sugarcoat that but once I landed there, it just was so amazing how technological advance advances were fundamentally changing the way businesses would work or how humans could get things done. And that whole, my whole career trajectory has been very much working at the forefront of new areas whether that be collaboration, software or supply chain which is, obviously we're all well aware, such a deep and important area and even low-code workflow automation before I came to Instacart. >> I love the transparency there. It's a indicator of a great leader and that level of authenticity. Were there any hurdles that you felt you had to overcome in the beginning or was the curiosity enough to power through the initial first few years that are always tough for anyone, no matter their gender or career? >> I think I was a very fortunate person. I do want to say that, sure, there are a lot of long hours and I often felt that I had to be more prepared, maybe than some of my colleagues that were men back, way back in the day. But I had the very good fortune of working for companies throughout my history that really believed in an equitable and respectful workplace. And I had wonderful mentors, both women and men, along the way who really were there to help develop talent. So I never felt that I had sort of a glass ceiling. I definitely felt that I had to to sit there and assert a point of view, at times. >> Savannah: Mm-Hm. >> But, I've seen this whole industry and space change and it's not just gender, but also racial backgrounds educational backgrounds, that neurodiversity I'm now seeing much greater respect for listening to that chorus of voices because we do get better, much better outcomes that way. >> Absolutely. I couldn't agree more and I'm happy to hear that you've been supported along your journey. I think the industry can definitely get a bad rap and there are a lot of people paving the way for us. I want to talk a little bit about supply chain because I don't know about you, but for me I don't think there were as many people talking about the industry and probably what you do, say four years ago, as are now. How did you find your way into supply chain and what is it about helping that be more efficient that excites you? >> Yes. There's nothing like a shortage of toilet paper to get people to. (Savannah laughs) Or to understand what supply chain means. And I, as tough as those times were, especially at the beginning of the pandemic and the uncertainty, it was so exciting for those of us in supply chain because suddenly people got what we did like- >> Savannah: Mm-Hm. >> And they were interested in hearing about it. So I really, I really have, we did enjoy that. I got exposed to that because ultimately I served as the Vice President of Product Management and Strategy for IBM, Sterling Supply Chain which was a very large brand within the IBM portfolio, serving over 10,000 clients worldwide, really focused on their omnichannel order management and their other supply chain processes around order to cash, procure to pay, logistics and things like that. And when you start to learn about the intricacies and that choreography needed across so many players in the value chain, it's an absolutely fascinating puzzle. And- >> Savannah: Yeah. >> Often the further away from the consumer experience you got, the more analog it became. And so the opportunity to start to digitize and transform that was really something that was very, very intriguing. And now here at Instacart, the opportunity to sort of parlay that into one of probably the most complex supply chains that there are, grocery, food just adds another level- >> Yeah. >> Of excitement intrigue to the work. >> I can only imagine there are, I'm just thinking about it right now. I'm not sure there are many supply chains, if any that touch as many lives as food does, as, I mean so is that what brought you, you joined Instacart relatively recently if I'm not mistaken, within the last year. Is that what brought you to them? Was the complexity of that global challenge? >> Absolutely. That was definitely the start of it, was so intriguing to me to see, to, the more I learned about Instacart when they approached me was also they're really changing an industry that's been very static for many, many years, right? And they're fundamentally reshaping that industry. One that's, as you said, is crucial to the everyday lives of pretty much everyone. And I was intrigued by that. But I was also intrigued by the breadth at which they're approaching this, not just the marketplace, but how we are helping retailers through our Instacart platform actually reach their consumers in ways that they like to shop whether it's online or in the store. We are also very, very committed to not just serving from a convenience standpoint, but actually improving access to healthy and nutritious food for as many people as might need that. So it just, core to the complexity of the problem the criticality of it, but also just frankly speaking to the core of who Instacart is as a company, I, it just felt like it was like a culmination of a lot of things to have this opportunity to work here. >> Sounds like a fantastic opportunity. I want to dive a little bit deeper into the technology side there. How is Instacart's technology helping grocers with varying levels of scale and geographical challenges and I'm sure a variety of other things and even a digital skillset. How are you helping them navigate their digital transformation? >> You know, this is probably one of the sectors that lags behind other retail sectors as far as digital transformation. And when the progress that's been made over the last four years is tremendous. And the road ahead is still before us is still a long way to go. I mean Instacart built the world's largest grocery marketplace, if you want to think about that. And so we have more than 10 years of experience in understanding the complexity of that. With, again a supply chain that is very, very complex. So last spring we announced the Instacart platform as a way of really putting a name to a lot of work we were already doing. And it's all about opening up the capability and the technology that we have to help grocers reach their customers directly as well as through our marketplace. So we help grocers like Publix, Wegmans, The Fresh Market just hundreds of grocers build out their own storefronts, their own mobile apps and that we are actually powering for them. We help them create some very unique fulfillment models that might serve customers or be new market opportunities. Certainly we have the traditional full service shop, but we also have virtual convenience that can enable delivery in minutes. And in certain geographies and demographics, that's, you know, really important. We are even going in the store with our connected stores technologies that we announced earlier this year, and that is everything from smart cards to scan and pay to wayfinding that it just, it's a lot of very interesting work we're doing and we're very, very fortunate to be able to partner with some of the best and brightest grocery retailers out there as well as retailers and other verticals as well. But grocery store is sort of our core. >> Yeah, I can only imagine some of the conversations that you have and the user behaviors that you get to learn about as people are on their food journey. You teased a little bit there about what's coming next. What else do you think is in our food future? >> Well, I think, you know, the pandemic pushed the grocery industry to get online to start to digitally transform itself, but we believe it's not an either or. There are virtually no one that's exclusively online and we know more and more there's no one that's exclusively you know, only in the store. We really expect to have that blend and I think as long as we're very, very savvy about understanding the, our retailers' needs as well as their customers' needs on how they can really traverse seamlessly between whether they're online or in store, how they can have an engaging experience that's consistent to the brand of the retailer. >> Savannah: Mm-Hm. >> How they can be rewarded for their loyalty. How they can be encouraged to try new things and just have a much more engaging experience with that grocer because food is a very emotional sort of buy, right? I mean, it's a very sensory rich. And so how- >> Sort of? I think you can go ahead and just make that claim. Just for a lot of people, yeah, yeah. We'll endorse that. >> You're right, yeah, it is. Right, we're passionate about our brand of this or that or we want to touch or smell or do things like that. So there's a tremendous amount of innovation you get online, like personalization and other things that you don't get when you get, you walk into the store, everybody's got the same end cap like I see the same end cap as you see and we might be very different. And then vice versa. I get a very much a sensory experience when I'm in the store, right? That I don't have, how do we blend that? And so there's some really interesting things that we're working on with our retail partners to embrace that omnichannel approach. So we create that flywheel of experience and innovation between the two. So I think you're going to see a lot more focus on an omnichannel experience that traverses between the on and the in, online and the in-store. >> Yeah, I, so I love this because you know, we, there's a continued debate around remote and in-person, working remote and in-person events, but it sounds like hybrid is here to stay when it comes to food and and how we eat, which is very exciting. Last question for you, Jeanette. What would you say to someone, a woman of any age who is looking at this video or maybe dreaming about a career in cloud technology? What's your moment of inspiration? >> You know, I think my best advice is all, you know, stay curious. Just be in love with not even just the technology for technology's sake, but what the technology can unlock as far as an experience and focus on building those experiences. Not only for your direct customer in my case, retailers, grocers, but for their customer. Trying to understand that. And I think if you can connect those dots, you know the cloud is the limit, let's put it that way. (Jeanette and Savannah laugh) >> I'll take it upon that. I love that. Jeanette Barlow, thank you so much for joining us. The team at Instacart is lucky to have you. And thank you to our audience for joining us for this special program on theCUBE featuring Women of the Cloud. My name is Savannah Peterson and I look forward to celebrating more brilliant women like Jeanette with you all soon. (upbeat, happy music)

(light upbeat music) >> Hey, everyone, welcome back to theCUBE's day two live coverage of VMware Explore 2022 from Moscone Center in San Francisco. Lisa Martin here as your host with Dave Nicholson. We've got a couple of guests here and we have some props on set. Get a load of this Nature Fresh Farms produce. Keith Bradley joins us, the VP of IT from Nature Fresh Farms, and Alison Biers is back, as well, director of marketing at Edge Solutions for Dell. Guys, welcome back to the program and thanks for bringin' some food. >> Well, thank you, yeah. >> Thank you so much. >> So, Keith, talk to us a little bit about technology from Nature Fresh Farm's perspective. How do we look at this farming organization as a tech company? >> As technical, we're something that measures everything we grow. So, we're 200 acres of greenhouse, spanning probably about 3 or 400 acres of land. Everything's entirely environmentally controlled. So, the peppers that we have in front of you, the tomatoes, they're all grown and controlled from everything they get from light to moisture to irrigation and nutrients. So, we do all that. >> So, should I be able to taste the Dell goodness in these cucumbers, for example? >> I'd like to say Nature Fresh slash Dell good. >> Connect the dots for us. So, let's go through that sort of mental exercise of how are these end products for consumers better because of what you're doing in IT? >> So, one of the things that we've been able to do, and one of the transformations we made is we are now able to run our ETLs. So, analyze the data realtime at the Edge. So, making decisions which used to be only once a day based on analytics to now multiple times a day. Our ETLs used to take 8 to 10 hours to run. Now they run- >> So, extraction, transformation and load. >> Yep, yep. >> Okay. So, we consider it a party foul if you use a TLA and you don't find it the first time. >> Okay. >> But you get a pass 'cause you're an actual and real person. >> I'll give you that one. >> I already had a claim laid on that. I'm sorry, so continue. >> Yeah, yeah. So, it allowed now the growers to make multiple decisions and then you start adding the next layer. As we expanded our technology base, we started introducing AI into it. So now, AI is even starting to make decisions before the grower even knows to make them based on historical data. So, it's allowed us to become more proactive in protecting the health and longevity and even taste of that plant and the product coming out to you. >> That's awesome. Alison, talk to us about from Dell's perspective how is it helping Nature Fresh to simplify the Edge which there's a lot of complexity there? You talked about the size of the organization but how do you help simplify it? >> I think Nature Fresh had a lot of common problems that we see customers have. So, they had some really interesting ambitions to improve their produce and do it in a GMO free way and really bring a quality product to their customer. But yet, they were each solving their problems on their individual farms in different ways. And so, one of the ways that we were able to help was to consolidate a lot of those silos as they were expanding the scope and scale of what they really wanted to do from a technology perspective. And then being able to do that in a secure way that's delivering the insights they need when they need them right there at the Edge is really critical. >> I think it's wonderful that we have the actual stuff here. Because we often talk in these abstract terms about outcomes. There's your outcome right there. >> Yeah. >> Right. >> But talk about this growing in the soil somewhere. You have growers. It's not an abstraction. These are actual actual people. Where does the technology organism interface occur here? You have organically grown crops. Where's that interface? Where's the first technology involved in this process? Literally physically. >> Physically. >> Yeah, yeah, yeah. Is there a shack with a server in it somewhere? >> So, we actually have, we have a core data center at the center of Nature Fresh set up basically where everything ends up. We have our Edge. So, we have computers, we're at the Edge analyzing stuff. But if you want to go right back to the grassroots of where it actually is, is it's right at, not dirt, but a ground up coconut husk. That is what the plants are grown in. And we analyze the data right there, 'cause that is our first Edge. And people think that's static for us. The Edge isn't static. 'Cause the Edge now moves. We have a plant that grows. Then we pick it. And then we have to store it and then we have to ship it. So, our Edge actually does move from area to area to area. So, statically one thing isn't the same all the time. It's a hard thing to say how it all starts but it's just a combination of everything from natural gas to everything. >> Okay, then are those, 'cause we think of things in terms of like internet of things and these sensors. >> Oh yeah. >> Things are being gathered. So, you've got stuff happily growing in husks and then being picked. What's the next step there? Where is that aggregated? Where does that go? Is that all going straight back to your data center or are there sort of intermediate steps in the process? >> So, what we do is we actually store everything at the Edge, and we do daily processes right there. And then it aggregates that data and it drops it down from a large number to a smaller number to go to the core. >> Got it. >> And then that way, at the core, it does the long term analysis. 'Cause again, a lot of the data that we collect, we don't need to keep. A lot of it is the temperature was X, the temperature was X, the temperature, we don't need that. So, it aggregates it all down. So, that way the information coming to the core doesn't overwhelm it. Because we do store enough information. And to give you an idea of how our 1.8 million plants are living and breathing. We actually have estimated 1.8 million plants throughout our 200 acres. >> At any moment. >> Yeah. >> That's how many plants they're tracking. And so, that realtime information is helping to make sure that they water the plants precisely with the amount that they need, that they're fertilizing them. And you were telling me about how the life of a plant, you're really maintaining that plant over the life of 12 months. So, if you make a mistake at any point along the line, then you're dealing with that in terms of their yield throughout the life of the plant. But you aggregate a lot of that data right there on site so that you're not having to send so much back to the cloud or to the core. And you do that a lot with VxRail as well as other technology you have on site. Right? >> Yeah. Our VxRail is the center of the core of how we process things. It allowed us to even expand, not even just for compute but GPUs for our AIs to do it. So, it's what we did. And it allowed us to mold how we do things. >> Alison, question for you, this sounds like a dynamic Edge the way that you described it, Keith, and you described it so eloquently. How does the partnership that Dell has with Nature Fresh, how is Dell enabling and accelerating and advancing its Edge solutions based on what you're seeing here and this need for realtime data analytics. >> Well, we spend a lot of time with customers like Keith and also across all kinds of other industries. And what we see is that they have a really common set of problems. They're all trying to derive realtime data right then and there so that they can make business decisions that impact their profitability and their competitiveness and all of their customers experience their product quality. And what we see a lot of times is that they have a common set of concerns around security. How to manage all of the hardware that they're implementing. And at the same time, they really want to be an enabler for the business outcome. So, people have creative ideas and they come to IT hoping for support in that journey. If you're managing everything as a snowflake, it becomes really hard and untenable. So, I think one of the things that we have as our mission is to help customers simplify their Edge so that they can be the enabler that's helping the business to transform and modernize. One of the things I really admire about Nature Fresh Farms is that they decided it from a full organization perspective. So, everybody from the operational technologists to the IT to the business decision makers and leaders at the company, they all decided to modernize together. And so, I think from a partnership perspective, too, that's one of the areas that we try to work with our customers on is really talking about total transformation and modernization. >> So, it sounds like, Keith, there was an appetite there as Alison was saying for a digital transformation and IT transformation. Talk to me a little bit about from a historical perspective, how old Nature Fresh is and how did you get the team on board sounds so eloquent. How did you get the team on board to go, "This is what we need to do and technology needs to fuel our business because it's going to impact the end user, consumer of our fabulous English cucumbers." >> So, it's actually really neat. Our owner, Pete Quiring, when he first started out he really wanted to embrace technology. And this is going back right to 2000. 2000 is when we first had our first planting. And he was actually a builder by nature. He actually was a builder and fabricator and he built greenhouses for other companies. But he said they're getting a little bigger and it's the labor amount, and the number of growers he needed for a range was getting exponentially higher. So, he was one of the first ones that said, "I'm going to put a computer right in the middle and control this 16 acre range." >> It's a pretty visionary view when you really think about it. He's trying to operate his farm. >> Yeah. >> Right? >> From one single computer. >> Operationalize it. It's really cool. >> So, it was neat concept and it was actually very much not a normal concept then. You go back to 2000, people weren't talking about internet of things. They didn't talk about automation. It wasn't there. And he basically said, this is the way to go. And unfortunately, he thought, "I'll sell it to somebody. I'll grow it, I'll put a product in for a year and I'll sell it." And then guess what happened? He didn't sell it. He says, "Ah, it's not big enough. I'll build another phase two." And then his comment to me was after he built the fourth phase, he says, "I guess I'm in the pepper and cucumber business now." And that's what he is just grown. But he said it was a great relationship we had and it's a great concept. And it even goes back, and I know we talked about before, is the computer allowed one senior grower to control large number of acreages. Where before, you'd need multiple growers that know exactly what to do, 'cause they'd have to manually change all these things. Now, from a single computer they can see everything that's going on in the entire range. >> You mentioned temperature and water. And this is kind of out of the blue question, but how have global circumstances and increases in the cost of fertilizer affected you? Or is that fertilizer that's not the type that you use in your operation? You have any insight into that. >> Yeah, everything has, the global change in cost has changed everybody. I don't think there's anybody that's exempt from it. The only thing that we've been able to do is we're able to control it. We don't need to rely on, I guess you can say, rely on the weather to help us do things. We can control how much is. And we recycle all of our water. So, what the plant doesn't absorb today for nutrients, we'll put it back in the system, sterilize- >> Wait, when you say 200 acres, it's all enclosed? >> Yep, 200 acres. >> 200 acres of greenhouse. >> Yep, at 200 acres of greenhouse entirely enclosed. >> Okay, okay. >> There is not a single portion of our greenhouse that's actually gets exposed to the outside. And if you ever see a picture of a greenhouse and you see one of these lovely plants here wet, that's not true. That's just a nice to make it look better. >> Spray it for the photo. >> Yeah, yeah. They spray it for the photo. But actually everything is dry. That water goes directly to the roots and we monitor how much we put in and how much comes out. And then we recycle it. We even get so much recycling, we run natural gas generators to heat the water to heat the greenhouse. We take the burn-off of natural gas, the CO2, and funnel that into the greenhouse to give it natural stimulant. >> So, this is starting to remind me of "The Martian", if you read the book or if you seen the movie. >> Oh yeah. >> But planting the potatoes inside the hab, in the habitat. >> Yeah, and you cut 'em in half and the little ones grow with that next ones. But yep, we recycle everything that we do. And that's what we do. >> That's amazing. >> And all that information at their fingertips. Really, I think what technology is enabling you all to do is focus on what you all are good at, which is focusing on your farming operation and not necessarily the technology. So, one of the places I think we deliver some value is in validating a lot of the solutions so that customers don't have to figure that all out themselves. >> Yeah, 'cause I'm not a security expert. I don't always understand the true depth of security, but that's where that relationship is. We need this and we need that. And we need a secure way to let those communicate. And we can hand that off to the experts at Dell and let us do what we do best. >> What have been some of the changes? In the last couple of years, we've seen the security elevate skyrocket to a board level conversation. Ransomware is a when, not if, we get attacked. How does Dell help you from a security perspective ensure that what you're able to do ultimately gets these products to market in a secure fashion so that all that data that you're generating isn't exposed? >> So, like I said, I agree 100%. It's not matter of if it's going to happen, it's when it's going to happen. So, one of the things that we've actually done is we started to use Dell solution, the PowerProtect Data Manager to back up our solutions on the VxRail. And it actually did twofold for us. It allowed us to do a lot of database manipulation from restores and stuff like that. But we're now actually even investing in the cyber recovery vault that gives us that protection. And it allows us to now look at how long will it take us to get back up. And we're doing some tests right now and the last test we did is we're able to get back up going as a company from a full attack in about an hour. >> Wow. >> We've actually done a few simulations now. So, we are able to recover what our core needs are within an hour. >> Which is a very different metric than simply saying, "Oh, the data's available." >> Yeah. >> No, no, no, no, no, no, no, no. You get zero credit for that. We need our operations to be back up and running. >> Even that hour is stressful to our growers. >> Sure. >> It's a variable within a variable because if you go in the summer when it's super hot, they'll be very stressed out within an hour. And then you got nice calm weather day, it's not as bad. But the weather can change in how they have to close the vents. And you're not just closing one vent, you're closing 32, 64, 100 acres of vents. And you're changing irrigation cycle. You need that automation to do it for you. >> How do you let people eat these things after all the care that goes into it? I'm going to feel mildly guilty for just about a second and a half before I sink my teeth into the cucumber. >> Oh, but that's the joy of it. That's one of the things that I love. >> This is serious. You're proud of this, aren't you? >> Oh yeah. You know what? There's not single person at Nature Fresh that isn't proud of what we do each day. We enjoy what we do and it's a culture that makes us strive to do better every day. It's just a great feeling to be there every day and to just enjoy what you're doing. >> And see, it's real. It's real. Isn't it great? Isn't it great to be a part of? My background's in economics. I think of these things in terms of driving efficiency. And this is just a beautiful thing. When you control those variables, you leverage the technology and what's the end result? You're essentially uplifting everything in the world. >> Yeah, so true. >> Not to get philosophical on ya. >> Right, and feeding the world, especially during the last couple of years, that access. One of the things we learned in the pandemic, one of many, is access to realtime data isn't a nice to have anymore, it's essential. >> Yeah. >> So true. >> And so, the story that you're telling here, the impact to the growers, enabling them to focus what you were saying, Alison, on what they do best, Dell Technologies, VxRail enabling Nature Fresh to focus on what it does best, ultimately delivering food to people during the last couple of years was huge. >> Yeah, and allowing even at a reduced labor number for us to keep growing and doing things by automation. We still need labor in the greenhouse to pick, prune and do stuff like that. But again, we're looking into technologies to help offset that. But again, it was one of those things that we just had to be efficient at everything we do. And we drove that through everything we have. >> Well, and you guys haven't stopped. Right? >> Yeah. >> You're continuing to figure out, he was just telling me a little bit about what their next step is. So, just getting more and more accurate, more intelligence as they grow. So, it's the possibilities, that's what's exciting to me about Edge. I think this example is great, 'cause it's so relatable. Everybody can understand what the Edge is in this context. And it's really driven by the fact that you can put compute into so many different places now. It's more though a matter about how do you gather it? How do you do it in a way where you can actually understand and glean information and insights from it? And that, I think, is what you all are really focused on. >> Yeah, yeah, information is key. >> It is key. What's next from Dell's perspective for Edge computing technologies? what are some of the things you guys got cooking? >> Yeah, we're going to try to help customers to continue to simplify their Edge. So, to deliver those insights that they need where they need them, to do it in a really secure way. I know we talked about security but to do it in really a zero trust fashion. And to help customers to do it also in a zero IT fashion. Because in this example, it's the growers that are out there in the fields, or in your greenhouse in this sense, helping people that aren't necessarily IT specialists to be able to get all the benefits from the technology. >> So, do you think that VxRail technology could be used to optimize say the production of olive oil? I'm looking here and we have the makings of a pretty good salad. >> Yeah, you do. >> There you go. >> It obviously doesn't just apply to food production. >> Yeah, it really goes across the board. Whether we're talking about manufacturing or retail or energy, putting technology right there at the point of data creation and being able to figure out how to manage that inflow of data, be able to figure out which portion of the data is really valuable, and then driving decisions and being able to understand and intelligently make decisions for your business based on that data is really important. >> Keith, what's next? Give us, as we wrap out this segment here, what's next from a technology perspective? You mentioned a couple things you're looking into. >> Yeah, so I think automation is really going to change the way we do things. And automation within the greenhouse is truly just becoming a reality. It's funny we go back and we say, can we do this stuff? And now it's like, oh, even three years ago, I don't think we were quite ready for it, but now it's right there. So, I see us doing a lot more work with vendors like Dell and to do automatic picking, automatic scouting, all that stuff that we do by hand, do it in an automated fashion. >> And at scale, right? >> Yeah. >> That's the important part. I think when you're managing a snowflake, you can only do it to some level, and to be able to automate it and to be able to break down those silos, you're going to be able to apply it to so many parts of your business. >> Yeah, wide applicability. Guys, thank you so much for joining us, sharing the Nature Fresh, Dell story, bringing us actual product. This is so exciting. We congratulate you on how you're leveraging technology in a really innovative way. And we look forward to hearing what's next. Maybe we'll see you at Dell Technologies World next year. >> Sounds great. >> Sounds great. >> Thank you so much. >> All right, our pleasure, guys. >> Thank you. >> For our guests and Dave Nicholson, I'm Lisa Martin. You're watching theCUBE live from VMware Explorer 2022. Dave and I will be right back with our next guest. So, stick around. (light upbeat music)

>>And welcome to this cube conversation here in Palo Alto, California. I'm John furrier, host of the cube. We have a great guest coming in remotely from LA Christian Hernandez developer experienced lead at code fresh code fresh IO. Recently they were on our feature at a startup showcase series, season two episode one cloud data innovations, open source innovations, all good stuff, Christian. Thanks for coming on this cube conversation. >>Thank you. Thank you, John. Thank you for having me on, >>You know, I'm I was really impressed with code fresh. My met with the founders on here on the cube because GI ops AI, everything's something ops devs dev sec ops. You've got AI ops. You've got now GI ops, essentially operationalizing the software future is here and software's eating the world is, was written many years ago, but it's open source is now all. So all things software's open source and that's kind of a done deal. It's only getting better and better. Mainstream companies are contributing. You guys are on this wave of, of this open source tsunami and you got cloud scale. Automation's right there, machine learning, all this stuff is now the next gen of, of, of code, right? So you, your code fresh and your title is developer experience lead. What does that mean right now? What does it mean to be a developer experience lead? Like you make sure people having a good experience. Are you developing you figuring out the product? What does that mean? >>Yeah. That's and it's also part of the, the whole Debre explosion that's happening right now. I believe it's, you know, everyone's always asking, well, what, you know, what is developer advocate? What does that mean developer experience? What does that mean? So, so you, you kind of hit the nail on the head a little bit up there in, in the beginning, is that the, the experience of the developer when using a particular platform, right? Especially the code flash platform. That is my responsibility there at code fresh to enable, to enable end users, to enable partners, to enable, you know, anyone that wants to use the code fresh platform for their C I C D and get ops square flows. So that's, that's really my, my corner of the world is to make sure their experience is great. So that's, it's really what, what I'm here to do >>At food fresh. You know, one of the things I can say of my career, you've been kind of become a historian over time. When I was a developer back in the old days, it was simply you compiled stuff, you did QA on it. You packaged it out. You wanted out the door and you know, that was a workflow right now with the cloud. I was talking with your founders, you got new abstraction layers. Cloud has changed again again, open source. So newer things are coming, right? Like, like, like Kubernetes for instance is a great example that came out of the open source kind of the innovations. But that, and Hadoop, we were mentioning before he came on camera from a storage standpoint, kind of didn't make it because it was just too hard. Right. And it made the developer's job harder. And then it made the developer's requirements to be specialized. >>So you had kind of two problems. You had hard to use a lot of friction and then it required certain expertise when the developers just want to code. Right. So, so you have now the motion of, with GI ops, you guys are in the middle of kinda this idea of frictionless based software delivery with the cloud. So what's different now, can you talk about that specific point because no one wants to be, do hard work and have to redo things. Yeah. Shift left and all that good stuff. What's hard now, what do you guys solve? What's the, what's the friction that you're taking out what's to become frictionless. >>Yeah. Yeah. And you, you, you mentioned a very interesting point about how, you know, things that are coming out almost makes it seem harder nowadays to develop an application. You used to have it to where, you know, kind of a, sort of a waterfall sort of workflow where, you know, you develop your code, you know, you compile it. Right. You know, I guess back in the day, Java was king. I think Java still is, has a, is a large footprint out there where you would just compile it, deploy it. If it works, it works. Alright cool. And you have it and you kind of just move it along in its process. Whereas I think the, the whole idea of, I think Netflix came out with like the, the fail often fail fast release often, you know, the whole Atlassian C I C D thing, agile thing came into play. >>Where now it's, it's a little bit more complex to get your code out there delivered to get your code from one environment to the other environment, especially with the, the Avan of Kubernetes and cloud native architecture, where you can deploy and have this imutable infrastructure where you can just deploy and automate so quickly. So often that there needs to be some sort of new process now into place where to have a new process, like GI ops to where it'll, it it's frictionless, meaning that it's, it, it makes it that process a little easier makes that little, that comp that complex process of deploying onto like a cloud native architecture easier. So that way, as you said before, returning the developers to back to what they care about, mot, the most is just code. I just want to code. >>Yeah. You know, the other thing, cool thing, Christian, I wanna bring up and we'll get into some of the specifics around Argo specifically CD is that the community is responding as a kind of, it takes a village kind of mindset. People are getting into this just saying, Hey, if we can get our act together around some de facto workflows and de facto capabilities, everyone wins. It's a rising tide, floats all boats, kind of concept. CNCF certainly has been a big part of that. Even seen some of the big hyper scales getting behind it. But you guys are part of the founding members of the open get ups working group, Amazon Azure, GitHub, red hat Weaveworks and then a ton of contributors. Okay. So this is kind of cool. This means that there's like people behind this thing. Look, we gotta get here faster. What happened at co con this year? You guys had some news around Argo and you had some news around the hosted solution. Can you take a minute to explain two things, one the open community vibe, and then two, what you guys announced at Coon in Spain. >>Yeah. Yeah. So as far as open get ups, that was, you know, as you said before, code fresh was part of that, that founding committee. Right. Of, of group of people trying to figure out, define what get ups is. Right. We're trying to bring it beyond the, you know, the, the hype word, right beyond just like a marketing term to where we actually define what it actually is, because it is actually something that's out there that people are doing. Right. A lot of people, you know, remember that the, the Chick-fil-A story where it's like, they, they are completely doing, you know, this get ops thing, we're just now wanting, putting definition around it. So that was just amazing to see out at there in, in Cuban. And, but like you said, in QAN, we, you know, we're, we're, we're taking some of that, that acceleration that we see in the community to, and we, we announce our, our hosted get ops offering. >>Right. So hosted get ops is something that our customers have been asking for for a while. Many times when, you know, someone wants to use something like Argo CD, the, in, they install it on their cluster, they get up and running. And, but with, with all that comes like the feed and care of that platform, and, you know, not only just keeping the lights on, but also management security, you know, general maintenance, you know, all the things that, that come along with managing a system. And on top of that comes like the scale aspect of it. Right. And so with scale, so a lot of people go with like a hub and spoke others, go with like a fleet design in, in either case, right. There's, there's a challenge for the feet and care of it. Right. And so with code fresh coast of get ups, we take that management headache away. >>Right? So we, we take the, the, the management of, of Argo CD, the management of, of all of that, and kind of just offer Argo CD as a surface, right. Which offers, you know, allows users to, you know, let us take care of all the, of the get offs, runtime. And so they can concentrate on, you know, their application deployments. Right. And you also get things like Dora metrics, right. Integrated with the platform, you have the ability to integrate multiple CI providers, you know, like get hub actions or whatever, existing Jenkins pipelines. And really that, that code fresh platform becomes like your get ops platform becomes like, you know, your, your central view of the world of, of your, you know, get ups processes. >>Yeah. I mean, that whole single source of truth concept is really kind of needed. I gotta ask you though, with the popularity of the Argo CD on get ups internally, right. That's been clear, right. Kubernetes, the way that's going, it's accelerating fast. People want simple it's scaling, you got automation built in all that good stuff. What was the driver behind the hosted get up solution? Was it customer needs? Was it efficiency all the above? What was specifically and, and why would someone want to have the hosted versus say internal? >>Yeah. So it's, it was really driven by, you know, customer need been something that the customers have been asking for. And it's also been something that, you know, you, you, you have a process of developing an application to, you know, you know, a fleet of clusters in a traditional, you know, I keep saying traditional, get outs practice as if get outs are so old. And, you know, in, you know, when, when, when people first start out, they'll start, you know, installing Argo city on all these clusters and trying to manage that at scale it's, it's, it, it seemed like there was, you know, it it'd be nice if we can just like, be able to consume this as a service. So we don't have to like, worry about, you know, you know, best practices. We don't have to worry about security. We don't just, all of that is taken care of and managed by us at code fresh. So this is like something that, you know, has been asked for and, and something that, you know, we believe will accelerate, you know, developers into actually developing their, their applications. They don't have to worry about managing >>The platform. So just getting this right. Hosted, managed service by you guys on this one, >>Correct? Yes. >>Okay. Got it. All right. So let me, let me get in the Argo real quick, just to kind of just level set for the folks that are, are leaning into this and then kicking the tires. Where are we with Argo? What, why was it so popular? What did it do specifically? Did it just make it easier for developers to manage and monitor Kubernetes, keep 'em updated? What was the specific value behind Argo? Where, where, where did it come from and why is it so popular? >>Yeah, so Argo the Argo project, which is made up of, of a few tools, usually when people say Argo, they meet, they they're talking about Argo CD, but there's also Argo workflows, Argo events, Argo notifications. And, and like I said before, CD with that, and that is something that was developed internally at Intuit. Right? So for those of who don't know, Intuit is the company behind turbo tax. So for those, those of us in the us, we, we know, you know, we know that season all too well, the tax season. And so that was a tool that was developed internally. >>And by the way, Intuit we've done many years. They're very huge cloud adopters. They've been on that train from the day one. They've been, they've been driving a lot of cloud scale too. Sorry >>To interrupt. Yeah. And, and, and yeah, no, and, and, and also, you know, they, they were always open source first, right. So they've always had, you know, they developed something internally. They always had the, the intention of opensourcing it. And so it was really a tool that was born internally, and it was a tool that helped them, you know, get stuff done with Kubernetes. And that's kind of like the tagline they use for, for the Argo project is you need to get stuff done. They wanted their developers to focus less on deploying the application and more right. More than on writing the application itself. And so the, and so the Argo project is a suite of tools essentially that helps deploy onto Kubernetes, you know, using get ups as that, you know, that cornerstone in design, right in the design philosophy, it's so popular because of the ease of use and developer friendliness aspect of it. It's, it's, it's, it's meant to be simple right. In and simple in a, in a good sense of getting up and running, which attracted, you know, developers from, you know, all around the world. You know, other companies like red hat got into it as well. BlackRock also is, is a, is a big contributor, thousands of other independent contributors as well to the Argo project. >>Yeah. Christian, if you bring up a good point and I'm gonna go on a little tangent here, but I wanna get your reaction to something that Dave ante and I, and our cube team has been kind of riffing on lately. You mentioned, you know, Netflix earlier, you mentioned Intuit. There's a kind of a story that's been developing and, and with traction and momentum and trajectory over the past, say 10 years, the companies that went on the cloud, like Netflix into it, snowflake, snowflake, not so much now, but in terms of open source, they're all contributing lift. They're all contributing back to open source, but they're not cloud providers. Right. So you're seeing that kind of first generation, I's a massive contribution to open source. So open source been around for a while, remember the early days, and we'd all participate on projects, but now you have real companies building IP going open source first because they're on a hyperscale cloud, but they're not the cloud themselves. They took advantage of that. So there's kind of this cycle of flywheel of cloud to open source, not from the vendors themselves like Amazon, which services or Azure, but the people who rode their CapEx and built on that scale, feeding into the open source. And then coming back, this is kind of an interesting dynamic. What's your reaction to that? Do you see that? Yeah. Super cloud kind of vibe there. >>Yeah. Yeah. Well, and, and also it, it, I think it's, it's a, it's indicative that, you know, open source is not only, you know, a way to develop, you know, applications, a way to engineer, you know, your project, but also kind of like a strategic advantage in, in, in such a way. Right. You know, you, you see, you see companies like, like, like even like Microsoft has been going into, you know, open source, right. They they've been going to open source first. They made a, a huge pivot to, you know, using open source as, you know, like, like a, like a strategic direction for, for the company. And I think that goes back to, you know, a little bit for my roots, you know, I, I, I always, I always talk about, you know, I always talk about red hat, right. I always talk about, you know, I was, I was, I was in red hat previously and, you know, you know, red hat being, you know, the first billion dollar open source company. >>Right. I, we always joke is like, well, you know, internally, like we know you were a billion dollar company that sold free software. How, you know, how, how does that happen? But it's, it's, it's really, you know, built into the, built into being able to tap into those expert resources. Yeah. You know, people love using software. People love the software they love using, and they wanna improve it. Companies are now just getting out of their way. Yeah. You know, companies now, essentially, it's just like, let's just get out of the way. Let's let people work on, you know, what they wanna work on. They love the software. They wanna improve it. Let's let them, >>It's interesting. A lot of people love the clouds have all this power. If you think about what we are just riffing on and what you just said, the economics and the organic self-governing has always been the open source way where commercial value is enabled. If you play ball, right. Like, oh, red hat, for instance. And now you're seeing the community kind of be that arbiter of the cloud. So, Hey, if everyone can create value on say AWS or Azure, bring it to open source, everyone benefits across all clouds hope eventually. So the choice aspect comes in. So this community angle is huge. And I think it's changing a lot for the better. And I think this is where we're seeing a lot of that growth. And you guys have been the middle level with the Argo project and get ups specifically in that, in that sector. How have you seen that growth? What some dynamics have you seen power dynamics, organic? Is it governed well, whats some of the, the successes, what are some of the challenges? Can you share your thoughts on the community's growth around get ops and Argo project? >>Yeah, yeah. Yeah. So I've been, you know, part of some of these communities, right? Like the, the open, get, get ops community, the Argos community pretty much from the beginning and, and seeing it developed from an idea to, you know, having all these contributors, having, you know, the, the, the buzzword come out of it, you know, the get ups and it be that being the, you know, having it, you know, all over the, you know, social media, all over LinkedIn, all over all, all these, all these different channels, you know, I I've seen things like get ops con, right. So, you know, being part of the, get ops open, get ops community, you know, one of the things we did was we did get ops con it started as a meetup, you know, couple years ago. And now, you know, it was a, you know, we had an actual event at Cuan in Los Angeles. >>You know, we had like, you know, about 50 people there, but then, you know, Cuan in Valencia this past Cuan we had over 200 people, it was a second largest co-located events in, at Cuan. So that just, just seeing that community and, you know, from a personal standpoint, you know, be being part of that, that the, the community being the, the event chair, right. Yeah. Being, being one of the co-chairs was a, was a moment of pride for me being able to stand up there and just seeing a sea of people was like, wow, we just started with a handful of people at a meetup. And now, you know, we're actually having conferences and, and, and speaking of conference, like the Argo community as well, we put in, you know, we put on a virtual only event on Argo con last year. We're gonna do it in person today. You know, this year. >>Do you have a date on that? Do you have a date on that Argo con 22? >>Two? Yeah, yeah, yeah. Argo con September 19th, 2022. So, you know, mark your calendars, it it's, you know, it's a multi-day event, you know, it's, it's part of something else that I've seen in the community where, you know, first we're talk talking about these meetups. Now we're doing multi-day events. We're, you know, in talks of the open, get ups, you know, get ups can also make that a multi-day event. There's just so many talks in so many people that want to be involved in network that, you know, we're saying, well, we're gonna need more days because there's just so many people coming to these events, you know, in, in, you know, seeing these communities grow, not just from like the engineering standpoint, but also from the end user standpoint, but also from the people that are actually doing these things. And, you know, seeing some of these use cases, seeing some of the success, seeing some of the failures, right? Like people love listening to those talks about postmortems, I think are part of my favorite talks as well. So seeing that community grow is, is, you know, on a personal level, it's, it's a point >>It's like CSI for software developers. You want to curious about >>Exactly >>What happened. You know, you know, it's interesting, you mentioned about the, the multiple events at Coon. You know, the vibe that's going on is a very festival vibe, right? You have organic groups coming together. I remember when they had just started doing the day zero programs. Now you have like, almost like multiple stages of content at these events. It feels like, like a Coachella vibe or some sort of like festival vibe, like a lot of things going on and you, and if you pick your kind of area, but you can move around, I find that the kind of the format de Azure I think is going well these days. What do you think about that? >>Yeah, yeah. No, for sure. It's and, and, and I love that that analogy of Coachella, it does feel like, you know, it's, there's something for everyone and you can find what you like, and you'll find a little, you know, a little group, right. A little click of, of, of people that's probably the wrong term to use, but you know, you, you find, you know, you, you know, like-minded people and, you know, passionate about the same thing, right? Like the security guys, they, you know, you see them all clump together, right? Like you see like the, the developer C I CD get ops guys, we all kind of clump together and start talking, you know, about everything that we're doing. And it's, that's, that's, I think that's really something special that coupon, you know, some, you know, it's gotten so big that it's almost impossible to fit everything in a, in a week, because unless there's just so much to do. And there's so much that that interests, you know, someone, but it's >>A code, a code party is what we call it. It's a code party. Yeah. >>It's, it's a code party for sure. For >>Sure. Nerd nerd Fest on, on steroids. Hey, I gotta get, I wanna wrap this up and give you the final word, Christian. Thanks for coming on. Great insight, great conversation. There's a huge, you guys are in the middle of a hot area, obviously large scale data growth. Kubernetes is scaling beautifully and making it easier at managed services. What people want machine learning's kicking in and, and you get automation building in all favoring, the developer and C I CD pipeline and all that good stuff. People want to learn more. Can you take a minute to put the plug in for code fresh on the certification? How do I get involved? Where are you? Is there levels if I want to jump in and get trained and get fluent on code fresh, can you share commentary and, and, and what the status is? >>Yeah, yeah, for sure. So code fresh is offering a free certification, right? For get ups or Argo CD and get ops. The first of it's kind for Argo CD, first of it's kind for get ops is you can actually go get certified with Argo CD and get ops. You know, we there level one is out right now. You can go take that code, fresh.io/certification. It's out there, sign up, you know, you, you don't, you don't need to pay anything, right. It's, it's something it's a, of a free course. You could take level two is coming soon. Right? So level two is coming soon in the next few months, I believe I don't wanna quote a specific day, but soon because I, but soon I, it it's soon, soon as in, as in months. Right? So, you know, we're, we're counting that down where you can not only level one cert level certification, but a level, two more advanced certification for those who have been using Argo for a while, they can still, you know, take that and be, you know, be able to get, you know, another level of certification for that. So also, you know, Argo con will be there. We're, we're part of the programming committee for Argo con, right? This is a community driven event, but, you know, code fresh is a proud diamond sponsor. So we'll be there. >>Where's it located up to us except for eptember 19th multiday or one day >>It's a, it's a multi-day event. So Argo con from 19, 19 20 and 21 in a mountain view. So it'll be in mountain view in the bay area. So for those of you who are local, you can just drive in. Great. >>I'm write that down. I'll plug it. I'll put in the show notes. >>Awesome. Awesome. Yeah. And you will be there so you can talk to me, you can talk to anyone else at code, fresh talking about Argo CD, you know, find, find out more about hosted, get ups code, fresh.io. You know, you can find us in the Argo project, open, get ups community, you know, we're, we're, we're deep in the community for both Argo and get ups. So, you know, you can find us there as well. >>Well, let's do a follow up in when you're in town, so's only a couple months away and getting through the summer, it's already, I can't believe events are back. So it's really great to see face to face in the community. And there was responding. I mean, co con in October, I think that was kind of on the, that was a tough call and then get to see your own in Spain. I couldn't make it. Unfortunately, I had got COVID came down with it, but our team was there. Open sources, booming continues to go. The next level, new power dynamics are developing in a great way. Christian. Thanks for coming on, sharing your insights as the developer experience lead at code fresh. Thanks so much. >>Thank you, John. I appreciate it. >>Okay. This is a cube conversation. I'm John feer, host of the cube. Thanks for watching.

thecube presents dell technologies world brought to you by dell good afternoon everyone welcome back to thecube's third day of coverage live from the show floor at dell technologies world 2022 lisa martin here with dave vellante we've been having lots of great conversations the last day and a half one of the things we love to do is really hear from the voice of dell's customers and we're going to do that next please welcome jonathan suckler the senior director of product marketing for dell and keith bradley the vp of i.t at nature fresh farms guys welcome hey great to be here thank you great thank you for letting us be here of course thanks for joining us so jonathan we're going to start with you we've been hearing a lot about we've been talking about ai for decades we've been hearing a lot about ai at the show it's it's so it's pervasive right it's in our refrigerators and our thermostats and our cars and that hockey puck thing that's in the kitchen that plays music when you're cooking right what's going on what is do you think from dell's perspective is fueling the adoption of ai now you know there's it i think that there's this huge interest in ai right now and you and you you're definitely pointed out a lot of the great success stories around ai but the the real benefit of is that you know with with with artificial intelligence applied to a lot of business problems you can solve them in ways that are that are much quicker than you would expect you know and you can solve them in ways you wouldn't have expected uh uh you know then than than you do what's really surprising though is as a as many as many people are interested in in using it and and all of the benefits that come from it though is that we really don't see the adoption being as quick as we would like to right i mean i want to say that like 80 percent of companies out there want to use ai they're testing ai you know they're they're they're planning uh projects around ai applications but when you ask them what's in production it really is still it's an innovator's game like you know companies like like nature fresh farms with uh what they're doing is truly at the tip of the spear what are some of the challenges jonathan that you're seeing from an adoption perspective of 80 say we want to actually be able to leverage this emerging technology in production the challenges are i think the pers it's a perceived challenge issue right i think there's like three big issues that people perceive as being uh barriers to adoption um the first one is pretty obvious it's cost right they they see artificial intelligence you they hear about all of the uh you know specialized hardware and and the software and the new and the people and the talent you've got to acquire to uh as being a barrier to that and they don't see the benefit or they they balance that against the benefit i think there's an issue also with uh complexity right because at the same time that you know you're building these these infrastructures around what you need to do for an artificial intelligence-enabled application there's this expectation that it needs to be separate and different and special and that becomes an issue from a management perspective right uh and i think finally uh it's uh it's change right i mean you you're you're bringing in new talent new new skill sets you're bringing in new technology and i think a lot of companies still today you know look at that as being like well what if if i do this am i really going to see the benefit if i am i stuck going down a path that i that i'm going to change later on and i think that's really the issue uh you know those but they're all perceived issues they're they're in in reality they're really not that true i mean keith has this done that nature fresh farms has done some incredible stuff right with with ai in an area that i i would never have guessed being a ripe for that kind of innovation you know so lisa keith knows that i love you know fresh tomatoes i live in the northeast where it's cold six months a year so we plant our tomatoes at memorial day weekend yeah right and then maybe you're lucky if you get tomatoes late august september and then you're done however you and i met a couple years ago you sent me all these vegetables i think i was popping the tomatoes like candy and then i interviewed you you were live in the giant greenhouse and it's just amazing what you guys have going to jonathan's point you're using ai to really create you know sustainable continuing flow of awesome vegetables tell us more about nature fresh so at nature fresh farms we're a 200 acre greenhouse just shy of 200 acres growing bell peppers and tomatoes and one of the biggest use cases for us in our ai is everything we do we need to be proactive so we need that ai to not be reactive to climate change to what happens to the weather to be proactive so it changes before the plant reacts because every time the plant will doesn't do as great we've lost production from it so we're always using our ai to help increase the yield per square meter inside of our greenhouses so everything from the growth the length the weight of the plant we monitor everything we want to know every aspect of that plant's life it's almost like doing an ekg on a plant 24 by 7 and wanting to know everything out of it how old is is the company nature fresh farms started in 1999 so we're just hitting 23 years now so we started off as a 16 acre little greenhouse our owner kind of got into it saying i think this is going to be new and he was one of the first ones to say i want to be all computers i want to do it culturally this is this was not an upsell or a hard sell for you from the vp of i.t perspective no no he's always been one saying that technology will change the greenhouse industry and that by adding technology the expertise is in the growers and letting technology help them do more because when we first started in the greenhouse industry you'd need a grower for every range so every 16 acre range would need a very senior grower now we have one grower that does 64 or almost 100 acres of greenhouse he'll have junior growers but he's able to do so much more so where do you specifically apply the ai can you talk about that uh so we talk specifically we apply the ai in almost all areas anything from picking the plant to the climate of the plant we'll do all those areas even on the packing line we actually have uh one robot well not a robot story a machine that looks at a box of tomatoes and basically tells us which one doesn't match the proper red because how you see red how you guys see red is slightly different so it'll tell us that this red tomato doesn't match so change out the right one so when it goes down the line into the consumers they're all exactly the same so it looks unified it looks beautiful like that how about that you're sending out red tomatoes yeah yeah that's what we do now what is dell's role in all this so dell's role has helped us grow what we do we started off with power scale and vxrail and stuff like that so everything's hosted on that and they have been a great partner at finding that solution to them i've been able to go to them and say hey i'm running into a storage problem i'm running into a compute problem they've been able to find a validated solution for us to use and to put out there and help us grow and then the next part that was really great that we've really now done is it's scalable as we're growing we've been able to community add more compute and more storage but not have to take things down to do it and that's what we really wanted to do yeah no i i think and i think what you're talking about there is really the one of the big issues that i was talking about earlier which is around complexity and cost right you know one of the answers to doing artificial intelligence in the enterprise is making sure that you can maintain and have an infrastructure that scales that's part of everything else and and to do that you've got to virtualize it and you know with power uh with a dell vxrail and power scale which it's all running vmware uh with with the uh with the containers and the vms on top of that actually managing you know and running those applications it takes a lot of the complexity of of worrying about where you're going to how you're going to manage that infrastructure and who's going to do it who's going to back it up how are you going to how you're going to you know keep costs down so it really really helps i think yeah yep and we just love it because we're able to take that solution make it better and make it do more and more every day and it's it's allowed our growers to see exponential time where they did it years ago it used to be overnight to get results sometimes from our system doing it now we're seeing it in real time and that's where i it really got to that point now where we're being reactive proactive to the to the plant the weather to stuff we know exactly what needs to happen before happens and that makes the plant grow more and that's what we're always aiming to do you know if you don't mind one of the things that i you were telling me about i think is really fascinating so is this idea that you know you need to have a data scientist you need a whole new staff to manage these applications these these technologies but you were talking about your growers are actually yeah they're actually data scientists that way right that's what we like to call them we call them grower scientists right now green sciences data scientists yeah because they've researched this data they know what the plant does and it's it's been a neat transition we talked about that how they went from being out in the greenhouse so much to being in front of the computer now but now with the help of ai they're more able to get back out into the greenhouse to now watch the plants see what's going on and be a part of the growth again and they said it's been great but they're the ones that are looking at these numbers every day every second if it's not remotely from home it's remote on the greenhouse they're launching everything because yeah think about they're watching 64 acres of land and making sure that does everything it needs to do so lisa this is a really good example of sort of distributed data at work right about this whole notion of data mesh where you have domain experts actually own the data you know they know they can bring context to the data it's not somebody who's just oh it's just data i don't really know what to do with it it's somebody who actually knows what it what it means that to me is a future use case that's going to explode yep it's like me i i look at their data and they always tease me because i'll look at it and i'll go yeah i have no idea but it's giving you numbers so are they right or not and it's a it's always a joke in the in the plant that i like ah you don't got question marks so it's working and then i'll go to them and say is this right and then they'll say yep we're on we're getting what we need i love the idea that you know we've we've heard of this term citizens citizen scientists or citizen data scientists and you have a grower data scientist yeah and i think that eliminates you talk again those problems like or challenges i mentioned earlier that kind of eliminates the complexity issue you know the uncertainty issue the fear of change when you've got your own uh teams who are who know what they need to do and they have the data to do it it just changes the game right yeah and the other two we found is i've always believed in it myself if you love what you do yeah you commit so much more to it and our growers they love what they do so their passion just exudes into the data and then it comes right back into the product well the technology is an enabler of their passion really i'm curious keith how the obviously the events of the last two years have been quite challenging how has ai been a facilitator of what seems like a competitive differentiation for your company uh it actually really accelerated it because we really had to invest in it that's when we started the the big journey to the vx rail the power protect data management we really had to invest in and then we heavily invested in the ai we've always had some lingerie in the background and it's always been there and we've been using it for years and years now but it really brought it right to the forefront though we have to do this better and we had to really push everything and as we grew it became more and more apparent that we were taking that road that investment was paying off for us now yeah how do i buy ai from you so you know it's interesting like i said we want to make it easy for for customers to implement an ai solution at dell and it's not so much that you go out and you buy an ai right or something like that what you're doing is is you're you're making your infrastructure ready for the applications that you need to run right and so at dell we have this uh these predefined uh architectures that we call validated designs they're validated uh to work in you know in a co in any a common environment we take the you know we take the guesswork out of uh how to put these systems together uh and in the case of artificial intelligence you know we we validate with our partners like uh uh vmware and like nvidia to make sure that the technologies work together so that they fit into the existing infrastructure they already have and uh you know in a way it's i think of it as virtualized ai but i think even more importantly it's it's ai for for any company it's not not for the not for the special scientists and you know not for the not for the uh the researcher at the university it's it's for you know it's for nature fresh farms with vxrail it's software defined you're able to bring in a gpu you've got the flexibility to do that for example yeah whereas with the traditional you know the old days you wouldn't be able to do that you'd be you'd have a lot of time on your hands and a lot of compute power you spent a lot of money doing what you need to do yeah oh yeah we'd be spending all the time working at it growing it and doing more and it just made our life easier not to manage the life the managed life cycle of the ai systems that we have is so much easier now because it's all predefined it's all it's all ready to go upgrade process all that is built into it yeah so life cycle is much easier from the i.t side so keith talk to talk to those folks in the audience who might have those those perceived challenges or limitations that jonathan was talking about because you're making it sound like this has been such an enabler of a business that's 23 years old we're taking growers who are experts at growing and they're playing and loving playing with data and ai how do you how do you advise folks to really eliminate some of those preconceived challenges that are out there i would say you have to sit there and just dive in you have to actually start to do it but you have to think about not where you the first two steps say where we want to be five steps from now and then say talk to a partner like dell with us and say this is where we want to get to this is and then figure out a way how to get there and committing to that path you can't get frustrated the first few times ai is very flustering sometimes the first few pass don't work and just saying going back to the drawing board each time we'll do it we've had a couple experiments where it didn't work and we didn't get the results we wanted and we had to just say let's change our thought process and how do we optimize this ai and then all of a sudden we started getting the right results but that it's it's like uh falling over the first time you fall over as a child it's gonna hurt but each time he gets a little less each time failure is progress yeah that's right that's right fail fast yeah failure can be a good f word yeah if you but you have to be open-minded yep oh yes every minute every minute you have to be open-minded and you have to you have to think outside the box too and that's the biggest part of things it's just not accepting things and just saying we have to do it but you have to have the culture that will embrace that and it sounds like the growers these are people that are expert and growing how it sounds like it wasn't an uphill battle to get them to come on board and become these citizen growers data scientists well you know it was funny because with the technology it kind of gave them that work-life balance that they didn't have before their life was inside the greenhouse because the plants grow 24 by 7. so now all of a sudden they just kept growing they could they could go home they kept doing their thing they could go home at five o'clock and because of the vdi solutions and stuff like that and the ai that's helping them grow they can kind of turn off and instead of having to come in sunday morning and that the the one joke we used to have is that on sundays if you're in church and there's clouds had come rolling out all the growers would stand up and leave because they had to go to their church they had to go back to their farm now the system does that automatically for them so they're able to get their work life home balance back so it was different for them it was a jump for them anybody that's not used to technology and jumping into it is hard but once they started to see the benefits and what more yield they can get and the home work life balance it was amazing there's no i can't underestimate the work-life balance enough i think it's challenge it's a very challenging thing for people in any industry to achieve we've we've seen that in the last two years with you know do i live at work do i work from home so achieving that is kudos to you and for del for enabling that because that's that's big that that affects everybody guys thank you so much for joining us talking about ai what you're doing at nature fresh the future what's possible yeah and how you buy ai from dell no i think it's great i think you know nature fresh farms is a great euro you've been a great like a great partner for sure but also this great kind of beacon to show people how it can be done and i think it's just a thank you very much we really enjoyed it excellent well thanks for thanks for bringing the beacon on the show we appreciate it we want to thank you for watching for our guests i'm lisa martin for dave vellante i'm lisa martin i should say you're watching thecube day three of our coverage live from the show floor of dell tech world 2022 stick around we'll be right back with our next guest after a short break [Music] you

>>Hello and welcome today's cube presentation of eight of us startup showcase. I'm john for your host highlighting the hottest companies and devops data analytics and cloud management lisa martin and David want are here to kick it off. We've got a great program for you again. This is our, our new community event model where we're doing every quarter, we have every new episode, this is quarter three this year or episode three, season one of the hottest cloud startups and we're gonna be featured. Then we're gonna do a keynote package and then 15 countries will present their story, Go check them out and then have a closing keynote with a practitioner and we've got some great lineups, lisa Dave, great to see you. Thanks for joining me. >>Hey guys, >>great to be here. So David got to ask you, you know, back in events last night we're at the 14 it's event where they had the golf PGA championship with the cube Now we got the hybrid model, This is the new normal. We're in, we got these great companies were showcasing them. What's your take? >>Well, you're right. I mean, I think there's a combination of things. We're seeing some live shows. We saw what we did with at mobile world Congress. We did the show with AWS storage day where it was, we were at the spheres, there was no, there was a live audience, but they weren't there physically. It was just virtual and yeah, so, and I just got pained about reinvent. Hey Dave, you gotta make your flights. So I'm making my flights >>were gonna be at the amazon web services, public sector summit next week. At least a lot, a lot of cloud convergence going on here. We got many companies being featured here that we spoke with the Ceo and their top people cloud management, devops data, nelson security. Really cutting edge companies, >>yes, cutting edge companies who are all focused on acceleration. We've talked about the acceleration of digital transformation the last 18 months and we've seen a tremendous amount of acceleration in innovation with what these startups are doing. We've talked to like you said, there's, there's C suite, we've also talked to their customers about how they are innovating so quickly with this hybrid environment, this remote work and we've talked a lot about security in the last week or so. You mentioned that we were at Fortinet cybersecurity skills gap. What some of these companies are doing with automation for example, to help shorten that gap, which is a big opportunity >>for the job market. Great stuff. Dave so the format of this event, you're going to have a fireside chat with the practitioner, we'd like to end these programs with a great experienced practitioner cutting edge in data february. The beginning lisa are gonna be kicking off with of course Jeff bar to give us the update on what's going on AWS and then a special presentation from Emily Freeman who is the author of devops for dummies, she's introducing new content. The revolution in devops devops two point oh and of course jerry Chen from Greylock cube alumni is going to come on and talk about his new thesis castles in the cloud creating moats at cloud scale. We've got a great lineup of people and so the front ends can be great. Dave give us a little preview of what people can expect at the end of the fireside chat. >>Well at the highest level john I've always said we're entering that sort of third great wave of cloud. First wave was experimentation. The second big wave was migration. The third wave of integration, Deep business integration and what you're >>going to hear from >>Hello Fresh today is how they like many companies that started early last decade. They started with an on prem Hadoop system and then of course we all know what happened is S three essentially took the knees out from, from the on prem Hadoop market lowered costs, brought things into the cloud and what Hello Fresh is doing is they're transforming from that legacy Hadoop system into its running on AWS but into a data mess, you know, it's a passionate topic of mine. Hello Fresh was scaling they realized that they couldn't keep up so they had to rethink their entire data architecture and they built it around data mesh Clements key and christoph Soewandi gonna explain how they actually did that are on a journey or decentralized data >>measure it and your posts have been awesome on data measure. We get a lot of traction. Certainly you're breaking analysis for the folks watching check out David Landes, Breaking analysis every week, highlighting the cutting edge trends in tech Dave. We're gonna see you later, lisa and I are gonna be here in the morning talking about with Emily. We got Jeff Barr teed up. Dave. Thanks for coming on. Looking forward to fireside chat lisa. We'll see you when Emily comes back on. But we're gonna go to Jeff bar right now for Dave and I are gonna interview Jeff. Mm >>Hey Jeff, >>here he is. Hey, how are you? How's it going really well. So I gotta ask you, the reinvent is on, everyone wants to know that's happening right. We're good with Reinvent. >>Reinvent is happening. I've got my hotel and actually listening today, if I just remembered, I still need to actually book my flights. I've got my to do list on my desk and I do need to get my >>flights. Uh, >>really looking forward >>to it. I can't wait to see the all the announcements and blog posts. We're gonna, we're gonna hear from jerry Chen later. I love the after on our next event. Get your reaction to this castle and castles in the cloud where competitive advantages can be built in the cloud. We're seeing examples of that. But first I gotta ask you give us an update of what's going on. The ap and ecosystem has been an incredible uh, celebration these past couple weeks, >>so, so a lot of different things happening and the interesting thing to me is that as part of my job, I often think that I'm effectively living in the future because I get to see all this really cool stuff that we're building just a little bit before our customers get to, and so I'm always thinking okay, here I am now, and what's the world going to be like in a couple of weeks to a month or two when these launches? I'm working on actually get out the door and that, that's always really, really fun, just kind of getting that, that little edge into where we're going, but this year was a little interesting because we had to really significant birthdays, we had the 15 year anniversary of both EC two and S three and we're so focused on innovating and moving forward, that it's actually pretty rare for us at Aws to look back and say, wow, we've actually done all these amazing things in in the last 15 years, >>you know, it's kind of cool Jeff, if I may is is, you know, of course in the early days everybody said, well, a place for startup is a W. S and now the great thing about the startup showcases, we're seeing the startups that >>are >>very near, or some of them have even reached escape velocity, so they're not, they're not tiny little companies anymore, they're in their transforming their respective industries, >>they really are and I think that as they start ups grow, they really start to lean into the power of the cloud. They as they start to think, okay, we've we've got our basic infrastructure in place, we've got, we were serving data, we're serving up a few customers, everything is actually working pretty well for us. We've got our fundamental model proven out now, we can invest in publicity and marketing and scaling and but they don't have to think about what's happening behind the scenes. They just if they've got their auto scaling or if they're survivalists, the infrastructure simply grows to meet their demand and it's it's just a lot less things that they have to worry about. They can focus on the fun part of their business which is actually listening to customers and building up an awesome business >>Jeff as you guys are putting together all the big pre reinvented, knows a lot of stuff that goes on prior as well and they say all the big good stuff to reinvent. But you start to see some themes emerged this year. One of them is modernization of applications, the speed of application development in the cloud with the cloud scale devops personas, whatever persona you want to talk about but basically speed the speed of of the app developers where other departments have been slowing things down, I won't say name names, but security group and I t I mean I shouldn't have said that but only kidding but no but seriously people want in minutes and seconds now not days or weeks. You know whether it's policy. What are some of the trends that you're seeing around this this year as we get into some of the new stuff coming out >>So Dave customers really do want speed and for we've actually encapsulate this for a long time in amazon in what we call the bias for action leadership principle >>where >>we just need to jump in and move forward and and make things happen. A lot of customers look at that and they say yes this is great. We need to have the same bias fraction. Some do. Some are still trying to figure out exactly how to put it into play. And they absolutely for sure need to pay attention to security. They need to respect the past and make sure that whatever they're doing is in line with I. T. But they do want to move forward. And the interesting thing that I see time and time again is it's not simply about let's adopt a new technology. It's how do we >>how do we keep our workforce >>engaged? How do we make sure that they've got the right training? How do we bring our our I. T. Team along for this. Hopefully new and fun and exciting journey where they get to learn some interesting new technologies they've got all this very much accumulated business knowledge they still want to put to use, maybe they're a little bit apprehensive about something brand new and they hear about the cloud, but there by and large, they really want to move forward. They just need a little bit of >>help to make it happen >>real good guys. One of the things you're gonna hear today, we're talking about speed traditionally going fast. Oftentimes you meant you have to sacrifice some things on quality and what you're going to hear from some of the startups today is how they're addressing that to automation and modern devoPS technologies and sort of rethinking that whole application development approach. That's something I'm really excited to see organization is beginning to adopt so they don't have to make that tradeoff anymore. >>Yeah, I would >>never want to see someone >>sacrifice quality, >>but I do think that iterating very quickly and using the best of devoPS principles to be able to iterate incredibly quickly and get that first launch out there and then listen with both ears just >>as much >>as you can, Everything. You hear iterate really quickly to meet those needs in, in hours and days, not months, quarters or years. >>Great stuff. Chef and a lot of the companies were featuring here in the startup showcase represent that new kind of thinking, um, systems thinking as well as you know, the cloud scale and again and it's finally here, the revolution of deVOps is going to the next generation and uh, we're excited to have Emily Freeman who's going to come on and give a little preview for her new talk on this revolution. So Jeff, thank you for coming on, appreciate you sharing the update here on the cube. Happy >>to be. I'm actually really looking forward to hearing from Emily. >>Yeah, it's great. Great. Looking forward to the talk. Brand new Premier, Okay, uh, lisa martin, Emily Freeman is here. She's ready to come in and we're going to preview her lightning talk Emily. Um, thanks for coming on, we really appreciate you coming on really, this is about to talk around deVOPS next gen and I think lisa this is one of those things we've been, we've been discussing with all the companies. It's a new kind of thinking it's a revolution, it's a systems mindset, you're starting to see the connections there she is. Emily, Thanks for coming. I appreciate it. >>Thank you for having me. So your teaser video >>was amazing. Um, you know, that little secret radical idea, something completely different. Um, you gotta talk coming up, what's the premise behind this revolution, you know, these tying together architecture, development, automation deployment, operating altogether. >>Yes, well, we have traditionally always used the sclc, which is the software delivery life cycle. Um, and it is a straight linear process that has actually been around since the sixties, which is wild to me um, and really originated in manufacturing. Um, and as much as I love the Toyota production system and how much it has shown up in devops as a sort of inspiration on how to run things better. We are not making cars, we are making software and I think we have to use different approaches and create a sort of model that better reflects our modern software development process. >>It's a bold idea and looking forward to the talk and as motivation. I went into my basement and dusted off all my books from college in the 80s and the sea estimates it was waterfall. It was software development life cycle. They trained us to think this way and it came from the mainframe people. It was like, it's old school, like really, really old and it really hasn't been updated. Where's the motivation? I actually cloud is kind of converging everything together. We see that, but you kind of hit on this persona thing. Where did that come from this persona? Because you know, people want to put people in buckets release engineer. I mean, where's that motivation coming from? >>Yes, you're absolutely right that it came from the mainframes. I think, you know, waterfall is necessary when you're using a punch card or mag tape to load things onto a mainframe, but we don't exist in that world anymore. Thank goodness. And um, yes, so we, we use personas all the time in tech, you know, even to register, well not actually to register for this event, but a lot events. A lot of events, you have to click that drop down. Right. Are you a developer? Are you a manager, whatever? And the thing is personas are immutable in my opinion. I was a developer. I will always identify as a developer despite playing a lot of different roles and doing a lot of different jobs. Uh, and this can vary throughout the day. Right. You might have someone who has a title of software architect who ends up helping someone pair program or develop or test or deploy. Um, and so we wear a lot of hats day to day and I think our discussions around roles would be a better, um, certainly a better approach than personas >>lease. And I've been discussing with many of these companies around the roles and we're hearing from them directly and they're finding out that people have, they're mixing and matching on teams. So you're, you're an S R E on one team and you're doing something on another team where the workflows and the workloads defined the team formation. So this is a cultural discussion. >>It absolutely is. Yes. I think it is a cultural discussion and it really comes to the heart of devops, right? It's people process. And then tools deVOps has always been about culture and making sure that developers have all the tools they need to be productive and honestly happy. What good is all of this? If developing software isn't a joyful experience. Well, >>I got to ask you, I got you here obviously with server list and functions just starting to see this kind of this next gen. And we're gonna hear from jerry Chen, who's a Greylock VC who's going to talk about castles in the clouds, where he's discussing the moats that could be created with a competitive advantage in cloud scale. And I think he points to the snowflakes of the world. You're starting to see this new thing happening. This is devops 2.0, this is the revolution. Is this kind of where you see the same vision of your talk? >>Yes, so DeVOps created 2000 and 8, 2000 and nine, totally different ecosystem in the world we were living in, you know, we didn't have things like surveillance and containers, we didn't have this sort of default distributed nature, certainly not the cloud. Uh and so I'm very excited for jerry's talk. I'm curious to hear more about these moz. I think it's fascinating. Um but yeah, you're seeing different companies use different tools and processes to accelerate their delivery and that is the competitive advantage. How can we figure out how to utilize these tools in the most efficient way possible. >>Thank you for coming and giving us a preview. Let's now go to your lightning keynote talk. Fresh content. Premier of this revolution in Devops and the Freemans Talk, we'll go there now. >>Hi, I'm Emily Freeman, I'm the author of devops for dummies and the curator of 97 things every cloud engineer should know. I am thrilled to be here with you all today. I am really excited to share with you a kind of a wild idea, a complete re imagining of the S DLC and I want to be clear, I need your feedback. I want to know what you think of this. You can always find me on twitter at editing. Emily, most of my work centers around deVOps and I really can't overstate what an impact the concept of deVOPS has had on this industry in many ways it built on the foundation of Agile to become a default a standard we all reach for in our everyday work. When devops surfaced as an idea in 2008, the tech industry was in a vastly different space. AWS was an infancy offering only a handful of services. Azure and G C P didn't exist yet. The majority's majority of companies maintained their own infrastructure. Developers wrote code and relied on sys admins to deploy new code at scheduled intervals. Sometimes months apart, container technology hadn't been invented applications adhered to a monolithic architecture, databases were almost exclusively relational and serverless wasn't even a concept. Everything from the application to the engineers was centralized. Our current ecosystem couldn't be more different. Software is still hard, don't get me wrong, but we continue to find novel solutions to consistently difficult, persistent problems. Now, some of these end up being a sort of rebranding of old ideas, but others are a unique and clever take to abstracting complexity or automating toil or perhaps most important, rethinking challenging the very premises we have accepted as Cannon for years, if not decades. In the years since deVOps attempted to answer the critical conflict between developers and operations, engineers, deVOps has become a catch all term and there have been a number of derivative works. Devops has come to mean 5000 different things to 5000 different people. For some, it can be distilled to continuous integration and continuous delivery or C I C D. For others, it's simply deploying code more frequently, perhaps adding a smattering of tests for others. Still, its organizational, they've added a platform team, perhaps even a questionably named DEVOPS team or have created an engineering structure that focuses on a separation of concerns. Leaving feature teams to manage the development, deployment, security and maintenance of their siloed services, say, whatever the interpretation, what's important is that there isn't a universally accepted standard. Well, what deVOPS is or what it looks like an execution, it's a philosophy more than anything else. A framework people can utilize to configure and customize their specific circumstances to modern development practices. The characteristic of deVOPS that I think we can all agree on though, is that an attempted to capture the challenges of the entire software development process. It's that broad umbrella, that holistic view that I think we need to breathe life into again, The challenge we face is that DeVOps isn't increasingly outmoded solution to a previous problem developers now face. Cultural and technical challenge is far greater than how to more quickly deploy a monolithic application. Cloud native is the future the next collection of default development decisions and one the deVOPS story can't absorb in its current form. I believe the era of deVOPS is waning and in this moment as the sun sets on deVOPS, we have a unique opportunity to rethink rebuild free platform. Even now, I don't have a crystal ball. That would be very handy. I'm not completely certain with the next decade of tech looks like and I can't write this story alone. I need you but I have some ideas that can get the conversation started, I believe to build on what was we have to throw away assumptions that we've taken for granted all this time in order to move forward. We must first step back. Mhm. The software or systems development life cycle, what we call the S. D. L. C. has been in use since the 1960s and it's remained more or less the same since before color television and the touch tone phone. Over the last 60 or so odd years we've made tweaks, slight adjustments, massaged it. The stages or steps are always a little different with agile and deVOps we sort of looped it into a circle and then an infinity loop we've added pretty colors. But the sclc is more or less the same and it has become an assumption. We don't even think about it anymore, universally adopted constructs like the sclc have an unspoken permanence. They feel as if they have always been and always will be. I think the impact of that is even more potent. If you were born after a construct was popularized. Nearly everything around us is a construct, a model, an artifact of a human idea. The chair you're sitting in the desk, you work at the mug from which you drink coffee or sometimes wine, buildings, toilets, plumbing, roads, cars, art, computers, everything. The sclc is a remnant an artifact of a previous era and I think we should throw it away or perhaps more accurately replace it, replace it with something that better reflects the actual nature of our work. A linear, single threaded model designed for the manufacturer of material goods cannot possibly capture the distributed complexity of modern socio technical systems. It just can't. Mhm. And these two ideas aren't mutually exclusive that the sclc was industry changing, valuable and extraordinarily impactful and that it's time for something new. I believe we are strong enough to hold these two ideas at the same time, showing respect for the past while envisioning the future. Now, I don't know about you, I've never had a software project goes smoothly in one go. No matter how small. Even if I'm the only person working on it and committing directly to master software development is chaos. It's a study and entropy and it is not getting any more simple. The model with which we think and talk about software development must capture the multithreaded, non sequential nature of our work. It should embody the roles engineers take on and the considerations they make along the way. It should build on the foundations of agile and devops and represent the iterative nature of continuous innovation. Now, when I was thinking about this, I was inspired by ideas like extreme programming and the spiral model. I I wanted something that would have layers, threads, even a way of visually representing multiple processes happening in parallel. And what I settled on is the revolution model. I believe the visualization of revolution is capable of capturing the pivotal moments of any software scenario. And I'm going to dive into all the discrete elements. But I want to give you a moment to have a first impression, to absorb my idea. I call it revolution because well for one it revolves, it's circular shape reflects the continuous and iterative nature of our work, but also because it is revolutionary. I am challenging a 60 year old model that is embedded into our daily language. I don't expect Gartner to build a magic quadrant around this tomorrow, but that would be super cool. And you should call me my mission with. This is to challenge the status quo to create a model that I think more accurately reflects the complexity of modern cloud native software development. The revolution model is constructed of five concentric circles describing the critical roles of software development architect. Ng development, automating, deploying and operating intersecting each loop are six spokes that describe the production considerations every engineer has to consider throughout any engineering work and that's test, ability, secure ability, reliability, observe ability, flexibility and scalability. The considerations listed are not all encompassing. There are of course things not explicitly included. I figured if I put 20 spokes, some of us, including myself, might feel a little overwhelmed. So let's dive into each element in this model. We have long used personas as the default way to do divide audiences and tailor messages to group people. Every company in the world right now is repeating the mantra of developers, developers, developers but personas have always bugged me a bit because this approach typically either oversimplifies someone's career are needlessly complicated. Few people fit cleanly and completely into persona based buckets like developers and operations anymore. The lines have gotten fuzzy on the other hand, I don't think we need to specifically tailor messages as to call out the difference between a devops engineer and a release engineer or a security administrator versus a security engineer but perhaps most critically, I believe personas are immutable. A persona is wholly dependent on how someone identifies themselves. It's intrinsic not extrinsic. Their titles may change their jobs may differ, but they're probably still selecting the same persona on that ubiquitous drop down. We all have to choose from when registering for an event. Probably this one too. I I was a developer and I will always identify as a developer despite doing a ton of work in areas like devops and Ai Ops and Deverell in my heart. I'm a developer I think about problems from that perspective. First it influences my thinking and my approach roles are very different. Roles are temporary, inconsistent, constantly fluctuating. If I were an actress, the parts I would play would be lengthy and varied, but the persona I would identify as would remain an actress and artist lesbian. Your work isn't confined to a single set of skills. It may have been a decade ago, but it is not today in any given week or sprint, you may play the role of an architect. Thinking about how to design a feature or service, developer building out code or fixing a bug and on automation engineer, looking at how to improve manual processes. We often refer to as soil release engineer, deploying code to different environments or releasing it to customers or in operations. Engineer ensuring an application functions inconsistent expected ways and no matter what role we play. We have to consider a number of issues. The first is test ability. All software systems require testing to assure architects that designs work developers, the code works operators, that infrastructure is running as expected and engineers of all disciplines that code changes won't bring down the whole system testing in its many forms is what enables systems to be durable and have longevity. It's what reassures engineers that changes won't impact current functionality. A system without tests is a disaster waiting to happen, which is why test ability is first among equals at this particular roundtable. Security is everyone's responsibility. But if you understand how to design and execute secure systems, I struggle with this security incidents for the most part are high impact, low probability events. The really big disasters, the one that the ones that end up on the news and get us all free credit reporting for a year. They don't happen super frequently and then goodness because you know that there are endless small vulnerabilities lurking in our systems. Security is something we all know we should dedicate time to but often don't make time for. And let's be honest, it's hard and complicated and a little scary def sec apps. The first derivative of deVOPS asked engineers to move security left this approach. Mint security was a consideration early in the process, not something that would block release at the last moment. This is also the consideration under which I'm putting compliance and governance well not perfectly aligned. I figure all the things you have to call lawyers for should just live together. I'm kidding. But in all seriousness, these three concepts are really about risk management, identity, data, authorization. It doesn't really matter what specific issue you're speaking about, the question is who has access to what win and how and that is everyone's responsibility at every stage site reliability engineering or sorry, is a discipline job and approach for good reason. It is absolutely critical that applications and services work as expected. Most of the time. That said, availability is often mistakenly treated as a synonym for reliability. Instead, it's a single aspect of the concept if a system is available but customer data is inaccurate or out of sync. The system is not reliable, reliability has five key components, availability, latency, throughput. Fidelity and durability, reliability is the end result. But resiliency for me is the journey the action engineers can take to improve reliability, observe ability is the ability to have insight into an application or system. It's the combination of telemetry and monitoring and alerting available to engineers and leadership. There's an aspect of observe ability that overlaps with reliability, but the purpose of observe ability isn't just to maintain a reliable system though, that is of course important. It is the capacity for engineers working on a system to have visibility into the inner workings of that system. The concept of observe ability actually originates and linear dynamic systems. It's defined as how well internal states of a system can be understood based on information about its external outputs. If it is critical when companies move systems to the cloud or utilize managed services that they don't lose visibility and confidence in their systems. The shared responsibility model of cloud storage compute and managed services require that engineering teams be able to quickly be alerted to identify and remediate issues as they arise. Flexible systems are capable of adapting to meet the ever changing needs of the customer and the market segment, flexible code bases absorb new code smoothly. Embody a clean separation of concerns. Are partitioned into small components or classes and architected to enable the now as well as the next inflexible systems. Change dependencies are reduced or eliminated. Database schemas accommodate change well components, communicate via a standardized and well documented A. P. I. The only thing constant in our industry is change and every role we play, creating flexibility and solutions that can be flexible that will grow as the applications grow is absolutely critical. Finally, scalability scalability refers to more than a system's ability to scale for additional load. It implies growth scalability and the revolution model carries the continuous innovation of a team and the byproducts of that growth within a system. For me, scalability is the most human of the considerations. It requires each of us in our various roles to consider everyone around us, our customers who use the system or rely on its services, our colleagues current and future with whom we collaborate and even our future selves. Mhm. Software development isn't a straight line, nor is it a perfect loop. It is an ever changing complex dance. There are twirls and pivots and difficult spins forward and backward. Engineers move in parallel, creating truly magnificent pieces of art. We need a modern model for this modern era and I believe this is just the revolution to get us started. Thank you so much for having me. >>Hey, we're back here. Live in the keynote studio. I'm john for your host here with lisa martin. David lot is getting ready for the fireside chat ending keynote with the practitioner. Hello! Fresh without data mesh lisa Emily is amazing. The funky artwork there. She's amazing with the talk. I was mesmerized. It was impressive. >>The revolution of devops and the creative element was a really nice surprise there. But I love what she's doing. She's challenging the status quo. If we've learned nothing in the last year and a half, We need to challenge the status quo. A model from the 1960s that is no longer linear. What she's doing is revolutionary. >>And we hear this all the time. All the cube interviews we do is that you're seeing the leaders, the SVP's of engineering or these departments where there's new new people coming in that are engineering or developers, they're playing multiple roles. It's almost a multidisciplinary aspect where you know, it's like going into in and out burger in the fryer later and then you're doing the grill, you're doing the cashier, people are changing roles or an architect, their test release all in one no longer departmental, slow siloed groups. >>She brought up a great point about persona is that we no longer fit into these buckets. That the changing roles. It's really the driver of how we should be looking at this. >>I think I'm really impressed, really bold idea, no brainer as far as I'm concerned, I think one of the things and then the comments were off the charts in a lot of young people come from discord servers. We had a good traction over there but they're all like learning. Then you have the experience, people saying this is definitely has happened and happening. The dominoes are falling and they're falling in the direction of modernization. That's the key trend speed. >>Absolutely with speed. But the way that Emily is presenting it is not in a brash bold, but it's in a way that makes great sense. The way that she creatively visually lined out what she was talking about Is amenable to the folks that have been doing this for since the 60s and the new folks now to really look at this from a different >>lens and I think she's a great setup on that lightning top of the 15 companies we got because you think about sis dig harness. I white sourced flamingo hacker one send out, I oh, okay. Thought spot rock set Sarah Ops ramp and Ops Monte cloud apps, sani all are doing modern stuff and we talked to them and they're all on this new wave, this monster wave coming. What's your observation when you talk to these companies? >>They are, it was great. I got to talk with eight of the 15 and the amount of acceleration of innovation that they've done in the last 18 months is phenomenal obviously with the power and the fuel and the brand reputation of aws but really what they're all facilitating cultural shift when we think of devoPS and the security folks. Um, there's a lot of work going on with ai to an automation to really kind of enabled to develop the develops folks to be in control of the process and not have to be security experts but ensuring that the security is baked in shifting >>left. We saw that the chat room was really active on the security side and one of the things I noticed was not just shift left but the other groups, the security groups and the theme of cultural, I won't say war but collision cultural shift that's happening between the groups is interesting because you have this new devops persona has been around Emily put it out for a while. But now it's going to the next level. There's new revolutions about a mindset, a systems mindset. It's a thinking and you start to see the new young companies coming out being funded by the gray locks of the world who are now like not going to be given the we lost the top three clouds one, everything. there's new business models and new technical architecture in the cloud and that's gonna be jerry Chen talk coming up next is going to be castles in the clouds because jerry chant always talked about moats, competitive advantage and how moats are key to success to guard the castle. And then we always joke, there's no more moz because the cloud has killed all the boats. But now the motor in the cloud, the castles are in the cloud, not on the ground. So very interesting thought provoking. But he's got data and if you look at the successful companies like the snowflakes of the world, you're starting to see these new formations of this new layer of innovation where companies are growing rapidly, 98 unicorns now in the cloud. Unbelievable, >>wow, that's a lot. One of the things you mentioned, there's competitive advantage and these startups are all fueled by that they know that there are other companies in the rear view mirror right behind them. If they're not able to work as quickly and as flexibly as a competitor, they have to have that speed that time to market that time to value. It was absolutely critical. And that's one of the things I think thematically that I saw along the eighth sort of that I talked to is that time to value is absolutely table stakes. >>Well, I'm looking forward to talking to jerry chan because we've talked on the queue before about this whole idea of What happens when winner takes most would mean the top 3, 4 cloud players. What happens? And we were talking about that and saying, if you have a model where an ecosystem can develop, what does that look like and back in 2013, 2014, 2015, no one really had an answer. Jerry was the only BC. He really nailed it with this castles in the cloud. He nailed the idea that this is going to happen. And so I think, you know, we'll look back at the tape or the videos from the cube, we'll find those cuts. But we were talking about this then we were pontificating and riffing on the fact that there's going to be new winners and they're gonna look different as Andy Jassy always says in the cube you have to be misunderstood if you're really going to make something happen. Most of the most successful companies are misunderstood. Not anymore. The cloud scales there. And that's what's exciting about all this. >>It is exciting that the scale is there, the appetite is there the appetite to challenge the status quo, which is right now in this economic and dynamic market that we're living in is there's nothing better. >>One of the things that's come up and and that's just real quick before we bring jerry in is automation has been insecurity, absolutely security's been in every conversation, but automation is now so hot in the sense of it's real and it's becoming part of all the design decisions. How can we automate can we automate faster where the keys to automation? Is that having the right data, What data is available? So I think the idea of automation and Ai are driving all the change and that's to me is what these new companies represent this modern error where AI is built into the outcome and the apps and all that infrastructure. So it's super exciting. Um, let's check in, we got jerry Chen line at least a great. We're gonna come back after jerry and then kick off the day. Let's bring in jerry Chen from Greylock is he here? Let's bring him in there. He is. >>Hey john good to see you. >>Hey, congratulations on an amazing talk and thesis on the castles on the cloud. Thanks for coming on. >>All right, Well thanks for reading it. Um, always were being put a piece of workout out either. Not sure what the responses, but it seemed to resonate with a bunch of developers, founders, investors and folks like yourself. So smart people seem to gravitate to us. So thank you very much. >>Well, one of the benefits of doing the Cube for 11 years, Jerry's we have videotape of many, many people talking about what the future will hold. You kind of are on this early, it wasn't called castles in the cloud, but you were all I was, we had many conversations were kind of connecting the dots in real time. But you've been on this for a while. It's great to see the work. I really think you nailed this. I think you're absolutely on point here. So let's get into it. What is castles in the cloud? New research to come out from Greylock that you spearheaded? It's collaborative effort, but you've got data behind it. Give a quick overview of what is castle the cloud, the new modes of competitive advantage for companies. >>Yeah, it's as a group project that our team put together but basically john the question is, how do you win in the cloud? Remember the conversation we had eight years ago when amazon re event was holy cow, Like can you compete with them? Like is it a winner? Take all? Winner take most And if it is winner take most, where are the white spaces for Some starts to to emerge and clearly the past eight years in the cloud this journey, we've seen big companies, data breaks, snowflakes, elastic Mongo data robot. And so um they spotted the question is, you know, why are the castles in the cloud? The big three cloud providers, Amazon google and Azure winning. You know, what advantage do they have? And then given their modes of scale network effects, how can you as a startup win? And so look, there are 500 plus services between all three cloud vendors, but there are like 500 plus um startups competing gets a cloud vendors and there's like almost 100 unicorn of private companies competing successfully against the cloud vendors, including public companies. So like Alaska, Mongo Snowflake. No data breaks. Not public yet. Hashtag or not public yet. These are some examples of the names that I think are winning and watch this space because you see more of these guys storm the castle if you will. >>Yeah. And you know one of the things that's a funny metaphor because it has many different implications. One, as we talk about security, the perimeter of the gates, the moats being on land. But now you're in the cloud, you have also different security paradigm. You have a different um, new kinds of services that are coming on board faster than ever before. Not just from the cloud players but From companies contributing into the ecosystem. So the combination of the big three making the market the main markets you, I think you call 31 markets that we know of that probably maybe more. And then you have this notion of a sub market, which means that there's like we used to call it white space back in the day, remember how many whites? Where's the white space? I mean if you're in the cloud, there's like a zillion white spaces. So talk about this sub market dynamic between markets and that are being enabled by the cloud players and how these sub markets play into it. >>Sure. So first, the first problem was what we did. We downloaded all the services for the big three clowns. Right? And you know what as recalls a database or database service like a document DB and amazon is like Cosmo dB and Azure. So first thing first is we had to like look at all three cloud providers and you? Re categorize all the services almost 500 Apples, Apples, Apples # one number two is you look at all these markets or sub markets and said, okay, how can we cluster these services into things that you know you and I can rock right. That's what amazon Azure and google think about. It is very different and the beauty of the cloud is this kind of fat long tail of services for developers. So instead of like oracle is a single database for all your needs. They're like 20 or 30 different databases from time series um analytics, databases. We're talking rocks at later today. Right. Um uh, document databases like Mongo search database like elastic. And so what happens is there's not one giant market like databases, there's a database market And 30, 40 sub markets that serve the needs developers. So the Great News is cloud has reduced the cost and create something that new for developers. Um also the good news is for a start up you can find plenty of white speeds solving a pain point, very specific to a different type of problem >>and you can sequence up to power law to this. I love the power of a metaphor, you know, used to be a very thin neck note no torso and then a long tail. But now as you're pointing out this expansion of the fat tail of services, but also there's big tam's and markets available at the top of the power law where you see coming like snowflake essentially take on the data warehousing market by basically sitting on amazon re factoring with new services and then getting a flywheel completely changing the economic unit economics completely changing the consumption model completely changing the value proposition >>literally you >>get Snowflake has created like a storm, create a hole, that mode or that castle wall against red shift. Then companies like rock set do your real time analytics is Russian right behind snowflakes saying, hey snowflake is great for data warehouse but it's not fast enough for real time analytics. Let me give you something new to your, to your parallel argument. Even the big optic snowflake have created kind of a wake behind them that created even more white space for Gaza rock set. So that's exciting for guys like me and >>you. And then also as we were talking about our last episode two or quarter two of our showcase. Um, from a VC came on, it's like the old shelf where you didn't know if a company's successful until they had to return the inventory now with cloud you if you're not successful, you know it right away. It's like there's no debate. Like, I mean you're either winning or not. This is like that's so instrumented so a company can have a good better mousetrap and win and fill the white space and then move up. >>It goes both ways. The cloud vendor, the big three amazon google and Azure for sure. They instrument their own class. They know john which ecosystem partners doing well in which ecosystems doing poorly and they hear from the customers exactly what they want. So it goes both ways they can weaponize that. And just as well as you started to weaponize that info >>and that's the big argument of do that snowflake still pays the amazon bills. They're still there. So again, repatriation comes back, That's a big conversation that's come up. What's your quick take on that? Because if you're gonna have a castle in the cloud, then you're gonna bring it back to land. I mean, what's that dynamic? Where do you see that compete? Because on one hand is innovation. The other ones maybe cost efficiency. Is that a growth indicator slow down? What's your view on the movement from and to the cloud? >>I think there's probably three forces you're finding here. One is the cost advantage in the scale advantage of cloud so that I think has been going for the past eight years, there's a repatriation movement for a certain subset of customers, I think for cost purposes makes sense. I think that's a tiny handful that believe they can actually run things better than a cloud. The third thing we're seeing around repatriation is not necessary against cloud, but you're gonna see more decentralized clouds and things pushed to the edge. Right? So you look at companies like Cloudflare Fastly or a company that we're investing in Cato networks. All ideas focus on secure access at the edge. And so I think that's not the repatriation of my own data center, which is kind of a disaggregated of cloud from one giant monolithic cloud, like AWS east or like a google region in europe to multiple smaller clouds for governance purposes, security purposes or legacy purposes. >>So I'm looking at my notes here, looking down on the screen here for this to read this because it's uh to cut and paste from your thesis on the cloud. The excellent cloud. The of the $38 billion invested this quarter. Um Ai and ml number one, um analytics. Number two, security number three. Actually, security number one. But you can see the bubbles here. So all those are data problems I need to ask you. I see data is hot data as intellectual property. How do you look at that? Because we've been reporting on this and we just started the cube conversation around workflows as intellectual property. If you have scale and your motives in the cloud. You could argue that data and the workflows around those data streams is intellectual property. It's a protocol >>I believe both are. And they just kind of go hand in hand like peanut butter and jelly. Right? So data for sure. I. P. So if you know people talk about days in the oil, the new resource. That's largely true because of powers a bunch. But the workflow to your point john is sticky because every company is a unique snowflake right? Like the process used to run the cube and your business different how we run our business. So if you can build a workflow that leverages the data, that's super sticky. So in terms of switching costs, if my work is very bespoke to your business, then I think that's competitive advantage. >>Well certainly your workflow is a lot different than the cube. You guys just a lot of billions of dollars in capital. We're talking to all the people out here jerry. Great to have you on final thought on your thesis. Where does it go from here? What's been the reaction? Uh No, you put it out there. Great love the restart. Think you're on point on this one. Where did we go from here? >>We have to follow pieces um in the near term one around, you know, deep diver on open source. So look out for that pretty soon and how that's been a powerful strategy a second. Is this kind of just aggregation of the cloud be a Blockchain and you know, decentralized apps, be edge applications. So that's in the near term two more pieces of, of deep dive we're doing. And then the goal here is to update this on a quarterly and annual basis. So we're getting submissions from founders that wanted to say, hey, you missed us or he screwed up here. We got the big cloud vendors saying, Hey jerry, we just lost his new things. So our goal here is to update this every single year and then probably do look back saying, okay, uh, where were we wrong? We're right. And then let's say the castle clouds 2022. We'll see the difference were the more unicorns were there more services were the IPO's happening. So look for some short term work from us on analytics, like around open source and clouds. And then next year we hope that all of this forward saying, Hey, you have two year, what's happening? What's changing? >>Great stuff and, and congratulations on the southern news. You guys put another half a billion dollars into early, early stage, which is your roots. Are you still doing a lot of great investments in a lot of unicorns. Congratulations that. Great luck on the team. Thanks for coming on and congratulations you nailed this one. I think I'm gonna look back and say that this is a pretty seminal piece of work here. Thanks for sharing. >>Thanks john thanks for having us. >>Okay. Okay. This is the cube here and 81 startup showcase. We're about to get going in on all the hot companies closing out the kino lisa uh, see jerry Chen cube alumni. He was right from day one. We've been riffing on this, but he nails it here. I think Greylock is lucky to have him as a general partner. He's done great deals, but I think he's hitting the next wave big. This is, this is huge. >>I was listening to you guys talking thinking if if you had a crystal ball back in 2013, some of the things Jerry saying now his narrative now, what did he have a crystal >>ball? He did. I mean he could be a cuBA host and I could be a venture capital. We were both right. I think so. We could have been, you know, doing that together now and all serious now. He was right. I mean, we talked off camera about who's the next amazon who's going to challenge amazon and Andy Jassy was quoted many times in the queue by saying, you know, he was surprised that it took so long for people to figure out what they were doing. Okay, jerry was that VM where he had visibility into the cloud. He saw amazon right away like we did like this is a winning formula and so he was really out front on this one. >>Well in the investments that they're making in these unicorns is exciting. They have this, this lens that they're able to see the opportunities there almost before anybody else can. And finding more white space where we didn't even know there was any. >>Yeah. And what's interesting about the report I'm gonna dig into and I want to get to him while he's on camera because it's a great report, but He says it's like 500 services I think Amazon has 5000. So how you define services as an interesting thing and a lot of amazon services that they have as your doesn't have and vice versa, they do call that out. So I find the report interesting. It's gonna be a feature game in the future between clouds the big three. They're gonna say we do this, you're starting to see the formation, Google's much more developer oriented. Amazon is much more stronger in the governance area with data obviously as he pointed out, they have such experience Microsoft, not so much their developer cloud and more office, not so much on the government's side. So that that's an indicator of my, my opinion of kind of where they rank. So including the number one is still amazon web services as your long second place, way behind google, right behind Azure. So we'll see how the horses come in, >>right. And it's also kind of speaks to the hybrid world in which we're living the hybrid multi cloud world in which many companies are living as companies to not just survive in the last year and a half, but to thrive and really have to become data companies and leverage that data as a competitive advantage to be able to unlock the value of it. And a lot of these startups that we talked to in the showcase are talking about how they're helping organizations unlock that data value. As jerry said, it is the new oil, it's the new gold. Not unless you can unlock that value faster than your competition. >>Yeah, well, I'm just super excited. We got a great day ahead of us with with all the cots startups. And then at the end day, Volonte is gonna interview, hello, fresh practitioners, We're gonna close it out every episode now, we're going to do with the closing practitioner. We try to get jpmorgan chase data measures. The hottest area right now in the enterprise data is new competitive advantage. We know that data workflows are now intellectual property. You're starting to see data really factoring into these applications now as a key aspect of the competitive advantage and the value creation. So companies that are smart are investing heavily in that and the ones that are kind of slow on the uptake are lagging the market and just trying to figure it out. So you start to see that transition and you're starting to see people fall away now from the fact that they're not gonna make it right, You're starting to, you know, you can look at look at any happens saying how much ai is really in there. Real ai what's their data strategy and you almost squint through that and go, okay, that's gonna be losing application. >>Well the winners are making it a board level conversation >>And security isn't built in. Great to have you on this morning kicking it off. Thanks John Okay, we're going to go into the next set of the program at 10:00 we're going to move into the breakouts. Check out the companies is three tracks in there. We have an awesome track on devops pure devops. We've got the data and analytics and we got the cloud management and just to run down real quick check out the sis dig harness. Io system is doing great, securing devops harness. IO modern software delivery platform, White Source. They're preventing and remediating the rest of the internet for them for the company's that's a really interesting and lumbago, effortless acres land and monitoring functions, server list super hot. And of course hacker one is always great doing a lot of great missions and and bounties you see those success continue to send i O there in Palo alto changing the game on data engineering and data pipe lining. Okay. Data driven another new platform, horizontally scalable and of course thought spot ai driven kind of a search paradigm and of course rock set jerry Chen's companies here and press are all doing great in the analytics and then the cloud management cost side 80 operations day to operate. Ops ramps and ops multi cloud are all there and sunny, all all going to present. So check them out. This is the Cubes Adria's startup showcase episode three.

>>Hello and welcome today's cube presentation of eight of us startup showcase. I'm john for your host highlighting the hottest companies and devops data analytics and cloud management lisa martin and David want are here to kick it off. We've got a great program for you again. This is our, our new community event model where we're doing every quarter, we have every new episode, this is quarter three this year or episode three, season one of the hottest cloud startups and we're gonna be featured. Then we're gonna do a keynote package and then 15 countries will present their story, Go check them out and then have a closing keynote with a practitioner and we've got some great lineups, lisa Dave, great to see you. Thanks for joining me. Hey >>guys, great to be here. >>So David got to ask you, you know, back in events last night we're at the 14 it's event where they had the golf PGA championship with the cube Now we got the hybrid model, This is the new normal. We're in, we got these great companies were showcasing them. What's your take? >>Well, you're right. I mean, I think there's a combination of things. We're seeing some live shows. We saw what we did with at mobile world Congress. We did the show with AWS storage day where it was, we were at the spheres, there was no, there was a live audience, but they weren't there physically. It was just virtual and yeah, so, and I just got pained about reinvent. Hey Dave, you gotta make your flights. So I'm making my flights >>were gonna be at the amazon web services, public sector summit next week. At least a lot, a lot of cloud convergence going on here. We got many companies being featured here that we spoke with the Ceo and their top people cloud management, devops data, nelson security. Really cutting edge companies, >>yes, cutting edge companies who are all focused on acceleration. We've talked about the acceleration of digital transformation the last 18 months and we've seen a tremendous amount of acceleration in innovation with what these startups are doing. We've talked to like you said, there's, there's C suite, we've also talked to their customers about how they are innovating so quickly with this hybrid environment, this remote work and we've talked a lot about security in the last week or so. You mentioned that we were at Fortinet cybersecurity skills gap. What some of these companies are doing with automation for example, to help shorten that gap, which is a big opportunity for the >>job market. Great stuff. Dave so the format of this event, you're going to have a fireside chat with the practitioner, we'd like to end these programs with a great experienced practitioner cutting edge in data february. The beginning lisa are gonna be kicking off with of course Jeff bar to give us the update on what's going on AWS and then a special presentation from Emily Freeman who is the author of devops for dummies, she's introducing new content. The revolution in devops devops two point oh and of course jerry Chen from Greylock cube alumni is going to come on and talk about his new thesis castles in the cloud creating moats at cloud scale. We've got a great lineup of people and so the front ends can be great. Dave give us a little preview of what people can expect at the end of the fireside chat. >>Well at the highest level john I've always said we're entering that sort of third great wave of cloud. First wave was experimentation. The second big wave was migration. The third wave of integration, Deep business integration and what you're going to hear from Hello Fresh today is how they like many companies that started early last decade. They started with an on prem Hadoop system and then of course we all know what happened is S three essentially took the knees out from, from the on prem Hadoop market lowered costs, brought things into the cloud and what Hello Fresh is doing is they're transforming from that legacy Hadoop system into its running on AWS but into a data mess, you know, it's a passionate topic of mine. Hello Fresh was scaling they realized that they couldn't keep up so they had to rethink their entire data architecture and they built it around data mesh Clements key and christoph Soewandi gonna explain how they actually did that are on a journey or decentralized data measure >>it and your posts have been awesome on data measure. We get a lot of traction. Certainly you're breaking analysis for the folks watching check out David Landes, Breaking analysis every week, highlighting the cutting edge trends in tech Dave. We're gonna see you later, lisa and I are gonna be here in the morning talking about with Emily. We got Jeff Barr teed up. Dave. Thanks for coming on. Looking forward to fireside chat lisa. We'll see you when Emily comes back on. But we're gonna go to Jeff bar right now for Dave and I are gonna interview Jeff. Mm >>Hey Jeff, >>here he is. Hey, how are you? How's it >>going really well. >>So I gotta ask you, the reinvent is on, everyone wants to know that's happening right. We're good with Reinvent. >>Reinvent is happening. I've got my hotel and actually listening today, if I just remembered, I still need to actually book my flights. I've got my to do list on my desk and I do need to get my flights. Uh, really looking forward to it. >>I can't wait to see the all the announcements and blog posts. We're gonna, we're gonna hear from jerry Chen later. I love the after on our next event. Get your reaction to this castle and castles in the cloud where competitive advantages can be built in the cloud. We're seeing examples of that. But first I gotta ask you give us an update of what's going on. The ap and ecosystem has been an incredible uh, celebration these past couple weeks, >>so, so a lot of different things happening and the interesting thing to me is that as part of my job, I often think that I'm effectively living in the future because I get to see all this really cool stuff that we're building just a little bit before our customers get to, and so I'm always thinking okay, here I am now, and what's the world going to be like in a couple of weeks to a month or two when these launches? I'm working on actually get out the door and that, that's always really, really fun, just kind of getting that, that little edge into where we're going, but this year was a little interesting because we had to really significant birthdays, we had the 15 year anniversary of both EC two and S three and we're so focused on innovating and moving forward, that it's actually pretty rare for us at Aws to look back and say, wow, we've actually done all these amazing things in in the last 15 years, >>you know, it's kind of cool Jeff, if I may is is, you know, of course in the early days everybody said, well, a place for startup is a W. S and now the great thing about the startup showcases, we're seeing the startups that are very near, or some of them have even reached escape velocity, so they're not, they're not tiny little companies anymore, they're in their transforming their respective industries, >>they really are and I think that as they start ups grow, they really start to lean into the power of the cloud. They as they start to think, okay, we've we've got our basic infrastructure in place, we've got, we were serving data, we're serving up a few customers, everything is actually working pretty well for us. We've got our fundamental model proven out now, we can invest in publicity and marketing and scaling and but they don't have to think about what's happening behind the scenes. They just if they've got their auto scaling or if they're survivalists, the infrastructure simply grows to meet their demand and it's it's just a lot less things that they have to worry about. They can focus on the fun part of their business which is actually listening to customers and building up an awesome business >>Jeff as you guys are putting together all the big pre reinvented, knows a lot of stuff that goes on prior as well and they say all the big good stuff to reinvent. But you start to see some themes emerged this year. One of them is modernization of applications, the speed of application development in the cloud with the cloud scale devops personas, whatever persona you want to talk about but basically speed the speed of of the app developers where other departments have been slowing things down, I won't say name names, but security group and I t I mean I shouldn't have said that but only kidding but no but seriously people want in minutes and seconds now not days or weeks. You know whether it's policy. What are some of the trends that you're seeing around this this year as we get into some of the new stuff coming out >>So Dave customers really do want speed and for we've actually encapsulate this for a long time in amazon in what we call the bias for action leadership principle where we just need to jump in and move forward and and make things happen. A lot of customers look at that and they say yes this is great. We need to have the same bias fraction. Some do. Some are still trying to figure out exactly how to put it into play. And they absolutely for sure need to pay attention to security. They need to respect the past and make sure that whatever they're doing is in line with I. T. But they do want to move forward. And the interesting thing that I see time and time again is it's not simply about let's adopt a new technology. It's how do we how do we keep our workforce engaged? How do we make sure that they've got the right training? How do we bring our our I. T. Team along for this. Hopefully new and fun and exciting journey where they get to learn some interesting new technologies they've got all this very much accumulated business knowledge they still want to put to use, maybe they're a little bit apprehensive about something brand new and they hear about the cloud, but there by and large, they really want to move forward. They just need a little bit of help to make it happen real >>good guys. One of the things you're gonna hear today, we're talking about speed traditionally going fast. Oftentimes you meant you have to sacrifice some things on quality and what you're going to hear from some of the startups today is how they're addressing that to automation and modern devoPS technologies and sort of rethinking that whole application development approach. That's something I'm really excited to see organization is beginning to adopt so they don't have to make that tradeoff anymore. >>Yeah, I would never want to see someone sacrifice quality, but I do think that iterating very quickly and using the best of devoPS principles to be able to iterate incredibly quickly and get that first launch out there and then listen with both ears just as much as you can, Everything. You hear iterate really quickly to meet those needs in, in hours and days, not months, quarters or years. >>Great stuff. Chef and a lot of the companies were featuring here in the startup showcase represent that new kind of thinking, um, systems thinking as well as you know, the cloud scale and again and it's finally here, the revolution of deVOps is going to the next generation and uh, we're excited to have Emily Freeman who's going to come on and give a little preview for her new talk on this revolution. So Jeff, thank you for coming on, appreciate you sharing the update here on the cube. Happy >>to be. I'm actually really looking forward to hearing from Emily. >>Yeah, it's great. Great. Looking forward to the talk.

>>Hello. And we're here at the cube startup showcase made possible by a Ws. Thanks so much for joining us today. You know when Jim McDaid Ghani was formulating her ideas around data mesh, She wasn't the only one thinking about decentralized data architecture. Hello, Fresh was going into hyper growth mode and realized that in order to support its scale, it needed to rethink how it thought about data. Like many companies that started in the early part of last decade, Hello Fresh relied on a monolithic data architecture and the internal team. It had concerns about its ability to support continued innovation at high velocity. The company's data team began to think about the future and work backwards from a target architecture which possessed many principles of so called data mesh even though they didn't use that term. Specifically, the company is a strong example of an early but practical pioneer of data mission. Now there are many practitioners and stakeholders involved in evolving the company's data architecture, many of whom are listed here on this on the slide to are highlighted in red are joining us today, we're really excited to welcome into the cube Clements cheese, the Global Senior Director for Data at Hello Fresh and christoph Nevada who's the Global Senior Director of data also, of course. Hello Fresh folks. Welcome. Thanks so much for making some time today and sharing your story. >>Thank you very much. Hey >>steve. All right, let's start with Hello Fresh. You guys are number one in the world in your field, you deliver hundreds of millions of meals each year to many, many millions of people around the globe. You're scaling christoph. Tell us a little bit more about your company and its vision. >>Yeah. Should I start or Clements maybe maybe take over the first piece because Clements has actually been a longer trajectory yet have a fresh. >>Yeah go ahead. Climate change. I mean yes about approximately six years ago I joined handle fresh and I didn't think about the startup I was joining would eventually I. P. O. And just two years later and the freshman public and approximately three years and 10 months after. Hello fresh was listed on the German stock exchange which was just last week. Hello Fresh was included in the Ducks Germany's leading stock market index and debt to mind a great great milestone and I'm really looking forward and I'm very excited for the future for the future for head of fashion. All our data. Um the vision that we have is to become the world's leading food solution group and there's a lot of attractive opportunities. So recently we did lounge and expand Norway. This was in july and earlier this year we launched the U. S. Brand green >>chef in the U. K. As >>well. We're committed to launch continuously different geographies in the next coming years and have a strong pipe ahead of us with the acquisition of ready to eat companies like factor in the U. S. And the planned acquisition of you foods in Australia. We're diversifying our offer now reaching even more and more untapped customer segments and increase our total addressable market. So by offering customers and growing range of different alternatives to shop food and consumer meals. We are charging towards this vision and the school to become the world's leading integrated food solutions group. >>Love it. You guys are on a rocket ship, you're really transforming the industry and as you expand your tam it brings us to sort of the data as a as a core part of that strategy. So maybe you guys could talk a little bit about your journey as a company specifically as it relates to your data journey. You began as a start up. You had a basic architecture like everyone. You made extensive use of spreadsheets. You built a Hadoop based system that started to grow and when the company I. P. O. You really started to explode. So maybe describe that journey from a data perspective. >>Yes they saw Hello fresh by 2015 approximately had evolved what amount of classical centralized management set up. So we grew very organically over the years and there were a lot of very smart people around the globe. Really building the company and building our infrastructure. Um This also means that there were a small number of internal and external sources. Data sources and a centralized the I team with a number of people producing different reports, different dashboards and products for our executives for example of our different operations teams, christian company's performance and knowledge was transferred um just via talking to each other face to face conversations and the people in the data where's team were considered as the data wizard or as the E. T. L. Wizard. Very classical challenges. And those et al. Reserves indicated the kind of like a silent knowledge of data management. Right? Um so a central data whereas team then was responsible for different type of verticals and different domains, different geographies and all this setup gave us to the beginning the flexibility to grow fast as a company in 2015 >>christoph anything that might add to that. >>Yes. Um Not expected to that one but as as clement says it right, this was kind of set up that actually work for us quite a while. And then in 2017 when L. A. Freshman public, the company also grew rapidly and just to give you an idea how that looked like. As was that the tech department self actually increased from about 40 people to almost 300 engineers And the same way as a business units as Clemens has described, also grew sustainable, sustainably. So we continue to launch hello fresh and new countries launching brands like every plate and also acquired other brands like much of a factor and with that grows also from a data perspective the number of data requests that centrally we're getting become more and more and more and also more and more complex. So that for the team meant that they had a fairly high mental load. So they had to achieve a very or basically get a very deep understanding about the business. And also suffered a lot from this context switching back and forth, essentially there to prioritize across our product request from our physical product, digital product from the physical from sorry, from the marketing perspective and also from the central reporting uh teams. And in a nutshell this was very hard for these people. And this that also to a situation that, let's say the solution that we have became not really optimal. So in a nutshell, the central function became a bottleneck and slowdown of all the innovation of the company. >>It's a classic case, isn't it? I mean Clements, you see you see the central team becomes a bottleneck and so the lines of business, the marketing team salesman's okay, we're going to take things into our own hands. And then of course I I. T. And the technical team is called in later to clean up the mess. Uh maybe, I mean was that maybe I'm overstating it, but that's a common situation, isn't it? >>Yeah. Uh This is what exactly happened. Right. So um we had a bottleneck, we have the central teams, there was always a little of tension um analytics teams then started in this business domains like marketing, trade chain, finance, HR and so on. Started really to build their own data solutions at some point you have to get the ball rolling right and then continue the trajectory um which means then that the data pipelines didn't meet the engineering standards. And um there was an increased need for maintenance and support from central teams. Hence over time the knowledge about those pipelines and how to maintain a particular uh infrastructure for example left the company such that most of those data assets and data sets are turned into a huge step with decreasing data quality um also decrease the lack of trust, decreasing transparency. And this was increasing challenge where majority of time was spent in meeting rooms to align on on data quality for example. >>Yeah. And and the point you were making christoph about context switching and this is this is a point that Jemaah makes quite often is we've we've we've contextualized are operational systems like our sales systems, our marketing system but not our our data system. So you're asking the data team, Okay. Be an expert in sales, be an expert in marketing, be an expert in logistics, be an expert in supply chain and it start stop, start, stop, it's a paper cut environment and it's just not as productive. But but on the flip side of that is when you think about a centralized organization you think, hey this is going to be a very efficient way, a cross functional team to support the organization but it's not necessarily the highest velocity, most effective organizational structure. >>Yeah, so so I agree with that. Is that up to a certain scale, a centralized function has a lot of advantages, right? That's clear for everyone which would go to some kind of expert team. However, if you see that you actually would like to accelerate that and specific and this hyper growth, right, you wanna actually have autonomy and certain teams and move the teams or let's say the data to the experts in these teams and this, as you have mentioned, right, that increases mental load and you can either internally start splitting your team into a different kind of sub teams focusing on different areas. However, that is then again, just adding another peace where actually collaboration needs to happen busy external sees, so why not bridging that gap immediately and actually move these teams and to end into into the function themselves. So maybe just to continue what, what was Clements was saying and this is actually where over. So Clements, my journey started to become one joint journey. So Clements was coming actually from one of these teams to build their own solutions. I was basically having the platform team called database housed in these days and in 2019 where basically the situation become more and more serious, I would say so more and more people have recognized that this model doesn't really scale In 2019, basically the leadership of the company came together and I identified data as a key strategic asset and what we mean by that, that if we leverage data in a proper way, it gives us a unique competitive advantage which could help us to, to support and actually fully automated our decision making process across the entire value chain. So what we're, what we're trying to do now or what we should be aiming for is that Hello, Fresh is able to build data products that have a purpose. We're moving away from the idea. Data is just a by problem products, we have a purpose why we would like to collect this data. There's a clear business need behind that. And because it's so important to for the company as a business, we also want to provide them as a trust versi asset to the rest of the organization. We say there's the best customer experience, but at least in a way that users can easily discover, understand and security access high quality data. >>Yeah, so and and and Clements, when you c J Maxx writing, you see, you know, she has the four pillars and and the principles as practitioners you look at that say, okay, hey, that's pretty good thinking and then now we have to apply it and that's and that's where the devil meets the details. So it's the four, you know, the decentralized data ownership data as a product, which we'll talk about a little bit self serve, which you guys have spent a lot of time on inclement your wheelhouse which is which is governance and a Federated governance model. And it's almost like if you if you achieve the first two then you have to solve for the second to it almost creates a new challenges but maybe you could talk about that a little bit as to how it relates to Hello fresh. >>Yes. So christophe mentioned that we identified economic challenge beforehand and for how can we actually decentralized and actually empower the different colleagues of ours. This was more a we realized that it was more an organizational or a cultural change and this is something that somebody also mentioned I think thought words mentioned one of the white papers, it's more of a organizational or cultural impact and we kicked off a um faced reorganization or different phases we're currently and um in the middle of still but we kicked off different phases of organizational reconstruct oring reorganization, try unlock this data at scale. And the idea was really moving away from um ever growing complex matrix organizations or matrix setups and split between two different things. One is the value creation. So basically when people ask the question, what can we actually do, what shall we do? This is value creation and how, which is capability building and both are equal in authority. This actually then creates a high urge and collaboration and this collaboration breaks up the different silos that were built and of course this also includes different needs of stuffing forward teams stuffing with more, let's say data scientists or data engineers, data professionals into those business domains and hence also more capability building. Um Okay, >>go ahead. Sorry. >>So back to Tzemach did johnny. So we the idea also Then crossed over when she published her papers in May 2019 and we thought well The four colors that she described um we're around decentralized data ownership, product data as a product mindset, we have a self service infrastructure and as you mentioned, Federated confidential governance. And this suited very much with our thinking at that point of time to reorganize the different teams and this then leads to a not only organisational restructure but also in completely new approach of how we need to manage data, show data. >>Got it. Okay, so your business is is exploding. Your data team will have to become domain experts in too many areas, constantly contact switching as we said, people started to take things into their own hands. So again we said classic story but but you didn't let it get out of control and that's important. So we actually have a picture of kind of where you're going today and it's evolved into this Pat, if you could bring up the picture with the the elephant here we go. So I would talk a little bit about the architecture, doesn't show it here, the spreadsheet era but christoph maybe you can talk about that. It does show the Hadoop monolith which exists today. I think that's in a managed managed hosting service, but but you you preserve that piece of it, but if I understand it correctly, everything is evolving to the cloud, I think you're running a lot of this or all of it in A W. S. Uh you've got everybody's got their own data sources, uh you've got a data hub which I think is enabled by a master catalog for discovery and all this underlying technical infrastructure. That is really not the focus of this conversation today. But the key here, if I understand it correctly is these domains are autonomous and not only that this required technical thinking, but really supportive organizational mindset, which we're gonna talk about today. But christoph maybe you could address, you know, at a high level some of the architectural evolution that you guys went through. >>Yeah, sure. Yeah, maybe it's also a good summary about the entire history. So as you have mentioned, right, we started in the very beginning with the model is on the operation of playing right? Actually, it wasn't just one model is both to one for the back end and one for the for the front and and or analytical plane was essentially a couple of spreadsheets and I think there's nothing wrong with spreadsheets, right, allows you to store information, it allows you to transform data allows you to share this information. It allows you to visualize this data, but all the kind of that's not actually separating concern right? Everything in one tool. And this means that obviously not scalable, right? You reach the point where this kind of management set up in or data management of isn't one tool reached elements. So what we have started is we've created our data lake as we have seen here on Youtube. And this at the very beginning actually reflected very much our operational populace on top of that. We used impala is a data warehouse, but there was not really a distinction between borders, our data warehouse and borders our data like the impala was used as a kind of those as the kind of engine to create a warehouse and data like construct itself and this organic growth actually led to a situation as I think it's it's clear now that we had to centralized model is for all the domains that will really lose kimball modeling standards. There was no uniformity used actually build in house uh ways of building materialized use abuse that we have used for the presentation layer, there was a lot of duplication of effort and in the end essentially they were missing feedbacks, food, which helped us to to improve of what we are filled. So in the end, in the natural, as we have said, the lack of trust and that's basically what the starting point for us to understand. Okay, how can we move away and there are a lot of different things that you can discuss of apart from this organizational structure that we have said, okay, we have these three or four pillars from from Denmark. However, there's also the next extra question around how do we implement our talking about actual right, what are the implications on that level? And I think that is there's something that we are that we are currently still in progress. >>Got it. Okay, so I wonder if we could talk about switch gears a little bit and talk about the organizational and cultural challenges that you faced. What were those conversations like? Uh let's dig into that a little bit. I want to get into governance as well. >>The conversations on the cultural change. I mean yes, we went through a hyper growth for the last year since obviously there were a lot of new joiners, a lot of different, very, very smart people joining the company which then results that collaboration uh >>got a bit more difficult. Of course >>there are times and changes, you have different different artifacts that you were created um and documentation that were flying around. Um so we were we had to build the company from scratch right? Um Of course this then resulted always this tension which I described before, but the most important part here is that data has always been a very important factor at l a fresh and we collected >>more of this >>data and continued to improve use data to improve the different key areas of our business. >>Um even >>when organizational struggles, the central organizational struggles data somehow always helped us to go through this this kind of change. Right? Um in the end those decentralized teams in our local geography ease started with solutions that serve the business which was very very important otherwise wouldn't be at the place where we are today but they did by all late best practices and standards and I always used sport analogy Dave So like any sport, there are different rules and regulations that need to be followed. These rules are defined by calling the sports association and this is what you can think about data governance and compliance team. Now we add the players to it who need to follow those rules and bite by them. This is what we then called data management. Now we have the different players and professionals, they need to be trained and understand the strategy and it rules before they can play. And this is what I then called data literacy. So we realized that we need to focus on helping our teams to develop those capabilities and teach the standards for how work is being done to truly drive functional excellence in a different domains. And one of our mission of our data literacy program for example is to really empower >>every employee at hello >>fresh everyone to make the right data informs decisions by providing data education that scaled by royal Entry team. Then this can be different things, different things like including data capabilities, um, with the learning paths for example. Right? So help them to create and deploy data products connecting data producers and data consumers and create a common sense and more understanding of each other's dependencies, which is important, for example, S. S. L. O. State of contracts and etcetera. Um, people getting more of a sense of ownership and responsibility. Of course, we have to define what it means, what does ownership means? But the responsibility means. But we're teaching this to our colleagues via individual learning patterns and help them up skill to use. Also, there's shared infrastructure and those self self service applications and overall to summarize, we're still in this progress of of, of learning, we are still learning as well. So learning never stops the tele fish, but we are really trying this um, to make it as much fun as possible. And in the end we all know user behavior has changed through positive experience. Uh, so instead of having massive training programs over endless courses of workshops, um, leaving our new journalists and colleagues confused and overwhelmed. >>We're applying um, >>game ification, right? So split different levels of certification where our colleagues can access, have had access points, they can earn badges along the way, which then simplifies the process of learning and engagement of the users and this is what we see in surveys, for example, where our employees that your justification approach a lot and are even competing to collect Those learning path batteries to become the # one on the leader board. >>I love the game ification, we've seen it work so well and so many different industries, not the least of which is crypto so you've identified some of the process gaps uh that you, you saw it is gloss over them. Sometimes I say paved the cow path. You didn't try to force, in other words, a new architecture into the legacy processes. You really have to rethink your approach to data management. So what what did that entail? >>Um, to rethink the way of data management. 100%. So if I take the example of Revolution, Industrial Revolution or classical supply chain revolution, but just imagine that you have been riding a horse, for example, your whole life and suddenly you can operate a car or you suddenly receive just a complete new way of transporting assets from A to B. Um, so we needed to establish a new set of cross functional business processes to run faster, dry faster, um, more robustly and deliver data products which can be trusted and used by downstream processes and systems. Hence we had a subset of new standards and new procedures that would fall into the internal data governance and compliance sector with internal, I'm always referring to the data operations around new things like data catalog, how to identify >>ownership, >>how to change ownership, how to certify data assets, everything around classical software development, which we know apply to data. This this is similar to a new thinking, right? Um deployment, versioning, QA all the different things, ingestion policies, policing procedures, all the things that suffer. Development has been doing. We do it now with data as well. And in simple terms, it's a whole redesign of the supply chain of our data with new procedures and new processes and as a creation as management and as a consumption. >>So data has become kind of the new development kit. If you will um I want to shift gears and talk about the notion of data product and, and we have a slide uh that we pulled from your deck and I'd like to unpack it a little bit. Uh I'll just, if you can bring that up, I'll read it. A data product is a product whose primary objective is to leverage on data to solve customer problems where customers, both internal and external. So pretty straightforward. I know you've gone much deeper and you're thinking and into your organization, but how do you think about that And how do you determine for instance who owns what? How did you get everybody to agree? >>I can take that one. Um, maybe let me start with the data product. So I think um that's an ongoing debate. Right? And I think the debate itself is an important piece here, right? That visit the debate, you clarify what we actually mean by that product and what is actually the mindset. So I think just from a definition perspective, right? I think we find the common denominator that we say okay that our product is something which is important for the company has come to its value what you mean by that. Okay, it's it's a solution to a customer problem that delivers ideally maximum value to the business. And yes, it leverages the power of data and we have a couple of examples but it had a fresh year, the historical and classical ones around dashboards for example, to monitor or error rates but also more sophisticated ways for example to incorporate machine learning algorithms in our recipe recommendations. However, I think the important aspects of the data product is a there is an owner, right? There's someone accountable for making sure that the product that we are providing is actually served and is maintained and there are, there is someone who is making sure that this actually keeps the value of that problem thing combined with the idea of the proper documentation, like a product description, right that people understand how to use their bodies is about and related to that peace is the idea of it is a purpose. Right? You need to understand or ask ourselves, Okay, why does this thing exist does it provide the value that you think it does. That leads into a good understanding about the life cycle of the data product and life cycle what we mean? Okay from the beginning from the creation you need to have a good understanding, we need to collect feedback, we need to learn about that. We need to rework and actually finally also to think about okay benefits time to decommission piece. So overall, I think the core of the data product is product thinking 11 right that we start the point is the starting point needs to be the problem and not the solution and this is essentially what we have seen what was missing but brought us to this kind of data spaghetti that we have built there in in Russia, essentially we built at certain data assets, develop in isolation and continuously patch the solution just to fulfill these articles that we got and actually these aren't really understanding of the stakeholder needs and the interesting piece as a result in duplication of work and this is not just frustrating and probably not the most efficient way how the company should work. But also if I build the same that assets but slightly different assumption across the company and multiple teams that leads to data inconsistency and imagine the following too narrow you as a management for management perspective, you're asking basically a specific question and you get essentially from a couple of different teams, different kind of grass, different kind of data and numbers and in the end you do not know which ones to trust. So there's actually much more ambiguity and you do not know actually is a noise for times of observing or is it just actually is there actually a signal that I'm looking for? And the same is if I'm running in a B test right, I have a new future, I would like to understand what has it been the business impact of this feature. I run that specific source in an unfortunate scenario. Your production system is actually running on a different source. You see different numbers. What you've seen in a B test is actually not what you see then in production typical thing then is you're asking some analytics tend to actually do a deep dive to understand where the discrepancies are coming from. The worst case scenario. Again, there's a different kind of source. So in the end it's a pretty frustrating scenario and that's actually based of time of people that have to identify the root cause of this divergence. So in a nutshell, the highest degree of consistency is actually achieved that people are just reusing Dallas assets and also in the media talk that we have given right, we we start trying to establish this approach for a B testing. So we have a team but just providing or is kind of owning their target metric associated business teams and they're providing that as a product also to other services including the A B testing team, they'll be testing team can use this information defines an interface is okay I'm joining this information that the metadata of an experiment and in the end after the assignment after this data collection face, they can easily add a graph to the dashboard. Just group by the >>Beatles Hungarian. >>And we have seen that also in other companies. So it's not just a nice dream that we have right. I have actually worked in other companies where we worked on search and we established a complete KPI pipeline that was computing all this information. And this information was hosted by the team and it was used for everything A B test and deep dives and and regular reporting. So uh just one of the second the important piece now, why I'm coming back to that is that requires that we are treating this data as a product right? If you want to have multiple people using the things that I am owning and building, we have to provide this as a trust mercy asset and in a way that it's easy for people to discover and actually work with. >>Yeah. And coming back to that. So this is to me this is why I get so excited about data mesh because I really do think it's the right direction for organizations. When people hear data product they say well, what does that mean? Uh but then when you start to sort of define it as you did, it's it's using data to add value, that could be cutting costs, that could be generating revenue, it could be actually directly you're creating a product that you monetize, So it's sort of in the eyes of the beholder. But I think the other point that we've made is you made it earlier on to and again, context. So when you have a centralized data team and you have all these P NL managers a lot of times they'll question the data because they don't own it. They're like wait a minute. If they don't, if it doesn't agree with their agenda, they'll attack the data. But if they own the data then they're responsible for defending that and that is a mindset change, that's really important. Um And I'm curious uh is how you got to, you know, that ownership? Was it a was it a top down with somebody providing leadership? Was it more organic bottom up? Was it a sort of a combination? How do you decide who owned what in other words, you know, did you get, how did you get the business to take ownership of the data and what is owning? You know, the data actually mean? >>That's a very good question. Dave I think this is one of the pieces where I think we have a lot of learnings and basically if you ask me how we could start the feeling. I think that would be the first piece. Maybe we need to start to really think about how that should be approached if it stopped his ownership. Right? It means somehow that the team has a responsibility to host and self the data efforts to minimum acceptable standards. This minimum dependencies up and down string. The interesting piece has been looking backwards. What what's happening is that under that definition has actually process that we have to go through is not actually transferring ownership from the central team to the distributor teams. But actually most cases to establish ownership, I make this difference because saying we have to transfer ownership actually would erroneously suggests that the data set was owned before. But this platform team, yes, they had the capability to make the changes on data pipelines, but actually the analytics team, they're always the ones who had the business understands, you use cases and but no one actually, but it's actually expensive expected. So we had to go through this very lengthy process and establishing ownership. We have done that, as in the beginning, very naively. They have started, here's a document here, all the data assets, what is probably the nearest neighbor who can actually take care of that and then we we moved it over. But the problem here is that all these things is kind of technical debt, right? It's not really properly documented, pretty unstable. It was built in a very inconsistent over years and these people who have built this thing have already left the company. So there's actually not a nice thing that is that you want to see and people build up a certain resistance, e even if they have actually bought into this idea of domain ownership. So if you ask me these learnings, but what needs to happen as first, the company needs to really understand what our core business concept that they have, they need to have this mapping from. These are the core business concept that we have. These are the domain teams who are owning this concept and then actually link that to the to the assets and integrated better with both understanding how we can evolve actually, the data assets and new data build things new in the in this piece in the domain. But also how can we address reduction of technical death and stabilizing what we have already. >>Thank you for that christoph. So I want to turn a direction here and talk about governance and I know that's an area that's passionate, you're passionate about. Uh I pulled this slide from your deck, which I kind of messed up a little bit sorry for that, but but by the way, we're going to publish a link to the full video that you guys did. So we'll share that with folks. But it's one of the most challenging aspects of data mesh, if you're going to decentralize you, you quickly realize this could be the Wild West as we talked about all over again. So how are you approaching governance? There's a lot of items on this slide that are, you know, underscore the complexity, whether it's privacy, compliance etcetera. So, so how did you approach this? >>It's yeah, it's about connecting those dots. Right. So the aim of the data governance program is about the autonomy of every team was still ensuring that everybody has the right interoperability. So when we want to move from the Wild West riding horses to a civilised way of transport, um you can take the example of modern street traffic, like when all participants can manoeuvre independently and as long as they follow the same rules and standards, everybody can remain compatible with each other and understand and learn from each other so we can avoid car crashes. So when I go from country to country, I do understand what the street infrastructure means. How do I drive my car? I can also read the traffic lights in the different signals. Um, so likewise as a business and Hello Fresh, we do operate autonomously and consequently need to follow those external and internal rules and standards to set forth by the redistribution in which we operate so in order to prevent a car crash, we need to at least ensure compliance with regulations to account for society's and our customers increasing concern with data protection and privacy. So teaching and advocating this advantage, realizing this to everyone in the company um was a key community communication strategy and of course, I mean I mentioned data privacy external factors, the same goes for internal regulations and processes to help our colleagues to adapt to this very new environment. So when I mentioned before the new way of thinking the new way of um dealing and managing data, this of course implies that we need new processes and regulations for our colleagues as well. Um in a nutshell then this means the data governance provides a framework for managing our people the processes and technology and culture around our data traffic. And those components must come together in order to have this effective program providing at least a common denominator, especially critical for shared dataset, which we have across our different geographies managed and shared applications on shared infrastructure and applications and is then consumed by centralized processes um for example, master data, everything and all the metrics and KPI s which are also used for a central steering. Um it's a big change day. Right. And our ultimate goal is to have this noninvasive, Federated um ultimatum and computational governance and for that we can't just talk about it. We actually have to go deep and use case by use case and Qc buy PVC and generate learnings and learnings with the different teams. And this would be a classical approach of identifying the target structure, the target status, match it with the current status by identifying together with the business teams with the different domains have a risk assessment for example, to increase transparency because a lot of teams, they might not even know what kind of situation they might be. And this is where this training and this piece of illiteracy comes into place where we go in and trade based on the findings based on the most valuable use case um and based on that help our teams to do this change to increase um their capability just a little bit more and once they hand holding. But a lot of guidance >>can I kind of kind of trying to quickly David will allow me I mean there's there's a lot of governance piece but I think um that is important. And if you're talking about documentation for example, yes, we can go from team to team and tell these people how you have to document your data and data catalog or you have to establish data contracts and so on the force. But if you would like to build data products at scale following actual governance, we need to think about automation right. We need to think about a lot of things that we can learn from engineering before. And that starts with simple things like if we would like to build up trust in our data products, right, and actually want to apply the same rigor and the best practices that we know from engineering. There are things that we can do and we should probably think about what we can copy and one example might be. So the level of service level agreements, service level objectives. So that level indicators right, that represent on on an engineering level, right? If we're providing services there representing the promises we made to our customers or consumers, these are the internal objectives that help us to keep those promises. And actually these are the way of how we are tracking ourselves, how we are doing. And this is just one example of that thing. The Federated Governor governance comes into play right. In an ideal world, we should not just talk about data as a product but also data product. That's code that we say, okay, as most as much as possible. Right? Give the engineers the tool that they are familiar basis and actually not ask the product managers for example to document their data assets in the data catalog but make it part of the configuration. Have this as a, as a C D C I, a continuous delivery pipeline as we typically see another engineering task through and services we say, okay, there is configuration, we can think about pr I can think about data quality monitoring, we can think about um the ingestion data catalog and so on and forest, I think ideally in the data product will become of a certain templates that can be deployed and are actually rejected or verified at build time before we actually make them deploy them to production. >>Yeah, So it's like devoPS for data product um so I'm envisioning almost a three phase approach to governance and you kind of, it sounds like you're in early phases called phase zero where there's there's learning, there's literacy, there's training, education, there's kind of self governance and then there's some kind of oversight, some a lot of manual stuff going on and then you you're trying to process builders at this phase and then you codify it and then you can automate it. Is that fair? >>Yeah, I would rather think think about automation as early as possible in the way and yes, there needs to be certain rules but then actually start actually use case by use case. Is there anything that small piece that we can already automate? It's as possible. Roll that out and then actually extended step by step, >>is there a role though that adjudicates that? Is there a central Chief state officer who is responsible for making sure people are complying or is it how do you handle that? >>I mean from a from a from a platform perspective, yes, we have a centralized team to uh implement certain pieces they'll be saying are important and actually would like to implement. However, that is actually working very closely with the governance department. So it's Clements piece to understand and defy the policies that needs to be implemented. >>So Clements essentially it's it's your responsibility to make sure that the policy is being followed. And then as you were saying, christoph trying to compress the time to automation as fast as possible percent. >>So >>it's really it's uh >>what needs to be really clear that it's always a split effort, Right? So you can't just do one thing or the other thing, but everything really goes hand in hand because for the right automation for the right engineering tooling, we need to have the transparency first. Uh I mean code needs to be coded so we kind of need to operate on the same level with the right understanding. So there's actually two things that are important which is one its policies and guidelines, but not only that because more importantly or even well equally important to align with the end user and tech teams and engineering and really bridge between business value business teams and the engineering teams. >>Got it. So just a couple more questions because we gotta wrap I want to talk a little bit about the business outcome. I know it's hard to quantify and I'll talk about that in a moment but but major learnings, we've got some of the challenges that you cited. I'll just put them up here. We don't have to go detailed into this, but I just wanted to share with some folks. But my question, I mean this is the advice for your peers question if you had to do it differently if you had a do over or a Mulligan as we like to say for you golfers, what would you do differently? Yeah, >>I mean can we start with from a from the transformational challenge that understanding that it's also high load of cultural change. I think this is this is important that a particular communication strategy needs to be put into place and people really need to be um supported. Right? So it's not that we go in and say well we have to change towards data mesh but naturally it's in human nature, you know, we're kind of resistance to to change right? Her speech uncomfortable. So we need to take that away by training and by communicating um chris we're gonna add something to that >>and definitely I think the point that I have also made before right we need to acknowledge that data mesh is an architecture of scale, right? You're looking for something which is necessary by huge companies who are vulnerable, data productive scale. I mean Dave you mentioned it right, there are a lot of advantages to have a centralized team but at some point it may make sense to actually decentralized here and at this point right? If you think about data Mash, you have to recognize that you're not building something on a green field. And I think there's a big learning which is also reflected here on the slide is don't underestimate your baggage. It's typically you come to a point where the old model doesn't doesn't broke anymore and has had a fresh right? We lost our trust in our data and actually we have seen certain risks that we're slowing down our innovation so we triggered that this was triggering the need to actually change something. So this transition implies that you typically have a lot of technical debt accumulated over years and I think what we have learned is that potentially we have decentralized some assets to earlier, this is not actually taking into account the maturity of the team where we are actually distributed to and now we actually in the face of correcting pieces of that one. Right? But I think if you if you if you start from scratch you have to understand, okay, is are my team is actually ready for taking on this new uh, this news capabilities and you have to make sure that business decentralization, you build up these >>capabilities and the >>teams and as Clements has mentioned, right, make sure that you take the people on your journey. I think these are the pieces that also here, it comes with this knowledge gap, right? That we need to think about hiring and literacy the technical depth I just talked about and I think the last piece that I would add now which is not here on the flight deck is also from our perspective, we started on the analytical layer because that's kind of where things are exploding, right, this is the thing that people feel the pain but I think a lot of the efforts that we have started to actually modernize the current state uh, towards data product towards data Mash. We've understood that it always comes down basically to a proper shape of our operational plane and I think what needs to happen is is I think we got through a lot of pains but the learning here is this need to really be a commitment from the company that needs to happen and to act. >>I think that point that last point you made it so critical because I I hear a lot from the vendor community about how they're gonna make analytics better and that's that's not unimportant, but but through data product thinking and decentralized data organizations really have to operationalize in order to scale. So these decisions around data architecture an organization, their fundamental and lasting, it's not necessarily about an individual project are why they're gonna be project sub projects within this architecture. But the architectural decision itself is an organizational, its cultural and what's the best approach to support your business at scale. It really speaks to to to what you are, who you are as a company, how you operate and getting that right, as we've seen in the success of data driven driven companies is yields tremendous results. So I'll ask each of you to give give us your final thoughts and then we'll wrap maybe >>maybe it quickly, please. Yeah, maybe just just jumping on this piece that you have mentioned, right, the target architecture. If we talk about these pieces right, people often have this picture of mind like OK, there are different kind of stages, we have sources, we have actually ingestion layer, we have historical transformation presentation layer and then we're basically putting a lot of technology on top of that kind of our target architecture. However, I think what we really need to make sure is that we have these different kind of viewers, right? We need to understand what are actually the capabilities that we need in our new goals. How does it look and feel from the different kind of personas and experience view? And then finally, that should actually go to the to the target architecture from a technical perspective um maybe just to give an outlook but what we're what we're planning to do, how we want to move that forward. We have actually based on our strategy in the in the sense of we would like to increase that to maturity as a whole across the entire company and this is kind of a framework around the business strategy and it's breaking down into four pillars as well. People meaning the data, cultural, data literacy, data organizational structure and so on that. We're talking about governance as Clements has actually mentioned that, right, compliance, governance, data management and so on. You talk about technology and I think we could talk for hours for that one. It's around data platform, better science platform and then finally also about enablement through data, meaning we need to understand that a quality data accessibility and the science and data monetization. >>Great, thank you christophe clement. Once you bring us home give us your final thoughts. >>Can't can just agree with christoph that uh important is to understand what kind of maturity people have to understand what the maturity level, where the company where where people organization is and really understand what does kind of some kind of a change replies to that those four pillars for example, um what needs to be taken first and this is not very clear from the very first beginning of course them it's kind of like Greenfield you come up with must wins to come up with things that we really want to do out of theory and out of different white papers. Um only if you really start conducting the first initiatives you do understand. Okay, where we have to put the starts together and where do I missed out on one of those four different pillars? People, process technology and governance. Right? And then that kind of an integration. Doing step by step, small steps by small steps not boiling the ocean where you're capable ready to identify the gaps and see where either you can fill um the gaps are where you have to increase maturity first and train people or increase your text text, >>you know Hello Fresh is an excellent example of a company that is innovating. It was not born in Silicon Valley which I love. It's a global company. Uh and I gotta ask you guys, it seems like this is an amazing place to work you guys hiring? >>Yes, >>definitely. We do >>uh as many rights as was one of these aspects distributing. And actually we are hiring as an entire company specifically for data. I think there are a lot of open roles serious. Please visit or our page from better engineering, data, product management and Clemens has a lot of rules that you can speak about. But yes >>guys, thanks so much for sharing with the cube audience, your, your pioneers and we look forward to collaborations in the future to track progress and really want to thank you for your time. >>Thank you very much. Thank you very much. Dave >>thank you for watching the cubes startup showcase made possible by A W. S. This is Dave Volonte. We'll see you next time. >>Yeah.

(upbeat music) >> Hello everyone. We're here at theCUBE startup showcase made possible by AWS. Thanks so much for joining us today. You know, when Zhamak Dehghani was formulating her ideas around data mesh, she wasn't the only one thinking about decentralized data architectures. HelloFresh was going into hyper-growth mode and realized that in order to support its scale, it needed to rethink how it thought about data. Like many companies that started in the early part of the last decade, HelloFresh relied on a monolithic data architecture and the internal team it had concerns about its ability to support continued innovation at high velocity. The company's data team began to think about the future and work backwards from a target architecture, which possessed many principles of so-called data mesh, even though they didn't use that term specifically. The company is a strong example of an early but practical pioneer of data mesh. Now, there are many practitioners and stakeholders involved in evolving the company's data architecture many of whom are listed here on this slide. Two are highlighted in red and joining us today. We're really excited to welcome you to theCUBE, Clemence Chee, who is the global senior director for data at HelloFresh, and Christoph Sawade, who's the global senior director of data also of course at HelloFresh. Folks, welcome. Thanks so much for making some time today and sharing your story. >> Thank you very much. >> Thanks, Dave. >> All right, let's start with HelloFresh. You guys are number one in the world in your field. You deliver hundreds of millions of meals each year to many, many millions of people around the globe. You're scaling. Christoph, tell us a little bit more about your company and its vision. >> Yeah. Should I start or Clemence? Maybe take over the first piece because Clemence has actually been longer a director at HelloFresh. >> Yeah go ahead Clemence. >> I mean, yes, about approximately six years ago I joined and HelloFresh, and I didn't think about the startup I was joining would eventually IPO. And just two years later, HelloFresh went public. And approximately three years and 10 months after HelloFresh was listed on the German stock exchange which was just last week, HelloFresh was included in the DAX Germany's leading stock market index and that, to mind a great, great milestone, and I'm really looking forward and I'm very excited for the future for HelloFresh and also our data. The vision that we have is to become the world's leading food solution group. And there are a lot of attractive opportunities. So recently we did launch and expand in Norway. This was in July. And earlier this year, we launched the US brand, Green Chef, in the UK as well. We're committed to launch continuously different geographies in the next coming years and have a strong path ahead of us. With the acquisition of ready to eat companies like factor in the US and the plant acquisition of Youfoodz in Australia, we are diversifying our offer, now reaching even more and more untapped customer segments and increase our total address for the market. So by offering customers and growing range of different alternatives to shop food and to consume meals, we are charging towards this vision and this goal to become the world's leading integrated food solutions group. >> Love it. You guys are on a rocket ship. You're really transforming the industry. And as you expand your TAM, it brings us to sort of the data as a core part of that strategy. So maybe you guys could talk a little bit about your journey as a company, specifically as it relates to your data journey. I mean, you began as a startup, you had a basic architecture and like everyone, you've made extensive use of spreadsheets, you built a Hadoop based system that started to grow. And when the company IPO'd, you really started to explode. So maybe describe that journey from a data perspective. >> Yes, Dave. So HelloFresh by 2015, approximately had evolved what amount, a classical centralized data management set up. So we grew very organically over the years, and there were a lot of very smart people around the globe, really building the company and building our infrastructure. This also means that there were a small number of internal and external sources, data sources, and a centralized BI team with a number of people producing different reports, different dashboards and, and products for our executives, for example, or for different operations teams to see a company's performance and knowledge was transferred just by our talking to each other face-to-face conversations. And the people in the data warehouse team were considered as the data wizard or as the ETL wizard. Very classical challenges. And it was ETL, who reserved, indicated the kind of like a style of knowledge of data management, right? So our central data warehouse team then was responsible for different type of verticals in different domains, different geographies. And all this setup gave us in the beginning, the flexibility to grow fast as a company in 2015. >> Christoph, anything to add to that? >> Yes, not explicitly to that one, but as, as Clemence said, right, this was kind of the setup that actually worked for us quite a while. And then in 2017, when HelloFresh went public, the company also grew rapidly. And just to give you an idea how that looked like as well, the tech departments have actually increased from about 40 people to almost 300 engineers. And in the same way as the business units, as there Clemence has described, also grew sustainably. So we continue to launch HelloFresh in new countries, launched new brands like Every Plate, and also acquired other brands like we have Factor. And that grows also from a data perspective, the number of data requests that the central (mumbles), we're getting become more and more and more, and also more and more complex. So that for the team meant that they had a fairly high mental load. So they had to achieve a very, or basically get a very deep understanding about the business and also suffered a lot from this context, switching back and forth. Essentially, they had to prioritize across our product requests from our physical product, digital product, from a physical, from, sorry, from the marketing perspective, and also from the central reporting teams. And in a nutshell, this was very hard for these people, and that altered situations that let's say the solution that we have built. We can not really optimal. So in a, in a, in a, in a nutshell, the central function became a bottleneck and slow down of all the innovation of the company. >> It's a classic case. Isn't it? I mean, Clemence, you see, you see the central team becomes a bottleneck, and so the lines of business, the marketing team, sales teams say "Okay, we're going to take things into our own hands." And then of course IT and the technical team is called in later to clean up the mess. Maybe, maybe I'm overstating it, but, but that's a common situation. Isn't it? >> Yeah this is what exactly happened. Right. So we had a bottleneck, we had those central teams, there was always a bit of tension. Analytics teams then started in those business domains like marketing, supply chain, finance, HR, and so on started really to build their own data solutions. At some point you have to get the ball rolling, right? And then continue the trajectory, which means then that the data pipelines didn't meet the engineering standards. And there was an increased need for maintenance and support from central teams. Hence over time, the knowledge about those pipelines and how to maintain a particular infrastructure, for example, left the company, such that most of those data assets and data sets that turned into a huge debt with decreasing data quality, also decreasing lack of trust, decreasing transparency. And this was an increasing challenge where a majority of time was spent in meeting rooms to align on, on data quality for example. >> Yeah. And the point you were making Christoph about context switching, and this is, this is a point that Zhamak makes quite often as we've, we've, we've contextualized our operational systems like our sales systems, our marketing systems, but not our, our data systems. So you're asking the data team, okay, be an expert in sales, be an expert in marketing, be an expert in logistics, be an expert in supply chain and it's start, stop, start, stop. It's a paper cut environment, and it's just not as productive. But, but, and the flip side of that is when you think about a centralized organization, you think, hey, this is going to be a very efficient way across functional team to support the organization, but it's not necessarily the highest velocity, most effective organizational structure. >> Yeah. So, so I agree with that piece, that's up to a certain scale. A centralized function has a lot of advantages, right? So it's a tool for everyone, which would go to a destined kind of expert team. However, if you see that you actually would like to accelerate that in specific as the type of growth. But you want to actually have autonomy on certain teams and move the teams, or let's say the data to the experts in these teams. And this, as you have mentioned, right, that increases mental load. And you can either internally start splitting your team into different kinds of sub teams focusing on different areas, however, that is then again, just adding another piece where actually collaboration needs to happen because the external seized, so why not bridging that gap immediately and actually move these teams end to end into the, into the function themselves. So maybe just to continue what Clemence was saying, and this is actually where our, so, Clemence and my journey started to become one joint journey. So Clemence was coming actually from one of these teams who builds their own solutions. I was basically heading the platform team called data warehouse team these days. And in 2019, where (mumbles) become more and more serious, I would say, so more and more people have recognized that this model does not really scale, in 2019, basically the leadership of the company came together and identified data as a key strategic asset. And what we mean by that, that if he leveraged it in a, in a, an appropriate way, it gives us a unique, competitive advantage, which could help us to, to support and actually fully automate our decision making process across the entire value chain. So once we, what we're trying to do now, or what we would be aiming for is that HelloFresh is able to build data products that have a purpose. We're moving away from the idea that it's just a bi-product. We have a purpose why we would like to collect this data. There's a clear business need behind that. And because it's so important to, for the company as a business, we also want to provide them as a trustworthy asset to the rest of the organization. We'd say, this is the best customer experience, but at least in a way that users can easily discover, understand and securely access, high quality data. >> Yeah. So, and, and, and Clemence, when you see Zhamak's writing, you see, you know, she has the four pillars and the principles. As practitioners, you look at that say, okay, hey, that's pretty good thinking. And then now we have to apply it. And that's where the devil meets the details. So it's the for, the decentralized data ownership, data as a product, which we'll talk about a little bit, self-serve, which you guys have spent a lot of time on, and Clemence your wheelhouse, which is, which is governance and a federated governance model. And it's almost like if you, if you achieve the first two, then you have to solve for the second two, it almost creates a new challenges, but maybe you could talk about that a little bit as to how it relates to HelloFresh. >> Yes. So Chris has mentioned that we identified kind of a challenge beforehand and said, how can we actually decentralized and actually empower the different colleagues of ours? And this was more a, we realized that it was more an organizational or a cultural change. And this is something that someone also mentioned. I think ThoughtWorks mentioned one of the white papers, it's more of an organizational or a cultural impact. And we kicked off a phased reorganization, or different phases we're currently on, in the middle of still, but we kicked off different phases of organizational restructuring or reorganization trying to lock this data at scale. And the idea was really moving away from ever growing complex matrix organizations or matrix setups and split between two different things. One is the value creation. So basically when people ask the question, what can we actually do? What should we do? This is value creation and the how, which is capability building, and both are equal in authority. This actually then creates a high urge in collaboration and this collaboration breaks up the different silos that were built. And of course, this also includes different needs of staffing for teams staffing with more, let's say data scientists or data engineers, data professionals into those business domains, enhance, or some more capability building. >> Okay, go ahead. Sorry. >> So back to Zhamak Dehghani. So we, the idea also then crossed over when she published her papers in May, 2019. And we thought, well, the four pillars that she described were around decentralized data ownership, product, data as a product mindset, we have a self-service infrastructure. And as you mentioned, federated computational governance. And this suited very much with our thinking at that point of time to reorganize the different teams and this then that to not only organizational restructure, but also in completely new approach of how we need to manage data, through data. >> Got it. Okay. So your businesses is exploding. The data team was having to become domain experts to many areas, constantly context switching as we said, people started to take things into their own hands. So again, we said classic story, but, but you didn't let it get out of control and that's important. And so we, we actually have a picture of kind of where you're going today and it's evolved into this, Pat, if you could bring up the picture with the, the elephant, here we go. So I will talk a little bit about the architecture. It doesn't show it here, the spreadsheet era, but Christoph, maybe you could talk about that. It does show the Hadoop monolith, which exists today. I think that's in a managed hosting service, but, but you, you preserve that piece of it. But if I understand it correctly, everything is evolving to the cloud. I think you're running a lot of this or all of it in AWS. You've got, everybody's got their own data sources. You've got a data hub, which I think is enabled by a master catalog for discovery and all this underlying technical infrastructure that is, is really not the focus of this conversation today. But the key here, if I understand correctly is these domains are autonomous and that not only this required technical thinking, but really supportive organizational mindset, which we're going to talk about today. But, but Christoph, maybe you could address, you know, at a high level, some of the architectural evolution that you guys went through. >> Yeah, sure. Yeah. Maybe it's also a good summary about the entire history. So as you have mentioned, right, we started in the very beginning, it's a monolith on the operational plan, right? Actually it wasn't just one model it was two, one for the backend and one for the front end. And our analytical plan was essentially a couple of spreadsheets. And I think there's nothing wrong with spreadsheets, but it allows you to store information, it allows you to transform data, it allows you to share this information, it allows you to visualize this data, but all kind of, it's not actually separating concern, right? Every single one tool. And this means that it's obviously not scalable, right? You reach the point where this kind of management's set up in, or data management is in one tool, reached elements. So what we have started is we created our data lake, as we have seen here on our dupe. And just in the very beginning actually reflected very much our operation upon this. On top of that, we used Impala as a data warehouse, but there was not really a distinction between what is our data warehouse and what is our data lakes as the Impala was used as kind of both as a kind of engine to create a warehouse and data lake constructed itself. And this organic growth actually led to a situation. As I think it's clear now that we had the centralized model as, for all the domains that were really lose Kimball, the modeling standards and there's new uniformity we used to actually build, in-house, a base of building materialized use, of use that we have used for the presentation there. There was a lot of duplication of effort. And in the end, essentially the amendments and feedback tool, which helped us to, to improve of what we, have built during the end in a natural, as you said, the lack of trust. And this basically was a starting point for us to understand, okay, how can we move away? And there are a lot of different things that we can discuss of apart from this organizational structure that we have set up here, we have three or four pillars from Zhamak. However, there's also the next, extra question around, how do we implement product, right? What are the implications on that level and I think that is, that's something that we are, that we are currently still in progress. >> Got it. Okay. So I wonder if we could talk about, switch gears a little bit, and talk about the organizational and cultural challenges that you faced. What were those conversations like? And let's, let's dig into that a little bit. I want to get into governance as well. >> The conversations on the cultural change. I mean, yes, we went through a hyper growth through the last year, and obviously there were a lot of new joiners, a lot of different, very, very smart people joining the company, which then results that collaborations got a bit more difficult. Of course, the time zone changes. You have different, different artifacts that you had recreated in documentation that were flying around. So we were, we had to build the company from scratch, right? Of course, this then resulted always this tension, which I described before. But the most important part here is that data has always been a very important factor at HelloFresh, and we collected more of this data and continued to improve, use data to improve the different key areas of our business. Even when organizational struggles like the central (mumbles) struggles, data somehow always helped us to grow through this kind of change, right? In the end, those decentralized teams in our local geographies started with solutions that serve the business, which was very, very important. Otherwise, we wouldn't be at the place where we are today, but they did violate best practices and standards. And I always use the sports analogy, Dave. So like any sport, there are different rules and regulations that need to be followed. These routes are defined by, I'll call it, the sports association. And this is what you can think about other data governance and then our compliance team. Now we add the players to it who need to follow those rules and abide by them. This is what we then call data management. Now we have the different players, the professionals they also need to be trained and understand the strategy and the rules before they can play. And this is what I then called data literacy. So we realized that we need to focus on helping our teams to develop those capabilities and teach the standards for how work is being done to truly drive functional excellence in the different domains. And one of our ambition of our data literacy program for example, is to really empower every employee at HelloFresh, everyone, to make the right data-informed decisions by providing data education that scales (mumbles), and that can be different things. Different things like including data capabilities with, in the learning path for example, right? So help them to create and deploy data products, connecting data, producers, and data consumers, and create a common sense and more understanding of each other's dependencies, which is important. For example, SIS, SLO, state of contracts, et cetera, people get more of a sense of ownership and responsibility. Of course, we have to define what it means. What does ownership means? What does responsibility mean? But we are teaching this to our colleagues via individual learning patterns and help them upscale to use also their shared infrastructure, and those self-service data applications. And of all to summarize, we are still in this progress of learning. We're still learning as well. So learning never stops at Hello Fresh, but we are really trying this to make it as much fun as possible. And in the end, we all know user behavior is changed through positive experience. So instead of having massive training programs over endless courses of workshops, leaving our new joiners and colleagues confused and overwhelmed, we're applying gamification, right? So split different levels of certification where our colleagues, can access, have had access points. They can earn badges along the way, which then simplifies the process of learning and engagement of the users. And this is what we see in surveys, for example, where our employees value this gamification approach a lot and are even competing to collect those learning pet badges, to become the number one on the leaderboard. >> I love the gamification. I mean, we've seen it work so well in so many different industries, not the least of which is crypto. So you've identified some of the process gaps that you, you saw, you just gloss over them. Sometimes I say, pave the cow path. You didn't try to force. In other words, a new architecture into the legacy processes, you really had to rethink your approach to data management. So what did that entail? >> To rethink the way of data management, 100%. So if I take the example of revolution, industrial revolution or classical supply chain revolution, but just imagine that you have been riding a horse, for example, your whole life, and suddenly you can operate a car or you suddenly receive just a complete new way of transporting assets from A to B. So we needed to establish a new set of cross-functional business processes to run faster, drive faster, more robustly, and deliver data products which can be trusted and used by downstream processes and systems. Hence we had a subset of new standards and new procedures that would fall into the internal data governance and compliance sector. With internal, I'm always referring to the data operations around new things like data catalog, how to identify ownership, how to change ownership, how to certify data assets, everything around classical is software development, which we now apply to data. This, this is some old and new thinking, right? Deployment, versioning, QA, all the different things, ingestion policies, the deletion procedures, all the things that software development has been doing, we do it now with data as well. And it's simple terms, it's a whole redesign of the supply chain of our data with new procedures and new processes in asset creation, asset management and asset consumption. >> So data's become kind of the new development kit, if you will. I want to shift gears and talk about the notion of data product, and we have a slide that, that we pulled from your deck. And I'd like to unpack it a little bit. I'll just, if you can bring that up, I'll, I'll read it. A data product is a product whose primary objective is to leverage on data to solve customer problems, where customers are both internal and external. so pretty straightforward. I know you've, you've gone much deeper in your thinking and into your organization, but how do you think about that and how do you determine for instance, who owns what, how did you get everybody to agree? >> I can take that one. Maybe let me start as a data product. So I think that's an ongoing debate, right? And I think the debate itself is the important piece here, right? You mentioned the debate, you've clarified what we actually mean by that, a product, and what is actually the mindset. So I think just from a definition perspective, right? I think we find the common denominator that we say, okay, that our product is something which is important for the company that comes with value. What do you mean by that? Okay. It's a solution to a customer problem that delivers ideally maximum value to the business. And yes, leverage is the power of data. And we have a couple of examples, and I'll hit refresh here, the historical and classical ones around dashboards, for example, to monitor our error rates, but also more sophisticated based for example, to incorporate machine learning algorithms in our recipe recommendation. However, I think the important aspects of a data product is A: there is an owner, right? There's someone accountable for making sure that the product that you're providing is actually served and has maintained. And there are, there's someone who's making sure that this actually keeps the value of what we are promising. Combined with the idea of the proper documentation, like a product description, right? The people understand how to use it. What is this about? And related to that piece is the idea of, there's a purpose, right? We need to understand or ask ourselves, okay, why does a thing exist? Does it provide the value that we think it does? Then it leads in to a good understanding of what the life cycle of the data product and product life cycle. What do we mean? Okay. From the beginning, from the creation, you need to have a good understanding. You need to collect feedback. We need to learn about that, you need to rework, and actually finally, also to think about, okay, when is it time to decommission that piece So overall I think the core of this data product is product thinking 101, right? That we start, the point is, the starting point needs to be the problem and not the solution. And this is essentially what we have seen, what was missing, what brought us to this kind of data spaghetti that we have built there in Rush, essentially, we built it. Certain data assets develop in isolation and continuously patch the solution just to fulfill these ad hoc requests that we got and actually really understanding what the stakeholder needs. And the interesting piece as a results in duplication of (mumbled) And this is not just frustrating and probably not the most efficient way, how the company should work. But also if I build the same data assets, but slightly different assumption across the company and multiple teams that leads to data inconsistency. And imagine the following scenario. You, as a management, for management perspective, you're asking basically a specific question and you get essentially from a couple of different teams, different kinds of graphs, different kinds of data and numbers. And in the end, you do not know which ones to trust. So there's actually much (mumbles) but good. You do not know what actually is it noise for times of observing or is it just actually, is there actually a signal that I'm looking for? And the same as if I'm running an AB test, right? I have a new feature, I would like to understand what is the business impact of this feature? I run that with a specific source and an unfortunate scenario. Your production system is actually running on a different source. You see different numbers. What you have seen in the AB test is actually not what you see then in production, typical thing. Then as you asking some analytics team to actually do a deep dive, to understand where the discrepancies are coming from, worst case scenario again, there's a different kind of source. So in the end, it's a pretty frustrating scenario. And it's actually a waste of time of people that have to identify the root cause of this type of divergence. So in a nutshell, the highest degree of consistency is actually achieved if people are just reusing data assets. And also in the end, the meetup talk they've given, right? We start trying to establish this approach by AB testing. So we have a team, but just providing, or is kind of owning their target metric associated business teams, and they're providing that as a product also to other services, including the AB testing team. The AB testing team can use this information to find an interface say, okay, I'm drawing information for the metadata of an experiment. And in the end, after the assignment, after this data collection phase, they can easily add a graph to a dashboard just grouped by the AB testing barrier. And we have seen that also in other companies. So it's not just a nice dream that we have, right? I have actually looked at other companies maybe looked on search and we established a complete KPI pipeline that was computing all these information and this information both hosted by the team and those that (mumbles) AB testing, deep dives and, and regular reporting again. So just one last second, the, the important piece, Now, why I'm coming back to that is that it requires that we are treating this data as a product, right? If we want to have multiple people using the thing that I am owning and building, we have to provide this as a trust (mumbles) asset and in a way that it's easy for people to discover and to actually work with. >> Yeah. And coming back to that. So this is, to me this is why I get so excited about data mesh, because I really do think it's the right direction for organizations. When people hear data product, they think, "Well, what does that mean?" But then when you start to sort of define it as you did, it's using data to add value that could be cutting costs, that could be generating revenue, it could be actually directly creating a product that you monetize. So it's sort of in the eyes of the beholder, but I think the other point that we've made, is you made it earlier on too, and again, context. So when you have a centralized data team and you have all these P&L managers, a lot of times they'll question the data 'cause they don't own it. They're like, "Well, wait a minute." If it doesn't agree with their agenda, they'll attack the data. But if they own the data, then they're responsible for defending that. And that is a mindset change that's really important. And I'm curious is how you got to that ownership. Was it a top-down or was somebody providing leadership? Was it more organic bottom up? Was it a sort of a combination? How do you decide who owned what? In other words, you know, did you get, how did you get the business to take ownership of the data and what does owning the data actually mean? >> That's a very good question, Dave. I think that one of the pieces where I think we have a lot of learning and basically if you ask me how we could stop the filling, I think that would be the first piece that we need to start. Really think about how that should be approached. If it's staff has ownership, right? That means somehow that the team has the responsibility to host themselves the data assets to minimum acceptable standards. That's minimum dependencies up and down stream. The interesting piece has to be looking backwards. What was happening is that under that definition, this extra process that we have to go through is not actually transferring ownership from a central team to the other teams, but actually in most cases to establish ownership. I make this difference because saying we have to transfer ownership actually would erroneously suggest that the dataset was owned before, but this platform team, yes, they had the capability to make the change, but actually the analytics team, but always once we had the business understand the use cases and what no one actually bought, it's actually expensive, expected. So we had to go through this very lengthy process and establishing ownership, how we have done that as in the beginning, very naively started, here's a document, here are all the data assets, what is probably the nearest neighbor who can actually take care of that. And then we, we moved it over. But the problem here is that all these things is kind of technical debt, right? It's not really properly documented, pretty unstable. It was built in a very inconsistent way over years. And these people that built this thing have already left the company. So this is actually not a nice thing that you want to see and people build up a certain resistance, even if they have actually bought into this idea of domain ownership. So if you ask me these learnings, what needs to happen is first, the company needs to really understand what our core business concept that we have the need to have this mapping from this other core business concept that we have. These are the domain teams who are owning this concept, and then actually linked that to the, the assets and integrate that better, but suppose understanding how we can evolve, actually the data assets and new data builds things new and the, in this piece and the domain, but also how can we address reduction of technical depth and stabilizing what we have already. >> Thank you for that Christoph. So I want to turn a direction here and talk Clemence about governance. And I know that's an area that's passionate, you're passionate about. I pulled this slide from your deck, which I kind of messed up a little bit, sorry for that. But, but, but by the way, we're going to publish a link to the full video that you guys did. So we'll share that with folks, but it's one of the most challenging aspects of data mesh. If you're going to decentralize, you, you quickly realize this could be the wild west, as we talked about all over again. So how are you approaching governance? There's a lot of items on this slide that are, you know, underscore the complexity, whether it's privacy compliance, et cetera. So, so how did you approach this? >> It's yeah, it's about connecting those dots, right? So the aim of the data governance program is to promote the autonomy of every team while still ensuring that everybody has the right interoperability. So when we want to move from the wild west, riding horses to a civilized way of transport, I can take the example of modern street traffic. Like when all participants can maneuver independently, and as long as they follow the same rules and standards, everybody can remain compatible with each other and understand and learn from each other so we can avoid car crashes. So when I go from country to country, I do understand what the street infrastructure means. How do I drive my car? I can also read the traffic lights and the different signals. So likewise, as a business in HelloFresh we do operate autonomously and consequently need to follow those external and internal rules and standards set forth by the tradition in which we operate. So in order to prevent a, a car crash, we need to at least ensure compliance with regulations, to account for societies and our customers' increasing concern with data protection and privacy. So teaching and advocating this imaging, evangelizing this to everyone in the company was a key community or communication strategy. And of course, I mean, I mentioned data privacy, external factors, the same goes for internal regulations and processes to help our colleagues to adapt for this very new environment. So when I mentioned before, the new way of thinking, the new way of dealing and managing data, this of course implies that we need new processes and regulations for our colleagues as well. In a nutshell, then this means that data governance provides a framework for managing our people, the processes and technology and culture around our data traffic. And that governance must come together in order to have this effective program providing at least a common denominator is especially critical for shared data sets, which we have across our different geographies managed, and shared applications on shared infrastructure and applications. And as then consumed by centralized processes, for example, master data, everything, and all the metrics and KPIs, which are also used for a central steering. It's a big change, right? And our ultimate goal is to have this non-invasive federated, automated and computational governance. And for that, we can't just talk about it. We actually have to go deep and use case by use case and QC by PUC and generate learnings and learnings with the different teams. And this would be a classical approach of identifying the target structure, the target status, match it with the current status, by identifying together with the business teams, with the different domains and have a risk assessment, for example, to increase transparency because a lot of teams, they might not even know what kind of situation they might be. And this is where this training and this piece of data literacy comes into place, where we go in and trade based on the findings, based on the most valuable use case. And based on that, help our teams to do this change, to increase their capability. I just told a little bit more, I wouldn't say hand-holding, but a lot of guidance. >> Can I kind of kind of chime in quickly and (mumbled) below me, I mean, there's a lot of governance piece, but I think that is important. And if you're talking about documentation, for example, yes, we can go from team to team and tell these people, hey, you have to document your data assets and data catalog, or you have to establish a data contract and so on and forth. But if we would like to build data products at scale, following actual governance, we need to think about automation, right? We need to think about a lot of things that we can learn from engineering before, and just starts as simple things. Like if we would like to build up trust in our data products, right? And actually want to apply the same rigor and the best practices that we know from engineering. There are things that we can do. And we should probably think about what we can copy. And one example might be so the level of service level agreements, so that level objectives. So the level of indicators, right, that represent on a, on an engineering level, right? Are we providing services? They're representing the promises we make to our customer and to our consumers. These are the internal objectives that help us to keep those promises. And actually these audits of, of how we are tracking ourselves, how we are doing. And this is just one example of where I think the federated governance, governance comes into play, right? In an ideal world, you should not just talk about data as a product, but also data product that's code. That'd be say, okay, as most, as much as possible, right? Give the engineers the tool that they are familiar with, and actually not ask the product managers, for example, to document the data assets in the data catalog, but make it part of the configuration has as, as a, as a CDCI continuous delivery pipeline, as we typically see in other engineering, tasks through it and services maybe say, okay, there is configuration, we can think about PII, we can think about data quality monitoring, we can think about the ingestion data catalog and so on and forth. But I think ideally in a data product goals become a sort of templates that can be deployed and are actually rejected or verified at build time before we actually make them and deploy them to production. >> Yeah so it's like DevOps for data product. So, so I'm envisioning almost a three-phase approach to governance. And you're kind of, it sounds like you're in the early phase of it, call it phase zero, where there's learning, there's literacy, there's training education, there's kind of self-governance. And then there's some kind of oversight, some, a lot of manual stuff going on, and then you, you're trying to process builders at this phase and then you codify it and then you can automate it. Is that fair? >> Yeah. I would rather think, think about automation as early as possible in a way, and yes, it needs to be separate rules, but then actually start actually use case by use case. Is there anything that small piece that we can already automate? If just possible roll that out at the next extended step-by-step. >> Is there a role though, that adjudicates that? Is there a central, you know, chief state officer who's responsible for making sure people are complying or is it, how do you handle it? >> I mean, from a, from a, from a platform perspective, yes. This applies in to, to implement certain pieces, that we are saying are important and actually would like to implement, however, that is actually working very closely with the governance department, So it's Clemence's piece to understand that defy the policies that needs to be implemented. >> So good. So Clemence essentially, it's, it's, it's your responsibility to make sure that the policy is being followed. And then as you were saying, Christoph, you want to compress the time to automation as fast as possible. Is that, is that-- >> Yeah, so it's a really, it's a, what needs to be really clear is that it's always a split effort, right? So you can't just do one or the other thing, but there is some that really goes hand in hand because for the right information, for the right engineering tooling, we need to have the transparency first. I mean, code needs to be coded. So we kind of need to operate on the same level with the right understanding. So there's actually two things that are important, which is one it's policies and guidelines, but not only that, because more importantly or equally important is to align with the end-user and tech teams and engineering and really bridge between business value business teams and the engineering teams. >> Got it. So just a couple more questions, because we got to wrap up, I want to talk a little bit about the business outcome. I know it's hard to quantify and I'll talk about that in a moment, but, but major learnings, we've got some of the challenges that, that you cited. I'll just put them up here. We don't have to go detailed into this, but I just wanted to share with some folks, but my question, I mean, this is the advice for your peers question. If you had to do it differently, if you had a do over or a Mulligan, as we like to say for you, golfers, what, what would you do differently? >> I mean, I, can we start with, from, from the transformational challenge that understanding that it's also high load of cultural exchange. I think this is, this is important that a particular communication strategy needs to be put into place and people really need to be supported, right? So it's not that we go in and say, well, we have to change into, towards data mash, but naturally it's the human nature, nature, nature, we are kind of resistant to change, right? And (mumbles) uncomfortable. So we need to take that away by training and by communicating. Chris, you might want to add something to that. >> Definitely. I think the point that I've also made before, right? We need to acknowledge that data mesh it's an architectural scale, right? If you're looking for something which is necessary by huge companies who are vulnerable, that are product at scale. I mean, Dave, you mentioned that right, there are a lot of advantages to have a centralized team, but at some point it may make sense to actually decentralize here. And at this point, right, if you think about data mesh, you have to recognize that you're not building something on a green field. And I think there's a big learning, which is also reflected on the slide is, don't underestimate your baggage. It's typically is you come to a point where the old model doesn't work anymore. And as had a fresh write, we lost the trust in our data. And actually we have seen certain risks of slowing down our innovation. So we triggered that, this was triggering the need to actually change something. So at this transition applies that you took, we have a lot of technical depth accumulated over years. And I think what we have learned is that potentially we have, de-centralized some assets too early. This is not actually taking into account the maturity of the team. We are actually investigating too. And now we'll be actually in the face of correcting pieces of that one, right? But I think if you, if you, if you start from scratch, you have to understand, okay, is all my teams actually ready for taking on this new, this new capability? And you have to make sure that this is decentralization. You build up these capabilities and the teams, and as Clemence has mentioned, right? Make sure that you take the, the people on your journey. I think these are the pieces that also here it comes with this knowledge gap, right? That we need to think about hiring literacy, the technical depth I just talked about. And I think the, the last piece that I would add now, which is not here on the slide deck is also from our perspective, we started on the analytical layer because it was kind of where things are exploding, right? This is the bit where people feel the pain. But I think a lot of the efforts that we have started to actually modernize the current stage and data products, towards data mesh, we've understood that it always comes down basically to a proper shape of our operational plan. And I think what needs to happen is I think we got through a lot of pains, but the learning here is this needs to really be an, a commitment from the company. It needs to have an end to end. >> I think that point, that last point you made is so critical because I, I, I hear a lot from the vendor community about how they're going to make analytics better. And that's not, that's not unimportant, but, but true data product thinking and decentralized data organizations really have to operationalize in order to scale it. So these decisions around data architecture and organization, they're fundamental and lasting, it's not necessarily about an individual project ROI. They're going to be projects, sub projects, you know, within this architecture. But the architectural decision itself is organizational it's cultural and, and what's the best approach to support your business at scale. It really speaks to, to, to what you are, who you are as a company, how you operate and getting that right, as we've seen in the success of data-driven companies is, yields tremendous results. So I'll, I'll, I'll ask each of you to give, give us your final thoughts and then we'll wrap. Maybe. >> Just can I quickly, maybe just jumping on this piece, what you have mentioned, right, the target architecture. If you talk about these pieces, right, people often have this picture of (mumbled). Okay. There are different kinds of stages. We have (incomprehensible speech), we have actually a gesture layer, we have a storage layer, transformation layer, presentation data, and then we are basically putting a lot of technology on top of that. That's kind of our target architecture. However, I think what we really need to make sure is that we have these different kinds of views, right? We need to understand what are actually the capabilities that we need to know, what new goals, how does it look and feel from the different kinds of personas and experience view. And then finally that should actually go to the, to the target architecture from a technical perspective. Maybe just to give an outlook what we are planning to do, how we want to move that forward. Yes. Actually based on our strategy in the, in the sense of we would like to increase the maturity as a whole across the entire company. And this is kind of a framework around the business strategy and it's breaking down into four pillars as well. People meaning the data culture, data literacy, data organizational structure and so on. If you're talking about governance, as Clemence had actually mentioned that right, compliance, governance, data management, and so on, you're talking about technology. And I think we could talk for hours for that one it's around data platform, data science platform. And then finally also about enablements through data. Meaning we need to understand data quality, data accessibility and applied science and data monetization. >> Great. Thank you, Christoph. Clemence why don't you bring us home. Give us your final thoughts. >> Okay. I can just agree with Christoph that important is to understand what kind of maturity people have, but I understand we're at the maturity level, where a company, where people, our organization is, and really understand what does kind of, it's just kind of a change applies to that, those four pillars, for example, what needs to be tackled first. And this is not very clear from the very first beginning (mumbles). It's kind of like green field, you come up with must wins to come up with things that you really want to do out of theory and out of different white papers. Only if you really start conducting the first initiatives, you do understand that you are going to have to put those thoughts together. And where do I miss out on one of those four different pillars, people process technology and governance, but, and then that can often the integration like doing step by step, small steps, by small steps, not pulling the ocean where you're capable, really to identify the gaps and see where either you can fill the gaps or where you have to increase maturity first and train people or increase your tech stack. >> You know, HelloFresh is an excellent example of a company that is innovating. It was not born in Silicon Valley, which I love. It's a global company. And, and I got to ask you guys, it seems like it's just an amazing place to work. Are you guys hiring? >> Yes, definitely. We do. As, as mentioned right as well as one of these aspects distributing and actually hiring as an entire company, specifically for data. I think there are a lot of open roles, so yes, please visit or our page from data engineering, data, product management, and Clemence has a lot of roles that you can speak to about. But yes. >> Guys, thanks so much for sharing with theCUBE audience, you're, you're pioneers, and we look forward to collaborations in the future to track progress, and really want to thank you for your time. >> Thank you very much. >> Thank you very much Dave. >> And thank you for watching theCUBE's startup showcase made possible by AWS. This is Dave Volante. We'll see you next time. (cheerful music)

(upbeat music) >> From the Cube studios in Palo Alto in Boston connecting with thought leaders all around the world. This is the CUBE Conversation. >> Hey everybody this is Dave Vellante and welcome to the special CUBE Conversation. I'm really excited to have Keith Bradley here he's the Vice President of IT at Nature Fresh Farms. Keith good to see you. >> Hey, good to see you too there Dave. >> All right, first of all I got to thank you for sending me these awesome veggies. I got these wonderful peppers. I got red, orange. I got the yellow. I got to tell you Keith these tomatoes almost didn't make it. It's my last one on the vine. >> (Laughs) >> These guys are like candy. It's amazing. >> Yap. They are the tasty thing. >> Wonderful. >> You know what, I'll probably just join you right here now too. I'll have one right here right now and I'll join you right now. >> My kids love these but I'm not bringing them home. And then I got these other grape tomatoes and then I've got these mini pepper poppers that are so sweet. You know which one I'm talking about here. And then we've got the tomatoes on the vine. I mean, it's just unbelievable that you guys are able to do this in a greenhouse. Big cukes, little cukes. Wow. Thank you so much for sending these. Delicious. Really appreciate it. >> Yeah. Well thank you for having them. It's a great little tree and it's something that I know you're going to enjoy. And I love for everybody to have it and there's not a person I haven't seen that hasn't enjoyed our tomatoes and peppers. >> Now tell me more about Nature Fresh Farms. Let's talk about your business I want to spend some time on that. We've got IoT, we got a data lifecycle. All kinds of cool stuff, scanners. Paint a picture for us. >> I like to even go... If you don't mind. I like to even go back to where our roots actually came from. So Peter Quiring, our owner actually was a builder by nature and he was actually back in the year 2000 really wanted to get into the greenhouse because he was a manufacturer. And he built our phase one facility back in 2000 under the concept that he said, "there's computers out there." And Peter will be the first one to say, "I don't know how to use them, "but I know that it can do a lot for us." So even back in 2000, we were starting to experiment with using the computers back then to control the greenhouse, to do much of the functionality. Then he bought it under the concept as our sister company, South Essex Fabricating that he would sell the greenhouse turnkey to somebody else. Well, talking to him and I've been around since about phase two. He basically said, "when I built phase three, "which is our first 32 acre range, I realized that is actually in the pepper business now," and he realized he was a grower and then he fell in love with the industry. And again, kept pushing how we can do things automated? How do we can do things? How do we get more yield, more everything out of what we do? And as a lover of technology he made it a great environment for everybody including the growers to work in and to just do something new. >> Well, I mean the thing that we know that as populations grow we're not getting more land. Okay (laughs). So, you have to get better yield and the answer is not to just pound vegetables with pesticides. So maybe talk about how you guys are different from sort of a conventional farming approach, just in terms of maybe your yield, how you treat the plants, how you're able to pick throughout the year, give us some insight there. >> So basically I'll start with through the lifecycle of a pepper. So it's basically planted at a propagator and then it comes to our facility and it comes in the little white boxes here behind me. And they actually are usually about that tall. They're about a foot tall. Maybe a little more when they come to us. And right from that point in time, we start keeping track of everything. How much we put water, how much water it doesn't take, what nutrients it takes, how much it weighs. We actually weigh the vines to know how much they are in real time. We do everything top to bottom. So we actually control the life cycle of the plant. On top of that, we also look and have a whole bio scout division. So it's a group of people that are starting to use AI to actually look at how the bugs are attacking the plants. And then at the same time, we release a good bug that will eventually die off to kill the bugs that are starting to harm the plant. So it basically allows us to basically do as close to natural way of growing a plant as possible without spraying or doing anything like that at night. It's actually funny 'cause there's a lot pictures out there and you think that a greenhouse, it's going to be wet in here. And actually for the most part, it is dry all the time. Like I'm very hot, it's very dry and it's just how we work. We don't let anything inside. We control everything in that plant's life. And now with our newest range, we even control how much light it gets. So we basically give it light all night too. And even some nights when it's a little days out, not like today, but when it's a little dark out and the sun's not up there, we'll actually make sure it gets more light to get that more yield out of it. So we can grow 24/7 12 months a year. >> Okay Keith. So it sounds like you're using data and AI to really inform you as to nature's best formula for the good bugs, the bad bugs, the lighting to really drive yields and quality. >> Yeah, we analyze, like I said, everything from the edge that we collect, like I said, we have over 2000 sensors out in the greenhouse and we keep expanding it more and more every year to collect everything from the length of the vine, the weight of the vine in real time. And we basically collect it from the day the plant is born to the day that we actually take it all out to be composted. We know how much light it got. Does it need to get light that day? We analyze everything in general and it allows us to take that data back in real time to make it better and to look at the past data to do better again. Like you hear, some times we have actually have a cart going by here now. That data from that cart, we'll go back to our growers and they will know how much weight they got out of that row in the next 15 to 20 minutes. So they can actually look, okay, how did that plant react to the sun, how's tomorrow? Does it need more nutrients? Does it need a little less? They take all that data from the core and make sure it's all accurate and as up to date as possible. >> So Keith, and maybe even you can give us approximations, but so how much acreage do you have? And how much acreage would you need with conventional farming techniques to get the kind of yields and quality that you guys are able to achieve? >> So we own 160 acres of greenhouse that's actually under glass. It's actually 200 acres total of land but what's 160 acres approximately of greenhouse that's actually under glass. 'A' we're always constantly growing. Our demand is up that that's why we grow so fast. Usually you're looking at both 12 to one. So for every foot squared of space, you're looking for equivalent is about 12 feet squared for a conventional farm. That's the general average. Mostly because we can harvest year round, we can continually harvest. We maximize the harvest amount and everything total. >> I'm also interested in your regime, your team. So obviously you're supporting from an IT perspective, but you've got all this AI going on. You've got this data life cycle. So what does the data team look like? >> We're actually... I always laugh though. I like to call our growers are basically data analysts. They're not really part of my IT team, but they basically have learned the role of how to analyze data. So we'll have basically one or two junior growers, per range. So probably about, I'd say about, we have about 10 to 12 junior growers and then one senior grower per whole farm. So probably about three or four senior growers at any one time. But my IT staff is actually about a team of four, five, including myself. And we are always constantly looking at how to improve data and how to automate the process. That's what drives us to do more. And that's where the robots even come in is every time we look at something, it's not even from an IT perspective, but even just from a picking perspective, how do we automate this? How do we do a better tomorrow? How do we continually clean this up? And it just never ends. And every year we look back, okay, it cost us a dollar per meter squared or per foot square for the people down South in America there now. We look at that and how do we do that better next year? How do we do better the next day? And it's a constant looking and it's something we look at refining and now that's why we're going so much into AI 'cause we want to not look at the data and decide what to do. We want the data to tell us what to do. >> You guys are on the cutting edge. I mean this is the future of farming. I wonder if we could talk about the IT, what does the IT group look like in the future of farming? I mean you guys, what's your infrastructure look like? Are you all in the cloud or you can't be in the cloud because this is really an extent of an IoT or an edge use case. Paint a picture of the IT infrastructure for us if you would. >> So the IT infrastructure it's a very large amount at the edge. We take a lot of the information from the edge and we bring it back to our core to do our analyzing. But for the most part, we don't really leverage the cloud much yet and most of it is on-prem. We are starting to experiment with moving out to the cloud. And a lot of it is, you'll laugh though, is because the farming and agriculture industry really was stagnant for a long time and not really stagnant, but just didn't really progress as fast as the rest of the world. So now they're just starting to catch up and realizing, wow, this is a growing industry. We can do a lot of cool things with technology in this range. And now it's just exploded. So I'm going to say in the next five to 10 years, you're going to see a lot more private clouds and things like that happening with us. I know we're right now starting to just look at creating with the VxRail, a private cloud, and a concept like that to start to test that water again of how to analyze and how to do more things onsite and in the cloud and leverage everything top to bottom. >> So you've got your own servers at the edge... So Intel based servers, what's your storage infrastructure look like? Maybe describe the network a little bit. >> Yap. Okay. So we are basically, I'll admit here, we are a Dell factory. We're basically everything top to bottom. Right now we're on an FX2, Dell FX2 platform. It's basically our core platform we've been using for the last five years. It does all of our analitics and stuff like that. And we have just transformed our unstructured data to Isilon. It's been one of the best things for us to clean that up and make things move forward. It was actually one of those things that management actually looked at me and kind of looked at me and said, "what are you nuts?" Because we basically bought our first Isilon and then four months later, I said, "I love this. I got to have more," because everybody loved it so much in the way of store things. So we actually doubled the size of it within four months, which was a great... It was actually very seamless to do, but we're now also in a position where the FX2 in that stage type of situation didn't quite work for us to expand it. It wasn't as easy to expand. So we wanted to get away that we could expand at a moment's notice. We can change, we can scale out much faster and do things easier. So that's why we're transforming to a VxRail to basically clean that up and allow us to expand as we grow. >> So you're essentially trying to replicate the agility and speed of the cloud but like you say, you're an edge use case. So you can't do everything in cloud. Is that the right way to think about it? You mentioned private cloud but just sort of cloud experience, but at the edge. >> Yeah. We try to keep everything at the edge. It just makes it a lot easier to control. Because we're so big. Think about it like you are bringing all this information back from everywhere. It's a lot of data to come back to one spot. So we're trying to push that more, to keep it at the edge so that we can analyze it right there in the moment instead of having to come back and do it but yeah. And I think you'll see in the next few years, a lot of change to the cloud, I think it'll start to be there, but again, like I said, the private cloud will probably be the way most will go. >> Okay. So I got to ask you then, I mean, you've really tested that agility over the last 60 days with this COVID pandemic. How were you able to respond? What role did data play? You had supply chain considerations. Obviously, you got a lot of online ordering going on. You got to get produce out. You've got social distancing. How were you able to handle that crisis? >> Well it was a really great thing for our team. Our team really came together in a great way. We had a lot of people that did have to go home and we started because we had so many ranges all over, already about a year and a half ago we started implementing an SD-WAN solution to allow us to connect to different areas and to do all kinds of stuff. So it was actually very quick for us to be able to send the others home. We used our VeloCloud SD-WAN to expand it. It was very seamless and we just started sending people home left, right and center. The staff that had to stay here, like the workers out in the greenhouse here now are offshore labor as we call it. They work great. They worked with at every moment of the day and they dug right in. We haven't lost heartbeat. Like actually our orders have gone up in the last... Through this COVID experience more than anything else. And it's really learned... It really helped from an IT perspective and I laugh about this and it's one of the greatest things about what I do, I love this moment, is where sometimes we were very hesitant to jump on this video collaboration. I said, "hey, that's a great way of doing this." But sometimes people they're very stuck in their ways and they love it and they're like, "I don't know about this whole team Zoom "and all that fun stuff," but because of this, they've now embraced it and it's actually really changed the way even they've worked. So in a way, it kind of sped up the processes of us becoming more agile that way in a way that would've taken a long time. They now love teams. They love being able to communicate that way. They love being able to just do a quick call. All that functionality has changed and even made us more efficient that way. (mumbles) >> How does this all affect your IT budget allocation? Did you get more budget? Was it flat budget? Did you have to shift budget to sort of work from home and securing the remote workers? Can you sort of describe that dynamic? >> So it did, I'll be true, there's no way around it to not up my budge. They basically said, "yep, "you have to do what you have to do. "We have to continue to function, "we cannot let our greenhouse go down "and what do you need to do to make it happen?" So I quickly contacted Dell and got things coming and improve our infrastructure as much as we could to get ready. I contacted (mumbles). I basically made it so that my team can support every single part of our facet from home if they actually had to go home. So for example, if I had to get stuck at home, I could do every single part of my job from home, including the growers as much as possible. So say our senior grower had to get home. I locked him up. He has to be able to see everything and do everything. So we actually expanded that very quickly and it was a cost to us. But again, there's no technology we didn't implement that we hadn't talked about before. We just hadn't said, "you know what? It's just not the right time to try that." And now we just went ahead and we just said, we got to do it now. And there's not one part of our aspect that we don't reuse. >> Was Dell able to deliver? Did they have supply constraint issues? I mean, I know there's been huge demand for that whole remote worker. Were able to get what you needed in time? >> Yeah. You know what, I think that we hit it a little ahead of the scope of when things started to go bad, our senior management, our president and all that. He basically said, "you know Keith, "we got to get ready on this. "We got to get some stuff coming." We never ran out of some things. The quirkiest thing and it is just a reality, the biggest thing was webcams was to kind of trying to get webcams. Other than that, there was issues with UPS and Purolator and FedEx because they were just inundated too. But for the most part, we kept everything moving. There wasn't a time that I was actually really waiting on something that we had to have. One of the other great things of our senior team that's here is they've really given me the latitude to say, "what do you need and how do you need to do it?" And so I have my own basically storage area of stuff everywhere. And my team does laugh at me 'cause they call me a hoarder and I basically have too much. And we were able to use either some older stuff or some newer stuff and combine it and we got everything running. There was only a little hiccups here and there but nothing ever is going to go perfect. >> Yeah. But it's enabling business results. We've asked a lot of it pros like yourself like what do you expect the shape of the recovery? And obviously our hearts go out to those small businesses that have been decimated. You're clearly seeing industries like airlines and hospitality and restaurants are obviously in rough shape, but there is a bifurcated story here. Some businesses and it sounds like in this camp where the pandemic was actually a tailwind, your online demand is up, food, vegetables, people... There were a lot of meat shortages. So people really turn to vegetables, is that right? Is that the shape of the recovery actually, is maybe not even V-shape, it's been a tailwind for Nature Fresh Farms. >> Yeah. You know what? It has been a tailwind and that's the right way to say it. We've just increased our yieldage. We've increased that, it's not unnew for us, that's been the biggest driving force for us is basically the demand for our product and building fast enough to keep up to that demand. Like we continually build and expand. We've got more ranges being built in the coming years like looking towards the 21, 22, 23 year. It's just going to just continue to expand and that is purely because of demand. And this COVID just again, escalated that little bit 'cause everybody's like, I really want the peppers and like you learned, we actually do have some tasty peppers and tomatoes. So it does make it a nice little treat to have at home for the kids. >> Well, it's an amazing story of tech meets farming. And as you said for years your industry kind of became quiet when it came to tech, but this is the future of farming, in my opinion. And Keith, thanks so much for coming on the CUBE and sharing the story of Nature Fresh Farms. >> Well, thank you for having me. It's been a great pleasure. >> Alright. Thank you for watching everybody this is Dave Vellante for the CUBE and we'll see you next time. (upbeat music)

(upbeat music) >> From the Cube studios in Palo Alto in Boston connecting with thought leaders all around the world. This is the CUBE Conversation. >> Hey everybody this is Dave Vellante and welcome to the special CUBE Conversation. I'm really excited to have Keith Bradley here he's the Vice President of IT at Nature Fresh Farms. Keith good to see you. >> Hey, good to see you too there Dave. >> All right, first of all I got to thank you for sending me these awesome veggies. I got these wonderful peppers. I got red, orange. I got the yellow. I got to tell you Keith these tomatoes almost didn't make it. It's my last one on the vine. >> (Laughs) >> These guys are like candy. It's amazing. >> Yap. They are the tasty thing. >> Wonderful. >> You know what, I'll probably just join you right here now too. I'll have one right here right now and I'll join you right now. >> My kids love these but I'm not bringing them home. And then I got these other grape tomatoes and then I've got these mini pepper poppers that are so sweet. You know which one I'm talking about here. And then we've got the tomatoes on the vine. I mean, it's just unbelievable that you guys are able to do this in a greenhouse. Big cukes, little cukes. Wow. Thank you so much for sending these. Delicious. Really appreciate it. >> Yeah. Well thank you for having them. It's a great little tree and it's something that I know you're going to enjoy. And I love for everybody to have it and there's not a person I haven't seen that hasn't enjoyed our tomatoes and peppers. >> Now tell me more about Nature Fresh Farms. Let's talk about your business I want to spend some time on that. We've got IoT, we got a data lifecycle. All kinds of cool stuff, scanners. Paint a picture for us. >> I like to even go... If you don't mind. I like to even go back to where our roots actually came from. So Peter Quiring, our owner actually was a builder by nature and he was actually back in the year 2000 really wanted to get into the greenhouse because he was a manufacturer. And he built our phase one facility back in 2000 under the concept that he said, "there's computers out there." And Peter will be the first one to say, "I don't know how to use them, "but I know that it can do a lot for us." So even back in 2000, we were starting to experiment with using the computers back then to control the greenhouse, to do much of the functionality. Then he bought it under the concept as our sister company, South Essex Fabricating that he would sell the greenhouse turnkey to somebody else. Well, talking to him and I've been around since about phase two. He basically said, "when I built phase three, "which is our first 32 acre range, I realized that is actually in the pepper business now," and he realized he was a grower and then he fell in love with the industry. And again, kept pushing how we can do things automated? How do we can do things? How do we get more yield, more everything out of what we do? And as a lover of technology he made it a great environment for everybody including the growers to work in and to just do something new. >> Well, I mean the thing that we know that as populations grow we're not getting more land. Okay (laughs). So, you have to get better yield and the answer is not to just pound vegetables with pesticides. So maybe talk about how you guys are different from sort of a conventional farming approach, just in terms of maybe your yield, how you treat the plants, how you're able to pick throughout the year, give us some insight there. >> So basically I'll start with through the lifecycle of a pepper. So it's basically planted at a propagator and then it comes to our facility and it comes in the little white boxes here behind me. And they actually are usually about that tall. They're about a foot tall. Maybe a little more when they come to us. And right from that point in time, we start keeping track of everything. How much we put water, how much water it doesn't take, what nutrients it takes, how much it weighs. We actually weigh the vines to know how much they are in real time. We do everything top to bottom. So we actually control the life cycle of the plant. On top of that, we also look and have a whole bio scout division. So it's a group of people that are starting to use AI to actually look at how the bugs are attacking the plants. And then at the same time, we release a good bug that will eventually die off to kill the bugs that are starting to harm the plant. So it basically allows us to basically do as close to natural way of growing a plant as possible without spraying or doing anything like that at night. It's actually funny 'cause there's a lot pictures out there and you think that a greenhouse, it's going to be wet in here. And actually for the most part, it is dry all the time. Like I'm very hot, it's very dry and it's just how we work. We don't let anything inside. We control everything in that plant's life. And now with our newest range, we even control how much light it gets. So we basically give it light all night too. And even some nights when it's a little days out, not like today, but when it's a little dark out and the sun's not up there, we'll actually make sure it gets more light to get that more yield out of it. So we can grow 24/7 12 months a year. >> Okay Keith. So it sounds like you're using data and AI to really inform you as to nature's best formula for the good bugs, the bad bugs, the lighting to really drive yields and quality. >> Yeah, we analyze, like I said, everything from the edge that we collect, like I said, we have over 2000 sensors out in the greenhouse and we keep expanding it more and more every year to collect everything from the length of the vine, the weight of the vine in real time. And we basically collect it from the day the plant is born to the day that we actually take it all out to be composted. We know how much light it got. Does it need to get light that day? We analyze everything in general and it allows us to take that data back in real time to make it better and to look at the past data to do better again. Like you hear, some times we have actually have a cart going by here now. That data from that cart, we'll go back to our growers and they will know how much weight they got out of that row in the next 15 to 20 minutes. So they can actually look, okay, how did that plant react to the sun, how's tomorrow? Does it need more nutrients? Does it need a little less? They take all that data from the core and make sure it's all accurate and as up to date as possible. >> So Keith, and maybe even you can give us approximations, but so how much acreage do you have? And how much acreage would you need with conventional farming techniques to get the kind of yields and quality that you guys are able to achieve? >> So we own 160 acres of greenhouse that's actually under glass. It's actually 200 acres total of land but what's 160 acres approximately of greenhouse that's actually under glass. 'A' we're always constantly growing. Our demand is up that that's why we grow so fast. Usually you're looking at both 12 to one. So for every foot squared of space, you're looking for equivalent is about 12 feet squared for a conventional farm. That's the general average. Mostly because we can harvest year round, we can continually harvest. We maximize the harvest amount and everything total. >> I'm also interested in your regime, your team. So obviously you're supporting from an IT perspective, but you've got all this AI going on. You've got this data life cycle. So what does the data team look like? >> We're actually... I always laugh though. I like to call our growers are basically data analysts. They're not really part of my IT team, but they basically have learned the role of how to analyze data. So we'll have basically one or two junior growers, per range. So probably about, I'd say about, we have about 10 to 12 junior growers and then one senior grower per whole farm. So probably about three or four senior growers at any one time. But my IT staff is actually about a team of four, five, including myself. And we are always constantly looking at how to improve data and how to automate the process. That's what drives us to do more. And that's where the robots even come in is every time we look at something, it's not even from an IT perspective, but even just from a picking perspective, how do we automate this? How do we do a better tomorrow? How do we continually clean this up? And it just never ends. And every year we look back, okay, it cost us a dollar per meter squared or per foot square for the people down South in America there now. We look at that and how do we do that better next year? How do we do better the next day? And it's a constant looking and it's something we look at refining and now that's why we're going so much into AI 'cause we want to not look at the data and decide what to do. We want the data to tell us what to do. >> You guys are on the cutting edge. I mean this is the future of farming. I wonder if we could talk about the IT, what does the IT group look like in the future of farming? I mean you guys, what's your infrastructure look like? Are you all in the cloud or you can't be in the cloud because this is really an extent of an IoT or an edge use case. Paint a picture of the IT infrastructure for us if you would. >> So the IT infrastructure it's a very large amount at the edge. We take a lot of the information from the edge and we bring it back to our core to do our analyzing. But for the most part, we don't really leverage the cloud much yet and most of it is on-prem. We are starting to experiment with moving out to the cloud. And a lot of it is, you'll laugh though, is because the farming and agriculture industry really was stagnant for a long time and not really stagnant, but just didn't really progress as fast as the rest of the world. So now they're just starting to catch up and realizing, wow, this is a growing industry. We can do a lot of cool things with technology in this range. And now it's just exploded. So I'm going to say in the next five to 10 years, you're going to see a lot more private clouds and things like that happening with us. I know we're right now starting to just look at creating with the VxRail, a private cloud, and a concept like that to start to test that water again of how to analyze and how to do more things onsite and in the cloud and leverage everything top to bottom. >> So you've got your own servers at the edge... So Intel based servers, what's your storage infrastructure look like? Maybe describe the network a little bit. >> Yap. Okay. So we are basically, I'll admit here, we are a Dell factory. We're basically everything top to bottom. Right now we're on an FX2, Dell FX2 platform. It's basically our core platform we've been using for the last five years. It does all of our analitics and stuff like that. And we have just transformed our unstructured data to Isilon. It's been one of the best things for us to clean that up and make things move forward. It was actually one of those things that management actually looked at me and kind of looked at me and said, "what are you nuts?" Because we basically bought our first Isilon and then four months later, I said, "I love this. I got to have more," because everybody loved it so much in the way of store things. So we actually doubled the size of it within four months, which was a great... It was actually very seamless to do, but we're now also in a position where the FX2 in that stage type of situation didn't quite work for us to expand it. It wasn't as easy to expand. So we wanted to get away that we could expand at a moment's notice. We can change, we can scale out much faster and do things easier. So that's why we're transforming to a VxRail to basically clean that up and allow us to expand as we grow. >> So you're essentially trying to replicate the agility and speed of the cloud but like you say, you're an edge use case. So you can't do everything in cloud. Is that the right way to think about it? You mentioned private cloud but just sort of cloud experience, but at the edge. >> Yeah. We try to keep everything at the edge. It just makes it a lot easier to control. Because we're so big. Think about it like you are bringing all this information back from everywhere. It's a lot of data to come back to one spot. So we're trying to push that more, to keep it at the edge so that we can analyze it right there in the moment instead of having to come back and do it but yeah. And I think you'll see in the next few years, a lot of change to the cloud, I think it'll start to be there, but again, like I said, the private cloud will probably be the way most will go. >> Okay. So I got to ask you then, I mean, you've really tested that agility over the last 60 days with this COVID pandemic. How were you able to respond? What role did data play? You had supply chain considerations. Obviously, you got a lot of online ordering going on. You got to get produce out. You've got social distancing. How were you able to handle that crisis? >> Well it was a really great thing for our team. Our team really came together in a great way. We had a lot of people that did have to go home and we started because we had so many ranges all over, already about a year and a half ago we started implementing an SD-WAN solution to allow us to connect to different areas and to do all kinds of stuff. So it was actually very quick for us to be able to send the others home. We used our VeloCloud SD-WAN to expand it. It was very seamless and we just started sending people home left, right and center. The staff that had to stay here, like the workers out in the greenhouse here now are offshore labor as we call it. They work great. They worked with at every moment of the day and they dug right in. We haven't lost heartbeat. Like actually our orders have gone up in the last... Through this COVID experience more than anything else. And it's really learned... It really helped from an IT perspective and I laugh about this and it's one of the greatest things about what I do, I love this moment, is where sometimes we were very hesitant to jump on this video collaboration. I said, "hey, that's a great way of doing this." But sometimes people they're very stuck in their ways and they love it and they're like, "I don't know about this whole team Zoom "and all that fun stuff," but because of this, they've now embraced it and it's actually really changed the way even they've worked. So in a way, it kind of sped up the processes of us becoming more agile that way in a way that would've taken a long time. They now love teams. They love being able to communicate that way. They love being able to just do a quick call. All that functionality has changed and even made us more efficient that way. (mumbles) >> How does this all affect your IT budget allocation? Did you get more budget? Was it flat budget? Did you have to shift budget to sort of work from home and securing the remote workers? Can you sort of describe that dynamic? >> So it did, I'll be true, there's no way around it to not up my budge. They basically said, "yep, "you have to do what you have to do. "We have to continue to function, "we cannot let our greenhouse go down "and what do you need to do to make it happen?" So I quickly contacted Dell and got things coming and improve our infrastructure as much as we could to get ready. I contacted (mumbles). I basically made it so that my team can support every single part of our facet from home if they actually had to go home. So for example, if I had to get stuck at home, I could do every single part of my job from home, including the growers as much as possible. So say our senior grower had to get home. I locked him up. He has to be able to see everything and do everything. So we actually expanded that very quickly and it was a cost to us. But again, there's no technology we didn't implement that we hadn't talked about before. We just hadn't said, "you know what? It's just not the right time to try that." And now we just went ahead and we just said, we got to do it now. And there's not one part of our aspect that we don't reuse. >> Was Dell able to deliver? Did they have supply constraint issues? I mean, I know there's been huge demand for that whole remote worker. Were able to get what you needed in time? >> Yeah. You know what, I think that we hit it a little ahead of the scope of when things started to go bad, our senior management, our president and all that. He basically said, "you know Keith, "we got to get ready on this. "We got to get some stuff coming." We never ran out of some things. The quirkiest thing and it is just a reality, the biggest thing was webcams was to kind of trying to get webcams. Other than that, there was issues with UPS and Purolator and FedEx because they were just inundated too. But for the most part, we kept everything moving. There wasn't a time that I was actually really waiting on something that we had to have. One of the other great things of our senior team that's here is they've really given me the latitude to say, "what do you need and how do you need to do it?" And so I have my own basically storage area of stuff everywhere. And my team does laugh at me 'cause they call me a hoarder and I basically have too much. And we were able to use either some older stuff or some newer stuff and combine it and we got everything running. There was only a little hiccups here and there but nothing ever is going to go perfect. >> Yeah. But it's enabling business results. We've asked a lot of it pros like yourself like what do you expect the shape of the recovery? And obviously our hearts go out to those small businesses that have been decimated. You're clearly seeing industries like airlines and hospitality and restaurants are obviously in rough shape, but there is a bifurcated story here. Some businesses and it sounds like in this camp where the pandemic was actually a tailwind, your online demand is up, food, vegetables, people... There were a lot of meat shortages. So people really turn to vegetables, is that right? Is that the shape of the recovery actually, is maybe not even V-shape, it's been a tailwind for Nature Fresh Farms. >> Yeah. You know what? It has been a tailwind and that's the right way to say it. We've just increased our yieldage. We've increased that, it's not unnew for us, that's been the biggest driving force for us is basically the demand for our product and building fast enough to keep up to that demand. Like we continually build and expand. We've got more ranges being built in the coming years like looking towards the 21, 22, 23 year. It's just going to just continue to expand and that is purely because of demand. And this COVID just again, escalated that little bit 'cause everybody's like, I really want the peppers and like you learned, we actually do have some tasty peppers and tomatoes. So it does make it a nice little treat to have at home for the kids. >> Well, it's an amazing story of tech meets farming. And as you said for years your industry kind of became quiet when it came to tech, but this is the future of farming, in my opinion. And Keith, thanks so much for coming on the CUBE and sharing the story of Nature Fresh Farms. >> Well, thank you for having me. It's been a great pleasure. >> Alright. Thank you for watching everybody this is Dave Vellante for the CUBE and we'll see you next time. (upbeat music)

>> Narrator: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Okay, welcome back everyone, it's theCUBE's virtual coverage of AWS Summit Online, they're virtual. Then I'm John Furrier, your host of theCUBE. We're here in our Palo Alto studios for theCUBE virtual. We're remotely doing interviews during this COVID crisis. We have our quarantine crew, we're doing our best now for two and a half months getting those stories out, and today is AWS Summit. It's going to continue online, it never ends. It's virtual, it's asynchronous, but more importantly, let's get to great content. Our next guest Jimmy Chen, CEO of Propel. Great entrepreneur, vision with real impact and this is a story that is super important in my opinion, because it's a tech story and it's a social impact story. And you don't have to do one or the other, you can do both these days. This is going to be great. Jimmy, thanks for spending the time with us today. >> Yeah John, thanks for having me on the show. >> So, I want to get into the broader entrepreneurship and social impact as an entrepreneurial thing, which I think is a total awesome opportunity. But, you guys are using AWS for good, Propel, Take a minute to explain Propel the company, the things you're working and what you're passionate about. >> So Propel, we're a tech company based in Brooklyn that build software to help people navigate safety net programs like the food stamp program. There are about 40 million Americans who get their food stamp benefits on a debit card, called an EBT card, which looks kind of like a debit card or a credit card you get from a bank. But, when we spent time talking to people who use these cards to buy groceries, we actually found that it has kind of a weird quirk, which is that everyone who goes grocery shopping with an EBT card has to call the 1-800 number on the back of the card first, because that's how they can check the balance. And if you try to check-out at the grocery store you don't have enough left on your card, you get into this really embarrassing experience of having to decide, do you want three apples or two, and trying to figure out how to get your balance to be appropriate for the amount of food they're trying to buy. And so, we actually found that this pain point of needing to call the 1-800 number to go check your balance on your EBT card is a really common one that's felt by all 40 million of these Americans who use the food stamp program to put food on the table. So, what be built at Propel is really simple, it's a mobile banking app for the EBT card, the same way that you have a mobile banking app or your banking product, that we've created a digital free app that allows someone who gets their food stamp benefit on an EBT card to check their balance, to see their transaction history and more broadly actually to improve their overall financial help. >> And mends also the quality of life, knowing confidence whether whatever they're going through, that's something they're going to feel about as well. Talk about the tech piece of it. Obviously, this is a good example of something that I've been really riffing on for many years now, and just trying to get people's attention to is that cloud computing changes the game on social impact, because the time to get to the value, which is well talked about in entrepreneurial circles, later got funded, I got product market fit, applies to anything. And this is really spawning a new generation of entrepreneurship. This is a real thing and Amazon does that. What's your experience with AWS in this area? >> Well, our experience over the last month and a half in the middle of the COVID crisis I think has really driven home the value of AWS for our business, which is that, you know, at the start of COVID we had about 2 million people who used the Fresh EBT app on a monthly basis to manage their existing SNAP benefits. Unfortunately, as the economy has worsen and people's usage of safety net services as has increased, so has our userbase. And AWS has been really key to us, being able to scale our services, to be able to help an extra million people start using the Fresh EBT app essentially over the last few weeks. And so, you know, to your point about infrastructure and scale and technology, for us it's really been about, what are the best practices in the consumer tech worlds? And how do we apply those to help people that are lower-income and generally deal with experiences that are less good. >> You know, I've talked about though is something that I've been really talking a lot about, and maybe I'm a little bit older, but the younger entrepreneurs, they love to be agile and everything else. But what you're doing and what you've done is really have agility, but when you have these hard times everyone uses the word pivot. Which I hate that word pivot, it means to me like, it didn't work out, I'm going to pivot to something else. But to me, I think what's available when you're using the cloud, like what new position you're in, you built an app for a use case, you had product market fit. This COVID crisis becomes a tailwind for you, because actually your app helps people that are in need, but it also might give you an opportunity to do other things really fast, which means jump on an opportunity, not necessarily pivot. I mean is that tacking, pivot? It's kind of semantics, but it's a cultural mindset. And I want to get your thoughts Jimmy on how you see your business changing where you can actually take what you've built on the trajectory in the climbs of scale, the steep learnings. And then also take new territory down, whether it's a new service, helping people in need, 'cause that's the mission. Now you have flexibility. >> Jimmy: That's right. >> Talk about how you think about that, and what are some of your opportunities that you see. >> Jimmy: Well, the reality is that financial life for people who are low-income and using safety net services changes rapid. And there's no better example of this over the last, you know, few decades than the COVID crisis. Over the past few months, people who are using food stamp benefits have had really an unprecedented challenge over the last few months. It's been tough for everyone, but our survey data shows that for people who were getting food stamp benefits and working in early March, 86% of them have now lost some source of income, or have had their hours cut. And so I think one of the things we're starting to hear from our users is just the unprecedented type of need that they're facing and that they're turning to apps like the Fresh EBT app, to help them to navigate this particular crisis. To answer questions like, "What are the nutrition programs "through the government that are available to me? "How do I get a stimulus check? "What about the unemployment program? "And just, what are the full set of safety net resources "that are available to help someone like me "to get back on my feet and to make it through "this unprecedented financial hardship?" So, to your point about pivoting, you know, it's not necessarily, I don't think of it as pivoting, I think of it as like as responding to the real changes in user need. >> Yeah, ceasing opportunity on your position of your value proposition. Jimmy talk about the company, that your company launched a new service, Project 100. What is that about? Can you take a minute to explain that? >> Project 100 is a partnership between Propel, Stand for Children and the GiveDirectly team, which is the other two are nonprofits that are focused on different aspects of serving people that are in financial need. And it is a partnership that we've created to raise a $100 million to be able to make cash transfers to a 100,000 people who use the Fresh EBT app and are in financial need. So, Propel's role in this is that we, because our app helps people that are getting their food stamp benefits, we can certify that this is a person who is in financial need and uses, essentially, the status on the food stamp program as a proxy for, this is a family who really needs help to get through this crisis. We've been fortunate to have a lot of donors who are very generous and interested in finding ways to support, you know, people that are going through these types of financial hardships. And so, we've been fortunate to raise already about $70 million through this program. But, I think we still have a ways to go to reach this $100 million goal, where we really think that, that was a material impact on helping low-income Americans weather this financial shock. >> Well, I really appreciate what you're doing and thanks for what you're doing, it's great, and I think it's a great opportunity. Got great product market fit and you got a lot of horizontal opportunities to go after as you're more successful. I also want to get your thoughts real quick on tech entrepreneurship. It's been very glamorous over the past couple decades, to be an entrepreneur, but ultimately it's about creating value. I think, you're seeing with the cloud a lot of opportunities that aren't the traditional, you know, go public, built, raise a bunch of money, really either for profit or nonprofit, really in highly social impact situations. This is a growing field and you're doing it. Can you share what you're seeing and what advice you could give folks who are really thinking about having a mission driven opportunity. >> Jimmy: Well, I think that people solve the problems that they understand, and that traditionally tech entrepreneurs understand the very specific set of challenges, because the demographics of tech entrepreneurs are a smaller set than the overall population in the United States, right? Tech entrepreneurs tend to be male, they tend to have a college education, they tend to live in cities like San Francisco or New York City, and they tend to have a lot of money. But the reality is, that's not the demographic of people who use technology in the United States and so if people solve the problems that they understand, whose going to solve the problems that people on food stamps understand, if there are not a lot of people who are on food stamps that are starting their own software companies? And so I think the power of tools like Amazon Web Services and the cloud that allow people to be able to create new technology in a record amount of time and scale that, is the ability to democratize who gets to build the technology that people use, right? It means, both being able to help people who traditionally would not have the resources to start a new type of organization, to start a new one, but it also means being able to help companies like mine identify these types of challenges, to learn about the needs that people who are low-income have and be able to scale a product. >> Phenomenal mission Propel. Jimmy Chen, CEO of Propel. If you're designing a product, or art, or anything, you got to know who you're designing it for. And great point, and people solve problems that they understand. Thank you for what you're doing. Congratulations and continue success. We'll keep in touch. Thanks for coming on the virtual CUBE, thank you. >> Jimmy: Thank you so much for having me John. >> I'm John Furrier here on theCUBE for theCUBE virtual coverage of AWS Summit Online. A virtual conference has gone a way to virtual, so is theCUBE. Until further notice, we're going to do our part in our studio in Palo Alto, the studio in Boston. Checking in with folks and getting the updates. We're all in this together, and I'm John Furrier with theCUBE. Thanks for watching. (bright music)

[Music] we're on welcome to the special cube conversation we're here with some breaking news we got some startup investment news here in the Q studios palo alto I'm John for your host here at Jerry Chen partnered Greylock and the CEO of rock said Venkat Venkat Rahmani welcome to the cube you guys announcing hot news today series a and seed and Series A funding 21 million dollars for your company congratulations thank you Roxette is a data company jerry great this is one of your nest you kept this secret forever it was John was really hard you know over the past two years every time I sat in this seat I'd say and one more thing you know I knew that part of the advantage was rocks I was a special company and we were waiting to announce it and that's right time so it's been about two and half years in the making I gotta give you credit Jerry I just want to say to everyone I try to get the secrets out of you so hard you are so strong and keeping a secret I said you got this hot startup this was two years ago yeah I think the probe from every different angle you can keep it secrets all the entrepreneurs out there Jerry Chen's your guide alright so congratulations let's talk about the startup so you guys got 21 million dollars how much was the seed round this is the series a the seed was three million dollars both Greylock and Sequoia participating and the series a was eighteen point five all right so other investors Jerry who else was in on this I just the two firms former beginning so we teamed up with their French from Sequoia and the seed round and then we over the course of a year and half like this is great we're super excited about the team bank had Andrew bhai belt we love the opportunity and so Mike for an office coin I said let's do this around together and we leaned in and we did it around alright so let's just get into the other side I'm gonna read your your about section of the press release roxette's visions to Korea to build the data-driven future provide a service search and analytics engine make it easy to go from data to applications essentially building a sequel layer on top of the cloud for massive data ingestion I want to jump into it but this is a hot area not a lot of people are doing this at the level you guys are now and what your vision is did this come from what's your background how did you get here did you wake up one Wednesday I'm gonna build this awesome contraction layer and build an operating system around data make this thing scalable how did it all start I think it all started from like just a realization that you know turning useful data to useful apps just requires lots of like hurdles right you have to first figure out what format the data is in you got to prepare the data you gotta find the right specialized you know data database or data management system to load it in and it often requires like weeks to months before useful data becomes useful apps right and finally you know after I you know my tenure at Facebook when I left the first thing I did was I was just talking you know talking to a lot of people with real-world companies and reload problems and I started walking away from moremore of them thinking that this is way too complex I think the the format in which a lot of the data is coming in is not the format in which traditional sequel based databases are optimized for and they were built for like transaction processing and analytical processing not for like real-time streams of data but there's JSON or you know you know parque or or any of these other formats that are very very popular and more and more data is getting produced by one set of applications and getting consumed by other applications but what we saw it was what is this how can we make it simpler why do we need all this complexity right what is a simple what is the most simple and most powerful system we can build and pulled in the hands of as many people as possible and so we very sort of naturally relate to developers and data scientists people who use code on data that's just like you know kind of like our past lives and when we thought about it well why don't we just index the data you know traditional databases were built when every byte mattered every byte of memory every byte on disk now in the cloud the economics are completely different right so when you rethink those things with fresh perspective what we said was like what if we just get all of this data index it in a format where we can directly run very very fast sequel on it how simple would the world be how much faster can people go from ideas to do experiments and experiments to production applications and how do we make it all faster also in the cloud right so that's really the genesis of it well the real inspiration came from actually talking to a lot of people with real-world problems and then figuring out what is the simplest most powerful thing we can build well I want to get to the whole complexity conversation cuz we were talking before we came on camera here about how complexity can kill and why and more complexity on top of more complexity I think there's a simplicity angle here that's interesting but I want to get back to your background of Facebook and I want to tell a story you've been there eight years but you were there during a very interesting time during that time in history Facebook was I think the first generation we've taught us on the cube all the time about how they had to build their own infrastructure at scale while they're scaling so they were literally blitzscaling as reid hoffman and would say and you guys do it the Greylock coverage unlike other companies at scale eBay Microsoft they had old-school one dotto Technology databases Facebook had to kind of you know break glass you know and build the DevOps out from generation one from scratch correct it was a fantastic experience I think when I started in 2007 Facebook had about 40 million monthly actives and I had the privilege of working with some of the best people and a lot of the problems we were very quickly around 2008 when I went and said hey I want to do some infrastructure stuff the mandate that was given to me and my team was we've been very good at taking open source software and customizing it to our needs what would infrastructure built by Facebook for Facebook look like and we then went into this journey that ended up being building the online data infrastructure at Facebook by the time I left the collectively these systems were surveying 5 plus billion requests per second across 25 plus geographical clusters and half a dozen data centers I think at that time and now there's more and the system continues to chug along so it was just a fantastic experience I think all the traditional ways of problem solving just would not work at that scale and when the user base was doubling early in the early days every four months every five months yeah and what's interesting you know you're young and here at the front lines but you're kind of the frog in boiling water and that's because you are you were at that time building the power DevOps equation automating scale growth everything's happening at once you guys were right there building it now fast forward today everyone who's got an enterprise it's it wants to get there they don't they're not Facebook they don't have this engineering staff they want to get scale they see the cloud clearly the value property has got clear visibility but the economics behind who they hire so they have all this data and they get more increasing amount of data they want to be like Facebook but can't be like Facebook so they have to build their own solutions and I think this is where a lot of the other vendors have to rebuild this cherry I want to ask you because you've been looking at a lot of investments you've seen that old guard kind of like recycled database solutions coming to the market you've seen some stuff in open source but nothing unique what was it about Roxette that when you first talk to them that but you saw that this is going to be vectoring into a trend that was going to be a perfect storm yeah I think you nailed it John historic when we have this new problems like how to use data the first thing trying to do you saw with the old technology Oh existing data warehouses akin databases okay that doesn't work and then the next thing you do is like okay you know through my investments in docker and B and the boards or a cloud aerosol firsthand you need kind of this rise of stateless apps but not stateless databases right and then I through the cloud area and a bunch of companies that I saw has an investor every pitch I saw for two or three years trying to solve this data and state problem the cloud dudes add more boxes right here's here's a box database or s3 let me solve it with like Oh another database elastic or Kafka or Mongo or you know Apache arrow and it just got like a mess because if almond Enterprise IT shop there's no way can I have the skill the developers to manage this like as Beckett like to call it Rube Goldberg machination of data pipelines and you know I first met Venkat three years ago and one of the conversations was you know complexity you can't solve complex with more complexity you can only solve complexity with simplicity and Roxette and the vision they had was the first company said you know what let's remove boxes and their design principle was not adding another boxes all a problem but how to remove boxes to solve this problem and you know he and I got along with that vision and excited from the beginning stood to leave the scene ah sure let's go back with you guys now I got the funding so use a couple stealth years to with three million which is good a small team and that goes a long way it certainly 2021 total 18 fresh money it's gonna help you guys build out the team and crank whatnot get that later but what did you guys do in the in those two years where are you now sequel obviously is lingua franca cool of sequel but all this data is doesn't need to be scheming up and built out so were you guys that now so since raising the seed I think we've done a lot of R&D I think we fundamentally believe traditional data management systems that have been ported over to run on cloud Williams does not make them cloud databases I think the cloud economics is fundamentally different I think we're bringing this just scratching the surface of what is possible the cloud economics is you know it's like a simple realization that whether you rent 100 CPUs for one minute or or one CPU 400 minutes it's cost you exactly the same so then if you really ask why is any of my query is slow right I think because your software sucks right so basically what I'm trying to say is if you can actually paralyze that and if you can really exploit the fluidity of the hardware it's not easy it's very very difficult very very challenging but it's possible I think it's not impossible and if you can actually build software ground-up natively in the cloud that simplifies a lot of this stuff and and understands the economics are different now and it's system software at the end of the day is how do I get the best you know performance and efficiency for the price being paid right and the you know really building you know that is really what I think took a lot of time for us we have built not only a ground-up indexing technique that can take raw data without knowing the shape of the data we can turn that and index it in ways and store them maybe in more than one way since for certain types of data and then also have built a distributed sequel engine that is cloud native built by ground up in the cloud and C++ and like really high performance you know technologies and we can actually run distributor sequel on this raw data very very fast my god and this is why I brought up your background on Facebook I think there's a parallel there from the ground this ground up kind of philosophy if you think of sequel as like a Google search results search you know keyword it's the keyword for machines in most database worlds that is the standard so you can just use that as your interface Christ and then you using the cloud goodness to optimize for more of the results crafty index is that right correct yes you can ask your question if your app if you know how to see you sequel you know how to use Roxette if you can frame your the question that you're asking in order to answer an API request it could be a micro service that you're building it could be a recommendation engine that you're that you're building or you could you could have recommendations you know trying to personalize it on top of real time data any of those kinds of applications where it's a it's a service that you're building an application you're building if you can represent ask a question in sequel we will make sure it's fast all right let's get into the how you guys see the application development market because the developers will other winners here end of the day so when we were covering the Hadoop ecosystem you know from the cloud era days and now the important work at the Claire merger that kind of consolidates that kind of open source pool the big complaint that we used to hear from practitioners was its time consuming Talent but we used to kind of get down and dirty the questions and ask people how they're using Hadoop and we had two answers we stood up Hadoop we were running Hadoop in our company and then that was one answer the other answer was we're using Hadoop for blank there was not a lot of those responses in other words there has to be a reason why you're using it not just standing it up and then the Hadoop had the problem of the world grew really fast who's gonna run it yeah management of it Nukem noose new things came in so became complex overnight it kind of had took on cat hair on it basically as we would say so how do you guys see your solution being used so how do you solve that what we're running Roxette oh okay that's great for what what did developers use Roxette for so there are two big personas that that we currently have as users right there are developers and data scientists people who program on data right - you know on one hand developers want to build applications that are making either an existing application better it could be a micro service that you know I want to personalize the recommendations they generated online I mean offline but it's served online but whether it is somebody you know asking shopping for cars on San Francisco was the shopping you know was the shopping for cars in Colorado we can't show the same recommendations based on how do we basically personalize it so personalization IOT these kinds of applications developers love that because often what what you need to do is you need to combine real-time streams coming in semi structured format with structured data and you have no no sequel type of systems that are very good at semi structured data but they don't give you joins they don't give you a full sequel and then traditional sequel systems are a little bit cumbersome if you think about it I new elasticsearch but you can do joins and much more complex correct exactly built for the cloud and with full feature sequel and joins that's how that's the best way to think about it and that's how developers you said on the other side because its sequel now all of a sudden did you know data scientist also loved it they had they want to run a lot of experiments they are the sitting on a lot of data they want to play with it run experiments test hypotheses before they say all right I got something here I found a pattern that I don't know I know I had before which is why when you go and try to stand up traditional database infrastructure they don't know how what indexes to build how do i optimize it so that I can ask you know interrogatory and all that complexity away from those people right from basically provisioning a sandbox if you will almost like a perpetual sandbox of data correct except it's server less so like you don't you never think about you know how many SSDs do I need how many RAM do I need how many hosts do I need what configure your programmable data yes exactly so you start so DevOps for data is finally the interview I've been waiting for I've been saying it for years when's is gonna be a data DevOps so this is kind of what you're thinking right exactly so you know you give us literally you you log in to rocks at you give us read permissions to battle your data sitting in any cloud and more and more data sources we're adding support every day and we will automatically cloudburst will automatically interested we will schematize the data and we will give you very very fast sequel over rest so if you know how to use REST API and if you know how to use sequel you'd literally need don't need to think about anything about Hardware anything about standing up any servers shards you know reindex and restarting none of that you just go from here is a bunch of data here are my questions here is the app I want to build you know like you should be bottleneck by your career and imagination not by what can my data employers give me through a use case real quick island anyway the Jarius more the structural and architectural questions around the marketplace take me through a use case I'm a developer what's the low-hanging fruit use case how would I engage with you guys yeah do I just you just ingest I just point data at you how do you see your market developing from the customer standpoint cool I'll take one concrete example from a from a developer right from somebody we're working with right now so they have right now offline recommendations right or every night they generate like if you're looking for this car or or this particular item in e-commerce these are the other things are related well they show the same thing if you're looking at let's say a car this is the five cars that are closely related this car and they show that no matter who's browsing well you might have clicked on blue cars the 17 out of 18 clicks you should be showing blue cars to them right you may be logging in from San Francisco I may be logging in from like Colorado we may be looking for different kinds of cars with different you know four-wheel drives and other options and whatnot there's so much information that's available that you can you're actually by personalizing it you're adding creating more value to your customer we make it very easy you know live stream all the click stream beta to rock set and you can join that with all the assets that you have whether it's product data user data past transaction history and now if you can represent the joins or whatever personalization that you want to find in real time as a sequel statement you can build that personalization engine on top of Roxanne this is one one category you're putting sequel code into the kind of the workflow of the code saying okay when someone gets down to these kinds of interactions this is the sequel query because it's a blue car kind of go down right so like tell me all the recent cars that this person liked what color is this and I want to like okay here's a set of candidate recommendations I have how do I start it what are the four five what are the top five I want to show and then on the data science use case there's a you know somebody building a market intelligence application they get a lot of third-party data sets it's periodic dumps of huge blocks of JSON they want to combine that with you know data that they have internally within the enterprise to see you know which customers are engaging with them who are the persons churning out what are they doing and they in the in the market and trying to bring they bring it all together how do you do that when you how do you join a sequel table with a with a JSON third party dumb and especially for coming and like in the real-time or periodic in a week or week month or one month literally you can you know what took this particular firm that we're working with this is an investment firm trying to do market intelligence it used age to run ad hoc scripts to turn all of this data into a useful Excel report and that used to take them three to four weeks and you know two people working on one person working part time they did the same thing in two days and Rock said I want to get to back to microservices in a minute and hold that thought I won't go to Jerry if you want to get to the business model question that landscape because micro services were all the world's going to Inc so competition business model I'll see you gets are funded so they said love the thing about monetization to my stay on the core value proposition in light of the red hat being bought by by IBM had a tweet out there kind of critical of the transactions just in terms of you know people talk about IBM's betting the company on RedHat Mike my tweet was don't get your reaction will and tie it to the visible here is that it seems like they're going to macro services not micro services and that the world is the stack is changing so when IBM sell out their stack you have old-school stack thinkers and then you have new-school stack thinkers where cloud completely changes the nature of the stack in this case this venture kind of is an indication that if you think differently the stack is not just a full stack this way it's this way in this way yeah as we've been saying on the queue for a couple of years so you get the old guard trying to get a position and open source all these things but the stacks changing these guys have the cloud out there as a tailwind which is a good thing how do you see the business model evolving do you guys talk about that in terms of you can hey just try to find your groove swing get customers don't worry about the monetization how many charging so how's that how do you guys talk about the business model is it specific and you guys have clear visibility on that what's the story on that I mean I think yeah I always tell Bank had this kind of three hurdles you know you have something worthwhile one well someone listen to your pitch right people are busy you like hey John you get pitched a hundred times a day by startups right will you take 30 seconds listen to it that's hurdle one her will to is we spend time hands on keyboards playing around with the code and step threes will they write you a check and I as a as a enter price offered investor in a former operator we don't overly folks in the revenue model now I think writing a check the biz model just means you're creating value and I think people write you checking screening value but you know the feedback I always give Venkat and the founders work but don't overthink pricing if the first 10 customers just create value like solve their problems make them love the product get them using it and then the monetization the actual specifics the business model you know we'll figure out down the line I mean it's a cloud service it's you know service tactically to many servers in that sentence but it's um it's to your point spore on the cloud the one that economists are good so if it works it's gonna be profitable yeah it's born the cloud multi-cloud right across whatever cloud I wanna be in it's it's the way application architects going right you don't you don't care about VMs you don't care about containers you just care about hey here's my data I just want to query it and in the past you us developer he had to make compromises if I wanted joins in sequel queries I had to use like postgrads if I won like document database and he's like Mongo if I wanted index how to use like elastic and so either one I had to pick one or two I had to use all three you know and and neither world was great and then all three of those products have different business models and with rocks head you actually don't need to make choices right yes this is classic Greylock investment you got sequoia same way go out get a position in the market don't overthink the revenue model you'll funded for grow the company let's scale a little bit and figure out that blitzscale moment I believe there's probably the ethos that you guys have here one thing I would add in the business model discussion is that we're not optimized to sell latte machines who are selling coffee by the cup right so like that's really what I mean we want to put it in the hands of as many people as possible and make sure we are useful to them right and I think that is what we're obsessed about where's the search is a good proxy I mean that's they did well that way and rocks it's free to get started right so right now they go to rocks calm get started for free and just start and play around with it yeah yeah I mean I think you guys hit the nail on the head on this whole kind of data addressability I've been talking about it for years making it part of the development process programming data whatever buzzword comes out of it I think the trend is it looks a lot like that depo DevOps ethos of automation scale you get to value quickly not over thinking it the value proposition and let it organically become part of the operation yeah I think we we the internal KPIs we track are like how many users and applications are using us on a daily and weekly basis this is what we obsess about I think we say like this is what excellence looks like and we pursue that the logos in the revenue would would you know would be a second-order effect yeah and it's could you build that core kernels this classic classic build up so I asked about the multi cloud you mention that earlier I want to get your thoughts on kubernetes obviously there's a lot of great projects going on and CN CF around is do and this new state problem that you're solving in rest you know stateless has been an easy solution VP is but API 2.0 is about state right so that's kind of happening now what's your view on kubernetes why is it going to be impactful if someone asked you you know at a party hey thank you why is what's all this kubernetes what party going yeah I mean all we do is talk about kubernetes and no operating systems yeah hand out candy last night know we're huge fans of communities and docker in fact in the entire rock set you know back-end is built on top of that so we run an AWS but with the inside that like we run or you know their entire infrastructure in one kubernetes cluster and you know that is something that I think is here to stay I think this is the the the programmability of it I think the DevOps automation that comes with kubernetes I think all of that is just like this is what people are going to start taking why is it why is it important in your mind the orchestration because of the statement what's the let's see why is it so important it's a lot of people are jazzed about it I've been you know what's what's the key thing I think I think it makes your entire infrastructure program all right I think it turns you know every aspect of you know for example yeah I'll take it I'll take a concrete example we wanted to build this infrastructure so that when somebody points that like it's a 10 terabytes of data we want to very quickly Auto scale that out and be able to grow this this cluster as quickly as possible and it's like this fluidity of the hardware that I'm talking about and it needs to happen or two levels it's one you know micro service that is ingesting all the data that needs to sort of burst out and also at the second level we need to be able to grow more more nodes that we we add to this cluster and so the programmability nature of this like just imagine without an abstraction like kubernetes and docker and containers and pods imagine doing this right you are building a you know a lots and lots of metrics and monitoring and you're trying to build the state machine of like what is my desired state in terms of server utilization and what is the observed state and everything is so ad hoc and very complicated and kubernetes makes this whole thing programmable so I think it's now a lot of the automation that we do in terms of called bursting and whatnot when I say clock you know it's something we do take advantage of that with respect to stateful services I think it's still early days so our our position on my partner it's a lot harder so our position on that is continue to use communities and continue to make things as stateless as possible and send your real-time streams to a service like Roxette not necessarily that pick something like that very separate state and keep it in a backhand that is very much suited to your micro service and the business logic that needs to live there continue should continue to live there but if you can take a very hard to scale stateful service split it into two and have some kind of an indexing system Roxette is one that you know we are proud of building and have your stateless communal application logic and continue to have that you know maybe use kubernetes scale it in lambdas you know for all we care but you can take something that is very hard to you know manage and scale today break it into the stateful part in the stateless part and the serval is back in like like Roxette will will sort of hopefully give you a huge boost in being able to go from you know an experiment to okay I'm gonna roll it out to a smaller you know set of audience to like I want to do a worldwide you know you can do all of that without having to worry about and think about the alternative if you did it the old way yeah yeah and that's like talent you'd need it would be a wired that's spaghetti everywhere so Jerry this is a kubernetes is really kind of a benefit off your your investment in docker you must be proud and that the industry has gone to a whole nother level because containers really enable all this correct yeah so that this is where this is an example where I think clouds gonna go to a whole nother level that no one's seen before these kinds of opportunities that you're investing in so I got to ask you directly as you're looking at them as a as a knowledgeable cloud guy as well as an investor cloud changes things how does that change how is cloud native and these kinds of new opportunities that have built from the ground up change a company's network network security application era formants because certainly this is a game changer so those are the three areas I see a lot of impact compute check storage check networking early days you know it's it's it's funny it gosh seems so long ago yet so briefly when you know I first talked five years ago when I first met mayor of Essen or docker and it was from beginning people like okay yes stateless applications but stateful container stateless apps and then for the next three or four years we saw a bunch of companies like how do I handle state in a docker based application and lots of stars have tried and is the wrong approach the right approach is what these guys have cracked just suffered the state from the application those are app stateless containers store your state on an indexing layer like rock set that's hopefully one of the better ways saw the problem but as you kind of under one problem and solve it with something like rock set to your point awesome like networking issue because all of a sudden like I think service mesh and like it's do and costs or kind of the technologies people talk about because as these micro services come up and down they're pretty dynamic and partially as a developer I don't want to care about that yeah right that's the value like a Roxanna service but still as they operate of the cloud or the IT person other side of the proverbial curtain I probably care security I matters because also India's flowing from multiple locations multiple destinations using all these API and then you have kind of compliance like you know GDP are making security and privacy super important right now so that's an area that we think a lot about as investors so can I program that into Roxette what about to build that in my nap app natively leveraging the Roxette abstraction checking what's the key learning feature it's just a I'd say I'm a prime agent Ariane gdpr hey you know what I got a website and social network out in London and Europe and I got this gdpr nightmare I don't we don't have a great answer for GDP are we are we're not a controller of the data right we're just a processor so I think for GDP are I think there is still the controller still has to do a lot of work to be compliant with GDP are I think the way we look at it is like we never forget that this ultimately is going to be adding value to enterprises so from day one we you can't store data and Roxette without encrypting it like it's just the on you know on by default the only way and all transit is all or HTTPS and SSL and so we never freaked out that we're building for enterprises and so we've baked in for enterprise customers if they can bring in their own custom encryption key and so everything will be encrypted the key never leaves their AWS account if it's a you know kms key support private VP ceilings like we have a plethora of you know security features so that the the control of the data is still with the data controller with this which is our customer but we will be the the processor and a lot of the time we can process it using their encryption keys if I'm gonna build a GDP our sleeves no security solution I would probably build on Roxette and some of the early developers take around rocks at our security companies that are trying to track we're all ideas coming and going so there the processor and then one of the companies we hope to enable with Roxette is another generation security and privacy companies that in the past had a hard time tracking all this data so I can build on top of rocks crack okay so you can built you can build security a gbbr solution on top rock set because rock set gives you the power to process all the data index all the data and then so one of the early developers you know stolen stealth is they looking at the data flows coming and go he's using them and they'll apply the context right they'll say oh this is your credit card the Social Security is your birthday excetera your favorite colors and they'll apply that but I think to your point it's game-changing like not just Roxette but all the stuff in cloud and as an investor we see a whole generation of new companies either a to make things better or B to solve this new category problems like pricing the cloud and I think the future is pretty bright for both great founders and investors because there's just a bunch of great new companies and it's building up from the ground up this is the thing I brought my mother's red hat IBM thing is that's not the answer at the root level I feel like right now I'd be on I I think's fastenings but it's almost like you're almost doubling down to your your comment on the old stack right it's almost a double down the old stack versus an aggressive bet on kind of what a cloud native stack will look like you know I wish both companies are great people I was doing the best and stuff do well with I think I'd like to do great with OpenStack but again their product company as the people that happen to contribute to open source I think was a great move for both companies but it doesn't mean that that's not we can't do well without a new stack doing well and I think you're gonna see this world where we have to your point oh these old stacks but then a category of new stack companies that are being born in the cloud they're just fun to watch it all it's all big all big investments that would be blitzscaling criteria all start out organically on a wave in a market that has problems yeah and that's growing so I think cloud native ground-up kind of clean sheet of paper that's the new you know I say you're just got a pic pick up you got to pick the right way if I'm oh it's gotta pick a big wave big wave is not a bad wave to be on right now and it's at the data way that's part of the cloud cracked and it's it's been growing bigger it's it's arguably bigger than IBM is bigger than Red Hat is bigger than most of the companies out there and I think that's the right way to bet on it so you're gonna pick the next way that's kind of cloud native-born the cloud infrastructure that is still early days and companies are writing that way we're gonna do well and so I'm pretty excited there's a lot of opportunities certainly this whole idea that you know this change is coming societal change you know what's going on mission based companies from whether it's the NGO to full scale or all the applications that the clouds can enable from data privacy your wearables or cars or health thing we're seeing it every single day I'm pretty sad if you took amazon's revenue and then edit edit and it's not revenue the whole ready you look at there a dybbuk loud revenue so there's like 20 billion run which you know Microsoft had bundles in a lot of their office stuff as well if you took amazon's customers to dinner in the marketplace and took their revenue there clearly would be never for sure if item binds by a long shot so they don't count that revenue and that's a big factor if you look at whoever can build these enabling markets right now there's gonna be a few few big ones I think coming on they're gonna do well so I think this is a good opportunity of gradual ations thank you thank you at 21 million dollars final question before we go what are you gonna spend it on we're gonna spend it on our go-to-market strategy and hiding amazing people as many as we can get good good answer didn't say launch party that I'm saying right yeah okay we're here Rex at SIA and Joe's Jerry Chen cube cube royalty number two all-time on our Keeble um nine list partner and Greylock guy states were coming in I'm Jeffrey thanks for watching this special cube conversation [Music]

>> Hi, I'm Peter Burris, and welcome once again to Action Item. (funky electronic music) Every week, Wikibon gathers together the research team to discuss seminal issues that are facing the IT industry. And this week is no different. In the next couple of weeks, somewhere near 100,000 people are gonna be heading to Las Vegas for the Amazon, or AWS re:Invent show from all over the world. And this week, what we wanna do is we wanna provide a preview of what we think folks are gonna be talking about. And I'm joined here in our lovely Palo Alto studio, theCUBE studio, by Rob Hof, who is the editor-in-chief of SiliconANGLE. David Floyer, who's in analyst at Wikibon. George Gilbert, who's an analyst Wikibon. And John Furrier, who's a CUBE host and co-CEO. On the phone we have Neil Raden, an analyst at Wikibon, and also Dave Vellante, who's co-CEO with John Furrier, an analyst at Wikibon as well. So guys, let's jump right into it. David Floyer, I wanna hit you first. AWS has done a masterful job of making the whole concept of infrastructure as a service real. Nobody should downplay how hard that was and how amazing their success has been. But they're moving beyond infrastructure as a service. What do we expect for how far up Amazon is likely to go up the stack this year at re:Invent? >> Well, I can say what I'm hoping for. I agree with your premise that they have to go beyond IAS. The overall market for cloud is much bigger than just IAS, with SaaS and other clouds as well, both on-premise and off-premise. So I would start with what enterprise CIOs are wanting, and they are wanting to see a multi-cloud strategy, both on-premise and multiple clouds. SaaS clouds, other clouds. So I'm looking for AWS to provide additional services to make that easier. in particular, services, I thought of private clouds for enterprises. I'm looking for distributed capabilities, particularly in the storage area so they can link different clouds together. I want to see edge data management capabilities. I'd love to see that because the edge itself, especially the low-latency stuff, the real-time stuff, that needs specialist services, and I'd like to see them integrate that much better than just Snowball. I want to see more details about AI I'd love to see what they're doing in that. There's tremendous potential for AI in operational and to improve security, to improve availability, recovery. That is an area where I think they could be a leader of the IT industry. >> So let me stop you there, and George I wanna turn to you. So AWS in AI how do we anticipate that's gonna play out at re:Invent this year? >> I can see three things in decreasing order of likelihood. The first one is, they have to do a better job of tooling, both for, sort of, developers who want to dabble in, well get their arms around AI, but who aren't real data scientists. And then also hardcore tools for data scientists that have been well served by, recently, Microsoft and IBM, among others. So this is this Iron Man Initiative that we've heard about. For the hardcore tools, something from Domino Data Labs that looks like they're gonna partner with them. It's like a data-science workbench, so for the collaborative data preparation, modeling, deployment. That whole life cycle. And then for the developer-ready tooling, I expect to see they'll be working with a company called DataRobot, which has a really nifty tool where you put in a whole bunch of training data, and it trains, could be a couple dozen models that it thinks that might fit, and it'll show you the best fits. It'll show you the features in the models that are most impactful. In other words, it provides a lot of transparency. >> So it's kind of like models for models. >> Yes, and it provides transparency. Now that's the highest likelihood. And we have names on who we think the likely suspects are. The next step down, I would put applying machine learning to application performance management and IT operations. >> So that's the whole AI for ITOM that David Floyer just mentioned. >> Yeah. >> Now, presumably, this is gonna have to extend beyond just AI for Amazon or AWS-related ITOM. Our expectation's that we're gonna see a greater distribution of, or Amazon take more of a leadership in establishing a framework that cuts across multi-cloud. Have I got that right, David Floyer? >> Absolutely. A massive opportunity for them to provide the basics on their own platform. That's obviously the starting point. They'll have the best instrumentation for all of the components they have there. But they will need to integrate that in with their own databases, with other people's databases. The more that they can link all the units together and get real instrumentation from an application point of view of the whole of the infrastructure, the more value AI can contribute. >> John Foyer, the whole concept of the last few years of AWS is that all roads eventually end up at AWS. However, there's been a real challenge associated with getting this migration momentum to really start to mature. Now we saw some interesting moves that they made with VMware over the last couple of years, and it's been quite successful. And some would argue it might even have given another round of life to VMware. Are there some things we expect to see AWS do this time that are gonna reenergize the ecosystem to start bringing more customers higher up the stack to AWS? >> Yeah, but I think I look at it, quickly, as VMware was a groundbreaking even for both companies, VMware and AWS. We talked about that at that research event we had with them. The issue that is happening is that AWS has had a run in the marketplace. They've been the leader in cloud. Every year, it's been a slew of announcements. This year's no different. They're gonna have more and more announcements. In fact, they had to release some announcements early, before the show, because they have, again, more and more announcements. So they have the under-the-hood stuff going on that David Floyer and George were pointing out. So the classic build strategy is to continue to be competitive by having more services layered on top of each other, upgrading those services. That's a competitive strategy frame that's under the hood. On the business side, you're seeing more competition this year than ever before. Amazon now is highly contested, certainly in the marketplace with competitors. Okay, you're seeing FUD, the uncertainty and doubt from other people, how they're bundling. But it's clear. The cloud visibility is clear to customers. The numbers are coming in, multiple years of financial performance. But now the ecosystem plays, really, the interesting one. I think the VMware move is gonna be a tell sign for other companies that haven't won that top-three position. >> Example? >> I will say SAP. >> Oh really? You think SAP is gonna have a major play this year where we might see some more stuff about AWS and SAP? >> I'm hearing rumblings that SAP is gonna be expanding their relationship. I don't have the facts yet on the ground, but from what I'm sensing, this is consistent with what they've been doing. We've seen them at Google cloud platform. We talked to them specifically about how they're dealing with cloud. And their strategy is clear. They wanna be on Azure, Google, and Amazon. They wanna provide that database functionality and their client base in from HANA, and roll that in. So it's clear that SAP wants to be multi-cloud. >> Well we've seen Oracle over the past couple of years, or our research has suggested, I would say, that there's been kind of two broad strategies. The application-oriented strategy that goes down to IAAS aggressively. That'd be Oracle and Microsoft. And then the IAAS strategy that's trying to move up through an ecosystem play, which is more AWS. David Floyer and I have been writing a lot of that research. So it sounds like AWS is really gonna start doubling down in an ecosystem and making strategic bets on software providers who can bring those large enterprise install bases with them. >> Yeah, and the thing that you pointed out is migration. That's a huge issue. Now you can get technical, and say, what does that mean? But Andy Jassy has been clear, and the whole Amazon Web Services Team has been clear from day one. They're customer centric. They listen to the customers. So if they're doing more migration this year, and we'll see, I think they will be, I think that's a good tell sign and good prediction. That means the customers want to use Amazon more. And VMware was the same way. Their customers were saying, hey, we're ops guys, we want to have a cloud strategy. And it was such a great move for VMware. I think that's gonna lift the fog, if you will, pun intended, between what cloud computing is and other alternatives. And I think companies are gonna be clear that I can party with Amazon Web Services and still run my business in a way that's gonna help customers. I think that's the number one thing that I'm looking for is, what is the customers looking for in multi-cloud? Or if it's server-less or other things. >> Well, or yeah I agree. Lemme run this by you guys. It sounds as though multi-cloud increasingly is going to be associated with an application set. So, for example, it's very difficult to migrate a database manager from one place to another, as a snowflake. The cost to the customer is extremely high. The cost to the migration team is extremely high, lotta risk. But if you can get an application provider to step up and start migrating elements of the database interface, then you dramatically reduce the overall cost of what that migration might look like. Have I got that right, David Floyer? >> Yeah, absolutely. And I think that's what AWS, what I'm expecting them to focus on is more integration with more SaaS vendors, making it a better place-- >> Paul: Or just software vendors. >> Or software vendors. Well, SaaS vendors in particular, but software vendors in particular-- >> Well SAP's not a SaaS player, right? Well, they are a little bit, but most of their installations are still SAP on Oracle and moving them over, then my ass is gonna require a significant amount of SAP help. >> And one of the things I would love to see them have is a proper tier-one database as a service. That's something that's hugely missing at the moment, and using HANA, for example, on SAP, it's a tier-one database in a particular area, but that would be a good move and help a lot of enterprises to move stuff into AWS. >> Is that gonna be sufficient, though, given how dominant Oracle is in that-- >> No, they need something general purpose which can compete with Oracle or come to some agreement with Oracle. Who knows what's gonna happen in the future? >> Yeah, I don't know. >> Yeah we're all kinda ignoring here. It will be interesting to see. But at the end of the day, look, Oracle has an incentive also to render more of what it has, as a service at some level. And it's gonna be very difficult to say, we're gonna render this as a service to a customer, but Amazon can't play. Or AWS can't play. That's gonna be a real challenge for them. >> The Oracle thing is interesting and I bring this up because Oracle has been struggling as a company with cloud native messaging. In other words, they're putting out, they have a lot of open source, we know what they have for tooling. But they own IT. I mean if you dug up Oracle, they got the database as David pointed out, tier one. But they know the IT guys, they've been doing business in IT for years as a legacy vendor. Now they're transforming, and they are trying hard to be the cloud native path, and they're not making it. They're not getting the credit, and I don't know if that's a cultural issue with Oracle. But Amazon has that positioning from a developer cloud DNA. Now winning real enterprise deals. So the question that I'm looking for is, can Amazon continue to knock down these enterprise deals in lieu of these incumbent or legacy players in IT. So if IT continues to transform more towards cloud native, docker containers, or containers in Kubernetes, these kinds of micro services, I would give the advantage to Amazon over Oracle even though that Oracle has the database because ultimately the developers are driving the behavior. >> Oh again I don't think any of us would disagree with that. >> Yeah so the trouble though is the cost of migrating the applications and the data. That is huge. The systems of record are there for a reason. So there are two fundamental strategies for Oracle. If they can get their developers to add the AI, add the systems of intelligence. Make them systems of intelligence, then they can win in that strategy. Or the alternative is that they move it to AWS and do that movement in AWS. That's a much more risky strategy. >> Right but I think our kind of concluding point here is that ultimately if AWS can get big application players to participate and assist and invest in and move customers along with some of these big application migrations, it's good for AWS. And to your point John, it's probably good for the customers too. >> Absolutely. >> Yeah I don't think it's mutually exclusive as David makes a point about migrating for Oracle. I don't see a lot of migration coming off of Oracle. I look at overall database growth is the issue. Right so Oracle will have that position, but it's kind of like when we argued about the internet growth back in 1997. Just internet users growing was so great that rising tide flows. So I believe that the database growth is going to happen so fast that Amazon is not necessarily targeting Oracle's market share, they're going after the overall database market, which might be a smaller tier two kind of configuration or new architectures that are developing. So I think it's interesting dynamic and Oracle certainly could play there and lock in the database, but-- >> Here's what I would say, I would say that they're going after the new workload world, and a lot of that new workload is gonna involve database as it always has. Not like there's anything that the notion that we have solved or that database is 90% penetrated for the applications that are gonna be dominant matter in 2025 is ridiculous. There's a lot of new database that's gonna be sold. I think you're absolutely right. Rob Hof what's the general scuttlebutt that you're hearing. You know you as editor of SiliconANGLE, editor-in-chief of SiliconANGLE. What is the journalist world buzzing about for re:Invent this year? >> Well I guess you know my questions is because of the challenges that we're facing like we just talked about with migrating, the difficulty in migrating some of these applications. We also see very fast growing rivals like Google. Still small, but growing fast. And then there's China. That's a big one where is there a natural limit there that they're gonna have? So you put these things together, and I guess we see Amazon Web Services still growing at 42% a year or whatever it's great. But is it gonna start to go down because of all these challenges? >> 'Cause some of the constraints may start to assert themselves. >> Rob: Exactly, exactly. >> So-- >> Rob: That's what I'm looking at. >> Kind of the journalism world is kinda saying, are there some speed bumps up ahead for AWS? >> Exactly, and we saw one just a couple, well just this week with China for example. They sold off $300 million worth of data centers, equipment and such to their partner in China Beijing Sinnet. And they say this is a way to comply with Chinese law. Now we're going to start expanding, but expanding while you're selling off $300 million worth of equipment, you know, it begs a question. So I'm curious how they're going to get past that. >> That does raise an interesting question, and I think I might go back to some of the AI on ITOM, AI on IT operations management. Is that do you need control of the physical assets in China to nonetheless sell great service. >> Rob: And that's a big question. >> For accessing assets in China. >> Rob: Right. >> And my guess is that if they're successful with AI for ITOM and some of these other initiatives we're talking about. It in fact may be very possible for them to offer a great service in China, but not actually own the physical assets. And that's, it's an interesting question for some of the Chinese law issues. Dave Vellante, anything you want to jump in on, and add to the conversation? For example, if we look at some of the ecosystem and some of the new technologies, and some of the new investments being made around new technologies. What are some of your thoughts about some of the new stuff that we might hear about at AWS this year? >> Dave: Well so, a couple things. Just a comment on some of the things you guys were saying about Oracle and migration. To me it comes down to three things, growth, which is clearly there, you've talked about 40% plus growth. Momentum, you know the flywheel effect that Amazon has been talking about for years. And something that really hasn't been discussed as much which is economics, and this is something that we've talked about a lot and Amazon is bringing a software like marginal economics model to infrastructure services. And as it potentially slows down its growth, it needs to find new areas, and it will expand its tan by gobbling up parts of the ecosystem. So, you know there's so much white space, but partners got to be careful about where they're adding value because ultimately Amazon is gonna target those much in the same way, in my view anyway that Microsoft and Intel have in the past. And so I think you've got to tread very carefully there, and watch where Amazon is going. And they're going into the big areas of AI, trying to do more stuff with the Edge. And anywhere there's automation they are going to grab that piece of value in the value chain. >> So one of the things that we've been, we've talked about two main things. We've talked about a lot of investments, lot of expectations about AI and how AI is gonna show up in a variety of different ways at re:Invent. And we've talked about how they're likely to make some of these migration initiatives even that much more tangible than they have been. So by putting some real operational clarity as to how they intend to bring enterprises into AWS. We haven't talked about IoT. Dave just mentioned it. What's happening with the Edge, how is the Edge going to work? Now historically what we've seen is we've seen a lot of promises that the Edge was all going to end up in the cloud from a data standpoint, and that's where everything was gonna be processed. We started seeing the first indications that that's not necessarily how AWS is gonna move last year with Snowball and server-less computing, and some of those initiatives. We have anticipated a real honest to goodness true private cloud, AWS stack with a partnership. Hasn't happened yet. David Floyer what are we looking for this year? Are we gonna see that this year or are we gonna see more kind of circumnavigating the issue and doing the best that they can? >> Yeah, well my prediction last year was that they would come out with some sort of data service that you could install on your on-premise machine as a starting point for this communication across a multi cloud environment. I'm still expecting that, whether it happens this year or early next year. I think they have to. The pressure from enterprises, and they are a customer driven organization. The pressure from enterprises is going to mandate that they have some sort of solution on-premise. It's a requirement in many countries, especially in Europe. They're gonna have to do that I think without doubt. So they can do it in multiple ways, they can do it as they've done with the US government by putting in particular data centers, whole data centers within the US government. Or they can do it with small services, or they can have a, take the Microsoft approach of having an AWS service on site as well. I think with pressure from Microsoft, the pressure from Europe in particular is going to make this an essential requirement of their whole strategy. >> I remember a number of years going back a couple decades when Dell made big moves because to win the business of a very large manufacturer that had 50,000 work stations. Mainly engineers were turning over every year. To get that business Dell literally put a distribution point right next to that manufacturer. And we expect to see something similar here I would presume when we start talking about this. >> Yeah I mean I would make a comment on the IoT. First of all I agree with what David said, and I like his prediction, but I'm kind of taking a contrarian view on this, and I'm watching a few things at Amazon. Amazon always takes an approach of getting into new markets either with a big idea, and small teams to figure it out or building blocks, and they listen to the customer. So IoT is interesting because IoT's hard, it's important, it's really a fundamental important infrastructure, architecture that's not going away. I mean it has to be nailed down, it's obvious. Just like blockchain kinda is obvious when you talk about decentralization. So it'll be interesting to see what Amazon does on those two fronts. But what's interesting to note is Amazon always becomes their first customer. In their retail business, AWS was powering retail. With Whole Foods, and the stuff they're doing on the physical side, it'll be very interesting to see what their IoT strategy is from a technology standpoint with what they're doing internally. We get food delivered to our house from Amazon Fresh, and they got Whole Foods and all the retail. So it'll be interesting to see that. >> They're buying a lot of real estate. And I thought about this as well John. They're buying a lot of real estate, and how much processing can they put in there. And the only limit is that I don't think Whole Foods would qualify as particularly secure locations (laughing) when we start talking about this. But I think you're absolutely right. >> That only brings the question, how will they roll out IoT. Because he's like okay roll out an appliance that's more of an infrastructure thing. Is that their first move. So the question that I'm looking for is just kind of read the tea leaves and saying, what is really their doing. So they have the tech, and it's gonna be interesting to see, I mean it's more of a high level kind of business conversation, but IoT is a really big challenging area. I mean we're hearing that all over the place from CIOs like what's the architecture, what's the playbook? And it's different per company. So it's challenging. >> Although one of the reasons why it looks different per company is because it is so uncertain as to how it's gonna play out. There's not a lot of knowledge to fuse. My guess is that in 10 years we're gonna look back and see that there was a lot more commonality and patterns of work that were in IoT that many people expected. So I'll tell you one of the things that I saw last year that particularly impressed me at AWS re:Invent. Was the scale at which the network was being built out. And it raised for me an interesting question. If in fact one of the chief challenges of IoT. There are multiple challenges that every company faces with IoT. One is latency, one is intellectual property control, one is legal ramification like GDPR. Which is one of the reasons why the whole Europe play is gonna be so interesting 'cause GDPR is gonna have a major impact on a global basis, it's not just Europe. Bandwidth however is an area that is not necessarily given, it's partly a function of cost. So what happens if AWS blankets the world with network, and customers to get access to at least some degree of Edge no longer have to worry about a telco. What happens to the telco business at least from a data communication standpoint? Anybody wanna jump in on that one? >> Well yeah I mean I've actually talked to a couple folks like Ericson, and I think AT&T. And they're actually talking about taking their central offices and even the base stations, and sort of outfitting them as mini data centers. >> As pops. >> Yeah. But I think we've been hearing now for about 12 months that, oh maybe Edge is going to take over before we actually even finish getting to the cloud. And I think that's about as sort of ill-considered as the notion that PCs were gonna put mainframes out of business. And the reason I use that as an analogy, at one point IBM was going to put all their mainframe based databases and communication protocol on the PC. That was called OS2 extended edition. And it failed spectacularly because-- >> Peter: For a lot of reasons. >> But the idea is you have a separation of concerns. Presentation on one side in that case, and data management communications on the other. Here in this, in what we're doing here, we're definitely gonna have the low latency inferencing on the Edge and then the question is what data goes back up into the cloud for training and retraining and even simulation. And we've already got, having talked to Microsoft's Azure CTO this week, you know they see it the same way. They see the compute intensive modeling work, and even simulation work done in the cloud, and the sort of automated decisioning on the Edge. >> Alright so I'm gonna make one point and then I want to hit the Action Item around here. The one point I wanna make is I have a feeling that over, and I don't know if it's gonna happen at re:Invent this year but I have a feeling that over the course of the next six to nine months, there's going to be a major initiative on the part of Amazon to start bringing down the cost of data communications, and use their power to start hitting the telcos on a global basis. And what's going to be very very interesting is whether Amazon starts selling services to its network independent of its other cloud services. Because that could have global implications for who wins and who loses. >> Well that's a good point, I just wanna add color on that. Just anecdotally from my perspective you asked a question and I went, haven't talked to anyone. But knowing the telco business, I think they're gonna have that VMware moment. Because they've been struggling with over the top for so long. The rapid pace of innovation going on, that I don't think Amazon is gonna go after the telcos, I think it's just an evolutionary steamroller effect. >> It's an inevitability. >> It's an inevitability that the steamroller's coming. >> So users, don't sign longterm data communications deals right now. >> Why wouldn't you do a deal with Amazon if you're a telco, you get relevance, you have stability, lock in your cash flows, cut your deal, and stay alive. >> You know it's an interesting thought. Alright so let's hit the Action Item around here. So really quickly, as a preface for this, the way we wanna do this is guys, is that John Furrier is gonna have a couple hour one on one with Andy Jassy sometime in the next few days. And so if you were to, well tell us a little about that first John. >> Well every re:Invent we've been doing re:Invent for multiple years, I think it's our sixth year, we do all the events, and we cover it as the media partner as you know. And I'm gonna have a one on one sit down every year prior to re:Invent to get his view, exclusive interview, for two hours. Talk about the future. We broke the first Amazon story years ago on the building blocks, and how they overcame, and now they're winning. So it's a time for me to sit down and get his insight and continue to tell the story, and document the growth of this amazing success story. And so I'm gonna ask him specific questions and I wanted, love to know what he's thinking. >> Alright guys so I want each of you to pretend that you are, so representing your community, what would your community, what's the one question your community would like answered by Andy Jassy. George let's start with you. >> So my question would be, are you gonna take IT operations management, machine learn enable it, and then as part of offering a hybrid cloud solution, do you extend that capability on-prem, and maybe to even other vendor clouds. >> Peter: That's a good one, David Floyer. >> I've got two if I may. >> The more the merrier. >> I'll say them very quickly. The first one, John, is you've, the you being AWS, developed a great international network, with fantastic performance. How is AWS going to avoid conflicts with the EU, China, Japan, and particularly about their resistance about using any US based nodes. And from in-country telecommunication vendors. So that's my first, and the second is, again on AI, what's going to be the focus of AWS in applying the value of AI. Where are you gonna focus first and to give value to your customers? >> Rob Hof do you wanna ask a question? >> Yeah I'd like to, one thing I didn't raise in terms of the challenges is, Amazon overall is expanding so fast into all kinds of areas. Whole Foods we saw this. I'd ask Jassy, how do you contend with reality that a lot of these companies that you're now bumping up against as an overall company. Now don't necessarily want to depend on AWS for their critical infrastructure because they're competitors. How do you deal with that? >> Great question, David Vellante. >> David: Yeah my question is would be, as an ecosystem partner, what advice would you give? 'Cause I'm really nervous that as you grow and you use the mantra of, well we do what customers want, that you are gonna eat into my innovation. So what advice would you give to your ecosystem partners about places that they can play, and a framework that they should think about where they should invest and add value without the fear of you consuming their value proposition. >> So it's kind of the ecosystem analog to the customer question that Rob asked. So the one that I would have for you John is, the promise is all about scale, and they've talked a lot about how software at scale has to turn into hardware. What will Amazon be in five years? Are they gonna be a hardware player on a global basis? Following his China question, are they gonna be a software management player on a global basis and are not gonna worry as much about who owns the underlying hardware? Because that opens up a lot of questions about maybe there is going to be a true private cloud option an AWS will just try to run on everything, and really be the multi cloud administrator across the board. The Cisco as opposed to the IBM in the internet transformation. Alright so let me summarize very quickly. Thank you very much all of you guys once again for joining us in our Action Item. So this week we talked about AWS re:Invent. We've done this for a couple of years now. theCUBE has gone up and done 30, 35, 40 interviews. We're really expanding our presence at AWS re:Invent this year. So our expectation is that Amazon has been a major player in the industry for quite some time. They have spearheaded the whole concept of infrastructure as a service in a way that, in many respects nobody ever expected. And they've done it so well and so successfully that they are having an enormous impact way beyond just infrastructure in the market place today. Our expectation is that this year at AWS re:Invent, we're gonna hear a lot about three things. Here's what we're looking for. First, is AWS as a provider of advanced artificial intelligence technologies that then get rendered in services for application developers, but also for infrastructure managers. AI for ITOM being for example a very practical way of envisioning how AI gets instantiated within the enterprise. The second one is AWS has had a significant migration as a service initiative underway for quite some time. But as we've argued in Wikibon research, that's very nice, but the reality is nobody wants to bond the database manager. They don't want to promise that the database manager's gonna come over. It's interesting to conceive of AWS starting to work with application players as a way of facilitating the process of bringing database interfaces over to AWS more successfully as an onboarding roadmap for enterprises that want to move some of their enterprise applications into the AWS domain. And we mentioned one in particular, SAP, that has an interesting potential here. The final one is we don't expect to see the kind of comprehensive Edge answers at this year's re:Invent. Instead our expectation is that we're gonna continue to see AWS provide services and capabilities through server-less, through other partnerships that allow AWS to be, or the cloud to be able to extend out to the Edge without necessarily putting out that comprehensive software stack as an appliance being moved through some technology suppliers. But certainly green grass, certainly server-less, lambda, and other technologies are gonna continue to be important. If we finalize overall what we think, one of the biggest plays is, we are especially intrigued by Amazon's continuing build out of what appears to be one of the world's fastest, most comprehensive networks, and their commitment to continue to do that. We think this is gonna have implications far beyond just how AWS addresses the Edge to overall how the industry ends up getting organized. So with that, once again thank you very much for enjoying Action Item, and participating, and we'll talk next week as we review some of the things that we heard at AWS. And we look forward to those further conversations with you. So from Peter Burris, the Wikibon team, SiliconANGLE, thank you very much and this has been Action Item. (funky electronic music)

>> Announcer: Live, from Orlando, Florida it's the Cube, covering Grace Hopper's Celebration of Women in Computing, brought to you by SiliconANGLE Media. >> Welcome back to the Cube's coverage of the Grace Hopper Conference, here at the Orange County Convention Center. I'm your host, Rebecca Knight. We're joined by Maureen Fan. She is the CEO and co-founder of Baobab Studio, which is the industry's leading VR animation studio, so, welcome Maureen. >> Thank you so much for having me. >> It's excited to talk to you, because you just won an Emmy. Congratulations. >> Thank you. >> You just won an Emmy for "Invasion", so, tell us a little bit about invasion. >> It was our first piece ever and it was just an experiment to see if we could even create VR and it's a story about these adorable little bunnies and you are actually a bunny too, you look down, you have a furry, little bunny body and these aliens that come to try to take over the Earth, with their advanced technology and you and your bunny friend end up saving the entire Earth and it's starring Ethan Hawk and it just came out last year. And we're really excited, because it became the number one top downloaded VR experience across all the headsets and it's getting turned into a Hollywood Feature Film. >> Very cool, very cool >> Thank you. >> And you have another film coming out too and this is "Rainbow Crow" >> Yes. >> Tell our viewers a little bit about "Rainbow". >> So, "Rainbow Crow" is based off of a Native American legend about how the crow used to have beautiful rainbow feathers and a beautiful singing voice and it's John Legend, in our piece and how he decides to sacrifice himself, by flying into the sun to bring warmth and fire back to the Earth and in the process, loses all his beautiful feathers, becomes black and burnt and his voice becomes like the crow's voice, but it's about how beauty is within and there's also, huge themes about diversity and how if you learn to accept yourself and your differences, that's when you can accept others and that's why we specifically cast minorities and women, so, we have John Legend, Constance Wu, from "Fresh off the Boat" as a skunk character, Diego Luna, from "Rogue One", for the moth character, as well as Randy Edmunds, as a Native American elder, narrator, and we have a whole bunch of other stars to announce, soon-- >> Well we cannot wait to hear. That's already an amazing line-up. >> Thank you. >> So, when you're thinking about "Rainbow Crow" and particularly, because it's VR, which is relatively new, still experimental, I mean, the messages of diversity, does it lend itself to VR, better than, say, a standard animation film? >> Absolutely, because if you think about stories that you just watch passively, the reason why we need stories and humanity, in general is to experience characters and stories beyond those we can experience in our real lives and we think, "Oh, how would I feel if I was in the "position of that character or what would I do?" but in VR, because you are actually playing a character in a role, you actually have to decide at that point, "what would I do?" so, it's not just a experience that I just see, it's one where I'm actively experiencing it, so, I create a memory and remember afterwards and there's all these research studies at Stanford by Jeremy Bailenson, who is head of the Stanford VR lab, that shows if you are made a homeless person, inside a VR experience and you have to go through a day in the life of a homeless person or you would look in the mirror and see that you are a black woman, that you, when you get out of the headset, you act completely differently. You have so much more empathy for these people than you would normally and so, it gets you to care about these characters, in a way that you don't normally and in VR, because you're doing it in a real-time game engine, these characters can act and react to what you do, so you can turn that empathy into action and actually act upon your caring, which we call compassion, so, it really changes you in a way, that normal, traditional story-telling doesn't, so, I think that having voices and characters that are different, in front of the screen, and also, behind the screen are really important to create role models and different perspectives for all the people out in the world. >> And these are movies that are targeted at kids, children, but do you see a future in which, where there is more targeted at adults, for VR? >> Absolutely. The funny thing is, in the beginning, the VR distributors didn't think that people would want our VR animation, because they're like, "Oh, it's just going to be these hardcore boys "that just love to play games. "Are they going to want this animation?" and VR is targeted towards adults, that's why they were surprised and we were surprised when "Invasion" became the number one downloaded VR experience. It shows that the audience for our content is from little kids to grandmas and everyone in between and that's probably why it became the top downloaded experience, is because it's universally appealing and has themes that are appealing to just, every single generation, so, absolutely, but for VR to become mainstream, there needs to be more universally appealing content. Right now, the content tends to be for games, like parkour games, as well as documentaries, which are two amazing pieces of content for this medium, but for it to become mainstream, we need more universally appealing content and I'm excited about, right now, it's a new industry. This is when minorities and women in particular, can enter the space and help shape the voices and the direction of the industry. >> That is exactly where I wanted to go next. So, let's talk a little bit about Baobab Studio. It's not that old and VR is not that old and so, why are there more opportunities, would you say, for women, and minorities? >> Well, if you look at traditional animation in the traditional entertainment fields that's a very mature industry and to break into that industry, you have to either have lots and lots of money or unfair distribution advantage, but VR, there's technological disruption, which means nobody has an advantage at all, means it's a level playing field and everybody can come in and start something, so, this is a perfect opportunity, when there's low barriers to entry of coming in, for women and minorities, anyone who wants their voice heard, to start companies or to make experiences and we can set the groundwork, because there's no one telling us what we can and can't do, because no one actually knows what we can and can't do yet. >> Right, right, but yet you are still of a female, asian figurehead of a studio, that will hopefully, someday be a major studio. You're working on it, but do you find that people take you as seriously in Hollywood? I mean, what are you coming up against? >> Well, it's really interesting, because I heard for even fundraising is one of the hardest parts of starting a company and there was a Stanford Research Study that showed that if you took a deck, a pitch deck for a company and you had a male voice-over versus a female voice-over the male voice-over was, I don't remember what, it was like 50% more likely to get funded than the woman with the same exact pitch deck, so I knew from that and they also show that if you are married and wear a ring you're taken more seriously, or if you're less attractive, also, you're taken more seriously and my hypothesis and some of the hypotheses out there, is it takes away the whole entire female attraction thing, like what does it mean to be an attractive female, so, I had to go into the meetings, knowing this. I even considered wearing a ring. I considered wearing a paper bag over my head. >> A bag over you head. Exactly, exactly. >> But at the same time I felt that I need to be myself and the best thing to, there's a correlation between the perceived leadership and confidence, that I needed to just go in there and be confident in myself so, I knew that, that could work against me, but I just needed to be myself, but I had to make sure that I was really confident and really believed in what I said and honestly, besides being confident and aggressive, I also, felt comfortable, because a lot of the people I talked to, I knew from my network and I had many of my male friends and female friends who knew these VC's, do the initial introduction, so I felt more comfortable going in, for them already knowing that I had somebody else saying that I was awesome. >> Yeah, and you've had many mentors and sponsors along the way too. >> Absolutely, I would say it's one of the most important things, for my career from the very beginning. When I graduated from business school, I actually emailed my mentors and said, "Here are the things I care about for finding a job." I didn't have to go find any jobs. They actually found all these jobs. for me, set up informational interviews, for me and I just went in and did it, all the informational interviews, got the offers and just choose one of them that I wanted to be in but, even for starting my company, my co-founder, Eric Darnell was a write and director of all four "Madagascar" films and I got introduced to him, through my mentor, Glen Entis who is the co-founder of PDI Dreamworks Animation and he was my mentor through Zynga and then, Gen Entis introduced me to Alvy Ray Smith, who is the co-founder of Pixar, who also became our advisor, Alvy Ray Smith, then introduced us to Glen Keane, who is the animator for "Little Mermaid", "Alaadin". >> The power of networks. >> It was all through the network and through my mentors that I found, a lot of the opportunities that I have and they also helped my through my personal life and how to navigate being entrepreneur and I rely on them so much. >> So, beyond finding the right mentor and sponsor what else would you give, your parting words to the young Maureen fans out there? >> I think there's a tendency for society to pressure you to conform, to money, fame, beauty and you don't need to listen to that and you don't need to be bucketed. I designed my own major at Stanford and with an eBay, I took four different roles. I just kept on creating my own roles and refusing to be bucketed as a creative or a suit and you can be who you are and create a category onto yourself and so, don't feel pressured to listen to what society is telling you. The other thing, is if you are faced with pushed back for being promoted and you feel like it's maybe because you're a woman, we have a tendency as women to start blaming ourselves and thinking there's something wrong with us, versus research shows men are most likely to blame the system, don't let it affect you and bring you down, because you need to actually be confident and believe in yourself in order to rise above. >> Great. Great advice. Maureen, it's been a pleasure having you on the show. Thanks so much. >> Thank you. >> And best of luck to you. >> Thank you, so much. >> Hope you win another Emmy. >> Thank you. >> Come back and talk to us again. >> Thank you. I'm Rebecca Knight, we'll have more from Grace Hopper, just after this. (techno music)